Conference item
RobotCycle: assessing cycling safety in urban environments
- Abstract:
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 22.9MB, Terms of use)
-
- Publisher copy:
- 10.1109/iv55156.2024.10588375
Authors
+ UK Research and Innovation
More from this funder
- Funder identifier:
- https://ror.org/001aqnf71
- Grant:
- EP/W011344/1
+ Engineering and Physical Sciences Research Council
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- Funder identifier:
- https://ror.org/0439y7842
- Publisher:
- IEEE
- Host title:
- 2024 IEEE Intelligent Vehicles Symposium (IV)
- Pages:
- 357-363
- Publication date:
- 2024-06-05
- Acceptance date:
- 2024-03-29
- Event title:
- 35th IEEE Intelligent Vehicles Symposium (IV 2024)
- Event location:
- Jeju Island, South Korea
- Event website:
- https://ieee-itss.org/event/iv2024/
- Event start date:
- 2024-06-02
- Event end date:
- 2024-06-05
- DOI:
- EISSN:
-
2642-7214
- ISSN:
-
1931-0587
- EISBN:
- 9798350348811
- ISBN:
- 9798350348828
- Language:
-
English
- Keywords:
- Pubs id:
-
1988187
- UUID:
-
uuid_edabcdff-78f8-4b8f-9913-d125d7da7fc3
- Local pid:
-
pubs:1988187
- Deposit date:
-
2025-12-09
- ARK identifier:
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2024
- Rights statement:
- © 2024 IEEE
- Notes:
-
This paper was presented at the 35th IEEE Intelligent Vehicles Symposium (IV 2024), 2nd-5th June 2024, Jeju Island, South Korea.
This is the accepted manuscript version of the article. The final version is available online from IEEE at https://dx.doi.org/10.1109/iv55156.2024.10588375
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