BikeActions: An Open Platform and Benchmark for Cyclist-Centric VRU Action Recognition
This work addresses a critical gap in VRU behavior modeling for autonomous driving by providing an open platform and benchmark, though it is incremental as it extends existing methods to a new cyclist-centric domain.
The authors tackled the problem of anticipating cyclist actions in dense shared spaces by introducing BikeActions, a multi-modal dataset with 852 annotated samples across 5 action classes, and established performance baselines using state-of-the-art models.
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/.