Matching Items (7)

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Distracted Driving Awareness Campaign at Arizona State University

Description

Through this creative project, I executed a Distracted Driving Awareness Campaign at Arizona State University to raise awareness about the dangers of distracted driving, specifically texting while driving. As an

Through this creative project, I executed a Distracted Driving Awareness Campaign at Arizona State University to raise awareness about the dangers of distracted driving, specifically texting while driving. As an Undergraduate Student Government Senator, my priority is the safety and success of students, both in and out of the classroom. By partnering with State Farm and AT&T, we were able to raise awareness about the dangers of distracted driving and collected over 200 pledges from students to never text and drive.

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Agent

Created

Date Created
  • 2014-05

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Information architecture in vehicle infotainment displays

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

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.

Contributors

Agent

Created

Date Created
  • 2018

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Human-centric detection and mitigation approach for various levels of cell phone-based driver distractions

Description

Driving a vehicle is a complex task that typically requires several physical interactions and mental tasks. Inattentive driving takes a driver’s attention away from the primary task of driving, which

Driving a vehicle is a complex task that typically requires several physical interactions and mental tasks. Inattentive driving takes a driver’s attention away from the primary task of driving, which can endanger the safety of driver, passenger(s), as well as pedestrians. According to several traffic safety administration organizations, distracted and inattentive driving are the primary causes of vehicle crashes or near crashes. In this research, a novel approach to detect and mitigate various levels of driving distractions is proposed. This novel approach consists of two main phases: i.) Proposing a system to detect various levels of driver distractions (low, medium, and high) using a machine learning techniques. ii.) Mitigating the effects of driver distractions through the integration of the distracted driving detection algorithm and the existing vehicle safety systems. In phase- 1, vehicle data were collected from an advanced driving simulator and a visual based sensor (webcam) for face monitoring. In addition, data were processed using a machine learning algorithm and a head pose analysis package in MATLAB. Then the model was trained and validated to detect different human operator distraction levels. In phase 2, the detected level of distraction, time to collision (TTC), lane position (LP), and steering entropy (SE) were used as an input to feed the vehicle safety controller that provides an appropriate action to maintain and/or mitigate vehicle safety status. The integrated detection algorithm and vehicle safety controller were then prototyped using MATLAB/SIMULINK for validation. A complete vehicle power train model including the driver’s interaction was replicated, and the outcome from the detection algorithm was fed into the vehicle safety controller. The results show that the vehicle safety system controller reacted and mitigated the vehicle safety status-in closed loop real-time fashion. The simulation results show that the proposed approach is efficient, accurate, and adaptable to dynamic changes resulting from the driver, as well as the vehicle system. This novel approach was applied in order to mitigate the impact of visual and cognitive distractions on the driver performance.

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Agent

Created

Date Created
  • 2017

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Warning a distracted driver: smart phone applications, informative warnings and automated driving take-over requests

Description

While various collision warning studies in driving have been conducted, only a handful of studies have investigated the effectiveness of warnings with a distracted driver. Across four experiments, the present

While various collision warning studies in driving have been conducted, only a handful of studies have investigated the effectiveness of warnings with a distracted driver. Across four experiments, the present study aimed to understand the apparent gap in the literature of distracted drivers and warning effectiveness, specifically by studying various warnings presented to drivers while they were operating a smart phone. Experiment One attempted to understand which smart phone tasks, (text vs image) or (self-paced vs other-paced) are the most distracting to a driver. Experiment Two compared the effectiveness of different smartphone based applications (app’s) for mitigating driver distraction. Experiment Three investigated the effects of informative auditory and tactile warnings which were designed to convey directional information to a distracted driver (moving towards or away). Lastly, Experiment Four extended the research into the area of autonomous driving by investigating the effectiveness of different auditory take-over request signals. Novel to both Experiment Three and Four was that the warnings were delivered from the source of the distraction (i.e., by either the sound triggered at the smart phone location or through a vibration given on the wrist of the hand holding the smart phone). This warning placement was an attempt to break the driver’s attentional focus on their smart phone and understand how to best re-orient the driver in order to improve the driver’s situational awareness (SA). The overall goal was to explore these novel methods of improved SA so drivers may more quickly and appropriately respond to a critical event.

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Agent

Created

Date Created
  • 2017

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Effects of cell phone notification levels on driver performance

Description

Previous literature was reviewed in an effort to further investigate the link between notification levels of a cell phone and their effects on driver distraction. Mind-wandering has been suggested as

Previous literature was reviewed in an effort to further investigate the link between notification levels of a cell phone and their effects on driver distraction. Mind-wandering has been suggested as an explanation for distraction and has been previously operationalized with oculomotor movement. Mind-wandering’s definition is debated, but in this research it was defined as off task thoughts that occur due to the task not requiring full cognitive capacity. Drivers were asked to operate a driving simulator and follow audio turn by turn directions while experiencing each of three cell phone notification levels: Control (no texts), Airplane (texts with no notifications), and Ringer (audio notifications). Measures of Brake Reaction Time, Headway Variability, and Average Speed were used to operationalize driver distraction. Drivers experienced higher Brake Reaction Time and Headway Variability with a lower Average Speed in both experimental conditions when compared to the Control Condition. This is consistent with previous research in the field of implying a distracted state. Oculomotor movement was measured as the percent time the participant was looking at the road. There was no significant difference between the conditions in this measure. The results of this research indicate that not, while not interacting with a cell phone, no audio notification is required to induce a state of distraction. This phenomenon was unable to be linked to mind-wandering.

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Agent

Created

Date Created
  • 2019

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Impatience and driving speeds: a driving simulator study

Description

Research on priming has shown that exposure to the concept of fast food can have an effect on human behavior by inducing haste and impatience (Zhong & E. DeVoe, 2010).

Research on priming has shown that exposure to the concept of fast food can have an effect on human behavior by inducing haste and impatience (Zhong & E. DeVoe, 2010). This research suggests that thinking about fast food makes individuals impatient and strengthens their desire to complete tasks such as reading and decision making as quickly and efficiently as possible. Two experiments were conducted in which the effects of fast food priming were examined using a driving simulator. The experiments examined whether fast food primes can induce impatient driving. In experiment 1, 30 adult drivers drove a course in a driving simulator after being exposed to images by rating aesthetics of four different logos. Experiment 1 did not yield faster driving speeds nor an impatient and faster break at the yellow light in the fast food logo prime condition. In experiment 2, 30 adult drivers drove the same course from experiment 1. Participants did not rate logos on their aesthetics prior to the drive, instead billboards were included in the simulation that had either fast food or diner logos. Experiment 2 did not yielded faster driving speeds, however there was a significant effect of faster breaking and a higher number of participants running the yellow light.

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Agent

Created

Date Created
  • 2014

Modeling and measuring cognitive load to reduce driver distraction in smart cars

Description

Driver distraction research has a long history spanning nearly 50 years, intensifying in the last decade. The focus has always been on identifying the distractive tasks and measuring the respective

Driver distraction research has a long history spanning nearly 50 years, intensifying in the last decade. The focus has always been on identifying the distractive tasks and measuring the respective harm level. As in-vehicle technology advances, the list of distractive activities grows along with crash risk. Additionally, the distractive activities become more common and complicated, especially with regard to In-Car Interactive System. This work's main focus is on driver distraction caused by the in-car interactive System. There have been many User Interaction Designs (Buttons, Speech, Visual) for Human-Car communication, in the past and currently present. And, all related studies suggest that driver distraction level is still high and there is a need for a better design. Multimodal Interaction is a design approach, which relies on using multiple modes for humans to interact with the car & hence reducing driver distraction by allowing the driver to choose the most suitable mode with minimum distraction. Additionally, combining multiple modes simultaneously provides more natural interaction, which could lead to less distraction. The main goal of MMI is to enable the driver to be more attentive to driving tasks and spend less time fiddling with distractive tasks. Engineering based method is used to measure driver distraction. This method uses metrics like Reaction time, Acceleration, Lane Departure obtained from test cases.

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Agent

Created

Date Created
  • 2015