基于车辆行驶状态的险态驾驶动态感知及协同诊断模型研究

项目名称： 基于车辆行驶状态的险态驾驶动态感知及协同诊断模型研究

项目编号： No.51305339

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 机械、仪表工业

项目作者： 赵栓峰

作者单位： 西安科技大学

项目金额： 25万元

中文摘要： 驾驶行为是影响交通安全最活跃的因素，如何对险态驾驶进行动态诊断已经成为世界各国科学家的研究热点。本项目拟以车辆行驶状态为对象，研究险态驾驶的动态感知及诊断方法。即在复杂路况条件下，建立以险态驾驶指标为输入的险态驾驶模型，揭示险态驾驶与车辆行驶状态的映射法则；从车辆行驶状态中分解出高信噪比的道路信息、车辆信息、驾驶员状态信息，借鉴瞬时频率分析方法提取频域信息，并与时域、相位和空间信息相结合，构建险态驾驶状态的高纬特征向量，采用核主成分分析方法进行特征约简，在低维空间中建立险态驾驶状态全周期评估方法；研究险态驾驶模型正向推演与车辆行驶状态反向分析协同耦合的险态行为动态诊断模型，解决险态驾驶演化中模型和特征均具有时变特性的难题，在对驾驶员"零干扰"的前提下，实现险态驾驶的动态诊断。项目研究为险态驾驶程度的精确划分和非接触式在线监测提供理论方法和使能技术支撑，推动险态驾驶诊断技术的实用化进程。

中文关键词： 危险驾驶；驾驶员模型；监测与诊断；神经网络；

英文摘要： With the increasing number of automobile, nowadays the transportation safety becomes a extremely important issue for scientific research. The status of driver plays the most significant role in the safety of road traffic due to driver is the core factor in road traffic system. Thus, study of risk driving behavior of the driver becomes a vital issue for road traffic safety. It is selected as the main context of this project which focuses on the development of a dynamic diagnosis model for risk driving behavior under zero distraction condition. The risk driving model whose input was the risk driving indexes was set up in complex road conditions. It reveal the mapping rules of the risk driving and vehicle running status; Decomposition of the high SNR road ,vehicle and driver status information form the vehicle running status, drawing on the instantaneous frequency analysis methods to extract frequency domain information, the time domain, spatial information to build the high-dimensional feature vector for risk driving, kernel principal component analysis for feature reduction in low-dimensional space-cycle assessment of the risk-state driving state; a dynamic risk driving synergistic diagnosis model is developed by coupling the forward method based on positive analysis of risk model and backward methods based on si

英文关键词： dangerous driving；driver model；Monitoring and diagnosis；neural networks；