Vehicle Guidance by Computer Vision

The 4D-approach for machine vision using integral spatio-temporal models is applied to road vehicle guidance. Both for road parameter estimation in a look ahead range and for vehicle state estimation relative to the road, dynamical models are given. A system architecture for active machine vision in road vehicle guidance is discussed. Based on the recursive, feature-based state estimation, numerically efficient situation recognition and process control has been achieved. With 0.1 second cycle time, speeds up to 60 mph have been driven autonomously over distances up to 24 km on a standard highway with the 5-ton test vehicle VaMoRs.

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