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Deep Learning Architectures for Skateboarder–Pedestrian Surrogate Safety Measures

1
Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA
2
Department of Civil, Construction and Environmental Engineering, San Diego State University, San Diego, CA 92182, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Dan Graham
Future Transp. 2021, 1(2), 387-413; https://doi.org/10.3390/futuretransp1020022
Received: 28 June 2021 / Revised: 25 August 2021 / Accepted: 9 September 2021 / Published: 12 September 2021
Skateboarding as a method of transportation has become prevalent, which has increased the occurrence and likelihood of pedestrian–skateboarder collisions and near-collision scenarios in shared-use roadway areas. Collisions between pedestrians and skateboarders can result in significant injury. New approaches are needed to evaluate shared-use areas prone to hazardous pedestrian–skateboarder interactions, and perform real-time, in situ (e.g., on-device) predictions of pedestrian–skateboarder collisions as road conditions vary due to changes in land usage and construction. A mechanism called the Surrogate Safety Measures for skateboarder–pedestrian interaction can be computed to evaluate high-risk conditions on roads and sidewalks using deep learning object detection models. In this paper, we present the first ever skateboarder–pedestrian safety study leveraging deep learning architectures. We view and analyze state of the art deep learning architectures, namely the Faster R-CNN and two variants of the Single Shot Multi-box Detector (SSD) model to select the correct model that best suits two different tasks: automated calculation of Post Encroachment Time (PET) and finding hazardous conflict zones in real-time. We also contribute a new annotated data set that contains skateboarder–pedestrian interactions that has been collected for this study. Both our selected models can detect and classify pedestrians and skateboarders correctly and efficiently. However, due to differences in their architectures and based on the advantages and disadvantages of each model, both models were individually used to perform two different set of tasks. Due to improved accuracy, the Faster R-CNN model was used to automate the calculation of post encroachment time, whereas to determine hazardous regions in real-time, due to its extremely fast inference rate, the Single Shot Multibox MobileNet V1 model was used. An outcome of this work is a model that can be deployed on low-cost, small-footprint mobile and IoT devices at traffic intersections with existing cameras to perform on-device inferencing for in situ Surrogate Safety Measurement (SSM), such as Time-To-Collision (TTC) and Post Encroachment Time (PET). SSM values that exceed a hazard threshold can be published to an Message Queuing Telemetry Transport (MQTT) broker, where messages are received by an intersection traffic signal controller for real-time signal adjustment, thus contributing to state-of-the-art vehicle and pedestrian safety at hazard-prone intersections. View Full-Text
Keywords: post encroachment time; deep learning; object detection; Faster R-CNN; Single Shot Multi-box Detector; skateboarder; hazardous region post encroachment time; deep learning; object detection; Faster R-CNN; Single Shot Multi-box Detector; skateboarder; hazardous region
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MDPI and ACS Style

Shourov, C.E.; Sarkar, M.; Jahangiri, A.; Paolini, C. Deep Learning Architectures for Skateboarder–Pedestrian Surrogate Safety Measures. Future Transp. 2021, 1, 387-413. https://doi.org/10.3390/futuretransp1020022

AMA Style

Shourov CE, Sarkar M, Jahangiri A, Paolini C. Deep Learning Architectures for Skateboarder–Pedestrian Surrogate Safety Measures. Future Transportation. 2021; 1(2):387-413. https://doi.org/10.3390/futuretransp1020022

Chicago/Turabian Style

Shourov, Chowdhury E., Mahasweta Sarkar, Arash Jahangiri, and Christopher Paolini. 2021. "Deep Learning Architectures for Skateboarder–Pedestrian Surrogate Safety Measures" Future Transportation 1, no. 2: 387-413. https://doi.org/10.3390/futuretransp1020022

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