A new multi-agent approach for lane detection and tracking

Abstract

This paper proposes an unconventional approach for multi-lane detection and tracking based on a reactive multi-agent system. Most of the algorithms use camera information with a two-step process to detect road marking (1) extraction of road marking features, (2) lane estimation and tracking, performed by studying the extracted point distribution. However, our proposed method is based on a confidence map instead of lane marking features, and a multi-agent model instead of geometric fitting. This approach takes better account of the specific features of road markings, and more precisely, parts defined by clothoids. The method has been tested on a real-world dataset of images in real condition and evaluated with a sequence of more than 2500 synthetic images provided by the SiVIC platform. First results are very promising, with more than 98% for ego lane detection and 97% for multi-lane detection with 4% of false alarm. Furthermore, this approach gives us new opportunities to improve lane detection which would be difficult to implement in a more conventional approach.