Description
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. Surrogate Safety Measures (SSM) for skateboarder-pedestrian interaction can be computed to evaluate high-risk conditions on roads and sidewalks using deep learning object detection models. This work demonstrates the first ever skateboarder-pedestrian safety study leveraging deep learning architectures. We captured and manually annotated skateboarder image data for input to object detection models. We evaluated the Faster Region Based Convolutional Neural Network (Faster R-CNN) and the Single Shot Multi-box Detector (SSD) models to select the model that best suits two different tasks: automated calculation of Post Encroachment Time (PET) and finding hazardous conflict zones in real-time. This study led to the creation of two new datasets that has been made publicly available. The trained 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, the 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 PET, whereas to determine hazardous regions in real-time, due to its extremely fast inference rate, a variation of the Single Shot Multi-box Detector 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). SSM values that exceed a hazard threshold can be published to a 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.
