Neural network based classification of highway scenes for vehicle guidance

Summary form only given. Current segmentation and classification algorithms are not sufficiently robust to provide reliable real-time sensor-based vehicle guidance for intelligent vehicle highway systems. An alternative technique based on neural networks was developed for scene classification as these algorithms can handle missing and fuzzy data. The backpropagation algorithm was successfully used in conjunction with new activation functions for classification of roads, lane markers, shadows, grass, and edge transitions in real-highway scenes. 131 subregions of size 3×3 with known classes such as roads, lane markers, shadows, grass, and low- and high-edge transitions were extracted from 15 training images. Two different neural network architectures based on image and edge data were defined. The neural network was then trained for learning the characteristics of desired classes. After convergence of the training phase was completed, the test images were correctly classified into the desired classes. The training time of 2.14 h was significantly lower than that of days to a week, as reported by other researchers for similar applications