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Semantic Evidential Lane Grids with Prior Maps for Autonomous Navigation


Real-time modeling of the surrounding environment is a key functionality for autonomous navigation. Bird view grid-based approaches have interesting advantages compared to feature-based ones. Methods able to encode occupancy information and to manage perception uncertainty in dynamic environments are quite well known but very few studies have been carried out on encoding semantic information in grids. This kind of information can be crucial in many situations in order to make the vehicle able to follow basic road rules, such as lane keeping or lane changes. Usual approaches often detect lane markings using on-board cameras or lidars but the problem is tricky when the road is multi-lane 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. From a given pose estimate provided by a localization system, we propose an Evidential model that encodes lane information into grids by propagating the pose uncertainty on every cell. This Evidential model is compared with a classical Bayesian one and some of its special characteristics are highlighted. Real results carried on public roads with the same real-time software are reported to support the comparison.
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hal-01378525 , version 1 (10-10-2016)


  • HAL Id : hal-01378525 , version 1


Chunlei Yu, Véronique Cherfaoui, Philippe Bonnifait. Semantic Evidential Lane Grids with Prior Maps for Autonomous Navigation. 19th IEEE Intelligent Transportation Systems Conference (ITSC 2016), Nov 2016, Rio de Janeiro, Brazil. pp.1875-1881. ⟨hal-01378525⟩
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