To safely navigate intricate real-world scenarios, autonomous vehicles must be able to adapt to diverse road conditions and anticipate future events. World model (WM) based reinforcement learning (RL) has emerged as a promising approach by learning and predicting the complex dynamics of various environments. Nevertheless, to the best of our knowledge, there does not exist an accessible platform for training and testing such algorithms in sophisticated driving environments. To fill this void, we introduce CarDreamer, the first open-source learning platform designed specifically for developing WM based autonomous driving algorithms. It comprises three key components: 1) World model backbone: CarDreamer has integrated some state-of-the-art WMs, which simplifies the reproduction of RL algorithms. The backbone is decoupled from the rest and communicates using the standard Gym interface, so that users can easily integrate and test their own algorithms. 2) Built-in tasks: CarDreamer offers a comprehensive set of highly configurable driving tasks which are compatible with Gym interfaces and are equipped with empirically optimized reward functions. 3) Task development suite: This suite streamlines the creation of driving tasks, enabling easy definition of traffic flows and vehicle routes, along with automatic collection of multi-modal observation data. A visualization server allows users to trace real-time agent driving videos and performance metrics through a browser. Furthermore, we conduct extensive experiments using built-in tasks to evaluate the performance and potential of WMs in autonomous driving. Thanks to the richness and flexibility of CarDreamer, we also systematically study the impact of observation modality, observability, and sharing of vehicle intentions on AV safety and efficiency. All code and documents are accessible on https://github.com/ucd-dare/CarDreamer.

翻译：为安全应对复杂真实场景，自动驾驶车辆需适应不同道路条件并预判未来事件。基于世界模型（WM）的强化学习（RL）通过学习并预测多种环境的复杂动态，已成为极具前景的方法。然而，据我们所知，目前尚缺乏用于在复杂驾驶环境中训练与测试此类算法的可访问平台。为填补这一空白，我们提出CarDreamer——首个专为开发基于WM的自动驾驶算法设计的开源学习平台。该平台包含三大核心组件：1）世界模型主干：CarDreamer集成了部分前沿WM，简化了RL算法的复现过程。主干模块与其他组件解耦，并通过标准Gym接口通信，使用户能轻松集成和测试自有算法。2）内置任务：CarDreamer提供全面且高度可配置的驾驶任务集，兼容Gym接口并配备经验优化的奖励函数。3）任务开发套件：该套件简化了驾驶任务的创建流程，支持交通流与车辆路径的便捷定义，并自动采集多模态观测数据。通过可视化服务器，用户可通过浏览器实时追踪智能体驾驶视频与性能指标。此外，我们利用内置任务开展大量实验，评估WM在自动驾驶中的性能与潜力。借助CarDreamer的丰富性与灵活性，我们还系统研究了观测模态、可观测性和车辆意图共享对自动驾驶安全性与效率的影响。所有代码与文档均可在https://github.com/ucd-dare/CarDreamer 获取。