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.

翻译：本文介绍了RobotCycle这一新颖的在研项目，该项目利用自动驾驶汽车（AV）研究来探究道路基础设施如何影响骑行者在真实道路出行中的行为与安全性。项目需求与城市规划者、骑行者和政策制定者等关键利益相关方共同制定，为风险与安全指标的设计以及数据采集标准提供依据。我们提出一种数据驱动方法，依托一套基于定制可穿戴传感单元采集的、包含多样化交通场景与情境的新颖丰富数据集。通过分析道路使用者的轨迹，我们识别出指示潜在风险或与环境基础设施要素相关的危险交互的正常路径偏离。我们的分析将驾驶特征与轨迹模式同局部道路路段、驾驶条件及道路使用者交互相关联，以预测交通行为并识别关键场景。此外，通过借鉴AV研究的最新进展，项目生成了详细的3D高清地图（HD Maps）、交通流模式及轨迹模型，实现对所有交通参与者行为的全面评估与分析。这些数据可为设计骑行友好型道路基础设施提供依据，最终提升道路安全性与骑行适宜性。该项目为加强骑行者保护及推动可持续城市交通提供了宝贵见解。