LAUSR

The core value of simulation-based autonomy tests is to create densely extreme traffic scenarios to test the performance and robustness of the algorithms and systems. Test scenarios are usually designed or extracted manually from the real-world data, which is inefficient with a remarkable domain gap compared with testing in real scenarios. Therefore, it is crucial to automatically generate realistic and diverse dynamic traffic scenarios making autonomy tests efficient. Moreover, scenario generation is expected to be interpretable, controllable, and diversified, which can be hard to achieve simultaneously by methods based on rules or deep networks. In this paper, we propose a dynamic traffic scenario generation method called SceGene, inspired by genetic inheritance and mutation processes in biological intelligence. SceGene applies biological processes, such as crossover and mutation, to exchange and mutate the content of scenarios, and involves the natural selection process to control generation direction. SceGene has three main parts: 1) a new representation method for describing the traffic scenarios’ feature; 2) a new scenario generation algorithm based on crossover, mutation, and selection; and 3) an abnormal scenario information repair method based on the microscopic driving model. Evaluation on the public traffic scenario dataset shows that SceGene can ensure highly realistic and diversified scenario generation in an interpretable and controllable way, significantly improving the efficiency of the simulation-based autonomy tests.