Decision-making and Trajectory Planning for Autonomous Heavy Truck in Dense Traffic

doi:10.3901/JME.2023.12.332

Abstract

Abstract: The lane-change safety and efficiency in the dense traffic will be greatly improved if the driver cooperativeness of the surrounding vehicles can be estimated for the autonomous heavy truck, which has inferior flexibility and dynamic stability, as well as greater destructiveness. Therefore, this study propose a lane-change decision-making and planning method based on the predicted driver cooperativeness of the surrounding vehicles and the asymmetrical risk assessment. The driver cooperativeness of the following vehicle in the target lane is estimated by considering the driving environment and the trajectories predicted by Gaussian mixture model, which is also used to construct the asymmetrical risk model. The utility theory is used to describe the probability of selecting the target lane, and then the driving decision will be made by combining the risk level of the target lane in the prediction horizon. Finally, a multi-objective cost function is designed to select the optimal trajectory from the candidates generated by the polynomial curve. The results of simulation using the naturalistic driving dataset show that the proposed method can accurately predict the driver cooperativeness and correctly recognize the risk level. Sequentially, the autonomous heavy truck can make the lane-change decision more safely and efficiently in the dense traffic.

Key words: autonomous heavy truck, trajectory prediction, driver cooperativeness, risk assessment, decision-making and planning

CLC Number:

U461

HU Wen, DENG Zejian, ZHANG Bangji, CAO Dongpu, YANG Yanding, CAO Kai, LI Shen. Decision-making and Trajectory Planning for Autonomous Heavy Truck in Dense Traffic[J]. Journal of Mechanical Engineering, 2023, 59(12): 332-342.

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