Adaptive forward vehicle collision warning based on driving behavior

Forward Vehicle Collision Warning (FCW) is one of the most important functions for the Advanced Driver Assistance System (ADAS). In this procedure, vehicle detection and distance measurement are core components, requiring accurate localization and estimation. In this paper, we propose a simple but efficient forward vehicle collision warning framework by aggregating monocular distance measurement and precise vehicle detection. In order to obtain forward vehicle distance, a quick camera calibration method which only needs three physical points to calibrate related camera parameters is utilized. As for the forward vehicle detection, a multi-scale detection algorithm that regards the result of calibration as distance prior is proposed to improve the precision. What's more, traditional deterministic FCW approaches cannot be personalized for different drivers, which will lead to false warnings when drivers are in diverse driving status. Therefore, abnormal driver behaviors are introduced to make FCW adaptive. Specifically, the proposed adaptive FCW generates warnings by considering the different behaviors of the driver. Intensive experiments are conducted in our established real scene dataset and the results have demonstrated the effectiveness of the proposed framework.