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Description
Despite the various driver assistance systems and electronics, the threat to life of driver, passengers and other people on the road still persists. With the growth in technology, the use of in-vehicle devices with a plethora of buttons and features is increasing resulting in increased distraction. Recently, speech recognition has

Despite the various driver assistance systems and electronics, the threat to life of driver, passengers and other people on the road still persists. With the growth in technology, the use of in-vehicle devices with a plethora of buttons and features is increasing resulting in increased distraction. Recently, speech recognition has emerged as an alternative to distraction and has the potential to be beneficial. However, considering the fact that automotive environment is dynamic and noisy in nature, distraction may not arise from the manual interaction, but due to the cognitive load. Hence, speech recognition certainly cannot be a reliable mode of communication.

The thesis is focused on proposing a simultaneous multimodal approach for designing interface between driver and vehicle with a goal to enable the driver to be more attentive to the driving tasks and spend less time fiddling with distractive tasks. By analyzing the human-human multimodal interaction techniques, new modes have been identified and experimented, especially suitable for the automotive context. The identified modes are touch, speech, graphics, voice-tip and text-tip. The multiple modes are intended to work collectively to make the interaction more intuitive and natural. In order to obtain a minimalist user-centered design for the center stack, various design principles such as 80/20 rule, contour bias, affordance, flexibility-usability trade-off etc. have been implemented on the prototypes. The prototype was developed using the Dragon software development kit on android platform for speech recognition.

In the present study, the driver behavior was investigated in an experiment conducted on the DriveSafety driving simulator DS-600s. Twelve volunteers drove the simulator under two conditions: (1) accessing the center stack applications using touch only and (2) accessing the applications using speech with offered text-tip. The duration for which user looked away from the road (eyes-off-road) was measured manually for each scenario. Comparison of results proved that eyes-off-road time is less for the second scenario. The minimalist design with 8-10 icons per screen proved to be effective as all the readings were within the driver distraction recommendations (eyes-off-road time < 2sec per screen) defined by NHTSA.
ContributorsMittal, Richa (Author) / Gaffar, Ashraf (Thesis advisor) / Femiani, John (Committee member) / Gray, Robert (Committee member) / Arizona State University (Publisher)
Created2015
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 harm level. As in-vehicle technology advances, the list of distractive activities grows along with crash risk. Additionally, the distractive activities

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.
ContributorsJahagirdar, Tanvi (Author) / Gaffar, Ashraf (Thesis advisor) / Ghazarian, Arbi (Committee member) / Gray, Robert (Committee member) / Arizona State University (Publisher)
Created2015
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Description
Text Classification is a rapidly evolving area of Data Mining while Requirements Engineering is a less-explored area of Software Engineering which deals the process of defining, documenting and maintaining a software system's requirements. When researchers decided to blend these two streams in, there was research on automating the process of

Text Classification is a rapidly evolving area of Data Mining while Requirements Engineering is a less-explored area of Software Engineering which deals the process of defining, documenting and maintaining a software system's requirements. When researchers decided to blend these two streams in, there was research on automating the process of classification of software requirements statements into categories easily comprehensible for developers for faster development and delivery, which till now was mostly done manually by software engineers - indeed a tedious job. However, most of the research was focused on classification of Non-functional requirements pertaining to intangible features such as security, reliability, quality and so on. It is indeed a challenging task to automatically classify functional requirements, those pertaining to how the system will function, especially those belonging to different and large enterprise systems. This requires exploitation of text mining capabilities. This thesis aims to investigate results of text classification applied on functional software requirements by creating a framework in R and making use of algorithms and techniques like k-nearest neighbors, support vector machine, and many others like boosting, bagging, maximum entropy, neural networks and random forests in an ensemble approach. The study was conducted by collecting and visualizing relevant enterprise data manually classified previously and subsequently used for training the model. Key components for training included frequency of terms in the documents and the level of cleanliness of data. The model was applied on test data and validated for analysis, by studying and comparing parameters like precision, recall and accuracy.
ContributorsSwadia, Japa (Author) / Ghazarian, Arbi (Thesis advisor) / Bansal, Srividya (Committee member) / Gaffar, Ashraf (Committee member) / Arizona State University (Publisher)
Created2016
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Description
When software design teams attempt to collaborate on different design docu-

ments they suffer from a serious collaboration problem. Designers collaborate either in person or remotely. In person collaboration is expensive but effective. Remote collaboration is inexpensive but inefficient. In, order to gain the most benefit from collaboration there needs to

When software design teams attempt to collaborate on different design docu-

ments they suffer from a serious collaboration problem. Designers collaborate either in person or remotely. In person collaboration is expensive but effective. Remote collaboration is inexpensive but inefficient. In, order to gain the most benefit from collaboration there needs to be remote collaboration that is not only cheap but also as efficient as physical collaboration.

Remotely collaborating on software design relies on general tools such as Word, and Excel. These tools are then shared in an inefficient manner by using either email, cloud based file locking tools, or something like google docs. Because these tools either increase the number of design building blocks, or limit the number

of available times in which one can work on a specific document, they drastically decrease productivity.

This thesis outlines a new methodology to increase design productivity, accom- plished by providing design specific collaboration. Using version control systems, this methodology allows for effective project collaboration between remotely lo- cated design teams. The methodology of this paper encompasses role management, policy management, and design artifact management, including nonfunctional re- quirements. Version control can be used for different design products, improving communication and productivity amongst design teams. This thesis outlines this methodology and then outlines a proof of concept tool that embodies the core of these principles.
ContributorsPike, Shawn (Author) / Gaffar, Ashraf (Thesis advisor) / Lindquist, Timothy (Committee member) / Whitehouse, Richard (Committee member) / Arizona State University (Publisher)
Created2016
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Description
A well-defined Software Complexity Theory which captures the Cognitive means of algorithmic information comprehension is needed in the domain of cognitive informatics & computing. The existing complexity heuristics are vague and empirical. Industrial software is a combination of algorithms implemented. However, it would be wrong to conclude that algorithmic space

A well-defined Software Complexity Theory which captures the Cognitive means of algorithmic information comprehension is needed in the domain of cognitive informatics & computing. The existing complexity heuristics are vague and empirical. Industrial software is a combination of algorithms implemented. However, it would be wrong to conclude that algorithmic space and time complexity is software complexity. An algorithm with multiple lines of pseudocode might sometimes be simpler to understand that the one with fewer lines. So, it is crucial to determine the Algorithmic Understandability for an algorithm, in order to better understand Software Complexity. This work deals with understanding Software Complexity from a cognitive angle. Also, it is vital to compute the effect of reducing cognitive complexity. The work aims to prove three important statements. The first being, that, while algorithmic complexity is a part of software complexity, software complexity does not solely and entirely mean algorithmic Complexity. Second, the work intends to bring to light the importance of cognitive understandability of algorithms. Third, is about the impact, reducing Cognitive Complexity, would have on Software Design and Development.
ContributorsMannava, Manasa Priyamvada (Author) / Ghazarian, Arbi (Thesis advisor) / Gaffar, Ashraf (Committee member) / Bansal, Ajay (Committee member) / Arizona State University (Publisher)
Created2016