scholarly journals Effect of Cognitive Distraction on Physiological Measures and Driving Performance in Traditional and Mixed Traffic Environments

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Qiang Hua ◽  
Lisheng Jin ◽  
Yuying Jiang ◽  
Baicang Guo ◽  
Xianyi Xie
Keyword(s):  
Repeated Measures ◽  
Traffic Accidents ◽  
Rapid Development ◽  
Driving Performance ◽  
Intelligent Vehicles ◽  
Distracted Driving ◽  
Steering Wheel ◽  
Physiological Measures ◽  
Mixed Traffic ◽  
Cognitive Distraction

Distracted driving is a dominant cause of traffic accidents. In addition, with the rapid development of intelligent vehicles, mixed traffic environments are expected to become more complicated with multiple types of intelligent vehicles sharing the road, thereby increasing the opportunities for distracted driving. However, the existing research on detecting driver distraction in mixed traffic environments is limited. Therefore, in this study, we analysed the effect of cognitive distraction on the driver physiological measures and driving performance in traditional and mixed traffic environments and compared the parameters extracted in the two environments. Sixty drivers were involved in the data collection, which included normal driving and two distracting tasks while driving in a simulator. Repeated-measures analysis of variance (ANOVA) was performed to examine the effect of cognitive distraction and traffic environments on all parameters. The results indicate that the effects of the pupil diameter, standard deviations (SDs) of the horizontal and vertical fixation angles, blink frequency, speed, SD of the lane positioning (SDLP), SD of the steering wheel angle (SDSWA), and steering entropy (SE) were significant. These findings provide a theoretical foundation for identifying the most appropriate parameters to detect cognitive distraction in traditional and mixed traffic environments to help reduce traffic accidents.

scholarly journals Cognitive Distraction State Recognition of Drivers at a Nonsignalized Intersection in a Mixed Traffic Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Qiang Hua ◽  
Lisheng Jin ◽  
Yuying Jiang ◽  
Ming Gao ◽  
Baicang Guo
Keyword(s):  
Road Traffic ◽  
Safety Concern ◽  
Autonomous Driving ◽  
Attention Mechanism ◽  
Intelligent Vehicles ◽  
Distracted Driving ◽  
Support Vector ◽  
Mixed Traffic ◽  
Cognitive Distraction ◽  
Traffic Environment

Distracted driving has become a growing traffic safety concern. With advances in autonomous driving and connected vehicle technology, a mixture of various types of intelligent vehicles will become normal in the near future, while more factors that may cause driver cognitive distraction are emerging. However, there are rarely studies on distracted driving in mixed traffic environments. To fill this gap, we conducted a natural driving experiment with three representative events at a nonsignalized intersection in a mixed traffic environment and proposed a novel method of identifying cognitive distraction based on bidirectional long short-term memory (Bi-LSTM) with attention mechanism. Forty participants were recruited for each event, who completed three different cognitive distraction experiments induced by three different secondary tasks in contrast with a normal driving process when passing a nonsignalized intersection. Related driving performance and eye movement data were collected to train and test the Bi-LSTM with attention mechanism model. Compared with the support vector machine (SVM) model, its recognition accuracy rate is 94.33%, which is 3.83% higher than that of the SVM in the total event, which has reasonable applicability for distraction recognition in a mixed traffic environment. Potential applications of this model include distraction alarm and autonomous driving assistance systems, which could avoid road traffic accidents.

scholarly journals Driver Cognitive Distraction Detection Using Driving Performance Measures

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Lisheng Jin ◽  
Qingning Niu ◽  
Haijing Hou ◽  
Huacai Xian ◽  
Yali Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Performance Measures ◽  
Traffic Accidents ◽  
Can Bus ◽  
Driving Performance ◽  
Cognitive State ◽  
Area Network ◽  
Characteristic Parameters ◽  
Secondary Tasks ◽  
Cognitive Distraction

Driver cognitive distraction is a hazard state, which can easily lead to traffic accidents. This study focuses on detecting the driver cognitive distraction state based on driving performance measures. Characteristic parameters could be directly extracted from Controller Area Network-(CAN-)Bus data, without depending on other sensors, which improves real-time and robustness performance. Three cognitive distraction states (no cognitive distraction, low cognitive distraction, and high cognitive distraction) were defined using different secondary tasks. NLModel, NHModel, LHModel, and NLHModel were developed using SVMs according to different states. The developed system shows promising results, which can correctly classify the driver’s states in approximately 74%. Although the sensitivity for these models is low, it is acceptable because in this situation the driver could control the car sufficiently. Thus, driving performance measures could be used alone to detect driver cognitive state.

Road Adhesion Coefficient Estimation Based on the Steering Wheel Rebound After Steering

2018 ◽  
Author(s):  
Liangyao Yu ◽  
Sheng Zheng ◽  
Xiaohui Liu ◽  
Jinghu Chang ◽  
Fei Li
Keyword(s):  
Stability Control ◽  
Front Wheel ◽  
Intelligent Vehicles ◽  
Steering Wheel ◽  
Adhesion Coefficient ◽  
The Road ◽  
Whole Process ◽  
Friction Moment ◽  
Stability Control System ◽  
The Coefficient Of Friction

Accurately estimating road adhesion coefficient is very important for vehicle stability control system. In this paper, an innovation method to estimate the road adhesion coefficient is proposed. This method can be used in vehicles without additional sensors. And this method is especially suitable to be used in the intelligent vehicle equipped with steer-by-wire (SBW) system. When vehicle steers, releasing the steering wheel suddenly will result in rebound to a certain angle. When the steer wheel turns the same angle on different road whose adhesion coefficients are different, the front wheel rebound angles are different. The friction moment between the road and tire is the main factor to prevent the tire from turning back, and the coefficient of friction is equal to road adhesion coefficient when the vehicle is stationary. In this paper, the detailed dynamical models describing the whole process of the front wheel and tire rebound are established. Furthermore, the Luenberger reduced-order disturbance observer is established to estimate the friction moment, and then the adhesion coefficient is estimated. The SBW system which is usually equipped in intelligent vehicles can control the steer moment and steer angle accurately. When the steer wheel turns to certain angle, the SBW system is able to stop outputting torque quickly and timely, which is important for improving the experiment accuracy. In this paper, the SBW system is used to conduct an experiment on different roads. The experiment results demonstrate the validity of this method.

scholarly journals Acute Effects of Intermittent Foot Cooling on 1 RM Leg Press Strength in Resistance-Trained Men: A Pilot Study

2021 ◽  
Vol 18 (18) ◽  
pp. 9594
Author(s):  
Chih-Min Wu ◽  
Mei-Hsien Lee ◽  
Wen-Yi Wang ◽  
Zong-Yan Cai
Keyword(s):  
Lower Limb ◽  
Repeated Measures ◽  
Vastus Lateralis ◽  
Crossover Design ◽  
Physiological Measures ◽  
Acute Effects ◽  
Leg Press ◽  
Recreational Athletes ◽  
Lower Limb Strength ◽  
Strength Based

Inter-set peripheral cooling can improve high-intensity resistance exercise performance. However, whether foot cooling (FC) would increase 1 repetition maximum (RM) lower-limb strength is unclear. This study investigated the effect of intermittent FC on 1 RM leg press strength. Ten recreational male lifters performed three attempts of 1 RM leg press with FC or non-cooling (NC) in a repeated-measures crossover design separated by 5 days. FC was applied by foot immersion in 10 °C water for 2.5 min before each attempt. During the 1 RM test, various physiological measures were recorded. The results showed that FC elicited higher 1 RM leg press strength (Δ [95% CI]; Cohen’s d effect size [ES]; 13.6 [7.6–19.5] kg; ES = 1.631) and electromyography values in vastus lateralis (57.7 [8.1–107.4] μV; ES = 0.831) and gastrocnemius (15.1 [−3.1–33.2] μV; ES = 0.593) than in NC. Higher arousal levels (felt arousal scale) were found in FC (0.6 [0.1–1.2]; ES = 0.457) than in NC. In conclusion, the preliminary findings, although limited, suggest intermittent FC has a potential ergogenic role for recreational athletes to enhance maximal lower-limb strength and may partly benefit strength-based competition events.

Research on Intelligent Steering Wheel Technology Based on Multi Sensor

2014 ◽  
Vol 488-489 ◽  
pp. 1130-1133
Author(s):  
Yuan Bai ◽  
Xiao Dong Tan
Keyword(s):  
Traffic Safety ◽  
Automobile Industry ◽  
Traffic Accidents ◽  
Drunk Driving ◽  
Steering Wheel ◽  
Private Car ◽  
Driving Condition ◽  
Fatigue Driving

At present, the automobile industry is developing rapidly, the private car is widely popularized, and the hidden dangers of traffic safety exist. The phenomenon of drunk driving and fatigue driving becomes more and more serious, and the improvement for steering wheel could effectively prevent traffic accidents. This paper introduces and analyzes the intelligence of steering wheel in three major aspects, they respectively include intelligent grip detection, which tests if a driver is of fatigue driving; hart rate detection, which tests if a driver is in normal driving condition; alcohol detection, which tests if a driver drinks too much, and it predicts the possibility of accident from the drivers state, and timely gives out signal to warn the driver.

scholarly journals Research on Mandatory Lane-Changing Behavior in Highway Weaving Sections

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Hao ◽  
Zhaolei Zhang ◽  
Zhibo Gao ◽  
Kefu Yi ◽  
Li Liu ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Traffic Accidents ◽  
Driving Behavior ◽  
Heterogeneous Traffic ◽  
Psychological Characteristics ◽  
Distribution Characteristics ◽  
Mixed Traffic ◽  
Lane Changing ◽  
Headway Distribution ◽  
Analysis Models

As the accident-prone sections and bottlenecks, highway weaving sections will become more complicated when it comes to the mixed-traffic environments with connected and automated vehicles (CAVs) and human-driven vehicles (HVs). In order to make CAVs accurately identify the driving behavior of manual-human vehicles to avoid traffic accidents caused by lane changing, it is necessary to analyze the characteristics of the mandatory lane-changing (MCL) process in the weaving area. An analytical MCL method based on the driver’s psychological characteristics is proposed in this study. Firstly, the driver’s MLC pressure concept was proposed by leading in the distance of the off-ramp. Then, the lane-changing intention was quantified by considering the driver’s MLC pressure and tendentiousness. Finally, based on the lane-changing intention and the headway distribution of the target lane, an MLC positions probability density model was proposed to describe the distribution characteristics of the lane-changing position. Through the NGSIM data verification, the lane-changing analysis models can objectively describe the vehicle lane-changing characteristics in the actual scenarios. Compared with the traditional lane-changing model, the proposed models are more interpretable and in line with the driving intention. The results show significant improvements in the lane-changing safe recognition of CAVs in heterogeneous traffic flow (both CAVs and HVs) in the future.

Non-Intrusive ECG Measurement on Vehicle Steering Wheel and Driver Seat

2008 ◽  
Author(s):  
Shan Hu ◽  
Xun Yu
Keyword(s):  
Continuous Monitoring ◽  
Traffic Accidents ◽  
Measurement Techniques ◽  
Measurement Methods ◽  
Steering Wheel ◽  
Sleep Stages ◽  
Ecg Signal ◽  
Driver Drowsiness ◽  
Waking And Sleep ◽  
Electrocardiogram Ecg

Driver drowsiness is one of the major causes of deadly traffic accidents. Continuous monitoring of drivers’ drowsiness thus is of great importance for preventing drowsiness-caused accidents. Previous psychophysiological studies have shown that heart rate variability (HRV) has established differences between waking and sleep stages [1, 2]. This offers a way to detect driver’s drowsiness by analyzing HRV, which is typically measured and analyzed from electrocardiogram (ECG) signal. Although ECG measurement techniques are well developed, most of them involve electrode contacts on chest or head. Wiring and discomfort problems inherent in those techniques prevent implementing them on cars. To address these problems, we make full use of the environment settings in a car to develop two non-intrusive real-time ECG measurement methods for drivers.

Analysis of Sleep Deprivation Effect to Driving Performance Using ReactionTest Simulation

2015 ◽  
Vol 72 (4) ◽  
Author(s):  
Ika Nurlaili Isnainiyah ◽  
Febriliyan Samopa ◽  
Hatma Suryotrisongko ◽  
Edwin Riksakomara
Keyword(s):  
Sleep Deprivation ◽  
Traffic Accidents ◽  
Driving Simulator ◽  
Driving Performance ◽  
Deprivation Condition ◽  
Falling Asleep ◽  
Sleep Condition ◽  
Lack Of Sleep ◽  
The Difference ◽  
Social Habits

Sleep deprivation condition might lead to falling asleep through inappropriate situations, such as driving. Driving in a state of fatigue or drowsy from lack of sleep will be far worse than driving after alcohol consumption. Hence, the authors develop a driving simulator using Java to modify the control and rules of OpenDS application in order to simulate and calculate the automatic ReactionTest for 25 respondents simulating in both normal conditions and sleepy conditions when driving. Through this study, the authors obtained that the difference of driving performance in terms of reaction rate when driving the car in sleep deprivation condition and the normal condition, is equal to 1.08 seconds. The results also shown that the risk of loss of control that can occur to the driver of the car in units of meters (m), is equal to 0.3024 x the car’s speed. This study aims to reduce the number of traffic accidents caused by sleep deprivation that occur in society by giving a recommendation to the driver that forced to drive in lack of sleep condition. In top of that, the authors propose to create an understanding for changing the social habits of driving toward a better way.  

Head motion coefficient-based algorithm for distracted driving detection

2019 ◽  
Vol 53 (2) ◽  
pp. 171-188 ◽  
Author(s):  
Kwok Tai Chui ◽  
Wadee Alhalabi ◽  
Ryan Wen Liu
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Video Stream ◽  
Detection Algorithm ◽  
Distracted Driving ◽  
Head Motion ◽  
Content Type ◽  
Cell Phone Use ◽  
The Road ◽  
Video Frames

PurposeConcentration is the key to safer driving. Ideally, drivers should focus mainly on front views and side mirrors. Typical distractions are eating, drinking, cell phone use, using and searching things in car as well as looking at something outside the car. In this paper, distracted driving detection algorithm is targeting on nine scenarios nodding, head shaking, moving the head 45° to upper left and back to position, moving the head 45° to lower left and back to position, moving the head 45° to upper right and back to position, moving the head 45° to lower right and back to position, moving the head upward and back to position, head dropping down and blinking as fundamental elements for distracted events. The purpose of this paper is preliminary study these scenarios for the ideal distraction detection, the exact type of distraction.Design/methodology/approachThe system consists of distraction detection module that processes video stream and compute motion coefficient to reinforce identification of distraction conditions of drivers. Motion coefficient of the video frames is computed which follows by the spike detection via statistical filtering.FindingsThe accuracy of head motion analyzer is given as 98.6 percent. With such satisfactory result, it is concluded that the distraction detection using light computation power algorithm is an appropriate direction and further work could be devoted on more scenarios as well as background light intensity and resolution of video frames.Originality/valueThe system aimed at detecting the distraction of the public transport driver. By providing instant response and timely warning, it can lower the road traffic accidents and casualties due to poor physical conditions. A low latency and lightweight head motion detector has been developed for online driver awareness monitoring.

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