| 1. |
- Pipkorn, Linda, 1991, et al.
(författare)
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Automation aftereffects: the influence of automation duration, test track and timings
- 2022
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 23:5, s. 4746-4757
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Driver conflict response during supervised automation: Do hands on wheel matter?
- 2021
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Ingår i: Transportation Research Part F: Traffic Psychology and Behaviour. - : Elsevier BV. - 1369-8478. ; 76, s. 14-25
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Out-of-the-loop crash prediction: The Automation Expectation Mismatch (AEM) algorithm
- 2019
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Ingår i: IET Intelligent Transport Systems. - : Institution of Engineering and Technology (IET). - 1751-9578 .- 1751-956X. ; 13:8, s. 1231-1240
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Tidskriftsartikel (refereegranskat)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.
(författare)
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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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Ingår i: International Conference on Driver Distraction and Inattention, 4th, 2015, Sydney, New South Wales, Australia.
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Konferensbidrag (refereegranskat)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.
(författare)
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Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel
- 2018
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Ingår i: Human Factors. - : SAGE Publications. - 1547-8181 .- 0018-7208. ; 60:8, s. 1095-1116
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Tidskriftsartikel (refereegranskat)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. |
- Habibovic, Azra, 1982, et al.
(författare)
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Driver behavior in car-to-pedestrian incidents: An application of the Driving Reliability and Error Analysis Method (DREAM)
- 2013
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Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 50, s. 554-565
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Tidskriftsartikel (refereegranskat)abstract
- To develop relevant road safety countermeasures, it is necessary to first obtain an in-depth understanding of how and why safety-critical situations such as incidents, near-crashes, and crashes occur. Video-recordings from naturalistic driving studies provide detailed information on events and circumstances prior to such situations that is difficult to obtain from traditional crash investigations, at least when it comes to the observable driver behavior. This study analyzed causation in 90 video-recordings of car-to-pedestrian incidents captured by onboard cameras in a naturalistic driving study in Japan. The Driving Reliability and Error Analysis Method (DREAM) was modified and used to identify contributing factors and causation patterns in these incidents. Two main causation patterns were found. In intersections, drivers failed to recognize the presence of the conflict pedestrian due to visual obstructions and/or because their attention was allocated towards something other than the conflict pedestrian. In incidents away from intersections, this pattern reoccurred along with another pattern showing that pedestrians often behaved in unexpected ways. These patterns indicate that an interactive advanced driver assistance system (ADAS) able to redirect the driver's attention could have averted many of the intersection incidents, while autonomous systems may be needed away from intersections. Cooperative ADAS may be needed to address issues raised by visual obstructions.
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| 7. |
- Ljung Aust, Mikael, 1973, et al.
(författare)
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Manual for DREAM version 3.2
- 2012
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The Driving Reliability and Error Analysis Method (DREAM) is based on the Cognitive Reliability and Error Analysis Method (CREAM; Hollnagel, 1998). CREAM was developed to analyse accidents within process control domains such as nuclear power plants and train operation, and DREAM is an adaptation of CREAM to suit the road traffic domain. The purpose of DREAM is to make it possible to systematically classify and store accident and incident causation information. This means that DREAM, like all other methods for accident/incident analysis, is not a provider but an organiser of explanations. For any of the contributing factor categories available in DREAM to be used, it must be supported by relevant empirical information. DREAM in itself cannot tell us why accidents happen (if it could, we would need neither on-scene investigations nor interviews).DREAM includes three main components: an accident model, a classification scheme and a detailed procedure description which step by step goes through what needs to be done in order to perform a DREAM analysis on an investigated accident/incident. Below, the accident model will be given more detailed descriptions. After this follows a description of the classification scheme, and then comes the analysis process, including example cases and recommendations for how to do the categorisation in certain typical scenarios.
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| 8. |
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| 9. |
- Pipkorn, Linda, 1991, et al.
(författare)
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Driver response to take-over requests in real traffic
- 2023
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Ingår i: IEEE Transactions on Human-Machine Systems. - 2168-2291 .- 2168-2305. ; 53:5, s. 823-833
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Tidskriftsartikel (refereegranskat)abstract
- Existing research on control-transitions from automated driving (AD) to manual driving mainly stems from studies in virtual settings. There is a need for studies conducted in real settings to better understand the impacts of increasing vehicle automation on traffic safety. This study aims specifically to understand how drivers respond to take-over requests (TORs) in real traffic by investigating the associations between 1) where drivers look when receiving the TOR, 2) repeated exposure to TORs, and 3) the drivers’ response process. In total, thirty participants were exposed to four TORs after about 5–6 min of driving with AD on public roads. While in AD, participants could choose to engage in non-driving-related tasks (NDRTs).When they received the TOR, for 38% of TORs, participants were already looking on path. For those TORs where drivers looked off path at the time of the TOR, the off-path glance was most commonly towards an NDRT item. Then, for 72% of TORs (independent on gaze direction), drivers started their response process to the TOR by looking towards the instrument cluster before placing their hands on the steering wheel and their foot on the accelerator pedal, and deactivating automation. Both timing and order of these actions varied among participants, but all participants deactivated AD within 10 s from the TOR. The drivers’ gaze direction at the TOR had a stronger association with the response process than the repeated exposure to TORs did. Drivers can respond to TORs in real traffic. However, the response should be considered as a sequence of actions that requires a certain amount of time.
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| 10. |
- Pipkorn, Linda, 1991, et al.
(författare)
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Driver Visual Attention Before and After Take-Over Requests During Automated Driving on Public Roads
- 2024
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Ingår i: Human Factors. - : SAGE Publications. - 1547-8181 .- 0018-7208. ; 66:2, s. 336-347
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Tidskriftsartikel (refereegranskat)abstract
- Objective This study aims to understand drivers’ visual attention before and after take-over requests during automated driving (AD), when the vehicle is fully responsible for the driving task on public roads. Background Existing research on transitions of control from AD to manual driving has mainly focused on take-over times. Despite its relevance for vehicle safety, drivers’ visual attention has received little consideration. Method Thirty participants took part in a Wizard of Oz study on public roads. Drivers’ visual attention was analyzed before and after four take-over requests. Visual attention during manual driving was also recorded to serve as a baseline for comparison. Results During AD, the participants showed reduced visual attention to the forward road and increased duration of single off-road glances compared to manual driving. In response to take-over requests, the participants looked away from the forward road toward the instrument cluster. Levels of visual attention towards the forward road did not return to the levels observed during manual driving until after 15 s had passed. Conclusion During AD, drivers may look toward non-driving related task items (e.g., mobile phone) instead of forward. Further, when a transition of control is required, drivers may take over control before they are aware of the driving environment or potential threat(s). Thus, it cannot be assumed that drivers are ready to respond to events shortly after the take-over request. Application It is important to consider the effect of the design of take-over requests on drivers’ visual attention alongside take-over times.
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