Spatial Sampling and Integrity in Lane Grid Maps

Autonomous vehicles have to take cautious decisions when driving in complex urban scenarios. Situation understanding is a key point towards safe navigation. High Definition maps supply different types of prior information such as road network topology, geometric description of the road, and semantic information including traffic laws. Conjointly with the perception system, they provide representations of the static environment and allow to model interactions. For safety issues, it is crucial to get a reliable understanding of the vehicle situation to avoid inappropriate decisions. Confidence on the information supplied to decision-making must be therefore provided. This paper proposes a spatial occupancy information representation at lane level with Lane Grid Maps (LGM). Based on areas of interest for the ego vehicle and sampled in the along-track direction, perception data is augmented to provide non-misleading information to the decision-making at a tactical level. An advantage of this representation is its ability to manage information integrity thanks to a good spatial sampling choice. The proposed approach takes into account the uncertainty of the ego vehicle localization, which has an impact on the estimated spatial occupancy of the perceived objects. This paper provides a method to set the proper sampling step in order to avoid oversampling and subsampling of the LGM for a given integrity risk level. The approach is evaluated with real data obtained thanks to several experimental vehicles.