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汽车安全与节能学报

JASE ›› 2019, Vol. 10 ›› Issue (4): 413-422.DOI: 10.3969/j.issn.1674-8484.2019.04.002

• 汽车安全 • 上一篇    下一篇

结构化道路中动态车辆的轨迹预测

谢辉，高斌，熊硕，王悦   

  1. （内燃机燃烧学国家重点实验室，天津大学，天津 300072，中国）
  • 收稿日期:2019-02-28 出版日期:2019-12-31 发布日期:2020-01-01
  • 作者简介:第一作者 / First author ： 谢辉 (1970—)，男( 汉)，天津，教授。E-mail: xiehui@tju.edu.cn。 第二作者 / Second author ： 高斌 (1993—)，男( 汉)，山东，硕士研究生。E-mail: gaobin2016@tju.edu.cn。
  • 基金资助:
    天津市科技计划项目（ 17ZXRGGX00140）

Trajectory prediction of dynamic vehicles in structured roads

XIE Hui, GAO Bin, XIONG Shuo, WANG Yue   

  1. (State Key Laboratory of Engines, Tianjin University, Tianjin 30072, China)
  • Received:2019-02-28 Online:2019-12-31 Published:2020-01-01

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摘要： 为提高结构化道路中自动驾驶汽车周边动态车辆运动轨迹预测的准确率，提出了基于交通场 景特征辨识的轨迹预测策略。基于激光雷达与组合导航系统实现周边车辆的检测跟踪与定位，通过驾 驶意图估计模型判断车辆驾驶行为并对交通场景分类，对于车道保持和变道场景分别基于车辆运动模 型与 Markov决策过程预测车辆运动轨迹。结果表明：对于车道保持场景，当预测时域为 4 s时基于 车辆运动模型的平均预测误差约为 0.12 m ；对于变道场景，当预测时域为 8 s时基于 Markov决策过 程的平均预测误差约为 0.17 m，因而，本文提出的预测策略能够实时准确地预测目标车辆运动轨迹。

关键词: 自动驾驶汽车, 轨迹预测, 驾驶意图估计,  Markov决策过程

Abstract: A trajectory prediction strategy based on traffic scene feature identification was proposed to improve the accuracy of dynamic vehicle trajectory prediction around autonomous cars in structured roads. The surrounding vehicles were detected, tracked and localized by lidars and integrated navigation system. The driving intention estimation model was used to judge the vehicle driving behavior and classify the traffic scenes. The vehicle motion trajectory was predicted based on vehicle motion model and Markov decision process for lane keeping and lane changing scenes, respectively. The results showed that the average prediction error based on the vehicle motion model is about 0.12 m for the lane keeping scene, when the prediction time domain is 4 s for the lane change scene, the average prediction error based on the Markov decision process is about 0.17 m when the prediction time domain is 8 s. Therefore, the strategy can accurately predict the trajectory of dynamic vehicles in real time.

Key words: autonomous cars, trajectory prediction, driving intention estimation, Markov decision processes