Integrity Management of the Reachable Space With Lane Grid Maps

For an autonomous vehicle, reliable situation understanding is a key component of safe navigation. An incorrect prediction of an upcoming situation means that erroneous information may be supplied to the decision-making process, leading to hazardous outcomes. It is therefore of great importance to estimate the driving areas that are reachable by other interacting road users, without introducing misleading information. This paper presents a means of handling the integrity of prediction information, given the imperfection of object prediction, via a Lane Grid Map, that is to say a spatial representation of the situation at a tactical level, based 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 prediction information. Moreover, addressing interactions during the prediction makes it possible to handle some particular situations safely. We show how some interactions can be utilized via the concept of neutralization. To quantify the integrity of the prediction, we propose the use of two metrics, namely False Negative Rate and Neutralized Time Interval. Experiments were carried out with three vehicles to evaluate the integrity of the prediction using these metrics.