A Lidar-based Dual-level Virtual Lanes Construction and Anticipation of Specific Road Infrastructure Events for Autonomous Driving.

Autonomous vehicles require clear road markings and a high-level quality of infrastructure. This paper addresses road course detection problem in non cooperative environments (i.e. absence or poor quality of road-marking, working zones, etc.). To cope with visual lane detection challenges in these difficult scenarios, we propose a virtual lane generation system to provide a comfortable and safe ride. Based on Lidar sensor, the dual-level virtual lane system consists of the combination of two blocks: the first constructs virtual lanes based on independent road-borders detection, while the second level uses dynamic objects detection and their trajectories in order to estimate the lane parameters. Furthermore, the system is able to anticipate road infrastructures thanks to the independent detection of road borders. Thus we are able to manage difficult use cases such as bifurcations and exit lanes without cartography. The performance is tested through extensive experiments with Cruise4U Valeo self-driving cars on highway and beltway roads. Experimental results demonstrate the accurate and robust performance of the proposed system.