RAKD: Risk-Aware Knowledge Distillation for Autonomous Driving in Adversarial Scenarios
Yuenan Zhao, Xiaoyu Xu, Ruitong Li, Ran Song, Wei Zhang · IEEE Transactions on Vehicular Technology · 2025
Aggressive driving behaviors of surrounding road users pose substantial threats to the safety of autonomous vehicles. However, current imitation learning methods struggle to learn a risk-aware driving policy, as they fail to learn the intrinsic decision-making principles and safety-relevant features from high-dimensional records. To address this issue, we propose a robust risk-aware knowledge distillation (RAKD) framework. RAKD employs a privileged teacher based on reinforcement learning to supervise a vision-based student via policy and feature distillation. The privileged teacher leverages two novel safetycritical representations in bird's-eye view and risk-aware reward shaping to enhance risk-sensitive learning, establishing superior imitation benchmarks through on-policy learning. Building on these benchmarks, we integrate a teachable module in the student network, aligning with the teacher's features through multiscale attention-based feature distillation, focusing on collision risk-related information. Experimental results demonstrate that our RAKD-trained student achieves superior driving score and collision avoidance in diverse adversarial scenarios compared to baselines. Our repository is publicly available at https://github. com/quliang93/RAKD.