Collecting a high-quality dataset is a critical task that demands meticulous attention to detail, as overlooking certain aspects can render the entire dataset unusable. Autonomous driving challenges remain a prominent area of research, requiring further exploration to enhance the perception and planning performance of vehicles. However, existing datasets are often incomplete. For instance, datasets that include perception information generally lack planning data, while planning datasets typically consist of extensive driving sequences where the ego vehicle predominantly drives forward, offering limited behavioral diversity. In addition, many real datasets struggle to evaluate their models, especially for planning tasks, since they lack a proper closed-loop evaluation setup. The CARLA Leaderboard 2.0 challenge, which provides a diverse set of scenarios to address the long-tail problem in autonomous driving, has emerged as a valuable alternative platform for developing perception and planning models in both open-loop and closed-loop evaluation setups. Nevertheless, existing datasets collected on this platform present certain limitations. Some datasets appear to be tailored primarily for limited sensor configuration, with particular sensor configurations. To support end-to-end autonomous driving research, we have collected a new dataset comprising over 2.85 million frames using the CARLA simulation environment for the diverse Leaderboard 2.0 challenge scenarios. Our dataset is designed not only for planning tasks but also supports dynamic object detection, lane divider detection, centerline detection, traffic light recognition, prediction tasks and visual language action models . Furthermore, we demonstrate its versatility by training various models using our dataset. Moreover, we also provide numerical rarity scores to understand how rarely the current state occurs in the dataset.

翻译：高质量数据集的收集是一项需要细致关注细节的关键任务，若忽视某些方面可能导致整个数据集不可用。自动驾驶挑战仍是研究的重点领域，需要进一步探索以提升车辆的感知和规划性能。然而，现有数据集往往不完整。例如，包含感知信息的数据集通常缺乏规划数据，而规划数据集多由大量驾驶序列组成，其中自车主要沿直线行驶，行为多样性有限。此外，许多真实数据集难以评估其模型，尤其是规划任务，因为它们缺乏合适的闭环评估设置。CARLA Leaderboard 2.0挑战提供了多样化的场景来解决自动驾驶中的长尾问题，已成为在开环和闭环评估设置下开发感知与规划模型的重要替代平台。然而，在该平台上收集的现有数据集存在一定局限性。部分数据集似乎主要针对有限的传感器配置，且采用特定的传感器配置。为支持端到端自动驾驶研究，我们利用CARLA仿真环境收集了一个包含超过285万帧的新数据集，覆盖Leaderboard 2.0挑战中的多样化场景。该数据集不仅适用于规划任务，还支持动态目标检测、车道线检测、中心线检测、交通灯识别、预测任务以及视觉语言动作模型。此外，我们通过使用该数据集训练多种模型，展示了其多功能性。同时，我们还提供了数值罕见性评分，以理解当前状态在数据集中出现的稀有程度。