Vehicle intention recognition at signalized intersections based on security-aware inverse reinforcement learning

Abstract

To address the challenge of distinguishing subjective aggressive driving (initiated by drivers) from hazardous behaviors caused by external cyberattacks, this study proposes an innovative intent recognition framework named Intent-Decipher. By integrating the information credibility outputted by an intrusion detection system (IDS) into a security-aware inverse reinforcement learning (SA-IRL) model, the framework infers the reward function behind vehicle behaviors and classifies three key driving intents: normal, aggressive, and malicious. Experiments were conducted on a semi-synthetic dataset containing 20 000 trajectories. Results show that Intent-Decipher significantly outperforms baseline methods in classification accuracy, achieving a macro-average F1-score of 0.94. Notably, Intent-Decipher excels at differentiating subjective aggressive driving from attack-induced behaviors: its F1-score for identifying malicious attack-induced (MAI) intent reaches 0.90, an absolute improvement of 0.16 compared with the standard inverse reinforcement learning (IRL) model (which lacks security awareness and only achieves an F1-score of 0.74).

Keywords

signalized intersections / intent recognition / anomaly detection / inverse reinforcement learning (IRL) / vehicular ad-hoc networks

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Funding

National Key Research and Development Program of China(2022YFB4300304)