End-to-end driving systems have made rapid progress, but have so far not been applied to the challenging new CARLA Leaderboard 2.0. Further, while there is a large body of literature on end-to-end architectures and training strategies, the impact of the training dataset is often overlooked. In this work, we make a first attempt at end-to-end driving for Leaderboard 2.0. Instead of investigating architectures, we systematically analyze the training dataset, leading to new insights: (1) Expert style significantly affects downstream policy performance. (2) In complex data sets, the frames should not be weighted on the basis of simplistic criteria such as class frequencies. (3) Instead, estimating whether a frame changes the target labels compared to previous frames can reduce the size of the dataset without removing important information. By incorporating these findings, our model ranks first and second respectively on the map and sensors tracks of the 2024 CARLA Challenge, and sets a new state-of-the-art on the Bench2Drive test routes. Finally, we uncover a design flaw in the current evaluation metrics and propose a modification for future challenges. Our dataset, code, and pre-trained models are publicly available at https://github.com/autonomousvision/carla_garage.

翻译：端到端驾驶系统已取得快速进展，但迄今尚未应用于具有挑战性的新型CARLA Leaderboard 2.0。此外，尽管有大量文献研究端到端架构与训练策略，训练数据集的影响却常被忽视。本研究首次尝试在Leaderboard 2.0上实现端到端驾驶。我们未探究架构设计，而是系统分析训练数据集，获得以下新发现：(1) 专家驾驶风格显著影响下游策略性能。(2) 在复杂数据集中，不应基于类别频率等简化标准对帧进行加权。(3) 通过估计某帧相较于先前帧是否改变目标标签，可在不丢失重要信息的前提下缩减数据集规模。基于这些发现，我们的模型在2024年CARLA挑战赛的地图赛道与传感器赛道分别获得第一与第二名，并在Bench2Drive测试路线上创造了新的最优性能。最后，我们揭示了当前评估指标的设计缺陷，并为未来挑战提出改进方案。数据集、代码与预训练模型已公开于https://github.com/autonomousvision/carla_garage。