Data-Driven Personalized Scenario Risk Map Construction for Intelligent Vehicles

被引：0
|
作者：
Cui G. [1 ]
Lü C. [1 ]
Li J. [1 ]
Zhang Z. [1 ]
Xiong G. [1 ]
Gong J. [1 ]
机构：
[1] School of Mechanical Engineering, Beijing Institute of Technology, Beijing
来源：
Qiche Gongcheng/Automotive Engineering | 2023年 / 45卷 / 02期
关键词：
driver-personalized learning; graph representation learning; machine learning; risky driving scenes recognition; scene understanding;
D O I：
10.19562/j.chinasae.qcgc.2023.02.008
中图分类号：
学科分类号：
摘要：
In order to realize the auxiliary function of danger warning of intelligent vehicle and accurately establish the personalized assistance system for individual drivers，a data-driven personalized scenario risk map construction method for intelligent vehicles is proposed. The graph representation of the attributes and implied interaction of both dynamic and static elements in complex traffic scenes is constructed. The graph kernel method is used to measure the similarity of the graph representation data，and the driver's operation data is processed and analyzed to obtain the driver's personalized scene risk evaluation label. The recognition model is trained based on support vector machine and the mapping relationship between the driver's personalized risk evaluation mechanism and scene features is established. The risk assessment label output by the model and the real value are compared experimentally. The results show that the recognition accuracy of the driver risky driving scene recognition model based on the personalized scenario risk map can reach 95.8%，which is 38.2% higher than that of the method based on feature vector representation，and it can effectively evaluate the risk degree of the personalized scene based on the driver's driving style. © 2023 SAE-China. All rights reserved.
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页码：231 / 242
页数：11
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