Automatic Test-Case Generation for Autonomous Vehicles

自动驾驶车辆的自动测试用例生成

• 批准号: 509824862
• 负责人: Professor Dr.-Ing. Matthias Althoff
• 金额: --
• 依托单位: Lehrstuhl für Informatik VI: Robotik, Künstliche Intelligenz und Echtzeitsysteme
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/509824862?language=en
• 关键词: Automatic; Test; Case; Generation; Autonomous

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.

显然，不能仅仅依靠物理测试来确保自动驾驶汽车的正确功能。由于物理测试驱动器是昂贵和耗时的，它是有利的，伴随着他们的计算机模拟。然而，由于大多数交通场景并不具有挑战性，即使是模拟也往往过于耗时。该提案的目标是提供自动合成自动驾驶汽车运动规划的挑战性测试用例的方法和工具。这可以被看作是运动规划算法的驾驶测试，为了在真实的车辆中使用，需要通过该测试。为了获得具有挑战性的测试用例，我们形式化的交通规则和计算措施，以估计交通规则的遵守程度。这使得我们可以控制我们的自动合成测试用例的交通规则的遵守程度。我们还将形式化场景的用户规范，以便用户可以控制场景生成过程。在下一步中，我们将合成初始场景。在从我们将要开发的数据库中提取与场景规范相关的初始场景后，我们优化了其他交通参与者和测试车辆的初始状态。因此，我们优化到所需的大小的交通规则兼容的可达集的车辆测试。从优化的初始交通场景开始，我们将优化周围交通参与者的行为，以伪造被测车辆的运动规划器。为了进一步测试碰撞缓解概念，我们还计划让其他交通参与者违反交通规则，导致被测车辆的解决方案空间变空。我们开发的方法将使用我们的运动规划基准套件CommonRoad（commonroad.in.tum.de）通过数值实验进行评估。为了评估生成场景的关键性，我们还计划在驾驶模拟器中进行用户研究，以比较主观感知风险以及合成场景的真实性等指标。