考虑交通参与者的城市交叉口车速预测模型

Speed prediction model at urban intersections considering traffic participants

为了提高车辆在城市交叉口自由行驶状态下的车速预测性能,提出一种考虑本车与其他交通参与者交互特性的车速预测新方法.首先,提出一种车辆目标细分方法来区分其他车辆相对于本车的行驶方向,并应用目标检测算法YOLOv5识别潜在的交通冲突和弱势交通参与者;然后,将识别的交通参与者信息与历史车速信息相结合,建立基于长短期记忆网络的车速预测模型,在左转、右转以及直行3种不同的驾驶场景下验证交通参与者信息对于提高车速预测性能的有效性.结果表明:与仅以历史车速为输入的基准模型相比,考虑交通参与者的车速预测模型表现出更好的性能,其在很大程度上解决了预测模型在一个预测时域内精度逐渐下降的问题,并对城市交叉口的复杂交通环境表现出更强的适应性.

In order to improve the performance of speed prediction in the state of free driving at urban intersections,a new method for speed prediction that considers the interaction characteristics of the host vehicle with other traffic participants is proposed.First,a vehicle target classification method is proposed to distinguish the driving direction of other vehicles relative to the host vehicle,and the target detection algorithm YOLOv5 is used to identify potential traffic conflicts and vulnerable traffic participants.Then,the identified traffic participant and historical speed are combined to establish a speed prediction model based on long short-term memory network.The effectiveness of traffic participant information in improving speed prediction performance is verified in three different driving scenarios,i.e.left turn,right turn and straight.The results show that compared with the baseline model that only takes historical speed as input,the speed prediction model considering traffic participants shows better performance.It solves the problem of the gradual decline in the accuracy of the prediction model in a prediction domain,and shows stronger adaptability to the complex traffic environment of urban intersections.