Research on a Cognitive Distraction Recognition Model for Intelligent Driving Systems Based on Real Vehicle Experiments

Sun, Qinyu; Wang, Chang; Guo, Yingshi; Yuan, Wei; Fu, Rui

doi:10.3390/s20164426

Cited by 15 publications

(13 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computational Intelligence and Neuroscience known behavior template for behavior matching is calculated, and finally, the recognition result is obtained [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Sports Deep Learning Method Based on Cognitive Human Behavior Recognition

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

An in-depth learning-based approach is designed to develop the ability to recognize human behavior on the move. We introduce 3D residual structures and create 3D residual models. In order to get the most out of the data relationship of several consecutive frames, this study introduces 3D techniques for assigning different values to the existing frames. Experiments show that both structures improve recognition performance. For the 3D residual model, 3D attention model, and 3D attention residual model, this study proposes two model fusion strategies: average and weighted. Among them, the weighted fusion is to give a higher fusion proportion to the high accuracy model by using the model weight calculation method designed in this study. The experimental results show that the additive fusion strategy based on feature contribution has an obvious improvement effect on the test results of the two benchmark datasets, with an increase of more than 2% points, including an increase of 2.69% on HMDB51. The effect of splicing and fusion strategy has also increased by more than 1% point, including 1.34% on UCF101 dataset and about 1.9% on HMDB51. It is proven that deep learning can effectively recognize human behavior in sports.

show abstract

“…Computational Intelligence and Neuroscience known behavior template for behavior matching is calculated, and finally, the recognition result is obtained [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Sports Deep Learning Method Based on Cognitive Human Behavior Recognition

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Only indicators which were found to be significantly affected by the secondary task were inputted into the 2 detection models. A suitable time window can help to improve the detection accuracy and the accuracy of the detection model will be reduced by a time window that is too short [ 49 ]. Thus, a time window of 2 s was set from 2 s ahead of the lead vehicle’s brake onset, with an overlap of 1.5 s.…”

Section: Methodsmentioning

confidence: 99%

Young Novice Drivers’ Cognitive Distraction Detection: Comparing Support Vector Machines and Random Forest Model of Vehicle Control Behavior

Xue

Wang

et al. 2023

Sensors

View full text Add to dashboard Cite

The use of mobile phones has become one of the major threats to road safety, especially in young novice drivers. To avoid crashes induced by distraction, adaptive distraction mitigation systems have been developed that can determine how to detect a driver’s distraction state. A driving simulator experiment was conducted in this paper to better explore the relationship between drivers’ cognitive distractions and traffic safety, and to better analyze the mechanism of distracting effects on young drivers during the driving process. A total of 36 participants were recruited and asked to complete an n-back memory task while following the lead vehicle. Drivers’ vehicle control behavior was collected, and an ANOVA was conducted on both lateral driving performance and longitudinal driving performance. Indicators from three aspects, i.e., lateral indicators only, longitudinal indicators only, and combined lateral and longitudinal indicators, were inputted into both SVM and random forest models, respectively. Results demonstrated that the SVM model with parameter optimization outperformed the random forest model in all aspects, among which the genetic algorithm had the best parameter optimization effect. For both lateral and longitudinal indicators, the identification effect of lateral indicators was better than that of longitudinal indicators, probably because drivers are more inclined to control the vehicle in lateral operation when they were cognitively distracted. Overall, the comprehensive model built in this paper can effectively identify the distracted state of drivers and provide theoretical support for control strategies of driving distraction.

show abstract

“…On the whole, having multiple measures has shown to be more accurate in detecting driver behaviour instead of using just a single type, as asserted in [19], [24], [26]. These measures are further processed, weighted depending on their effect on driver behaviour before being fed into algorithms such as Support Vector Machines (SVM), Dynamic Bayesian Networks, Neural Networks, AdaBoost classifier, Hidden Markov Model and bidirectional long short-term memory network (Bi-LSTM) among others for distraction detection and recognition [17], [18], [22], [26], [27], [28], [29].…”

Section: B Ways To Detect Driver Distractionmentioning

confidence: 99%

Detection of Driver Cognitive Distraction Using Machine Learning Methods

Misra¹

2023

IEEE Access

View full text Add to dashboard Cite

Driver distraction is one of the primary causes of crashes. As a result, there is a great need to continuously observe driver state and provide appropriate interventions to distracted drivers. Cognitive distraction refers to the "look but not see" situations when the drivers' eyes are focused on the forward roadway, but their mind is not. Typically, cognitive distractions can result from fatigue, conversation with a co-passenger, listening to the radio, or other similarly loading secondary tasks that do not necessarily take a driver's eyes off the roadway. This makes it one of the hardest distractions to detect as there are no visible clues of driver distraction. In this study, we have identified features from different sources including eyetracking, physiological, and vehicle kinematics data that are relevant towards the classification of distracted and non-distracted drivers via the analysis of data collected from a driving simulator study involving 40 drivers across multiple driving scenarios. The key classification algorithms implemented include Random Forest, Decision Trees and Support Vector Machines. A reduced feature set including pupil area, pupil vertical and horizontal motion was found to be predictive of driver distraction while maintaining an average accuracy of 90% across various road types. Additionally, the impact of road types on driver behaviour was also identified. The findings of the study has practical application towards the design of driver distraction monitoring systems.

show abstract

Research on a Cognitive Distraction Recognition Model for Intelligent Driving Systems Based on Real Vehicle Experiments

Cited by 15 publications

References 38 publications

Sports Deep Learning Method Based on Cognitive Human Behavior Recognition

Sports Deep Learning Method Based on Cognitive Human Behavior Recognition

Young Novice Drivers’ Cognitive Distraction Detection: Comparing Support Vector Machines and Random Forest Model of Vehicle Control Behavior

Detection of Driver Cognitive Distraction Using Machine Learning Methods

Contact Info

Product

Resources

About