The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice
Open accessAbstract
One of the major challenges that autonomous cars are facing today is driving in urban environments. To make it a reality, autonomous vehicles require the ability to communicate with other road users and understand their intentions. Such interactions are essential between vehicles and pedestrians, the most vulnerable road users. Understanding pedestrian behavior, however, is not intuitive and depends on various factors, such as demographics of the pedestrians, traffic dynamics, environmental conditions, and so on. In this paper, we identify these factors by surveying pedestrian behavior studies, both the classical works on pedestrian-driver interaction and the modern ones that involve autonomous vehicles. To this end, we will discuss various methods of studying pedestrian behavior and analyze how the factors identified in the literature are interrelated. We will also review the practical applications aimed at solving the interaction problem, including design approaches for autonomous vehicles that communicate with pedestrians and visual perception and reasoning algorithms tailored to understanding pedestrian intention. Based on our findings, we will discuss the open problems and propose future research directions.
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Cites
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913
You'll never walk alone: Modeling social behavior for multi-target tracking
Pellegrini, S., Ess, A., Schindler, K., & van Gool, L. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459260 · Cited by 1,683 · cited 2×
“treated as a dynamic navigation problem in which pedestrians choose the path that minimizes the likelihood of colliding with others [143].”
BackgroundPedestrian detection: A benchmark
Dollar, P., Wojek, C., Schiele, B., & Perona, P. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206631 · Cited by 1,313
Perceptions of autonomous vehicles: Relationships with road users, risk, gender and age
Hulse, L. M., Xie, H., & Galea, E. R. (2018) · Safety Science · doi:10.1016/j.ssci.2017.10.001 · Cited by 698
Visual attention while driving: sequences of eye fixations made by experienced and novice drivers
Underwood, G., Chapman, P., Brocklehurst, N., Underwood, J., & Crundall, D. (2003) · Ergonomics · doi:10.1080/0014013031000090116 · Cited by 517
Did you see the unicycling clown? Inattentional blindness while walking and talking on a cell phone
Hyman, I. E., Boss, S. M., Wise, B. M., McKenzie, K. E., & Caggiano, J. M. (2010) · Applied Cognitive Psychology · doi:10.1002/acp.1638 · Cited by 438
Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior
Rasouli, A., Kotseruba, I., & Tsotsos, J. K. (2017) · 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw.2017.33 · Cited by 425
Self-citationSisiopiku, V. P., & Akın, D. (2003) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2003.06.001 · Cited by 380
Visual attention and the transition from novice to advanced driver
Underwood, G. (2007) · Ergonomics · doi:10.1080/00140130701318707 · Cited by 373
Communicating Awareness and Intent in Autonomous Vehicle-Pedestrian Interaction
Mahadevan, K., Somanath, S., & Sharlin, E. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174003 · Cited by 366
Cited by
A Review on YOLOv8 and Its Advancements
Sohan, M., Sai Ram, T., & Rami Reddy, C. V. (2024) · Algorithms for Intelligent Systems · doi:10.1007/978-981-99-7962-2_39 · Cited by 898
Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions
Muhammad, K., Ullah, A., Lloret, J., Ser, J. D., & de Albuquerque, V. H. C. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3032227 · Cited by 659
Explanations in Autonomous Driving: A Survey
Omeiza, D., Webb, H., Jirotka, M., & Kunze, L. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3122865 · Cited by 355
Prattico, F. G., Lamberti, F., Cannavo, A., Morra, L., & Montuschi, P. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3054312 · Cited by 59
Designing Communication Strategies of Autonomous Vehicles with Pedestrians: An Intercultural Study
Lanzer, M., Babel, F., Yan, F., Zhang, B., You, F., Wang, J., & Baumann, M. (2020) · 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications · doi:10.1145/3409120.3410653 · Cited by 52
Metadata
- Authors
- Rasouli, A., & Tsotsos, J. K.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2020-03
- Volume
- 21
- Issue
- 3
- Pages
- 900-918
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2805439952
- Cited by
- 855
- References
- 82
- FWCI
- 43.15
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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