Prediction of Pedestrian Crossing Intentions at Intersections Based on Long Short-Term Memory Recurrent Neural Network
Prediction of Pedestrian Crossing Intentions at Intersections Based on Long Short-Term Memory Recurrent Neural Network
- New search for: Zhang, Shile
- New search for: Abdel-Aty, Mohamed
- New search for: Yuan, Jinghui
- New search for: Li, Pei
- New search for: Zhang, Shile
- New search for: Abdel-Aty, Mohamed
- New search for: Yuan, Jinghui
- New search for: Li, Pei
Traffic violations of pedestrians at intersections are major causes of road crashes involving pedestrians, especially red-light crossing behaviors. To predict the pedestrians’ red-light crossing intentions, video data from real traffic scenes are collected. Using detection and tracking techniques in computer vision, some pedestrians’ characteristics, including location information, are generated. A long short-term memory neural network is established and trained to predict pedestrians’ red-light crossing intentions. The experimental results show that the model has an accuracy rate of 91.6% based on internal testing at one signalized crosswalk. This model can be further implemented in the vehicle-to-infrastructure communication environment and prevent crashes because of the pedestrians’ red-light crossing behaviors.
Document information
Prediction of Pedestrian Crossing Intentions at Intersections Based on Long Short-Term Memory Recurrent Neural Network
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 2674 , 4 ; 57-65
2020-03-03
Article (Journal)
Electronic Resource
English
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