Object Detection for Self-Driving Car in Complex Traffic Scenarios

Abstract

The application of convolutional neural networks (CNNs) in particular has greatly enhanced the object detection capabilities of self-driving cars, because of recent advancements in artificial intelligence (AI). However, striking a balance in vehicular settings between high precision and fast processing continues to be a persistent challenge. Developing nations such as India, possessing the second-largest global population, introduce unique intricacies to road scenarios. Numerous challenges arise on Indian roads, such as unique vehicle kinds and a variety of traffic patterns, such as auto-rickshaws, which are only seen in India. This study presents the outcomes of evaluating the YOLOv8 models, which have demonstrated superior performance in Indian traffic conditions when compared to other existing YOLO models. The examination utilized the dataset, compiled from data collected in the cities of Bangalore and Hyderabad, as well as their surrounding areas. The investigation's findings demonstrate how well the YOLOv8 models work to address the unique problems that Indian road conditions present. This study advances the development of autonomous vehicles designed for intricate traffic situations such as those found on Indian Roads.