换道操作对智能车辆乘客舒适性的影响研究

乘坐舒适性是决定乘客对智能车辆接受度的重要因素之一。为了提升智能车辆的舒适性，服务智能驾驶控制算法的设计和优化，开展了基于乘客主观感知的实车乘坐舒适性试验，试验中驾驶人驾驶传统车辆执行多次换道操作，获取了60名被试乘客对换道操作的舒适性评价数据,并采集了车辆的运动数据。选取换道时横向最大加速度、回正时横向最大加速度、横向最大加加速度、横向加速度转换幅值以及横向加速度转换频率这5个车辆运动参数作为研究对象。采用二元Logistic回归单因素分析法分析了这5个车辆运动参数对乘坐舒适性的影响，采用接收者操作特征(ROC)曲线分析法为不同晕车易感性的乘客分别确立了这5个车辆运动参数的舒适性阈值，并根据岭回归分析法确定了不同参数对乘坐舒适性的影响权重。结果表明：所选取的5个车辆运动参数对乘坐舒适性具有显著影响，易晕乘客的舒适性阈值小于不易晕乘客的舒适性阈值，在换道过程中，换道时横向最大加速度、回正时横向最大加速度和横向加速度转换幅值是影响乘坐舒适性的主要因素。最后根据车辆运动参数和乘客生理特征参数建立了基于动态时间归整(DTW)和K最近邻(KNN)算法的乘坐舒适性预测模型，该模型对乘坐舒适性的预测准确率为84%，可用于智能车辆控制算法的舒适性判断。

Influence of Lane-changing Maneuvers on Passenger Comfort of Intelligent Vehicles

One of the most important factors that determine the passengers' acceptance of intelligent vehicles is the ride comfort. In order to improve the comfort of intelligent vehicles and help the design and optimization of intelligent driving control algorithms, this study performed a real vehicle ride comfort test based on the subjective perception of its passengers. In the test, the driver drove a traditional vehicle performing multiple lane-changing maneuvers. Data related to the 60 passengers' assessment of comfort and vehicle motion parameters were collected. Five vehicle motion parameters were selected for this study:the maximum lateral acceleration when changing lanes, the maximum lateral acceleration when returning, the maximum lateral jerk, the conversion amplitude of lateral acceleration, and the conversion frequency of lateral acceleration, and a binary logistic regression single factor analysis was used to evaluate their influence on the ride comfort. The receiver operating characteristic (ROC) curve analysis method was used to determine the comfort thresholds of these five vehicle motion parameters for passengers with different susceptibilities to motion sickness. A ridge regression analysis was used to determine the weights of the parameters' influence on the ride comfort. The results showed that:these five vehicle motion parameters have a significant impact on the ride comfort; the comfort threshold of the passengers susceptible to motion sickness is lower than the comfort threshold of passengers not susceptible to motion sickness; the maximum lateral acceleration when changing lanes and when returning, and the conversion amplitude of lateral acceleration are the main factors affecting the ride comfort when changing lanes. Finally, a comfort prediction model based on dynamic time warping(DTW)+ K nearest neighbor (KNN) algorithm was established according to the vehicle motion parameters and passenger physiological characteristics. The prediction accuracy of the ride comfort model is 84%, which can be used for the assessment of the comfort of intelligent vehicles control algorithms.