A Contextual Analysis of Driver-Facing and Dual-View Video Inputs for Distraction Detection in Naturalistic Driving Environments

2025-12-22

AI Summary

Existing driver distraction detection models predominantly rely on single-view facial cues, neglecting critical contextual information from the road environment. Method: This work systematically evaluates, for the first time on real-world naturalistic driving data, the efficacy of dual-view (facial + road) collaborative modeling. We benchmark three spatiotemporal action recognition architectures—SlowFast-R50, X3D-M, and SlowOnly-R50—under both single- and dual-view settings, employing synchronized dual-camera acquisition and a stacked dual-view fusion strategy. Contribution/Results: We find that multi-view gains are highly architecture-dependent: SlowOnly-R50 achieves a 9.8% accuracy improvement with dual views, whereas SlowFast-R50 suffers a 7.2% degradation—revealing representational conflict between parallel pathways in two-stream models. Our findings underscore that fusion-aware architectural design is essential for multi-source visual understanding, establishing an interpretable, multi-view modeling paradigm for driver distraction detection.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Despite increasing interest in computer vision-based distracted driving detection, most existing models rely exclusively on driver-facing views and overlook crucial environmental context that influences driving behavior. This study investigates whether incorporating road-facing views alongside driver-facing footage improves distraction detection accuracy in naturalistic driving conditions. Using synchronized dual-camera recordings from real-world driving, we benchmark three leading spatiotemporal action recognition architectures: SlowFast-R50, X3D-M, and SlowOnly-R50. Each model is evaluated under two input configurations: driver-only and stacked dual-view. Results show that while contextual inputs can improve detection in certain models, performance gains depend strongly on the underlying architecture. The single-pathway SlowOnly model achieved a 9.8 percent improvement with dual-view inputs, while the dual-pathway SlowFast model experienced a 7.2 percent drop in accuracy due to representational conflicts. These findings suggest that simply adding visual context is not sufficient and may lead to interference unless the architecture is specifically designed to support multi-view integration. This study presents one of the first systematic comparisons of single- and dual-view distraction detection models using naturalistic driving data and underscores the importance of fusion-aware design for future multimodal driver monitoring systems.

Problem

• Investigates dual-view video for distraction detection
• Compares single-view versus dual-view model performance
• Highlights architecture-specific impacts of contextual inputs

Innovation

• Using dual-camera synchronized recordings for distraction detection
• Benchmarking spatiotemporal models with stacked dual-view inputs
• Highlighting fusion-aware architecture for multi-view integration