Good Intentions? Willingness to Engage with Technology on the Road and in a Driving Simulator

2018 ◽  
pp. 147-176 ◽  
Author(s):  
Katie J. Parnell ◽  
Neville A. Stanton ◽  
Katherine L. Plant
Keyword(s):  
Driving Simulator ◽  
The Road ◽  
On The Road ◽  
Willingness To Engage

Advance Information on the Road: A Simulator Study of the Effect of Road Markings

1976 ◽  
Vol 18 (6) ◽  
pp. 521-532 ◽  
Author(s):  
Harald Witt ◽  
Carl G. Hoyos
Keyword(s):  
Driving Simulator ◽  
Driving Behavior ◽  
Speed Profile ◽  
The Road ◽  
Positive Effects ◽  
Road Signs ◽  
Advance Information ◽  
Simulator Study ◽  
On The Road ◽  
Accident Statistics

Accident statistics and studies of driving behavior have shown repeatedly that curved roads are hazardous. It was hypothesized that the safety of curves could be improved by indicating in advance the course of the road in a more effective way than do traditional road signs. A code of sequences of stripes put on right edge of the pavement was developed to indicate to the driver the radius of the curve ahead. The main characteristic of this code was the frequency of transitions from code elements to gaps between elements. The effect of these markings was investigated on a driving simulator. Twelve subjects drove on simulated roads of different curvature and with different placement of the code in the approach zone. Some positive effects of the advance information could be observed. The subjects drove more steadily, more precisely, and with a more suitable speed profile.

Analysis of Preference for Autonomous Driving Under Different Traffic Conditions Using a Driving Simulator

2015 ◽  
Vol 27 (6) ◽  
pp. 660-670 ◽  
Author(s):  
Udara Eshan Manawadu ◽  
◽  
Masaaki Ishikawa ◽  
Mitsuhiro Kamezaki ◽  
Shigeki Sugano ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Driving Experience ◽  
The Road ◽  
Traffic Conditions ◽  
Passenger Vehicles ◽  
New Type ◽  
On The Road ◽  
Near Future

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/08.jpg"" width=""300"" /> Driving simulator</div>Intelligent passenger vehicles with autonomous capabilities will be commonplace on our roads in the near future. These vehicles will reshape the existing relationship between the driver and vehicle. Therefore, to create a new type of rewarding relationship, it is important to analyze when drivers prefer autonomous vehicles to manually-driven (conventional) vehicles. This paper documents a driving simulator-based study conducted to identify the preferences and individual driving experiences of novice and experienced drivers of autonomous and conventional vehicles under different traffic and road conditions. We first developed a simplified driving simulator that could connect to different driver-vehicle interfaces (DVI). We then created virtual environments consisting of scenarios and events that drivers encounter in real-world driving, and we implemented fully autonomous driving. We then conducted experiments to clarify how the autonomous driving experience differed for the two groups. The results showed that experienced drivers opt for conventional driving overall, mainly due to the flexibility and driving pleasure it offers, while novices tend to prefer autonomous driving due to its inherent ease and safety. A further analysis indicated that drivers preferred to use both autonomous and conventional driving methods interchangeably, depending on the road and traffic conditions.

scholarly journals Good intentions: drivers’ decisions to engage with technology on the road and in a driving simulator

2018 ◽  
Vol 20 (4) ◽  
pp. 597-619 ◽  
Author(s):  
Katie J. Parnell ◽  
Neville A. Stanton ◽  
Katherine L. Plant
Keyword(s):  
Driving Simulator ◽  
The Road ◽  
On The Road

scholarly journals Validity of Mental Workload Measures in a Driving Simulation Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Francesco Galante ◽  
Fabrizio Bracco ◽  
Carlo Chiorri ◽  
Luigi Pariota ◽  
Luigi Biggero ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Mental Workload ◽  
Critical Issue ◽  
Simulation Environment ◽  
Driving Simulators ◽  
The Road ◽  
Mixed Support ◽  
Vehicle Systems ◽  
Driving Task ◽  
On The Road

Automated in-vehicle systems and related human-machine interfaces can contribute to alleviating the workload of drivers. However, each new functionality can also introduce a new source of workload, due to the need to attend to new tasks and thus requires careful testing before being implemented in vehicles. Driving simulators have become a viable alternative to on-the-road tests, since they allow optimal experimental control and high safety. However, for each driving simulator to be a useful research tool, for each specific task an adequate correspondence must be established between the behavior in the simulator and the behavior on the road, namely, the simulator absolute and relative validity. In this study we investigated the validity of a driving-simulator-based experimental environment for research on mental workload measures by comparing behavioral and subjective measures of workload of the same large group of participants in a simulated and on-road driving task on the same route. Consistent with previous studies, mixed support was found for both types of validity, although results suggest that allowing more and/or longer familiarization sessions with the simulator may be needed to increase its validity. Simulator sickness also emerged as a critical issue for the generalizability of the results.

scholarly journals An Integrated Model for User State Detection of Subjective Discomfort in Autonomous Vehicles

Vehicles ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 764-777
Author(s):  
Dario Niermann ◽  
Alexander Trende ◽  
Klas Ihme ◽  
Uwe Drewitz ◽  
Cornelia Hollander ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Linear Models ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
The Road ◽  
Novel Approach ◽  
Driving Scenario ◽  
On The Road ◽  
Using Data ◽  
Subjective Discomfort

The quickly rising development of autonomous vehicle technology and increase of (semi-) autonomous vehicles on the road leads to an increased demand for more sophisticated human–machine-cooperation approaches to improve trust and acceptance of these new systems. In this work, we investigate the feeling of discomfort of human passengers while driving autonomously and the automatic detection of this discomfort with several model approaches, using the combination of different data sources. Based on a driving simulator study, we analyzed the discomfort reports of 50 participants for autonomous inner city driving. We found that perceived discomfort depends on the driving scenario (with discomfort generally peaking in complex situations) and on the passenger (resulting in interindividual differences in reported discomfort extend and duration). Further, we describe three different model approaches on how to predict the passenger discomfort using data from the vehicle’s sensors as well as physiological and behavioral data from the passenger. The model’s precision varies greatly across the approaches, the best approach having a precision of up to 80%. All of our presented model approaches use combinations of linear models and are thus fast, transparent, and safe. Lastly, we analyzed these models using the SHAP method, which enables explaining the models’ discomfort predictions. These explanations are used to infer the importance of our collected features and to create a scenario-based discomfort analysis. Our work demonstrates a novel approach on passenger state modelling with simple, safe, and transparent models and with explainable model predictions, which can be used to adapt the vehicles’ actions to the needs of the passenger.

scholarly journals Driving while navigating: On-road driving performance using GPS or printed instructions

2019 ◽  
Vol 86 (1) ◽  
pp. 61-69 ◽  
Author(s):  
Lauren Marchman Cochran ◽  
Anne E. Dickerson
Keyword(s):  
Driving Simulator ◽  
Electronic Devices ◽  
Point System ◽  
The Road ◽  
Significant Difference ◽  
On The Road ◽  
And Performance ◽  
High Level ◽  
Paired T Test ◽  
Better Than

Background. Route navigation is a high-level skill and requires intact executive functioning to successfully find one’s way while driving in unfamiliar environments. Purpose. Driving performances were compared while navigating using electronic devices and printed directions on unfamiliar driving routes as well as in an interactive driving simulator. Method. Twenty-four participants drove two on-road routes using GPS and printed directions, and navigated using printed directions in the simulator, using a point system to evaluate performance. The two unfamiliar routes, order of simulator and on-road driving, and use of GPS and printed directions were counterbalanced. Paired t test were used to compare both GPS versus printed directions and performance between on-road driving and the simulator. Findings. Participants’ performance using GPS on the road was significantly better than with printed directions. There was no significant difference between performance in the simulator and on the road. Implications. Using GPS may be an effective strategy for improving safety. Using a driving simulator may be an efficient means of evaluating the strategic level of driving, executive function, and readiness to drive.

Pink Elephants on the Road: Visual Illusions and Distortions when Driving during Significant Sleep Deprivation

2020 ◽  
Vol 64 (1) ◽  
pp. 1360-1364
Author(s):  
Curtis M. Craig ◽  
Nichole L. Morris ◽  
Katelyn R. Schwieters ◽  
Conrad Iber
Keyword(s):  
Sleep Deprivation ◽  
Driving Simulator ◽  
Mental Workload ◽  
Visual Illusions ◽  
Safe Driving ◽  
The Road ◽  
Time Period ◽  
Visual Phenomena ◽  
On The Road ◽  
Study Participants

Visual hallucinations, illusions, and distortions have been observed in individuals undergoing severe periods of extended wakefulness. However, the incidence of these perceptual phenomena occurring during applied domains such as driving have been underreported. This study investigates effects of a 30-hour period of extended wakefulness during which participants abstained from stimulants and were not allowed to sleep or nap. Participants drove every 4 hours during this period on an uneventful 30-minute driving route in a fullcab high fidelity driving simulator. At the end of the study, participants reported whether they experienced significant visual illusions or distortions, and when the events occurred. Participants reported visual distortions and illusions during drives comprising a time period between 22 and 30 hours awake. Furthermore, self-reported mental workload and extroversion predicted the likelihood of experiencing the visual phenomena. Potential mechanisms for this relationship and possible consequences for safe driving performance during significant sleep deprivation are discussed.

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