Evidential grids with semantic lane information for intelligent vehicles

Abstract : —Occupancy grids are popular in autonomous navigation for encoding obstacle information into grid cells to provide real-time environmental models. However, very few studies have been carried out on encoding lane and traffic information in grids. This information refines the world model up to the lane level which is important in many situations to enable vehicles to follow basic road rules, such as lane keeping or lane changes in case of overtaking for instance. Usual approaches consist in detecting lane boundaries using on-board cameras or lidars but the problem is tricky when the road is multi-lanes or in challenging weather conditions. In this work, we propose to tackle this problem by using a vectorial prior map that stores detailed lane level information. We take advantage of the pose estimation from a localization solver and propagate the estimation uncertainty over the grids cells. Both Bayesian and Evidential models are presented and some of their special characteristics are highlighted and compared. Real results carried on public roads with the same real-time software are reported to support the comparison.
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Chunlei Yu, Véronique Cherfaoui, Philippe Bonnifait. Evidential grids with semantic lane information for intelligent vehicles. RFIA- Journée Transports Intelligents, Jun 2016, Clermont-Ferrand, France. ⟨hal-01378521⟩

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