Detection of lateral hazardous driving events using in-vehicle gyro sensor data

Jeong, Eui Jun; Oh, Cheol; Kim, Ikki

doi:10.1007/s12205-013-0387-9

Cited by 15 publications

(9 citation statements)

References 5 publications

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“…Impressive training accuracy of 99.9% and validation accuracy of 100% were attained by the RNN-LSTM model. (Jeong et al, 2013) conducted a study using data from the in-car gyro sensor to identify lateral risky driving incidents. They equipped a probe vehicle with a customized data collection setup.…”

Section: Speed and Velocity Sensorsmentioning

confidence: 99%

Machine Learning Approaches for Detecting Driver Drowsiness: A Critical Review

Ahmad,

Em,

Aziz

2023

ijmst

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Driver drowsiness is a serious issue that poses a significant threat to road safety, as it can lead to accidents and injuries. In response to this problem, a thorough review of machine learning techniques for detecting driver drowsiness was conducted. The review examined a range of techniques, including more recent approaches that use machine learning and deep learning algorithms as well as different types of data sources driver behaviours, physiological signals, and vehicle behaviours. The primary objective of this paper was to critically analyse and provide a comprehensive overview of the current state-of-the-art in detecting driver drowsiness, evaluate the effectiveness of each technique in terms of accuracy and reliability, and identify potential areas for future research and improvement. In order to achieve this, a systematic review of relevant research studies was undertaken. The review determined that machine learning-based techniques can improve the accuracy and reliability of driver drowsiness detection systems. However, certain limitations, such as the need for large amounts of data, feature extraction, and model structure, must be addressed. By overcoming these limitations, machine learning-based systems have the potential to enhance road safety and prevent accidents. In conclusion, this paper provides a thorough review of machine learning techniques for driver drowsiness detection, evaluates their effectiveness, identifies potential research directions, and highlights their significance and contribution to road safety. The insights gained from this study can be used to guide the development of more effective driver drowsiness detection systems and improve road safety for the community.

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Section: Speed and Velocity Sensorsmentioning

confidence: 99%

Machine Learning Approaches for Detecting Driver Drowsiness: A Critical Review

Ahmad,

Em,

Aziz

2023

ijmst

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“…Omerustaoglu et al combined in-car data and image data to study distracted driving behaviors through deep learning [ 11 ]. Jeong et al used the data collected by the built-in 3-axis gyroscope of the vehicle to identify two driving behaviors by support vector machine [ 12 ]. Other studies described various aggressive driving behaviors and formulated their standards (Tasca [ 13 ], Abou-Zeid [ 14 ], Li [ 15 ], Yang [ 16 ]).…”

Section: Related Workmentioning

confidence: 99%

A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data

Meng

Wang

et al. 2022

PLoS ONE

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Distracted driving behavior is one of the main factors of road accidents. Accurately predicting the risk of driving behavior is of great significance to the active safety of road transportation. The large amount of information collected by the sensors installed on the vehicle can be identified by the algorithm to obtain the distracted driving behavior data, which can be used to predict the driving behavior risk of the vehicle and the area. In this paper, a new neural network named Driving Behavior Risk Prediction Neural Network (DBRPNN) is developed for prediction based on the distracted driving behavior data. The network consists of three modules: the Feature Processing Module, the Memory Module, and the Prediction Module. In this process, attribute data (time in a day, daily driving time, and daily driving mileage) that can reflect external factors and driver statuses, are added to the network to increase the accuracy of the model. We predicted the driving behavior risk of different objects (Vehicle and Area). For the applicability improvement of the model, we further classify the distracted driving behavior categories, and DBRPNN can provide more accurate risk prediction. The results show that compared with traditional models (Classification and Regression Tree, Support Vector Machines, Recurrent Neural Network, and Long Short-Term Memory), DBRPNN has better prediction performance. The method proposed in this paper has been fully verified and may be transplanted into active safety early warning system for more accurate and flexible application.

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“…Xu and Wang (2020) provided a safety prewarning mechanism, which can automatically extract safety information from surveillance cameras based on computer vision, assess risks based on the embedded comprehensive risk assessment model, categorize risks into five levels and provide timely suggestions. Jeong et al (2021) developed a computer vision-based solitary work detection model that considers interactive operations between heavy equipment and spotters. Li et al (2022) took a reinforcement processing area as a research case and proposed a new method for recognizing each worker's activity through the position relationship of objects detected by Faster R-CNN.…”

Section: Literature Review 21 Application Of Computer Vision In Const...mentioning

confidence: 99%

Pose estimation method for construction machine based on improved AlphaPose model

Zhao

Cao

Xiang

2022

ECAM

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PurposeThe safety management of construction machines is of primary importance. Considering that traditional construction machine safety monitoring and evaluation methods cannot adapt to the complex construction environment, and the monitoring methods based on sensor equipment cost too much. This paper aims to introduce computer vision and deep learning technologies to propose the YOLOv5-FastPose (YFP) model to realize the pose estimation of construction machines by improving the AlphaPose human pose model.Design/methodology/approachThis model introduced the object detection module YOLOv5m to improve the recognition accuracy for detecting construction machines. Meanwhile, to better capture the pose characteristics, the FastPose network optimized feature extraction was introduced into the Single-Machine Pose Estimation Module (SMPE) of AlphaPose. This study used Alberta Construction Image Dataset (ACID) and Construction Equipment Poses Dataset (CEPD) to establish the dataset of object detection and pose estimation of construction machines through data augmentation technology and Labelme image annotation software for training and testing the YFP model.FindingsThe experimental results show that the improved model YFP achieves an average normalization error (NE) of 12.94 × 10–3, an average Percentage of Correct Keypoints (PCK) of 98.48% and an average Area Under the PCK Curve (AUC) of 37.50 × 10–3. Compared with existing methods, this model has higher accuracy in the pose estimation of the construction machine.Originality/valueThis study extends and optimizes the human pose estimation model AlphaPose to make it suitable for construction machines, improving the performance of pose estimation for construction machines.

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Detection of lateral hazardous driving events using in-vehicle gyro sensor data

Cited by 15 publications

References 5 publications

Machine Learning Approaches for Detecting Driver Drowsiness: A Critical Review

Machine Learning Approaches for Detecting Driver Drowsiness: A Critical Review

A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data

Pose estimation method for construction machine based on improved AlphaPose model

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