Bridging Connected Vehicles with Artificial Intelligence for Smart First Responder Services

Citizen-centric methods leverage connectivity and Artificial Intelligence (AI) methodologies to improve smart city services. Among these services, intelligent re-design of first responder services call for novel solutions that save city resources and result in faster response time. To this end, we propose to utilize sensory data in connected vehicles to capture contextual details that can be obtained through various Convolutional Neural Network (CNN) architectures to determine which set of first responders should be called in the case of an accident. We use real images from a rich dataset of accidents involving different types of vehicles to train and test the CNNs. Through experimental results, we show that when ResNet-34 network is augmented with one fit cycle, image augmentation and hidden layer unfreezing methods can result in 88.9% accuracy in the prediction of the required first responder(s) in case of an accident solely based on the captured images.