“…It is crucial to determine how the automatic security system, such as the seatbelt tension increase, affects the user when dealing with this type of information. According to Günthner and Proff [37], a properly implemented security system needs to consider the social norm, acceptance, perceived usefulness, perceived ease-of-use, and trust in technology of the user's treats to prevent undesired outcomes, like a panic attack.…”
Automobile security became an essential theme over the last years, and some automakers invested much money for collision avoidance systems, but personalization of their driving systems based on the user’s behavior was not explored in detail. Furthermore, efficiency gains could be had with tailored systems. In Mexico, 80% of automobile accidents are caused by human beings; the remaining 20% are related to other issues such as mechanical problems. Thus, 80% represents a significant opportunity to improve safety and explore driving efficiency gains. Moreover, when driving aggressively, it could be connected with mental health as a post-traumatic stress disorder. This paper proposes a Tailored Collision Mitigation Braking System, which evaluates the driver’s personality driving treats through signal detection theory to create a cognitive map that understands the driving personality of the driver. In this way, aggressive driving can be detected; the system is then trained to recognize the personality trait of the driver and select the appropriate stimuli to achieve the optimal driving output. As a result, when aggressive driving is detected continuously, an automatic alert could be sent to the health specialists regarding particular risky behavior linked with mental problems or drug consumption. Thus, the driving profile test could also be used as a detector for health problems.
“…It is crucial to determine how the automatic security system, such as the seatbelt tension increase, affects the user when dealing with this type of information. According to Günthner and Proff [37], a properly implemented security system needs to consider the social norm, acceptance, perceived usefulness, perceived ease-of-use, and trust in technology of the user's treats to prevent undesired outcomes, like a panic attack.…”
Automobile security became an essential theme over the last years, and some automakers invested much money for collision avoidance systems, but personalization of their driving systems based on the user’s behavior was not explored in detail. Furthermore, efficiency gains could be had with tailored systems. In Mexico, 80% of automobile accidents are caused by human beings; the remaining 20% are related to other issues such as mechanical problems. Thus, 80% represents a significant opportunity to improve safety and explore driving efficiency gains. Moreover, when driving aggressively, it could be connected with mental health as a post-traumatic stress disorder. This paper proposes a Tailored Collision Mitigation Braking System, which evaluates the driver’s personality driving treats through signal detection theory to create a cognitive map that understands the driving personality of the driver. In this way, aggressive driving can be detected; the system is then trained to recognize the personality trait of the driver and select the appropriate stimuli to achieve the optimal driving output. As a result, when aggressive driving is detected continuously, an automatic alert could be sent to the health specialists regarding particular risky behavior linked with mental problems or drug consumption. Thus, the driving profile test could also be used as a detector for health problems.
“…This study implemented a self-developed questionnaire, consisting of section 1 with questions on the respondent’s demographic data and section 2 with the items relevant to the extended theory of acceptance model (TAM) ( Günthner and Proff, 2021 ), the model for which appears in Figure 1 . A five-point Likert scale was used for the responses (1 = strongly disagree, 5 = strongly agree).…”
Section: Methodsmentioning
confidence: 99%
“…Two statements each represented the constructs of intention to use, perceived usefulness, perceived ease of use, subjective norm, and trust in technology. An example of the statements of intention to use is, “I will use distracted-driving-reducing technology,” The personal innovativeness construct consisted of four statements; for example, “I like to experiment with new information technologies.” Figure 1 The conceptual model of the intention to use distracted-driving-reduction technology ( Günthner and Proff, 2021 ). …”
Section: Methodsmentioning
confidence: 99%
“… Günthner and Proff (2021) define personal innovativeness as the extent to which a person buys a new product at an earlier stage of the market than other consumers. It can also be an individual’s enthusiasm for engaging with new technology and willingness to accept an innovation earlier than others.…”
Section: Introductionmentioning
confidence: 99%
“…Such studies find a relationship between personal innovativeness and acceptance of various applications, with regard to perceived usefulness, ease of use, and intention to use. These applications include driver-assistant systems among older adults ( Günthner and Proff, 2021 ), mobile device options ( Lee, 2019 ), and e-commerce ( Bigne-Alcaniz et al., 2008 ).…”
New driver assistance systems play an important role to rise safety and comfort in todays´ traffic. Those systems should be developed with the needs of the user in mind and tested for the users´ requirements. In this, user acceptance is a central variable of interest – both in scientific and in practical applications of user-centered research on driver assistance systems. In some cases, applied research settings need simplified measurements in order to be efficiently applicable in the driving situations. In the present paper, we explored the applicability and validity of a single-item acceptance measurement (SIAM) for practical study settings covering the attitude towards using new driver assistance systems. To provide a theoretical framing, we tested the one-item measure against the widely used Technology Acceptance Model (TAM) and the van der Laan acceptance scale (VDL) in a driving simulator study. Participants experienced four different complex driving scenarios using a driver assistance system. Acceptance was measured repeatedly throughout the drive. The results supported construct validity for the SIAM, correlating with the VDL. The SIAM further predicted the intention to use the system. Being carefully aware of the psychometric drawbacks of short scales and acknowledging the importance of multi-item scales, the SIAM is promising for efficiently approaching the acceptance of driver assistance systems in applied settings.
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