基于相邻车道安全态势划分的换道决策

doi:10.3901/JME.2023.02.233

机械工程学报 ›› 2023, Vol. 59 ›› Issue (2): 233-244.doi: 10.3901/JME.2023.02.233

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基于相邻车道安全态势划分的换道决策

王慧然¹, 陈无畏², 王其东^1,2, 赵林峰², 朱茂飞¹, 魏振亚²

1. 合肥学院先进制造工程学院合肥 230601;
2. 合肥工业大学汽车与交通工程学院合肥 230009

收稿日期:2021-12-15 修回日期:2022-07-25 发布日期:2023-03-30
通讯作者: 陈无畏(通信作者),男,1951年出生,博士,教授,博士研究生导师。主要研究方向为车辆动力学与控制、辅助驾驶和自动驾驶技术,发表论文300余篇。E-mail:hfgdcjs@126.com
作者简介:王慧然,男,1991年出生,博士。主要研究方向为车辆动力学与控制技术和自动驾驶技术。E-mail:wanghuiran7@163.com;王其东,男,1964年出生,博士,教授,博士研究生导师。主要研究方向为现代车辆动力学及其控制、辅助驾驶和自动驾驶技术。E-mail:qidongwang1964@163.com
基金资助:
安徽省高校自然科学研究（KJ2021A0985）、合肥学院人才科研基金（21-22RC05、20RC06）和安徽省新能源汽车产业创新发展和推广应用政策支持研发创新（wfgcyh2020477）资助项目。

Lane Change Decision Based on the Safety State Division of the Adjacent Lanes

WANG Huiran¹, CHEN Wuwei², WANG Qidong^1,2, ZHAO Linfeng², ZHU Maofei¹, WEI Zhenya²

1. School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601;
2. School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009

Received:2021-12-15 Revised:2022-07-25 Published:2023-03-30

摘要/Abstract

摘要： 针对大多数换道决策研究中存在的相邻车道的交通状态分析不足致使换道安全性难以保证的问题，提出一种基于相邻车道安全态势划分的换道决策方法。根据车-车相对位置关系，建立关联车辆分类准则，确定当前车道、相邻车道和相邻第二车道上需要监测横、纵向运动状态的车辆。设计深度神经网络和基于左、右换道差异的横向偏离判断标准对关联车辆的车道保持、车道偏离和换道的横向运动行为进行预测。考虑关联车辆不同横向运动行为对自车相邻车道安全态势的影响，结合车-车相对纵向运动状态，设计自车相邻车道的安全态势划分方法，确定自车相邻车道的安全等级。在此基础上，设计换道决策准则，实现车辆换道的精准决策。仿真结果表明，所提出的换道决策方法能够准确的预测关联车辆的横向运动行为，可以在不同行驶环境下实现更加精准的换道决策，提高了车辆换道的安全性，并通过实车试验，进一步验证了所提出的换道决策方法的有效性。

关键词: 相邻车道, 安全态势, 横向运动行为, 换道决策

Abstract: According to the shortcoming that lane change safety is difficult to ensure due to insufficient analysis of the traffic state of the adjacent lane in most lane change decision studies, a lane change decision method based on the safety state division of the adjacent lane is proposed to solve this problem. Based on the vehicle-vehicle relative position relationship, the classification criteria for associated vehicles is established to determine the vehicle that need to be monitored for lateral and longitudinal motion in the current lane, adjacent lanes, and adjacent second lanes. A deep neural network and a lateral deviation judgment standard based on the difference between left and right lane changes are designed to predict the lateral movement behaviors of lane keeping, lane departure and lane change for the associated vehicles. Considering the influence of different lateral motion behaviors of related vehicles on the security situation of the adjacent lanes of the vehicle and the relative longitudinal motion of the vehicle-vehicle, the safety state division method of the adjacent lanes is designed to determine the security level of the adjacent lanes. Then, the lane change decision criterion is designed to realize the precise decision-making for lane change. The simulation results show that the proposed lane change decision method can accurately predict the lateral motion behavior of associated vehicles, and can achieve more accurate lane change decision for different driving environments, and improve lane change safety of the vehicle. And the effectiveness of the method is validated through the real vehicle experiments.

Key words: adjacent lanes, safety state, lateral movement behavior, lane change decision

中图分类号:

U461

王慧然, 陈无畏, 王其东, 赵林峰, 朱茂飞, 魏振亚. 基于相邻车道安全态势划分的换道决策[J]. 机械工程学报, 2023, 59(2): 233-244.

WANG Huiran, CHEN Wuwei, WANG Qidong, ZHAO Linfeng, ZHU Maofei, WEI Zhenya. Lane Change Decision Based on the Safety State Division of the Adjacent Lanes[J]. Journal of Mechanical Engineering, 2023, 59(2): 233-244.

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基于相邻车道安全态势划分的换道决策

Lane Change Decision Based on the Safety State Division of the Adjacent Lanes

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