Decoupled Functional Evaluation of Autonomous Driving Models via Feature Map Quality Scoring
End-to-end models are emerging as the mainstream in autonomous driving perception and planning. However, the lack of explicit supervision signals for intermediate functional modules leads to opaque operational mechanisms and limited interpretability, making it challenging for traditional methods to independently evaluate and train these modules. Pioneering in the issue, this study builds upon the feature map-truth representation similarity-based evaluation framework and proposes an independent evaluation method based on Feature Map Convergence Score (FMCS). A Dual-Granularity Dynamic Weighted Scoring System (DG-DWSS) is constructed, formulating a unified quantitative metric - Feature Map Quality Score - to enable comprehensive evaluation of the quality of feature maps generated by functional modules. A CLIP-based Feature Map Quality Evaluation Network (CLIP-FMQE-Net) is further developed, combining feature-truth encoders and quality score prediction heads to enable real-time quality analysis of feature maps generated by functional modules. Experimental results on the NuScenes dataset demonstrate that integrating our evaluation module into the training improves 3D object detection performance, achieving a 3.89 percent gain in NDS. These results verify the effectiveness of our method in enhancing feature representation quality and overall model performance.
Code (0)
등록된 구현이 없습니다.
Tasks
3D Object DetectionAutonomous DrivingSimilar Papers 제목 키워드 기반
CBDES MoE: Hierarchically Decoupled Mixture-of-Experts for Functional Modules in Autonomous Driving
Bird's Eye View (BEV) perception systems based on multi-sensor feature fusion have become a fundamental cornerstone for end-to-end autonomous driving. However, existing multi-modal BEV methods commonly suffer from limite…
3D Object DetectionAutonomous DrivingEvaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles
In recent years, many deep learning models have been adopted in autonomous driving. At the same time, these models introduce new vulnerabilities that may compromise the safety of autonomous vehicles. Specifically, recent…
3D Object DetectionAutonomous DrivingAutonomous VehiclesDeep Learning+2TRIG: Trajectory-Rig Decoupled Metric Geometry Learning
Vision-centric autonomous driving requires accurate metric geometry and ego-motion estimation from synchronized multi-camera observations. Recent visual geometry models show strong performance in pose estimation, depth p…
Autonomous Driving3D ReconstructionPose EstimationFeature Map Convergence Evaluation for Functional Module
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…
Autonomous Drivingimage-classificationImage ClassificationVLDrive: Vision-Augmented Lightweight MLLMs for Efficient Language-grounded Autonomous Driving
Recent advancements in language-grounded autonomous driving have been significantly promoted by the sophisticated cognition and reasoning capabilities of large language models (LLMs). However, current LLM-based approache…
Autonomous Driving