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.

翻译：在自动驾驶领域，感知系统至关重要，其通过解析传感器数据来理解环境，这是决策与规划的基础。确保这些感知系统的安全性是实现高级自动驾驶的根本，使我们能够放心地将驾驶与监控任务委托给机器。本报告旨在通过检视和总结基于视觉的系统的最新进展，以及自动驾驶中感知任务的评估指标，来提升感知系统的安全性。报告同时强调了当前该领域研究所取得的重大成就与公认的挑战。本项目侧重于通过基于深度的感知与计算机视觉技术，增强自动驾驶机器人的理解与导航能力。具体而言，它探讨了如何利用现有的检测与跟踪算法在未知的二维地图中实现更优的导航，并在此基础上，基于深度的感知如何能够增强轮式机器人的导航能力，从而提升自动驾驶的感知水平。