An XGBoost approach to detect driver visual distraction based on vehicle dynamics

Guo, Youguang; Ding, Hua; ShangGuan, Xingxing

doi:10.1080/15389588.2023.2218513

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…Ahangari et al [14] achieved an accuracy of 76.5% on an independent test set collected from a driving simulator using a random forest (RF) classifier. Gao et al [15] used time-window and fast Fourier transform methods to obtain the vehicle dynamics parameters required for the model and achieved 85.68% interference discrimination accuracy using the XGBoost model. However, these methods usually rely on manually designed features and may fail to adequately capture complex patterns of distraction.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Road Safety: Advancements in Lightweight YOLOv8 Models and GhostC2f Design for Real-Time Distracted Driving Detection

Du,

Liu,

et al. 2023

Sensors

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The rapid detection of distracted driving behaviors is crucial for enhancing road safety and preventing traffic accidents. Compared with the traditional methods of distracted-driving-behavior detection, the YOLOv8 model has been proven to possess powerful capabilities, enabling it to perceive global information more swiftly. Currently, the successful application of GhostConv in edge computing and embedded systems further validates the advantages of lightweight design in real-time detection using large models. Effectively integrating lightweight strategies into YOLOv8 models and reducing their impact on model performance has become a focal point in the field of real-time distracted driving detection based on deep learning. Inspired by GhostConv, this paper presents an innovative GhostC2f design, aiming to integrate the idea of linear transformation to generate more feature maps without additional computation into YOLOv8 for real-time distracted-driving-detection tasks. The goal is to reduce model parameters and computational load. Additionally, enhancements have been made to the path aggregation network (PAN) to amplify multi-level feature fusion and contextual information propagation. Furthermore, simple attention mechanisms (SimAMs) are introduced to perform self-normalization on each feature map, emphasizing feature maps with valuable information and suppressing redundant information interference in complex backgrounds. Lastly, the nine distinct distracted driving types in the publicly available SFDDD dataset were expanded to 14 categories, and nighttime scenarios were introduced. The results indicate a 5.1% improvement in model accuracy, with model weight size and computational load reduced by 36.7% and 34.6%, respectively. During 30 real vehicle tests, the distracted-driving-detection accuracy reached 91.9% during daylight and 90.3% at night, affirming the exceptional performance of the proposed model in assisting distracted driving detection when driving and contributing to accident-risk reduction.

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Section: Introductionmentioning

confidence: 99%