Human Factors Issues Associated with Limited Ability Autonomous Driving Systems: Drivers’ Allocation of Visual Attention to the Forward Roadway

Llaneras, Robert E.; Salinger, Jeremy; Green, Charles A.

doi:10.17077/drivingassessment.1472

Cited by 108 publications

(68 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, it has been experimentally found that people are inclined to watch DVDs in a highly automated car (Carsten, Lai, Barnard, Jamson, & Merat, 2012;Llaneras, Salinger, & Green, 2013). Furthermore, over 30 driving simulator studies have measured a reduction of self-reported workload in sophisticated driving simulator studies, (see De Winter, Happee, Martens, & Stanton, 2014, for a review), which matches the results of our questions regarding task difficulty (Q25-27).…”

Section: Discussionsupporting

confidence: 58%

Public opinion on automated driving: Results of an international questionnaire among 5000 respondents

Kyriakidis

Happee

Winter

2015

Transportation Research Part F: Traffic Psychology and Behaviou

1,035

548

View full text Add to dashboard Cite

a b s t r a c tThis study investigated user acceptance, concerns, and willingness to buy partially, highly, and fully automated vehicles. By means of a 63-question Internet-based survey, we collected 5000 responses from 109 countries (40 countries with at least 25 respondents). We determined cross-national differences, and assessed correlations with personal variables, such as age, gender, and personality traits as measured with a short version of the Big Five Inventory. Results showed that respondents, on average, found manual driving the most enjoyable mode of driving. Responses were diverse: 22% of the respondents did not want to pay more than $0 for a fully automated driving system, whereas 5% indicated they would be willing to pay more than $30,000, and 33% indicated that fully automated driving would be highly enjoyable. 69% of respondents estimated that fully automated driving will reach a 50% market share between now and 2050. Respondents were found to be most concerned about software hacking/misuse, and were also concerned about legal issues and safety. Respondents scoring higher on neuroticism were slightly less comfortable about data transmitting, whereas respondents scoring higher on agreeableness were slightly more comfortable with this. Respondents from more developed countries (in terms of lower accident statistics, higher education, and higher income) were less comfortable with their vehicle transmitting data, with cross-national correlations between q = À0.80 and q = À0.90. The present results indicate the major areas of promise and concern among the international public, and could be useful for vehicle developers and other stakeholders.

show abstract

Section: Discussionsupporting

confidence: 58%

Public opinion on automated driving: Results of an international questionnaire among 5000 respondents

Kyriakidis

Happee

Winter

2015

Transportation Research Part F: Traffic Psychology and Behaviou

1,035

548

View full text Add to dashboard Cite

show abstract

“…Carsten and colleagues (Carsten, Lai, Barnard, Jamson, & Merat, 2012) found that engagement in non-driving tasks, like reading or usage of a DVD player, increased from manual to highly automated driving as a substitution for the vehicle control task. Llaneras, Salinger and Green (2013) reported similar results with engagements like eating or e-mailing. Summed up, engagement in a non-driving task is very likely in highly automated driving.…”

Section: Engagement In a Non-driving Task As Use Case In Highly Automsupporting

confidence: 62%

The influence of age on the take-over of vehicle control in highly automated driving

Körber

Gold

Lechner

et al. 2016

Transportation Research Part F: Traffic Psychology and Behaviou

187

View full text Add to dashboard Cite

The growing proportion of older drivers in the population plays an increasingly relevant role in road traffic that is currently awaiting the introduction of automated vehicles. In this study, it was investigated how older drivers (≥ 60 years) compared to younger drivers (≤ 28 years) perform in a critical traffic event when driving highly automated. Conditions of the take-over situation were manipulated by adding a verbal non-driving task (20 questions task) and by variation of traffic density. Two age groups consisting of 36 younger and 36 older drivers drove either with or without a non-driving task on a six-lane highway. They encountered three situations with either no, medium or high traffic density where they had to regain vehicle control and evade an obstacle on the road. Older drivers reacted as fast as younger drivers, however, they differed in their modus operandi as they braked more often and more strongly and maintained a higher time-to-collision (TTC). Deterioration of take-over time and quality caused by increased traffic density and engagement in a non-driving task was on the same level for both age groups. Independent of the traffic density, there was a learning effect for both younger and older drivers in a way that the take-over time decreased, minimum TTC increased and maximum lateral acceleration decreased between the first and the last situation of the experiment. Results highlight that older drivers are able to solve critical traffic events as well as younger drivers, yet their modus operandi differs. Nevertheless, both age groups adapt to the experience of take-over situations in the same way.

show abstract

“…Beyond that, it is also possible that the system might require a transfer to Level 2 if the conditions necessitate it (Creaser & Fitch, 2015). An automated driving system will allow the driver to take his eyes off the road and engage in NDRTs and driving simulator studies show that drivers are willing to do so, possibly increasing the demand of a take-over situation (Carsten, Lai, Barnard, Jamson, & Merat, 2012;Llaneras, Salinger, & Green, 2013;Radlmayr, Gold, Lorenz, Farid, & Bengler, 2014). This act of reliance is only performed if the driver trusts driving automation enough to completely hand over the driving task.…”

Section: The Role Of Trust In Automated Drivingmentioning

confidence: 99%

Introduction matters: Manipulating trust in automation and reliance in automated driving

2018

View full text Add to dashboard Cite

Trust in automation is a key determinant for the adoption of automated systems and their appropriate use. Therefore, it constitutes an essential research area for the introduction of automated vehicles to road traffic. In this study, we investigated the influence of trust promoting (Trust promoted group) and trust lowering (Trust lowered group) introductory information on reported trust, reliance behavior and takeover performance. Forty participants encountered three situations in a 17-minute highway drive in a conditionally automated vehicle (SAE Level 3). Situation 1 and Situation 3 were non-critical situations where a take-over was optional. Situation 2 represented a critical situation where a take-over was necessary to avoid a collision. A non-driving-related task (NDRT) was presented between the situations to record the allocation of visual attention. Participants reporting a higher trust level spent less time looking at the road or instrument cluster and more time looking at the NDRT. The manipulation of introductory information resulted in medium differences in reported trust and influenced participants' reliance behavior. Participants of the Trust promoted group looked less at the road or instrument cluster and more at the NDRT. The odds of participants of the Trust promoted group to overrule the automated driving system in the non-critical situations were 3.65 times (Situation 1) to 5 times (Situation 3) higher. In Situation 2, the Trust promoted group's mean take-over time was extended by 1154 ms and the mean minimum time-to-collision was 933 ms shorter. Six participants from the Trust promoted group compared to no participant of the Trust lowered group collided with the obstacle. The results demonstrate that the individual trust level influences how much drivers monitor the environment while performing an NDRT. Introductory information influences this trust level, reliance on an automated driving system, and if a critical take-over situation can be successfully solved.

show abstract

Human Factors Issues Associated with Limited Ability Autonomous Driving Systems: Drivers’ Allocation of Visual Attention to the Forward Roadway

Cited by 108 publications

References 5 publications

Public opinion on automated driving: Results of an international questionnaire among 5000 respondents

Public opinion on automated driving: Results of an international questionnaire among 5000 respondents

The influence of age on the take-over of vehicle control in highly automated driving

Introduction matters: Manipulating trust in automation and reliance in automated driving

Contact Info

Product

Resources

About