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Cycling as a mode of transportation has been recording an upward trend in both the U.S. and Europe. Unfortunately, the safety of cyclists has been a point of growing concern. Data from the National Highway Traffic Safety Administration (NHTSA) show that the crashes that occur during the events of motorists overtaking cyclists was one of the leading categories involving cyclists in fatal crashes. In support of the efforts to understand the driving behavior of drivers of motorized vehicles while overtaking cyclists, this research project is aimed at developing an algorithm to identify the overtaking events. Most existing quantitative studies on cycling safety leverage instrumented bicycles or vehicles with sensors for extracting naturalistic driving trajectories. Whereas we use data from a recent research that provides naturalistic driving trajectories of road users collected at select intersections in urban areas in Germany using drones equipped with cameras. Using these videos with a data frequency of 25 Hz, the authors of this study have output inD dataset. The inD dataset contains trajectories of road users that are captured in form of coordinates on a two-dimensional plane obtained from the ariel or bird's eye view of the road. Additionally, the data also captures velocity, acceleration, heading angles, dimensions of driver's vehicle etc. Overtaking can be thought of as four phases of approaching, steering away, passing, and returning. Using the inD dataset, we have developed an algorithm to identify events when a driver of motor vehicle overtakes a cyclist. This work fits into our broader goal to contribute to the body of knowledge for improving road safety of cyclists. The work is expected to provide inputs to governmental/ traffic authorities in aspects such as design of intersections and design of bicycle lanes by providing insights into overtaking events.
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Cycling-Net: A Deep Learning Approach to Predicting Cyclist Behaviors from Geo-Referenced Egocentric Video Data
Cycling, as a green transportation mode, provides an environmentally friendly transportation choice for short-distance traveling. However, cyclists are also getting involved in fatal accidents more frequently in recent years. Thus, understanding and modeling their road behaviors is crucial in helping improving road safety laws and infrastructures. Traditionally, people understand road user behavior using either purely spatial trajectory data, or videos from fixed surveillance camera through tracking or predicting their paths. However, these data only cover limited areas and do not provide information from the cyclist's field of view. In this paper, we take advantage of geo-referenced egocentric video data collected from the handlebar cameras of cyclists to learn how to predict their behaviors. This approach is technically more challenging, because both the observer and objects in the scene might be moving, and there are strong temporal dependencies in both the behaviors of cyclists and the video scenes. We propose Cycling-Net, a novel deep learning model that tracks different types of objects in consecutive scenes and learns the relationship between the movement of these objects and the behavior of the cyclist. Experiment results on a naturalistic trip dataset show the Cycling-Net is effective in behavior prediction and outperforms a baseline model.
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- PAR ID:
- 10225180
- Date Published:
- Journal Name:
- Proceedings of the 28th International Conference on Advances in Geographic Information Systems
- Page Range / eLocation ID:
- 337 to 346
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3397536.3422258
