The Oxford RobotCycle Project: A Multimodal Urban Cycling Dataset for Assessing the Safety of Vulnerable Road Users

The Oxford RobotCycle Project is a novel initiative aiming to understand how road and traffic infrastructure influence road users’ behavior, 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 3-D point clouds, and a detailed ontology capturing static and dynamic agents and their relations, the dataset provides a comprehensive framework for analyzing and understanding traffic dynamics. Heatmaps derived from the cyclists’ vision reveal attention patterns and focal points during various traffic scenarios. We also analyze traffic interactions and risk, either perceived or actual, and correlate them with road infrastructure and traffic volumes. To complement the dataset, we also provide a complete set of tools for risk and traffic analysis, visualization, 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 toward improving cycling safety, enhancing urban mobility, and supporting sustainable transportation initiatives. The dataset and tools are made available at https://ori-mrg.github.io/robotcycle-dataset/