# PCLA: A Framework for Testing Autonomous Agents in the CARLA Simulator

arXiv: https://arxiv.org/abs/2503.09385 · HTML: https://mlsanity.com/paper/2503.09385/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2025.

## Built on by (most cited first, 3 of 3)

- [InterFuserDVS: Event-Enhanced Sensor Fusion for Safe RL-Based Decision Making](https://mlsanity.com/paper/2605.04355/) (2026)
- [Multi-Resolution End-to-End Deep Neural Network for Optimizing Latency-Accuracy Tradeoff in Autonomous Driving](https://mlsanity.com/paper/2605.29138/) (2026)
- [Evaluating Fuzz Testing for Reinforcement Learning Agents](https://mlsanity.com/paper/2607.24577/) (2026)

## Abstract (arXiv)

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.