  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.
