Radar-Vision Fusion with an Application to Car-Following using an Improved AdaBoost Detection Algorithm

This work outlines an object detection system for usage within driver assistance systems. The system detects vehicles that are driving ahead of an equipped vehicle by means of vision and radar data fusion. The radar provides a first estimation of vehicle candidates with related lateral position and distance information. This information is used to define a region of interest (ROI) on an image obtained by a video camera. An AdaBoost object detection algorithm is utilized to scan the ROI and verify radar detection. Due to the visual detection more specific data of the vehicle's 3D position and width can be given. Moreover, the distance information provided by radar is used to choose optimal parameters during the visual detection process, e.g. properties of the scan window and parameters for fusing detections. In addition, this work will show that mutual information for haar-like feature selection can significantly increase detection rates using a new adaptive threshold.