One of the major impediments in deployment of Autonomous Driving Systems (ADS) is their safety and reliability. The primary reason for the complexity of testing ADS is that it operates in an open world characterized by its non-deterministic, high-dimensional and non-stationary nature where the actions of other actors in the environment are uncontrollable from the ADS's perspective. This leads to a state space explosion problem and one way of mitigating this problem is by concretizing the scope for the system under test (SUT) by testing for a set of behavioral competencies which an ADS must demonstrate. A popular approach to testing ADS is scenario-based testing where the ADS is presented with driving scenarios from real world (and synthetically generated) data and expected to meet defined safety criteria while navigating through the scenario. We present SAFR-AV, an end-to-end ADS testing platform to enable scenario-based ADS testing. Our work addresses key real-world challenges of building an efficient large scale data ingestion pipeline and search capability to identify scenarios of interest from real world data, creating digital twins of the real-world scenarios to enable Software-in-the-Loop (SIL) testing in ADS simulators and, identifying key scenario parameter distributions to enable optimization of scenario coverage. These along with other modules of SAFR-AV would allow the platform to provide ADS pre-certifications.

翻译：自动驾驶系统部署的主要障碍之一是其安全性和可靠性。测试自动驾驶系统复杂性的主要原因在于其运行于一个开放世界环境中，该环境具有非确定性、高维度和非平稳的特性，且环境中其他参与者的行为从自动驾驶系统的视角来看是不可控的。这导致了状态空间爆炸问题，缓解该问题的途径之一是通过测试自动驾驶系统必须展现的一组行为能力来具体化被测系统的范围。一种流行的自动驾驶系统测试方法是基于场景的测试，即向自动驾驶系统呈现来自真实世界（及合成生成）数据的驾驶场景，并期望其在导航通过场景时满足既定的安全标准。我们提出了SAFR-AV，一个端到端的自动驾驶系统测试平台，以实现基于场景的自动驾驶系统测试。我们的工作应对了构建高效大规模数据摄取管道和搜索能力以从真实世界数据中识别关注场景、创建真实世界场景的数字孪生以支持自动驾驶系统模拟器中的软件在环测试，以及识别关键场景参数分布以优化场景覆盖等关键现实挑战。这些功能连同SAFR-AV的其他模块，将使得该平台能够提供自动驾驶系统预认证。