Toward Perception-Driven Urban Environment Modeling for Automated Road Vehicles

Automated driving is a widely discussed topic nowadays. Impressive demonstrations have shown the potentials of vehicle automation. However, many projects in the context of automated driving use a priori data in order to compensate insufficiencies in perceiving and understanding the vehicle´s environment. Additionally, in terms of functional safety and redundancy, it is not yet known whether such localization-and map-based approaches are really path breaking. This is the reason why we focus on on-board perception also of the stationary urban environment. While object tracking is a commonly used approach, the combination of grid-based and object-based representations for environment perception is still a research topic. The sufficient perception of lanes and drivable areas is an unsolved issue in urban environment. Several perception modules have to collaborate for a suitable representation of the vehicles´ surroundings. In this paper, we present the latest contributions of the project Stadtpilot to a perception-driven modeling of urban environments. We propose a lane detection approach which is based on a grid-based representation of different environmental features. Our approach is able to detect multi-lane structures and it is capable to deal with complex lane structures which are typical of urban roads. The extracted features are stabilized by a tracking module. Additionally, we incorporate a free-space representation which data is not derived implicitly from detected targets, but based on an explicit ground representation. Extensions of our dynamic classification module focus on the start/stop behavior of other road users in order to enhance the completeness of track list (mobile objects) and grid (stationary environment). The presented algorithms run in real-time on a standard PC and are evaluated with real sensor data.