密集交通场景下无人驾驶重卡换道决策规划方法

doi:10.3901/JME.2023.12.332

机械工程学报 ›› 2023, Vol. 59 ›› Issue (12): 332-342.doi: 10.3901/JME.2023.12.332

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密集交通场景下无人驾驶重卡换道决策规划方法

胡文¹, 邓泽健², 张邦基^1,3, 曹东璞⁴, 杨彦鼎⁵, 曹恺⁵, 李深⁶

1. 湖南大学机械与运载工程学院长沙 410082;
2. 滑铁卢大学机电工程系滑铁卢N2L3G1 加拿大;
3. 浙大城市学院智慧交通运输工程研究中心杭州 310015;
4. 清华大学车辆与运载学院北京 100084;
5. 东风悦享科技有限公司武汉 430058;
6. 清华大学土木水利学院北京 100084

收稿日期:2022-07-13 修回日期:2023-05-11 出版日期:2023-06-20 发布日期:2023-08-15
通讯作者: 张邦基(通信作者),男,1968年出生,教授,博士研究生导师。主要研究方向为车辆智能底盘、无人驾驶车辆路径跟踪控制。E-mail:zhangbj@zucc.edu.cn
作者简介:胡文,男,1992年出生,博士研究生。主要研究方向为无人驾驶车辆风险评估、社会认知导向的驾驶行为决策与路径规划。E-mail:huxiaowen@hnu.edu.cn
基金资助:
国家自然科学基金资助项目(52202498)。

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

HU Wen¹, DENG Zejian², ZHANG Bangji^1,3, CAO Dongpu⁴, YANG Yanding⁵, CAO Kai⁵, LI Shen⁶

1. College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082;
2. University of Waterloo, Waterloo, N2L3G1, Canada;
3. Intelligent Transportation System Research Center, Zhejiang University City College, Hangzhou 310015;
4. School of Vehicle and Mobility, Tsinghua University, Beijing 100084;
5. Dongfeng USharing Technology Co. Ltd, Wuhan 430058;
6. School of Civil Engineering, Tsinghua University, Beijing 100084

Received:2022-07-13 Revised:2023-05-11 Online:2023-06-20 Published:2023-08-15

摘要/Abstract

摘要： 准确估计周围车辆的驾驶员合作程度能提高无人驾驶车辆在密集交通场景下的换道安全性与效率，特别是对于自身灵活性和稳定性较差、对周围车辆安全威胁较大的重型卡车。因此，提出一种基于周围车辆驾驶员合作程度预测以及非对称风险评估的无人驾驶重型卡车换道决策规划方法。该方法基于高斯混合模型对周围车辆进行运动轨迹预测，结合当前驾驶环境估计目标车道后车的驾驶员合作程度，并用于构建非对称风险模型；基于轨迹预测结果，采用效用理论建模无人驾驶重型卡车当前和未来的车道选择概率，综合当前和未来的风险评估，输出最终的驾驶行为决策；设计多目标代价函数用于从多项式候选轨迹中选取最优轨迹。基于自然驾驶数据集的仿真试验表明，提出的方法可以准确地预测目标车道后车的驾驶员合作程度以及对周围车辆的风险等级，使无人驾驶重型卡车在密集交通流下也能安全高效地执行换道决策和轨迹规划。

关键词: 无人驾驶重型卡车, 轨迹预测, 驾驶员合作程度, 风险评估, 决策规划

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

中图分类号:

U461

胡文, 邓泽健, 张邦基, 曹东璞, 杨彦鼎, 曹恺, 李深. 密集交通场景下无人驾驶重卡换道决策规划方法[J]. 机械工程学报, 2023, 59(12): 332-342.

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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密集交通场景下无人驾驶重卡换道决策规划方法

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

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