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基于LSTM和改进TTR算法的车辆辅助驾驶侧翻预警

周兵 梁帅 吴晓建 许艳 潘倩兮 柴天

湖南大学学报(自然科学版)2023,Vol.50Issue(12):155-167,13.
湖南大学学报(自然科学版)2023,Vol.50Issue(12):155-167,13.DOI:10.16339/j.cnki.hdxbzkb.2023192

基于LSTM和改进TTR算法的车辆辅助驾驶侧翻预警

Vehicle Assisted Driving Rollover Warning Based on LSTM and Improved TTR Algorithm

周兵 1梁帅 1吴晓建 2许艳 1潘倩兮 1柴天1

作者信息

  • 1. 湖南大学汽车车身先进设计制造国家重点实验室,湖南长沙 410082
  • 2. 南昌大学先进制造学院,江西南昌 330031
  • 折叠

摘要

Abstract

Aiming at improving the prediction and judgment of rollover risk in rollover warning,a more efficient and accurate rollover warning algorithm was proposed to provide an important basis for drivers or other driving assistance systems to determine the intervention time of vehicle control.Firstly,a 3-DOF vehicle pre-warning reference model was established.The phase-plane method was selected to divide the roll stability region as the rollover index,an improved time-to-rollover(TTR)algorithm was designed and TTR was calculated according to the response of the 3-DOF vehicle model.The analysis results show that the phase plane rollover index is close to the actual lateral-load transfer rate(LTR),which is more accurate than the common expression of LTR,and the improved TTR is closer to the actual TTR.Then,to improve the computational efficiency of pre-warning,a long short-term memory(LSTM)model was established to replace the improved TTR algorithm and the TTR value output by the model was used as the basis for vehicle pre-warning control.Finally,the LSTM model was trained by collecting data through driver-in-the-loop(DIL)tests.In two working conditions,the proposed rollover warning method was verified to have the accuracy of rollover risk prediction and higher real-time performance.

关键词

高级驾驶辅助系统/侧翻预警/改进侧翻预测时间算法/相平面法/长短期记忆网络

Key words

advanced driver assistance system/rollover warning/improved time-to-rollover algorithm/phase-plane method/long short-term memory

分类

交通运输

引用本文复制引用

周兵,梁帅,吴晓建,许艳,潘倩兮,柴天..基于LSTM和改进TTR算法的车辆辅助驾驶侧翻预警[J].湖南大学学报(自然科学版),2023,50(12):155-167,13.

基金项目

国家自然科学基金资助项目(51875184,52002163,52262054),National Natural Science Foundation of China(51875184,52002163,52262054) (51875184,52002163,52262054)

湖南大学学报(自然科学版)

OACSCDCSTPCD

1674-2974

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