PCLA : a framework for testing autonomous agents in the CARLA simulator
Additional information
Authors
Tehrani M. J.,
Kim J.,
Tonella P.
Type
Article in conference proceedings
Year
2025
Language
English
Abstract
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.
Keywords
Autonomous driving, Simulator, Testing, CARLA
Conference proceedings
ACM International Conference on the Foundations of Software Engineering (FSE Companion)
Meeting name
FSE Companion '25: 33rd ACM International Conference on the Foundations of Software Engineering
Meeting place
Clarion Hotel Trondheim Trondheim Norway
Meeting date
June 23-27, 2025
Pages (or article number)
1040 - 1044
Diffusion
License
CC BY
Visibility
Public
Status open access
Hybrid
