DETERMINATION OF ROAD DIRECTIONS USING FEEDBACK NEURAL NETS

Autonomous vehicles may be driven by image data of real-world scenes c ollected through a TV camera. Detecting the clues for safe navigation requires, among other things, the estimation of the path to be followe d by the vehicle, which has proven to be a formidable task in outdoor scenes. In this paper, an innovative system for road direction detecti on is proposed which is composed of three specialized blocks performin g edge extraction, image-segments detection and road estimation. The r oad direction estimation block is implemented as a feedback neural net work and is not fed directly with image data but with higher-level ima ge features which are extracted through the preprocessing stages. The use of feedback, while reducing the complexity of the network, improve s the estimation robustness and the noise immunity. A novel algorithm is defined and employed for the training step and experimental results in outdoor scenes are reported.