Determination of road directions using feedback neural nets

Abstract Autonomous vehicles may be driven by image data of real-world scenes collected through a TV camera. Detecting the clues for safe navigation requires, among other things, the estimation of the path to be followed by the vehicle, which has proven to be a formidable task in outdoor scenes. In this paper, an innovative system for road direction detection is proposed which is composed of three specialized blocks performing edge extraction, image-segments detection and road estimation. The road direction estimation block is implemented as a feedback neural network and is not fed directly with image data but with higher-level image features which are extracted through the preprocessing stages. The use of feedback, while reducing the complexity of the network, improves 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.

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