Integrity Management of the Reachable Space With Lane Grid Maps
Résumé
For an autonomous vehicle, reliable situation understanding is a key point towards safe navigation.
A wrong prediction of the upcoming situation may lead to hazardous cases by providing improper information to the decision-making process.
It is therefore of great importance to estimate the driving areas reachable by the other interacting road users without introducing misleading information.
This paper presents how integrity of prediction information can be handled given the imperfection of objects prediction using a spatial representation of the situation at a tactical level, called lane grid map, which relies on the topological layer of a high-definition map.
We demonstrate experimentally, using real data, how the spatial sampling step of the grid representation can be used to manage the integrity of the prediction information.
Moreover, handling interactions during the prediction makes it possible to handle some particular situations safely.
We show how some interactions can be exploited thanks to the neutralization concept.
To quantify the integrity of the prediction, we propose to use two metrics, namely False Negative Rate and Neutralized Time Interval. Experiments have been carried out with three experimental vehicles to evaluate the integrity of the prediction using these metrics.
Domaines
Automatique / Robotique
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2022_Integrity_Management_of_the_Reachable_Space_with_Lane_Grid_Maps.pdf (11.72 Mo)
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