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：为安全应对复杂的现实场景，自动驾驶车辆必须具备适应多样化道路条件并预测未来事件的能力。基于世界模型的强化学习方法通过学习并预测各类环境的复杂动态，已成为一种极具前景的研究方向。然而，据我们所知，目前尚缺乏能够在复杂驾驶环境中训练与测试此类算法的开放平台。为填补这一空白，我们推出了CarDreamer——首个专为开发基于世界模型的自动驾驶算法而设计的开源学习平台。该平台包含三个核心模块：1）世界模型主干网络：CarDreamer集成了多种前沿世界模型，简化了强化学习算法的复现流程。该主干网络与其他模块解耦，并通过标准Gym接口进行通信，用户可便捷地集成并测试自有算法。2）内置任务集：平台提供一套高度可配置的完整驾驶任务，这些任务兼容Gym接口，并配备经实证优化的奖励函数。3）任务开发套件：该套件简化了驾驶任务的创建流程，支持轻松定义交通流与车辆路径，同时自动采集多模态观测数据。可视化服务器允许用户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了大量实验，以评估世界模型在自动驾驶中的性能与潜力。得益于CarDreamer的丰富性与灵活性，我们还系统研究了观测模态、可观测性及车辆意图共享对自动驾驶安全性与效率的影响。所有代码与文档均公开于https://github.com/ucd-dare/CarDreamer。