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Despite large advances in recent years, real-time capable motion planning forautonomous road vehicles remains a huge challenge. In this work, we present adecision module that is based on set-based reachability analysis: First, weidentify all possible driving corridors by computing the reachable set for thelongitudinal position of the vehicle along the lanelets of the road network,where lane changes are modeled as discrete events. Next, we select the bestdriving corridor based on a cost function that penalizes lane changes anddeviations from a desired velocity profile. Finally, we generate a referencetrajectory inside the selected driving corridor, which can be used to guide orwarm start low-level trajectory planners. For the numerical evaluation wecombine our decision module with a motion-primitive-based and anoptimization-based planner and evaluate the performance on 2000 challengingCommonRoad traffic scenarios as well in the realistic CARLA simulator. Theresults demonstrate that our decision module is real-time capable and yieldssignificant speed-ups compared to executing a motion planner standalone withouta decision module.

Real-Time Capable Decision Making for Autonomous Driving Using Reachable Sets