# Unveiling the Black Box: Independent Functional Module Evaluation for Bird's-Eye-View Perception Model

arXiv: https://arxiv.org/abs/2409.11969 · HTML: https://mlsanity.com/paper/2409.11969/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 30 references that are in the map.
- First posted on arXiv 18 Sep 2024.

## Builds on (most cited first, 26 of 26)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection](https://mlsanity.com/paper/2206.10092/) (2022)
- [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023)
- [DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model](https://mlsanity.com/paper/2310.01412/) (2023)
- [BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision](https://mlsanity.com/paper/2211.10439/) (2022)
- [Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe](https://mlsanity.com/paper/2209.05324/) (2022)
- [Towards Visually Explaining Variational Autoencoders](https://mlsanity.com/paper/1911.07389/) (2019)
- [MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization](https://mlsanity.com/paper/1811.10247/) (2018)
- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular Backbones](https://mlsanity.com/paper/2207.12716/) (2022)
- [LGD: Label-guided Self-distillation for Object Detection](https://mlsanity.com/paper/2109.11496/) (2021)
- [LabelEnc: A New Intermediate Supervision Method for Object Detection](https://mlsanity.com/paper/2007.03282/) (2020)
- [CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow](https://mlsanity.com/paper/2403.08919/) (2024)
- [Regularizing Deep Networks by Modeling and Predicting Label Structure](https://mlsanity.com/paper/1804.02009/) (2018)
- [LabelDistill: Label-guided Cross-modal Knowledge Distillation for Camera-based 3D Object Detection](https://mlsanity.com/paper/2407.10164/) (2024)
- [Label-guided Attention Distillation for Lane Segmentation](https://mlsanity.com/paper/2304.01636/) (2023)
- [Exploring the Causality of End-to-End Autonomous Driving](https://mlsanity.com/paper/2407.06546/) (2024)
- [Label-Guided Auxiliary Training Improves 3D Object Detector](https://mlsanity.com/paper/2207.11753/) (2022)
- [Exploring Latent Pathways: Enhancing the Interpretability of Autonomous Driving with a Variational Autoencoder](https://mlsanity.com/paper/2404.01750/) (2024)
- [Feature Map Convergence Evaluation for Functional Module](https://mlsanity.com/paper/2405.04041/) (2024)
- [Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving](https://mlsanity.com/paper/2407.12491/) (2024)

## Built on by (most cited first, 1 of 1)

- [Decoupled Functional Evaluation of Autonomous Driving Models via Feature Map Quality Scoring](https://mlsanity.com/paper/2508.07552/) (2025)

## Abstract (arXiv)

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.