Map-optimized probabilistic traffic rule evaluation

Abstract

Current traffic rule handling in autonomous driving relies on non-scalable methods such as explicitly hard-coding sets of logical statements to evaluate rules. This is sufficient for early prototypes but is not scalable for larger systems designed to work in arbitrary locations. Such methods can also become problematic when traversing different geographical entities with changing traffic rules. Additionally, they do not adequately convey the uncertainty of the traffic rule evaluation stemming from uncertainty in sensor measurements. We propose an exchangeable traffic rules module which takes a knowledge base of sentences in first-order logic as input. These sentences consist of a limited number of high-level queries which are independent of local jurisdiction (e.g. can I turn right?). The knowledge base is then compiled into a potentially large logic graph. However, only a relatively small subset of the knowledge base is relevant for specific road geometries (e.g. some of the rules applicable to an intersection is not relevant for a T-junction). Since detailed road maps are available for autonomous driving, this information can be used to resolve these subsets in the knowledge base which only require map knowledge. Therefore, map information can be used to convert a single, large logic graph into a set of smaller, map-optimized logic graphs pruned for specific road geometries. The optimized graphs are then converted into Bayesian networks to facilitate probabilistic inference. Experiments were conducted using a traffic and scenario simulation framework. The results demonstrate a significant improvement in performance when using map-optimized logic graphs over a traditional first-order logic knowledge base.