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

• URL: http://arxiv.org/abs/2409.10524v1
• Date: Fri, 30 Aug 2024 12:38:22 GMT
• Title: 3CSim: CARLA Corner Case Simulation for Control Assessment in Autonomous Driving
• Authors: Matúš Čávojský, Eugen Šlapak, Matúš Dopiriak, Gabriel Bugár, Juraj Gazda,
• Abstract summary: This framework is designed to address the limitations of traditional AD model training by focusing on non-standard, rare, and cognitively challenging scenarios. 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.
• Score: 0.44938884406455726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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