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，一个正在进行的新项目，旨在利用自动驾驶汽车（AV）研究成果，探究真实骑行过程中道路基础设施对骑行者行为及安全性的影响。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协作定义，为风险与安全指标的设计及数据采集标准提供了依据。我们提出了一种数据驱动方法，依托通过定制可穿戴传感单元采集的、包含多样化交通场景和情境的新型丰富数据集。通过分析道路使用者的轨迹，我们识别出指示与环境中基础设施要素相关的潜在风险或危险交互的常规路径偏差。我们的分析将驾驶特征和轨迹模式与局部道路路段、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研究的进展，项目生成了详细的3D高精地图（HD Maps）、交通流模式及轨迹模型，为所有交通参与者的行为提供全面评估与分析。这些数据可用于指导骑行友好型道路基础设施的设计，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。