An integrated spatio-temporal approach to automatic visual guidance of autonomous vehicles

The Kalman filter approach to recursive state estimation making use of dynamic models for the motion of massive objects has been extended to image sequence processing. This confines image processing to the last frame of the sequence only, and derives a direct spatial interpretation including spatial velocity components by smoothing integrations of prediction errors. Results are presented for road-vehicle guidance at high speeds including obstacle detection and monocular relative spatial state estimation. The corresponding data-processing architecture is discussed; the system has been implemented on a MIMD parallel processing system. Speeds up to 100 km/h have been demonstrated.<>