The Application of Depthₐnythingᵥ2 in Autonomous Driving: Depth Estimation

Depthₐnythingᵥ2 is a deep learning-based depth estimation model that can effectively capture long-range spatial dependencies, thereby enhancing the accuracy and generalization ability of image depth estimation. Depth estimation is a crucial element for precise environmental perception in autonomous driving systems, enabling vehicles to identify obstacles, predict routes, and make decisions. The speed and accuracy of depth estimation directly impact the safety and adaptability of autonomous driving systems. In real-world applications, autonomous vehicles must navigate through various weather conditions, such as rain, fog, and snow, which can significantly degrade the performance of depth estimation algorithms. Additionally, the presence of diverse objects, including pedestrians, cyclists, and other vehicles, further complicates the task. To address these challenges, Depthₐnythingᵥ2 incorporates advanced techniques such as multi-scale feature fusion and attention mechanisms to improve robustness. Based on an extensive literature review, this paper explores the depth estimation capabilities of DepthAnythingV2, evaluates its performance in terms of speed and accuracy in complex environments, and provides an outlook on the remaining issues to be addressed and future research directions. Future work may focus on further optimizing the model architecture and exploring real-time implementation strategies to enhance its practicality in autonomous driving applications.