End-to-end autonomous driving (E2E-AD) has achieved remarkable progress. However, one practical and useful function has been long overlooked: users may wish to customize the desired speed of the policy or specify whether to allow the autonomous vehicle to overtake. To bridge this gap, we present Bench2Drive-Speed, a benchmark with metrics, dataset, and baselines for desired-speed conditioned autonomous driving. We introduce explicit inputs of users' desired target-speed and overtake/follow instructions to driving policy models. We design quantitative metrics, including Speed-Adherence Score and Overtake Score, to measure how faithfully policies follow user specifications, while remaining compatible with standard autonomous driving metrics. To enable training of speed-conditioned policies, one approach is to collect expert demonstrations that strictly follow speed requirements, an expensive and unscalable process in the real world. An alternative is to adapt existing regular driving data by treating the speed observed in future frames as the target speed for training. To investigate this, we construct CustomizedSpeedDataset, composed of 2,100 clips annotated with experts demonstrations, enabling systematic investigation of supervision strategies. Our experiments show that, under proper re-annotation, models trained on regular driving data perform comparably to on expert demonstrations, suggesting that speed supervision can be introduced without additional complex real-world data collection. Furthermore, we find that while target-speed following can be achieved without degrading regular driving performance, executing overtaking commands remains challenging due to the inherent difficulty of interactive behaviors. All code, datasets and baselines are available at https://github.com/Thinklab-SJTU/Bench2Drive-Speed

翻译：端到端自动驾驶（E2E-AD）已取得显著进展。然而，一项实用且便捷的功能长期被忽视：用户可能希望自定义策略的期望速度，或指定是否允许自动驾驶车辆超车。为弥补这一空白，我们提出Bench2Drive-Speed，这是一个涵盖指标、数据集和基线方法的期望速度条件自动驾驶基准。我们向驾驶策略模型引入用户显式输入的期望目标速度及超车/跟驰指令，并设计了包括速度遵从得分（Speed-Adherence Score）和超车得分（Overtake Score）在内的量化指标，用于衡量策略遵循用户指令的忠实程度，同时保持与标准自动驾驶指标的兼容性。为训练速度条件策略，一种方法是在现实世界中收集严格遵循速度要求的专家演示数据，但这一过程成本高昂且不可扩展。另一种替代方案是改造现有常规驾驶数据，将未来帧中观测到的速度作为训练的目标速度。为此，我们构建了CustomizedSpeedDataset数据集，包含2100个经专家演示标注的片段，从而系统研究监督策略。实验表明，在合理重标注条件下，基于常规驾驶数据训练的模型表现与专家演示数据相当，这意味着无需额外复杂的现实数据采集即可引入速度监督。此外，我们发现目标速度跟随可在不降低常规驾驶性能的前提下实现，但由于交互行为的固有难度，执行超车指令仍具挑战性。所有代码、数据集和基线方法均开源在https://github.com/Thinklab-SJTU/Bench2Drive-Speed。