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《工程(英文)》 >> 2022年 第19卷 第12期 doi: 10.1016/j.eng.2021.12.020

一种用于自动驾驶的车辆概率性长期轨迹预测框架

State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China

收稿日期: 2021-04-04 修回日期: 2021-09-23 录用日期: 2021-12-28 发布日期: 2022-03-18

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摘要

在混合动态交通环境中,准确地预测周围车辆长期范围内的运动轨迹是自动驾驶车辆(AV)实现合理行为决策和保障行车安全不可或缺的前提条件之一。本文提出了一种车辆长期轨迹预测的概率框架,由驾驶意图推理模型(DIM)和轨迹预测模型(TPM)组成。DIM基于动态贝叶斯网络进行设计和应用,用于准确推断车辆潜在的驾驶意图。文中所提出的DIM结合了基本的交通规则和车辆多维运动信息。为了进一步提高轨迹预测精度并实现预测不确定性识别,本文开发了基于高斯过程(GP)的TPM,综合考虑了车辆模型的短期预测结果和运动特性。最后,在高速换道场景下进行仿真验证,说明了新方法的有效性。通过与其他先进方法进行对比,展示并验证了该框架在车辆长期轨迹预测任务中的优异性能。

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