BikeScenes: Online LiDAR Semantic Segmentation for Bicycles
This addresses cyclist safety by adapting perception technology for bicycles, though it is incremental as it applies existing methods to a new domain.
The paper tackles the problem of adapting automotive LiDAR segmentation for bicycle safety by creating a domain-specific dataset (BikeScenes) with 3021 annotated scans, achieving a mean IoU of 63.6% after fine-tuning compared to 13.8% with automotive pre-training alone.
The vulnerability of cyclists, exacerbated by the rising popularity of faster e-bikes, motivates adapting automotive perception technologies for bicycle safety. We use our multi-sensor 'SenseBike' research platform to develop and evaluate a 3D LiDAR segmentation approach tailored to bicycles. To bridge the automotive-to-bicycle domain gap, we introduce the novel BikeScenes-lidarseg Dataset, comprising 3021 consecutive LiDAR scans around the university campus of the TU Delft, semantically annotated for 29 dynamic and static classes. By evaluating model performance, we demonstrate that fine-tuning on our BikeScenes dataset achieves a mean Intersection-over-Union (mIoU) of 63.6%, significantly outperforming the 13.8% obtained with SemanticKITTI pre-training alone. This result underscores the necessity and effectiveness of domain-specific training. We highlight key challenges specific to bicycle-mounted, hardware-constrained perception systems and contribute the BikeScenes dataset as a resource for advancing research in cyclist-centric LiDAR segmentation.