Advancing Autonomous Driving Perception: Analysis of Sensor Fusion and Computer Vision Techniques
This work addresses safety challenges in autonomous driving perception, but it is incremental as it builds on existing methods without introducing new paradigms.
The report analyzes sensor fusion and computer vision techniques to enhance safety in autonomous driving perception systems, focusing on improving navigation in unknown 2D maps using existing detection and tracking algorithms and depth-based perception.
In autonomous driving, perception systems are piv otal as they interpret sensory data to understand the envi ronment, which is essential for decision-making and planning. Ensuring the safety of these perception systems is fundamental for achieving high-level autonomy, allowing us to confidently delegate driving and monitoring tasks to machines. This re port aims to enhance the safety of perception systems by examining and summarizing the latest advancements in vision based systems, and metrics for perception tasks in autonomous driving. The report also underscores significant achievements and recognized challenges faced by current research in this field. This project focuses on enhancing the understanding and navigation capabilities of self-driving robots through depth based perception and computer vision techniques. Specifically, it explores how we can perform better navigation into unknown map 2D map with existing detection and tracking algorithms and on top of that how depth based perception can enhance the navigation capabilities of the wheel based bots to improve autonomous driving perception.