Security Strategy against Generalized Inter-Vehicle Cyberattacks in Car-following Scenarios for Connected and Autonomous Vehicles
This repository contains the source code and data for the following paper:
Zhou, H., Ma, C., Cai, X., Ma, K., Li, X., & Ran, B. (2025). Security strategy against generalized inter-vehicle cyberattacks in car-following scenarios for connected and autonomous vehicles. Transportation Research Part C: Emerging Technologies.
Connected autonomous vehicles (CAVs) can improve safety and efficiency by sharing future driving intentions with nearby vehicles. However, overreliance on inter-vehicle communication may lead to critical safety risks when facing cyberattacks. This repository provides the implementation of a security-aware car-following control strategy under inter-vehicle cyberattacks, which dynamically adjusts its reliance on inter-vehicle information. The framework includes four control modes, a cyberattack misbehavior identification module, and two trajectory optimization models for connected and non-connected driving. We also include a cyberattack generation algorithm to simulate safety-critical attack scenarios and evaluate the proposed strategy's effectiveness.
The project is developed using Python 3. Please ensure you have the corresponding environment set up.
The code related to our data processing and algorithm are included in folder 'src'. As you proceed through all code, always verify the paths for both the input and output files. This ensures that everything runs smoothly.
The 'data' folder contains the benchmark reference trajectories used in our paper. Some of the realistic AV trajectory are extracted from project ULTra-AV.
Developers - Hang Zhou (hzhou364@wisc.edu).
If you have any questions, please feel free to contact the CATS Lab at UW-Madison. We're here to help!