面向团雾的信息突失下驾驶应激行为机理及信息补偿方法

The sudden-loss of driving information occurs a lot and will occur in the far future. It has a significant negative impact on driving behavior. The space-time sudden change of agglomerate fog will bring about the sudden-loss of driving information, and easily leads to serious traffic accidents. However, most of the existing researches focus on normal fog, which can not reflect short time visibility mutation characteristics. In view of this, this project intends to collect road environment data to extract features of the information sudden-loss caused by agglomerate fog. At the same time through long-term continuous observation on vehicles trajectory to capture the natural driving behavior data which can overcome the uncontrollability of the experimental vehicle test and the unreliability of simulation test. Thus, the driving behavior data can reveals the driving stress behavior characteristic. Then, establish a relation model between the information sudden- loss characteristics and driving behavior, and analysis the mechanism of how sudden-loss item, sudden-loss rate, and sudden-loss duration effect driving stress behavior. On the basis of achievements above, the combination of the road and vehicle information giving device which can compensate the sudden-loss information will be artificial designed, verified and optimized to improve the traffic safety level on expressway in agglomerate fog. The project will study the driving mechanism in the fog from the normal fog extended to agglomerate fog, is helpful to understand the cause of traffic accidents in agglomerate fog, provide direction for the improvement of traffic safety in the agglomerate fog. At the same time, provide the bionic reference for intelligent vehicle active/assistant safety system in agglomerate fog.

驾驶信息突失现象大量并将长期存在，其易对驾驶行为产生显著不利影响，团雾的时空突变性导致驾驶信息突失，极易诱发重特大交通事故，而现有研究主要集中于常态雾，无法反映短时能见度突变特征。鉴于此，本项目拟从信息突失出发，采集道路环境数据以提取团雾导致的信息突失特征，同时通过路上长期连续轨迹观测采集真实团雾环境下自然车流驾驶行为数据以克服实验车实验不可控和单纯模拟实验可信度低的缺陷，进而揭示团雾信息突失下驾驶应激行为特征，建立信息突失特征与驾驶行为间的因果关系模型，解析突失信息项、突失率、持续时间等对驾驶应激行为的影响机理，在此基础上人工设计、验证并优化可补偿突失信息的路上及车载信息组合，以期改善高速公路团雾气候下的交通安全水平。项目将雾天驾驶行为机理的研究从常态雾延伸至团雾，有助于更深入地理解团雾交通事故致因，为团雾交通安全改善研究提供方向，同时为团雾下智能车辆主动/辅助安全系统研究提供仿生参考。

驾驶信息突失现象大量并将长期存在，其易对驾驶行为产生显著不利影响，团雾的时空突变性导致驾驶信息突失，极易诱发重特大交通事故，而现有研究主要集中于常态雾，无法反映短时能见度突变特征。鉴于此，本项目拟从信息突失出发，采集道路环境数据以提取团雾导致的信息突失特征，进而揭示团雾信息突失下驾驶应激行为特征，建立信息突失特征与驾驶行为间的因果关系模型，解析突失信息项、突失率、持续时间等对驾驶应激行为的影响机理，在此基础上人工设计、验证并优化可补偿突失信息的路上及车载信息组合，以期改善高速公路团雾气候下的交通安全水平。项目将雾天驾驶行为机理的研究从常态雾延伸至团雾，有助于更深入地理解团雾交通事故致因，为团雾交通安全改善研究提供方向，同时为团雾下智能车辆主动/辅助安全系统研究提供仿生参考。. 项目在研期间，共发表学术论文7篇，其中SCI收录4篇，EI收录3篇，授权发明专利7项，获中国公路学会科学技术一等奖1项，特等奖1项。