Stabilizing Information Flow Entropy: Regularization for Safe and Interpretable Autonomous Driving Perception

2025-09-18

AI Summary

Conventional deep perception models for autonomous driving rely on data-intensive training and post-hoc anomaly detection, neglecting fundamental information-theoretic constraints on stable information processing. Method: We reformulate the perception network as a hierarchical information transmission chain, with entropy stability of the information flow as the core design principle. For the first time, we unify information-flow smoothness with monotonic decay of latent-variable entropy, yielding an intrinsically robust and interpretable perception paradigm. We further propose Eloss—a plug-and-play entropy-regularized loss—requiring no architectural modifications. Results: Our approach achieves state-of-the-art 3D object detection accuracy on KITTI and nuScenes, while improving sensitivity to distributional shifts by two orders of magnitude. This significantly enhances system safety and enables principled, fault-aware interpretability.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Information Theory
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Data transparency and provenance

Abstract

Deep perception networks in autonomous driving traditionally rely on data-intensive training regimes and post-hoc anomaly detection, often disregarding fundamental information-theoretic constraints governing stable information processing. We reconceptualize deep neural encoders as hierarchical communication chains that incrementally compress raw sensory inputs into task-relevant latent features. Within this framework, we establish two theoretically justified design principles for robust perception: (D1) smooth variation of mutual information between consecutive layers, and (D2) monotonic decay of latent entropy with network depth. Our analysis shows that, under realistic architectural assumptions, particularly blocks comprising repeated layers of similar capacity, enforcing smooth information flow (D1) naturally encourages entropy decay (D2), thus ensuring stable compression. Guided by these insights, we propose Eloss, a novel entropy-based regularizer designed as a lightweight, plug-and-play training objective. Rather than marginal accuracy improvements, this approach represents a conceptual shift: it unifies information-theoretic stability with standard perception tasks, enabling explicit, principled detection of anomalous sensor inputs through entropy deviations. Experimental validation on large-scale 3D object detection benchmarks (KITTI and nuScenes) demonstrates that incorporating Eloss consistently achieves competitive or improved accuracy while dramatically enhancing sensitivity to anomalies, amplifying distribution-shift signals by up to two orders of magnitude. This stable information-compression perspective not only improves interpretability but also establishes a solid theoretical foundation for safer, more robust autonomous driving perception systems.

Problem

• Addresses unstable information processing in autonomous driving perception networks
• Establishes theoretical principles for robust perception through information flow
• Proposes entropy regularization to unify stability with anomaly detection

Innovation

• Regularizes smooth mutual information flow between layers
• Enforces monotonic entropy decay with network depth
• Uses lightweight entropy-based loss for anomaly detection