How does training effect users’ attitudes and skills needed for highly automated driving?

Ebnali, Mahdi; Hulme, Kevin F.; Ebnali-Heidari, Aliakbar; Mazloumi, Adel

doi:10.1016/j.trf.2019.09.001

Cited by 67 publications

(20 citation statements)

References 38 publications

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“…Lowering the perception of risk of driving AV may have a positive impact upon the public confidence in AV technology. However conversely, drivers who report excessive trust towards assistive technologies tend to overestimate their capabilities (Ebnali et al, 2019). Over-trust tends to distort the accuracy of perceived risk.…”

Section: How Responsibility Leads To Liabilitymentioning

confidence: 95%

“…'Learning by doing' is essential (Boelhouwer et al, 2019) and drivers require training strategies to support them during handover situations. Drivers who are trained in 'real-life' AV may benefit from improved skills, knowledge and safer automated-to manual-recovery, as their driving experience provides a more transparent view of how highly automated cars operate (Ebnali et al, 2019), and specialised training improves response time (Payre et al, 2016). The current state of this research provides a basis on which the Guide2Autonomy aims to build.…”

Section: How Responsibility Leads To Liabilitymentioning

confidence: 99%

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Legal issues in automated vehicles: critically considering the potential role of consent and interactive digital interfaces

Pattinson

Chen

Basu

2020

Humanit Soc Sci Commun

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Some of the first ‘automated’ vehicles to be deployed on our roads will require a system of shared driving with a human driver. While this creates technical and operational challenges, the law must also facilitate such a transfer. One method may be to obtain the driver’s consent to share operational responsibility and to delineate legal responsibility between vehicle and driver in the event of an accident. Consent is a voluntary agreement where an individual is aware of the potential consequences of their consent, including the risks. The driver of a partially automated vehicle must be informed of potential risks before giving consent to share operational responsibility. This paper will refer to the inherent dangers associated with shared operational responsibility, in particular where there has been a request for the driver to take back control from the automated vehicle during the journey. Drivers are likely to experience delay in regaining situational awareness, making such operational transfers hazardous. It is argued that where an interactive digital interface is used to convey information, such as driver responsibility, risk and legal terms, drivers may fail to sufficiently process such communications due to fundamental weaknesses in human–machine interaction. The use of an interactive digital interface alone may be inadequate to effectively communicate information to drivers. If the problems identified are not addressed, it is argued that driver consent may be inconsequential, and fail to facilitate a predicable demarcation of legal responsibility between automated vehicles and drivers. Ongoing research into automated vehicle driver training is considered as part of the preparation required to design driver education to a level whereby drivers may be able to sufficiently understand the responsibilities involved in operating a partially automated vehicle, which has implications for future driver training, licensing and certification.

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Section: How Responsibility Leads To Liabilitymentioning

confidence: 95%

Section: How Responsibility Leads To Liabilitymentioning

confidence: 99%

Legal issues in automated vehicles: critically considering the potential role of consent and interactive digital interfaces

Pattinson

Chen

Basu

2020

Humanit Soc Sci Commun

View full text Add to dashboard Cite

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“…People who are early adopters (Zmud et al, 2016b), male (Bansal et al, 2016;Jiang et al, 2018) and younger (Abraham et al, 2017;Payre et al, 2014;Wang et al, 2019) have shown more WTU for advanced and automated vehicles. Also, some studies proved that eagerness to purchase and use automation technologies has a direct relationship with the education attainment and the level of knowledge about emerging vehicles (Daziano et al, 2017;Ebnali et al, 2019;Shin et al, 2015;Zmud et al, 2016a). Purchasing and using automated and emerging features in the vehicle may also impact travel behavior and commuting patterns of the drivers, causing them to increase vehicle mile traveled (VMT) or, inversely, forcing drivers to take shorter distances (Gkartzonikas and Gkritza, 2019;Shin et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Willingness to Pay for Connected Vehicles: An Alternative-Specific Mixed Logit Regression Approach

2020

IJTTE

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The study of connected vehicles (CVs) has become a hot topic in recent years. Understanding the characteristics that lead consumers to relate to CVs motivates researchers to conduct market analysis studies. The current research investigated the socio-demographic attributes that may contribute to the individual preferences for purchasing CVs. Researchers constructed a series of Alternative-Specific Mixed Logit models to examine the associations between individual preferences of respondents and their willingness to pay for CV features in their future vehicle. The results indicate that hours spent driving play a privileged role among sociodemographic characteristics and driving behavior attributes of respondents. People who drive longer hours tended to purchase CV features. Also, the factor of age had a noticeable effect as the results showed that older people are more likely to purchase CV features.

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“…Previous studies have reported the negative impact of internal distraction factors such as a cellphone[29] [30], or talking to passengers[24], as well as eating and drinking[30] [31] on doing a proper performance by the driver. The effect of external tools, such as traffic signs[32] [33] or digital billboards[7] on driver distraction and the likelihood of an accident has also been reported. The effects of other factors such as using drugs and alcohol on the drivers' behavior such as driving at unauthorized speeds or overtaking have been stated in the previous studies[32] [33].…”

mentioning

confidence: 99%

“…The effect of external tools, such as traffic signs[32] [33] or digital billboards[7] on driver distraction and the likelihood of an accident has also been reported. The effects of other factors such as using drugs and alcohol on the drivers' behavior such as driving at unauthorized speeds or overtaking have been stated in the previous studies[32] [33]. However, in this study, one of the important factors that increase the effects of those factors is the drivers' physical disability such as poor vision.…”

mentioning

confidence: 99%

Analyzing Drivers’ Mental Patterns Using Q-Methodology

Sheykhfard¹,

Haghighi²,

Soltaninejad³

et al. 2020

JTTs

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The human factor is the most important cause of road accidents. Investigating the drivers' mental patterns can lead to a better understanding of the factors that affect drivers to make a mistake and thus increase the likelihood of an accident. In this study, mental patterns of drivers as a human characteristic are determined through a questionnaire survey. To do this, 166 participants (18 -65 years) were asked to express their opinion on the possible effect of 25 factors on the occurrence of accidents. These factors were selected through the investigation of the accident database during the last three years in different areas of the case study. To analyze the data extracted from the survey, Q-methodology was used. The results of the factor analysis showed that there are 5 mental patterns among the participants. Based on the driver's opinion, human factors and road conditions were the most and least influential accident-generating items, respectively. The most significant reason for accidents determined by drivers was human errors including 1) unauthorized overtaking, 2) unauthorized speed, 3) driver distractions (such as cell phone), and 4) driver physical disability (such as visual impairment). Moreover, the failure of the vehicle was mostly reported as another influential contributor to accidents. It is worth mentioning that the results of this study can be used to minimize accidents resulted from drivers' behavioral errors by suggesting strategies for enhancing their performance through new manuals which is a step towards a safer road.

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How does training effect users’ attitudes and skills needed for highly automated driving?

Cited by 67 publications

References 38 publications

Legal issues in automated vehicles: critically considering the potential role of consent and interactive digital interfaces

Legal issues in automated vehicles: critically considering the potential role of consent and interactive digital interfaces

Willingness to Pay for Connected Vehicles: An Alternative-Specific Mixed Logit Regression Approach

Analyzing Drivers’ Mental Patterns Using Q-Methodology

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