| 1. |
- Pipkorn, Linda, 1991, et al.
(author)
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Automation aftereffects: the influence of automation duration, test track and timings
- 2022
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In: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 23:5, s. 4746-4757
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Journal article (peer-reviewed)abstract
- Automation aftereffects (i.e., degraded manual driving performance, delayed responses, and more aggressive avoidance maneuvers) have been found in driving simulator studies. In addition, longer automation duration seems to result in more severe aftereffects, compared to shorter duration. The extent to which these findings generalize to real-world driving is currently unknown. The present study investigated how automation duration affects drivers' take-over response quality and driving performance in a road-work zone. Seventeen participants followed a lead vehicle on test track. They encountered the road-work zone four times: two times while driving manually, and after a short and a long automation duration. The take-over request was issued before the lead vehicle changed lane to reveal the road-work zone. After both short and long automation durations, all drivers deactivated automation well ahead of the road-work zone. Compared to manual, drivers started their steering maneuvers earlier or at similar times after automation (independently of duration), and none of the drivers crashed. However, slight increases in vehicle speed and accelerations were observed after exposure to automation. In sum, the present study did not observe as large automation aftereffects on the test track as previously found in driving simulator studies. The extent to which these results are a consequence of a more realistic test environment, or due to the duration between the timings for the take-over request and the conflict appearance, is still unknown.
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| 2. |
- Pipkorn, Linda, 1991, et al.
(author)
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Driver conflict response during supervised automation: Do hands on wheel matter?
- 2021
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In: Transportation Research Part F: Traffic Psychology and Behaviour. - : Elsevier BV. - 1369-8478. ; 76, s. 14-25
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Journal article (peer-reviewed)abstract
- Securing appropriate driver responses to conflicts is essential in automation that is not perfect (because the driver is needed as a fall-back for system limitations and failures). However, this is recognized as a major challenge in the human factors literature. Moreover, in-depth knowledge is lacking regarding mechanisms affecting the driver response process. The first aim of this study was to investigate how driver conflict response while using highly reliable (but not perfect) supervised automation differ for drivers that (a) crash or avoid a conflict object and (b) report high trust or low trust in automation to avoid the conflict object. The second aim was to understand the influence on the driver conflict response of two specific factors: a hands-on-wheel requirement (with vs. without), and the conflict object type (garbage bag vs. stationary vehicle). Seventy-six participants drove with highly reliable but supervised automation for 30 minutes on a test track. Thereafter they needed to avoid a static object that was revealed by a lead-vehicle cut-out. The driver conflict response was assessed through the response process: timepoints for driver surprise reaction, hands-on-wheel, driver steering, and driver braking. Crashers generally responded later in all actions of the response process compared to non-crashers. In fact, some crashers collided with the conflict object without even putting their hands on the wheel. Driver conflict response was independent of the hands-on-wheel requirement. High-trust drivers generally responded later than the low-trust drivers or not at all, and only high trust drivers crashed. The larger stationary vehicle triggered an earlier surprise reaction compared to the garbage bag, while hands-on-wheel and steering response were similar for the two conflict object types. To conclude, crashing is associated with a delay in all actions of the response process. In addition, driver conflict response does not change with a hands-on-wheel requirement but changes with trust-level and conflict object type. Simply holding the hands on the wheel is not sufficient to prevent collisions or elicit earlier responses. High trust in automation is associated with late response and crashing, whereas low trust is associated with appropriate driver response.
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| 3. |
- Tivesten, Emma, 1968, et al.
(author)
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Out-of-the-loop crash prediction: The Automation Expectation Mismatch (AEM) algorithm
- 2019
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In: IET Intelligent Transport Systems. - : Institution of Engineering and Technology (IET). - 1751-9578 .- 1751-956X. ; 13:8, s. 1231-1240
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Journal article (peer-reviewed)abstract
- This study uses behavioural data from the complete drive for a subset of 54 participants from the automation expectation mismatch set of test track experiments and aims to develop an algorithm that can predict which drivers are likely to crash. Participants experienced 30 min of highly reliable supervised automation and were required to intervene to avoid crashing with a stationary object at the end of the drive. Many of them still crashed, despite having their eyes on the conflict object. They were informed about their role as supervisors, automation limitations, and received attention reminders if visually distracted. Three pre-conflict behavioural patterns were found to be associated with increased risk of crash involvement: low levels of visual attention to the forward path, high per cent road centre (i.e. gaze concentration), and long visual response times to attention reminders. One algorithm showed very high performance in classifying crashers when combining metrics related to all three behaviours. This algorithm is possible to implement as a real-time function in eye-tracker equipped vehicles. The algorithm can detect drivers that are not sufficiently engaged in the driving task, and provide feedback (e.g. reduce function performance, turn off function) to increase their engagement.
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| 4. |
- Tivesten, Emma, 1968, et al.
(author)
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The timecourse of driver visual attention in naturalistic driving with Adaptive Cruise Control and Forward Collision Warning
- 2015
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In: International Conference on Driver Distraction and Inattention, 4th, 2015, Sydney, New South Wales, Australia.
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Conference paper (peer-reviewed)abstract
- Adaptive Cruise Control (ACC) and Forward Collision Warning (FCW) have been shown to have a positive effect on safety-related measures despite a general increase in secondary task involvement. To understand this effect, this study examined the relationship between drivers glance locations and ACC hard braking or FCW events when ACC is active. The study analyzed naturalistic driving on motorways where the car remained in the same lane. Four subsets of driving segments were included: ACC braking (peak deceleration ≥ 3 m/s2), FCW+ACC (driving with ACC when a forward collision warning was issued) ACC maintaining speed, and Driver braking without ACC or FCW. The results indicate that although drivers do take their eyes off path more when using ACC, this conclusion seems to be valid only in non-critical (baseline-similar) situations. Drivers showed a steady increase in %EyesOnPath well before critical situations, resulting in 95% EyesOnPath both at the onset of ACC braking and at the onset of driver braking, and 98% when FCW were issued. At braking onset, headway was significantly longer when ACC braked compared to when the driver braked.
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| 5. |
- Victor, Trent, 1968, et al.
(author)
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Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel
- 2018
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In: Human Factors. - : SAGE Publications. - 1547-8181 .- 0018-7208. ; 60:8, s. 1095-1116
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Journal article (peer-reviewed)abstract
- Objective: The aim of this study was to understand how to secure driver supervision engagement and conflict intervention performance while using highly reliable (but not perfect) automation. Background: Securing driver engagement—by mitigating irony of automation (i.e., the better the automation, the less attention drivers will pay to traffic and the system, and the less capable they will be to resume control) and by communicating system limitations to avoid mental model misconceptions—is a major challenge in the human factors literature. Method: One hundred six drivers participated in three test-track experiments in which we studied driver intervention response to conflicts after driving highly reliable but supervised automation. After 30 min, a conflict occurred wherein the lead vehicle cut out of lane to reveal a conflict object in the form of either a stationary car or a garbage bag. Results: Supervision reminders effectively maintained drivers’ eyes on path and hands on wheel. However, neither these reminders nor explicit instructions on system limitations and supervision responsibilities prevented 28% (21/76) of drivers from crashing with their eyes on the conflict object (car or bag). Conclusion: The results uncover the important role of expectation mismatches, showing that a key component of driver engagement is cognitive (understanding the need for action), rather than purely visual (looking at the threat), or having hands on wheel. Application: Automation needs to be designed either so that it does not rely on the driver or so that the driver unmistakably understands that it is an assistance system that needs an active driver to lead and share control.
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| 6. |
- Ahlström, Christer, et al.
(author)
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Processing of Eye/Head-Tracking Data in Large-Scale Naturalistic Driving Data Sets
- 2012
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In: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; vol.13:no.2, s. pp.553-564
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Journal article (peer-reviewed)abstract
- Driver distraction and driver inattention are frequently recognized as leading causes of crashes and incidents. Despite this fact, there are few methods available for the automatic detection of driver distraction. Eye tracking has come forward as the most promising detection technology, but the technique suffers from quality issues when used in the field over an extended period of time. Eye-tracking data acquired in the field clearly differs from what is acquired in a laboratory setting or a driving simulator, and algorithms that have been developed in these settings are often unable to operate on noisy field data. The aim of this paper is to develop algorithms for quality handling and signal enhancement of naturalistic eye- and head-tracking data within the setting of visual driver distraction. In particular, practical issues are highlighted. Developed algorithms are evaluated on large-scale field operational test data acquired in the Sweden-Michigan Field Operational Test (SeMiFOT) project, including data from 44 unique drivers and more than 10 000 trips from 13 eye-tracker-equipped vehicles. Results indicate that, by applying advanced data-processing methods, sensitivity and specificity of eyes-off-road glance detection can be increased by about 10%. In conclusion, postenhancement and quality handling is critical when analyzing large databases with naturalistic eye-tracking data. The presented algorithms provide the first holistic approach to accomplish this task.
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| 7. |
- Bärgman, Jonas, 1972, et al.
(author)
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Holistic assessment of driver assistance systems: how can systems be assessed with respect to how they impact glance behaviour and collision avoidance?
- 2020
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In: IET Intelligent Transport Systems. - : Institution of Engineering and Technology (IET). - 1751-9578 .- 1751-956X. ; 14:9, s. 1058-1067
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Journal article (peer-reviewed)abstract
- This study demonstrates the need for a holistic safety-impact assessment of an advanced driver assistance system (ADAS) and its effect on eye-glance behaviour. It implements a substantial incremental development of the what-if (counterfactual) simulation methodology, applied to rear-end crashes from the SHRP2 naturalistic driving data. This assessment combines (i) the impact of the change in drivers’ off-road glance behaviour due to the presence of the ADAS, and (ii) the safety impact of the ADAS alone. The results illustrate how the safety benefit of forward collision warning and autonomous emergency braking, in combination with adaptive cruise control (ACC) and driver assist (DA) systems, may almost completely dominate the safety impact of the longer off-road glances that activated ACC and DA systems may induce. Further, this effect is shown to be robust to induced system failures. The accuracy of these results is tempered by outlined limitations, which future estimations will benefit from addressing. On the whole, this study is a further step towards a successively more accurate holistic risk assessment which includes driver behavioural responses such as off-road glances together with the safety effects provided by the ADAS.
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| 8. |
- Bärgman, Jonas, 1972, et al.
(author)
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How does glance behavior influence crash and injury risk? A ‘what-if’ counterfactual simulation using crashes and near-crashes from SHRP2
- 2015
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In: Transportation Research Part F: Traffic Psychology and Behaviour. - : Elsevier BV. - 1369-8478. ; 35, s. 152-169
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Journal article (peer-reviewed)abstract
- As naturalistic driving data become increasingly available, new analyses are revealing the significance of drivers’ glance behavior in traffic crashes. Due to the rarity of crashes, even in the largest naturalistic datasets, near-crashes are often included in the analyses and used as surrogates for crashes. However, to date we lack a method to assess the extent to which driver glance behavior influences crash and injury risk across both crashes and near-crashes. This paper presents a novel method for estimating crash and injury risk from off-road glance behavior for crashes and near-crashes alike; this method can also be used to evaluate the safety impact of secondary tasks (such as tuning the radio). We apply a ‘what-if’ (counterfactual) simulation to 37 lead-vehicle crashes and 186 lead-vehicle near-crashes from lead-vehicle scenarios identified in the SHRP2 naturalistic driving data. The simulation combines the kinematics of the two conflicting vehicles with a model of driver glance behavior to estimate two probabilities: (1) that each event becomes a crash, and (2) that each event causes a specific level of injury. The usefulness of the method is demonstrated by comparing the crash and injury risk of normal driving with the risks of driving while performing one of three secondary tasks: the Rockwell radio-tuning task and two hypothetical tasks. Alternative applications of the method and its metrics are also discussed. The method presented in this paper can guide the design of safer driver–vehicle interfaces by showing the best tradeoff between the percent of glances that are on-road, the distribution of off-road glances, and the total task time for different tasks.
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| 9. |
- Engström, Johan A Skifs, 1973, et al.
(author)
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Attention selection and multitasking in everyday driving: A conceptual model
- 2013
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In: Driver Distraction and Inattention: Advances in Research and Countermeasures. - 9781409425854 ; , s. 27-54
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Book chapter (other academic/artistic)abstract
- This chapter outlines a conceptual model of attention selection and multitasking in everyday driving. While existing theoretical and empirical work on attention in driving has mainly focused on dual-task interference in experimental settings, the present model aims to account for attention selection in natural driving situations. The model starts from the view of attention as a form of adaptive behaviour and emphasises the key role of expectancy, the dynamic interplay between top-down and bottom-up selection, the often habitual nature of attention selection in real driving and how attention selection is driven by perceived and expected value. However, the model also offers a novel characterisation of dual-task interference mechanisms and more precise definitions of key concepts such as driver inattention and driver distraction. Based on the model, a general conceptualisation of the relation between attention selection and crash causation is proposed and implications for the design of driver support systems and automotive human-machine interfaces are discussed.
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| 10. |
- Engström, Johan A Skifs, 1973, et al.
(author)
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Great expectations: A predictive processing account of automobile driving
- 2018
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In: Theoretical Issues in Ergonomics Science. - 1464-536X .- 1463-922X. ; 19:2, s. 156-194
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Journal article (peer-reviewed)abstract
- Predictive processing has been proposed as a unifying framework for understanding brain function, suggesting that cognition and behaviour can be fundamentally understood based on the single principle of prediction error minimisation. According to predictive processing, the brain is a statistical organ that continuously attempts get a grip on states in the world by predicting how these states cause sensory input and minimising the deviations between the predicted and actual input. While these ideas have had a strong influence in neuroscience and cognitive science, they have so far not been adopted in applied human factors research. The present paper represents a first attempt to do so, exploring how predictive processing concepts can be used to understand automobile driving. It is shown how a framework based on predictive processing may provide a novel perspective on a range of driving phenomena and offer a unifying framework for traditionally disparate human factors models.
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