驾驶人换道意图实时识别模型评价及测试

doi:10.13229/j.cnki.jdxbgxb201606011

吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (6): 1836-1844.doi: 10.13229/j.cnki.jdxbgxb201606011

驾驶人换道意图实时识别模型评价及测试

郭应时¹, 付锐^{1, 2}, 赵凯¹, 马勇¹, 袁伟¹

1.长安大学汽车学院,西安 710064;
2.长安大学汽车运输安全保障技术交通行业重点实验室,西安 710064

收稿日期:2015-05-20 出版日期:2016-11-20 发布日期:2016-11-20
通讯作者: 付锐(1965-),女,教授,博士生导师.研究方向:道路交通安全技术.E-mail:furui@chd.edu.cn
作者简介:郭应时(1964-),男,教授,博士生导师.研究方向:车辆安全技术.E-mail:guoys@chd.edu.cn
基金资助:
教育部创新团队发展计划项目(IRT1286); 国家自然科学基金项目(61473046, 61374196,51305041); 中央高校基本科研业务费专项资金项目(310822153101, 310822161006,2014G3222004,2013G2222029)

Evaluation and test of real-time identification models of driver's lane change intention

GUO Ying-shi¹, FU Rui^{1, 2}, ZHAO Kai¹, MA Yong¹, YUAN Wei¹

1.School of Automobile, Chang'an University, Xi'an 710064, China;
2.Key Laboratory of Automotive Transportation Safety Technology, Ministry of Transport, Chang'an University, Xi'an 710064, China

Received:2015-05-20 Online:2016-11-20 Published:2016-11-20

摘要/Abstract

摘要：

通过分析驾驶人换道行为和车辆运动状态,研究了意图换道和车道保持阶段的差异性,并基于BP神经网络模型和证据理论识别模型,对意图换道进行了实时识别试验。结果表明:两种模型在换道前3 s对意图换道样本识别准确率分别为78.26%、45.22%,在换道时刻的识别准确率分别为99.13%、86.96%;随机选择样本对两种识别模型进行验证,意图换道样本的识别准确率分别为86.00%、96.00%,车道保持样本的识别准确率分别为21.05%、78.95%,同时模型识别出正确样本的最长时间均小于0.5 s,表明证据理论识别模型具有较高的优越性和实用性。

关键词: 交通运输安全工程, 驾驶行为, 意图识别, BP神经网络, 证据理论

Abstract:

The difference between lane change intention stage and lane keeping stage was studied by analyzing the driver lane change performance and the vehicle movement parameters. Based on BP neural networks model and D-S evidence theory model, the real-time identification of lane change intension tests were carried out. The results show that that sample identification accuracies of the two models in three seconds before the lane change are 78.3% and 45.2% respectively. The identification accuracies just at the moment of lane change are 99.13% and 86.96% respectively. By verifying the two models with random sample data, the identification accuracies for lane change intension are 86% and 96%, while for lane keeping are 21.05% and 78.95% respectively. The maximum time of the models for accurate identification is less than 0.5 second. It indicates that the evidence theory identification model is more applicable and reliable in both accuracy and timeliness.

Key words: engineering of communications and transportation safety, driving behavior, intent identification, BP neural network, theory evidence

中图分类号:

U491.25

郭应时, 付锐, 赵凯, 马勇, 袁伟. 驾驶人换道意图实时识别模型评价及测试[J]. 吉林大学学报(工学版), 2016, 46(6): 1836-1844.

GUO Ying-shi, FU Rui, ZHAO Kai, MA Yong, YUAN Wei. Evaluation and test of real-time identification models of driver's lane change intention[J]. 吉林大学学报(工学版), 2016, 46(6): 1836-1844.

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驾驶人换道意图实时识别模型评价及测试

Evaluation and test of real-time identification models of driver's lane change intention

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