  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.
