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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.07789 |
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| _version_ | 1866917650997706752 |
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| author | Panagiotaki, Efimia Reinmund, Tyler Mouton, Stephan Pitt, Luke Shanthini, Arundathi Shaji Tubby, Wayne Towlson, Matthew Sze, Samuel Liu, Brian Prahacs, Chris De Martini, Daniele Kunze, Lars |
| author_facet | Panagiotaki, Efimia Reinmund, Tyler Mouton, Stephan Pitt, Luke Shanthini, Arundathi Shaji Tubby, Wayne Towlson, Matthew Sze, Samuel Liu, Brian Prahacs, Chris De Martini, Daniele Kunze, Lars |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07789 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RobotCycle: Assessing Cycling Safety in Urban Environments Panagiotaki, Efimia Reinmund, Tyler Mouton, Stephan Pitt, Luke Shanthini, Arundathi Shaji Tubby, Wayne Towlson, Matthew Sze, Samuel Liu, Brian Prahacs, Chris De Martini, Daniele Kunze, Lars Robotics 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. |
| title | RobotCycle: Assessing Cycling Safety in Urban Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2403.07789 |