Abstract

With the rapid development of autonomous driving technology, real-time ranging of preceding vehicles has become a critical component to ensure driving safety. Although monocular vision-based ranging methods offer advantages of low cost and easy deployment, they still suffer from limited accuracy in long-distance targets, small objects, and complex traffic scenarios. To address these challenges, this paper improves the classic Smoke monocular 3D detection model by introducing a multi-scale feature enhancement module and a dynamic Gaussian heatmap generation mechanism, which effectively strengthen feature representation and stabilize depth estimation. Experiments conducted on the KITTI dataset demonstrate that the improved model outperforms the baseline in both 3D AP and BEV AP metrics, with a significant reduction in average ranging error, especially in small-target and long-distance scenarios. This study provides a feasible improvement strategy for monocular vision-based ranging in complex traffic environments and has important implications for enhancing the robustness of autonomous driving perception systems.