Matching Items (4)
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- All Subjects: Transportation
- Genre: Masters Thesis
- Creators: Cooke, Nancy J.
Description
This study exmaines the effect of in-vehicle infotainment display depth on driving performance. More features are being built into infotainment displays, allowing drivers to complete a greater number of secondary tasks while driving. However, the complexity of completing these tasks can take attention away from the primary task of driving, which may present safety risks. Tasks become more time consuming as the items drivers wish to select are buried deeper in a menu’s structure. Therefore, this study aims to examine how deeper display structures impact driving performance compared to more shallow structures.
Procedure. Participants complete a lead car following task, where they follow a lead car and attempt to maintain a time headway (TH) of 2 seconds behind the lead car at all times, while avoiding any collisions. Participants experience five conditions where they are given tasks to complete with an in-vehicle infotainment system. There are five conditions, each involving one of five displays with different structures: one-layer vertical, one-layer horizontal, two-layer vertical, two-layer horizontal, and three-layer. Brake Reaction Time (BRT), Mean Time Headway (MTH), Time Headway Variability (THV), and Time to Task Completion (TTC) are measured for each of the five conditions.
Results. There is a significant difference in MTH, THV, and TTC for the three-layer condition. There is a significant difference in BRT for the two-layer horizontal condition. There is a significant difference between one- and two-layer displays for all variables, BRT, MTH, THV, and TTC. There is also a significant difference between one- and three-layer displays for TTC.
Conclusions. Deeper displays negatively impact driving performance and make tasks more time consuming to complete while driving. One-layer displays appear to be optimal, although they may not be practical for in-vehicle displays.
Procedure. Participants complete a lead car following task, where they follow a lead car and attempt to maintain a time headway (TH) of 2 seconds behind the lead car at all times, while avoiding any collisions. Participants experience five conditions where they are given tasks to complete with an in-vehicle infotainment system. There are five conditions, each involving one of five displays with different structures: one-layer vertical, one-layer horizontal, two-layer vertical, two-layer horizontal, and three-layer. Brake Reaction Time (BRT), Mean Time Headway (MTH), Time Headway Variability (THV), and Time to Task Completion (TTC) are measured for each of the five conditions.
Results. There is a significant difference in MTH, THV, and TTC for the three-layer condition. There is a significant difference in BRT for the two-layer horizontal condition. There is a significant difference between one- and two-layer displays for all variables, BRT, MTH, THV, and TTC. There is also a significant difference between one- and three-layer displays for TTC.
Conclusions. Deeper displays negatively impact driving performance and make tasks more time consuming to complete while driving. One-layer displays appear to be optimal, although they may not be practical for in-vehicle displays.
ContributorsGran, Emily (Author) / Gray, Robert (Thesis advisor) / Branaghan, Russell (Committee member) / Carrasquilla, Christina (Committee member) / Arizona State University (Publisher)
Created2018
Description
Future autonomous vehicle systems will be diverse in design and functionality since they will be produced by different brands. In the automotive industry, trustworthiness of a vehicle is closely tied to its perceived safety. Trust involves dependence on another agent in an uncertain situation. Perceptions of system safety, trustworthiness, and performance are important because they guide people’s behavior towards automation. Specifically, these perceptions impact how reliant people believe they can be on the system to do a certain task. Over or under reliance can be a concern for safety because they involve the person allocating tasks between themselves and the system in inappropriate ways. If a person trusts a brand they may also believe the brand’s technology will keep them safe. The present study measured brand trust associations and performance expectations for safety between twelve different automobile brands using an online survey.
The literature and results of the present study suggest perceived trustworthiness for safety of the automation and the brand of the automation, could together impact trust. Results revelated that brands closely related to the trust-based attributes, Confidence, Secure, Integrity, and Trustworthiness were expected to produce autonomous vehicle technology that performs in a safer way. While, brands more related to the trust-based attributes Harmful, Deceptive, Underhanded, Suspicious, Beware, and Familiar were expected to produce autonomous vehicle technology that performs in a less safe way.
These findings contribute to both the fields of Human-Automation Interaction and Consumer Psychology. Typically, brands and automation are discussed separately however, this work suggests an important relationship may exist. A deeper understanding of brand trust as it relates to autonomous vehicles can help producers understand potential for over or under reliance and create safer systems that help users calibrate trust appropriately. Considering the impact on safety, more research should be conducted to explore brand trust and expectations for performance between various brands.
The literature and results of the present study suggest perceived trustworthiness for safety of the automation and the brand of the automation, could together impact trust. Results revelated that brands closely related to the trust-based attributes, Confidence, Secure, Integrity, and Trustworthiness were expected to produce autonomous vehicle technology that performs in a safer way. While, brands more related to the trust-based attributes Harmful, Deceptive, Underhanded, Suspicious, Beware, and Familiar were expected to produce autonomous vehicle technology that performs in a less safe way.
These findings contribute to both the fields of Human-Automation Interaction and Consumer Psychology. Typically, brands and automation are discussed separately however, this work suggests an important relationship may exist. A deeper understanding of brand trust as it relates to autonomous vehicles can help producers understand potential for over or under reliance and create safer systems that help users calibrate trust appropriately. Considering the impact on safety, more research should be conducted to explore brand trust and expectations for performance between various brands.
ContributorsCelmer, Natalie (Author) / Branaghan, Russell (Thesis advisor) / Chiou, Erin (Thesis advisor) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Vehicular automation and autonomy are emerging fields that are growing at an
exponential rate, expected to alter the very foundations of our transportation system within the next 10-25 years. A crucial interaction has been born out this new technology: Human and automated drivers operating within the same environment. Despite the well- known dangers of automobiles and driving, autonomous vehicles and their consequences on driving environments are not well understood by the population who will soon be interacting with them every day. Will an improvement in the understanding of autonomous vehicles have an effect on how humans behave when driving around them? And furthermore, will this improvement in the understanding of autonomous vehicles lead to higher levels of trust in them? This study addressed these questions by conducting a survey to measure participant’s driving behavior and trust when in the presence of autonomous vehicles. Participants were given several pre-tests to measure existing knowledge and trust of autonomous vehicles, as well as to see their driving behavior when in close proximity to autonomous vehicles. Then participants were presented with an educational intervention, detailing how autonomous vehicles work, including their decision processes. After examining the intervention, participants were asked to repeat post-tests identical to the ones administered before the intervention. Though a significant difference in self-reported driving behavior was measure between the pre-test and post- test, there was no significant relation found between improvement in scores on the education intervention knowledge check and driving behavior. There was also no significant relation found between improvement in scores on the education intervention knowledge check and the change in trust scores. These findings can be used to inform autonomous vehicle and infrastructure design as well as future studies of the effects of autonomous vehicles on human drivers in experimental settings.
exponential rate, expected to alter the very foundations of our transportation system within the next 10-25 years. A crucial interaction has been born out this new technology: Human and automated drivers operating within the same environment. Despite the well- known dangers of automobiles and driving, autonomous vehicles and their consequences on driving environments are not well understood by the population who will soon be interacting with them every day. Will an improvement in the understanding of autonomous vehicles have an effect on how humans behave when driving around them? And furthermore, will this improvement in the understanding of autonomous vehicles lead to higher levels of trust in them? This study addressed these questions by conducting a survey to measure participant’s driving behavior and trust when in the presence of autonomous vehicles. Participants were given several pre-tests to measure existing knowledge and trust of autonomous vehicles, as well as to see their driving behavior when in close proximity to autonomous vehicles. Then participants were presented with an educational intervention, detailing how autonomous vehicles work, including their decision processes. After examining the intervention, participants were asked to repeat post-tests identical to the ones administered before the intervention. Though a significant difference in self-reported driving behavior was measure between the pre-test and post- test, there was no significant relation found between improvement in scores on the education intervention knowledge check and driving behavior. There was also no significant relation found between improvement in scores on the education intervention knowledge check and the change in trust scores. These findings can be used to inform autonomous vehicle and infrastructure design as well as future studies of the effects of autonomous vehicles on human drivers in experimental settings.
ContributorsReagan, Taylor (Author) / Cooke, Nancy J. (Thesis advisor) / Chiou, Erin (Committee member) / Gray, Robert (Committee member) / Arizona State University (Publisher)
Created2019
Description
Communications between air traffic controllers and pilots are critical to national airspace traffic management. Measuring communications in real time made by pilots and air traffic controllers has the potential to predict human error. In this thesis a measure for Deviations from Closed Loop Communications is defined and tested to predict a human error event, Loss of Separation (LOS). Six retired air traffic controllers were recruited and tested in three conditions of varying workload in an Terminal Radar Approach Control Facility (TRACON) arrival radar simulation. Communication transcripts from simulated trials were transcribed and coding schemes for Closed Loop Communication Deviations (CLCD) were applied. Results of the study demonstrated a positive correlation between CLCD and LOS, indicating that CLCD could be a variable used to predict LOS. However, more research is required to determine if CLCD can be used to predict LOS independent of other predictor variables, and if CLCD can be used in a model that considers many different predictor variables to predict LOS.
ContributorsLieber, Christopher Shane (Author) / Cooke, Nancy J. (Thesis advisor) / Gutzwiller, Robert S (Committee member) / Niemczyk, Mary (Committee member) / Arizona State University (Publisher)
Created2020