Use of a monocular camera to analyze a ground vehicle’s lateral movements for reliable autonomous city driving

Abstract — For safe urban driving, one prerequisite is to keep a car within a road-lane boundary. This requires human and robotic drivers to recognize the boundary of a road-lane and the location of the vehicle with respect to the boundary of a road-lane that the vehicle happens to be driving in. We present a new computer vision system that analyzes a stream of perspective images to produce information about a vehicle’s lateral movements, such as distances from a vehicle to a road-lane’s boundary and detection of lane-changing maneuvers. We improve existing work in this field and develop new algorithms to tackle more challenging cases, such as driving on inter-city highways. Tests on real inter-city highways showed that our system provides stable and reliable performance in terms of computing lateral distances, while yielding reasonable performance in detecting lane-changing maneuvers. I