Decoupled Functional Evaluation of Autonomous Driving Models via Feature Map Quality Scoring

TL;DR

This paper introduces a new evaluation framework for autonomous driving models that assesses feature map quality independently, leading to improved detection performance and better interpretability of model modules.

Contribution

It proposes the Feature Map Convergence Score and a CLIP-based evaluation network for real-time, independent assessment of feature map quality in autonomous driving models.

Findings

Improved 3D object detection NDS by 3.89% using the evaluation method.

Demonstrated enhanced feature representation quality and model performance.

Validated effectiveness on the NuScenes dataset.

Abstract

End-to-end models are emerging as the mainstream in autonomous driving perception and planning. However, the lack of explicit supervision signals for intermediate functional modules leads to opaque operational mechanisms and limited interpretability, making it challenging for traditional methods to independently evaluate and train these modules. Pioneering in the issue, this study builds upon the feature map-truth representation similarity-based evaluation framework and proposes an independent evaluation method based on Feature Map Convergence Score (FMCS). A Dual-Granularity Dynamic Weighted Scoring System (DG-DWSS) is constructed, formulating a unified quantitative metric - Feature Map Quality Score - to enable comprehensive evaluation of the quality of feature maps generated by functional modules. A CLIP-based Feature Map Quality Evaluation Network (CLIP-FMQE-Net) is further developed, combining feature-truth encoders and quality score prediction heads to enable real-time quality analysis of feature maps generated by functional modules. Experimental results on the NuScenes dataset demonstrate that integrating our evaluation module into the training improves 3D object detection performance, achieving a 3.89 percent gain in NDS. These results verify the effectiveness of our method in enhancing feature representation quality and overall model performance.