  This paper introduces RobotCycle, a novel ongoing project that leverages
Autonomous Vehicle (AV) research to investigate how road infrastructure
influences cyclist behaviour and safety during real-world journeys. The
project's requirements were defined in collaboration with key stakeholders,
including city planners, cyclists, and policymakers, informing the design of
risk and safety metrics and the data collection criteria. We propose a
data-driven approach relying on a novel, rich dataset of diverse traffic scenes
and scenarios captured using a custom-designed wearable sensing unit. By
analysing road-user trajectories, we identify normal path deviations indicating
potential risks or hazardous interactions related to infrastructure elements in
the environment. Our analysis correlates driving profiles and trajectory
patterns with local road segments, driving conditions, and road-user
interactions to predict traffic behaviours and identify critical scenarios.
Moreover, by leveraging advancements in AV research, the project generates
detailed 3D High-Definition Maps (HD Maps), traffic flow patterns, and
trajectory models to provide a comprehensive assessment and analysis of the
behaviour of all traffic agents. These data can then inform the design of
cyclist-friendly road infrastructure, ultimately enhancing road safety and
cyclability. The project provides valuable insights for enhancing cyclist
protection and advancing sustainable urban mobility.
