Towards Knowledge-based Road Modeling for Automated Vehicles: Analysis and Concept for Incorporating Prior Knowledge

Abstract

Typically, automated driving functions rely on high-definition maps for modeling the stationary environment (SE). However, outdated or erroneous maps pose a risk to both safety and performance of such a driving function. To address the issue of false map data provided to the vehicle, deviations ahead of the vehicle must be detected and corrected, preferably within the vehicle. To enable the continued operation of the driving function, a SE model as input to the driving function has to be generated on the fly. Moreover, to reduce the probability to encounter deviations in the first place, map update hypotheses have to be provided, e.g., to compute an update in an external server. In this paper, we present a concept for integrating prior knowledge, e.g., regarding rule-compliant lane configurations, into the generation of the SE model. Prior knowledge enables the evaluation of undetected elements, the interpretation of connections between elements, and an overall plausibility check. Last, we provide an example for SE modeling for which we demonstrate the benefit of incorporating prior knowledge. The main novelity of this work is to show a way of deriving and representing required knowledge for SE modeling. Instead of focussing on individual infrastructure entities (e.g., intersection) as typically discussed in related works, we establish our derivation by analyzing traffic regulations and exemplary critical scenarios that arise due to the presence of map deviations.