Lane and Obstacle Detection System for Autonomous Vehicles using Computer Vision

This research study proposes a multi-modal sensor fusion system combined with a hybrid Artificial Intelligence (AI) model. The data is integrated from cameras, LiDAR, and radar sensors, using an advanced deep learning architecture that efficiently processes and fuses this information. The proposed model incorporates both, traditional computer vision techniques and advanced neural networks, trained on a diverse dataset of real-world scenarios. The aim of the model is to implement an attention mechanism for focusing on critical areas of the environment, and reducing computational load. Further, the model also aims to provide a robust error-handling system to enable continuous online learning capabilities with adaptivity to new scenarios situations. The results of this study show that by combining strengths of various state-of-the-art techniques, the proposed approach creates a more comprehensive and reliable system for autonomous vehicle navigation.