Bridging Connected Vehicles with Artificial Intelligence for Smart First Responder Services Citizencentric methods leverage connectivity and Artificial Intelligence (AI) methodologies to improve smart city services. Among these services, intelligent redesign 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 ResNet34 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.

Routing Autonomous Emergency Vehicles in Smart Cities Using Real Time Systems Analogy: A Conceptual Model Emergency service vehicles like ambulance, fire, police etc. should respond to emergencies on time. Existing barriers like increased congestion, multiple signalized intersections, queued vehicles, traffic phase timing etc. can prevent emergency vehicles (EVs) achieving desired response times. Existing solutions to route EVs have not been successful because they do not use dynamic traffic parameters. Real time information on increased congestion, halts on road, pedestrian flow, queued vehicles, real and adaptive speed, can be used to properly actuate preemption and minimise the impact that EV movement can have on other traffic.Smart cities provide the necessary infrastructure to enable two critical factors in EV routing: realtime traffic data and connectivity. In addition, using autonomous vehicles (AVs) in place of normal emergency service vehicles can have further advantages in terms of safety and adaptability in smart city environments. AVs feature several sensors and connectivity that can help them make realtime decisions. We propose a novel idea of using autonomous emergency vehicles (AEVs) that can meet the critical response time and drive through a complex road network in smart cities efficiently and safely. This is achieved by considering traffic network analogous to realtime systems (RTS) where we use mixedcriticality realtime system (MCRTS) task scheduling to schedule AEVs for meeting response time.