Advancing Autonomous Driving Perception: Analysis of Sensor Fusion and Computer Vision Techniques

TL;DR

This paper reviews recent advancements in vision-based perception systems for autonomous driving, emphasizing sensor fusion, depth perception, and computer vision techniques to improve safety and navigation in self-driving vehicles.

Contribution

It provides a comprehensive analysis of current perception methods, highlighting how depth perception and sensor fusion enhance autonomous vehicle navigation and safety.

Findings

Depth-based perception improves navigation accuracy.

Sensor fusion enhances perception robustness.

Current challenges include environmental variability.

Abstract

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.