QUESTION
Design an experiment to do the following:
Possible Scenarios
Scenario 1 – Task analysis of office worker (for example secretary) in an
office environment.
Scenario 2 – Situational Awareness of heavy vehicle driver ( simulation)
Scenario 3 – Mental Workload during driving ( simulation)
Scenario 4 – Environmental and ergonomic evaluation in a warehouse.
Your report should cover the following points ( as applicable):
1. Experiment Title
2. Outline the problem area or Objectives of this experiment and the
hypothesis
3. Outline the assumptions for the scenario – Lab or field study.
4. Outline equipment required (consult with the Instructor – if required)
5. Define inclusion and exclusion criteria
6. Outline sample size ( if relevant)
7. Experimental protocols for human subject studies ( if applicable)
8. Do a literature review and identify methods that can be used to do
task analysis/ Situational Awareness/ mental workload/ ergonomic or
environmental evaluation in the scenario chosen.
9. Compare and contrast a few methods and define why you choose a
particular method for your study
10. Apply the HF method to collect data from your subjects.
11. Conduct data analysis, apply the statistical tools to perform the data
analysis.
12. Explain your findings and analysis.
13. Relevant concept drawings and graphs / tables with clear
explanations are encouraged in your Lab report
14. Discuss solutions to the problems that have been identified. Identify
the practicable solutions that can be implemented.
15. Conclusion
SOLUTION
The Experiment here in this Lab report has been designed to find out and investigate the real-time effects of an Adaptive Cruise Control (ACC) system along with cell phone which increase the mental workload on the driver while driving through simulation techniques.This lab experiment will be helpful in finding the effects of ACC and cell phone while doing multiple driving tasks and communication tasks for the driver and how it will affect the drivers performance in case of the eighteen participants who drove in driving simulation a virtual car while changing the lateral position and the speed. In this experiment 9 participants responded while simulation driving to the cell phones without using the ACC system and the measurement of task performance was done in the terms of speed controls and lane deviation while tracking simultaneously the lead vehicle along with the headway distance in this experiment.Simulation freeze technique was used to measure thesituation awareness, while the uni-dimensionalmental workload rating was used to measure the subjective workload. The results clearly indicate that the ACC system is the perfect way to enhance the driving task in case of typical conditions of driving and in order to diminish eth driver mental workload too. But the cell phone increased the mental workload on the driver and caused dangerous effects on the situation awareness too.The secondary task of conversing on the cell phone created a zone of competition for the driver with limited mental resources which he has, whereas the ACC system has multiple dimensional effects by enhancing the driving performance. During the conducting of the experiment the cell phone durations was of very short duration tats why they didn’t show any kind of multiple dimensional effects on the driver’s performance.This experiment is helpful in findingtheimplications of implementing the in-vehicle automation in order to support and enhance the prospect ofsituation awareness amongst the drivers under normal driving conditions and restriction on using cell phones while driving.
Introduction
The central processing resources of the driver are placed under high level of demands while steering a car or manoeuvring through several tricky traffic situations and in such cases we define mental workload as the total and cognitive effort which the driver or any person invests to deliver best performance while doping any task (Hart & Wickens, 1990). From this definition it is clear that mental workload is a complex and multidimensional phenomenon and several parameters are needed to measure it in the operation of various machines and while driving too (Gopher & Donchin, 1986) (Bao, Kiss, & Wittmann, 2002).
Driving is a dynamic control system just like flying aircraft, because the system input variables change with the task time in case of driving too. The input variables mainly comprise of the environmental factors which have some level of uncertainty attached to them. Few of the input variables are:
• Weather Conditions
• Roadway Conditions
• Driver Conditions
• Vehicle Conditions
On the basis of the information found on the state of environment the driver chooses the course of action which might or might not change the state of the system. In general there are five types of time-phased information processing systems (IP) which are:
• Perception
• Projection
• Comprehension
• Decision of action course
• Implementation of picked action
In US too the ACC control systems got implemented in year 2005 , when Ford announced that ACC will be optional in the new Jaguar 2005 S-Type (Ford, 2005). With increased cell phone usage the drivers might opt to use the much advanced automation technologies while driving like ACC in near future as it is helpful in enhancing the efficiency and thecomfort along with improved system and safety of the operator too, by giving perfect control and navigation assistance.It might also have negative effect on the behaviour of the driver as it increases drivers monitoring workload and causes the distraction thus diverting the attention from the driving task performance (Ward, 2000).
Literature Review
The study on-in-vehicle automation techniques like ACC system and workload by fully automating the task of driving by adjusting speed of the vehicle in relation to the least following distance and efficient monitoring of the traffic and initiating the activities like braking or accelerating when the detected headway is larger or smaller than the set criterion. It has been found that ACC enhanced the safety levels of driving as it minimised the instances of unsafe headway distance in the driving tasks (Ward, 2000). However many negative effects have been found in case of SA ,which gets reduced due to poor concentration to lane-positioning and failure in traffic compliance and response times which are slow to the sudden events whenthe driver uses the ACC system.
Studies done empirically show that in-vehicle automation does affect the performance, attention allocation and the mental workload of the driver (Parker, Malisia, & Rudin-Brown, 2003) (Rudin-Brown, Parker, & Malisia, 2003). The main concern of drivers today is the the in-vehicle device’s impact on the actions of the drivers and their safety (Jerome, Ganey, Mouloua, & Hancock, 2002). It is critical to find with the increased cell phone usage while driving, that whether the cell phone conversations cause any impact on the driver’s mental workload and decrease the SA and finally result in decr4esed driving performance or not.
The results of the study (Chen & Lin, 2003) indicated that cell phone usage while driving can have hazardous effects on the safety of the driver.
Dramatic increase of cell phone usage and the implementation of ACC hinder the performance of the driver due to increased IP loads because he has the additional task of collecting information from ACC and listening to the cell phone conversation while driving. The rationale of the proposed experiment is to study:
• The effects of ACC and cell phone on driver and theperceived mental workload of the driver by measuring it.
• To find and assess the multiple driving competitive environment and the task of communicating for limited mental resources.
• To find ways that how to facilitate and support the driver through in-vehicle implementation techniques in case when automation is vital to enhance the driving performance of driver.
This Experiment will also help in finding how drivers should create a perfect balance between the secondary tasks and riving so that the driving performance is enhanced with reduction of workload.
Method
4.1 Task
The main task which has been utilised in this experiment is Freeway-driving environment of medium fidelity in 3-dimensional simulation .It is being presented using the stereo type of display with the help ofthe virtual reality (VR) system .
Fig 1: Experiment Setup Equipment
Realistic automobile interfaces have been used to input the user controls like brake pedals, Physical gas and physical steering wheel as seen in the above diagram. The participants had to drive on the roadway while maintainingtheir vehicles in the lanes on the right hand side and needed to keep their vehicle in teh middle of specific lane while following the lead vehicle and while following they had to keepfollowing all the present road signs too. The partakers had to go through the ACC as well as no ACC controls with the help of simulator and without any physical boundarywith reference to the automation.
Fig.2: Close view of Driving Simulation Display
As a secondary task cell phone conversation was induced for some of the participants cell and the experimenter asked the driver or participant 10 arithmetic problems per call.
The participants drove on the freeway which was about 2 miles longfor around 25 minutes while undergoing every trial . The freeway length of 2 miles included the curves as well as thestraightway and the giant loop configuration.
4.2 Experimental Design
The experiment was conducted with the ACC modes and the cell phone usage conditions as the independent variables. The cell phone arithmetic problems or questions were altered or manipulated for different subjects to reduce thecarry-over effects.
The dependent variable that is the driver’s situation awareness was used measuring the Situation Awareness Global Assessment technique (SAGAT). SAGAT is a technique which is used for simulation freezingin which various queries are put forth in front of the drivers when they are in the state of simulation at different points in time (Endsley, 1995b). In this experiment the simulation of driving in every trail was being frozen at 7, 14 and 21 minutes and followed by closing the display screens . Now the participants had to fill a questionnaire on another workstation which included Level 1, Level 2, and Level 3 queries . After recalling the car locations and colours or traffic signals they have gone through participants were asked to spotdifferent driving behaviours like braking, accelerating or turning and thesubjective workload was measured by using the rating scale used for mental demand which contains anchors marked as “Low” along with the “ High” rating scales. An “X” was marked by the participant where they felt the most accurate demand for trail and then the distance was measured form the “Low” and response measure was thus measured distance which was divided by the total height of the scale.
Measurement of Task performance wasdone using criterion like lane maintenance, keeping safe headway distanced from lead car and tracking lane changes which was traced after every one second using the VR computer system.
4.3 Equipment
The VR engine used VirtualEnvironmentSoftware Sandbox and was programmed with Visual C++. The participants had to wear stereographic goggles to view in 3D mode and all the cell phone conversations were conducted using the Motorola T720 cell phone.
4.4 Participants
All eighteen participants were studentsof college with 20/20 clear vision and all the partakers had bare minimum 1 year of experience of driving, with an average age of 26.6 years.
4.5 Procedure
With a 20 minute driving simulation instruction the participants performed the simulation driving task and performed a subjective workload rating scale with instructions related to it.The participants had to typically concentrate on the task of driving with residual attention to the cell phone response and the participants took the calls at an interval of 3, 10 and 17 minutes in every trial while the calls were not more than 2 minutes and the whole experiment took total 3.5 to 4 hrs and the schedule of experiment is:
Data Analysis and Results
5.1 Driving Workload
The results show that there is found to be not any kind of interaction effect on the mental workload of the driver of the ACC control mode and cell phone conversation. The results of Analysis of Variance (ANOVA) show the subjective ratings depicted for mental demand in case of task performed while driving gets influenced by ACC control mode (F (1, 16) = 68.46, p< 0.0001) along withthe cell phone conversation(F (1, 16) = 8.54, p= 0.01).Without the ACC control mode the mean percent mental demand was much more and greater perception of mental workload was present whenever the cell phone conversations were occurring.
Fig. 3: Mean Subjective rating of percent mental demand in case of ACC control and Cell phone conversationconditions.
Thus the findings of this experiment support the hypothesis that during typical driving conditions the usage of ACC will help in reduction of mental workload on driver and on the contrary the usage of cell phone will increase the workload on the driver.
5.2 Driving Performance
The study suggests that the vehicle control becomes more proficient with the help of ACC system, whereas cell phone conversation does not have any effect. In case of Headway distance the drivers showed more deviation in case of ACC control being inactive from the ANOVA results it is visible, which indicated towards need for being morecautious.
Fig. 4: RMSE headway distance for cell phone conversation conditions and ACC control
In terms of variation in the following speed there has been no interaction between the ACC control and cell phone conversation and our findings that ACC control will support better lane maintenance on curves. However the results obtained do not maintain and go along with the hypothesis suggested(Chen & Lin, 2003) who were of the opinion that the task performance gets decreased due to cell phone conversation while driving, which is again related to the time span or the call duration of the cell phone conversations.
Discussion
A Correlation Analysis was done to find the relationship between not just the secondary task performance and situation awareness but also included the mental workload also. But thePearson correlation coefficient was more on negative linear association as found between workload ratings and the performance of the secondary task(r = -0.447, p = 0.0063). This shows that as the mental demand rating increased in tend task of driving the secondary task performance decreased.Thus this clearly depicts the competition between the cell phone usage and driving tasks for the mental resources and there should be secondary task measures for mental workload which are required during the simulated task of driving.
Conclusion
Thus the experiment results clearly support that in-vehicle automation techniques like ACC help in increasing the driver’s situation awareness as it relives the driver of not just the vehicle monitoring but also the motor control workload and thus eth driver can pay more attention to the primary task of driving. This also concludes that the cell-phone usage is detrimental to the drivers safety while driving as it competes with the limited mental resources of the driver (Gugerty, 2003).Thus in future there is need to research the effect of interaction of the in-vehicle automation and the usage of device on the driver’s awareness of situation while the driver is exposed to hazardous or emergency driving situations. Thus the need is to develop and find out measures which are driver friendly and support safe driving and performance under not juts normal circumstances but also under accidental or emergency situations too.
Bibliography
Bao, Y., Kiss, M., & Wittmann, M. (2002). Effects of age and memory grouping on simulated car driving. Proceedings of the Human Factors and Ergonomics Society 46th Annual Meeting, (pp. 1853–1857.).
Chen, H., & Lin, C. (2003). Effects of wireless communicationon driving performance using a desktop driving simulator. XVth Triennial Congress of the International Ergonomics Association Conference. Seoul, Korea.
Endsley, M. (1995b). Measurement of situation awareness in dynamic systems. Human Factors , pp. 65-84.
Ford. (2005). Adaptive cruise control. Retrieved March 17, 2012, from Ford Website: http://www.ford.com/en/innovation/vehicleFeatures/adaptiveCruiseControl.htm
Gopher, D., & Donchin, E. (1986). Workload – an examination of the concept. New York: Wiley.
Gugerty, L. R. (2003). Differences in remote versus in-person communications while performing a driving task. 47th Annual Meeting of the Human Factors and Ergonomics Society. Santa Monica, CA: Human Factors and Ergonomics Society.
Hart, S., & Wickens, C. (1990). Workload assessment and prediction. New York: Van Nostrand Reinhold.
Jerome, C., Ganey, H., Mouloua, M., & Hancock, P. (2002). Driver workload response to in-vehicle device operations. International Journal of Occupational Safety and Ergonomics , 539–548.
Parker, H., Malisia, A., & Rudin-Brown, C. (2003). Adaptive cruise control and driver workload. XVth Triennial Congress of the International Ergonomics Association Conference (CD-ROM),. Seoul, Korea.
Rudin-Brown, C., Parker, H., & Malisia, A. (2003). Behavioral adaptation to adaptive cruise control. 47th Annual Meeting of the Human Factors and Ergonomics Society. Santa Monica, CA: Human Factors and Ergonomics Society.
Ward, N. (2000). Automation of task processed: an example of intelligent transportation systems. Human Factors and Ergonomics in Manufacturing , 395–408.
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