预见性驾驶风险场模型

doi:10.3901/JME.2024.10.160

机械工程学报 ›› 2024, Vol. 60 ›› Issue (10): 160-170.doi: 10.3901/JME.2024.10.160

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预见性驾驶风险场模型

褚端峰¹, 彭赛骞^1,2, 胡海洋¹, 皮大伟³

1. 武汉理工大学智能交通系统研究中心武汉 430063;
2. 武汉理工大学机电工程学院武汉 430070;
3. 南京理工大学机械工程学院南京 210094

收稿日期:2023-06-20 修回日期:2024-01-15 出版日期:2024-05-20 发布日期:2024-07-24
作者简介:褚端峰,男,1983年出生,博士,教授,博士研究生导师。主要研究方向为自动驾驶、车路协同等。
E-mail:chudf@whut.edu.cn
彭赛骞,男,1998年出生,硕士研究生。主要研究方向为车辆轨迹预测、驾驶风险评估。
E-mail:psq@whut.edu.cn
胡海洋,男,1990年出生,博士研究生。主要研究方向为自动驾驶、车路协同。
E-mail:huhaiyang@whut.edu.cn
皮大伟(通信作者),男,1983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、智能网联汽车等。
E-mail:pidawei@mail.njust.edu.cn
基金资助:
国家重点研发计划(2021YFB2501104)、湖北省杰出青年基金(2022CFA091)、武汉市科技重大(2022013702025184)和武汉市重点研发计划(2022012202015027)资助项目。

Predictive Driving Risk Field Model

CHU Duanfeng¹, PENG Saiqian^1,2, HU Haiyang¹, PI Dawei³

1. Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063;
2. School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070;
3. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094

Received:2023-06-20 Revised:2024-01-15 Online:2024-05-20 Published:2024-07-24

摘要/Abstract

摘要： 为真实反映车辆在行驶过程中的风险，并准确预测未来潜在的驾驶风险，提出一种考虑目标车辆预测轨迹的预见性驾驶风险场模型。首先，根据场景中的地图信息和所有车辆的历史轨迹信息，来预测目标车辆的未来轨迹；然后，根据预测的目标车辆未来轨迹，以及规划的自车未来轨迹，计算自车与目标车辆之间的相对位置，以及它们之间靠近或远离的趋势；最后，通过构建的驾驶风险场模型计算自车在当前和未来时刻的驾驶风险。试验表明相比于传统的碰撞时间(Time to collision，TTC)方法，建立的预见性驾驶风险场模型能更真实地反映驾驶风险；相比于不考虑目标车辆轨迹预测的驾驶风险建模方法，建立的预见性驾驶风险场模型能判断自车与目标车辆之间靠近或远离的趋势，预测风险与真实风险的偏差约为5%，能够准确预测未来时刻的潜在驾驶风险。

关键词: 驾驶风险场, 轨迹预测, 预见性驾驶, 风险分析, 智能车辆

Abstract: In order to accurately assess driving risks and predict potential hazards, it is necessary to consider the trajectory predictions of target vehicles. To address this issue, a predictive driving risk field model that takes into account the future trajectories of target vehicles is proposed. The model is comprised of three main steps. First, this study predict the future trajectories of target vehicles based on the map information in the scene and the historical trajectory information of all vehicles. Second, this study calculate the relative positions and motion trends (i.e., approaching or moving away from each other) between the ego vehicle and its target vehicles, based on the predicted future trajectories of target vehicles and the planned future trajectory of the ego vehicle. Finally, this study use the constructed driving risk field model to calculate the driving risk of the ego vehicle at both the current and future time intervals. The experimental results demonstrate that our predictive driving risk field model is more effective at reflecting driving risks compared to the traditional time-to-collision(TTC) method. Moreover, the deviation between predicted risk and real risk is about 5%. It shows that our model provides more accurate predictions of potential driving risks in the future, compared to a driving risk modeling method that does not consider the trajectory predictions of target vehicles.

Key words: driving risk field, trajectory prediction, predictive driving, risk assessment, intelligent vehicle

中图分类号:

U461

褚端峰, 彭赛骞, 胡海洋, 皮大伟. 预见性驾驶风险场模型[J]. 机械工程学报, 2024, 60(10): 160-170.

CHU Duanfeng, PENG Saiqian, HU Haiyang, PI Dawei. Predictive Driving Risk Field Model[J]. Journal of Mechanical Engineering, 2024, 60(10): 160-170.

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预见性驾驶风险场模型

Predictive Driving Risk Field Model

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