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.

Cooperative ScheduleDriven Intersection Control with Connected and Autonomous Vehicles Recent work in decentralized, scheduledriven traffic control has demonstrated the ability to improve the efficiency of traffic flow in complex urban road networks. In this approach, a scheduling agent is associated with each intersection. Each agent senses the traffic approaching its intersection and in realtime constructs a schedule that minimizes the cumulative wait time of vehicles approaching the intersection over the current lookahead horizon. In this paper, we propose a cooperative algorithm that utilizes both connected and autonomous vehicles (CAV) and scheduledriven traffic control to create better traffic flow in the city. The algorithm enables an intersection scheduling agent to adjust the arrival time of an approaching platoon through use of wireless communication to control the velocity of vehicles. The sequence of approaching platoons is thus shifted toward a new shape that has smaller cumulative delay. We demonstrate how this algorithm outperforms the original approach in a realtime traffic signal control problem.