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RobotCycle: Assessing Cycling Safety in Urban Environments (2403.07789v3)

Published 12 Mar 2024 in cs.RO

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.

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References (22)
  1. M. M. Shoman, H. Imine, E. M. Acerra, and C. Lantieri, “Evaluation of Cycling Safety and Comfort in Bad Weather and Surface Conditions Using an Instrumented Bicycle,” IEEE Access, vol. 11, no. January, pp. 15 096–15 108, 2023.
  2. I. Kaparias, S. Miah, S. Clegg, Y. Gao, B. Waterson, and E. Milonidis, “Measuring the effect of highway design features on cyclist behavior using an instrumented bicycle,” 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2021, pp. 1–6, 2021.
  3. P. Vieira, J. P. Costeira, S. Brandao, and M. Marques, “SMARTcycling: Assessing cyclists’ driving experience,” IEEE Intelligent Vehicles Symposium, Proceedings, vol. 2016-August, no. Iv, pp. 1321–1326, 2016.
  4. M. R. Ibrahim, J. Haworth, N. Christie, and T. Cheng, “CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning,” IET Intelligent Transport Systems, vol. 15, no. 10, pp. 1331–1344, 2021.
  5. S. M. Kumar, “Smart Biking : IoT-Connected Cycling Gear for Training and Safety,” 2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon), pp. 652–656, 2023.
  6. C. Wen, S. Pan, C. Wang, and J. Li, “An Indoor Backpack System for 2-D and 3-D Mapping of Building Interiors,” IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 7, pp. 992–996, 2016.
  7. Z. Gong, J. Li, Z. Luo, C. Wen, C. Wang, and J. Zelek, “Mapping and Semantic Modeling of Underground Parking Lots Using a Backpack LiDAR System,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 2, pp. 734–746, 2021.
  8. P. Chen, W. Shi, S. Bao, M. Wang, W. Fan, and H. Xiang, “Low-Drift Odometry, Mapping and Ground Segmentation Using a Backpack LiDAR System,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7285–7292, 2021.
  9. A. Nüchter, D. Borrmann, P. Koch, M. Kühn, and S. May, “A Man-Portable, Imu-Free Mobile Mapping System,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 2, no. 3W5, pp. 17–23, 2015.
  10. S. Lagüela, I. Dorado, M. Gesto, P. Arias, D. González-Aguilera, and H. Lorenzo, “Behavior analysis of novel wearable indoor mapping system based on 3d-slam,” Sensors (Switzerland), vol. 18, no. 3, pp. 1–16, 2018.
  11. N. Corso and A. Zakhor, “Indoor localization algorithms for an ambulatory human operated 3D mobile mapping system,” Remote Sensing, vol. 5, no. 12, pp. 6611–6646, 2013.
  12. J. Holmgren, H. M. Tulldahl, J. Nordlöf, M. Nyström, K. Olofsson, J. Rydell, and E. Willen, “Estimation of tree position and stem diameter using simultaneous localization and mapping with data from a backpack-mounted laser scanner,” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, vol. 42, no. 3W3, pp. 59–63, 2017.
  13. A. Rasch and M. Dozza, “Modeling Drivers’ Strategy When Overtaking Cyclists in the Presence of Oncoming Traffic,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 2180–2189, 2022.
  14. S. Daraei, K. Pelechrinis, and D. Quercia, “A data-driven approach for assessing biking safety in cities,” EPJ Data Science, vol. 10, no. 1, 2021.
  15. D. Castells-Graells, C. Salahub, and E. Pournaras, “On cycling risk and discomfort: urban safety mapping and bike route recommendations,” Computing, vol. 102, no. 5, pp. 1259–1274, 2020.
  16. L. Meuleners, M. Fraser, and P. Roberts, “Improving cycling safety through infrastructure design: A bicycle simulator study,” Transportation Research Interdisciplinary Perspectives, vol. 18, no. June 2022, p. 100768, 2023.
  17. M. R. Ibrahim, J. Haworth, and N. Christie, “Re-designing cities with conditional adversarial networks,” 2021. [Online]. Available: http://arxiv.org/abs/2104.04013
  18. W. Maddern, G. Pascoe, C. Linegar, and P. Newman, “1 year, 1000 km: The oxford robotcar dataset,” The International Journal of Robotics Research, vol. 36, no. 1, pp. 3–15, 2017.
  19. D. Barnes, M. Gadd, P. Murcutt, P. Newman, and I. Posner, “The oxford radar robotcar dataset: A radar extension to the oxford robotcar dataset,” in 2020 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2020, pp. 6433–6438.
  20. OpenStreetMap contributors, “Planet dump retrieved from https://planet.osm.org ,” https://www.openstreetmap.org, 2017.
  21. LevelXData, “The unid dataset: Naturalistic trajectories of vehicles and vulnerable road users recorded at the rwth aachen university campus,” Tech. Rep., 2023. [Online]. Available: https://levelxdata.com/unid-dataset/
  22. M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 3213–3223.
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