Socially Game-Theoretic Lane-Change for Autonomous Heavy Vehicle Based on Asymmetric Driving Aggressiveness
Wen Hu, Zejian Deng, Yanding Yang, Pingyi Zhang, Kai Cao, Duanfeng Chu, Bangji Zhang, Dongpu Cao
Abstract
The existing autonomous driving systems present poor performance for heavy vehicles, and face challenges gaining acceptance from surrounding vehicles in the interactive scenarios due to the deficiency of sociality. Therefore, a socially game-theoretic decision-making method is proposed in this study. Firstly, the concept of asymmetric driving aggressiveness is introduced to construct the decision-making model, ensuring driving safety and sociality across different vehicle types. Secondly, a socialized lane-change model for autonomous heavy vehicles is proposed based on the asymmetric aggressiveness and game theory with incomplete information. Subsequently, the cooperativeness of the interactive vehicle is estimated to eliminate the uncertainty through the combination of trajectory prediction and driving environment assessment. Finally, two conventional decisionmaking models are used as benchmarks to validate the effectiveness and robustness of the proposed method using naturalistic driving data. The results demonstrate that the socially game-theoretic lane-change model can generate safe and socialized decisions by estimating the cooperativeness of interactive vehicle. Besides, the proposed method exhibits enhanced social-friendliness with minimal impact on other vehicles and excellent robustness in navigating dynamic and complex traffic environment. Despite a slight reduction in driving efficiency, it still demonstrates a better adaptability and compatibility for autonomous heavy vehicles because the depressed performances are not the primary concerns.