The Oxford RobotCycle Project: A multimodal urban cycling dataset for assessing the safety of vulnerable road users

Panagiotaki, Efimia; Thuremella, Divya; Baghabrah, Jumana; Sze, Samuel; Fu, Lanke Frank Tarimo; Hardin, Benjamin; Reinmund, Tyler; Flatscher, Tobit; Marques, Daniel; Prahacs, Chris; Kunze, Lars; Martini, Daniele De

Abstract

The Oxford RobotCycle Project is a novel initiative aiming to understand how road and traffic infrastructure influence road users’ behaviour, affecting cyclists’ journeys and safety. By leveraging state-of-the-art technology and methods used in Autonomous Vehicles (AVs), this project introduces a novel multimodal dataset, capturing dynamic cycling data in complex and diverse urban traffic environments. The dataset consists of range, visual, and inertial sensors, mounted on a backpack, and eye-gaze tracking glasses, coupled with an analysis of road infrastructure and interactions with other road users. Enhanced by annotated maps, reconstructed 3D pointclouds, and a detailed ontology capturing static and dynamic agents and their relations, the dataset provides a comprehensive framework for analysing and understanding traffic dynamics. Heatmaps derived from the cyclists’ vision reveal attention patterns and focal points during various traffic scenarios. We also analyse traffic interactions and risk, either perceived or actual, and correlate it with road infrastructure and traffic volumes. To complement the dataset we also provide a complete set of tools for risk and traffic analysis, visualisation, automatic calibration, and data annotation. The dataset can also be used to evaluate the robustness of odometry estimation methods, due to the highly dynamic cyclist movements. Combining multimodal data with traffic and risk analysis, the Oxford RobotCycle Project facilitates identifying safety-critical scenarios to derive actionable insights for safer, cyclist-friendly road design. This work contributes towards improving cycling safety, enhancing urban mobility, and supporting sustainable transportation initiatives.

• Journal Article Type: Article
• Acceptance Date: Apr 22, 2025
• Online Publication Date: May 1, 2025
• Publication Date: May 1, 2025
• Deposit Date: May 2, 2025
• Publicly Available Date: May 2, 2025
• Journal: IEEE Transactions on Field Robotics
• Print ISSN: 2997-1101
• Electronic ISSN: 2997-1101
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 2
• Pages: 308 - 335
• DOI: https://doi.org/10.1109/tfr.2025.3566304
• Public URL: https://uwe-repository.worktribe.com/output/14402139