A Survey on Safety-Critical Driving Scenario Generation—A Methodological Perspective

Autonomous driving systems have witnessed significant development during the past years thanks to advance in machine learning-enabled sensing and decision-making algorithms. One critical challenge for their massive deployment real world is safety evaluation. Most existing are still trained evaluated on naturalistic scenarios collected from daily life or heuristically-generated adversarial ones. However, large population of cars, general, leads an extremely low collision rate, indicating that safety-critical rare real-world data. Thus, methods artificially generate become crucial measure risk reduce cost. In this survey, we focus algorithms scenario generation autonomous driving. We first provide a comprehensive taxonomy by dividing them into three categories: data-driven generation, knowledge-based generation. Then, discuss useful tools including simulation platforms packages. Finally, extend our discussion five main challenges current works– fidelity, efficiency, diversity, transferability, controllability– research opportunities lighted up these challenges.