Bridging Data-Driven and Knowledge-Driven Approaches for Safety-Critical Scenario Generation in Automated Vehicle Validation

• URL: http://arxiv.org/abs/2311.10937v1
• Date: Sat, 18 Nov 2023 02:11:14 GMT
• Title: Bridging Data-Driven and Knowledge-Driven Approaches for Safety-Critical Scenario Generation in Automated Vehicle Validation
• Authors: Kunkun Hao, Lu Liu, Wen Cui, Jianxing Zhang, Songyang Yan, Yuxi Pan and Zijiang Yang
• Abstract summary: Automated driving vehicles (ADV) promise to enhance driving efficiency and safety, yet they face challenges in safety-critical scenarios. This paper investigates the complexities of employing two major scenario-generation solutions: data-driven and knowledge-driven methods. We introduce BridgeGen, a safety-critical scenario generation framework, designed to bridge the benefits of both solutions.
• Score: 5.063522035689929
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated driving vehicles~(ADV) promise to enhance driving efficiency and safety, yet they face intricate challenges in safety-critical scenarios. As a result, validating ADV within generated safety-critical scenarios is essential for both development and performance evaluations. This paper investigates the complexities of employing two major scenario-generation solutions: data-driven and knowledge-driven methods. Data-driven methods derive scenarios from recorded datasets, efficiently generating scenarios by altering the existing behavior or trajectories of traffic participants but often falling short in considering ADV perception; knowledge-driven methods provide effective coverage through expert-designed rules, but they may lead to inefficiency in generating safety-critical scenarios within that coverage. To overcome these challenges, we introduce BridgeGen, a safety-critical scenario generation framework, designed to bridge the benefits of both methodologies. Specifically, by utilizing ontology-based techniques, BridgeGen models the five scenario layers in the operational design domain (ODD) from knowledge-driven methods, ensuring broad coverage, and incorporating data-driven strategies to efficiently generate safety-critical scenarios. An optimized scenario generation toolkit is developed within BridgeGen. This expedites the crafting of safety-critical scenarios through a combination of traditional optimization and reinforcement learning schemes. Extensive experiments conducted using Carla simulator demonstrate the effectiveness of BridgeGen in generating diverse safety-critical scenarios.

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