Abstract
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.
Abstract (translated)
自动驾驶感知模型通常由多个功能模块组成，通过复杂的关系来理解环境。然而，通过端到端的训练优化感知模型，缺乏对功能模块的独立评估，这使得模型的可解释性和优化存在困难。在这个问题上，我们提出了基于特征图分析的评估方法来衡量模型的收敛，从而评估功能模块的训练成熟度。我们构建了一个名为特征图收敛分数（FMCS）的定量度量，并开发了特征图收敛评估网络（FMCE-Net）来分别测量和预测模型的收敛程度。在多个图像分类实验中，FMCE-Net在FMCS上的预测准确性非常显著，验证了所引入方法的有效性和鲁棒性。据我们所知，这是第一个关于功能模块的独立评估方法，为感知模型的训练评估提供了一个新的范式。
URL
https://arxiv.org/abs/2405.04041
