A Hybrid Framework for Expediting Emergency Vehicle Movement on Indian Roads

Unhindered and smooth movement of emergency vehicles within a city is a crucial aspect of any intelligent transport system. It is common to observe emergency vehicles such as ambulances and fire engines obstructed by traffic snarls on Indian roads, especially in the proximity of busy intersections. Existing literature primarily advocates the deployment of RFID technology to terminate the round-robin sequence of the signal system and switch the signal to green in the required direction. However, this technology has proven to be susceptible to electromagnetic interferences and also the economic feasibility is questionable. This paper proposes a model that employs real time image processing and object detection using a convolutional neural network (CNN) architecture called SSD Mobilenet. Unlike a few other architectures, SSD Mobilenet requires very limited computation, hence enabling swift detection. Furthermore, an acoustic signal (sound) processing (pitch detection) algorithm is employed to detect the sirens of emergency vehicles to nullify the potential false positives (e.g. an ambulance in a non-emergency scenario) that creep into object detection using image processing. Both algorithms work in unison, bolstering the accuracy of detection. Upon detection, the signal instantly switches to green, facilitating the expedited movement of emergency vehicles, even in high traffic conditions.