3CSim: CARLA Corner Case Simulation for Control Assessment in Autonomous Driving
This addresses safety and reliability issues for autonomous vehicles by providing a tool for testing in non-standard scenarios, though it is incremental as it builds on existing simulation methods.
The authors tackled the problem of evaluating autonomous driving systems by developing 3CSim, a framework in CARLA simulator that generates rare and challenging corner case scenarios, resulting in 32 unique cases with adjustable parameters like weather and traffic density.
We present the CARLA corner case simulation (3CSim) for evaluating autonomous driving (AD) systems within the CARLA simulator. This framework is designed to address the limitations of traditional AD model training by focusing on non-standard, rare, and cognitively challenging scenarios. These corner cases are crucial for ensuring vehicle safety and reliability, as they test advanced control capabilities under unusual conditions. Our approach introduces a taxonomy of corner cases categorized into state anomalies, behavior anomalies, and evidence-based anomalies. We implement 32 unique corner cases with adjustable parameters, including 9 predefined weather conditions, timing, and traffic density. The framework enables repeatable and modifiable scenario evaluations, facilitating the creation of a comprehensive dataset for further analysis.