基于边缘计算的交叉路口无人驾驶车辆通行轨迹预测算法

Trajectory prediction algorithm of unmanned vehicles at urban intersection based on edge computing

为给无人驾驶车辆精确制定城市路口通行轨迹提供先验信息,提出了在边缘计算平台中基于驾驶员意图分类和Bezier曲线相结合的轨迹预测算法。分析2个路口230辆车的真实通行数据,提出了基于支持向量机的驾驶员意图识别算法,预测路口车辆直行、左转及右转的概率;用基于Bezier曲线和代价函数相结合的通行轨迹预测方法,预测其路口通行轨迹。结果表明:经过与采集的120辆车的实际数据对比,驾驶员意图分类算法准确度在92.5%以上,车辆预测轨迹与真实轨迹间最大偏差范围在22.3~57.9 cm之间,所有车辆预测轨迹与真实轨迹间平均偏差为21.4 cm。因此,本方法能够满足城市路口车辆通行轨迹预测需求。

A trajectory prediction algorithm was proposed based on the combination of driver intent classification and Bezier curve in the edge computing platform to provide prior information for unmanned vehicles to accurately formulate urban intersection trajectories.Proposed a support vector machine-based driver intention recognition algorithm to predict the probability of vehicles going straight,turning left or turning right at the intersection after analyzing the actual traffic data of 230 vehicles at two intersections.And proposed a traffic trajectory prediction method based on the combination of Bezier curve and cost function to predict the traffic trajectory of the intersection.The results show that the accuracy of the driver’s intention classification algorithm is above 92.5%comparing with the actual data collected from 120 vehicles.The maximum deviation range between the predicted trajectory of the vehicle and the true trajectory is 0.223~0.579 m.The average deviation between the predicted trajectory of all vehicles and the true trajectory is 0.214 m.Therefore,this method meets the needs of vehicle trajectory prediction at urban intersections.