Highway Autonomous Driving Decision Making Using Reweighting Ego-Attention and Driver Assistance Module

Decision making is challenging in autonomous driving (AD) under highway scenarios because of the unpredictable behaviors of neighbor vehicles, leading to the necessity of accurately modelling interactions between vehicles. Though ego-attention, a variant of self-attention, provides a way for object interaction extraction, its feature expression still needs to improve. This paper improves the original ego-attention by reweighting the encoding vehicle features, forcing them to pay more attention to significant features. Moreover, we designed a rule-based driver assistance module (DAM) to alleviate mis-decisions by constraining action space. Finally, we constructed our final AD decision-making model by integrating the proposed reweighting ego-attention and the DAM into the dual-input decision-making framework trained by enhanced deep reinforcement learning (DRL). We evaluated our decision-making model on highway scenarios. The results show that our model achieves better performance in success step (39.95 steps/episode), speed (29.15 m/s), lane-changing times (5.64 times/episode), and task completion rate (98%) than existing models, including DRL-GAT-SA, AE-D3QN-DA, and ego-attention-based ones, implying the competitive driving accuracy, safety, and comfort of our model.