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

Matúš Čávojský, Eugen Šlapak, Matúš Dopiriak, Gabriel Bugár, Juraj Gazda
8/30/2024
cs.ROcs.AI

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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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Cite this paper

@article{vojsk2024csim,
  title  = {3CSim: CARLA Corner Case Simulation for Control Assessment in Autonomous Driving},
  author = {Matúš Čávojský and Eugen Šlapak and Matúš Dopiriak and Gabriel Bugár and Juraj Gazda},
  year   = {2024},
  eprint = {2409.10524},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.10524v1}
}

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