The last few decades witnessed the birth and growth of a new sensibility to transportation efficiency. In particular, the need for efficient and improved people and goods mobility pushed researchers to address the problem of intelligent transportation systems. This paper surveys the most advanced approaches to the (partial) customization of road following task, using on-board systems based on artificial vision. The functionalities of lane detection, obstacle detection and pedestrian detection are described and classified, and their possible application on future road vehicles is discussed.

No abstract

No abstract

No abstract

This paper presents an evolutionary approach able to process a digital image and detect tracks left by preceding vehicles on ice and snow in Antarctica. Biologically inspired by a colony of ants able to interact and cooperate to determine the shortest path to the food, this approach is based on autonomous agents moving along the image pixels and iteratively improving an initial coarse solution.The unfriendly Antarctic environment makes this image analysis problem extremely challenging, since light reflections, abruptly varying brightness conditions, and different terrain slopes must be considered as well.The ant-based approach is compared to a more traditional Hough-based solution and the results are discussed.