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Driving Simulator - Science topic

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Hi,
I am trying to analyze the duration of the gaze toward a specific object. Our lab has the Smart Eye Pro systems (mounted on a driving simulator), and I would like to find a way to annotate the locations of road users/objects by time so that we can track their locations video-frame by frame and determine whether the gaze landed on the road users/objects.
That being said, is there any video annotation software to save the coordinates data of the road users frame by frame?
Thank you in advance!
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If you are using smart eye pro together with iMotions, you would be able to define areas of interest (AOI). For dynamic stimuli such as moving objects this does require you move the AOI periodically and the algorithm can interpolate the points in between to give you smooth movements. Gaze based metrics are then calculated on the area automatically.
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We are assembling a simulator in our lab and we have a Sensowheel SD-LC motor that we want to work with SCANeR studio 1.9. At the moment we have the motor and controller wired through a P-CAN (CANbus - USB). The motor works correctly through the Sensodrive GUI however it doesnt seem to be detected by SCANeR studio.
Does anybody have experience with Sensodrive or SCANeR that could offer some advice?
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Dear Christopher Wilson:
You can benefit from these Links about your topic:
I hope it will be helpful..
Best wishes..
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I want to model a Nigerian Based Scenario to aid training of drivers on the simulator
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May I suggest you begin with a generic model of driving simulation that seems to you to be a good fit with questions you are seeking to answer.
which provides a useful bibliography.
Then you could progress to exploring how scenario are created in say
which also has a useful bibliography.
Then you could focus in on what may distinctive about Nigerian driving in
say
Having looked at a broad range of literature, what questions are you seeking to answer using driving simulator scenarios?
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I'm searching for the dominant acceleration and frequency of whole-body vibration in the driving simulator to have the best result in the simulation field and the most valid data to present.
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Thanks a million for your helpful answers.@ Mohamed-Mourad Lafifi @ Ijaz Durrani
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Does anyone know how we can model two different kinds of drivers on the conflict point (connector) at the highway exit ramp using microsimulation (VISSIM or Aimsun)? 1. drivers use the right-turn indicator to exit from the highway, 2. drivers do NOT use the right-turn indicator (violating drivers) but they are exiting from the highway. #VISSIM #TrafficModelling #Drivingbehaviour #Aimsum
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Using VISSIM's External Driver Model API may solve your problem. It gives you access to a lot of driving variables, in particular:
DRIVER_DATA_VEH_TURNING_INDICATOR
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Measuring driver workload subjectively is a useful way of further understanding the effects of (for example) changing environmental conditions throughout a journey. In a driving simulator study where a participants shall experience multiple 'interventions' within the same homogeneous driving scenario it would be useful to gain a quick understanding of perceived workload for each intervention. For example, drive for 5 minutes under condition X, measure workload for condition X, continue to drive into condition Y, measure workload for condition Y ... etc...
Methods such as a Driver Activity Load Index (DALI) are very useful when participants have time to stop a scenario and engage with this pen-and-paper method directly after each intervention. However, when running multiple interventions back-to-back are there any recommended metrics (non physiology-based) through which one can measure perceived workload through verbal questioning/answering in a time-efficient manner?
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An older European Union funded project, in which a couple of car and car parts manufacturers had participated, issued a report that lists a couple of methods, including the above mentioned ISI:
As to validation, I have just found a study in which several driver workload assessment methods have been compared with each other and also with EEG activity during driving:
I have only skimmed these sources, but maybe you find them worth a closer look.
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SSQ (Simulator Sickness Questionnaire) is known to have a complex factor structure, with items loading on multiple dimensions.
In the original study (Kennedy et al., 1993), it is stated that "The N, O, and D scores are then calculated from the weighted totals using the conversion formulas given at the bottom of the table."
Those formulas are:
Nausea = [ Sum obtained by adding symptom scores ] x 9.54
Oculomotor = [ Sum obtained by adding symptom scores ] x 7.58
Disorientation = [ Sum obtained by adding symptom scores ] x 13.92
Total Severity = (Nausea + Oculomotor + Disorientation) x 3.74
It is not clear in the article that how those multipliers, 9.54, 7.58, 13.92 and 3.74 were derived.
Question A: How did they derive those multipliers?
I am working on a Turkish translation of SSQ, and my results are promising. However, it looks like I need to remove some items, and make some changes in scoring.
Attached file contains a comparison of factor weights of my results and Kennedy et al's. original work, besides Bark et al.'s (2013) results on some driving simulator experiments. My results are more similar to Kennedy et al. study, compared to Balk et al study.
The data is collected through 84 participants who had 2 different VR game sessions. SSQ-TR factor analysis is done using Principal Components with Varimax rotation and 3 factors emerged based on eigenvalue>1 assumption.
Question B: I seek for suggestions for factoring the SSQ-TR.
I have some ideas on removing some items and re-adjusting item/load structure, indicated on the shared spreadsheet.
References
Kennedy, R. S., Lane, N. E., Berbaum, K. S., & Lilienthal, M. G. (1993). Simulator Sickness Questionnaire: An Enhanced Method for Quantifying Simulator Sickness. The International Journal of Aviation Psychology, 3(3), 203–220. doi:10.1207/s15327108ijap0303_3
Balk, S. A., Bertola, M. A., & Inman, V. W. (2013). Simulator sickness questionnaire: Twenty years later.
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Good Answer Joseph Smyth
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I find difficult locating recent studies of driving simulators made for learning more adaptive car-following behaviors and whose learning transference has been tested. Any hint?
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Hi,
I will have data for driver behavior which will be collected with driving simulator. Steer angle and lateral car displacement is common variable for driving performance. Im thinking about how can i categorized good and bad driving performance by determining threshold or range of lateral displacement or steer angle as bad driving behavior.
Regards,
Faezeh
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Robert Barbour Thanks Robert. I appreciate your help.
Good luck,
Faezeh
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Hello researchers!
I'm looking to acquire some general commentary (pros/cons) from researchers either using Tobii Glasses Pro or the Smart Eye Pro, and other setups!
I'm curious about things like sampling resolution for studying fixations and saccades, current gaze accuracy and precision, calibration procedures, presence of company support, but am open to hearing about other things as well.
Our context: driving simulator with wraparound display.
Generally, I've understood that Smart Eye Pro is more reliable and pragmatic, but also more expensive. Wondering whether the extra cost merits the added degree of quality.
Thank you!
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Hi Hanna,
So one thing to consider carefully are the demographics of the participants you want to test with these. If you have a lot of elder participants, they tend to wear glasses and have complex lens corrections that aren't provided by the Tobii lens kit. Also, wearing both corrective lenses and tobii pro is a big hassle and not comfortable at all. Smart Eye Pro is expensive, but their setup is mounted and very reliable to use even with corrective lenses (without causing comfort issues). Its probably the best money can buy and depends on the number of cameras you require for your setup.
If you are looking at other options, there are some such as EyeWorks (Fovio FX3-bar tracker) and DKablis glasses 3 by Ergoneers (far more suitable for corrective lenses since they are just frames).
You should also consider what type of experiments you will be carrying out, as pupillometry might be more accurate with head-mounted gear (Tobii and Dkablis). The effort placed to systematically control the lighting can play a huge role in the accuracy of these devices.
I have worked extensively with the Fovio Fx3 in simulators and it is good for saccades, fixations, gaze distributions, and easy calibration. However, pupil diameter and tracking range/angles could be much better. They also provide pretty good analysis software. Their customer support could be better but hey, its cheaper.
Keep in mind that most of these companies offer to travel to visit your location and provide free demos of their devices, so do consider shooting them an email before finalizing the purchase. It is best to see in person and do not hesitate to say NO if it does not fit your needs.
Hope this helps.
Good luck!
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Six levels (1 = low demand, 6 = high demand) of progressively increasing task demand tested with 90 participants in a driving simulator (18-65 yrs). Lighting conditions were kept constant across all scenarios. Level 6 demand involved the same scenario as level 5 but with short moments of performing a secondary task (operating the GPS to set coordinates). Secondary task took up 20% of drive time.
Here are some other trends observed from the data in question:
> Average headway initially decreases (level 1 to 3 of task demand) then increases (level 3 to 6) with task demand
> NASA-TLX records increasing workload with increasing task demand
> SART records decreasing situation awareness with increasing task demand
> Average velocity decreases with increasing task demand
> Heart rate shows no significant change with the increase in task demand
> Gaze radius (X, Y axis) decreases with increasing task demand
> Average standard deviation of lane position decreases till level 5 demand then increases. (level 6 demand involved a secondary-task with distraction)
My take is based on the "accommodation reflex": The decrease/constriction in pupil diameter is a direct result of the larger headway selected by the driver, so they can better focus on the further object. But could it be that decrease/increase in task based average pupil diameter (not event based), especially when driving, has little correlation with cognitive workload but more with situation awareness. Also, the gaze field gets narrower.
Please let me know what you think.
Regards,
Vishal
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What I am writing partly repeats Frank's comment, but perhaps different formulations of the same idea can be helpful as well.
The first question is whether the construction of of your driving simulator is such that it enforces accommodation changes. The simulator might include a model dashbord at arm's length and a projection screen for the driving environment that is farther away, so that the subjects' gaze is alternating between near and far targets and perhaps is more often directed to near targets during the more demanding tasks. In this case accommodation could explain the seemingly paradox behaviour of the pupil.
Otherwise, if all vision targets are at the same distance, it might be that the tasks with the higher mental workload require the gaze to linger on brighter areas of the display for a longer time, which could again explain a decrease in pupil size. All this has in principle been said above.
But there is still some other possibility. It might be the case that your pupil measuring equipment has a gaze dependent bias. I just saw an article which mentions that "pupil size is underestimated for leftward and downward gaze, and overestimated for rightward and upward gaze when using an EyeLink© 1000 eye-tracker; the opposite result is obtained when using Tobii© (T120 and X120) eye-trackers. (https://www.sciencedirect.com/science/article/pii/S0960982214001961#bib7)
Hope you can find the correct explanation
Detlef
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Hi guys,
Reading my question title surely has given at least some of you a flashback on their experiences during the estimation of a nonlinear system model. I hope to get some tips, tricks and useful critic on my proceedings with the model estimation. The project I’m working on is part of my bachelor’s thesis. I am thankful for every useful input form you!
What am I identifying?
My task is to identify a fully-fledged driving simulator capable of movements in nine degrees of freedom in total. The main goal is to obtain a good system model which fits the estimation and validation data and can be used for further investigation (if needed). Most importantly, as I’m conducting the thesis with an automotive OEM, not only the identification per se but also the whole process from generating measurement data, selecting a suitable model and optimizing the parameters shall be worked out in order to have a reference for future research purposes.
The driving simulator has been shown to by nonlinear and dynamic and shall be investigated as a MIMO system.
What has been done so far?
Measurement: All nine degrees of freedom have been excited with suitable position signals (Sine sweeps, discrete sine excitations, white and pink noise, Amplitude modulated pseudo binary signals) and the output has been measured as acceleration. Not only have the individual degrees of freedom (longitudinal, lateral, …) been excited (which would be a SISO case) but also a multidimensional excitation (by exciting all degrees of freedom) has been performed to identify the MIMO system.
Model selection: I am working with Matlab’s built-in toolbox from Prof. Ljung as well as a Lolimot identification toolbox from Prof. Nelles. So far, I have gained deep insight in both toolboxes and examined the different approaches in more or less full detail. In the beginning, I have played around with the GUI to get a feeling for the system models. Now, I’m exclusively working with the toolbox functions in Matlab scripts to change the model and estimation parameters arbitrarily. I want to concentrate my thesis on the estimation of a Lolimot, Narx, Hammerstein-Wiener and a linear model. This way, I want to compare the different models and I want to show that a linear model for example is not sufficient for the underlying driving simulator. In conclusion, I want to find the model that performs best for my system.
What am I planning to do next?
In the next steps of my bachelor’s thesis, I want to examine the above mentioned system models and thus have to perform a parameter optimization. The models rely on a different set of parameters (e.g. time delays, nonlinearity estimator parameters, …). As testing out all parameter combinations does not seem to be a viable option w.r.t. computing time, I have defined a DoE and want to perform a subset selection which will be representative of all parameter combinations. Using this subset (which is noticeably smaller than the huge amount of parameter possibilities of the DoE) the models shall be estimated and compared using their respective loss function values. This allows me to assign a unique value to every parameter set of the obtained subset which reflects whether the model is better or worse. Next, I want to build a response surface model and find its global minimum to find the best parameter combination of the whole subset and consequently of all parameter variations.
What questions do I have?
Before I work on the above mentioned parameter optimization, I want to make sure that I have understood everything this far and that my data is suitable for an identification. I have gained quite some understanding reading various system identification publications, however I still am not sure on two things.
Excitation signals:
The above mentioned excitations have been measured with a set of acceleration sensors all around the vehicle mockup. The measurement output has shown some pretty good results, which I used to identify other system properties like latency, phase lag, etc. I am sure that the measured signals themselves are pretty good and show minor noise in the relevant frequencies and obviously a bit more noise for lower frequencies where the noise characteristics of the sensor itself takes over. However, I am not sure whether the type of excitation is right. For dynamic systems sine sweeps and APRBS signals have yielded good results in the literature. However, an APRBS signal (step excitations with different amplitudes) shows steep peaks in the measured output of the simulator. The vehicle moves (for vertical signals) up, idles a few seconds and moves down again. The peaks result in the steep movement up and back down again. Between that is just dead time. Thus, I am not sure, whether the system dynamic has been excited strong enough. A sine sweep seems to be better and the system models estimated with both toolboxes seem to confirm that or at least manage to obtain a fit to the estimation sweep data, whereas the APRBS data is very hard to fit.
So the question here is: Is such an excitation with dead time between measurement output peaks even suitable for an identification?
Another question is: The discrete sinusoidal excitations have been designed to excite the system with one sinusoidal signal which is faded in and out, then there is 2 seconds of dead time and then the next sinusoid follows. The measured output follows suit and shows excitation with dead time between the sinusoids. Is this critical as well?
The final question here is: I have also conducted measurements with white and pink noise inputs. The statistical character makes this kind of input especially useful. Though, the signals had to be manipulated in amplitude and smoothened to not overexcite the simulator dynamics (and eventually to crash the simulator). This means, that the frequency band is not as wide as a ‘normal’ white noise, but should be in the relevant are of the simulator. Is an identification with that kind of estimation signal suitable?
Estimation and validation data:
When estimating the system models, estimation, validation and test data can be assigned. The system is being estimated based on the estimation data (training data) and can be validated by plotting the system output for the validation data. What I fundamentally do not seem to understand, or have not read yet, is whether the estimation and validation data can be fundamentally different. In most examples I have seen that the system has been trained with e.g. step inputs and validated with a different independent set of step inputs. It was then tested again with a third independent step input. What I am trying to do however, is to estimate the system based on e.g. the Sweep data, to and to validate it on the white noise signals. The question thus is: Is that even a good approach? The signals are fundamentally different.
As far as I understand or want to understand is that a successful identification of a system should be capable to represent all input-output combinations possible for the system. It is very clear to me that this will never be the case. But the underlying system in my case is able to perform sine and step excitations and many more. Should I have measured an input-output combination that contained all kinds of excitations?
In other words: What is the best way to estimate and validate the model in my case? Ljung’s toolbox does not even take validation data in consideration during estimation, it much rather relies on the user to evaluate the fit to the validation data. This is very understandable, since in most cases the evaluation is a mere decision of the user.
I am thanking all of you for input to my problems!
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Hi Ziad. I recommand you read this book
"The Volterra And Wiener Theories of Nonlinear Systems" by Dr. MARTIN SCHETZEN. This book, like all his other books, is very easy to understand .
I believe Dr. Schetzen's was Norbert Wiener's student at MIT and is among the best experts of this subject. I strongly recommend this book to anyone interested in the subject
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Hi guys,
Reading my question title surely has given at least some of you a flashback on their experiences during the estimation of a nonlinear system model. I hope to get some tips, tricks and useful critic on my proceedings with the model estimation. The project I’m working on is part of my bachelor’s thesis. I am thankful for every useful input form you !
What am I identifying?
My task is to identify a fully-fledged driving simulator capable of movements in nine degrees of freedom in total. The main goal is to obtain a good system model which fits the estimation and validation data and can be used for further investigation (if needed). Most importantly, as I’m conducting the thesis with an automotive OEM, not only the identification per se but also the whole process from generating measurement data, selecting a suitable model and optimizing the parameters shall be worked out in order to have a reference for future research purposes.
The driving simulator has been shown to by nonlinear and dynamic and shall be investigated as a MIMO system.
What has been done so far?
Measurement: All nine degrees of freedom have been excited with suitable position signals (Sine sweeps, discrete sine excitations, white and pink noise, Amplitude modulated pseudo binary signals) and the output has been measured as acceleration. Not only have the individual degrees of freedom (longitudinal, lateral, …) been excited (which would be a SISO case) but also a multidimensional excitation (by exciting all degrees of freedom) has been performed to identify the MIMO system.
Model selection: I am working with Matlab’s built-in toolbox from Prof. Ljung as well as a Lolimot identification toolbox from Prof. Nelles. So far, I have gained deep insight in both toolboxes and examined the different approaches in more or less full detail. In the beginning, I have played around with the GUI to get a feeling for the system models. Now, I’m exclusively working with the toolbox functions in Matlab scripts to change the model and estimation parameters arbitrarily. I want to concentrate my thesis on the estimation of a Lolimot, Narx, Hammerstein-Wiener and a linear model. This way, I want to compare the different models and I want to show that a linear model for example is not sufficient for the underlying driving simulator. In conclusion, I want to find the model that performs best for my system.
What am I planning to do next?
In the next steps of my bachelor’s thesis, I want to examine the above mentioned system models and thus have to perform a parameter optimization. The models rely on a different set of parameters (e.g. time delays, nonlinearity estimator parameters, …). As testing out all parameter combinations does not seem to be a viable option w.r.t. computing time, I have defined a DoE and want to perform a subset selection which will be representative of all parameter combinations. Using this subset (which is noticeably smaller than the huge amount of parameter possibilities of the DoE) the models shall be estimated and compared using their respective loss function values. This allows me to assign a unique value to every parameter set of the obtained subset which reflects whether the model is better or worse. Next, I want to build a response surface model and find its global minimum to find the best parameter combination of the whole subset and consequently of all parameter variations.
What questions do I have?
Before I work on the above mentioned parameter optimization, I want to make sure that I have understood everything this far and that my data is suitable for an identification. I have gained quite some understanding reading various system identification publications, however I still am not sure on two things.
Excitation signals:
The above mentioned excitations have been measured with a set of acceleration sensors all around the vehicle mockup. The measurement output has shown some pretty good results, which I used to identify other system properties like latency, phase lag, etc. I am sure that the measured signals themselves are pretty good and show minor noise in the relevant frequencies and obviously a bit more noise for lower frequencies where the noise characteristics of the sensor itself takes over. However, I am not sure whether the type of excitation is right. For dynamic systems sine sweeps and APRBS signals have yielded good results in the literature. However, an APRBS signal (step excitations with different amplitudes) shows steep peaks in the measured output of the simulator. The vehicle moves (for vertical signals) up, idles a few seconds and moves down again. The peaks result in the steep movement up and back down again. Between that is just dead time. Thus, I am not sure, whether the system dynamic has been excited strong enough. A sine sweep seems to be better and the system models estimated with both toolboxes seem to confirm that or at least manage to obtain a fit to the estimation sweep data, whereas the APRBS data is very hard to fit.
So the question here is: Is such an excitation with dead time between measurement output peaks even suitable for an identification?
Another question is: The discrete sinusoidal excitations have been designed to excite the system with one sinusoidal signal which is faded in and out, then there is 2 seconds of dead time and then the next sinusoid follows. The measured output follows suit and shows excitation with dead time between the sinusoids. Is this critical as well?
The final question here is: I have also conducted measurements with white and pink noise inputs. The statistical character makes this kind of input especially useful. Though, the signals had to be manipulated in amplitude and smoothened to not overexcite the simulator dynamics (and eventually to crash the simulator). This means, that the frequency band is not as wide as a ‘normal’ white noise, but should be in the relevant are of the simulator. Is an identification with that kind of estimation signal suitable?
Estimation and validation data:
When estimating the system models, estimation, validation and test data can be assigned. The system is being estimated based on the estimation data (training data) and can be validated by plotting the system output for the validation data. What I fundamentally do not seem to understand, or have not read yet, is whether the estimation and validation data can be fundamentally different. In most examples I have seen that the system has been trained with e.g. step inputs and validated with a different independent set of step inputs. It was then tested again with a third independent step input. What I am trying to do however, is to estimate the system based on e.g. the Sweep data, to and to validate it on the white noise signals. The question thus is: Is that even a good approach? The signals are fundamentally different.
As far as I understand or want to understand is that a successful identification of a system should be capable to represent all input-output combinations possible for the system. It is very clear to me that this will never be the case. But the underlying system in my case is able to perform sine and step excitations and many more. Should I have measured an input-output combination that contained all kinds of excitations?
In other words: What is the best way to estimate and validate the model in my case? Ljung’s toolbox does not even take validation data in consideration during estimation, it much rather relies on the user to evaluate the fit to the validation data. This is very understandable, since in most cases the evaluation is a mere decision of the user.
I am thanking all of you for input to my problems!
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That's a very nicely formulated and detailed question. However, the classification of dynamic systems into 'linear' and 'nonlinear' systems is like classification of animal kingdom into 'elephants' and 'non-elephants'. There is no one best way of identifying and validating a nonlinear model. There is no clear way of characterising all input-output pairs either (as one would by using a graph space for linear systems).
If the model has separable nonlinearities, one can 'mock validate' the linear part only using standard statistical techniques, assuming the nonlinear part to be exact. In general, in my limited experience, assuming exactness on SOME part of the model would normally be required for any statistical validation of nonlinear model.
I don't know if this helps your specific case, though!
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Below is the project title:
This project will investigate how psychological stress and mental workload impact physiology. Using Heart Rate Variability in comparison to an array of ‘gold standard’ physiological measures to investigate how task interaction impacts on psychophysiological stress.
Physiometric analysis methods -
•Stethography
•Galvanometry
•Blood pressure
•Core Body Temperature
Which of one of the physiological measures stated above would be best to use to measure acute stress during a driving simulation and why ?
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cortisol, heart rate, blood pressure
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Hi, hope you are doing well all. I need some suggestions regarding participants selection in driving simulator experiments. Sears (1986) stated that many psychological research findings are poorly generalizable, because the research has been conducted with university students.
According to you what should be the criteria to choose participant for driver behaviour research (specially in Automated Vehicle) with driving simulator? such as age groups, proportions of young and old, male and female etc.
Are there any research papers in your knowledge on this subjects?
Thanks a million in advance.
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Hi Sarang,
I have ran a few experiments with a driving simulator, and although I don't know of any specific papers which suggest guidelines for participant inclusion criteria - I can give you a few examples of things the research team I worked with the be important co-variates.
Handedness (left or right).
Years driving experience.
Estimated average millage over the last year.
Age.
Gender.
Depending on your study, it might also be important to consider other variables; such as familiarity with automated vehicles. Often you may also wish to issue your participants with questionnaires such as a scale for trust in automation.
I would say that aiming to get a range of ages and gender parity is important, but often studies conducted in universities may be limited in this respect.
One important thing to note when recruiting for these studies, is also "simulator sickness", there is an informal consensus that women (particularly older women) may be more prone to this, so have a procedure in place for withdrawal.
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To investigate the effects of stress on driving behaviour one can observe stressed drivers in a driving simulator. In order to do this, the participants of such a simulation study needs to be in stress of different levels.
Which possibilities to induce stress of different levels in a participant of a simulation study has proven to be useful?
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Needing to find a restroom on a crowded highway, during rush hour, after leaving late with a belly of coffee is a very real source of stress! It would be equivalent to having young children fighting in the back seat during a family outing!
If we fail to hold onto a sense of humor in a world that seems to be on the brink of chaos, we cannot go forward with important work!
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In a driving simulator it is aimed to use a game as the input of the moving platform. But when the game is being played on the computer, I need to access the linear and angular velocity and acceleration datas in the free space of the vehicle. How can I do that?
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Yasin,
Then first set of items was about simulation physics. The second set below is more specifically about data sources.
More simulation data sources here:
and here:
and here:
industry data source here:
.
With a context, what sort of study, with what goal in mind ,more precise sources can be located.
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How can we decrease the effect of cameras on driver behavior during a naturalistic driving study?
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Thank you very much!
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Hi,
Anyone knows where I can get sample EV's drive cycle data like mentioned in the picture ? I will need time,current,voltage values.
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I want to select a motor for electric vehicle. 
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The reference book for standard drive cycles is attached. I used this book for implementing several drive-cycles in my researches. This will help you.
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I just start to study about synchronous reluctance motor and want to simulate it and control its speed in Matlb .
What is the best synchronous reluctance motor state equation which can use in drive simulations?
(Reluctance Synchronous machine)
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hi,
i only recommend PSIM software.
of course if suitable for your application.
regards,
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We are planning to conduct a EEG study in our driving simulator. Now we need to purchase a new EEG amplifier for the study.
We actually planned to buy a wireless device but now we're unsure about interfering signals in the simulation environment. Does anyone have advice?
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We pioneered the measurement of EEG acquisition from drivers on the open highway in Mackie & Miller 1978.  The most reliable approach is to have 100-gain pre-amps located very close to the EEG electrodes.  Once the EEG signal has been pre-amplified, you can go wireless.  You won't go wrong with products from Advanced Brain Monitoring (ABM), for example, http://www.advancedbrainmonitoring.com/xseries/.
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Rear-end crash is a common traffic crash type, especially in expressway. When a moving car is rear-ended by another car, the driver’s emergency response may be braking the car soon, or sped to run, or feel overwhelmed and just go with the rear car. And the special behavior types will depend on driver’s different factors and characters, e.g. age, gender, driving experience. There will be some potential methods to explore this issue. We can integrate the details by analyzing real accident cases. Some related crash data can be used, such as traces on the scene, review of drivers, video of the event, VDR and OBD data of vehicles involved in accidents. Meanwhile, we can use driving simulation to test the drivers’ reaction and use questionnaire to obtain the relevant experience of drivers. The research findings will help reconstruct the crashes accurately and promote the developing of automotive safety. Do you have some good idea or recommended documents about this issue? Thanks!
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I would say both the individual differences and the traffic situation may influence drivers' response and performance. The research findings will help us understand the accidents in details and improve the design of the warning systems. On the other hand, with the deployment of the automatic emergency braking and connected vehicle technology, such type of accidents could be reduced significantly in the future. 
I hope the following references may be helpful for your research. The first 3 papers tested the driver behavior with the scenario involved with a hazard vehicle tailgating the subject vehicle from the rear. The 4th paper also includes such a scenario with other types of scenarios and investigated the subjects' subjective rating of the warnings. The last paper has a detailed breakdown of driver response and reasons of the collision with the following vehicle incidents using real world data. 
  1. Bliss, J. P., & Acton, S. A. (2003). Alarm mistrust in automobiles: how collision alarm reliability affects driving. Applied Ergonomics, 34(6), 499-509.
  2. Baldwin, C. L., & May, J. F. (2011). Loudness interacts with semantics in auditory warnings to impact rear-end collisions. Transportation research part F: Traffic psychology and behavior, 14(1), 36-42.
  3. Lehto, M. R., Papastavrou, J. D., Ranney, T. A., & Simmons, L. A. (2000). An experimental comparison of conservative versus optimal collision avoidance warning system thresholds. Safety Science, 36(3), 185-209.
  4. Baldwin, C. L. (2011). Verbal collision avoidance messages during simulated driving: perceived urgency, alerting effectiveness, and annoyance. Ergonomics, 54(4), 328-337.
  5. Dingus, T. A., Klauer, S. G., Neale, V. L., Petersen, A., Lee, S. E., Sudweeks, J. D., ... & Bucher, C. (2006). The 100-car naturalistic driving study, Phase II-results of the 100-car field experiment (No. HS-810 593).
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I intend to use Lane Change Test (LCT) on a driving test track. Can someone please suggest me what is the ideal no. of lane change markers in one kilometer distance.
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I would hope that the test protocol defines what lane change is required.  Suggest that you start by having a look at ISO 3888
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Governments around the world are pushing hard to put automated vehicles on the road. Indeed, even when it comes to testing driver behaviour there is a trend towards on-road testing. However, we are still at the very early stages of understanding how safe drivers will be in their interaction with highly automated driving systems. Every mile that is travelled or tested on the road with an automated system where there are no interaction issues lends further support to the idea that the on-road testing methodology is safe and preferred.
However, absence of evidence is not evidence of absence. With that in mind, outside of it being a safe and controllable environment, how do we convince governments and funding bodies of the added and continued value of studying behaviour in a simulator? How do simulators remain relevant?
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I agree with all above writing people that simulators will remain important for scientific purposes. We are at the beginning of higher automation in cars.
Aviation is about 30 years ahead of road traffic with respect to automation. Does aviation have many research flight simulators today? Yes! Does aviation have even far more simulators and different simulator types for training today? Yes!
Maybe driving simulators will also be used for training in future.
- There is basic/initial training. Training of manual driving skills, traffic rules (in real situations compared to learning with books), behavior of pedestrians, etc. Parts of this training could be done by the use of simulators.
- There will be training for the use of automation; to operate a kind of autopilot and set different modes. Monitoring is also an important issue: understanding of system boundarys and take over performance.    
- And what about an additional license for highly automated road vehicles? For busses and trucks only? Also for highly automated cars?
- What about additional training for non-automated drivers to understand how other highly automated car behaves in certain situations?
- Recurrent / Refresher training?
I guess some of these issues will be addressed and discussed in future and simulators will remain important tools for different purposes.   
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Driving simulator is used to simulate the driving behavior like normal traveling on roads. It will stop the vehicle when crash happens. At that time, I always wonder why it couldn’t be designed as working continuously even a short period of time, and then we could know the behavior of drivers during collision. Due to the zero risk of this type of test, if necessary, maybe some accidents could be reconstructed through human involvement simulation in place of computer simulation.
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Dear Quan Yan,
Yes, you can continuously run the simulation post-crash, depending on your  simulation engine/software. We developed our own for research purposes and thus we are able to control the behaviour of the simulation software like what we want. Perhaps you might want to contact simulation professional software developers ( like drive square above ) for assistance. 
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I conducted a driving simulator study in which each participant (32 participants in total) passed each of the four infrastructural conditions in a randomized order. The road segment nearby the infrastructural condition was subdivided in ten sections of 50 meter. For each section, we recorded the mean speed and the mean lateral position for each participant. My dataset has thus 40 columns (4 conditions x 10 road sections).
Based on this research design, I would like to perform a 4 (condition) x 10 (section) within-subjects MANOVA for mean speed and mean lateral position. In SPSS I run a GLM_Repeated measures with two within-subject factors (condition and section) which have 4 and 10 levels respectively. The measure names are speed and LP (from lateral position). Than, I select my columns and drag them to the field "within-subjects variables".
In my SPSS output I find two tables which attract my attention: first there is the table called “Multivariate Tests” and second a table “Multivariate” under the heading “Tests of within-subjects effects”. Because the study has a full within-subjects design, my question is “Which table do I have to use in my analysis description?”. It is important to note that some of the test statistics differ between both tables and that some cells of the table “Multivariate Tests” are empty because SPSS “Cannot produce multivariate test statistics because of insufficient residual degrees of freedom”.
Can someone explain the difference between the two tables (Multivariate Tests and Tests of within-subjects effect_Multivariate) and which table is preferable to use in my data analysis?
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If you are conducting a repeated measures then you are just doing an ANOVA, a MANOVA would begin by you doing a Multivariate GLM. Within-subjects effects is what you would need to focus on.
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I am trying to write a scenario that includes a lead vehicle. I have chosen V (Vehicle) event from the SDL events. The problem is the lead vehicle needs to brake randomly during 15 min. drive. However, there is only 5 separate sets of parameters in triggered event section. How can I solve this problem?
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I understand that your question is more practical - related to the software - rather than theoretical - how to generate the set of random braking events, for good representativeness of real world events. However, if you are interested in discussing also the latter, feel free to contact me.
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In a driving simulator, participants are asked to null out the scene that has been perturbed by a sum-of-sine wave forms (3), using a steering wheel. After processing the data using a Fast Fourier Transform, some participants have a negative phase angle for some or all of the frequencies. 
We expect participants to have positive phase values, meaning their response signal lags behind the input signal. I understand that a negative phase value means that the response signal is leading the input signal. However, this is not what we would expect, as people should not be able to "predict" the input signal.
Are we missing something or are there questions that we haven't thought about yet?
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Are you looking at the phase of the Fourier spectrum of the steering wheel angle data...or...a Fourier-domain "coherence" measure (which would capture the relationship between your forcing function and the driver's response inputs)???
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My lab is looking into integrating the two systems and was curious to see if anyone had experience with combining the two methods? It is definitely present in the literature but I wanted to hear from people directly on what systems worked well together. Ideally, we'd want two portable systems that could potentially be wireless and not sensitive to some movement, since the project is geared towards driving and in a driving simulator. Also, I am trying to use eye tracking with it as well and leaning towards Tobii. If anyone has experiencing coupling eye tracking with NIRS or EEG that'd be helpful as well. Hope to hear from some of you.
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Hi Andrew, we developed custom sensors and also used biopac fnir devices sensors with Neuroscan EEG systems in our studies. Also, for eye tracking used SMI systems' goggle based version. Hope these help.
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I will need to display an image file and a sound file for a short period of time in one of the driving scenarios. I will use STISIM drive version 3 software. As I have no experience with this software, does anyone know if it's possible to do this? 
By the way, the image file needs to be displayed above a moving vehicle. I'm aware that I will need to use the programmable module for this.
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STISIM Drive uses something called "Open Module" which is a kind of plug-in software that lets you add functionality via programming languages such as C++
An easier route may be the included Scenario Definition Language (SDL) which is a scripting language to detail and add environments and events along the way (hazardous situations, objects, changes).
There is a basic overview of these features in these two document:
Each has links to the advisory who can provide details on the SDL (there is a guide available), and can also advise on the API for linking via C++
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I will run an experiment with a driving simulator and oculus rift. I would like to analyse the behavioural responses through video analysis. Does anyone know a scheme of behaviours that is correlated with Sense of Presence? 
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The ITC-SOPI is one option (Tom Flint linked to it above) if you want to get subjective measures through a subject-provided questionnaire. There have been some studies that you could use to start with for forming an observational scoring technique, but nothing that has been extensively validated. To start, take a look at:
Huang, M., & Alessi, N. (1999). Presence as an emotional experience. In J. D. Westwood, H. M. Hoffman, R. A. Robb, & D. Stredney, Medicine meets virtual reality: The convergence of physical and informational technologies options for a new era in healthcare. Amsterdam, The Netherlands: IOS Press.
Nichols, S., Haldane, C., & Wilson, J. R. (2000). Measurement of presence and its consequences in virtual environments. Internation Journal of Human Computer Studies , 52, 471-491
Meehan, M. (2001). Physiological reaction as an objective measure of presence in virtual environments, Doctoral Dissertation. University of North Carolina at Chapel Hill.
But keep in mind that behavior observation measures of presence can be very difficult. For one, an experimenter bias can exist, where an experimenter reads into a behavioral response too much or too little. You can mitigate this bias by having independent experimenters score the behavioral responses based on a pre-agreed upon scoring system. Of course, this requires that all the experimenters be trained on how to properly encode behavioral responses.
Another issue is that behavioral responses cannot always be generalized across all environments. Often an expected response is limited to the single situation and content where the response was observed. So you would need to be extremely careful with any implications you draw from your experiment.
If you want more information on developing a measurement device for Presence, I'd invite you to read my dissertation, available at http://etd.fcla.edu/CF/CFE0002779/Chertoff_Dustin_B_200908_PhD.pdf.
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I will run an experiment with oculus rift DK2 and I am searching a drive simulator (preferably not a racing simulator). Any Idea? 
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Euro Truck Simulator 2 has a decent Oculus Rift support and I think you'll find it quite enjoyable to do a slow-paced driving with Rift.
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A lot has been written about this model (6 chassis DoFs, 4 spindle DoFs, 4 tyre rotations and the steering rack). However, I couldn't find the mathematics/modeling assumptions behind this model.
My aim would be to develop a driving simulator with master students on the long term.
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Hi, Laurent,
I think that this is a rather complicated problem. I've been studying the torsional behavior of automotive powertrains for the last seven years and I can tell you that a model with so many degrees of freedom may harm your analysis.
Depending on the NVH phenomenon I'm studying, I can derive a 3 or 4 DoF model which describes well the problem I have to face. I say this because I've already developed an 18 DoF model for the whole powertrain and my conclusion was that I had no gain for analysing results from such a large model. However, integration became very difficult. It took me a week to simulate 10s of data.
Furthermore it can be rather complicated to link torsional dynamics and body vehicle dynamics, both with a good degree of accuracy. Keep in mind that the more sophisticated is your model, the more data you will need, and in my case this is the worst problem I have to face: finding companies who could obtain and provide me these data so that I can validate my models.
The way I found to address this problem was to develop a very detailed model and simulate it with estimated data. Then I applied model reduction techniques so that I could find the "smallest" model which could still represent the phenomena I wanted to study. Finally, with a minimal model in hands I contacted the companies to obtain only the data I really needed.
These would be my two cents about your problem. =)
Best wishes,
Vinícius
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Is there any EEG database for distraction or attention in driving? I've searched for nearly one week but find none.
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Hi,
I suggest you contact Benjamin Blankertz team. Have a look at their paper entitled 'EEG potentials predict upcoming emergency brakings during simulated driving'.