A Learning-Based Discretionary Lane-Change Decision-Making Model With Driving Style Awareness

Discretionary lane change (DLC) is a basic but complex maneuver in driving, which aims at reaching faster speed or better driving conditions, e.g., further line of sight ride quality. Although modeling DLC decision-making has been studied for years, the impact human factors, crucial accurately modelling strategies, largely ignored existing literature. In this paper, we integrate factors that are represented by styles to design new model. Specifically, our proposed model takes not only contextual traffic information also surrounding vehicles into consideration and makes lane-change/keep decisions. Moreover, can imitate drivers’ maneuvers learning style ego vehicle. Our evaluation results show captures strategies imitates lane-change maneuvers, achieve 98.66% prediction accuracy. analyze compared with drivers terms improving safety traffic.