3CSim: CARLA Corner Case Simulation for Control Assessment in Autonomous Driving

Abstract

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.

References in corpus (5)

• Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives
• MagicDrive: Street View Generation with Diverse 3D Geometry Control
• Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation
• Safe Navigation: Training Autonomous Vehicles using Deep Reinforcement Learning in CARLA
• First-principles Based 3D Virtual Simulation Testing for Discovering SOTIF Corner Cases of Autonomous Driving