ADAS at Work: assessing professional bus drivers\u27 experience and acceptance of a narrow navigation system.

Due to the argued benefits of passenger comfort, cost savings, and road safety, the bus sector is showing increasing interest in advanced driver-assistance systems (ADAS). Despite this growth of interest in ADAS and the fact that work tasks are sometimes complicated (especially docking at bus-stops which may occur several hundred times per shift), there has been little research into ADAS in buses. Therefore, the aim of this study was to develop further knowledge of how professional bus drivers experience and accept an ADAS which can help them dock at bus-stops. The study was conducted on a public route in an industrial area with five different bus-stops. Ten professional bus drivers got to use a narrow navigation system (NNS) that could dock automatically at bus-stops. The participants’ experience and acceptance were investigated using objective as well as subjective data (during and after the test-drive) and data were collected using interviews, questionnaires, and video recordings. The participants indicated high levels of trust in and acceptance of the NNS and felt that it had multiple benefits in terms of cognitive and physical ergonomics, safety, and comfort. However, the relatively slow docking process (which was deemed comfortable) was also expected to negatively affect, e.g., timetabling, possibly resulting in high stress levels. Therefore, when investigating users’ acceptance of ADAS in a work context, it is important to consider acceptance in terms of the operation, use, and work system levels and how those levels interact and affect each other

Quantifying cognitive workload and defining training time requirements using thermography

Effective mental workload measurement is critical because mental workload significantly affects human performance. A non-invasive and objective workload measurement tool is needed to overcome limitations of current mental workload measures. Further, training/learning increases mental workload during skill or knowledge acquisition, followed by a decreased mental workload, though sufficient training times are unknown. The objectives of this study were to: (1) investigate the efficacy of using thermography as a non-contact physiological measure to quantify mental workload, (2) quantify and describe the relationship between mental workload and learning/training, and, (3) introduce a method to determine a sufficient training time and an optimal human performance level for a novel task by using thermography. Three studies were conducted to address these objectives. The first study investigated the efficacy of using thermography to quantity the relationship between mental workload and facial temperature changes while learning an alpha-numeric task. Thermography measured and quantified the mental workload level successfully. Strong and significant correlations were found among thermography, performance, and subjective workload measures (MCH and SWAT ratings). The second study investigated the utility of using a psychophysical approach to determine workload levels that maximize performance on a cognitive task. The second study consisted of an adjustment session (participants adjusted their own workload levels) and work session (participants worked at the chosen workload level). Participants were found to fall into two performance groups (low and high performers by accuracy rate) and results were significantly different. Thermography demonstrated whether both group found their optimal workload level. The last study investigated efficacy of using thermography to quantify mental workload level in a complex training/learning environment. Experienced drivers’ performance data was used as criteria to indicate whether novice drivers mastered the driving skills. Strong and significant correlations were found among thermography, subjective workload measures, and performance measures in novice drivers. This study verified that thermography is a reliable and valid way to measure workload as a non-invasive and objective method. Also, thermography provided more practical results than subjective workload measures for simple and complex cognitive tasks. Thermography showed the capability to identify a sufficient training time for simple or complex cognitive tasks

A Quantitative analysis of the mental workload demands of MRAP vehicle drivers using physiological, subjective, and performance assessments

United States Special Operations Command (USSOCOM) Operators and vehicle Commanders are specially trained United States military Warfighters that have the demanding task of operating or working onboard Mine Resistant Ambush Protected (MRAP) All Terrain Vehicles (M-ATVs). Their missions encounter significant mental demands resulting from fatigue, highly stressful situations, and interactions with Government Furnished Equipment (GFE). Excessive mental demands can be the primary factor leading to compromised vehicle communication, missed improvised explosive device (IED) detection, and increased incidents of vehicle roll-over. Research has demonstrated the consequences of mental overloading including increased errors, performance decrements, distraction, cognitive tunneling and inadequate time to appropriately process information. The objectives of this thesis were to evaluate the extent to which task-related factors impact the mental workload of Warfighters and to evaluate the consistency among the three categories of mental workload metrics. The 14 participants studied in this research were Marine Corps personnel who had heavy vehicle driving experience. Physiological, subjective and performance measures were collected during a four-segment course that progressed in difficulty and analyzed across all participants to assess changes in mental workload. It was found that task-related factors impacted the mental workload of Warfighters. The subjective metric was able to capture changes in workload more accurately than biosignals. Due to technical problems with the biosignal data, comparison of consistency across metrics was inconclusive. The subjective workload ratings were significantly different between course segments and experience levels. The experiment resulted in workload ratings that increased by as much as 94% between segments and were 18% higher among novice drivers. This study showed that mental workload fluctuates while driving in a stressful situation, despite training and experience, and consequently, detection performance will be impacted which could have very adverse consequences. There is the need for additional research to have a better understanding of the true impact of mental workload on MRAP vehicle drivers, especially in an operational environment

A comparison between the responsiveness of selected physiological and subjective mental workload indicators during real-world driving scenarios

Sub-optimal levels of mental workload in automobile drivers is a risk factor for road accidents. However, mental workload as a construct cannot be directly measured. Common indicators of mental workload include heart rate frequency and variability, eye motion and subjective rating tools. Namely, the National Aeronautics and Space Administration Task Load Index (NASA-TLX), its modified, unweighted version called the Raw-TLX, and the Rating Scale of Mental Effort (RSME). Comparisons between the suitability and responsiveness of these mental workload indicators have been almost exclusively examined in driving simulators. However, real-world driving research is important as even high-fidelity simulators cannot capture the complexity of driving scenarios. Hence, this research aimed to compare the suitability and responsiveness of these mental workload indicators in response to real-world driving scenarios. Six participants drove along a set route for an hour while wearing a heart rate monitor and eye tracker. A dashcam was used to capture footage of the different driving scenarios encountered. The set route comprised of driving through the industrial, residential, provincial main road and Rhodes University campus areas. RSME scores were taken during brief stops after driving though each zone. The NASA-TLX questionnaire was administered on completion of the drive and analysed later as the modified Raw-TLX version. The data collected in response to the encountered driving scenarios were sorted into three meta-groupings. (1) Data was segmented according to the different areas that participants drove through. This was termed Area Events and were long duration scenarios of between five and thirty minutes. These driving scenarios were further segmented into two meta-groups with short duration driving scenarios (< 90 seconds). (2) The Common Events meta-group consisted of driving scenarios that were encountered by all participants. These were scenarios were anticipated by drivers. (3) The All Events meta-group was grouped according to all the driving scenarios that were encountered by participants. It consisted of both anticipated and unanticipated driving scenarios of short durations. Data were further analysed using a method of systematically selecting a threshold value for each mental workload indicator. Responses to driving scenarios which surpassed the threshold were considered indicative of an increase in mental workload. The total frequency of higher mental workload events was used as a determiner responsiveness (or ‘sensitivity’) for each mental workload indicator. Mental workload indicators were evaluated for their responsiveness and suitability for assessing mental workload. Results found blink frequency to be a responsive mental workload indicator for all categories of driving scenarios. Blink frequency and duration were the most responsive short duration mental workload indicators. Furthermore, the indicators were able to distinguish between higher and lower mental workload driving scenarios. However, blink parameters are also sensitive to driver fatigue and drowsiness. Further research on distinguishing mental workload from that of fatigue in response to real-world driving was recommended. Pupil diameter, fixation duration, saccade saccade duration and saccade amplitude were found to be responsive short duration mental workload indicators. However, these measures were not determined to be suitable for real-world driving applications. Pupil diameter was confounded by changing illumination levels. Fixation and saccade responses were confounded by the driving task itself as gaze could not be accounted for. For long duration driving scenarios heart rate frequency, heart rate variability: high-frequency power, blink frequency and RSME were found to be responsive and suitable MWL indicators. The Raw-TLX results could not be assessed for responsiveness as it was administered once. However, it was confirmed as a suitable cumulative mental workload indicator in the application of real-world driving. The moderate levels of workload reported by participants agreed with the experimental protocol that prevented inducing sub-optimal mental workload. Blink frequency shows promise as a responsive and suitable mental workload indicator for different types of driving scenarios. More research is needed regarding the assessment of mental workload during short durations using blink frequency and blink duration. For driving durations between five and thirty minutes long, further research into heart rate frequency, heart rate variability: high frequency power, and the RSME was recommended