基于相关向量机的换道安全性评估

针对传统换道预警系统算法存在的准确率和可靠率等问题,利用毫米波雷达、AWS视觉传感器、车载陀螺仪等设备搭建了试验车,在普通高速公路上对19名被试人员进行了实际道路驾驶试验,并从试验数据中筛选出近1000次换道行为数据.以3段式换道轨迹模型为基础,将横摆角速度统计值作为确定各个阶段曲率半径的依据,根据横摆角速度α= 0.05的上侧分位数确定可接受安全域的上限;并以换道持续时间的极大值为基准,通过分析12 s以上的换道过程,形成一系列控制点,采用B样条曲线规划确定了可接受安全域的下限.通过利用相关向量机(RVM)模型对换道过程参数进行估计,并利用7次多项式模型对换道轨迹进行拟合,然后将拟合轨迹与可接受安全域上限或者下限所围成面积与可接受安全域面积的比值作为预警参数,实现了对换道安全性的评估.利用真实数据对上述评估算法进行验证,结果表明,所提出的换道安全算法的评估结果能正确反映换道的真实安全性,并且与驾驶人的操作行为具有良好的相关性.

A Safety Evaluation of Lane Change Based on Relevance Vector Machine

The accuracy and reliability are the major problems that have existed in traditional of lane change warning algorithms.Therefore,a test vehicle is set by using millimeter-wave radar,AWS vision sensor,vehicle gyroscope and other devices aiming at solving the above-mentioned problems.A field tests is conducted on highways and 19 subjects are recruited.Nearly 1 000 lane change samples are extracted from recorded driving test data.Based on a three-stage lane change trajectory model,the statistical value of yaw rate is regarded as the basis to determine radius of curvature in each stage.The upper limit for acceptable safety threshold is determined according to the yaw rate quantile when α=0.05.A series of control points are formed by analyzing lane change processes for which lasted more than 12s and the maximum value of lane change duration is selected as the benchmark.B-spline curve planning is adopted to determine the lower limit of the acceptable safety threshold.Parameters in lane change are estimated by Relevance Vector Machine(RVM),and 7th polynomial model is used to fit lane change trajectory.The area enclosed by the fitted trajectories and the upper(or lower)limit of the acceptable safety threshold is used as the warning parameters.The ratio is used as a warning parameter to evaluate safety of lane change.Actual data is used to verify this algorithm.The evaluation results of this algorithm can reflect actual safety level of the lane change,and have strong correlation with operating behaviors of drivers.