Matching Items (5)

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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

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Created

Date Created
  • 2018

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Sequencing Behavior in an Intelligent Pro-active Co-Driver System

Description

Driving is the coordinated operation of mind and body for movement of a vehicle, such as a car, or a bus. Driving, being considered an everyday activity for many people,

Driving is the coordinated operation of mind and body for movement of a vehicle, such as a car, or a bus. Driving, being considered an everyday activity for many people, still has an issue of safety. Driver distraction is becoming a critical safety problem. Speed, drunk driving as well as distracted driving are the three leading factors in the fatal car crashes. Distraction, which is defined as an excessive workload and limited attention, is the main paradigm that guides this research area. Driver behavior analysis can be used to address the distraction problem and provide an intelligent adaptive agent to work closely with the driver, fay beyond traditional algorithmic computational models. A variety of machine learning approaches has been proposed to estimate or predict drivers’ fatigue level using car data, driver status or a combination of them.

Three important features of intelligence and cognition are perception, attention and sensory memory. In this thesis, I focused on memory and attention as essential parts of highly intelligent systems. Without memory, systems will only show limited intelligence since their response would be exclusively based on spontaneous decision without considering the effect of previous events. I proposed a memory-based sequence to predict the driver behavior and distraction level using neural network. The work started with a large-scale experiment to collect data and make an artificial intelligence-friendly dataset. After that, the data was used to train a deep neural network to estimate the driver behavior. With a focus on memory by using Long Short Term Memory (LSTM) network to increase the level of intelligence in two dimensions: Forgiveness of minor glitches, and accumulation of anomalous behavior., I reduced the model error and computational expense by adding attention mechanism on the top of LSTM models. This system can be generalized to build and train highly intelligent agents in other domains.

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Created

Date Created
  • 2020

Smart car technologies: a comprehensive study of the state of the art with analysis and trends

Description

Driving is already a complex task that demands a varying level of cognitive and physical load. With the advancement in technology, the car has become a place for media consumption,

Driving is already a complex task that demands a varying level of cognitive and physical load. With the advancement in technology, the car has become a place for media consumption, a communications center and an interconnected workplace. The number of features in a car has also increased. As a result, the user interaction inside the car has become overcrowded and more complex. This has increased the amount of distraction while driving and has also increased the number of accidents due to distracted driving. This thesis focuses on the critical analysis of today’s in-car environment covering two main aspects, Multi Modal Interaction (MMI), and Advanced Driver Assistance Systems (ADAS), to minimize the distraction. It also provides deep market research on future trends in the smart car technology. After careful analysis, it was observed that an infotainment screen cluttered with lots of small icons, a center stack with a plethora of small buttons and a poor Voice Recognition (VR) results in high cognitive load, and these are the reasons for the increased driver distraction. Though the VR has become a standard technology, the current state of technology is focused on features oriented design and a sales driven approach. Most of the automotive manufacturers are focusing on making the VR better but attaining perfection in VR is not the answer as there are inherent challenges and limitations in respect to the in-car environment and cognitive load. Accordingly, the research proposed a novel in-car interaction design solution: Multi-Modal Interaction (MMI). The MMI is a new term when used in the context of vehicles, but it is widely used in human-human interaction. The approach offers a non-intrusive alternative to the driver to interact with the features in the car. With the focus on user-centered design, the MMI and ADAS can potentially help to reduce the distraction. To support the discussion, an experiment was conducted to benchmark a minimalist UI design. An engineering based method was used to test and measure distraction of four different UIs with varying numbers of icons and screen sizes. Lastly, in order to compete with the market, the basic features that are provided by all the other competitors cannot be eliminated, but the hard work can be done to improve the HCaI and to make driving safer.

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Created

Date Created
  • 2015

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In-vehicle multimodal interaction: an approach to mitigate driver distraction

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

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.

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Agent

Created

Date Created
  • 2015

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