让自动驾驶按用户指定速度行驶并判断是否超车，提供可量化评估的新基准。
Can Users Specify Driving Speed? Bench2Drive-Speed: Benchmark and Baselines for Desired-Speed Conditioned Autonomous Driving
- 引入目标速度与超车指令作为输入，改造驾驶模型以响应用户需求。
- 用2100段标注数据验证：常规数据重标注后性能接近专家示范数据。
- 支持个性化驾驶设定，适合研究可定制化自动驾驶的开发者。
端到端自动驾驶虽取得显著进展，但用户自定义行驶速度或指定是否超车等功能长期被忽视。为此，我们提出Bench2Drive-Speed基准，包含评估指标、数据集和基线模型，支持目标速度条件下的自动驾驶。通过显式输入用户期望速度及超车/跟随指令，设计了速度遵循度得分（Speed-Adherence Score）和超车得分（Overtake Score）等量化指标，兼顾传统驾驶评价标准。为训练速度可控模型，我们构建CustomizedSpeedDataset，含2,100段由专家标注的视频片段。实验表明，在合理重标注下，基于常规驾驶数据训练的模型性能可媲美专家示范数据；速度控制可实现而不影响常规驾驶表现，但执行超车指令仍具挑战性。所有代码、数据集与基线已公开于GitHub。
原文摘要 · Abstract (English)
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
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