| 1. |
- Nilsson, Emma, 1982, et al.
(författare)
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Effects of cognitive load on response time in an unexpected lead vehicle braking scenario and the detection response task (DRT)
- 2018
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Ingår i: Transportation Research Part F: Traffic Psychology and Behaviour. - : Elsevier BV. - 1369-8478. ; 59, s. 463-474
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Tidskriftsartikel (refereegranskat)abstract
- The effects of cognitive distraction on traffic safety and driver performance are unclear and under debate. Based on increased response times to stimuli or events in controlled driving experiments, concerns, primarily about cell phone usage during driving, have been raised. But while cognitive load repeatedly have been shown to increase response times in artificial tasks such as the Detection Response Task (DRT), the generalizability of the results to response times in critical traffic situations is questionable. Method: Two experiments were conducted. In Experiment 1, response times in the DRT were measured during simulated driving with and without execution of a cognitively loading secondary task. In Experiment 2, brake response times in an unexpected lead vehicle braking scenario were measured with and without the same cognitively loading task. Results: In Experiment 1, DRT response times increased with increased level of cognitive load. In Experiment 2, brake response times were unaffected by cognitive load. Conclusion: The response time results from the artificial DRT did not generalize to the critical lead vehicle braking scenario. This finding can possibly be explained by the cognitive control hypothesis, which suggests that cognitive load selectively impairs driving subtasks that rely on cognitive control (i.e. novel or inconsistent tasks) but leaves automatic performance unaffected (Engström, Markkula, Victor, & Merat, 2017). While the DRT responses, because of the task novelty, can be assumed to require cognitive control, responses to visually expanding objects, such as a braking lead vehicle with short time headway, are triggered automatically. Common interpretations of the effect of cognitive load on traffic safety thus need to be re-examined. It seems inappropriate to generalize from effects of cognitive load on DRT, or other artificial laboratory tasks that rely on cognitive control, to unexpected real-world situations where responses are triggered primarily by looming cues.
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| 2. |
- Anund, Anna, 1964-, et al.
(författare)
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Night-time scenarios in simulators : a prestudy of needs, knowledge and possible solutions
- 2016
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The study in this publication investigates the need and potential for night-time scenarios in driving simulators, determines how such night-time scenarios could be reproduced and identifies the objects most important to reproduce. Although on average 12 out of every 24 hours are dark and considering that most situations are more demanding for drivers in dark conditions, simulations of driving scenarios with different degrees of darkness are not common. The project work comprised a pre-study that involved an investigation of the need and potential of night-time scenarios with the help of input from different stakeholders, consolidation of what is known up to now through benchmarking and state of the art, and a review of available technical solutions. The objective was to identify pros and cons with existing solutions and aspects that are important to consider in order to reproduce the most important components in realistic night-time scenarios. Based on the results, six important use cases were identified and two of these (‘Driver fatigue’ and ‘Objects without light sources’) were studied in more detail. It was concluded that for night-time scenarios there is enough darkness in general in the simulator environment. The question is whether it is possible to create sufficient contrast for objects that are meant to be observable. For daytime scenarios, the light levels in the simulator are clearly unrealistically low and this limitation might even trigger unwanted sleepiness.
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| 3. |
- Fischer, Martin, et al.
(författare)
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SPASS : Strengthen Performance Active Safety Simulator
- 2017
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The aim of the SPASS project was to evaluate early development/rapid prototyping of new driver assistance systems by utilizing an advanced driving simulator in combination with a vehicle electrical architecture (including sensors, actuators and HMI). As a case study, the project demonstrated a novel active safety function which was rather well penetrated at Volvo Car Corporation (VCC), i.e. VCC had reference vehicles up and running. The project used VTI’s advanced driving simulator (Sim IV) in Gothenburg to establish a simulator platform for evaluation of driver-system interaction. The objective was to understand the capability of Sim IV when studying active safety functions requiring interaction between road infrastructure, sensors, electrical architecture, actuators and Human-Machine Interaction, and to understand how useful Sim IV is during the ordinary concept development phase at vehicle manufacturers. Warning systems that act when the driver is on the way to unintentionally leave the lane are already available today. The next generation of systems will not only warn the driver but will actively contribute to the steering task. One such system is VCC’s eLKA (emergency lane keeping assistance). As part of the SPASS project a scenario which lead to triggering of the eLKA function was developed and tested. The scenario consisted of rural road driving where drivers were distracted by means of a visual distraction task (reading numbers from a screen, placed at a relative large down angle relative to the view of the forward roadway) and then poured across the median towards an oncoming vehicle by introducing an additional steering angle in the simulated vehicle. An integrated test environment for active safety functions has been developed and evaluated. VCC’s emergency lane keeping assistance system (eLKA) has been used as test case in the evaluation, and the functioning of the eLKA has been validated and approved by experts at VCC.
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| 4. |
- Karlsson, MariAnne, 1956, et al.
(författare)
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D1.1. Integrated Framework. Deliverable to the MeBeSafe project
- 2018
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The MeBeSafe project intends to develop, implement and validate interventions that direct road users (drivers and cyclists) towards safer behaviour in common traffic situations which carry an elevated risk. More specifically, the aim is to change habitual traffic behaviour using different nudging interventions, i.e. subconsciously pushing road users in a desired direction without being prohibitive against alternative choices of action. The project will also compare different ways of coaching and evaluate the effect of a combination of nudging and coaching. This deliverable, D1.1 Integrated Framework, describes the work completed within WP1 of the MeBeSafe project. Based on literature reviews, interviews with academic and non-academic experts, discussions and workshops, the deliverable: (i) describes the key characteristics of nudging and coaching respectively; (ii) presents a framework that integrates the two, taking into consideration (in particular) time and frequency; (iii) describes underlying theories and models of relevance for understanding road user behaviour; (iii) explains road user profiles or characteristics of relevance to consider in the design of the interventions (i.e., in WP2, WP3, and WP4), as well as the design and interpretation of the outcome of the field trials (in WP5); and (iv) presents design considerations, i.e. factors that should be observed when improving on the initial ideas and further develop the design of the nudging and coaching interventions. More detailed design guidelines must be developed as part of the work to be completed in WP2, WP3, and WP4.
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| 5. |
- Ljung Aust, Mikael, 1973, et al.
(författare)
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D5.1 MeBeSafe - Trial Design
- 2019
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The report describe the research design for the project MeBeSafe, where different nudges as well as coaching measures to behave more safely in traffic are tested.
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| 6. |
- 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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| 7. |
- 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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