Adaptive Neural Networks for Intelligent Data-Driven Development

2025-02-14

AI Summary

To address the challenges of unknown-object recognition and continual model evolution in safety-critical autonomous driving scenarios, this paper proposes a perception system development framework supporting real-time class-incremental learning and online adaptation. The core innovation is a unified adaptive pipeline comprising: (1) an extensible network architecture enabling zero-forgetting incremental expansion; (2) a retraining-free dynamic out-of-distribution detection mechanism ensuring open-set robustness; and (3) retrieval-based data augmentation enhancing safety-critical deployment reliability. Evaluated on CARLA and nuScenes benchmarks, the framework achieves 98.2% accuracy on known classes and 91.7% detection rate for unknown classes. Integrating new classes takes under three minutes, with less than 0.3% performance degradation on prior tasks—marking the first unified optimization of zero-forgetting incremental learning and open-set recognition.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Integrating machine learning into autonomous driving lifecycle.
• Adapting neural networks to recognize novel instances.
• Ensuring reliable deployment in dynamic environments.

Innovation

• adaptive neural network architecture
• dynamic OoD detection component
• retrieval-based data augmentation