提升自动驾驶感知安全性，融合视觉与深度信息实现更可靠导航
Advancing Autonomous Driving Perception: Analysis of Sensor Fusion and Computer Vision Techniques
- 结合视觉与深度感知，优化未知环境下的2D地图导航
- 现有检测追踪算法基础上，深度信息显著增强机器人路径规划能力
- 适合关注自动驾驶感知安全与多传感器融合的研究者
在自动驾驶中，感知系统至关重要，它负责解析感官数据以理解环境，是决策与规划的基础。确保感知系统的安全性是实现高级自动驾驶的关键，使我们能放心将驾驶与监控任务交由机器完成。本报告旨在通过分析和总结基于视觉的感知系统及自动驾驶感知任务的评估指标，提升感知系统的安全性。报告还强调了当前研究中的重要进展与公认挑战。项目聚焦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对环境的理解与导航能力。具体探讨如何利用现有检测与追踪算法，在未知二维地图中实现更优导航，并进一步研究深度感知如何提升轮式机器人的自主导航性能，从而改善自动驾驶感知能力。
原文摘要 · Abstract (English)
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.
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