More than 90% of traffic accidents are caused by driver behavior, with lane change behavior being a major contributor. Recently, driving assistance systems are being introduced on vehicles to reduce traffic accidents, and a reliable vehicle lane change collision detection system is a key component of these systems. Besides, the foundation of the vehicle lane change detection system is the effective vehicle lane change detection model. In this paper, based on the support vector machine, we propose a model for detecting driver lane change maneuvers and take into account the real-time vehicle dynamic features transmitted via Vehicle to X (V2X) Communication. The accuracy is ideal for lane keep and lane change situations, and it is also robust for zigzag driving situations, according to tests conducted using the NGSIM real traffic dataset. The detection accuracy for left and right lane change maneuvers is 97.5% and 99.09%, respectively, while the false alarm rate is 8.56%. Additionally, the average advance detection time is 1.7 s, which is suitable for actual driving application scenarios.

超过 90% 的交通事故是由驾驶员行为引起的，其中变道行为是主要原因。最近，为了减少交通事故，车辆上引入了驾驶辅助系统，可靠的车道变换碰撞检测系统是这些系统的关键组成部分。此外，车辆变道检测系统的基础是有效的车辆变道检测模型。在本文中，基于支持向量机，我们提出了一种检测驾驶员变道机动的模型，并考虑了通过车辆到 X (V2X) 通信传输的实时车辆动态特征。根据使用 NGSIM 真实交通数据集进行的测试，该精度非常适合车道保持和变道情况，并且对于曲折驾驶情况也很稳健。左右变道动作的检测准确率分别为97.5%和99.09%，误报率为8.56%。此外，平均提前检测时间为1.7 s，适合实际驾驶应用场景。