Awesome-LLM-for-Autonomous-Driving-Resources

This is a collection of research papers about LLM-for-Autonomous-Driving(LLM4AD). And the repository will be continuously updated to track the frontier of LLM4AD. Maintained by SJTU-ReThinklab.

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Citation

Our survey paper is at https://arxiv.org/abs/2311.01043 which includes more detailed discussions and will be continuously updated. The GitHub Pages was updated on June 20, 2025. The Survey Paper latest version was updated on August 12, 2024.

If you find our repo is helpful, please consider cite it.

@misc{yang2023survey,
      title={LLM4Drive: A Survey of Large Language Models for Autonomous Driving}, 
      author={Zhenjie Yang and Xiaosong Jia and Hongyang Li and Junchi Yan},
      year={2023},
      eprint={2311.01043},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}

Table of Contents

• Awesome LLM-for-Autonomous-Driving(LLM4AD)
  • Table of Contents
  • Overview of LLM4AD
  • ICLR 2024 Under Review
  • Papers
  • Datasets
  • Citation
  • License

Overview of LLM4AD

LLM-for-Autonomous-Driving (LLM4AD) refers to the application of Large Language Models(LLMs) in autonomous driving. We divide existing works based on the perspective of applying LLMs: planning, perception, question answering, and generation.

Motivation of LLM4AD

The orange circle represents the ideal level of driving competence, akin to that possessed by an experienced human driver. There are two main methods to acquire such proficiency: one, through learning-based techniques within simulated environments; and two, by learning from offline data through similar methodologies. It’s important to note that due to discrepancies between simulations and the real-world, these two domains are not fully the same, i.e. sim2real gap. Concurrently, offline data serves as a subset of real-world data since it’s collected directly from actual surroundings. However, it is difficult to fully cover the distribution as well due to the notorious long-tailed nature of autonomous driving tasks. The final goal of autonomous driving is to elevate driving abilities from a basic green stage to a more advanced blue level through extensive data collection and deep learning.

Papers

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format:
- [title](paper link) [links]
  - author1, author2, and author3...
  - publisher
  - task
  - keyword
  - code or project page
  - datasets or environment or simulator
  - publish date
  - summary
  - metrics

• A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving

  • Yupeng Zhou, Can Cui, Juntong Peng, Zichong Yang, Juanwu Lu, Jitesh H Panchal, Bin Yao, Ziran Wang
  • Publisher: Purdue University
  • Publish Date: 2025.06.17
  • Task: Evaluation
  • Summary:
    • The paper introduces a lightweight, structured, and low-latency middleware pipeline on the vehicle and develops a form of customizable real-world traffic scenarios on a closed test track.

• AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning

  • Zewei Zhou, Tianhui Cai, Seth Z. Zhao, Yun Zhang, Zhiyu Huang, Bolei Zhou, Jiaqi Ma
  • Publisher: University of California, Los Angeles
  • Publish Date: 2025.06.16
  • Project Page: AutoVLA
  • Code: AutoVLA
  • Task: Planning
  • Datasets: nuPlan, nuScenes, Waymo, Bench2Drive(Using CARLA-Garage Dataset for Training)
  • Summary:
    • AutoVLA is an end-to-end autonomous driving framework leveraging a pretrained VLM backbone integrated with physical discrete action tokens.
    • Use GRPO to enable adaptive reasoning and further enhance the model’s performance on end-to-end driving tasks.

• On the Natural Robustness of Vision-Language Models Against Visual Perception Attacks in Autonomous Driving

  • Pedram MohajerAnsari, Amir Salarpour, Michael Kühr, Siyu Huang, Mohammad Hamad, Sebastian Steinhorst, Habeeb Olufowobi, Mert D. Pesé
  • Publisher: Clemson University, Technical University of Munich, University of Texas at Arlington
  • Publish Date: 2025.06.13
  • Task: Perception
  • Code: V2LM
  • Summary:
    • This Paper introduce Vehicle Vision Language Models (V2LMs), fine-tuned VLMs specifically for AV perception task.

• STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving

  • Christian Fruhwirth-Reisinger, Dušan Malić, Wei Lin, David Schinagl, Samuel Schulter, Horst Possegger
  • Publisher: Graz University of Technology, Christian Doppler Laboratory for Embedded Machine Learning, Johannes Kepler University Linz, Amazon
  • Publish Date: 2025.06.06
  • Task: QA
  • Code: STSBench
  • Dataset: STSBench
  • Summary:
    • STSBench, a framework for automatic scenario mining from large-scale autonomous driving datasets with rich ground truth annotations.
    • Applied to the NuScenes dataset, present STSnu, the first benchmark that evaluates the spatio-temporal reasoning capabilities of VLMs based on comprehensive 3D perception.

• Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving

  • Hao Jiang, Chuan Hu, Yukang Shi, Yuan He, Ke Wang, Xi Zhang, Zhipeng Zhang
  • Publisher: Shanghai Jiao Tong University, KargoBot
  • Publish Date: 2025.06.05
  • Task: VQA
  • Datasets: nuScenes
  • Summary:
    • The paper introduces a structured and concise benchmark dataset, NuScenes-S, which is derived from the NuScenes dataset and contains machine-friendly structured representations.

• AD-EE: Early Exiting for Fast and Reliable Vision-Language Models in Autonomous Driving

  • Lianming Huang, Haibo Hu, Yufei Cui, Jiacheng Zuo, Shangyu Wu, Nan Guan, Chun Jason Xue
  • Publisher: City University of Hong Kong, McGill University, MBZUAI, Soochow University
  • Publish Date: 2025.06.04
  • Task: Perception, VQA
  • Dataset: Waymo, corner-case-focused CODA
  • Summary:
    • AD-EE, an Early Exit framework incorporates domain characteristics of autonomous driving and leverages causal inference to identify optimal exit layers.
    • AD-EE propose a Causal Inference-based approach to identify and analyze the optimal early exit layers for enhanced VLM inference.

• DriveRX: A Vision-Language Reasoning Model for Cross-Task Autonomous Driving

  • Muxi Diao, Lele Yang, Hongbo Yin, Zhexu Wang, Yejie Wang, Daxin Tian, Kongming Liang, Zhanyu Ma
  • Publisher: Beijing University of Posts and Telecommunications, Zhongguancun Academy, Beihang University
  • Publish Date: 2025.05.27
  • Project Page: DriveRX
  • Task: VQA
  • Summary:
    • AutoDriveRL, a unified training framework that formulates autonomous driving as a structured reasoning process over four core tasks. Each task is independently modeled as a vision-language QA problem and optimized using task-specific reward models, enabling fine-grained reinforcement signals at different reasoning stages.
    • Within this framework, train DriveRX, a cross-task reasoning VLM designed for real-time decision-making.

• FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving

  • Shuang Zeng, Xinyuan Chang, Mengwei Xie, Xinran Liu, Yifan Bai, Zheng Pan, Mu Xu, Xing Wei
  • Publisher: Alibaba Group, Xi’an Jiaotong University
  • Publish Date: 2025.05.23
  • Task: Generation, Planning
  • Code: FSDrive
  • Datasets: nuScenes
  • Summary:
    • A spatio-temporal CoT reasoning method that allows the model to enhance trajectory planning by thinking visually from future temporal and spatial dimensions.
    • A unified pre-training paradigm for visual generation and understanding.

• Human-like Semantic Navigation for Autonomous Driving using Knowledge Representation and Large Language Models

  • Augusto Luis Ballardini, Miguel Ángel Sotelo
  • Publisher: University of Alcal ́a
  • Publish Date: 2025.05.22
  • Task: QA
  • Summary:
    • The paper explores the use of Large Language Models to generate Answer Set Programming rules by translating informal navigation instructions into structured, logicbased reasoning.

• VL-SAFE: Vision-Language Guided Safety-Aware Reinforcement Learning with World Models for Autonomous Driving

  • Yansong Qu, Zilin Huang, Zihao Sheng, Jiancong Chen, Sikai Chen, Samuel Labi
  • Publisher: Purdue University, University of Wisconsin-Madison,
  • Publish Date: 2025.05.22
  • Project Page: VL-SAFE
  • Code: VL-SAFE
  • Task: Framework
  • Summary:
    • VL-SAFE, a world model-based safe RL framework with Vision-Language model (VLM)-as safety guidance paradigm, designed for offline safe policy learning.

• DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving

  • Zhenjie Yang, Yilin Chai, Xiaosong Jia, Qifeng Li, Yuqian Shao, Xuekai Zhu, Haisheng Su, Junchi Yan
  • Publisher: Shanghai Jiao Tong University
  • Publish Date: 2025.05.22
  • Project Pages: DriveMoE
  • Code: DriveMoE
  • Dataset: Bench2Drive
  • Task: Planning
  • Summary:
    • DriveMoE, a novel MoE-based E2E-AD framework, with a Scene-Specialized Vision MoE and a Skill-Specialized Action MoE.
    • DriveMoE is built upon our Vision-Language-Action (VLA) baseline (originally from the embodied AI field), called Drive-π0.

• AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving

  • Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang
  • Publisher: Tsinghua University, McGill University, Xiaomi Corporation, University of Wisconsin – Madison
  • Publish Date: 2025.06.12
  • Task: VQA
  • Summary:
    • AgentThink, the first framework to integrate dynamic, agent-style tool invocation into vision-language reasoning for autonomous driving tasks.
    • Two-stage training pipeline that combines SFT with GRPO.

• Extending Large Vision-Language Model for Diverse Interactive Tasks in Autonomous Driving

  • Zongchuang Zhao, Haoyu Fu, Dingkang Liang, Xin Zhou, Dingyuan Zhang, Hongwei Xie, Bing Wang, Xiang Bai
  • Publisher: Huazhong University of Science and Technology, Xiaomi EV
  • Publish Date: 2025.05.13
  • Task: VQA
  • Code: DriveMonkey
  • Summary:
    • NuInteract, a large-scale dataset for advancing LVLMs in autonomous driving. With 239K images,34K frames, and over 1.5M image-language pairs across 850 scenes, NuInteract provides dense captions detailing the surrounding environment and 2D/3D annotations for tasks like 2D/3D visual grounding, enabling comprehensive perception, prediction, and planning.
    • DriveMonkey, a flexible framework supporting multiple interactive tasks via user prompts.

• Towards Human-Centric Autonomous Driving: A Fast-Slow Architecture Integrating Large Language Model Guidance with Reinforcement Learning

  • Chengkai Xu, Jiaqi Liu, Yicheng Guo, Yuhang Zhang, Peng Hang, Jian Sun
  • Publisher: Tongji University
  • Publish Date: 2025.05.11
  • Task: Planning
  • Env: Highway-Env
  • Summary:
    • A “fast-slow” decision-making framework that integrates a Large Language Model (LLM) for high-level instruction parsing with a Reinforcement Learning (RL) agent for low-level real-time decision.

• Natural Reflection Backdoor Attack on Vision Language Model for Autonomous Driving

  • Ming Liu, Siyuan Liang, Koushik Howlader, Liwen Wang, Dacheng Tao, Wensheng Zhang
  • Publisher: Iowa State University, National University of Singapore
  • Publish Date: 2025.05.09
  • Task: VQA
  • Datasets: DriveLM
  • Summary:
    • It proposes a natural reflection-based backdoor attack targeting VLM systems in autonomous driving scenarios, aiming to induce substantial response delays when specific visual triggers are present.

• DSDrive: Distilling Large Language Model for Lightweight End-to-End Autonomous Driving with Unified Reasoning and Planning

  • Wenru Liu, Pei Liu, Jun Ma
  • Publisher: The Hong Kong University of Science and Technology
  • Publish Date: 2025.05.08
  • Task: Planning
  • Video: DSDrive
  • Summary:
    • DSDrive, a lightweight E2E AD framework that employs a compact LLM to process multi-modal inputs for explicit reasoning and closed-loop planning. Specifically, we utilize knowledge distillation to empower the compact LLM to undertake the reasoning and planning tasks, thereby improving its overall performance.
    • A novel waypoint-driven dual-head coordination module that bridges high-level reasoning and lowlevel trajectory planning.

• X-Driver: Explainable Autonomous Driving with Vision-Language Models

  • Wei Liu, Jiyuan Zhang, Binxiong Zheng, Yufeng Hu, Yingzhan Lin, Zengfeng Zeng
  • Publisher: Harbin Institute of Technology, Baidu Inc
  • Publish Date: 2025.05.08
  • Task: VQA, Planning
  • Dataset: Bench2Drive
  • Summary:
    • X-Driver, a unified multi-modal large language models(MLLMs) framework designed for closed-loop autonomous driving, leveraging Chain-of-Thought(CoT) and autoregressive modeling to enhance perception and decisionmaking.

• Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models

  • Yuewen Mei, Tong Nie, Jian Sun, Ye Tian
  • Publisher: Tongji University, The Hong Kong Polytechnic University,
  • Publish Date: 2025.05.02
  • Task: Generation
  • Dataset: Waymo
  • Summary:
    • An LLM-based agent framework is proposed to generate interactive and safety-critical scenarios online.
    • A memorization and retrieval mechanism is developed to continuously adapt LLMs to changing scenarios.

• V3LMA: Visual 3D-enhanced Language Model for Autonomous Driving

  • Jannik Lübberstedt, Esteban Rivera, Nico Uhlemann, Markus Lienkamp
  • Publisher: Technical University of Munich, Munich Institute of Robotics and Machine Intelligenc
  • Publish Date: 2025.04.30
  • Task: VQA
  • Datasets: LingoQA
  • Summary:
    • Proposal and evaluation of V3LMA, a novel method that combines the strengths of LLMs and LVLMs to enhance 3D scene understanding in traffic scenarios—without requiring model training or fine-tuning.

• Enhancing Autonomous Driving Systems with On-Board Deployed Large Language Models

  • Nicolas Baumann, Cheng Hu, Paviththiren Sivasothilingam, Haotong Qin, Lei Xie, Michele Magno, Luca Benini RSS 2025
  • Publisher: ETH Zurich, Zhejiang University
  • Publish Date: 2025.04.15
  • Task: Planning
  • Summary:
    • A hybrid architecture combining low level Model Predictive Controller (MPC) with locally deployed Large Language Models (LLMs) to enhance decision-making and Human Machine Interaction (HMI).

• NuScenes-SpatialQA: A Spatial Understanding and Reasoning Benchmark for Vision-Language Models in Autonomous Driving

  • Kexin Tian, Jingrui Mao, Yunlong Zhang, Jiwan Jiang, Yang Zhou, Zhengzhong Tu
  • Publisher: Texas A&M University, University of Wisconsin-Madison
  • Publish Date: 2025.04.07
  • Project Page: NuScenes-SpatialQA
  • Task: VQA
  • Summary:
    • NuScenes-SpatialQA, the first large-scale ground-truth-based Question-Answer (QA) benchmark specifically designed to evaluate the spatial understanding and reasoning capabilities of VLMs in autonomous driving.

• OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model

  • Xingcheng Zhou, Xuyuan Han, Feng Yang, Yunpu Ma, Alois C. Knoll
  • Publisher: Technical University of Munich, Ludwig Maximilian University of Munich
  • Publish Date: 2025.03.30
  • Project Page: OpenDriveVLA
  • Code: OpenDriveVLA
  • Task: VQA, Planning
  • Summary:
    • OpenDriveVLA, a Vision-Language Action (VLA) model designed for end-to-end autonomous driving.

• VLM-C4L: Continual Core Dataset Learning with Corner Case Optimization via Vision-Language Models for Autonomous Driving

  • Haibo Hu, Jiacheng Zuo, Yang Lou, Yufei Cui, Jianping Wang, Nan Guan, Jin Wang, Yung-Hui Li, Chun Jason Xue COLM 2025
  • Publisher: City University of Hong Kong, Soochow University, McGill University, Hon Hai Research Institute, Mohamed bin Zayed University of Artificial Intelligence
  • Publish Date: 2025.03.29
  • Project Pages: VLM-C4L
  • Task: Perception
  • Summary:
    • VLM-C4L, a continual learning framework that introduce Vision-Language Models (VLMs) to dynamically optimize and enhance corner case datasets, and VLM-C4L combines VLM-guided high-quality data extraction with a core data replay strategy, enabling the model to incrementally learn from diverse corner cases while preserving performance on previously routine scenarios, thus ensuring long-term stability and adaptability in real-world autonomous driving.

• Fine-Grained Evaluation of Large Vision-Language Models in Autonomous Driving

  • Yue Li, Meng Tian, Zhenyu Lin, Jiangtong Zhu, Dechang Zhu, Haiqiang Liu, Zining Wang, Yueyi Zhang, Zhiwei Xiong, Xinhai Zhao
  • Publisher: University of Science and Technology of China, Huawei Noah’s Ark Lab, University of California, Berkeley
  • Publish Date: 2025.03.27
  • Code: VLADBench
  • Task: VQA
  • Summary:
    • VLADBench, specifically designed to rigorously evaluate the capabilities of VLMs in AD. VLADBench employes a hierarchical structure that reflects the complex skill set required for reliable driving, progressing from fundamental scene and traffic elements comprehension to advanced reasoning and decision-making.

• ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation

  • Haoyu Fu, Diankun Zhang, Zongchuang Zhao, Jianfeng Cui, Dingkang Liang, Chong Zhang, Dingyuan Zhang, Hongwei Xie, Bing Wang, Xiang Bai
  • Publisher: Huazhong University of Science and Technology, Xiaomi EV
  • Publish Data: 2025.03.25
  • Task: Planning
  • Project Page: ORION
  • Code: ORION
  • Dataset: Bench2Drive
  • Summary:
    • ORION, a hOlistic E2E autonomous dRiving framework by vIsion-language instructed actiON generation. ORION uniquely combines a QT-Former to aggregate long-term history context, a Large Language Model (LLM) for driving scenario reasoning, and a generative planner for precision trajectory prediction.

• AED: Automatic Discovery of Effective and Diverse Vulnerabilities for Autonomous Driving Policy with Large Language Models

  • Le Qiu, Zelai Xu, Qixin Tan, Wenhao Tang, Chao Yu, Yu Wang
  • Publisher: Tsinghua University, Beijing Zhongguancun Academy
  • Publish Date: 2025.03.24
  • Task: Planning
  • Env: Highway-Env
  • Summary:
    • AED, a framework that uses large language models (LLMs) to Automatically discover Effective and Diverse vulnerabilities in autonomous driving policies.
    • AED first utilize an LLM to automatically design reward functions for RL training.

• AutoDrive-QA- Automated Generation of Multiple-Choice Questions for Autonomous Driving Datasets Using Large Vision-Language Models

  • Boshra Khalili, Andrew W.Smyth
  • Publisher: Columbia University
  • Publish Date: 2025.03.20
  • Task: VQA
  • Summary:
    • AutoDrive-QA, Automatic pipeline that converts existing driving QA datasets (including DriveLM, NuScenes-QA, and LingoQA) into a structured multiple-choice question (MCQ) format.

• RAD: Retrieval-Augmented Decision-Making of Meta-Actions with Vision-Language Models in Autonomous Driving

  • Yujin Wang, Quanfeng Liu, Zhengxin Jiang, Tianyi Wang, Junfeng Jiao, Hongqing Chu, Bingzhao Gao, Hong Chen
  • Publisher: Tongji University, Yale University, University of Texas at Austin
  • Publish Date: 2025.03.18
  • Task: VQA
  • Summary:
    • Propose a retrieval-augmented decision-making (RAD) framework, a novel architecture designed to enhance VLMs’ capabilities to reliably generate meta-actions in autonomous driving scenes.

• A Framework for a Capability-driven Evaluation of Scenario Understanding for Multimodal Large Language Models in Autonomous Driving

  • Tin Stribor Sohn, Philipp Reis, Maximilian Dillitzer, Johannes Bach, Jason J. Corso, Eric Sax
  • Publisher: Dr. Ing. h.c. F. Porsche AG, Forschungszentrum Informatik，Hochschule Esslingen, University of Michigan, Karlsruher Institut f ̈ur Technologie
  • Publish Date: 2025.03.14
  • Task: Evaluation
  • Summary:
    • This paper proposes a holistic framework for a capability-driven evaluation of MLLMs in autonomous driving. The framework structures scenario understanding along the four core capability dimensions semantic, spatial, temporal, and physical.

• DynRsl-VLM: Enhancing Autonomous Driving Perception with Dynamic Resolution Vision-Language Models

  • Xirui Zhou, Lianlei Shan, Xiaolin Gui
  • Publisher: Xi’an Jiaotong University, University of Chinese Academy of Sciences
  • Publish Date: 2025.03.14
  • Task: VQA
  • Summary:
    • DynRsl-VLM incorporates a dynamic resolution image input processing approach that captures all entity feature information within an image while ensuring that the image input remains computationally tractable for the Vision Transformer (ViT).

• SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment

  • Katrin Renz, Long Chen, Elahe Arani, Oleg Sinavski CVPR 2025
  • Publisher: Wayve, University of T ̈ubingen, T ̈ubingen AI Center
  • Publish Date: 2025.03.12
  • Task: Planning
  • Datasets: Carla Leadboard V2, Bench2Drive
  • Summary:
    • A VLM-based driving model that achieves state-of-the-art driving performance on the official CARLA Leaderboard 2.0 and the local benchmark Bench2Drive in the CARLA simulator. (2) A new task (Action Dreaming), which comes with a methodology to collect instruction-action pairs and a benchmark to evaluate the connection of language and action understanding without having to execute unsafe actions. (3) A generalist model that achieves not only good driving performance but also includes several language related tasks in the same model.

• CoT-Drive: Efficient Motion Forecasting for Autonomous Driving with LLMs and Chain-of-Thought Prompting

  • Haicheng Liao, Hanlin Kong, Bonan Wang, Chengyue Wang, Wang Ye, Zhengbing He, Chengzhong Xu, Zhenning Li IEEE TAI 2025
  • Publisher: University of Macau, Massachusetts Institute of Technology
  • Publish Date: 2025.03.10
  • Task: VQA
  • Summary:
    • Introduce a teacher-student knowledge distillation strategy to effectively transfer LLMs’ advanced scene understanding capabilities to lightweight language models (LMs), ensuring that CoT-Drive operates in real-time on edge devices while maintaining comprehensive scene understanding and generalization capabilities.

• Evaluation of Safety Cognition Capability in Vision-Language Models for Autonomous Driving

  • Enming Zhang, Peizhe Gong, Xingyuan Dai, Yisheng Lv, Qinghai Miao
  • Publisher: University of Chinese Academy of Sciences,
  • Publish Date: 2025.03.09
  • Code: SCD-Bench
  • Task: Evaluation
  • Summary:
    • Propose a novel evaluation method: Safety Cognitive Driving Benchmark (SCD-Bench) and debelop the Autonomous Driving Image-Text Annotation System (ADA).

• VLM-E2E: Enhancing End-to-End Autonomous Driving with Multimodal Driver Attention Fusion

  • Pei Liu, Haipeng Liu, Haichao Liu, Xin Liu, Jinxin Ni, Jun Ma
  • Publisher: The Hong Kong University of Science and Technology (Guangzhou), Li Auto Inc., Xiamen University, The Hong Kong University of Science and Technology
  • Publish Date: 2025.02.25
  • Task: Planning
  • Datasets: nuScenes
  • Summary:
    • VLME2E, a novel framework that uses the VLMs to enhance training by providing attentional cues.
    • Integrates textual representations into Bird’s-Eye-View (BEV) features for semantic supervision, which enables the model to learn richer feature representations that explicitly capture the driver’s attentional semantics.

• CurricuVLM: Towards Safe Autonomous Driving via Personalized Safety-Critical Curriculum Learning with Vision-Language Models

  • Zihao Sheng, Zilin Huang, Yansong Qu, Yue Leng, Sruthi Bhavanam, Sikai Chen
  • Publisher: University of Wisconsin-Madison, Purdue University
  • Publish Date: 2025.02.21
  • Task: Planning
  • Project Page: CurricuVLM
  • Datasets: MetaDrive, Waymo
  • Summary:
    • CurricuVLM, a novel framework that leverages Vision-Language Models (VLMs) to enable personalized curriculum learning for autonomous driving agents.
    • CurricuVLM is the first work to utilize VLMs for dynamic curriculum generation in closed-loop autonomous driving training.

• V2V-LLM: Vehicle-to-Vehicle Cooperative Autonomous Driving with Multi-Modal Large Language Models

  • Hsu-kuang Chiu, Ryo Hachiuma, Chien-Yi Wang, Stephen F. Smith, Yu-Chiang Frank Wang, Min-Hung Chen
  • Publisher: NVIDIA, Carnegie Mellon University
  • Publish Date: 2025.02.14
  • Task: VQA
  • Summary:
    • Create and introduce the V2V-QA dataset to support the development and evaluation of LLM-based approaches to end-to-end cooperative autonomous driving.
    • Propose a baseline method V2V-LLM for cooperative autonomous driving to provide an initial benchmark for V2V-QA.

• Occ-LLM: Enhancing Autonomous Driving with Occupancy-Based Large Language Models

  • Tianshuo Xu, Hao Lu, Xu Yan, Yingjie Cai, Bingbing Liu, Yingcong Chen ICRA 2025
  • Publisher: Hong Kong University of Science and Technology (Guangzhou), Huawei Noah’s Ark Lab
  • Publish Date: 2025.02.10
  • Task: Perception, Planning
  • Dataset: nuScenes
  • Summary:
    • Introduce an occupancy-based large language model (Occ-LLM) for autonomous driving, demonstrating superior scene comprehension.

• INSIGHT: Enhancing Autonomous Driving Safety through Vision-Language Models on Context-Aware Hazard Detection and Edge Case Evaluation

  • Dianwei Chen, Zifan Zhang, Yuchen Liu, Xianfeng Terry Yang
  • Publisher: University of Maryland, North Carolina State University
  • Publish Date: 2025.02.01
  • Task: VQA
  • Summary:
    • INSIGHT (Integration of Semantic and Visual Inputs for Generalized Hazard Tracking), a hierarchical vision-language model (VLM) framework designed to enhance hazard detection and edge-case evaluation.

• LLM-attacker: Enhancing Closed-loop Adversarial Scenario Generation for Autonomous Driving with Large Language Models

  • Yuewen Mei, Tong Nie, Jian Sun, Ye Tian IEEE TITS 2025
  • Publisher: Tongji University, The Hong Kong Polytechnic University
  • Publish Date: 2025.01.27
  • Viddeo: LLM-attacker
  • Task: Generation
  • Dataset: MetaDrive, Waymo
  • Summary:
    • LLM-attacker: a closedloop adversarial scenario generation framework leveraging large language models (LLMs).

• Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving

  • Lu Wang, Tianyuan Zhang, Yang Qu, Siyuan Liang, Yuwei Chen, Aishan Liu, Xianglong Liu, Dacheng Tao
  • Publisher: Beihang University, National University of Singapore, Aviation Industry Development Research Center of China, Nanyang Technological University
  • Publish Date: 2025.01.23
  • Task: VQA
  • Summary:
    • Cascading Adversarial Disruption (CAD) first introduces Decision Chain Disruption, which targets low-level reasoning breakdown by generating and injecting deceptive semantics, ensuring the perturbations remain effective across the entire decision-making chain.

• Distilling Multi-modal Large Language Models for Autonomous Driving

  • Deepti Hegde, Rajeev Yasarla, Hong Cai, Shizhong Han, Apratim Bhattacharyya, Shweta Mahajan, Litian Liu, Risheek Garrepalli, Vishal M. Patel, Fatih Porikli
  • Publisher: Johns Hopkins University, Qualcomm AI Research
  • Publish Date: 2025.01.16
  • Task: Planning
  • Summary:
    • DiMA, an end-to-end autonomous driving framework that distills knowledge from an MLLM to a vision-based planner to ensure robustness to long-tail events while maintaining efficiency.
    • Propose a distillation task along with the following surrogate tasks to align the objectives of the vision-based planner and the MLLM: (i) masked token reconstruction (ii) future token prediction (iii) scene editing.

• Modeling Language for Scenario Development of Autonomous Driving Systems

  • Toshiaki Aoki, Takashi Tomita, Tatsuji Kawai, Daisuke Kawakami, Nobuo Chida
  • Publisher: JAIST, Kochi University, Mitsubishi Electric Corporation
  • Publish Date: 2025.01.16
  • Task: Development
  • Summary:
    • This study introduces a notation called the car position diagram (CPD). The CPD allows for the concise representation of numerous scenarios and is particularly suitable for scenario analysis and design.

• Application of Vision-Language Model to Pedestrians Behavior and Scene Understanding in Autonomous Driving

  • Haoxiang Gao, Yu Zhao
  • Publisher: Motional AD LLC, University of Toronto
  • Publish Date: 2025.01.12
  • Task: VQA
  • Summary:
    • Analyze effective knowledge distillation of LLM semantic labels to smaller Vision networks, which can be used for the semantic representation of complex scenes for downstream decision-making for planning and control.

• DriVLM: Domain Adaptation of Vision-Language Models in Autonomous Driving

  • Xuran Zheng, Chang D. Yoo
  • Publisher: KAIST
  • Publish Date: 2025.01.09
  • Task: VQA
  • Summary:
    • Explore the utility of small-scale MLLMs and applied small-scale MLLMs to the field of autonomous driving.

• Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding

  • Mohammed Elhenawy, Huthaifa I. Ashqar, Andry Rakotonirainy, Taqwa I. Alhadidi, Ahmed Jaber, Mohammad Abu Tami
  • Publisher: Queensland University of Technology, Arab American University, Columbia University
  • Publish Date: 2025.01.09
  • Task: Scene Understanding
  • Summary:
    • This study developed a dynamic scene retrieval system using Contrastive Language–Image Pretraining (CLIP) models, which can be optimized for real-time deployment on edge devices.

• Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner

  • Aizierjiang Aiersilan AAAI 2025 Oral
  • Publisher: University of Macau
  • Publish Date: 2024.12.24
  • Task: Generation
  • Env : CARLA
  • Project Pages: AutoSceneGen
  • Code: AutoSceneGen
  • Summary:
    • A universal, general, and cost-effective framework, “AutoSceneGen”, is proposed to automatically enhance the heterogeneity of traffic scenarios through scenario descriptions, thereby accelerating the simulation and testing process.

• Large Language Model guided Deep Reinforcement Learning for Decision Making in Autonomous Driving

  • Hao Pang, Zhenpo Wang, Guoqiang Li
  • Publisher: Beijing Institute of Technology
  • Publish Date: 2024.12.24
  • Task: Planning
  • Code: LGDRL
  • Env: HighwayEnv
  • Summary:
    • Propose a novel large language model (LLM) guided deep reinforcement learning (LGDRL) framework for addressing the decision-making problem of autonomous vehicles. Within this framework, an LLM-based driving expert is integrated into the DRL to provide intelligent guidance for the learning process of DRL.

• Application of Multimodal Large Language Models in Autonomous Driving

  • Md Robiul Islam
  • Publisher: William & Mary
  • Publish Date: 2025.12.21
  • Task: VQA
  • Summary:
    • Conduct a Virtual Question Answering (VQA) dataset to fine-tune the model and address problems with the poor performance of MLLM on AD.

• VLM-RL: A Unified Vision Language Models and Reinforcement Learning Framework for Safe Autonomous Driving

  • Zilin Huang, Zihao Sheng, Yansong Qu, Junwei You, Sikai Chen
  • Publisher: University of Wisconsin-Madison, Purdue University,
  • Publish Date: 2024.12.20
  • Task: Reward Design
  • Project Page: VLM-RL
  • Summary:
    • VLM-RL, a unified framework that integrates pre-trained Vision-Language Models (VLMs) with RL to generate reward signals using image observation and natural language goals.

• AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving

  • Shuo Xing, Hongyuan Hua, Xiangbo Gao, Shenzhe Zhu, Renjie Li, Kexin Tian, Xiaopeng Li, Heng Huang, Tianbao Yang, Zhangyang Wang, Yang Zhou, Huaxiu Yao, Zhengzhong Tu
  • Publisher: Texas A&M University, University of Toronto, University of Michigan, University of Wisconsin-Madison, University of Maryland, University of Texas at Austin, University of North Carolina at Chapel Hill
  • Publish Date: 2024.12.19
  • Task: VQA
  • Code: AutoTrust
  • Leaderboard: AutoTrust
  • Summary:
    • AutoTrust is a groundbreaking benchmark designed to assess the trustworthiness of DriveVLMs. This work aims to enhance public safety by ensuring DriveVLMs operate reliably across critical dimensions.

• VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision

  • Yi Xu, Yuxin Hu, Zaiwei Zhang, Gregory P. Meyer, Siva Karthik Mustikovela, Siddhartha Srinivasa, Eric M. Wolff, Xin Huang
  • Publisher: Cruise LLC, Northeastern University
  • Publish Date: 2024.12.19
  • Task: Planning
  • Dataset: nuScenes
  • Summary:
    • VLM-AD, a method that leverages vision-language models (VLMs) as teachers to enhance training by providing additional supervision that incorporates unstructured reasoning information and structured action labels.

• RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Models

  • Yujin Wang, Quanfeng Liu, Jiaqi Fan, Jinlong Hong, Hongqing Chu, Mengjian Tian, Bingzhao Gao, Hong Chen
  • Publisher: Tongji University, Shenzhen Technology University
  • Publish Date: 2024.12.15
  • Task: VQA
  • Datasets: CODA-LM
  • Summary:
    • RAC3, a novel framework designed to enhance the performance of VLMs in corner case comprehension.
    • RAC3 integrates a frequencyspatial fusion (FSF) image encoder, a cross-modal alignment training method for embedding models with hard and semihard negative mining, and a fast querying and retrieval pipeline based on K-Means clustering and hierarchical navigable small world (HNSW) indexing.

• WiseAD: Knowledge Augmented End-to-End Autonomous Driving with Vision-Language Model

  • Songyan Zhang, Wenhui Huang, Zihui Gao, Hao Chen, Chen Lv
  • Publisher: Nanyang Technology University, Zhejiang University
  • Publish Date: 2024.12.13
  • Task: Planning
  • Summary:
    • Investigate the effects of the depth and breadth of fundamental driving knowledge on closed-loop trajectory planning and introduce WiseAD, a specialized VLM tailored for end-to-end autonomous driving capable of driving reasoning, action justification, object recognition, risk analysis, driving suggestions, and trajectory planning across diverse scenarios.

• PKRD-CoT: A Unified Chain-of-thought Prompting for Multi-Modal Large Language Models in Autonomous Driving

  • Xuewen Luo, Fan Ding, Yinsheng Song, Xiaofeng Zhang, Junnyong Loo ICONIP 2024
  • Publisher: Monash University Malaysia
  • Task: QA, Prompt Engineer
  • Publish Date: 2024.12.02
  • Summary:
    • PKRD-CoT is constructed based on the four fundamental capabilities of autonomous driving—perception, knowledge, reasoning, and decision-making.

• Visual Adversarial Attack on Vision-Language Models for Autonomous Driving

  • Tianyuan Zhang, Lu Wang, Xinwei Zhang, Yitong Zhang, Boyi Jia, Siyuan Liang, Shengshan Hu, Qiang Fu, Aishan Liu, Xianglong Liu
  • Publisher: Beihang University, National University of Singapore, Huazhong University of Science and Technology
  • Task: VQA
  • Publish Date: 2024.11.27
  • Summary:
    • ADvLM, the first visual adversarial attack framework specifically designed for VLMs in AD.
    • Semantic-Invariant Induction in the textual domain and Scenario-Associated Enhancement in the visual domain, ensuring attack effectiveness across varied instructions and sequential viewpoints.

• Explanation for Trajectory Planning using Multi-modal Large Language Model for Autonomous Driving

  • Shota Yamazaki, Chenyu Zhang, Takuya Nanri, Akio Shigekane, Siyuan Wang, Jo Nishiyama, Tao Chu, Kohei Yokosawa ECCV 2024 VCAD Workshop
  • Publisher: Nissan Motor Co., Ltd,
  • Publish Date: 2024.11.15
  • Task: VQA
  • Summary:
    • Propose a reasoning model that takes future planning trajectories of the ego vehicle as input to generate reasoning text.

• Integrating Object Detection Modality into Visual Language Model for Enhanced Autonomous Driving Agent

  • Linfeng He, Yiming Sun, Sihao Wu, Jiaxu Liu, Xiaowei Huang NeurIPS 2024 SafeGenAI Workshop
  • Publisher: University of Liverpool, University of Nottingham
  • Publish Date: 2024.11.08
  • Task: Precption
  • Dataset: DriveLM
  • Summary:
    • Extend the Llama-Adapter architecture by incorporating a YOLOS-based detection network alongside the CLIP perception network, addressing limitations in object detection and localisation.

• Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving

  • Bo Jiang, Shaoyu Chen, Bencheng Liao, Xingyu Zhang, Wei Yin, Qian Zhang, Chang Huang, Wenyu Liu, Xinggang Wang
  • Publisher: Huazhong University of Science and Technology
  • Publish Date: 2024.10.29
  • Task: VQA, Planning
  • Code: Senna
  • Dataset: nuScenes, DriveX
  • Summary:
    • Senna, an autonomous driving system that integrates an LVLM with an end-to-end model, achieving structured planning from high-level decisions to low-level trajectory prediction.

• Words to Wheels: Vision-Based Autonomous Driving Understanding Human Language Instructions Using Foundation Models

  • Chanhoe Ryu, Hyunki Seong, Daegyu Lee, Seongwoo Moon, Sungjae Min, D.Hyunchul Shim
  • Publisher: KAIST, ETRI
  • Publish Date: 2024.10.14
  • Task: VQA
  • Summary:
    • Introduce an innovative application of foundation models, enabling Unmanned Ground Vehicles (UGVs) equipped with an RGB-D camera to navigate to designated destinations based on human language instructions.

• LeGEND: A Top-Down Approach to Scenario Generation of Autonomous Driving Systems Assisted by Large Language Models

  • Shuncheng Tang, Zhenya Zhang, Jixiang Zhou, Lei Lei, Yuan Zhou, Yinxing Xue ASE 2024
  • Publisher: University of Science and Technology of China, Kyushu University, Zhejiang Sci-Tech University
  • Task: Generation
  • Publish Date: 2024.09.16
  • Code: LeGEND
  • Summary:
    • LeGEND, a top-down scenario generation approach that can achieve both criticality and diversity of scenarios.
    • Devise a two-stage transformation, by using an intermediate language, from accident reports to logical scenarios; so, LeGEND involves two LLMs, each in charge of one different stage.
    • Implement LeGEND and demonstrate its effectiveness on Apollo, and we detect 11 types of critical concrete scenarios that reflect different aspects of system defects.

• MiniDrive: More Efficient Vision-Language Models with Multi-Level 2D Features as Text Tokens for Autonomous Driving

  • Enming Zhang, Xingyuan Dai, Yisheng Lv, Qinghai Miao
  • Publisher: University of Chinese Academy of Sciences, CASIA
  • Task: QA
  • Publish Date: 2024.09.14
  • Code: MiniDrive
  • Summary:
    • MiniDrive addresses the challenges of efficient deployment and real-time response in VLMs for autonomous driving systems. It can be fully trained simultaneously on an RTX 4090 GPU with 24GB of memory.
    • Feature Engineering Mixture of Experts (FE-MoE) addresses the challenge of efficiently encoding 2D features from multiple perspectives into text token embeddings, effectively reducing the number of visual feature tokens and minimizing feature redundancy.
    • Dynamic Instruction Adapter through a residual structure, which addresses the problem of fixed visual tokens for the same image before being input into the language model.

• Hint-AD: Holistically Aligned Interpretability in End-to-End Autonomous Driving

  • Kairui Ding, Boyuan Chen, Yuchen Su, Huan-ang Gao, Bu Jin, Chonghao Sima, Wuqiang Zhang, Xiaohui Li, Paul Barsch, Hongyang Li, Hao Zhao CoRL 2024
  • Publisher: Institute for AI Industry Researc, Mercedes-Benz Group China Ltd, Tsinghua University, Shanghai AI Lab
  • Publish Date: 2024.09.10
  • Project Page: Hint-AD
  • Task: VQA
  • Summary:
  • Hint-AD, an integrated AD-language system that generates language aligned with the holistic perception-prediction-planning outputs of the AD model. By incorporating the intermediate outputs and a holistic token mixer sub-network for effective feature adaptation, Hint-AD achieves desirable accuracy, achieving state-of-the-art results in driving language tasks including driving explanation, 3D dense captioning, and command prediction.
  • Contribute a human-labeled driving explanation dataset, Nu-X on nuScenesto address the lack of driving explanation data on this widely-used AD dataset.

• OccLLaMA: An Occupancy-Language-Action Generative World Model for Autonomous Driving

  • Julong Wei, Shanshuai Yuan, Pengfei Li, Qingda Hu, Zhongxue Gan, Wenchao Ding
  • Publisher: Fudan University, Tsinghua University
  • Task: Perception(Occ) + Reasoning
  • Publish Date: 2024.09.05
  • Summary:
    • OccLLaMA, a unified 3D occupancy-language-action generative world model, which unifies VLA-related tasks including but not limited to scene understanding, planning, and 4D occupancy forecasting.
    • A novel scene tokenizer(VQVAE-like architecture) that efficiently discretize and reconstruct Occ scenes, considering sparsity and classes imbalance.

• ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models

  • Shounak Sural, Naren, Ragunathan Rajkumar ITSC 2024
  • Publisher: Carnegie Mellon University
  • Task: Context Recognition
  • Code: ContextVLM
  • Publish Date: 2024.08.30
  • Summary:
    • DrivingContexts, a large publicly-available datasetwith a combination of hand-annotated and machine annnotated labels to improve VLMs for better context recognition.
    • ContextVLM uses vision-language models to detect contexts using zero- and few-shot approaches.

• DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving

  • Yongjie Fu, Anmol Jain, Xuan Di, Xu Chen, Zhaobin Mo IAVVC 2024
  • Publisher: Columbia University
  • Task: Generation
  • Dataset: Waymo open dataset
  • Publish Date: 2024.08.29
  • Summary:
    • DriveGenVLM employ a video generation framework based on Denoising Diffusion Probabilistic Models to create realistic video sequences that mimic real-world dynamics.
    • The videos generated are then evaluated for their suitability in Visual Language Models (VLMs) using a pre-trained model called Efficient In-context Learning on Egocentric Videos (EILEV).

• Edge-Cloud Collaborative Motion Planning for Autonomous Driving with Large Language Models

  • Jiao Chen, Suyan Dai, Fangfang Chen, Zuohong Lv, Jianhua Tang
  • Publisher: South China University of Technology, Pazhou Lab
  • Task: Planning + QA
  • Project Page: EC-Drive
  • Publish Date: 2024.08.19
  • Summary:
    • EC-Drive, a novel edge-cloud collaborative autonomous driving system.

• V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models

  • Junwei You, Haotian Shi, Zhuoyu Jiang, Zilin Huang, Rui Gan, Keshu Wu, Xi Cheng, Xiaopeng Li, Bin Ran
  • Publisher: University of Wisconsin-Madison, Nanyang Technological University, Texas A&M University, Cornell University
  • Task: Planning
  • Projcet Page: V2X-VLM
  • Code: V2X-VLM
  • Dataset: DAIR-V2X
  • Publish Date: 2024.08.09
  • Summary:
    • V2X-VLM, a large vision-language model empowered E2E VICAD framework, which improves the ability of autonomous vehicles to navigate complex traffic scenarios through advanced multimodal understanding and decision-making.
    • A contrastive learning technique is employed to refine the model’s ability to distinguish between relevant and irrelevant features, which ensures that the model learns robust and discriminative representations of specific driving environments, leading to improved accuracy in trajectory planning in V2X cooperation scenarios.

• AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging

  • Senkang Hu, Zhengru Fang, Zihan Fang, Yiqin Deng, Xianhao Chen, Yuguang Fang, Sam Kwong IEEE TMC
  • Publisher: City University of Hong Kong, The University of Hong Kong, Lingnan University
  • Task: Multi Agent Planning
  • Publish Date: 2024.08.07
  • Summary:
    • AgentsCoMerge, a large language model empowered collaborative decision making for ramp merging. It includes observation, planning, communication, and reinforcement training modules. Experiments demonstrate its effectiveness in improving multi-agent collaboration and efficiency.

• VLM-MPC: Vision Language Foundation Model (VLM)-Guided Model Predictive Controller (MPC) for Autonomous Driving

  • Keke Long, Haotian Shi, Jiaxi Liu, Xiaopeng Li
  • Publisher: University of Wisconsin-Madison
  • Task: Planning
  • Publish Date: 2024.08.04
  • Summary:
    • It proposed a closed-loop autonomous driving controller that applies VLMs for high-level vehicle control.
    • The upper-level VLM uses the vehicle's front camera images, textual scenario description, and experience memory as inputs to generate control parameters needed by the lower-level MPC.
    • The lower-level MPC utilizes these parameters, considering vehicle dynamics with engine lag, to achieve realistic vehicle behavior and provide state feedback to the upper level.
    • This asynchronous two-layer structure addresses the current issue of slow VLM response speeds.

• SimpleLLM4AD: An End-to-End Vision-Language Model with Graph Visual Question Answering for Autonomous Driving

  • Peiru Zheng, Yun Zhao, Zhan Gong, Hong Zhu, Shaohua Wu IEIT Systems
  • Publisher: University of Wisconsin-Madison
  • Task: QA
  • Publish Date: 2024.07.31
  • Summary:
    • SimpleLLM4AD reimagines the traditional autonomous driving pipeline by structuring the task into four interconnected stages: perception, prediction, planning, and behavior.
    • Each stage is framed as a series of visual question answering (VQA) pairs, which are interlinked to form a Graph VQA (GVQA). This graph-based structure allows the system to reason about each VQA pair systematically, ensuring a coherent flow of information and decision-making from perception to action.

• Testing Large Language Models on Driving Theory Knowledge and Skills for Connected Autonomous Vehicles

  • Zuoyin Tang, Jianhua He, Dashuai Pei, Kezhong Liu, Tao Gao
  • Publisher: Aston University, Essex University, Wuhan University of Technology, Chang’An University
  • Task: Evaluation
  • Publish Date: 2024.07.24
  • Data: UK Driving Theory Test Practice Questions and Answers
  • Summary:
    • Design and run driving theory tests for several proprietary LLM models (OpenAI GPT models, Baidu Ernie and Ali QWen) and open-source LLM models (Tsinghua MiniCPM-2B and MiniCPM-Llama3-V2.5) with more than 500 multiple-choices theory test questions.

• KoMA: Knowledge-driven Multi-agent Framework for Autonomous Driving with Large Language Models

  • Kemou Jiang, Xuan Cai, Zhiyong Cui, Aoyong Li, Yilong Ren, Haiyang Yu, Hao Yang, Daocheng Fu, Licheng Wen, Pinlong Cai
  • Publisher: Beihang University, Johns Hopkins University, Shanghai Artificial Intelligence Laboratory
  • Task: Multi Agent Planning
  • Env: HighwayEnv
  • Project Page: KoMA
  • Publish Date: 2024.07.19
  • Summary:
    • Introduce a knowledge-driven autonomous driving framework KoMA that incorporates multiple agents empowered by LLMs, comprising five integral modules: Environment, Multi-agent Interaction, Multi-step Planning, Shared Memory, and Ranking-based Reflection.

• WOMD-Reasoning: A Large-Scale Language Dataset for Interaction and Driving Intentions Reasoning

  • Yiheng Li, Chongjian Ge, Chenran Li, Chenfeng Xu, Masayoshi Tomizuka, Chen Tang, Mingyu Ding, Wei Zhan
  • Publisher: UC Berkeley, UT Austin
  • Task: Dataset + Reasoning
  • Publish Date: 2024.07.05
  • Datasets: WOMD-Reasoning
  • Summary:
    • WOMD-Reasoning, a language dataset centered on interaction descriptions and reasoning. It provides extensive insights into critical but previously overlooked interactions induced by traffic rules and human intentions.
    • Develop an automatic language labeling pipeline, leveraging a rule-based translator to interpret motion data into language descriptions, and a set of manual prompts for ChatGPT to generate Q&A pairs.

• Exploring the Potential of Multi-Modal AI for Driving Hazard Prediction

  • Korawat Charoenpitaks, Van-Quang Nguyen, Masanori Suganuma, Masahiro Takahashi, Ryoma Niihara, Takayuki Okatani IEEE TIV 2024
  • Publisher: Tohoku University, RIKEN Center for AIP, DENSO CORPORATION
  • Task: Prediction
  • Code: DHPR
  • Publish Date: 2024.06.21
  • Summary:
    • DHPR (Driving Hazard Prediction and Reasoning) dataset, consists of 15K dashcam images of street scenes, and each image is associated with a tuple containing car speed, a hypothesized hazard description, and visual entities present in the scene.
    • Present several baseline methods and evaluate their performance.

• Asynchronous Large Language Model Enhanced Planner for Autonomous Driving

  • Yuan Chen, Zi-han Ding, Ziqin Wang, Yan Wang, Lijun Zhang, Si Liu ECCV 2024
  • Publisher: Beihang University, Tsinghua University
  • Task: Planning
  • Publish Date: 2024.06.20
  • Code: AsyncDriver
  • Datasets: nuPlan Closed-Loop Reactive Hard20
  • Summary:
    • AsyncDriver, a novel asynchronous LLM-enhanced framework, in which the inference frequency of LLM is controllable and can be decoupled from that of the real-time planner.
    • Adaptive Injection Block, which is model-agnostic and can easily integrate scene-associated instruction features into any transformer based real-time planner, enhancing its ability in comprehending and following series of language-based routing instructions.
    • Compared with existing methods, our approach demonstrates superior closedloop evaluation performance in nuPlan’s challenging scenarios.

• A Superalignment Framework in Autonomous Driving with Large Language Models

  • Xiangrui Kong, Thomas Braunl, Marco Fahmi, Yue Wang
  • Publisher: University of Western Australia, Queensland Government, Brisbane, Queensland University of Technology
  • Task: QA
  • Publish Date: 2024.06.09
  • Summary
    • Propose a secure interaction framework for LLMs to effectively audit data interacting with cloud-based LLMs.
    • Analyze 11 autonomous driving methods based on large language models, including driving safety, token usage, privacy, and consistency with human values.
    • Evaluate the effectiveness of driving prompts in the nuScenesQA dataset and compare different results between gpt-35-turbo and llama2-70b LLM backbones.

• PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning

  • Yupeng Zheng, Zebin Xing, Qichao Zhang, Bu Jin, Pengfei Li, Yuhang Zheng, Zhongpu Xia, Kun Zhan, Xianpeng Lang, Yaran Chen, Dongbin Zhao
  • Publisher: Chinese Academy of Sciences, Beijing University of Posts and Telecommunications, Beihang University, Tsinghua University, Li Auto
  • Task: Planning
  • Publish Date: 2024.06.04
  • Summary:
    • PlanAgent is the first closed-loop mid-to-mid(use bev, no raw sensor) autonomous driving planning agent system based on a Multi-modal Large Language Model.
    • Propose an efficient Environment Transformation module that extracts multi-modal information inputs with lanegraph representation.
    • Design a Reasoning Engine module that introduces a hierarchical chain-of-thought (CoT) to instruct MLLM to generate planner code and a Reflection module that combines simulation and scoring to filter out unreasonable proposals generated by the MLLM.

• ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles

  • Jiawei Zhang, Chejian Xu, Bo Li CVPR 2024
  • Publisher: UIUC, UChicago
  • Task: Scenario Generation
  • Env: Carla
  • Code: ChatScene
  • Publish Date: 2024.05.22
  • Summary:
    • ChatScene, a novel LLM-based agent capable of generating safety-critical scenarios by first providing textual descriptions and then carefully transforming them into executable simulations in CARLA via Scenic programming language.
    • An expansive retrieval database of Scenic code snippets has been developed. It catalogs diverse adversarial behaviors and traffic configurations, utilizing the rich knowledge stored in LLMs, which significantly augments the variety and critical nature of the driving scenarios generated.

• Probing Multimodal LLMs as World Models for Driving

  • Shiva Sreeram, Tsun-Hsuan Wang, Alaa Maalouf, Guy Rosman, Sertac Karaman, Daniela Rus
  • Publisher: MIT CSAIL, TRI, MIT LID
  • Task: Benchmark & Evaluation
  • Code: DriveSim
  • Publish Date: 2024.05.09
  • Summary:
    • A comprehensive experimental study to evaluate the capability of different MLLMs to reason/understand scenarios involving closed-loop driving and making decisions.
    • DriveSim, a specialized simulator designed to generate a diverse array of driving scenarios, thereby providing a platform to test and evaluate/benchmark the capabilities of MLLMs in understanding and reasoning about real-world driving scenes from a fixed in-car camera perspective, the same as the drive viewpoint.

• OmniDrive: A Holistic LLM-Agent Framework for Autonomous Driving with 3D Perception Reasoning and Planning

  • Shihao Wang, Zhiding Yu, Xiaohui Jiang, Shiyi Lan, Min Shi, Nadine Chang, Jan Kautz, Ying Li, Jose M. Alvarez
  • Publisher: Beijing Inst of Tech, NVIDIA, Huazhong Univ of Sci and Tech
  • Task: Benchmark & Planning
  • Publisher Data: 2024.05.02
  • Code: OmniDrive
  • Summary:
    • OmniDrive, a holistic framework for strong alignment between agent models and 3D driving tasks.
    • Propose a new benchmark with comprehensive visual question-answering (VQA) tasks, including scene description, traffic regulation, 3D grounding, counterfactual reasoning, decision making and planning.

• Chat2Scenario: Scenario Extraction From Dataset Through Utilization of Large Language Model

  • Yongqi Zhao, Wenbo Xiao, Tomislav Mihalj, Jia Hu, Arno Eichberger IEEE IV 2024
  • Publisher:
  • Publisher Data: 2024.04.26
  • Task: Generation
  • Dataset: Chat2Scenario
  • Summary:
    • Chat2Scenario is a web-based tool that allows users to search for specific driving scenarios within a dataset by inputting descriptive functional scenario text.

• VLAAD: Vision and Language Assistant for Autonomous Driving

  • SungYeon Park, MinJae Lee, JiHyuk Kang, Hahyeon Choi, Yoonah Park, Juhwan Cho, Adam Lee, DongKyu Kim WACV 2024 WorkShop
  • Publisher: Seoul National University, University of California, Berkeley
  • Publisher Data: 2024.04.16
  • Task: VQA
  • Code: VLAAD
  • Summary:
    • Introduce a multi-modal instruction tuning dataset that facilitates language models in learning visual instructions across diverse driving scenarios.
    • Capitalizing on this dataset, present a multi-modal LLM driving assistant named VLAAD.

• REvolve: Reward Evolution with Large Language Models for Autonomous Driving

  • Rishi Hazra, Alkis Sygkounas, Andreas Persson, Amy Loutfi, Pedro Zuidberg Dos Martires
  • Publisher: Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Swede
  • Task: Reward Generation
  • Env: AirSim
  • Project Page: REvolve
  • Publish Date: 2024.04.09
  • Summary:
    • Reward Evolve (REvolve), a novel evolutionary framework using LLMs, specifically GPT-4, to output reward functions (as executable Python codes) for AD and evolve them based on human feedback.

• AGENTSCODRIVER: Large Language Model Empowered Collaborative Driving with Lifelong Learning

  • Senkang Hu, Zhengru Fang, Zihan Fang, Xianhao Chen, Yuguang Fang
  • Publisher: City University of Hong Kong, The University of Hong Kong
  • Task: Planning(Multiple vehicles collaborative)
  • Publish Date: 2024.04.09
  • Env: HighwayEnv
  • Summary:
    • AGENTSCODRIVER, an LLM-powered multi-vehicle collaborative driving framework with lifelong learning, which allows different driving agents to communicate with each other and collaboratively drive in complex traffic scenarios.
    • It features reasoning engine, cognitive memory, reinforcement reflection, and communication module.

• Multi-Frame, Lightweight & Efficient Vision-Language Models for Question Answering in Autonomous Driving

  • Akshay Gopalkrishnan, Ross Greer, Mohan Trivedi
  • Publisher: UCSD
  • Task: QA
  • Publish Date: 2024.03.28
  • Code: official
  • Datasets: DriveLM
  • Summary:
    • EM-VLM4AD, an efficient, lightweight, multi-frame vision language model which performs Visual Question Answering for autonomous driving.
    • EM-VLM4AD requires at least 10 times less memory and floating point operations, while also achieving higher BLEU-4, METEOR, CIDEr, and ROGUE scores than the existing baseline on the DriveLM dataset.

• LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models

  • Mingxing Peng, Xusen Guo, Xianda Chen, Meixin Zhu, Kehua Chen, Hao (Frank) Yang, Xuesong Wang, Yinhai Wang
  • Publisher: The Hong Kong University of Science and Technology, Johns Hopkins University, Tongji University, STAR Lab
  • Task: Trajectory Prediction
  • Publish Date: 2024.03.27
  • Datasets: highD
  • Summary:
    • LC-LLM, the first Large Language Model for lane change prediction. It leverages the powerful capabilities of LLMs to understand complex interactive scenarios, enhancing the performance of lane change prediction.
    • LC-LLM achieves explainable predictions. It not only predicts lane change intentions and trajectories but also generates explanations for the prediction results.

• AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving

  • Mingfu Liang, Jong-Chyi Su, Samuel Schulter, Sparsh Garg, Shiyu Zhao, Ying Wu, Manmohan Chandraker
  • Publisher: Northwestern University, NEC Laboratories America, Rutgers University, UC San Diego
  • Publish Date: 2024.03.26
  • Task: Object Detection
  • Datasets: Mapillary, Cityscapes, nuImages, BDD100k, Waymo, KITTI
  • Summary:
    • An Automatic Data Engine (AIDE) that can automatically identify the issues, efficiently curate data, improve the model using auto-labeling, and verify the model through generated diverse scenarios.

• Engineering Safety Requirements for Autonomous Driving with Large Language Models

  • Ali Nouri, Beatriz Cabrero-Daniel, Fredrik Törner, Hȧkan Sivencrona, Christian Berger
  • Publisher: Chalmers University of Technology, University of Gothenburg, Volvo Cars, Zenseact, University of Gothenburg
  • Task: QA
  • Publish Date: 2024.03.24
  • Summary:
    • Propose a prototype of a pipeline of prompts and LLMs that receives an item definition and outputs solutions in the form of safety requirements.

• LeGo-Drive: Language-enhanced Goal-oriented Closed-Loop End-to-End Autonomous Driving

  • Pranjal Paul, Anant Garg, Tushar Choudhary, Arun Kumar Singh, K. Madhava Krishna
  • Publisher: The International Institute of Information Technology, Hyderabad, University of Tartu, Estonia
  • Project Page: LeGo-Drive
  • Code: LeGo-Drive
  • Env: Carla
  • Task: Trajectory Prediction
  • Publish Date: 2024.03.20
  • Summary:
    • A novel planning-guided end-to-end LLM-based goal point navigation solution that predicts and improves the desired state by dynamically interacting with the environment and generating a collision-free trajectory.

• Hybrid Reasoning Based on Large Language Models for Autonomous Car Driving

  • Mehdi Azarafza, Mojtaba Nayyeri, Charles Steinmetz, Steffen Staab, Achim Rettberg
  • Publisher: Univ. of Applied Science Hamm-Lippstadt, University of Stuttgart
  • Publish Date: 2024.03.18
  • Task: Reasoning
  • Env: Carla
  • Summary:
    • Combining arithmetic and commonsense elements, utilize the objects detected by YOLOv8.
    • Regarding the "location of the object," "speed of our car," "distance to the object," and "our car’s direction" are fed into the large language model for mathematical calculations within CARLA.
    • After formulating these calculations based on overcoming weather conditions, precise control values for brake and speed are generated.

• Large Language Models Powered Context-aware Motion Prediction

  • Xiaoji Zheng, Lixiu Wu, Zhijie Yan, Yuanrong Tang, Hao Zhao, Chen Zhong, Bokui Chen, Jiangtao Gong
  • Publisher: Tsinghua University
  • Task: Motion Prediction
  • Publish Data: 2024.03.17
  • Dataset: WOMD
  • Summary:
    • Design and conduct prompt engineering to enable an unfine-tuned GPT4-V to comprehend complex traffic scenarios.
    • Introduced a novel approach that combines the context information outputted by GPT4-V with MTR.

• Generalized Predictive Model for Autonomous Driving

  • Jiazhi Yang, Shenyuan Gao, Yihang Qiu, Li Chen, Tianyu Li, Bo Dai, Kashyap Chitta, Penghao Wu, Jia Zeng, Ping Luo, Jun Zhang, Andreas Geiger, Yu Qiao, Hongyang Li ECCV 2024
  • Publisher: OpenDriveLab and Shanghai AI Lab, Hong Kong University of Science and Technology, University of Hong Kong, University of Tubingen, Tubingen AI Center
  • Task: Datasets + Generation
  • Code: DriveAGI
  • Publish Date: 2024.03.14
  • Summary:
    • Introduce the first large-scale video prediction model in the autonomous driving discipline.
    • The resultant dataset accumulates over 2000 hours of driving videos, spanning areas all over the world with diverse weather conditions and traffic scenarios.
    • GenAD, inheriting the merits from recent latent diffusion models, handles the challenging dynamics in driving scenes with novel temporal reasoning blocks.

• LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments

  • Maonan Wang, Aoyu Pang, Yuheng Kan, Man-On Pun, Chung Shue Chen, Bo Huang
  • Publisher: The Chinese University of Hong Kong, Shanghai AI Laboratory, SenseTime Group Limited, Nokia Bell Labs
  • Publish Date: 2024.03.13
  • Task: Generation
  • Code: LLM-Assisted-Light
  • Summary:
    • LA-Light, a hybrid TSC framework that integrates the human-mimetic reasoning capabilities of LLMs, enabling the signal control algorithm to interpret and respond to complex traffic scenarios with the nuanced judgment typical of human cognition.
    • A closed-loop traffic signal control system has been developed, integrating LLMs with a comprehensive suite of interoperable tools.

• DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation

  • Guosheng Zhao, Xiaofeng Wang, Zheng Zhu, Xinze Chen, Guan Huang, Xiaoyi Bao, Xingang Wang
  • Publisher: Institute of Automation, Chinese Academy of Sciences, GigaAI
  • Publish Date: 2024.03.11
  • Task: Generation
  • Project: DriveDreamer-2
  • Datasets: nuScenes
  • Summary:
    • DriveDreamer-2, which builds upon the framework of DriveDreamer and incorporates a Large Language Model (LLM) to generate user-defined driving videos.
    • UniMVM(Unified Multi-View Model) enhances temporal and spatial coherence in the generated driving videos.
    • HDMap generator ensure the background elements do not conflict with the foreground trajectories.
    • Utilize the constructed text-to-script dataset to finetune GPT-3.5 into an LLM with specialized trajectory generation knowledge.

• Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents

  • Yuxi Wei, Zi Wang, Yifan Lu, Chenxin Xu, Changxing Liu, Hao Zhao, Siheng Chen, Yanfeng Wang
  • Publisher: Shanghai Jiao Tong University, Shanghai AI Laboratory, Carnegie Mellon University, Tsinghua University
  • Publish Date: 2024.03.11
  • Task: Generation
  • Code: ChatSim
  • Datasets: Waymo
  • Summary:
    • ChatSim, the first system that enables editable photo-realistic 3D driving scene simulations via natural language commands with external digital assets.
    • McNeRF, a novel neural radiance field method that incorporates multi-camera inputs, offering a broader scene rendering. It helps generate photo-realistic outcomes.
    • McLight, a novel multicamera lighting estimation that blends skydome and surrounding lighting. It makes external digital assets with their realistic textures and materials.

• Embodied Understanding of Driving Scenarios

  • Yunsong Zhou, Linyan Huang, Qingwen Bu, Jia Zeng, Tianyu Li, Hang Qiu, Hongzi Zhu, Minyi Guo, Yu Qiao, Hongyang Li ECCV 2024
  • Shanghai AI Lab, Shanghai Jiao Tong University, University of California, Riverside
  • Publish Date: 2024.03.07
  • Task: Benchmark & Scene Understanding
  • Code: ELM
  • Summary:
    • ELM is an embodied language model for understanding the long-horizon driving scenarios in space and time.

• DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models

  • Xiaoyu Tian, Junru Gu, Bailin Li, Yicheng Liu, Chenxu Hu, Yang Wang, Kun Zhan, Peng Jia, Xianpeng Lang, Hang Zhao
  • Publisher: IIIS, Tsinghua University, Li Auto
  • Publish Date: 2024.02.25
  • Task: + Planning
  • Project: DriveVLM
  • Datasets: nuScenes, SUP-AD
  • Summary:
    • DriveVLM, a novel autonomous driving system that leverages VLMs for effective scene understanding and planning.
    • DriveVLM-Dual, a hybrid system that incorporates DriveVLM and a traditional autonomous pipeline.

• GenAD: Generative End-to-End Autonomous Driving

  • Wenzhao Zheng, Ruiqi Song, Xianda Guo, Long Chen ECCV 2024
  • University of California, Berkeley, Waytous, Institute of Automation, Chinese Academy of Sciences
  • Publish Date: 2024.02.20
  • Task: Generation
  • Code: GenAD
  • Datasets: nuScenes
  • Summary:
    • GenAD models autonomous driving as a trajectory generation problem to unleash the full potential of endto-end methods.
    • Propose an instance-centric scene tokenizer that first transforms the surrounding scenes into map-aware instance tokens.
    • Employ a variational autoencoder to learn the future trajectory distribution in a structural latent space for trajectory prior modeling and adopt a temporal model to capture the agent and ego movements in the latent space to generate more effective future trajectories.

• RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model

  • Jianhao Yuan, Shuyang Sun, Daniel Omeiza, Bo Zhao, Paul Newman, Lars Kunze, Matthew Gadd
  • Publisher: University of Oxford, Beijing Academy of Artificial Intelligence
  • Publish Date: 2024.02.16
  • Task: Explainable Driving
  • Project: RAG-Driver
  • Summary:
    • RAG-Driver is a Multi-Modal Large Language Model with Retrieval-augmented In-context Learning capacity designed for generalisable and explainable end-to-end driving with strong zero-shot generalisation capacity.
    • Achieve State-of-the-art action explanation and justification performance in both BDD-X (in-distribution) and SAX (out-distribution) benchmarks.

• Driving Everywhere with Large Language Model Policy Adaptation

  • Boyi Li, Yue Wang, Jiageng Mao, Boris Ivanovic, Sushant Veer, Karen Leung, Marco Pavone CVPR 2024
  • Publisher: NVIDIA, University of Southern California, University of Washington, Stanford University
  • Publish Date: 2024.02.08
  • Task: Planning
  • Datasets: nuScenes
  • Project: LLaDA
  • Summary:
    • LLaDA is a training-free mechanism to assist human drivers and adapt autonomous driving policies to new environments.
    • Traffic Rule Extractor (TRE), which aims to organize and filter the inputs (initial plan+unique traffic code) and feed the output into the frozen LLMs to obtain the final new plan.
    • LLaDA set GPT-4 as default LLM.

• LimSim++

  • Daocheng Fu, Wenjie Lei, Licheng Wen, Pinlong Cai, Song Mao, Min Dou, Botian Shi, Yu Qiao
  • Publisher: Shanghai Artificial Intelligence Laboratory, Zhejiang University
  • Publish Date: 2024.02.02
  • Project: LimSim++
  • Summary:
    • LimSim++, an extended version of LimSim designed for the application of (M)LLMs in autonomous driving.
    • Introduce a baseline (M)LLM-driven framework, systematically validated through quantitative experiments across diverse scenarios.

• LangProp: A code optimization framework using Language Models applied to driving

  • Shu Ishida, Gianluca Corrado, George Fedoseev, Hudson Yeo, Lloyd Russell, Jamie Shotton, João F. Henriques, Anthony Hu
  • Publisher: Wayve Technologies, Visual Geometry Group, University of Oxford
  • Publish Date: 2024.01.18
  • Task: Code generation, Planning
  • Code: LangProp
  • Env: CARLA
  • Summary:
    • LangProp is a framework for iteratively optimizing code generated by large language models (LLMs) in a supervised/reinforcement learning setting.
    • Use LangProp in CARLA and generate driving code based on the state of the scene.

• VLP: Vision Language Planning for Autonomous Driving

  • Chenbin Pan, Burhaneddin Yaman, Tommaso Nesti, Abhirup Mallik, Alessandro G Allievi, Senem Velipasalar, Liu Ren CVPR 2024
  • Publisher: Syracuse University, Bosch Research North America & Bosch Center for Artificial Intelligence (BCAI)
  • Publish Date: 2024.01.14
  • Datasets: nuScenes
  • Summary:
    • Propose VLP, a Vision Language Planning model, which is composed of novel components ALP and SLP, aiming to improve the ADS from self-driving BEV reasoning and self-driving decision-making aspects, respectively.
    • ALP(agent-wise learning paradigm) aligns the produced BEV with a true bird’s-eye-view map.
    • SLP(selfdriving-car-centric learning paradigm) aligns the ego-vehicle query feature with the ego-vehicle textual planning feature.

• DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving

  • Wencheng Han, Dongqian Guo, Cheng-Zhong Xu, and Jianbing Shen
  • Publisher: SKL-IOTSC, CIS, University of Macau
  • Publish Date: 2024.01.08
  • Summary:
    • DME-Driver = Decision-Maker + Executor + CL
    • Executor network which is based on UniAD incorporates textual information for the OccFormer and the Planning module.
    • Decision-Maker which is based on LLaVA process inputs from three different modalities: visual inputs from the current and previous scenes textual inputs in the form of prompts, and current status information detailing the vehicle’s operating state.
    • CL is a consistency loss mechanism, slightly reducing performance metrics but significantly enhancing decision alignment between Executor and Decision-Maker.

• AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model

  • Lening Wang, Yilong Ren, Han Jiang, Pinlong Cai, Daocheng Fu, Tianqi Wang, Zhiyong Cui, Haiyang Yu, Xuesong Wang, Hanchu Zhou, Helai Huang, Yinhai Wang
  • Publisher: Beihang University, Shanghai Artificial Intelligence Laboratory, The University of Hong Kong, Zhongguancun Laboratory, Tongji University, Central South University, University of Washington, Seattle
  • Publish Date: 2023.12.29
  • Project page: AccidentGPT
  • Summary:
    • AccidentGPT, a comprehensive accident analysis and prevention multi-modal large model.
    • Integrates multi-vehicle collaborative perception for enhanced environmental understanding and collision avoidance.
    • Offer advanced safety features such as proactive remote safety warnings and blind spot alerts.
    • Serve traffic police and management agencies by providing real-time intelligent analysis of traffic safety factors.

• Holistic Autonomous Driving Understanding by Bird’s-Eye-View Injected Multi-Modal Large Models

  • Xinpeng Ding, Jinahua Han, Hang Xu, Xiaodan Liang, Wei Zhang, Xiaomeng Li CVPR 2024
  • Publisher: Hong Kong University of Science and Technology, Huawei Noah’s Ark Lab, Sun Yat-Sen University
  • Publish Date: 2023.12.21
  • Task: Datasets + VQA
  • Code: official
  • Summary:
    • Introduce NuInstruct, a novel dataset with 91K multi-view video-QA pairs across 17 subtasks, which based on nuScenes.
    • Propose BEV-InMLMM to integrate instructionaware BEV features with existing MLLMs, enhancing them with a full suite of information, including temporal, multi-view, and spatial details.

• LLM-ASSIST: Enhancing Closed-Loop Planning with Language-Based Reasoning

  • S P Sharan, Francesco Pittaluga, Vijay Kumar B G, Manmohan Chandraker
  • Publisher: UT Austin， NEC Labs America， UC San Diego
  • Publish Date: 2023.12.30
  • Task: Planning
  • Env/Datasets: nuPlan Closed-Loop Non-Reactive Challenge
  • Project: LLM-ASSIST
  • Summary:
    • LLM-Planner takes over scenarios that PDM-Closed cannot handle
    • Propose two LLM-based planners.
      • LLM-ASSIST(unc) considers the most unconstrained version of the planning problem, in which the LLM must directly return a safe future trajectory for the ego car.
      • LLM-ASSIST(par) considers a parameterized version of the planning problem, in which the LLM must only return a set of parameters for a rule-based planner, PDM-Closed.

• DriveLM: Driving with Graph Visual Question Answering

  • Chonghao Sima, Katrin Renz, Kashyap Chitta, Li Chen, Hanxue Zhang, Chengen Xie, Ping Luo, Andreas Geiger, Hongyang Li ECCV 2024
  • Publisher: OpenDriveLab, University of Tübingen, Tübingen AI Center, University of Hong Kong
  • Code: official
  • Publish Date: 2023.12.21
  • Summary:
    • DriveLM-Task
      • Graph VQA involves formulating P1-3(Perception, Prediction, Planning) reasoning as a series of questionanswer pairs (QAs) in a directed graph.
    • DriveLM-Data
      • DriveLM-Carla
        • Collect data using CARLA 0.9.14 in the Leaderboard 2.0 framework [17] with a privileged rule-based expert.
      • Drive-nuScenes
        • Selecting key frames from video clips, choosing key objects within these key frames, and subsequently annotating the frame-level P1−3 QAs for these key objects. A portion of the Perception QAs are generated from the nuScenes and OpenLane-V2 ground truth, while the remaining QAs are manually annotated.
    • DriveLM-Agent
      • DriveLMAgent is built upon a general vision-language model and can therefore exploit underlying knowledge gained during pre-training.

• LingoQA: Video Question Answering for Autonomous Driving

  • Ana-Maria Marcu, Long Chen, Jan Hünermann, Alice Karnsund, Benoit Hanotte, Prajwal Chidananda, Saurabh Nair, Vijay Badrinarayanan, Alex Kendall, Jamie Shotton, Oleg Sinavski
  • Publisher: Wayve
  • Task: VQA + Evaluation/Datasets
  • Code: official
  • Publish Date: 2023.12.21
  • Summary:
    • Introduce a novel benchmark for autonomous driving video QA using a learned text classifier for evaluation.
    • Introduce a Video QA dataset of central London consisting of 419k samples with its free-form questions and answers.
    • Establish a new baseline based on Vicuna-1.5-7B for this field with an identified model combination.

• DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving

  • Wenhai Wang, Jiangwei Xie, ChuanYang Hu, Haoming Zou, Jianan Fan, Wenwen Tong, Yang Wen, Silei Wu, Hanming Deng, Zhiqi Li, Hao Tian, Lewei Lu, Xizhou Zhu, Xiaogang Wang, Yu Qiao, Jifeng Dai
  • Publisher: OpenGVLab, Shanghai AI Laboratory, The Chinese University of Hong Kong, SenseTime Research, Stanford University, Nanjing University, Tsinghua University
  • Task: Planning + Explanation
  • Code: official
  • Env: Carla
  • Publish Date: 2023.12.14
  • Summary:
    • DriveMLM, the first LLM-based AD framework that can perform close-loop autonomous driving in realistic simulators.
    • Design an MLLM planner for decision prediction, and develop a data engine that can effectively generate decision states and corresponding explanation annotation for model training and evaluation.
    • Achieve 76.1 DS, 0.955 MPI results on CARLA Town05 Long.

• Large Language Models for Autonomous Driving: Real-World Experiments

  • Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Yang Zhou, Kaizhao Liang, Jintai Chen, Juanwu Lu, Zichong Yang, Kuei-Da Liao, Tianren Gao, Erlong Li, Kun Tang, Zhipeng Cao, Tong Zhou, Ao Liu, Xinrui Yan, Shuqi Mei, Jianguo Cao, Ziran Wang, Chao Zheng
  • Publisher: Purdue University
  • Publish Date: 2023.12.14
  • Project: official
  • Summary:
    • Introduce a Large Language Model (LLM)-based framework Talk-to-Drive (Talk2Drive) to process verbal commands from humans and make autonomous driving decisions with contextual information, satisfying their personalized preferences for safety, efficiency, and comfort.

• LMDrive: Closed-Loop End-to-End Driving with Large Language Models

  • Hao Shao, Yuxuan Hu, Letian Wang, Steven L. Waslander, Yu Liu, Hongsheng Li CVPR 2024
  • Publisher: CUHK MMLab, SenseTime Research, CPII under InnoHK, University of Toronto, Shanghai Artificial Intelligence Laboratory
  • Task: Planning + Datasets
  • Code: official
  • Env: Carla
  • Publish Date: 2023.12.12
  • Summary:
    • LMDrive, a novel end-to-end, closed-loop, languagebased autonomous driving framework.
    • Release 64K clips dataset, including navigation instruction, notice instructions, multi-modal multi-view sensor data, and control signals.
    • Present the benchmark LangAuto for evaluating the autonomous agents.

• Evaluation of Large Language Models for Decision Making in Autonomous Driving

  • Kotaro Tanahashi, Yuichi Inoue, Yu Yamaguchi, Hidetatsu Yaginuma, Daiki Shiotsuka, Hiroyuki Shimatani, Kohei Iwamasa, Yoshiaki Inoue, Takafumi Yamaguchi, Koki Igari, Tsukasa Horinouchi, Kento Tokuhiro, Yugo Tokuchi, Shunsuke Aoki
  • Publisher: Turing Inc., Japan
  • Task: Evalution
  • Publish Date: 2023.12.11
  • Summary:
    • Evaluate the two core capabilities
      • the spatial awareness decision-making ability, that is, LLMs can accurately identify the spatial layout based on coordinate information;
      • the ability to follow traffic rules to ensure that LLMs Ability to strictly abide by traffic laws while driving

• LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programs

  • Yunsheng Ma, Can Cui, Xu Cao, Wenqian Ye, Peiran Liu, Juanwu Lu, Amr Abdelraouf, Rohit Gupta, Kyungtae Han, Aniket Bera, James M. Rehg, Ziran Wang
  • Publisher: Purdue University, University of Illinois Urbana-Champaign, University of Virginia, InfoTech Labs, Toyota Motor North American
  • Task: Benchmark
  • Publish Date: 2023.12.07
  • Summary:
    • LaMPilot is the first interactive environment and dataset designed for evaluating LLM-based agents in a driving context.
    • It contains 4.9K scenes and is specifically designed to evaluate command tracking tasks in autonomous driving.

• Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous Driving

  • Ming Nie, Renyuan Peng, Chunwei Wang, Xinyue Cai, Jianhua Han, Hang Xu, Li Zhang
  • Publisher: Fudan University, Huawei Noah’s Ark Lab
  • Task: VQA + Datasets
  • Code: official
  • Datasets:
    • nuScenes
    • Waymo
    • ONCE
  • Publish Date: 2023.12.06
  • Summary:
    • Reason2Drive, a benchmark dataset with over 600K video-text pairs, aimed at facilitating the study of interpretable reasoning in complex driving.
    • Introduce a novel evaluation metric to assess chain-based reasoning performance in autonomous driving environments, and address the semantic ambiguities of existing metrics such as BLEU and CIDEr.
    • Introduce a straightforward yet effective framework that enhances existing VLMs with two new components: a prior tokenizer and an instructed vision decoder.

• GPT-4 Enhanced Multimodal Grounding for Autonomous Driving: Leveraging Cross-Modal Attention with Large Language Models

  • Haicheng Liao, Huanming Shen, Zhenning Li, Chengyue Wang, Guofa Li, Yiming Bie, Chengzhong Xu
  • Publisher: University of Macau, UESTC, Chongqing University, Jilin University
  • Task: Detection/Prediction
  • Code: official
  • Datasets:
    • Talk2car
  • Publish Date: 2023.12.06
  • Summaray:
    • Utilize five encoder Text, Image, Context, and Cross-Modal—with a Multimodal decoder to pridiction object bounding box.

• Dolphins: Multimodal Language Model for Driving

  • Yingzi Ma, Yulong Cao, Jiachen Sun, Marco Pavone, Chaowei Xiao ECCV 2024
  • Publisher: University of Wisconsin-Madison, NVIDIA, University of Michigan, Stanford University
  • Task: VQA
  • Project: Dolphins
  • Code: Dolphins
  • Datasets:
    • Image instruction-following dataset
      • GQA
      • MSCOCO: VQAv2, OK-VQA, TDIUC, Visual Genome dataset
    • Video instruction-following dataset
      • BDD-X
  • Publish Date: 2023.12.01
  • Summary:
    • Dolphins which is base on OpenFlamingo architecture is a VLM-based conversational driving assistant.
    • Devise grounded CoT (GCoT) instruction tuning and develop datasets.

• Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving

  • Yuqi Wang, Jiawei He, Lue Fan, Hongxin Li, Yuntao Chen, Zhaoxiang Zhang
  • Publisher: CASIA, CAIR, HKISI, CAS
  • Task: Generation
  • Project: Drive-WM
  • Code: Drive-WM
  • Datasets: nuScenes, Waymo Open Dataset
  • Publish Date: 2023.11.29
  • Summary:
    • Drive-WM, a multiview world model, which is capable of generating high-quality, controllable, and consistent multiview videos in autonomous driving scenes.
    • The first to explore the potential application of the world model in end-to-end planning for autonomous driving.

• Empowering Autonomous Driving with Large Language Models: A Safety Perspective

  • Yixuan Wang, Ruochen Jiao, Chengtian Lang, Sinong Simon Zhan, Chao Huang, Zhaoran Wang, Zhuoran Yang, Qi Zhu
  • Publisher: Northwestern University, University of Liverpool, Yale University
  • Task: Planning
  • Env: HighwayEnv
  • Code: official
  • Publish Date: 2023.11.28
  • Summary:
    • Deploys the LLM as an intelligent decision-maker in planning, incorporating safety verifiers for contextual safety learning to enhance overall AD performance and safety.

• GPT-4V Takes the Wheel: Evaluating Promise and Challenges for Pedestrian Behavior Prediction

  • Jia Huang, Peng Jiang, Alvika Gautam, Srikanth Saripalli
  • Publisher: Texas A&M University, College Station, USA
  • Task: Evaluation(Pedestrian Behavior Prediction)
  • Datasets:
    • JAAD
    • PIE
    • WiDEVIEW
  • Summary:
    • Provides a comprehensive evaluation of the potential of GPT-4V for pedestrian behavior prediction in autonomous driving using publicly available datasets.
    • It still falls short of the state-of-the-art traditional domain-specific models.
    • While GPT-4V represents a considerable advancement in AI capabilities for pedestrian behavior prediction, ongoing development and refinement are necessary to fully harness its capabilities in practical applications.

• ADriver-I: A General World Model for Autonomous Driving

  • Fan Jia, Weixin Mao, Yingfei Liu, Yucheng Zhao, Yuqing Wen, Chi Zhang, Xiangyu Zhang, Tiancai Wang
  • Publisher: MEGVII Technology, Waseda University, University of Science and Technology of China, Mach Drive
  • Task: Generation + Planning
  • Datasets: nuScenes, Largescale private datasets
  • Publish Date: 2023.11.22
  • Summary:
    • ADriver-I takes the vision-action pairs as inputs and autoregressively predicts the control signal of current frame. The generated control signals together with the historical vision-action pairs are further conditioned to predict the future frames.
    • MLLM(Multimodal large language model)=LLaVA-7B-1.5, VDM(Video Diffusion Model)=latent-diffusion
  • Metrics:
    • L1 error including speed and steer angle of current frame.
    • Quality of Generation: Frechet Inception Distance(FID), Frechet Video Distance(FVD).

• A Language Agent for Autonomous Driving

  • Jiageng Mao, Junjie Ye, Yuxi Qian, Marco Pavone, Yue Wang
  • University of Southern California, Stanford University, NVIDIA
  • Task: Generation + Planning
  • Project: Agent-Driver
  • Datasets: nuScenes
  • Publish Date: 2023.11.17
  • Summary:
    • Agent-Driver integrates a tool library for dynamic perception and prediction, a cognitive memory for human knowledge, and a reasoning engine that emulates human decision-making.
    • For motion planning, follow GPT-Driver(#GPT-Driver) and fine-tune the LLM with human driving trajectories in the nuScenes training set for one epoch.
    • For neural modules, adopte the modules in UniAD.
  • Metric:
    • L2 error (in meters) and collision rate (in percentage).

• Human-Centric Autonomous Systems With LLMs for User Command Reasoning

  • Yi Yang, Qingwen Zhang, Ci Li, Daniel Simões Marta, Nazre Batool, John Folkesson
  • Publisher: KTH Royal Institute of Technology, Scania AB
  • Task: QA
  • Code: DriveCmd
  • Datasets: UCU Dataset
  • Publish Date: 2023.11.14
  • Summary:
    • Propose to leverage the reasoning capabilities of Large Language Models (LLMs) to infer system requirements from in-cabin users’ commands.
    • LLVM-AD Workshop @ WACV 2024
  • Metric:
    • Accuracy at the question level(accuracy for each individual question).
    • Accuracy at the command level(accuracy is only acknowledged if all questions for a particular command are correctly identified).

• On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving

  • Licheng Wen, Xuemeng Yang, Daocheng Fu, Xiaofeng Wang, Pinlong Cai, Xin Li, Tao Ma, Yingxuan Li, Linran Xu, Dengke Shang, Zheng Zhu, Shaoyan Sun, Yeqi Bai, Xinyu Cai, Min Dou, Shuanglu Hu, Botian Shi
  • Publisher: Shanghai Artificial Intelligence Laboratory, GigaAI, East China Normal University, The Chinese University of Hong Kong, WeRide.ai
  • Project: official
  • Datasets:
    • Scenario Understanding: nuScenes, BDD-X, Carla, TSDD, Waymo, DAIR-V2X, CitySim.
    • Reasoning Capability: nuScenes, D2-city, Carla, CODA and the internet
    • Act as a driver: Real-world driving scenarios.
  • Publish Date: 2023.11.9
  • Summary:
    • Conducted a comprehensive and multi-faceted evaluation of the GPT-4V in various autonomous driving scenarios.
    • Test the capabilities of GPT-4V in Scenario Understanding, Reasoning, Act as a driver.

• ChatGPT as Your Vehicle Co-Pilot: An Initial Attempt

  • Shiyi Wang, Yuxuan Zhu, Zhiheng Li, Yutong Wang, Li Li, Zhengbing He
  • Publisher: Tsinghua University, Institute of Automation, Chinese Academy of Sciences, Massachusetts Institute of Technology
  • Task: Planning
  • Publish Date: 2023.10.17
  • Summary:
    • Design a universal framework that embeds LLMs as a vehicle "Co-Pilot" of driving, which can accomplish specific driving tasks with human intention satisfied based on the information provided.

• MagicDrive: Street View Generation with Diverse 3D Geometry Control

  • Ruiyuan Gao, Kai Chen, Enze Xie, Lanqing Hong, Zhenguo Li, Dit-Yan Yeung, Qiang Xu
  • Publisher: The Chinese University of Hong Kong, Hong Kong University of Science and Technology, Huawei Noah’s Ark Lab
  • Task: Generation
  • Project: MagicDrive
  • Code: MagicDrive
  • Datasets: nuScenes
  • Publish Date: 2023.10.13
  • Summary:
    • MagicDrive generates highly realistic images, exploiting geometric information from 3D annotations by independently encoding road maps, object boxes, and camera parameters for precise, geometry-guided synthesis. This approach effectively solves the challenge of multi-camera view consistency.
    • It also faces huge challenges in some complex scenes, such as night views and unseen weather conditions.

• Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles

  • Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Ziran Wang
  • Publisher: Purdue University, University of Illinois Urbana-Champaign，University of Virginia，PediaMed.AI.
  • Task: Planning
  • Project: video
  • Env: HighwayEnv
  • Publish Date: 2023.10.12
  • Summary:
    • Utilize LLMs’ linguistic and contextual understanding abilities with specialized tools to integrate the language and reasoning capabilities of LLMs into autonomous vehicles.

• DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model

  • Xiaofan Li, Yifu Zhang, Xiaoqing Ye
  • Publisher: Baidu Inc.
  • Task: Generation
  • Project: official
  • Datasets: nuScenes
  • Summary:
    • Address the new problem of multi-view video data generation from 3D layout in complex urban scenes.'
    • Propose a generative model DrivingDiffusion to ensure the cross-view, cross-frame consistency and the instance quality of the generated videos.
    • Achieve state-of-the-art video synthesis performance on nuScenes dataset.
  • Metrics:
    • Quality of Generation: Frechet Inception Distance(FID), Frechet Video Distance(FVD)
    • Segmentation Metrics: mIoU

• LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving

  • Hao Sha, Yao Mu, Yuxuan Jiang, Li Chen, Chenfeng Xu, Ping Luo, Shengbo Eben Li, Masayoshi Tomizuka, Wei Zhan, Mingyu Ding
  • Publisher: Tsinghua University, The University of Hong Kong, University of California, Berkeley
  • Task: Planning/Control
  • Code: official
  • Env:
    • ComplexUrbanScenarios
    • Carla
  • Publish Date: 2023.10.04
  • Summary:
    • Leverage LLMs to provide high-level decisions through chain-of-thought.
    • Convert high-level decisions into mathematical representations to guide the bottom-level controller(MPC).
    • Metrics: Number of failure/collision cases， Inefficiency，time, Penalty

• Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving

  • Long Chen, Oleg Sinavski, Jan Hünermann, Alice Karnsund, Andrew James Willmott, Danny Birch, Daniel Maund, Jamie Shotton
  • Publisher: Wayve
  • Task: Planning + VQA
  • Code: official
  • Simulator: a custom-built realistic 2D simulator.(The simulator is not open source.)
  • Datasets: Driving QA, data collection using RL experts in simulator.
  • Publish Date: 2023.10.03
  • Summary:
    • Propose a unique object-level multimodal LLM architecture(Llama2+Lora), using only vectorized representations as input.
    • Develop a new dataset of 160k QA pairs derived from 10k driving scenarios(control commands collected by RL(PPO), QA pair generated by GPT-3.5)
    • Metrics:
      • Accuracy of traffic light detection
      • MAE for traffic light distance prediction
      • MAE for acceleration
      • MAE for brake pressure
      • MAE for steering wheel angle

• Talk2BEV: Language-enhanced Bird’s-eye View Maps for Autonomous Driving

  • Vikrant Dewangan, Tushar Choudhary, Shivam Chandhok, Shubham Priyadarshan, Anushka Jain, Arun K. Singh, Siddharth Srivastava, Krishna Murthy Jatavallabhula, K. Madhava Krishna
  • Publisher: IIIT Hyderabad, University of British Columbia, University of Tartu, TensorTour Inc, MIT
  • Project Page: official
  • Code: Talk2BEV
  • Publish Date: 2023.10.03
  • Summary:
    • Introduces Talk2BEV, a large visionlanguage model (LVLM) interface for bird’s-eye view (BEV) maps in autonomous driving contexts.
    • Does not require any training or finetuning, relying instead on pre-trained image-language models
    • Develop and release Talk2BEV-Bench, a benchmark encom- passing 1000 human-annotated BEV scenarios, with more than 20,000 questions and ground-truth responses from the NuScenes dataset.

• DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model

  • Zhenhua Xu, Yujia Zhang, Enze Xie, Zhen Zhao, Yong Guo, Kenneth K. Y. Wong, Zhenguo Li, Hengshuang Zhao
  • Publisher: The University of Hong Kong, Zhejiang University, Huawei Noah’s Ark Lab, University of Sydney
  • Project Page: official
  • Task: Planning/Control + VQA
  • Datasets:
    • BDD-X dataset.
  • Publish Date: 2023.10.02
  • Summary:
    • Develop a new visual instruction tuning dataset(based on BDD-X) for interpretable AD assisted by ChatGPT/GPT4.
    • Present a novel multimodal LLM called DriveGPT4(Valley + LLaVA).
  • Metrics:
    • BLEU4, CIDEr and METETOR, ChatGPT Score.
    • RMSE for control signal prediction.

• GPT-DRIVER: LEARNING TO DRIVE WITH GPT

  • Jiageng Mao, Yuxi Qian, Hang Zhao, Yue Wang
  • Publisher: University of Southern California, Tsinghua University
  • Task: Planning(Fine-tuning Pre-trained Model)
  • Project: official
  • Datasets: nuScenes
  • Code: GPT-Driver
  • Publish Date: 2023.10.02
  • Summary:
    • Motion planning as a language modeling problem.
    • Align the output of the LLM with human driving behavior through fine-tuning strategies using the OpenAI fine-tuning API.
    • Leverage the LLM to generate driving trajectories.
  • Metrics:
    • L2 metric and Collision rate

• GAIA-1: A Generative World Model for Autonomous Driving

  • Anthony Hu, Lloyd Russell, Hudson Yeo, Zak Murez, George Fedoseev, Alex Kendall, Jamie Shotton, Gianluca Corrado
  • Publisher: Wayve
  • Task: Generation
  • Datasets:
    • Training dataset consists of 4,700 hours at 25Hz of proprietary driving data collected in London, UK between 2019 and 2023. It corresponds to approximately 420M unique images.
    • Validation dataset contains 400 hours of driving data from runs not included in the training set.
    • text coming from either online narration or offline metadata sources
  • Publish Date: 2023.09.29
  • Summary:
    • Introduce GAIA-1, a generative world model that leverages video(pre-trained DINO), text(T5-large), and action inputs to generate realistic driving scenarios.
    • Serve as a valuable neural simulator, allowing the generation of unlimited data.

• DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models

  • Licheng Wen, Daocheng Fu, Xin Li, Xinyu Cai, Tao Ma, Pinlong Cai, Min Dou, Botian Shi, Liang He, Yu Qiao ICLR 2024
  • Publisher: Shanghai AI Laboratory, East China Normal University, The Chinese University of Hong Kong
  • Publish Date: 2023.09.28
  • Task: Planning
  • Env:
    • HighwayEnv
    • CitySim, a Drone-Based vehicle trajectory dataset.
  • Summary:
    • Propose the DiLu framework, which combines a Reasoning and a Reflection module to enable the system to perform decision-making based on common-sense knowledge and evolve continuously.

• SurrealDriver: Designing Generative Driver Agent Simulation Framework in Urban Contexts based on Large Language Model

  • Ye Jin, Xiaoxi Shen, Huiling Peng, Xiaoan Liu, Jingli Qin, Jiayang Li, Jintao Xie, Peizhong Gao, Guyue Zhou, Jiangtao Gong
  • Keywords: human-AI interaction, driver model, agent, generative AI, large language model, simulation framework
  • Env: CARLA
  • Publisher: Tsinghua University
  • Summary: Propose a generative driver agent simulation framework based on large language models (LLMs), capable of perceiving complex traffic scenarios and providing realistic driving maneuvers.

• Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles

  • Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Ziran Wang
  • Publisher: Purdue University, PediaMed.AI Lab, University of Virginia
  • Task: Planning
  • Publish Date: 2023.09.18
  • Summary:
    • Provide a comprehensive framework for integrating Large Language Models (LLMs) into AD.

• DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving

  • Xiaofeng Wang, Zheng Zhu, Guan Huang, Xinze Chen, Jiwen Lu ECCV 2024
  • Publisher: GigaAI, Tsinghua University
  • Task: Generation
  • Project Page: official
  • Datasets: nuScenes
  • Publish Date: 2023.09.18
  • Summary:
    • Harness the powerful diffusion model to construct a comprehensive representation of the complex environment.
    • Generate future driving videos and driving policies by a multimodal(text, image, HDMap, Action, 3DBox) world model.

• Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving

  • Ali Keysan, Andreas Look, Eitan Kosman, Gonca Gürsun, Jörg Wagner, Yu Yao, Barbara Rakitsch
  • Publisher: Bosch Center for Artificial Intelligence, University of Tubingen,
  • Task: Prediction
  • Datasets: nuScenes
  • Publish Date: 2023.09.13
  • Summary:
    • Integrating pre-trained language models as textbased input encoders for the AD trajectory prediction task.
  • Metrics:
    • minimum Average Displacement Error (minADEk)
    • Final Displacement Error (minFDEk)
    • MissRate over 2 meters

• TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models

  • Siyao Zhang, Daocheng Fu, Zhao Zhang, Bin Yu, Pinlong Cai
  • Publisher: Beihang University, Key Laboratory of Intelligent Transportation Technology and System, Shanghai Artificial Intelligence Laboratory
  • Task: Planning
  • Code: official
  • Publish Date: 2023.09.13
  • Summary:
    • Present TrafficGPT—a fusion of ChatGPT and traffic foundation models.
    • Bridges the critical gap between large language models and traffic foundation models by defining a series of prompts.

• HiLM-D: Towards High-Resolution Understanding in Multimodal Large Language Models for Autonomous Driving

  • Xinpeng Ding, Jianhua Han, Hang Xu, Wei Zhang, Xiaomeng Li
  • Publisher: The Hong Kong University of Science and Technology, Huawei Noah’s Ark Lab
  • Task: Detection + VQA
  • Datasets: DRAMA
  • Publish Date: 2023.09.11
  • Summary:
    • Propose HiLM-D (Towards High-Resolution Understanding in MLLMs for Autonomous Driving), an efficient method to incorporate HR information into MLLMs for the ROLISP task.
    • ROLISP that aims to identify, explain and localize the risk object for the ego-vehicle meanwhile predicting its intention and giving suggestions.
  • Metrics:
    • LLM metrics, BLEU4, CIDEr and METETOR, SPICE.
    • Detection metrics, mIoU, IoUs so on.

• Language Prompt for Autonomous Driving

  • Dongming Wu, Wencheng Han, Tiancai Wang, Yingfei Liu, Xiangyu Zhang, Jianbing Shen
  • Publisher: Beijing Institute of Technology, University of Macau, MEGVII Technology, Beijing Academy of Artificial Intelligence
  • Task: Tracking
  • Code: official
  • Datasets: NuPrompt(not open), based on nuScenes.
  • Publish Date: 2023.09.08
  • Summary:
    • Propose a new large-scale language prompt set(based on nuScenes) for driving scenes, named NuPrompt(3D object-text pairs).
    • Propose an efficient prompt-based tracking model with prompt reasoning modification on PFTrack, called PromptTrack.

• MTD-GPT: A Multi-Task Decision-Making GPT Model for Autonomous Driving at Unsignalized Intersections

  • Jiaqi Liu, Peng Hang, Xiao Qi, Jianqiang Wang, Jian Sun. ITSC 2023
  • Publisher: Tongji University, Tsinghua University
  • Task: Prediction
  • Env: HighwayEnv
  • Publish Date: 2023.07.30
  • Summary:
    • Design a pipeline that leverages RL algorithms to train single-task decision-making experts and utilize expert data.
    • Propose the MTD-GPT model for multi-task(left-turn, straight-through, right-turn) decision-making of AV at unsignalized intersections.

• Domain Knowledge Distillation from Large Language Model: An Empirical Study in the Autonomous Driving Domain

  • Yun Tang, Antonio A. Bruto da Costa, Xizhe Zhang, Irvine Patrick, Siddartha Khastgir, Paul Jennings. ITSC 2023
  • Publisher: University of Warwick
  • Task: QA
  • Publish Date: 2023.07.17
  • Summary:
    • Develop a web-based distillation assistant enabling supervision and flexible intervention at runtime by prompt engineering and the LLM ChatGPT.

• Drive Like a Human: Rethinking Autonomous Driving with Large Language Models

  • Daocheng Fu, Xin Li, Licheng Wen, Min Dou, Pinlong Cai, Botian Shi, Yu Qiao
  • Publisher: Shanghai AI Lab, East China Normal University
  • Task: Planning
  • Code: official
  • Env: HighwayEnv
  • Publish Date: 2023.07.14
  • Summary:
    • Identify three key abilities: Reasoning, Interpretation and Memorization(accumulate experience and self-reflection).
    • Utilize LLM in AD as decision-making to solve long-tail corner cases and increase interpretability.
    • Verify interpretability in closed-loop offline data.

• Language-Guided Traffic Simulation via Scene-Level Diffusion

  • Ziyuan Zhong, Davis Rempe, Yuxiao Chen, Boris Ivanovic, Yulong Cao, Danfei Xu, Marco Pavone, Baishakhi Ray
  • Publisher: Columbia University, NVIDIA Research, Stanford University, Georgia Tech
  • Task: Diffusion
  • Publish Date: 2023.07.10
  • Summary:
    • Present CTG++, a language-guided scene-level conditional diffusion model for realistic query-compliant traffic simulation.
    • Leverage an LLM for translating a user query into a differentiable loss function and propose a scene-level conditional diffusion model (with a spatial-temporal transformer architecture) to translate the loss function into realistic, query compliant trajectories.

• ADAPT: Action-aware Driving Caption Transformer

  • Bu Jin, Xinyu Liu, Yupeng Zheng, Pengfei Li, Hao Zhao, Tong Zhang, Yuhang Zheng, Guyue Zhou, Jingjing Liu ICRA 2023
  • Publisher: Chinese Academy of Sciences, Tsinghua University, Peking University, Xidian University, Southern University of Science and Technology, Beihang University
  • Code: ADAPT
  • Datasets: BDD-X dataset
  • Summary:
    • Propose ADAPT, a new end-to-end transformerbased action narration and reasoning framework for self-driving vehicles.
    • propose a multi-task joint training framework that aligns both the driving action captioning task and the control signal prediction task.

WorkShop

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• Large Language and Vision Models for Autonomous Driving(LLVM-AD) Workshop @ WACV 2024
  • Publisher: Tencent Maps HD Map T.Lab, University of Illinois Urbana- Champaign, Purdue University, University of Virginia
  • Challenge 1: MAPLM: A Large-Scale Vision-Language Dataset for Map and Traffic Scene Understanding
    • Datasets: Download
    • Task: QA
    • Code: https://github.com/LLVM-AD/MAPLM
    • Description: MAPLM combines point cloud BEV (Bird's Eye View) and panoramic images to provide a rich collection of road scenario images. It includes multi-level scene description data, which helps models navigate through complex and diverse traffic environments.
    • Metric:
      • Frame-overall-accuracy (FRM): A frame is considered correct if all closed-choice questions about it are answered correctly.
      • Question-overall-accuracy (QNS): A question is considered correct if its answer is correct.
      • LAN: How many lanes in current road?
      • INT: Is there any road cross, intersection or lane change zone in the main road?
      • QLT: What is the point cloud data quality in current road area of this image?
      • SCN: What kind of road scene is it in the images? (SCN)
  • Challenge 2: In-Cabin User Command Understanding (UCU)
    • Datasets: Download
    • Task: QA
    • Code: https://github.com/LLVM-AD/ucu-dataset
    • Description:
      • This dataset focuses on understanding user commands in the context of autonomous vehicles. It contains 1,099 labeled commands. Each command is a sentence that describes a user’s request to the vehicle.
    • Metric:
      • Command-level accuracy: A command is considered correctly understood if all eight answers are correct.
      • Question-level accuracy: Evaluation at the individual question level.

Datasets

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format:
- [title](dataset link) [links]
  - author1, author2, and author3...
  - keyword
  - experiment environments or tasks

• Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning

  • Enna Sachdeva, Nakul Agarwal, Suhas Chundi, Sean Roelofs, Jiachen Li, Behzad Dariush, Chiho Choi, Mykel Kochenderfer
  • Publisher: Honda Research Institute, Stanford University
  • Publish Date: 2023.09.10
  • Summary:
    • A multi-modal ego-centric dataset for Ranking the importance level and Telling the reason for the importance.
    • Introduce a joint model for joint importance level ranking and natural language captions generation to benchmark our dataset.

• DriveLM: Drive on Language

  • Publisher: Sima, Chonghao and Renz, Katrin and Chitta, Kashyap and Chen, Li and Zhang, Hanxue and Xie, Chengen and Luo, Ping and Geiger, Andreas and Li, Hongyang ECCV 2024
  • Dataset: DriveLM
  • Publish Date: 2023.08
  • Summary:
    • Construct dataset based on the nuScenes dataset.
    • Perception questions require the model to recognize objects in the scene.
    • Prediction questions ask the model to predict the future status of important objects in the scene.
    • Planning questions prompt the model to give reasonable planning actions and avoid dangerous ones.

• WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models

  • Aboli Marathe, Deva Ramanan, Rahee Walambe, Ketan Kotecha. CVPR 2023
  • Publisher: Carnegie Mellon University, Symbiosis International University
  • Dataset: WEDGE
  • Publish Date: 2023.05.12
  • Summary:
    • A multi-weather autonomous driving dataset built from generative vision-language models.

• NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario

  • Tianwen Qian, Jingjing Chen, Linhai Zhuo, Yang Jiao, Yu-Gang Jiang
  • Publisher: Fudan University
  • Dataset: NuScenes-QA
  • Summary:
    • NuScenes-QA provides 459,941 question-answer pairs based on the 34,149 visual scenes, with 376,604 questions from 28,130 scenes used for training, and 83,337 questions from 6,019 scenes used for testing, respectively.
    • The multi-view images and point clouds are first processed by the feature extraction backbone to obtain BEV features.

• DRAMA: Joint Risk Localization and Captioning in Driving

  • Srikanth Malla, Chiho Choi, Isht Dwivedi, Joon Hee Choi, Jiachen Li
  • Publisher:
  • Datasets: DRAMA
  • Summary:
    • Introduce a novel dataset DRAMA that provides linguistic descriptions (with the focus on reasons) of driving risks associated with important objects and that can be used to evaluate a range of visual captioning capabilities in driving scenarios.

• Language Prompt for Autonomous Driving

  • Datasets: Nuprompt(Not open)
  • Previous summary

• Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving

  • Datasets: official, data collection using RL experts in simulator.
  • Previous summary

• Textual Explanations for Self-Driving Vehicles

  • Jinkyu Kim, Anna Rohrbach, Trevor Darrell, John Canny, Zeynep Akata ECCV 2018.
  • Publisher: University of California, Berkeley, Saarland Informatics Campus, University of Amsterdam
  • BDD-X dataset

• Grounding Human-To-Vehicle Advice for Self-Driving Vehicles

  • Jinkyu Kim, Teruhisa Misu, Yi-Ting Chen, Ashish Tawari, John Canny CVPR 2019
  • Publisher: UC Berkeley, Honda Research Institute USA, Inc.
  • HAD dataset

License

Awesome LLM for Autonomous Driving Resources is released under the Apache 2.0 license.