PCLA让研究者轻松测试CARLA顶尖自动驾驶模型。
PCLA: A Framework for Testing Autonomous Agents in the CARLA Simulator
- 提供9个预训练顶尖自动驾驶代理，可直接部署到自定义场景
- 支持在最新CARLA环境下无缝切换不同代理，无需改代码
- 开源框架，适合做自动驾驶测试与对比实验的研究者
近期自动驾驶代理的测试研究显著增长，尤其在仿真环境中。CARLA模拟器是常用平台，其排行榜中的自动驾驶代理被视为该环境下的最佳表现者。然而，研究者若要测试这些代理而非从头训练，常面临难以在自定义环境和场景中使用的问题。为此，我们提出PCLA（Pretrained CARLA Leaderboard Agents），一个开源的Python测试框架，包含九个来自排行榜挑战的高性能预训练代理。PCLA是首个专为在任意CARLA环境/场景中测试多种自动驾驶代理而设计的基础架构。它无需依赖排行榜代码库，即可将代理部署到车辆上；支持研究人员轻松切换代理，无需修改CARLA版本或编程环境；且完全兼容最新版CARLA，同时独立于排行榜特定版本。PCLA已公开发布于https://github.com/MasoudJTehrani/PCLA。
原文摘要 · Abstract (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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