Model-Based Car Tracking Integrated with a Road-Follower

Abstract

This paper discusses how we integrated our 3D car tracking approach with the lane following module RALPH on the Navlab autonomous vehicles, obtaining a hybrid vision system that tracks both the road and cars better than those two systems in isolation. The tracking system brings precise and crisp measurements of the car in the image, and performs image stabilization. However, because it does not know about the yaw or lateral offset of the ego-vehicle, its curvature estimate can be misguided. RALPH takes a more global image processing approach and can provide this missing information, as well as a good estimate of curvature, so that the combined curvature estimate is superior to both taken in isolation. The additional information provided by RALPH also improves tracking performance, and allows us to estimate properties of the tracked car that were previously unobservable, in particular its in-lane displacement. Better car tracking, and a better idea of where the road is, gives us a substantial foundation on which to base other capabilities needed to realize fully autonomous vehicles.