为了在发生轻微交通事故时, 快速使事故车辆驶离现场, 保证道路畅通, 提出了一种车辆碰撞检测及责任判定模型. 首先结合SSD目标检测算法(single shot multibox detector)和MobileNet轻量级深度网络模型, 对其进行改进以获取每一帧视频图像中运动目标的位置和大小信息, 实现对车辆识别与检测. 其次, 利用卡尔曼滤波器对连续图像帧之间的运动目标建立对应匹配关系, 预测目标的运动状态, 对目标的位置及运动趋势做出判断, 实现车辆轨迹跟踪. 随后通过车辆目标检测框的交并比判断是否发生碰撞. 最后针对直行道路中车辆的速度、方向信息结合道路安全条例及机动车事故快速方法对事故车辆进行责任判定. 结果分析表明, 该研究可实现直行道路场景下的追尾及变道引发的车辆碰撞检测及责任判定.
In order to quickly drive accident vehicles away from the scene and ensure a clear road during a minor traffic accident, this study proposes a vehicle collision detection and liability determination model. First, the study combines the SSD (single shot multibox detector) target detection algorithm and the MobileNet lightweight deep network model to make improvements and obtain the position and size information of the moving target in each frame of video images, so as to identify and detect the vehicle. Secondly, the study employs a Kalman filter to establish a corresponding matching relationship between moving targets in consecutive image frames, predict their motion states, and judge their positions and motion trend, in a bid to track the vehicle. Then, the study determines whether there is a collision by the intersection over union of the vehicle target detection frame. Finally, according to the speed and direction information of the vehicle on a straight road, the liability of the accident vehicle is determined under the road safety regulations and the fast method of motor vehicle accidents. The results show that the research can help to detect and determine the liability during vehicle collisions caused by rear-end collisions and lane changes on straight roads.