Relevance estimation of traffic elements using Markov logic networks

Abstract

Complex traffic situations e.g. at intersections consist of many traffic participants, traffic elements and relations between them. The behavior of participants is constrained by implicit and explicit traffic rules. We are interested in estimating whether a given traffic element — a traffic sign, a traffic light — is relevant in the current driving situation, i.e. affects the set of possible legal actions. A wide variety of properties influences the relevance. The route to take for example affects which traffic lights are relevant and the current weather situation affects whether a speed limit restricted by a supplementary sign is in effect. We use first-order logic to model such relations and apply reasoning to decide upon the relevance of static traffic elements. The need for perfect information is alleviated with the help of Markov logic networks, reconciling hard decision rules on the one hand and uncertainty intrinsic to the environment perception process on the other hand. The evaluation of twelve intersection scenes shows very promising results for the relevance estimation of traffic lights: Markov logic networks are able to judge whether enough information is available and determine the relevant traffic lights reliably in such cases.