基于驾驶人特性的自适应换道预警算法研究
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摘要
本文中建立了基于驾驶人行为特性的换道危险感知模型,提出一种参数在线辨识、阈值可调的换道预警算法。通过模糊逻辑方法,以速度关联度、换道安全系数及横向偏移为指标确定周围车辆对自车换道的影响程度,修正换道参数;利用递推极大似然估计对模型参数进行在线辨识,获得实时危险评估值;并基于信息熵搜索最佳报警阈值,将实时评估值与报警阈值进行比较,判断系统的报警状态。采用实车试验的自然驾驶行为数据进行验证,结果表明,自适应预警模型的准确率达92.1%,预测危险状态的时间可提前0.3～1 s,符合驾驶员的心理预期,具备可操作性。
A lane-changing hazard perception model based on the behavior characteristics of the driver is established, and a new algorithm is proposed with on-line parameter identification and adjustable threshold. By using the fuzzy logic method, the influence of the surrounding vehicles on lane change is determined by the speed correlation degree, the safety factor and the lateral deviation to modify hazard perception model parameters. Then the model parameters are on-line identified by recursive maximum likelihood estimation, and the real-time risk assessment value is obtained. Finally, based on the information entropy, the optimal alarm threshold is searched, and the real-time evaluation value is compared with the alarm threshold to judge the alarm state of the system. Verification results using natural driving behavior data from real-car experiments show that the accuracy of the adaptive warning model is 92.1% and the time to predict the state of danger can be advanced by 0.3-1 s, which accords with the psychological expectation and practical operating characteristics of the driver.
A lane-changing hazard perception model based on the behavior characteristics of the driver is established, and a new algorithm is proposed with on-line parameter identification and adjustable threshold. By using the fuzzy logic method, the influence of the surrounding vehicles on lane change is determined by the speed correlation degree, the safety factor and the lateral deviation to modify hazard perception model parameters. Then the model parameters are on-line identified by recursive maximum likelihood estimation, and the real-time risk assessment value is obtained. Finally, based on the information entropy, the optimal alarm threshold is searched, and the real-time evaluation value is compared with the alarm threshold to judge the alarm state of the system. Verification results using natural driving behavior data from real-car experiments show that the accuracy of the adaptive warning model is 92.1% and the time to predict the state of danger can be advanced by 0.3-1 s, which accords with the psychological expectation and practical operating characteristics of the driver.
引文
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[2] 杨旭春.同济大学朱西产:智能网联汽车发展及应用场景分析[N/OL].http://auto.sina.com.cn/zz/sh/2017-10-20/detail-ifymyyxw3791753.shtml,2017-10-20.
[3] GONG S,DU L.Optimal location of advance warning for mandatory lane change near a two-lane highway off-ramp[J].Transportation Research Part B Methodological,2016,84:1-30.
[4] WANG C,FU R,ZHANG Q,et al.Research on parameter TTC characteristics of lane change warning system[J].China Journal of Highway and Transport,2015,28(8):91-100,108.
[5] MINDERHOUD M M,BOVY P H L.Extended time-to-collision measures for road traffic safety assessment[J].Accident Analysis & Prevention,2001,33(1):89-97.
[6] 陈秀锋,杨万三,曲大义.基于最小安全距离的车辆换道安全研究[J].昆明理工大学学报:自然科学版,2013,38(3):42-46.
[7] 游峰,张荣辉,王海玮,等.基于纵向安全距离的超车安全预警模型[J].华南理工大学学报:自然科学版,2013,41(8):87-92.
[8] 王畅,付锐,彭金栓,等.应用于换道预警的驾驶风格分类方法[J].交通运输系统工程与信息,2014,14(3):187-193.
[9] SCHMIDT K W.Cooperative adaptive cruise control for vehicle following during lane changes[J].IFAC-Papers Online,2017,50(1):12582-12587.
[10] SAIGO S,RAKSINCHAROENSAK P,NAGAI M.Estimation of driving performance level using longitudinal and lateral driver models[J].IFAC Proceedings Volumes,2013,46(21):145-150.
[11] 黎胜,史忠科,刘通.基于FVD模型的改进模型和试验比较[J].公路交通科技,2014,31(2):109-113.
[12] 虞辰霏.自适应驾驶人特性的前撞预警方法研究[D].北京:清华大学,2013.
[13] NIE J,ZHANG J,DING W,et al.Decentralized cooperative lane-changing decision-making for connected autonomous vehicles[J].IEEE Access,2016,4:9413-9420.
[14] BOCKLISCH F,BOCKLISCH S F,BEGGIATO M,et al.Adaptive fuzzy pattern classification for the online detection of driver lane change intention[J].Neurocomputing,2017,262(1):148-158.
[15] 侯媛彬,周莉,王立琦,等.系统辨识[M].西安:西安电子科技大学出版社,2014.
[16] 吴斌,朱西产,沈剑平,等.基于自然驾驶数据的危险评估算法研究[J].汽车工程,2017,39(8):907-914.
[17] 陈明志,陈国龙.对区间值属性决策树分类的一种割点快速筛选法[C].全国理论计算机科学学术年会,2004.
[18] ABDI H.Signal detection theory[M].//PETERSONP,et al.International Encyclopedia of Education.Elsevier Science,2010:407-410.