Abstract—The development of advanced driver assistance systems (ADAS) is of importance to prevent the potential driving risks on the road, which relies on the perceptive sensors to detect and identify the traffic conditions. This paper examines a method to track multiple objects and determine the high risk obstacles using radar. We experiment the scenarios as the vehicle and pedestrians in front, as well as the pedestrian crossing the street. The multiple tracks are achieved based on the Kalman filter. This work implements the state prediction model to compensate the missing targets, and the false positives are excluded through a threshold of bad tracks. To identify the potential obstacles, we impose two boundaries, i.e., lane and dynamic boundaries as the region of interest on the multiple tracks. It is demonstrated that the dynamic boundary benefits the finding of high risk bstacles and excludes most of the background noises. The lane boundary takes the advantage of tracking bjects along the driving path: however, it could underestimate the risk of transverse moving objects in which the tracks are not continuous crossing the lanes.
- IA-06-0005 (434K)