Development of 4K driving simulator and Measurement of eye movement and physiological data during the driving

Author(s):  
Gaku Iizuka ◽  
Yuta Saito ◽  
Mitsuho Yamada
1989 ◽  
Vol 1 (2) ◽  
pp. 230-241 ◽  
Author(s):  
Thomas J. Anastasio ◽  
David A. Robinson

The mechanisms of eye-movement control are among the best understood in motor neurophysiology. Detailed anatomical and physiological data have paved the way for theoretical models that have unified existing knowledge and suggested further experiments. These models have generally taken the form of black-box diagrams (for example, Robinson 1981) representing the flow of hypothetical signals between idealized signal-processing blocks. They approximate overall oculomotor behavior but indicate little about how real eye-movement signals would be carried and processed by real neural networks. Neurons that combine and transmit oculomotor signals, such as those in the vestibular nucleus (VN), actually do so in a diverse, seemingly random way that would be impossible to predict from a block diagram. The purpose of this study is to use a neural-network learning scheme (Rumelhart et al. 1986) to construct parallel, distributed models of the vestibulo-oculomotor system that simulate the diversity of responses recorded experimentally from VN neurons.


Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1029 ◽  
Author(s):  
Thomas Kundinger ◽  
Nikoletta Sofra ◽  
Andreas Riener

Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.


Safety ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 24 ◽  
Author(s):  
Darko Babić ◽  
Dario Babić ◽  
Hrvoje Cajner ◽  
Ana Sruk ◽  
Mario Fiolić

The study investigates how the presence of traffic signalling elements (road markings and traffic signs) affects the behaviour of young drivers in night-time conditions. Statistics show that young drivers (≤30 years old) are often involved in road accidents, especially those that occur in night-time conditions. Among other factors, this is due to lack of experience, overestimation of their ability or the desire to prove themselves. A driving simulator scenario was developed for the purpose of the research and 32 young drivers took two runs using it: (a) one containing no road markings and traffic signs and (b) one containing road markings and traffic signs. In addition to the driving simulator, eye tracking glasses were used to track eye movement and an electrocardiograph was used to monitor the heart rate and to determine the level of stress during the runs. The results show statistically significant differences (dependent samples t-test) between the two runs concerning driving speed, lateral position of the vehicle, and visual scanning of the environment. The results prove that road markings and traffic signs provide the drivers with timely and relevant information related to the upcoming situation, thus enabling them to adjust their driving accordingly. The results are valuable to road authorities and provide an explicit confirmation of the importance of traffic signalling for the behaviour of young drivers in night-time conditions, and thus for the overall traffic safety.


2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Haiwei Wang ◽  
Jianrong Liu ◽  
Feng You

With the rapid development of advanced mobile intelligent terminals, driving tasks are diverse, and new traffic safety problems occur. We propose a new research on physiological characteristics and nonparametric tests for the master-slave driving task, especially for evaluation of drivers’ mental workload in mountain area highway in nighttime scenario. First, we establish the experimental platform based driving simulator and design the master-slave driving task. Second, based on the physiological data and subjective evaluation for mental workload, we use statistical methods to composite the physical changes evolution analysis in a driving simulator. Finally, we finished nonparametric test of the drivers’ psychological load and road test. The results show that in compassion with the daytime scenario, drivers should pay much effort to driving skills and risk identification in the nighttime scenario. Thus, in the same driving condition, drivers should bear the higher level of mental workload, and it has been subjected to even greater pressures and intensity of emotions.


Author(s):  
Kim R. Hammel ◽  
Donald L. Fisher ◽  
Anuj K. Pradhan

Driving simulators and eye tracking technology are increasingly being used to evaluate advanced telematics. Many such evaluations are easily generalizable only if drivers' scanning in the virtual environment is similar to their scanning behavior in real world environments. In this study we developed a virtual driving environment designed to replicate the environmental conditions of a previous, real world experiment (Recarte & Nunes, 2000). Our motive was to compare the data collected under three different cognitive loading conditions in an advanced, fixed-base driving simulator with that collected in the real world. In the study that we report, a head mounted eye tracker recorded eye movement data while participants drove the virtual highway in half-mile segments. There were three loading conditions: no loading, verbal loading and spatial loading. Each of the 24 subjects drove in all three conditions. We found that the patterns that characterized eye movement data collected in the simulator were virtually identical to those that characterized eye movement data collected in the real world. In particular, the number of speedometer checks and the functional field of view significantly decreased in the verbal conditions, with even greater effects for the spatial loading conditions.


2018 ◽  
Author(s):  
Emilio Salinas ◽  
Terrence R. Stanford

Diverse psychophysical and neurophysiological results show that oculomotor networks are continuously active, such that plans for making the next eye movement are always ongoing. So, when new visual information arrives unexpectedly, how are those plans affected? At what point can the new information start guiding an eye movement, and how? Here, based on modeling and simulation results, we make two observations that are relevant to these questions. First, we note that many experiments, including those investigating the phenomenon known as “saccadic inhibition,” are consistent with the idea that sudden-onset stimuli briefly interrupt the gradual rise in neural activity associated with the preparation of an impending saccade. And second, we show that this stimulus-driven interruption is functionally adaptive, but only if perception is fast. In that case, putting on hold an ongoing saccade plan toward location A allows the oculomotor system to initiate a concurrent, alternative plan toward location B (where a stimulus just appeared), deliberate (briefly) on the priority of each target, and determine which plan should continue. Based on physiological data, we estimate that the actual advantage of this strategy, relative to one in which any plan once initiated must be completed, is of several tens of milliseconds.


Author(s):  
Yidu Lu ◽  
Nadine Sarter

Trust miscalibration remains a major challenge for human-machine interaction. It can lead to misuse or disuse of automated systems. To date, most trust research has relied on subjective ratings and behavioral or physiological data to assess trust. Those trust measurements are discrete, disruptive and quite difficult to implement. To better understand the process of trust calibration, we propose eye tracking as an unobtrusive method for inferring trust levels in real time. Using an Unmanned Aerial Vehicle simulation, participants were exposed to varying levels of reliability of an automated target detection system. Eye movement data were captured and labeled as high or low trust based on subjective trust ratings. Feature extraction and raw eye movement data were compared as input for multiple classification modeling methods. Accuracy rates of 92% and 80%, respectively, were achieved with individual-level and group-level modeling, suggesting that eye tracking is a promising technique for tracing trust levels.


Author(s):  
Thomas Kundinger ◽  
Phani Krishna Yalavarthi ◽  
Andreas Riener ◽  
Philipp Wintersberger ◽  
Clemens Schartmüller

Purpose Drowsiness is a common cause of severe road accidents. Therefore, numerous drowsiness detection methods were developed and explored in recent years, especially concepts using physiological measurements achieved promising results. Nevertheless, existing systems have some limitations that hinder their use in vehicles. To overcome these limitations, this paper aims to investigate the development of a low-cost, non-invasive drowsiness detection system, using physiological signals obtained from conventional wearable devices. Design/methodology/approach Two simulator studies, the first study in a low-level driving simulator (N = 10) to check feasibility and efficiency, and the second study in a high-fidelity driving simulator (N = 30) including two age groups, were conducted. An algorithm was developed to extract features from the heart rate signals and a data set was created by labelling these features according to the identified driver state in the simulator study. Using this data set, binary classifiers were trained and tested using various machine learning algorithms. Findings The trained classifiers reached a classification accuracy of 99.9%, which is similar to the results obtained by the studies which used intrusive electrodes to detect ECG. The results revealed that heart rate patterns are sensitive to the drivers’ age, i.e. models trained with data from one age group are not efficient in detecting drowsiness for another age group, suggesting to develop universal driver models with data from different age groups combined with individual driver models. Originality/value This work investigated the feasibility of driver drowsiness detection by solely using physiological data from wrist-worn wearable devices, such as smartwatches or fitness trackers that are readily available in the consumer market. It was found that such devices are reliable in drowsiness detection.


2003 ◽  
Vol 90 (6) ◽  
pp. 3809-3815 ◽  
Author(s):  
Diana M. Dimitrova ◽  
Mary S. Shall ◽  
Stephen J. Goldberg

Recent studies have suggested that extraocular muscle (EOM) pulleys, composed of collagen, elastin, and smooth muscle, are among the tissues surrounding the eye. High-resolution magnetic-resonance imaging appears to indicate that the pulleys serve to both constrain and alter the pulling paths of the EOMs. The active pulley hypothesis suggests that the orbital layer of the EOMs inserts on the pulley and serves to control it. Based on anatomical data, the active pulley hypothesis also suggests that the orbital layer does not rotate the eye within the orbit; this is done by the global layer of the muscle. However, no physiological data exist to confirm this hypothesis. Here we used stimulation-evoked eye movements in anesthetized monkeys and cats before and after destruction of the lateral rectus muscle pulley by removal of the lateral bony orbit and adjacent orbital tissue. The absence of these structures resulted in increased lateral, in the primate, and medial, in the cat, eye-movement amplitude and velocity. Vertical eye movements in the cat were not significantly affected. The results indicate that these increases, confined to horizontal eye-movement amplitude and velocity, may be attributed to passive properties within the orbit. In relation to the active pulley hypothesis, we could discern no clear impact (in terms of amplitude or velocity profile of the movements) of lateral eye exposure that could be directly attributable to the active lateral pulley system.


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