Autonomous driving perception models are typically composed of multiple functional modules that interact through complex relationships to accomplish environment understanding. However, perception models are predominantly optimized as a black box through end-to-end training, lacking independent evaluation of functional modules, which poses difficulties for interpretability and optimization. Pioneering in the issue, we propose an evaluation method based on feature map analysis to gauge the convergence of model, thereby assessing functional modules' training maturity. We construct a quantitative metric named as the Feature Map Convergence Score (FMCS) and develop Feature Map Convergence Evaluation Network (FMCE-Net) to measure and predict the convergence degree of models respectively. FMCE-Net achieves remarkable predictive accuracy for FMCS across multiple image classification experiments, validating the efficacy and robustness of the introduced approach. To the best of our knowledge, this is the first independent evaluation method for functional modules, offering a new paradigm for the training assessment towards perception models.

翻译：自动驾驶感知模型通常由多个功能模块组成，这些模块通过复杂关系交互完成环境理解。然而，现有感知模型大多通过端到端训练以黑箱方式优化，缺乏对功能模块的独立评估，这给模型的可解释性与优化带来困难。针对这一开创性问题，我们提出一种基于特征图分析的评估方法，用于衡量模型收敛性，从而评估功能模块的训练成熟度。我们构建了名为特征图收敛分数（FMCS）的量化指标，并开发了特征图收敛评估网络（FMCE-Net），分别用于测量和预测模型的收敛程度。在多组图像分类实验中，FMCE-Net对FMCS实现了卓越的预测精度，验证了所提方法的有效性与鲁棒性。据我们所知，这是首个针对功能模块的独立评估方法，为感知模型的训练评估提供了新范式。