A two-stage-training support vector machine approach to predicting unintentional vehicle lane departure

Albousefi, Alhadi Ali; Ying, Hao; Filev, Dimitar; Syed, Fazal; Prakah-Asante, Kwaku O.; Tseng, Finn; Yang, Hsin-Hsiang

doi:10.1080/15472450.2016.1196141

Cited by 14 publications

(4 citation statements)

References 23 publications

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“…It then uses the MapMinMax function to normalize the training data according to the coefficients and parameters required for training [19], such as…”

Section: R E T R a C T E Dmentioning

confidence: 99%

Application of Internet of Things Technology in Vocal Music Teaching Recording Equipment Assisted by Machine Learning

Guo

2022

Wireless Communications and Mobile Computing

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Vocal music teaching is a professional, technical, and practical subject. It is also an important part of music and art education and has certain social and educational significance. In vocal music teaching, recording equipment is an essential teaching tool. It plays a pivotal role in vocal music teaching. In recent years, with the rapid development of China’s social economy, education has also achieved great development, and it has also brought development opportunities to vocal music education. More and more advanced technologies and equipment have appeared to assist the smooth progress of vocal music teaching, especially recording equipment. This paper is aimed at studying the application of IoT technology in vocal music teaching recording equipment assisted by machine learning. Combined with machine learning and Internet of Things technology, the experiment of recognition effect of vocal music teaching recording equipment was carried out. It designs an end-to-end vocal music teaching recording device recognition model based on the Internet of Things technology assisted by machine learning. The experimental results show that the use of this model improves the recording recognition accuracy of vocal music teaching recording equipment by 20%.

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“…It then uses the MapMinMax function to normalize the training data according to the coefficients and parameters required for training [19], such as…”

Section: R E T R a C T E Dmentioning

confidence: 99%

Application of Internet of Things Technology in Vocal Music Teaching Recording Equipment Assisted by Machine Learning

Guo

2022

Wireless Communications and Mobile Computing

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“…Others [12,13,67,68] Used to identify sharp deceleration, sharp steering, lane departure, and other abnormal driving behavior Journal of Advanced Transportation Although the above model has been proved to be effective in driving behavior recognition by some studies, it does not form well in all driving behavior recognition tasks. In this problem, there is no general model for driving behavior recognition, and the performance of the model depends heavily on the task for which it is applicable.…”

Section: Recurrent Neuralmentioning

confidence: 99%

A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

Zhao

Zhong

Zhi-jun

et al. 2022

Journal of Advanced Transportation

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The frequent traffic accidents lead to a large number of casualties and large related financial losses every year; this serious state is owed to several factors; among those, driving behavior is one of the most imperative subjects to discuss. Driving behaviors mainly include behavior characteristics such as car-following, lane change, and risky driving behavior such as distraction, fatigue, or aggressive driving, which are of great help to various tasks in traffic engineering. An accurate and reliable method of driving behavior recognition is of great significance and guidance for vehicle driving safety. In this paper, the vehicle multisensor information, vehicle CAN bus data acquisition system, and typical feature extraction methods are summarized at first. And then, several driving behavior recognition models based on machine learning and deep learning are reviewed. Through a detailed analysis of the features of random forests, support vector machines, convolutional neural networks, and recurrent neural networks used to build driving behavior recognition models, the following findings are obtained: the driving behavior model constructed by traditional machine learning model is relatively mature but it is greatly affected by feature extraction, data scale, and model structure, which affects the accuracy of the final driving behavior recognition. Deep learning model based on a neural network has achieved high accuracy in identifying driving behavior, and it may gradually become the mainstream of constructing the driving behavior model with the development of big data, artificial intelligence technology, and computer hardware. Finally, this paper points out some content that needs to be further explored, to provide reference and inspiration for scholars in this field to continue to study the driving behavior recognition model in depth.

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“…TLC is defined as the time duration available for the driver before lane-boundary crossing. However, the TLC-based method has been criticized for having a high FAR [8], [13] because of its inability to predict driver's intention. The TLC-based warning could be triggered when the TLC reaches the predefined critical value (usually more than 0.9 s).…”

Section: B Related Researchmentioning

confidence: 99%

“…Many studies have observed that the TLC-based methods tend to have a higher FAR when the ego vehicle drives close to the lane boundary [8], [13], [14]. This is primarily due to using an oversimplified model to reduce computational complexity, viz.…”

Section: B Excessive False Warningmentioning

confidence: 99%

A Learning-Based Approach for Lane Departure Warning Systems With a Personalized Driver Model

Wang

Zhao

Han

et al. 2018

IEEE Trans. Veh. Technol.

106

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Misunderstanding of driver correction behaviors (DCB) is the primary reason for false warnings of lane-departureprediction systems. We propose a learning-based approach to predicting unintended lane-departure behaviors (LDB) and the chance for drivers to bring the vehicle back to the lane. First, in this approach, a personalized driver model for lanedeparture and lane-keeping behavior is established by combining the Gaussian mixture model and the hidden Markov model. Second, based on this model, we develop an online model-based prediction algorithm to predict the forthcoming vehicle trajectory and judge whether the driver will demonstrate an LDB or a DCB. We also develop a warning strategy based on the model-based prediction algorithm that allows the lane-departure warning system to be acceptable for drivers according to the predicted trajectory. In addition, the naturalistic driving data of 10 drivers is collected through the University of Michigan Safety Pilot Model Deployment program to train the personalized driver model and validate this approach. We compare the proposed method with a basic time-to-lane-crossing (TLC) method and a TLC-directional sequence of piecewise lateral slopes (TLC-DSPLS) method. The results show that the proposed approach can reduce the falsewarning rate to 3.07%.Index Terms-Learning-based approach, lane departure warning system, Gaussian mixture model, hidden Markov model, personalized driver model

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A two-stage-training support vector machine approach to predicting unintentional vehicle lane departure

Cited by 14 publications

References 23 publications

Application of Internet of Things Technology in Vocal Music Teaching Recording Equipment Assisted by Machine Learning

Application of Internet of Things Technology in Vocal Music Teaching Recording Equipment Assisted by Machine Learning

A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

A Learning-Based Approach for Lane Departure Warning Systems With a Personalized Driver Model

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