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Lane level context and hidden space characterization for autonomous driving


For an autonomous vehicle, situation understanding is a key capability towards safe and comfortable decision-making and navigation. Information is in general provided by multiple sources. Prior information about the road topology and traffic laws can be given by a High Definition (HD) map while the perception system provides the description of the space and of road entities evolving in the vehicle surroundings. In complex situations such as those encountered in urban areas, the road user behaviors are governed by strong interactions with the others, and with the road network. In such situations, reliable situation understanding is therefore mandatory to avoid inappropriate decisions. Nevertheless, situation understanding is a complex task that requires access to a consistent and non-misleading representation of the vehicle surroundings. This paper proposes a formalism (an interaction lane grid) which allows to represent, with different levels of abstraction, the navigable and interacting spaces which must be considered for safe navigation. A top-down approach is chosen to assess and characterize the relevant information of the situation. On a high level of abstraction, the identification of the areas of interest where the vehicle should pay attention is depicted. On a lower level, it enables to characterize the spatial information in a unified representation and to infer additional information in occluded areas by reasoning with dynamic objects.
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Dates and versions

hal-02947233 , version 1 (30-08-2021)



Corentin Sanchez, Philippe Xu, Alexandre Armand, Philippe Bonnifait. Lane level context and hidden space characterization for autonomous driving. 31st IEEE Intelligent Vehicles Symposium (IV 2020), Oct 2020, Las Vegas, United States. pp.144-149, ⟨10.1109/IV47402.2020.9304697⟩. ⟨hal-02947233⟩
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