基于车内外视觉信息的行人碰撞预警方法

行人碰撞预警系统通常依据行人检测与碰撞时间判断的方式为驾驶员提供预警信息。为了提供更加可靠的危险判断依据，本文提出一种同时分析道路状况与驾驶员头部姿态的行人碰撞预警方法，用两个单目相机分别获取车辆内外环境图像。通道特征检测器用于定位行人，根据单目视觉距离测量方法估计出行人与自车间的纵向与横向距离。多任务级联卷积网络用于定位驾驶员面部特征点，通过求解多点透视问题获取头部方向角以反映驾驶员注意状态。结合行人位置信息与驾驶员状态信息，本文构建模糊推理系统判断碰撞风险等级。在实际路况下的实验结果表明，根据模糊系统输出的风险等级可以为预防碰撞提供有效的指导。

Pedestrian collision warning system based on looking-in and looking-out visual information analysis

Pedestrian collision warning systems usually provide early warning for drivers based on the technologies of pedestrian detection and collision time measurement. To provide a more reliable basis for risk assessment, a pedestrian collision warning method that involves analyzing the road condition and driver’s head pose simultaneously is proposed in this paper. Two monocular cameras are used to capture vehicle exterior and interior images, and a channel features detector is applied to locate pedestrians. The vertical and horizontal distances between pedestrians and ego-vehicle are estimated based on monocular vision distance measurement. The multi-task cascaded convolutional network is utilized for facial landmark detection. By solving a perspective-n-point (PnP) problem, the estimated head angles can reflect driver’s attention states. By combining both pedestrian location information and driver’s attention information, we implemented a fuzzy inference system to assess collision risk level. An experiment in real-world driving conditions demonstrated that the risk levels obtained from the fuzzy system are reliable and can provide guidance for collision avoidance.