Key Performance and Application Prospects of Image Depth Estimation Technology in Autonomous Driving Systems
<p>Guo Xin</p> · Academic Journal of Engineering and Technology Science · 2024
This paper explores the key performance metrics and application prospects of image depth estimation technology in autonomous driving systems. Beginning with an examination of the fundamentals of image depth estimation, including monocular and stereo approaches, the paper delves into performance metrics such as accuracy, robustness, real-time processing, and comparison with other sensor modalities. It then explores the diverse applications of image depth estimation in autonomous driving, including object detection, collision avoidance, path planning, and localization. Finally, the paper discusses future prospects and challenges, highlighting advancements in depth estimation algorithms, integration with emerging technologies, and addressing challenges such as occlusion and scalability. This comprehensive analysis underscores the pivotal role of image depth estimation in shaping the future of transportation and mobility.