Machine Learning for Autonomous Vehicle Emergency Response Systems

Abstract

Technological advancements in artificial intelligence, deep learning, and wireless communication have brought autonomous vehicles closer to reality. Unlike other avenues, such as production and infrastructure, communication and safety are crucial aspects of this technological area. Autonomous vehicles must be able to communicate with each other and utilize the environment to detect, track, and classify design elements. An autonomous vehicle must respond to emergency vehicles approaching a traffic intersection by making an “educated” decision. Although many current traffic regulations impose rules to deal with emergency vehicles, autonomous cars that can drive safely in cooperation with emergency vehicles have not yet been commercialized.[2] Machine-learning-based algorithms generate and improve models over time by learning from their experiences. With this in mind, machine learning models can be trained in a variety of scenarios, including the recognition of emergency vehicles and the detection of overpasses. Emergencies have played an indispensable role in human life, regardless of location. The emergency can be defined specifically as urgent and unplanned medical care for injuries or illnesses, but, most broadly, as the sudden and unexpected adverse environment of individuals or groups that makes them feel threatened. For this reason, various emergency vehicles, such as ambulances, police cars, and fire trucks, have been given priority on the road, because their goals were to save lives and reduce property damage. Placeholder eco-friendly autonomous vehicles’ systems must provide for emergency vehicles in all traffic scenarios, and that these systems should ideally be able to predict emergency vehicle behaviors, before making any observations of any emergency vehicle. Autonomously detecting and tracking emergency vehicles and effectively responding to them can notably improve the penetration of autonomous vehicles into society, and such systems become increasingly important for safety-conscious autonomous vehicles.