AuthorsC. Lu, T. Yue and S. Ali
TitleDeepScenario: An Open Driving Scenario Dataset for Autonomous Driving System Testing
AfilliationSoftware Engineering
Project(s)Department of Engineering Complex Software Systems, Co-Evolver: Uncertainty-Aware Coevolution Design of Self-Adaptive Cyber-Physical Systems
Publication TypeProceedings, refereed
Year of Publication2023
Conference Name2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR)
Date Published05/2023
Place PublishedMelbourne, Australia
Keywordsautonomous driving system testing, dataset, driving scenario, Open Source

With the rapid development of autonomous driving systems (ADSs), testing ADSs under various environmental conditions has become a key method to ensure the successful deployment of ADS in the real world. However, it is impossible to test all the scenarios due to the inherent complexity and uncertainty of ADSs and the driving tasks. Further, testing of ADSs is expensive regarding time and computational resources. Therefore, a large-scale driving scenario dataset consisting of various driving conditions is needed. To this end, we present an open driving scenario dataset DeepScenario, containing over 30K executable driving scenarios, which are collected by 2880 test executions of three driving scenario generation strategies. Each scenario in the dataset is labeled with six attributes characterizing test results. We further show the attribute statistics and distribution of driving scenarios. For example, there are 1050 collision scenarios, in 917 scenarios there were collisions with other vehicles, 105 and 28 with pedestrians and static obstacles, respectively. Target users include ADS developers who need to validate their systems under various environmental conditions.

Citation Key43359

Contact person