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/.

翻译：预测弱势道路参与者（VRUs）的意图是实现安全自动驾驶（AD）与移动机器人技术的关键挑战。当前研究主要集中于从车辆视角预测行人横穿行为，而在密集共享空间内的交互行为仍缺乏深入探索。为填补这一空白，我们推出了首个完全开放的感知平台FUSE-Bike。该平台配备两台激光雷达、一台相机及全球导航卫星系统（GNSS），能够直接从骑行者视角采集高保真、近距离数据。基于此平台，我们构建了新颖的多模态数据集BikeActions，包含涵盖5个不同动作类别的852个标注样本，专门用于改进VRU行为建模。我们通过评估最先进的图卷积网络与基于Transformer的模型在公开数据划分上的性能，建立了严谨的基准测试体系，并为这一挑战性任务设立了首个性能基线。我们在https://iv.ee.hm.edu/bikeactions/ 发布了完整数据集、数据管理工具、开源硬件设计方案及基准代码，以促进未来在VRU行为理解领域的研究。