推进自动驾驶感知：传感器融合与计算机视觉技术分析
Advancing Autonomous Driving Perception: Analysis of Sensor Fusion and Computer Vision Techniques
AI总结：
本报告通过分析基于视觉的系统与感知指标的最新进展，探讨了利用基于深度的感知和计算机视觉技术增强轮式自动驾驶机器人未知2D地图导航能力的方法，旨在提升自动驾驶感知系统的安全性与性能。
摘要：
在自动驾驶中，感知系统至关重要，因为它们解释感官数据以理解环境，这对决策和规划是必不可少的。确保这些感知系统的安全性是实现高级别自动驾驶的基础，使我们能够放心地将驾驶和监控任务委托给机器。本报告旨在通过审查和总结基于视觉的系统以及自动驾驶感知任务指标的最新进展，来提高感知系统的安全性。报告还强调了该领域当前研究所取得的重要成就和面临的公认挑战。本项目致力于通过基于深度的感知和计算机视觉技术，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和跟踪算法在未知2D地图中进行更好的导航，并在此基础上研究基于深度的感知如何增强轮式机器人的导航能力，以改善自动驾驶感知。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
