Connected and automated vehicles (CAVs) rely on digitally acquired traffic state information for real-time maneuver decisions, making them vulnerable to cyberattacks that manipulate perceived traffic states or communication channels. Such manipulation can subtly alter vehicle behavior and, in interactive traffic environments, may propagate disturbances beyond the attacked vehicle. The objective of this study is to develop frameworks for (i) detecting abnormal lane-changing under cyberattacks and (ii) enabling robust decision-making under adversarial conditions. For detection, a physics-guided neural network integrating a game-theoretic lane-changing model with LSTM learning is developed and evaluated using the I-24 MOTION dataset under simulated false data injection and denial-of-service attacks. Results show improved prediction accuracy and effective detection of falsified lane-changing behaviors. For robust control, a hierarchical adversarial reinforcement learning framework is proposed for discretionary lane-changing under bounded perturbations. Simulation results indicate improved traffic efficiency while maintaining safety under worst-case perturbations. These findings provide a modeling foundation for cybersecurity-aware monitoring and robust tactical decision-making in CAV systems.

Infrastructure Assets: Highway Assets, ITS, Transit Assets
Resource Types: Research Report
Capabilities: Data & Information Systems, Tools & Technology
Management Processes: Risk Management, Strategic Direction