The effectiveness of law enforcement and public safety is directly dependent on the time taken by first responders to arrive at the scene of an emergency. The primary objective of this thesis is to develop techniques and actions of response for an autonomous vehicle in emergency scenarios. This work discusses the methods developed to identify Emergency Vehicles (EV) and use its localized information to develop response actions for autonomous vehicles in emergency scenarios using an Infrastructure-Enabled Autonomy (IEA) setup. IEA is a new paradigm in autonomous vehicles research that aims at distributed intelligence architecture by transferring the core functionalities of sensing and localization to a roadside infrastructure setup. In this work two independent frameworks were developed to identify Emergency vehicles in a video feed using computer vision techniques: (1) A one-stage framework where an object detection algorithm is trained on a custom dataset to detect EVs, (2) A two-stage framework where an object classification is independently implemented in series with an object detection pipeline to classify vehicles into EVs and nonEVs.
The performance of many popular classification models were compared on a combination of multi-spectral feature vectors of an image to identify the ideal combination to be used for EV identification rule. Localized position co-ordinates of an EV are obtained by deploying the classification routine on IEA. This position information is used as an input in an autonomous vehicle and an ideal response action is developed
