Road and relative ego-state recognition

A road interpretation module is presented, which is part of a real-time vehicle guidance system for autonomous driving. Based on bifocal computer vision, the complete system is able to drive a vehicle on marked or unmarked roads, to detect obstacle, and to react appropriately. The hardware is a network of 23 transputers, organized in modular clusters. Parallel modules performing image analysis, feature extraction, object modelling, sensor data integration and vehicle control, are organized in hierarchical levels. The road interpretation module is based on the principle of recursive state estimation by Kalman filter techniques. Internal 4D-models of road, vehicle position, and orientation are updated by data, gained by the image-processing module. The system has been implemented on two vehicles (VITA and VaMoRs) and demonstrated in the framework of PROMETHEUS, where the ability of autonomous driving through narrow curves (radius ⩽40 m) and of lane changing were shown. Meanwhile the system has been tested on public roads in real traffic situations, driving on a German Autobahn autonomously at speeds up to 85 km/h