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清华大学学报(自然科学版)  2018, Vol. 58 Issue (4): 432-437    DOI: 10.16511/j.cnki.qhdxxb.2018.21.011
  汽车工程 本期目录 | 过刊浏览 | 高级检索 |
基于时间延迟动态预测的自动驾驶控制
赵建辉1, 高洪波2, 张新钰3, 张颖麟4
1. 清华大学 计算机科学技术系, 北京 100084;
2. 清华大学 汽车安全与节能国家重点实验室, 北京 100084;
3. 清华大学 信息技术中心, 北京 100084;
4. 湖南大学 汽车车身先进设计制造国家重点实验室, 长沙 410000
Automatic driving control based on time delay dynamic predictions
ZHAO Jianhui1, GAO Hongbo2, ZHANG Xinyu3, ZHANG Yinglin4
1. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China;
3. Information Technology Center, Tsinghua University, Beijing 100084, China;
4. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410000, China
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摘要 由于驾驶过程中的延迟和前视距离等因素,无人车无法准确跟踪规划轨迹。该文通过选择简化的自行车车辆模型,在纯跟踪模型的基础上对原有的算法进行优化,提出了一种基于动态延迟预测的自动驾驶控制方法。通过车辆运动学模型预测延迟后的车辆运动方向和位置信息,并根据行驶方向和轨迹方向之间的偏差值,获得最佳前视距离。MATLAB仿真结果表明,改进的算法可以以7 m/s的行驶速度跟踪规划轨迹,平均误差可以控制在0.3 m以内,跟踪性能优于传统的纯跟踪方法。
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赵建辉
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张颖麟
关键词 智能驾驶车载摄像头复杂交通环境    
Abstract:Signal delays, limited frontal view distances and other factors during self-driving limit the ability of self-driving cars to accurately track their planning trajectory. A simplified bicycle model was used to optimize a classical pure tracking model in an automatic driving control method based on dynamic delay prediction. A vehicle kinematics model is used to predict the vehicle motion direction and position after the delay. The optimal front sight distance is obtained according to difference between driving the actual direction and the tracking direction. MATLAB simulations show that this algorithm can track the planning trajectory at a maximum speed of 7 m/s with the average error controlled to within 0.3 m. Thus, the tracking performance is better than the traditional pure pursuit method.
Key wordsintelligent driving    on-board camera    complex traffic environment
收稿日期: 2017-12-30      出版日期: 2018-04-15
ZTFLH:  TP399  
基金资助:国家重点研究和发展计划(2016YFB0100903);北京市科学技术委员会重大专项(d171100005017002,d171100005117002);中国博士后基金(2017M620765)
通讯作者: 高洪波,助理研究员,E-mail:ghb48@tsinghu.edu.cn     E-mail: ghb48@tsinghu.edu.cn
作者简介: 赵建辉(1982-),男,讲师。
引用本文:   
赵建辉, 高洪波, 张新钰, 张颖麟. 基于时间延迟动态预测的自动驾驶控制[J]. 清华大学学报(自然科学版), 2018, 58(4): 432-437.
ZHAO Jianhui, GAO Hongbo, ZHANG Xinyu, ZHANG Yinglin. Automatic driving control based on time delay dynamic predictions. Journal of Tsinghua University(Science and Technology), 2018, 58(4): 432-437.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2018.21.011  或          http://jst.tsinghuajournals.com/CN/Y2018/V58/I4/432
  图1 纯跟踪模型的几何分析
  图2 车辆运动学自行车模型
  图3 时间延迟过程中的车辆轨迹
  图4 传统预测点选择方法
  图5 改进后的预测点选择方法
  图6 0.3s延迟跟踪效果(网络版彩图)
  图7 0.4s延迟跟踪效果(网络版彩图)
  图8 0.5s延迟跟踪效果(网络版彩图)
  表1 具有时间延迟的轨迹跟踪横向误差
  表2 消除时间延迟后的轨迹跟踪横向误差
  图9 消除0.3s延迟后的跟踪效果(网络版彩图)
  图10 消除0.4s延迟后的跟踪效果(网络版彩图)
  图11 消除0.5s延迟后的跟踪效果(网络版彩图)
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