通过特征相似度评估自动驾驶感知模块训练成熟度
Unveiling the Black Box: Independent Functional Module Evaluation for Bird's-Eye-View Perception Model
- 将模块特征图与真实标签在统一语义空间比对，量化相似性
- 相似度得分与鸟瞰图指标相关性高达0.9387，验证评估可靠性
- 适合关注模型可解释性与开发效率的自动驾驶研发人员
端到端模型正成为自动驾驶感知的主流，但其内部机制难以精细拆解，导致开发效率下降并阻碍信任建立。本文提出鸟瞰图感知模型独立功能模块评估框架（BEV-IFME），通过在统一语义表示空间中对比模块特征图与真实标签，量化其相似性，从而评估各功能模块的训练成熟度。该框架核心为两阶段对齐自编码器，实现特征编码与表示对齐，确保关键信息保留与特征结构一致性。评估指标相似度得分与鸟瞰图指标平均相关系数达0.9387，证明该框架具备可靠评估能力。
原文摘要 · Abstract (English)
End-to-end models are emerging as the mainstream in autonomous driving perception. However, the inability to meticulously deconstruct their internal mechanisms results in diminished development efficacy and impedes the establishment of trust. Pioneering in the issue, we present the Independent Functional Module Evaluation for Bird's-Eye-View Perception Model (BEV-IFME), a novel framework that juxtaposes the module's feature maps against Ground Truth within a unified semantic Representation Space to quantify their similarity, thereby assessing the training maturity of individual functional modules. The core of the framework lies in the process of feature map encoding and representation aligning, facilitated by our proposed two-stage Alignment AutoEncoder, which ensures the preservation of salient information and the consistency of feature structure. The metric for evaluating the training maturity of functional modules, Similarity Score, demonstrates a robust positive correlation with BEV metrics, with an average correlation coefficient of 0.9387, attesting to the framework's reliability for assessment purposes.
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