从骑车人视角构建数据集，助力自动驾驶识别行人行为。
BikeActions: An Open Platform and Benchmark for Cyclist-Centric VRU Action Recognition
- 用双激光雷达+相机从骑车人视角采集高精度数据
- 发布包含852个样本的多模态数据集，5类动作标注
- 提供开源平台与基准测试代码，推动行人行为研究
预判弱势道路使用者（VRUs）的行为是实现安全自动驾驶与移动机器人的重要挑战。当前研究大多从车辆视角关注行人过街行为，而密集共享空间中的交互仍缺乏探索。为此，我们推出了FUSE-Bike——首个完全开源的感知平台，配备双激光雷达、摄像头与GNSS，可直接从骑车人视角实现高保真近距离数据采集。基于该平台，我们构建了BikeActions数据集，包含852个标注样本，涵盖5类特定动作，专为提升VRU行为建模而设计。通过在公开数据划分上评估最先进的图卷积与Transformer模型，我们建立了该任务的首个性能基准。完整数据集、数据处理工具、开源硬件设计及基准代码已发布于https://iv.ee.hm.edu/bikeactions/，以促进未来对VRU行为理解的研究。
原文摘要 · Abstract (English)
Anticipating the intentions of Vulnerable Road Users (VRUs) is a critical challenge for safe autonomous driving (AD) and mobile robotics. While current research predominantly focuses on pedestrian crossing behaviors from a vehicle's perspective, interactions within dense shared spaces remain underexplored. To bridge this gap, we introduce FUSE-Bike, the first fully open perception platform of its kind. Equipped with two LiDARs, a camera, and GNSS, it facilitates high-fidelity, close-range data capture directly from a cyclist's viewpoint. Leveraging this platform, we present BikeActions, a novel multi-modal dataset comprising 852 annotated samples across 5 distinct action classes, specifically tailored to improve VRU behavior modeling. We establish a rigorous benchmark by evaluating state-of-the-art graph convolution and transformer-based models on our publicly released data splits, establishing the first performance baselines for this challenging task. We release the full dataset together with data curation tools, the open hardware design, and the benchmark code to foster future research in VRU action understanding under https://iv.ee.hm.edu/bikeactions/.
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