The vulnerability of cyclists, exacerbated by the rising popularity of faster e-bikes, motivates adapting automotive perception technologies for bicycle safety. This paper presents an online 3D LiDAR semantic segmentation pipeline developed using our multi-sensor ‘SenseBike’ research platform. To bridge the automotive-to-bicycle domain gap, we introduce the novel BikeScenes LidarSeg Dataset, comprising 3021 consecutive LiDAR scans around the TU Delft campus, 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.