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Abstract

This paper proposes a road direction detection system for outdoor autonomous vehicles that must navigate on roads according to visual information. The system consists of three major stages. At the first stage a self-similar family of Gabor kernels are used to extract low-level image features from a road image. Next, unsupervised learning principles with self-organising maps are used to cluster the Gabor coefficients to higher level image features suitable for road direction detection. At the last stage a multilayer perceptron network trained with a backpropagation algorithm carried out the road direction estimation according to the Gabor coefficients clusters. Several road images taken by a camera mounted on a passenger car are used to test the performance of the system.

Additional information

Authors: SURAKKA M, Mechanical Engineering Laboratory, Machine Intelligence Division, Tsukuba-shi, Ibaraki-ken (JP);HEIKKONEN J, JRC Ispra (IT)
Bibliographic Reference: Paper presented: 2nd IFAC Conference on Intelligent Autonomous Vehicles, IAV 95, Espoo (FI), June 12-15, 1995
Availability: Available from (1) as Paper EN 39100 ORA
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