为重卡高速自动驾驶提供长达1000米的多模态数据集，填补长距感知空白。
TruckDrive: Long-Range Autonomous Highway Driving Dataset
- 部署七套长距FMCW激光雷达，实现千米级场景感知
- 模型在150米外3D检测性能下降31%至99%，暴露长距短板
- 适合研究长距离自动驾驶感知与规划的科研人员
重型卡车的高速公路自动驾驶仍面临重大挑战：因制动距离长，需对数百米范围内的场景进行理解以提前规划并预留安全余量。然而现有驾驶数据集主要覆盖城市环境，感知范围通常不超过100米。为此，我们推出TruckDrive——一个专为长距离感知设计的高速公路多模态驾驶数据集，配备七套长距FMCW激光雷达（测距与径向速度）、三套高分辨率短距激光雷达、十一台8MP全景摄像头（不同焦距）及十套4D FMCW雷达。数据集包含47.5万组样本，其中16.5万帧被密集标注，可用于2D检测（最远1000米）、3D检测（最远400米）、深度估计、跟踪、规划及端到端驾驶任务，序列时长超过20秒，模拟高速行驶场景。我们发现，当前主流自动驾驶模型在150米以外的3D感知任务中性能下降31%至99%，暴露出现有架构与训练信号难以弥补的长距感知鸿沟。
原文摘要 · Abstract (English)
Safe highway autonomy for heavy trucks remains an open and unsolved challenge: due to long braking distances, scene understanding of hundreds of meters is required for anticipatory planning and to allow safe braking margins. However, existing driving datasets primarily cover urban scenes, with perception effectively limited to short ranges of only up to 100 meters. To address this gap, we introduce TruckDrive, a highway-scale multimodal driving dataset, captured with a sensor suite purpose-built for long range sensing: seven long-range FMCW LiDARs measuring range and radial velocity, three high-resolution short-range LiDARs, eleven 8MP surround cameras with varying focal lengths and ten 4D FMCW radars. The dataset offers 475 thousands samples with 165 thousands densely annotated frames for driving perception benchmarking up to 1,000 meters for 2D detection and 400 meters for 3D detection, depth estimation, tracking, planning and end to end driving over 20 seconds sequences at highway speeds. We find that state-of-the-art autonomous driving models do not generalize to ranges beyond 150 meters, with drops between 31% and 99% in 3D perception tasks, exposing a systematic long-range gap that current architectures and training signals cannot close.
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