一句话：提出 PCLA，首个用于在任意 CARLA 环境/场景中测试多种预训练自动驾驶智能体的开源 Python 框架。
论文关注在 CARLA 模拟器中测试自动驾驶智能体的难题：研究者虽可复用 CARLA Leaderboard 挑战中的高性能智能体，却难以将其部署到自定义测试环境与场景。为此，作者提出 PCLA，一个开源 Python 测试框架，集成九个来自 Leaderboard 的高性能预训练智能体。PCLA 是首个面向在任意 CARLA 环境/场景中测试多种自动驾驶智能体的基础设施；它无需依赖 Leaderboard 代码库即可将智能体部署到车辆，支持在不修改 CARLA 版本或编程环境的情况下切换智能体，并兼容最新版 CARLA，同时独立于 Leaderboard 所用的特定 CARLA 版本。框架已公开于 GitHub。
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.