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Adaptive Neural Networks for Intelligent Data-Driven Development

Youssef Shoeb, Azarm Nowzad, Hanno Gottschalk

TL;DR

This work tackles the challenge of safely deploying data-driven perception in autonomous driving by addressing semantic shifts and OoD objects through an adaptive, data-driven loop. It introduces a modular pipeline with (i) a scalable extension mechanism for new classes using probabilistic, GMM-based segmentation, (ii) a likelihood-ratio OoD detector that adapts to growing in-distribution classes without retraining, and (iii) a retrieval-based data augmentation strategy that sources informative samples from unlabeled data, guided by both text and image queries. Key contributions include a parameter-efficient continual learning scheme that freezes the backbone and adds lightweight adaptive heads with a contrastive objective, a DinoV2-based segmentation backbone, and a retrieval framework with CLIP-based and image-based search plus online clustering. The approach demonstrates competitive OoD detection with far fewer parameters than prior methods, effective retrieval performance, and feasible continual learning dynamics, enabling real-world, safety-conscious adaptation of perception systems. The work has practical impact for automotive development by enabling continuous, user-guided evolution of perception models with controlled computational overhead and memory usage.

Abstract

Advances in machine learning methods for computer vision tasks have led to their consideration for safety-critical applications like autonomous driving. However, effectively integrating these methods into the automotive development lifecycle remains challenging. Since the performance of machine learning algorithms relies heavily on the training data provided, the data and model development lifecycle play a key role in successfully integrating these components into the product development lifecycle. Existing models frequently encounter difficulties recognizing or adapting to novel instances not present in the original training dataset. This poses a significant risk for reliable deployment in dynamic environments. To address this challenge, we propose an adaptive neural network architecture and an iterative development framework that enables users to efficiently incorporate previously unknown objects into the current perception system. Our approach builds on continuous learning, emphasizing the necessity of dynamic updates to reflect real-world deployment conditions. Specifically, we introduce a pipeline with three key components: (1) a scalable network extension strategy to integrate new classes while preserving existing performance, (2) a dynamic OoD detection component that requires no additional retraining for newly added classes, and (3) a retrieval-based data augmentation process tailored for safety-critical deployments. The integration of these components establishes a pragmatic and adaptive pipeline for the continuous evolution of perception systems in the context of autonomous driving.

Adaptive Neural Networks for Intelligent Data-Driven Development

TL;DR

This work tackles the challenge of safely deploying data-driven perception in autonomous driving by addressing semantic shifts and OoD objects through an adaptive, data-driven loop. It introduces a modular pipeline with (i) a scalable extension mechanism for new classes using probabilistic, GMM-based segmentation, (ii) a likelihood-ratio OoD detector that adapts to growing in-distribution classes without retraining, and (iii) a retrieval-based data augmentation strategy that sources informative samples from unlabeled data, guided by both text and image queries. Key contributions include a parameter-efficient continual learning scheme that freezes the backbone and adds lightweight adaptive heads with a contrastive objective, a DinoV2-based segmentation backbone, and a retrieval framework with CLIP-based and image-based search plus online clustering. The approach demonstrates competitive OoD detection with far fewer parameters than prior methods, effective retrieval performance, and feasible continual learning dynamics, enabling real-world, safety-conscious adaptation of perception systems. The work has practical impact for automotive development by enabling continuous, user-guided evolution of perception models with controlled computational overhead and memory usage.

Abstract

Advances in machine learning methods for computer vision tasks have led to their consideration for safety-critical applications like autonomous driving. However, effectively integrating these methods into the automotive development lifecycle remains challenging. Since the performance of machine learning algorithms relies heavily on the training data provided, the data and model development lifecycle play a key role in successfully integrating these components into the product development lifecycle. Existing models frequently encounter difficulties recognizing or adapting to novel instances not present in the original training dataset. This poses a significant risk for reliable deployment in dynamic environments. To address this challenge, we propose an adaptive neural network architecture and an iterative development framework that enables users to efficiently incorporate previously unknown objects into the current perception system. Our approach builds on continuous learning, emphasizing the necessity of dynamic updates to reflect real-world deployment conditions. Specifically, we introduce a pipeline with three key components: (1) a scalable network extension strategy to integrate new classes while preserving existing performance, (2) a dynamic OoD detection component that requires no additional retraining for newly added classes, and (3) a retrieval-based data augmentation process tailored for safety-critical deployments. The integration of these components establishes a pragmatic and adaptive pipeline for the continuous evolution of perception systems in the context of autonomous driving.

Paper Structure

This paper contains 15 sections, 7 equations, 3 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. SMART DATA LOOP FOR LEARNING NEW CLASSES
  3. Overview
  4. Initial Segmentation Network
  5. OoD Detection
  6. Retrieval
  7. Continual Learning
  8. EVALUATIONS
  9. Experimental Setup
  10. Metrics
  11. Results
  12. Inlier and OoD Detection Performance
  13. Retrieval Performance
  14. Continual Learning
  15. CONCLUSIONS AND FUTURE WORK

Figures (3)

  • Figure 1: V-Model structure of the Data Engine, illustrating the key stages of data collection, model development, deployment, and shadow testing/error detection within a fully data-driven machine learning methodology.
  • Figure 2: Development cycle for a dynamic perception system.
  • Figure 3: Adaptive neural network architecture with an OoD detection mechanism.