Cluster Regularized Extreme Learning Machine for Detecting Mixed-Type Distraction in Driving

Liu, Tianchi; Yan, Yang; Huang, Guang-Bin; Lin, Zhiping; Klanner, Felix; Denk, Cornelia; Rasshofer, R.H.

doi:10.1109/itsc.2015.217

Cited by 12 publications

(7 citation statements)

References 9 publications

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“…Many of the reviewed algorithms use gaze and head pose features obtained via an eye-tracker [86], [100], [103], [104], [106], [108] or manually annotated [85], [89], [105], [109]. Fewer approaches incorporate explicit feature extraction pipelines (such as those discussed in Section IV-A).…”

Section: B Distration Detectionmentioning

confidence: 99%

“…3) Classifiers: Support Vector Machine (SVM) is the most commonly used classifier for this problem, used in nearly half of the reviewed algorithms [84], [100]- [106], [108]. Other approaches, such as boosting [84], extreme learning machines [102], [104], K-means [90], and Hidden Markov Models (HMM) [89] have demonstrated high performance on the distraction detection task. Recent works that use deep learning methods, such as recurrent [86], [88], [91] and convolutional neural networks [65], [87], [88], demonstrate their superior performance across most metrics and ability to extract information directly from raw images or multi-modal data.…”

Section: B Distration Detectionmentioning

confidence: 99%

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Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

Kotseruba

Tsotsos

2022

IEEE Trans. Intell. Transport. Syst.

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Driving safety has been a concern since the first cars appeared on the streets. Driver inattention has been singled out as a major cause of accidents early on. This is hardly surprising, as drivers routinely perform other tasks in addition to controlling the vehicle. Decades of research into what causes lapses or misdirection of drivers' attention resulted in improvements in road safety through better design of infrastructure, driver training programs, in-vehicle interfaces, and, more recently, the development of driving assistance systems (ADAS) and driving automation. This review focuses on the methods for modeling and detecting spatio-temporal aspects of drivers' attention, i.e. where and when they look, for the two latter categories of applications.We start with a brief theoretical background on human visual attention, methods for recording and measuring attention in the driving context, types of driver inattention, and factors causing it. We then discuss machine learning approaches for 1) modeling gaze for assistive and self-driving applications and 2) detecting gaze for driver monitoring. Following the overview of state-of-the-art models, we provide an extensive list of publicly available datasets that feature recordings of drivers' gaze and other attention-related annotations. We conclude with a general overview of the remaining challenges, such as data availability and quality, evaluation methods, and the limited scope of attention modeling, and outline steps toward rectifying some of these issues. Categorized and annotated lists of the reviewed models and datasets are available at https://github.com/ykotseruba/attention_and_driving

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Section: B Distration Detectionmentioning

confidence: 99%

Section: B Distration Detectionmentioning

confidence: 99%

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

Kotseruba

Tsotsos

2022

IEEE Trans. Intell. Transport. Syst.

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“…Liu et al [196] applied Cluster Regularized Extreme Learning Machine (CR-ELM) for detecting mixing types of distraction. Compared with the traditional ELM, CR-ELM introduces an additional regularization term penalizing large covariance of training data within the same clusters in the output space.…”

Section: Mixing Types Of Distractionmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

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Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

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“…e real vehicle test can be subdivided into fieldcontrolled experiment and naturalistic driving experiment [17,18]. A driving simulator has the advantages of low cost and convenient operation [19], but drivers' behaviors are not natural enough and the risk of operation awareness is lower. Compared with that, the data authenticity of real car test is higher.…”

Section: Introductionmentioning

confidence: 99%

Identification of Driver Distraction Based on SHRP2 Naturalistic Driving Study

Liu

Ren

Peng

2021

Mathematical Problems in Engineering

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Abundant evidence shows that driver distraction is one of the fundamental causes of traffic accidents. Current detected methods of driver distraction are mostly based on intrusive or semi-intrusive. The methods not only interfere with the driving task but also are restricted by various environmental factors, resulting in a high false positive rate. This paper only considers noninvasive vehicle kinematics indicators and proposes a recognition method based on deep learning. Firstly, some car following segments are obtained from the naturalistic driving database, and typical distracted segments are extracted by using situation awareness. Then, distracted recognition indexes’ set is established and only contains vehicle kinematic features. Thirdly, the gradient boosted decision tree recursive feature elimination (GBDT-RFE) and random forest recursive feature elimination (RF-RFE) are used to rank the importance of features. The indexes with higher importance are obtained. Finally, the long short-term memory neural network (LSTM-NN) is utilized to realize the classification and recognition of distracted driving, and the results are compared with SVM and AdaBoost. The results show that the F1-scores of LSTM-NN are 89% and 91% in distracted and normal driving, which are higher than SVM and AdaBoost. The average F1-score of distracted recognition (12% and 7%) is higher than SVM and AdaBoost. The false positive rate of different distracted types is less than 15%. LSTM-NN can effectively learn the information before and after the distracted sequence, which is conducive to accurately estimate the driver’s attention state. The study provides a method for vehicle distraction warning system and driving risk propensity assessment.

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Cluster Regularized Extreme Learning Machine for Detecting Mixed-Type Distraction in Driving

Cited by 12 publications

References 9 publications

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

Driver Distraction Using Visual-Based Sensors and Algorithms

Identification of Driver Distraction Based on SHRP2 Naturalistic Driving Study

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