Estimation and Tracking of Local Road Geometry for Assisting Reliable Urban Driving
October 16, 2013, 5PM
두산인문관(8동) 1층 연강홀
For safe urban driving, keeping a car within a road-lane boundary is a critical prerequisite. It requires human and robotic drivers to recognize the boundary of a road-lane and the vehicle’s location with respect to the boundary of a road-lane that the vehicle happens to be driving in. To provide such a perception capability, we present a new computer vision system that analyzes a stream of perspective images to produce information about a vehicle’s relative location to a road-lane’s boundary, and information about the detecting of lane-crossing and lane-changing maneuvers. To assist the vehicle’s lateral localization, our algorithm also estimates the host road-lane’s geometry, including the number of road lanes and their widths. The local road geometry estimated by frame-by-frame may be inconsistent over frames due to variations in the image features. To handle such inconsistent estimations, we implement a Bayes filter to smooth out, over time, the estimated road geometry. Tests on inter-city highway showed that our system provides stable and reliable performance in terms of computing lateral distances and detecting lane-crossing and lane-changing maneuvers.