Lane level context and hidden space characterization for autonomous driving

TL;DR

This paper introduces an interaction lane grid formalism for autonomous vehicles to represent and analyze complex urban situations at multiple abstraction levels, enhancing safe navigation and decision-making.

Contribution

It proposes a novel formalism that captures navigable and interacting spaces at different abstraction levels for improved situation understanding in autonomous driving.

Findings

The interaction lane grid effectively represents complex urban scenarios.

It enables inference of hidden information through reasoning with dynamic objects.

The approach improves safety and decision-making in urban autonomous driving.

Abstract

For an autonomous vehicle, situation understand-ing is a key capability towards safe and comfortable decision-making and navigation. Information is in general provided bymultiple sources. Prior information about the road topology andtraffic laws can be given by a High Definition (HD) map whilethe perception system provides the description of the spaceand of road entities evolving in the vehicle surroundings. Incomplex situations such as those encountered in urban areas,the road user behaviors are governed by strong interactionswith the others, and with the road network. In such situations,reliable situation understanding is therefore mandatory to avoidinappropriate decisions. Nevertheless, situation understandingis a complex task that requires access to a consistent andnon-misleading representation of the vehicle surroundings. Thispaper proposes a formalism (an interaction lane grid) whichallows to represent, with different levels of abstraction, thenavigable and interacting spaces which must be considered forsafe navigation. A top-down approach is chosen to assess andcharacterize the relevant information of the situation. On a highlevel of abstraction, the identification of the areas of interestwhere the vehicle should pay attention is depicted. On a lowerlevel, it enables to characterize the spatial information in aunified representation and to infer additional information inoccluded areas by reasoning with dynamic objects.