交通运输系统工程与信息

交通运输系统工程与信息 ›› 2020, Vol. 20 ›› Issue (3): 61-66.

• 智能交通系统与信息技术 • 上一篇    下一篇

基于行为识别的智能车纵向决策研究

曹波1，李永乐*2，赵凯2，朱愿2   

  1. 1. 陆军军事交通学院镇江校区，江苏镇江 212003；2. 陆军军事交通学院军事交通运输研究所，天津 300161
  • 收稿日期:2020-01-15 修回日期:2020-03-09 出版日期:2020-06-25 发布日期:2020-06-28
  • 作者简介:曹波(1994-)，男，重庆綦江人，助教.
  • 基金资助:

    国家重点研发计划/National Key Research and Development Program of China(2016YFB0100903).

Intelligent Vehicle Longitudinal Decision Making Based on Behavior Recognition

CAO Bo1, LI Yong-le2, ZHAO Kai2, ZHU Yuan2   

  1. 1. Zhenjiang Campus, Army Military Transportation University, Zhenjiang 212003, Jiangsu, China; 2. Military Transportation Research Institute, Army Military Transportation University, Tianjin 300161, China
  • Received:2020-01-15 Revised:2020-03-09 Online:2020-06-25 Published:2020-06-28

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摘要：

针对智能车纵向决策问题，提出基于环境车辆偏离车道程度识别运动模式的方法；构建动态环境车辆横纵向轨迹预测模型，并求解；构建保持、先行、避让在内的决策集，提出基于预测轨迹的单个车辆决策方法，并基于所有动态环境车辆的决策结果在加速、减速和匀速3 种结果中做出综合决策. 实车实验表明：在直行、换道和转弯运动模式下轨迹预测平均误差分别为0.11，0.29，0.80 m，预测精度较高；复杂动态环境下，本文提供的纵向决策信息提升了智能车行驶的安全性和舒适性.

关键词: 智能交通, 智能车, 纵向决策, 行为识别, 运动模式, 轨迹预测

Abstract:

This paper focuses on the longitudinal decision-making of intelligent vehicles. A method based on the degree of deviation from travel lane was proposed to identify the mode of vehicle in certain environment. Then, the longitudinal and transverse trajectory prediction model was developed considering dynamic traffic environment and being solved. The decision sets include maintaining, leading and avoiding behaviors of vehicle, and the proposed single vehicle decision method was based on the predicted trajectory. Then the comprehensive decision was made for three situations including acceleration, deceleration, and driving with constant speed and considering all dynamic environment. The real vehicle experiment shows that the average error of trajectory prediction in driving straight, lane changing, and making turns is 0.11 meters, 0.29 meters and 0.8 meters, respectively. The results show a high prediction accuracy. The proposed longitudinal decision- making methods can be used to improve the safety and comfort of intelligent vehicle driving under complex dynamic environment.

Key words: intelligent transportation, intelligent vehicles, longitudinal decision-making, behavior recognition, movement patterns, track prediction

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