Brain-Inspired Traffic Incident Detection for Effective Communication

Abstract

Traffic accidents are a significant global concern, leading to numerous fatalities and injuries, especially in remote areas where rapid detection and reporting to emergency services are crucial. This paper presents a novel application for identifying traffic accidents using brain-inspired neural network approaches such as spiking neural networks (SNNs) and Legendre Memory Unit-based recurrent neural networks (RNNs). These brain-inspired systems operate on a neuromorphic computing architecture with energy-efficient abilities including spatiotemporal processing. These brain-based architectures are briefly discussed and contrasted with conventional Artificial Neural Networks (ANNs) to produce metrics on the performance of the models for an in-depth comparison, using an accident detection data set generated from CCTV footage. This research also recommends the use of Predictive Quality of Service (PQoS) using different machine learning models to effectively communicate the findings of an accident scenario to emergency services. This is done by evaluating the effectiveness of the methods to locate nodal points in a V2X data set. Object detection methods like YOLOv5 have been briefly investigated for vehicle detection in an accident scenario in order to relay extra information about an incident and identify potential improvement areas. The main motivation for this method of accident detection and communication is the decrease of fatalities, especially in distant locations where quick detection and reporting of emergency services is crucial. This paper illustrates the relevance of the work in 5G and future 6G use cases, the role of AI and ML in visual image processing, and the social significance of this research after demonstrating reliable performance utilising a variety of machine learning algorithms in both accident detection and predicting QoS parameters.