Visual Dominance and Emerging Multimodal Approaches in Distracted Driving Detection: A Review of Machine Learning Techniques

• URL: http://arxiv.org/abs/2505.01973v1
• Date: Sun, 04 May 2025 02:51:00 GMT
• Title: Visual Dominance and Emerging Multimodal Approaches in Distracted Driving Detection: A Review of Machine Learning Techniques
• Authors: Anthony Dontoh, Stephanie Ivey, Logan Sirbaugh, Andrews Danyo, Armstrong Aboah,
• Abstract summary: Distracted driving continues to be a significant cause of road traffic injuries and fatalities worldwide.<n>Recent developments in machine learning (ML) and deep learning (DL) have primarily focused on visual data to detect distraction.<n>This systematic review assesses 74 studies that utilize ML/DL techniques for distracted driving detection across visual, sensor-based, multimodal, and emerging modalities.
• Score: 3.378738346115004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Distracted driving continues to be a significant cause of road traffic injuries and fatalities worldwide, even with advancements in driver monitoring technologies. Recent developments in machine learning (ML) and deep learning (DL) have primarily focused on visual data to detect distraction, often neglecting the complex, multimodal nature of driver behavior. This systematic review assesses 74 peer-reviewed studies from 2019 to 2024 that utilize ML/DL techniques for distracted driving detection across visual, sensor-based, multimodal, and emerging modalities. The review highlights a significant prevalence of visual-only models, particularly convolutional neural networks (CNNs) and temporal architectures, which achieve high accuracy but show limited generalizability in real-world scenarios. Sensor-based and physiological models provide complementary strengths by capturing internal states and vehicle dynamics, while emerging techniques, such as auditory sensing and radio frequency (RF) methods, offer privacy-aware alternatives. Multimodal architecture consistently surpasses unimodal baselines, demonstrating enhanced robustness, context awareness, and scalability by integrating diverse data streams. These findings emphasize the need to move beyond visual-only approaches and adopt multimodal systems that combine visual, physiological, and vehicular cues while keeping in checking the need to balance computational requirements. Future research should focus on developing lightweight, deployable multimodal frameworks, incorporating personalized baselines, and establishing cross-modality benchmarks to ensure real-world reliability in advanced driver assistance systems (ADAS) and road safety interventions.

Related papers

• Contrastive Learning-Driven Traffic Sign Perception: Multi-Modal Fusion of Text and Vision [2.0720154517628417]
  We propose a novel framework combining open-vocabulary detection and cross-modal learning.<n>For traffic sign detection, our NanoVerse YOLO model integrates a vision-language path aggregation network (RepVL-PAN) and an SPD-Conv module.<n>For traffic sign classification, we designed a Traffic Sign Recognition Multimodal Contrastive Learning model (TSR-MCL)<n>On the TT100K dataset, our method achieves a state-of-the-art 78.4% mAP in the long-tail detection task for all-class recognition.
  arXiv  Detail & Related papers  (2025-07-31T08:23:30Z)
• Lane-Wise Highway Anomaly Detection [8.086502588472783]
  This paper proposes a scalable and interpretable framework for lane-wise highway traffic anomaly detection.<n>Unlike traditional sensor-dependent methods, our approach uses AI-powered vision models to extract lane-specific features.<n>Our framework outperforms state-of-the-art methods in precision, recall, and F1-score.
  arXiv  Detail & Related papers  (2025-05-05T12:32:23Z)
• Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework [62.47416496137193]
  We propose a surveillance video assisted federated digital twin (SV-FDT) framework to empower ITSs with pedestrians and vehicles in-the-loop.<n>The architecture consists of three layers: (i) the end layer, which collects traffic surveillance videos from multiple sources; (ii) the edge layer, responsible for semantic segmentation-based visual understanding, twin agent-based interaction modeling, and local digital twin system (LDTS) creation in local regions; and (iii) the cloud layer, which integrates LDTSs across different regions to construct a global DT model in realtime.
  arXiv  Detail & Related papers  (2025-03-06T07:36:06Z)
• A Survey of World Models for Autonomous Driving [63.33363128964687]
  Recent breakthroughs in autonomous driving have been propelled by advances in robust world modeling.<n>This paper systematically reviews recent advances in world models for autonomous driving.
  arXiv  Detail & Related papers  (2025-01-20T04:00:02Z)
• CRASH: Crash Recognition and Anticipation System Harnessing with Context-Aware and Temporal Focus Attentions [13.981748780317329]
  Accurately and promptly predicting accidents among surrounding traffic agents from camera footage is crucial for the safety of autonomous vehicles (AVs) This study introduces a novel accident anticipation framework for AVs, termed CRASH. It seamlessly integrates five components: object detector, feature extractor, object-aware module, context-aware module, and multi-layer fusion. Our model surpasses existing top baselines in critical evaluation metrics like Average Precision (AP) and mean Time-To-Accident (mTTA)
  arXiv  Detail & Related papers  (2024-07-25T04:12:49Z)
• Probing Multimodal LLMs as World Models for Driving [72.18727651074563]
  We look at the application of Multimodal Large Language Models (MLLMs) in autonomous driving. Despite advances in models like GPT-4o, their performance in complex driving environments remains largely unexplored.
  arXiv  Detail & Related papers  (2024-05-09T17:52:42Z)
• MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild [81.32127423981426]
  Multimodal emotion recognition based on audio and video data is important for real-world applications. Recent methods have focused on exploiting advances of self-supervised learning (SSL) for pre-training of strong multimodal encoders. We propose a different perspective on the problem and investigate the advancement of multimodal DFER performance by adapting SSL-pre-trained disjoint unimodal encoders.
  arXiv  Detail & Related papers  (2024-04-13T13:39:26Z)
• Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives [56.2139730920855]
  We present a systematic analysis of MM-VUFMs specifically designed for road scenes. Our objective is to provide a comprehensive overview of common practices, referring to task-specific models, unified multi-modal models, unified multi-task models, and foundation model prompting techniques. We provide insights into key challenges and future trends, such as closed-loop driving systems, interpretability, embodied driving agents, and world models.
  arXiv  Detail & Related papers  (2024-02-05T12:47:09Z)
• Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction [69.29802752614677]
  RouteFormer is a novel ego-trajectory prediction network combining GPS data, environmental context, and the driver's field-of-view.<n>To tackle data scarcity and enhance diversity, we introduce GEM, a dataset of urban driving scenarios enriched with synchronized driver field-of-view and gaze data.
  arXiv  Detail & Related papers  (2023-12-13T23:06:30Z)
• AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking Dataset for Assistive Driving Perception [26.84439405241999]
  We present an AssIstive Driving pErception dataset (AIDE) that considers context information both inside and outside the vehicle. AIDE facilitates holistic driver monitoring through three distinctive characteristics. Two fusion strategies are introduced to give new insights into learning effective multi-stream/modal representations.
  arXiv  Detail & Related papers  (2023-07-26T03:12:05Z)
• A Novel Driver Distraction Behavior Detection Method Based on Self-supervised Learning with Masked Image Modeling [5.1680226874942985]
  Driver distraction causes a significant number of traffic accidents every year, resulting in economic losses and casualties. Driver distraction detection primarily relies on traditional convolutional neural networks (CNN) and supervised learning methods. This paper proposes a new self-supervised learning method based on masked image modeling for driver distraction behavior detection.
  arXiv  Detail & Related papers  (2023-06-01T10:53:32Z)
• Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification [168.67190934250868]
  Federated learning empowered connected autonomous vehicle (FLCAV) has been proposed. FLCAV preserves privacy while reducing communication and annotation costs. It is challenging to determine the network resources and road sensor poses for multi-stage training.
  arXiv  Detail & Related papers  (2022-06-03T23:55:45Z)
• Artificial Intelligence Enabled Traffic Monitoring System [3.085453921856008]
  This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs.
  arXiv  Detail & Related papers  (2020-10-02T22:28:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.