Autonomous Multi-Sensor Fusion Techniques for Environmental Perception in Self-Driving Vehicles

Assuming strong environmental awareness, the arrival of autonomous vehicles signals a revolutionary age in transportation. In order for autonomous vehicles to have trustworthy perception, this study explores the complex field of autonomous multi-sensor fusion methods. It highlights how different types of sensors, such as LiDAR, radar, and cameras, may work together to provide more complete and accurate environmental data. Examining the relative merits and practicality of early, late, and deep fusion, the article methodically investigates the several sensor fusion systems and how they fare in complicated and dynamic driving situations. The adaptive fusion technique, which improves detection and classification accuracy and optimizes computational resources by dynamically adjusting to ambient variables, is a novel component of this study. Intelligent data integration and interpretation from sensors is the goal of this strategy, which makes use of machine learning techniques like recurrent neural networks and convolutional neural networks. Results show that the suggested method outperforms the state-of-the-art when compared to well-known benchmarks in terms of accuracy, reliability, and computing efficiency. In addition, this study emphasizes the important problems and potential solutions related to multi-sensor fusion, including handling data that is not updated in real-time, overcoming bad weather, and guaranteeing the ability to analyze data in real-time. To realize self-driving vehicles' full potential and create smarter, more efficient transportation networks, the results show how crucial new fusion techniques are.