| 1. |
- Mirnig, Alexander G., et al.
(författare)
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External communication of automated shuttles: Results, experiences, and lessons learned from three European long-term research projects
- 2022
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Ingår i: Frontiers Robotics AI. - : Frontiers Media SA. - 2296-9144. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Automated shuttles are already seeing deployment in many places across the world and have the potential to transform public mobility to be safer and more accessible. During the current transition phase from fully manual vehicles toward higher degrees of automation and resulting mixed traffic, there is a heightened need for additional communication or external indicators to comprehend automated vehicle actions for other road users. In this work, we present and discuss the results from seven studies (three preparatory and four main studies) conducted in three European countries aimed at investigating and providing a variety of such external communication solutions to facilitate the exchange of information between automated shuttles and other motorized and non-motorized road users.
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| 2. |
- Dahlman, Anna Sjörs, et al.
(författare)
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D6.1: Evaluation framework, plans and material. Deliverable of the Horizon-2020 PANACEA project, Grant Agreement No. 953426
- 2022
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This deliverable presents the evaluation framework, plans and material for all data collections of the PANACEA project. It describes the objectives of the studies and how they will be realised. The purpose of the PANACEA evaluation framework is to create a common framework to be used in all studies to make sure the data are collected in a way that makes it possible to consolidate the results at the end and to provide what is needed for impact analysis (WP7). This version of the deliverable has its focus on setting the framework and the work process. An update of this deliverable will be done in M22 (D6.2: ‘Evaluation framework, plans and material - an update’). The key content of D6.1 is structured as follows: Chapter 1 is the introduction to the deliverable, specifying its purpose, the intended audience, and interrelations with other project activities. Chapter 2 introduces the project objectives related to the WP6 data collections. Chapter 3 provides a brief overview of each Use Case and Chapter 4 presents the various studies within the project including descriptions of the main actors, environment, vehicles, PANACEA sensors/technologies, and countermeasures. Chapter 5 describes the process of developing the evaluation framework for the project and presents the PANACEA evaluation framework. Chapters 6-18 then follow the steps defined in the evaluation framework. Chapters 6-11 describe the planning phase and present the Use Case Scenarios, Research Questions, Key Performance Indicators, study designs, data gathering tools, and data analysis plan. Chapters 12-14 describe the implementation phase, including pilot site preparations, data collection, and data delivery. Chapters 15-18 describe the data analysis phase and includes chapters about data analysis, results reporting, results consolidation, and impact assessment. Lastly, Chapter 19 provides the conclusions of the deliverable. The deliverable presents both a horizontal perspective of the pilot sites as well as more detailed descriptions of what will be included in the different studies. The general data gathering tools (objective and subjective) are identified and will be further refined in the update of the deliverable. A set of guidelines on practicalities and ethical aspects to take into consideration before and during data collection are presented. The update of the deliverable, planned for M22, will include the detailed evaluation protocols, with ready-made templates for pilot sites, questionnaires to use, performance criteria, indicators, log files to use, crucial timelines, etc. In addition, the final pilot and experimental plans will be defined and described per pilot site and type of evaluation activity.
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| 3. |
- Dahlman, Anna Sjörs, et al.
(författare)
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D6.2: Evaluation framework, plans and material - an update. Deliverable of the Horizon-2020 PANACEA project, Grant Agreement No. 953426
- 2023
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This deliverable presents the evaluation framework, plans and material for all data collections connected to work package 6 (WP6) of the PANACEA project. It describes the objectives of the studies and how they will be realised. The purpose of the PANACEA evaluation framework is to create a common framework to be used in all studies to make sure the data are collected in a way that makes it possible to consolidate the results at the end and to provide what is needed for impact analysis (WP7). The first version of the deliverable (D6.1: ‘Evaluation framework, plans and material´) had its focus on setting the framework and the work process. In this updated version, the focus is on the evaluation protocols for all studies, including templates for the pilot sites, questionnaires to use, key performance indicators (KPI), log files to use, crucial timelines, etc. The experimental plans are described per pilot site and type of evaluation activity. The key content of D6.2 is structured as follows: Chapter 1 is the introduction to the deliverable, specifying its purpose, the intended audience, and interrelations with other project activities. Chapter 2 introduces the project objectives related to the WP6 data collections. Chapter 3 provides a brief overview of each Use Case and Chapter 4 presents the various studies within the project including descriptions of the main actors, environment, vehicles, PANACEA sensors/technologies, and countermeasures. Chapter 5 describes the PANACEA evaluation framework. Chapters 6-15 then describe the steps defined in the evaluation framework. Chapters 6-11 include the planning phase and present the Use Case Scenarios, Research Questions, Key Performance Indicators, study designs, data gathering tools, and data analysis plan. Chapters 12-13 describe the implementation phase, including pilot site preparations, and data collection. Chapters 13-15 describe the data analysis phase and includes chapters about data delivery, data analysis, results reporting, results consolidation, and impact assessment. Lastly, Chapter 16 provides the conclusions of the deliverable. The deliverable presents both a horizontal perspective of the pilot sites as well as more detailed descriptions of what will be included in the different studies. The main text of the deliverable provides an overview of all studies and evaluations within PANACEA. Research questions and KPIs are defined for each study (Appendix III). The general data gathering tools (objective and subjective) are identified. The questionnaires used for the evaluations are included in Appendix IV. A set of guidelines on practicalities and ethical aspects to take into consideration before and during data collection are presented. Experimental plans for all WP6 data collections are included as appendices to the deliverable (Appendix II).
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| 4. |
- Vadeby, Anna, et al.
(författare)
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Sleepiness and prediction of driver impairment in simulator studies using a Cox proportional hazard approach
- 2010
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Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575 .- 1879-2057. ; 42:3, s. 835-41
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Tidskriftsartikel (refereegranskat)abstract
- Cox proportional hazard models were used to study relationships between the event that a driver is leaving the lane caused by sleepiness and different indicators of sleepiness. In order to elucidate different indicators' performance, five different models developed by Cox proportional hazard on a data set from a simulator study were used. The models consisted of physiological indicators and indicators from driving data both as stand alone and in combination. The different models were compared on two different data sets by means of sensitivity and specificity and the models' ability to predict lane departure was studied.In conclusion, a combination of blink indicators based on the ratio between blink amplitude and peak closing velocity of eyelid (A/PCV) (or blink amplitude and peak opening velocity of eyelid (A/POV)), standard deviation of lateral position and standard deviation of lateral acceleration relative road (ddy) was the most sensitive approach with sensitivity 0.80. This is also supported by the fact that driving data only shows the impairment of driving performance while blink data have a closer relation to sleepiness. Thus, an effective sleepiness warning system may be based on a combination of lane variability measures and variables related to eye movements (particularly slow eye closure) in order to have both high sensitivity (many correct warnings) and acceptable specificity (few false alarms).
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| 5. |
- Olstam, Johan, 1979-, et al.
(författare)
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Dynamic bus lanes in Sweden – a pre-study : PROVDYK – Final report
- 2015
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Dynamic bus lanes are only dedicated for buses when and where the buses need them, and otherwise open for all vehicles to use. This report presents the results from a pre-study, investigating the potential that dynamic bus lanes could have as a priority measure for public transport in a Swedish context. Knowledge of situations in which dynamic bus lanes have the highest potential, and their implementation requirements is scarce. It is moreover uncertain how they would affect traffic safety, level of service, user experience, travel time and delays for other vehicles.A workshop was conducted within this pre-study in order to further investigate plausible user experiences. The results indicate that:bus drivers’ stress levels could be reducedthe relative attractiveness of travelling by bus might risethat motorists probably would experience the introduction of dynamic bus lanes as neither good nor bad, as long as the system is fairly intuitiveTechnical solutions for implementing dynamic bus lanes exist. A dynamic bus lane system would require development of a system control unit and integration with bus sensors, sensors for traffic flow measurement, variable message signs (to inform road users of the current status of the dynamic bus lane) and traffic signals. The overall conclusion form the pre-study is that dynamic bus lanes could be a useful complementary priority measure for public transport vehicles in Sweden, especially when dedicated bus lanes are not feasible or desirable.
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| 6. |
- Åkerstedt, Torbjörn, et al.
(författare)
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Reaction of sleepiness indicators to partial sleep deprivation, time of day and time on task in a driving simulator - the DROWSI project
- 2010
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Ingår i: Journal of Sleep Research. - : Wiley. - 1365-2869 .- 0962-1105. ; 19:2, s. 298-309
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Tidskriftsartikel (refereegranskat)abstract
- Studies of driving and sleepiness indicators have mainly focused on prior sleep reduction. The present study sought to identify sleepiness indicators responsive to several potential regulators of sleepiness: sleep loss, time of day (TOD) and time on task (TOT) during simulator driving. Thirteen subjects drove a high-fidelity moving base simulator in six 1-h sessions across a 24-h period, after normal sleep duration (8 h) and after partial sleep deprivation (PSD; 4 h). The results showed clear main effects of TOD (night) and TOT but not for PSD, although the latter strongly interacted with TOD. The most sensitive variable was subjective sleepiness, the standard deviation of lateral position (SDLAT) and measures of eye closure [duration, speed (slow), amplitude (low)]. Measures of electroencephalography and line crossings (LCs) showed only modest responses. For most variables individual differences vastly exceeded those of the fixed effects, except for subjective sleepiness and SDLAT. In a multiple regression analysis, SDLAT, amplitude/peak eye-lid closing velocity and blink duration predicted subjective sleepiness bouts with a sensitivity and specificity of about 70%, but were mutually redundant. The prediction of LCs gave considerably weaker, but similar results. In summary, SDLAT and eye closure variables could be candidates for use in sleepiness-monitoring devices. However, individual differences are considerable and there is need for research on how to identify and predict individual differences in susceptibility to sleepiness.
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| 7. |
- Anund, Anna, 1964-, et al.
(författare)
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Driving automation and its effects on drivers : a human factor perspective
- 2019
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Ingår i: Cooperative Intelligent Transport Systems. - : IET Digital Library. - 9781839530135 ; , s. 87-103
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This chapter will cover some of the issues mentioned by European Road Transport Research Advisory Council (ERTRAC) and has its starting point in the knowledge and experience from four different EU-funded projects dealing with automation and human factors. It might be expected that there is no difference in how human factors should be considered in relation to automation depending on the country it is used in. However, there are reasons to believe that there are differences in understanding and in acceptance of new functions depending on experience or not from modern vehicles with, for example integrated driver support systems. This chapter has its starting point mainly from work done in Europe, which may be considered to be generic and valid for other countries in the world with the same type of car fleets. The focus will be on challenges covering: the need to have an adaptive Human-Machine Interface (HMI) to achieve trust and acceptance in relation to automated functionalities and system, the importance of considering different driver states and finally the evaluations of automated systems.
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| 8. |
- Sandberg, David, 1980, et al.
(författare)
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Detecting driver sleeepiness using optimized non-linear combinations of sleepiness indicators
- 2011
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 12:1, s. 97-108
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Tidskriftsartikel (refereegranskat)abstract
- This paper addresses the problem of detecting sleepiness in car drivers. First, a variety of sleepiness indicators (based on driving behavior) proposed in the literature were evaluated. These indicators were then subjected to parametric optimization using stochastic optimization methods. To improve performance, the functional form of some of the indicators was generalized before optimization. Next, using a neural network, the best performing sleepiness indicators were combined with a mathematical model of sleepiness, i.e., the sleep/wake predictor (SWP). The analyses were based on data obtained from a study that involved 12 test subjects at the moving-base driving simulator at the Swedish National Road and Transportation Research Institute (VTI), Linkping, Sweden. The data were derived from 12 1-h driving sessions for each test subject, with varying degrees of sleepiness. The performance measure (range [0,1]) for indicators was taken as the average of sensitivity and specificity. Starting with indicators proposed in the literature, the best such indicator, i.e., the standard deviation of the yaw angle, reached a performance score of 0.72 on previously unseen test data. It was found that indicators based on a given signal gave essentially equal performance after parametric optimization, but in no case was it better than 0.72. The best generalized indicator (the generic variability indicator) obtained a performance score of 0.74. SWP achieved a score of 0.78. However, by nonlinearly combining SWP with the generic variability indicator, a score of 0.83 was obtained. Thus, the results imply that a nonlinear combination of a measure based on driving behavior with a model of sleepiness significantly improves driver sleepiness detection.
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| 9. |
- Sandberg, David, 1980, et al.
(författare)
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The impact of sleepiness on lane positioning in truck driving
- 2013. - 1
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Ingår i: Driver Distraction and Inattention. - Farnham : Ashgate. - 9781409425854 - 9781315578156 ; 1, s. 405-416
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Bokkapitel (refereegranskat)abstract
- This chapter concerns the detection of sleepiness in truck drivers. Data obtained from a driver sleepiness study involving real-world driving are used in order to analyse the performance of several sleepiness indicators based on driving behavior; such as, for example, variability in lateral position and heading angle. Contrary to the results obtained for passenger cars, for heavy trucks it is found that indicators based on variability provide little or no information; their performance does not rise significantly above chance levels.However, the data indicate that there is a significant difference in the average lane position for sleepy and alert drivers, respectively, such that a sleepy driver generally places the vehicle closer (by about 0.2 m) to the centre of the road than an alert driver. The analysis also shows a significant, monotonous, increase in average lateral position (measured from the right, outer, lane boundary towards the lane centre) between the four cases of (i) daytime alert driving, (ii) daytime sleepy driving, (iii) night-time alert driving and (iv) nighttime sleepy driving.
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