Automatic Test-Case Generation for Autonomous Vehicles

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It is apparent that one cannot rely solely on physical test drives for ensuring the correct functionality of autonomous vehicles. Since physical test drives are costly and time-consuming, it is advantageous to accompany them with computer simulations. However, since most traffic scenarios are not challenging, even simulations are often too time-consuming. The goal of this proposal is to provide methods and tools for automatically synthesizing challenging test cases for motion planning of autonomous vehicles. This can be seen as a driving test for motion planning algorithms that needs to be passed in order to be used in real vehicles. In order to obtain challenging test cases, we formalize traffic rules and compute measures to estimate the degree of traffic rule compliance. This makes it possible to control the degree of traffic rule compliance for our automatic synthesis of test cases. We will also formalize the user specification of the scenario so that users can control the scenario generation process. In a next step, we will synthesize the initial scene. After extracting initial scenes from our to-be-developed database that are relevant for the scenario specification, we optimize the initial states of other traffic participants and the vehicle under test. Thereto, we optimize towards a desired size of the traffic-rule-compliant reachable set of the vehicle under test. Starting from the optimized initial traffic scene, we will optimize the behavior of surrounding traffic participants to falsify the motion planner of the vehicle under test. To additionally test collision mitigation concepts, we also plan to let other traffic participants violate traffic rules causing the solution space of the vehicle under test to become empty.Our developed methods will be evaluated by numerical experiments using our motion planning benchmark suite CommonRoad (commonroad.in.tum.de). To evaluate the criticality of the generated scenarios, we additionally plan to conduct user studies in our driving simulator to compare measures like the subjectively perceived risk as well as the realism of our synthesized scenarios.