Automatic Identification of Emergency Sounds for Self-Driving Cars

Abstract

As the development of Self-Driving Cars (SDCs) advances, one important feature that requires attention is their ability to sense and respond to emergencies on the road. Drivers of emergency vehicles prioritize speed over safety during emergencies to save lives, potentially leading to accidents. However, manual cars sometimes may not understand the urgency of the situation, further increasing the risk of collisions. In such cases, self-driving cars offer a better solution to replace manual cars and minimize road accidents. These cars with emergency sound detections offer a level of responsiveness, accuracy, and consistency that surpasses manual cars, contributing to improved road safety and efficiency in emergencies. Therefore, the key objective of the research is to improve the listening capability of self-driving cars to detect emergency vehicles with the help of a hybrid feature extraction technique. The suggested technique leverages a combination of Complex Morlet Wavelet and Co-occurrence Matrix to obtain statistical features from the emergency sounds. The proposed technique can work with the input length of 1.2 seconds of raw waveforms. This research work investigates that self-driving cars can accurately identify emergency vehicles by examining the distinctive emergency sound patterns emitted by the emergency vehicle with the highest accuracy of 94%. At the same time, the proposed technique reduces the computational cost by 20 – 40 milliseconds when compared with other techniques. The result of this work not only provides better accuracy but also reduces detection time, which is a crucial requirement for real-time applications such as self-driving cars.