Driver Cognitive Distraction Detection Using Driving Performance Measures

Jin, Lisheng; Niu, Qingning; Hou, Haijing; Xian, Huacai; Wang, Yali; Shi, Dongdong

doi:10.1155/2012/432634

Cited by 53 publications

(37 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work, we assume this set of mental states to be attentive, partially attentive, and distracted. This is similar to work in psychology and discrete event systems, which identifies tasks to have no, low, or high mental workload or cognitive distraction, and adjusts based on discrete mental modes [19], [22]. Existence of Distinct Driver Modes: Given that driver behavior heavily depends on context and mental state, we assume that there exist distinct driver modes that depend on this information.…”

Section: A Modeling Human Behaviormentioning

confidence: 99%

Improved driver modeling for human-in-the-loop vehicular control

Driggs-Campbell

Shia

Bajcsy

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

In order to develop provably safe human-in-theloop systems, accurate and precise models of human behavior must be developed. Driving is a good example of such a system because the driver has full control of the vehicle, and her likely actions are highly dependent on her mental state and the context of the current situation. This paper presents a testbed for collecting driver data that allows us to collect realistic data, while maintaining safety and control of the environmental surroundings. We extend previous work that focuses on set predictions consisting of trajectories observed from the nonlinear dynamics and behaviors of the human driven car, accounting for the driver mental state, the context or situation that the vehicle is in, and the surrounding environment in both highway and intersection scenarios. This allows us to predict driving behavior over long time horizons with extremely high accuracy. By using this realistic data and flexible algorithm, a precise and accurate driver model can be developed that is tailored to an individual and usable in semi-autonomous frameworks.

show abstract

Section: A Modeling Human Behaviormentioning

confidence: 99%

Improved driver modeling for human-in-the-loop vehicular control

Driggs-Campbell

Shia

Bajcsy

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

show abstract

“…As sleepiness affects driving performance complexly, the learning technique of the SVM method makes it very suitable for measuring the sleepy state while driving. 26…”

Section: Detection Modelmentioning

confidence: 99%

“…Characteristic parameters 26,29. driving performance during the transition from alert to sleepy state were different as well.…”

mentioning

confidence: 96%

Effects of driver behavior style differences and individual differences on driver sleepiness detection

Jin

Jiang

et al. 2015

Advances in Mechanical Engineering

Self Cite

View full text Add to dashboard Cite

Driving sleepiness is still a major causes of traffic accidents. Individual drivers, under various conditions, act and respond in different manners. This article presents the attempt of a straight-line driving simulator study that examined the effects of driver behavior style differences and individual differences on driver sleepiness detection which is based on driving performance measures. A total of 15 drivers who were classified into two categories through subjective assessment based on a Driver Behavior Questionnaire participated in driving simulator experiments. A total of 18 detection models, including 15 SE models for each subject, an A model for the aggressive drivers, an NA model for the non-aggressive drivers, and a G model for all experiment participants, were developed using support vector machine method based on driving performance characteristic parameters. The results show that the G model is not suitable for all drivers due to its lower mean accuracy of 69.88% (standard deviation = 7.70%) and higher standard deviation. The SE models for each subject show the best detection accuracy performance of 84.26% (standard deviation = 5.38%); however, it is impossible to set up a special detection model for every individual driver. The SD models on different style categories show an accuracy value of 77.54% (standard deviation = 5.78%). The results demonstrate that driver style differences as well as individual differences have great effects on driver sleepiness detection (F = 19.148, p \ 0.000).

show abstract

“…Blink duration is the average over duration of each blink in secondary task conducting. Blink percentage shows the percent in terms of time when blink happened in secondary tasks [10]. A repeated measure ANOVA found that the radio and computer tasks had no significant effect on driver blink behavior.…”

Section: Saccadesmentioning

confidence: 99%

Research on Evaluation Model for Secondary Task Driving Safety Based on Driver Eye Movements

Jin

Xian

Jiang

et al. 2014

Advances in Mechanical Engineering

Self Cite

View full text Add to dashboard Cite

This study was designed to gain insight into the influence of performing different types of secondary task while driving on driver eye movements and to build a safety evaluation model for secondary task driving. Eighteen young drivers were selected and completed the driving experiment on a driving simulator. Measures of fixations, saccades, and blinks were analyzed. Based on measures which had significant difference between the baseline and secondary tasks driving conditions, the evaluation index system was built. Method of principal component analysis (PCA) was applied to analyze evaluation indexes data in order to obtain the coefficient weights of indexes and build the safety evaluation model. Based on evaluation scores, the driving safety was grouped into five levels (very high, high, average, low, and very low) using K-means clustering algorithm. Results showed that secondary task driving severely distracts the driver and the evaluation model built in this study could estimate driving safety effectively under different driving conditions.

show abstract

Driver Cognitive Distraction Detection Using Driving Performance Measures

Cited by 53 publications

References 18 publications

Improved driver modeling for human-in-the-loop vehicular control

Improved driver modeling for human-in-the-loop vehicular control

Effects of driver behavior style differences and individual differences on driver sleepiness detection

Research on Evaluation Model for Secondary Task Driving Safety Based on Driver Eye Movements

Contact Info

Product

Resources

About