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Tonella_2025_ACM_3696630.3728577
Conference paper (in proceedings)

PCLA : a framework for testing autonomous agents in the CARLA simulator

  • 2025
Published in:
  • ACM International Conference on the Foundations of Software Engineering (FSE Companion). - 2025, p. 1040 - 1044
Autonomous driving
Simulator
Testing
CARLA
English Recent research on testing autonomous driving agents has grown significantly, especially in simulation environments. The CARLA simulator is often the preferred choice, and the autonomous agents from the CARLA Leaderboard challenge are regarded as the best-performing agents within this environment. However, researchers who test these agents, rather than training their own ones from scratch, often face challenges in utilizing them within customized test environments and scenarios. To address these challenges, we introduce PCLA (Pretrained CARLA Leaderboard Agents), an open-source Python testing framework that includes nine high-performing pre-trained autonomous agents from the Leaderboard challenges. PCLA is the first infrastructure specifically designed for testing various autonomous agents in arbitrary CARLA environments/scenarios. PCLA provides a simple way to deploy Leaderboard agents onto a vehicle without relying on the Leaderboard codebase, it allows researchers to easily switch between agents without requiring modifications to CARLA versions or programming environments, and it is fully compatible with the latest version of CARLA while remaining independent of the Leaderboard's specific CARLA version. PCLA is publicly accessible at https://github.com/MasoudJTehrani/PCLA.
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Language
  • English
Classification
Computer science and technology
Notes
  • FSE Companion '25: 33rd ACM International Conference on the Foundations of Software Engineering
  • Clarion Hotel Trondheim Trondheim Norway
  • June 23-27, 2025
License
CC BY
Open access status
hybrid
Identifiers
Persistent URL
https://n2t.net/ark:/12658/srd1333970
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