In The Truman Show: Generating Dynamic Scenarios in a Driving Simulator

Wassink, Ingo; Dijk, Betsy van; Zwiers, Job; Nijholt, Anton; Kuipers, Jorrit; Brugman, Arnd

doi:10.1109/mis.2006.97

Cited by 12 publications

(8 citation statements)

References 3 publications

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“…Advantages of a simulator are that goal-oriented virtual worlds and scenarios can be presented and that driving performance can be assessed objectively and accurately in a standardised fashion (de Winter et al 2006a, Wassink et al 2006. It is impossible to incorporate these aspects into driver training on the road.…”

Section: Data Quality In Simulation-based Driver Trainingmentioning

confidence: 99%

Relationships between driving simulator performance and driving test results

et al. 2009

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This article is considered relevant because: 1) car driving is an everyday and safety-critical task; 2) simulators are used to an increasing extent for driver training (related topics: training, virtual reality, human-machine interaction); 3) the article addresses relationships between performance in the simulator and driving test results--a relevant topic for those involved in driver training and the virtual reality industries; 4) this article provides new insights about individual differences in young drivers' behaviour. Simulators are being used to an increasing extent for driver training, allowing for the possibility of collecting objective data on driver proficiency under standardised conditions. However, relatively little is known about how learner drivers' simulator measures relate to on-road driving. This study proposes a theoretical framework that quantifies driver proficiency in terms of speed of task execution, violations and errors. This study investigated the relationships between these three measures of learner drivers' (n=804) proficiency during initial simulation-based training and the result of the driving test on the road, occurring an average of 6 months later. A higher chance of passing the driving test the first time was associated with making fewer steering errors on the simulator and could be predicted in regression analysis with a correlation of 0.18. Additionally, in accordance with the theoretical framework, a shorter duration of on-road training corresponded with faster task execution, fewer violations and fewer steering errors (predictive correlation 0.45). It is recommended that researchers conduct more large-scale studies into the reliability and validity of simulator measures and on-road driving tests.

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Section: Data Quality In Simulation-based Driver Trainingmentioning

confidence: 99%

Relationships between driving simulator performance and driving test results

et al. 2009

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“…Janson Olstam and Espié (2007) present the play preparation problem using a theater metaphor, which is an extension of the theater metaphors 4 presented in Alloyer et al (1997), Espié and Rousseau (1998), and Wassink et al (2005and Wassink et al ( , 2006. The theater metaphor includes definitions of the terms play, manuscript, role, actor, stage, etc.…”

Section: The Play Preparation Problemmentioning

confidence: 99%

“…Ideas and frameworks, but no complete algorithms, for combining autonomous and controlled vehicles are provided by Alloyer et al (1997), Wassink et al (2005Wassink et al ( , 2006 and Janson Olstam and Espié (2007). Inspired by the work of Alloyer et al (1997) and Wassink et al (2005Wassink et al ( , 2006, Janson Olstam and Espié (2007) proposed an alternative design methodology for driving simulator experiments in which periods with autonomous simulated road users are combined with periods with only controlled simulated road users. The basic idea is to let the surrounding vehicles run in autonomous mode between the predetermined situations at which measurements are taken.…”

Section: Introductionmentioning

confidence: 99%

An algorithm for combining autonomous vehicles and controlled events in driving simulator experiments

Olstam

Éspié

Måardh

et al. 2011

Transportation Research Part C: Emerging Technologies

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Autonomous vehicles can be used to create realistic simulations of surrounding vehicles in driving simulators. However, the use of autonomous vehicles makes it difficult to ensure reproducibility between subjects. In this paper, an effort is made to solve the problem by combining autonomous vehicles and controlled events. A controlled event can be compared to a theatre play. The aim is to achieve the same initial play conditions for each subject, which can be problematic since the traffic situation around the subject will be dependent upon each subject's actions while driving in autonomous traffic. This paper presents an algorithm that achieves the transition from autonomous traffic to a predefined start condition for a play. The algorithm has been tested in the Swedish National Road and Transport Research Institute (VTI) driving simulator III with promising results. In most of the cases we examined the algorithm could reconstruct the specified start condition and conduct the transition from autonomous to controlled mode in a inconspicuous way. Some problems were observed regarding moving unwanted vehicles away from the closest area around the simulator vehicle, and this part of the algorithm has to be enhanced. The experiment also showed that the subjects drove faster in the presence of controlled everyday life traffic normally used in the VTI driving simulator than in autonomous traffic.

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“…Behaviour of other vehicles, weather conditions, or the virtual environment can be manipulated in real time according to the training needs (Wassink et al, 2006). It is also possible to confront a trainee with novel technical devices such as adaptive cruise control, front wheel drive or rear wheel drive, or vehicles of different masses, which can make them aware that they should adapt their behaviour (Hoeschen et al, 2001).…”

Section: Control Over Training Conditions and Standardizationmentioning

confidence: 99%

“…Advantages of a simulator are that goal-oriented virtual worlds and scenarios can be presented, and that driving performance can be assessed objectively and accurately in a standardized fashion (De Winter et al, 2006b;Wassink et al, 2006). It is impossible to incorporate these aspects into driver training on the road.…”

Section: Data Quality In Simulation-based Driver Trainingmentioning

confidence: 99%