Inferring a spatial road representation from the behavior of real world traffic participants

The detection of road area in the surroundings of the ego-vehicle is a key issue for modern ADAS. Camera-based direct detection systems are able to reliably accomplish this task only within a limited spatial range or in simple environments, due to hardware limitations and unfavorable situations, like shadows or occlusions. In complex environments, like inner city, traffic is a real issue, since the mere presence of other cars can significantly restrict the field of view of the ego-vehicle. In order to extend the spatial range of road detection, indirect detection systems are a viable resource. They can complement state-of-the-art direct detection systems and help motion control systems to plan smooth and stable trajectories. In this paper we propose a probabilistic grid-based approach based on the interpretation of the motion of other vehicles in the scene. The approach uses the position and velocity of those vehicles in order to infer the presence and location of occluded road area. We will show that this approach can complement an already established feature-based detection system, taking advantage of those situations that are the most challenging for the latter. Evaluations on real-world scenes show that the union between this approach and direct road detection significantly extends the spatial range of detection, thus is able to provide a motion control system a longer horizon for planning trajectories.