Efficiently Ensuring Traffic Rule Compliance of Motion Plans by Incorporating Scenario Knowledge

Autonomous vehicles must obey the rules of the road to safely participate in road traffic. To enforce these rules during motion planning, they are often formalized in temporal logic. Such formalizations need to be very general to cover all possible traffic situations, resulting in large and complex logic formulas. During motion planning, however, we are usually confronted with a concrete scenario in which parts of the formulas may be irrelevant. Since specification-compliant motion planning under complex specifications is computationally challenging, we aim to simplify the traffic rules by removing these irrelevant parts. To this end, we first present a general algorithm that augments linear temporal logic formulas with scenario-specific knowledge. Then, we provide a method for extracting knowledge from traffic scenarios to augment traffic rules. We can formally guarantee that the augmented specification is equivalent to the original formula in the given scenario. Therefore, subsequent motion planning modules that handle temporal logic specifications need only consider the augmented formulas. We benchmark our approach in recorded real-world scenarios to demonstrate that it can significantly accelerate specification-compliant motion planning.     «

Autonomous vehicles must obey the rules of the road to safely participate in road traffic. To enforce these rules during motion planning, they are often formalized in temporal logic. Such formalizations need to be very general to cover all possible traffic situations, resulting in large and complex logic formulas. During motion planning, however, we are usually confronted with a concrete scenario in which parts of the formulas may be irrelevant. Since specification-compliant motion planning unde...     »