In-Vehicle Interface Adaptation to Environment-Induced Cognitive Workload (original) (raw)
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Development of an Adaptive Human-Machine-Interface to Minimize Driver Distraction and Workload
The original use of the vehicle dashboard was to provide enough sensory information to inform the driver of the current engine and vehicle status and performance. Over time, it has evolved into an entertainment system that includes person-toperson communication, global positioning information, and the Internet, just to name a few. Each of these new features adds to the amount of information that drivers must absorb, leading to potential distraction and possible increases in the number and types of accidents. In order to provide an overview of these issues, this paper summarizes previous work on driver distraction and workload, demonstrating the importance of addressing those issues that compete for driver attention and action. In addition, a test platform vehicle is introduced which has the capability of assessing modified dashboards and consoles, as well as the ability to acquire relevant driving performance data. Future efforts with this test platform will be directed toward helping to resolve the critical tug-of-war between providing more information and entertainment while keeping drivers and their passengers safe. The long-term goal of this research is to evaluate the various technological innovations available for inclusion in the driving environment and determining how to optimize driver information delivery without excessive distraction and workload. The information presented herein is the first step in that effort of developing an adaptive distraction/workload management system that monitors performance metrics and provides selected feedback to drivers.
The Impact of an Adaptive User Interface on Reducing Driver Distraction
This paper discusses the impact of an adaptive prototype in-car communication system (ICCS), called MIMI (Multimodal Interface for Mobile Info-communication), on driver distraction. Existing ICCSs attempt to minimise the visual and manual distraction, but more research needs to be done to reduce cognitive distraction. MIMI was designed to address usability and safety issues with existing ICCSs. Few ICCSs available today consider the driver’s context in the design of the user interface. An adaptive user interface (AUI) was designed and integrated into a conventional dialogue system in order to prevent the driver from receiving calls and sending text messages under high distraction conditions. The current distraction level is detected by a neural network using the driving speed and steering wheel angle of the car as inputs. An adaptive version of MIMI was compared to a non-adaptive version in a user study conducted using a simple driving simulator. The results obtained showed that the adaptive version provided several usability and safety benefits, including reducing the cognitive load, and that the users preferred the adaptive version.
Proceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Several researchers have focused on studying driver cognitive behavior and mental load for in-vehicle interaction while driving. Adaptive interfaces that vary with mental and perceptual load levels could help in reducing accidents and enhancing the driver experience. In this paper, we analyze the effects of mental workload and perceptual load on psychophysiological dimensions and provide a machine learning-based framework for mental and perceptual load estimation in a dual task scenario for in-vehicle interaction (https://github.com/amrgomaaelhady/MWL-PL-estimator). We use off-the-shelf non-intrusive sensors that can be easily integrated into the vehicle's system. Our statistical analysis shows that while mental workload influences some psychophysiological dimensions, perceptual load shows little effect. Furthermore, we classify the mental and perceptual load levels through the fusion of these measurements, moving towards a real-time adaptive invehicle interface that is personalized to user behavior and driving conditions. We report up to 89% mental workload classification accuracy and provide a real-time minimally-intrusive solution. CCS CONCEPTS • Human-centered computing → User centered design; • Applied computing → Psychology; • Computing methodologies → Classification and regression trees.
CLW 2014: The Fourth Workshop on Cognitive Load and In-Vehicle Human- Machine Interaction
2020
Interactions with in-vehicle electronic devices can interfere with the primary task of driving and increase crash risk. Interactions with in-vehicle interfaces draw upon visual, manipulative and cognitive resources, with this workshop focusing on cognitive resources for which measurement processes are less well known or established. This workshop will focus on two methods of measuring cognitive load, the Decision Response Time Task and collecting eye fixation data. The workshop will describe and demonstrate how they are collected, and discuss how the resulting data are reduced and analyzed. The focus will be on practical aspects of collecting and analyzing data using these methods, not on reporting research results.
Testing Road Vehicle User Interfaces Concerning the Driver’s Cognitive Load
Infrastructures
This paper investigates the usability of touch screens used in mass production road vehicles. Our goal is to provide a detailed comparison of conventional physical buttons and capacitive touch screens taking the human factor into account. The pilot test focuses on a specific Non-driving Related Task (NDRT): the control of the on-board climate system using a touch screen panel versus rotating knobs and push buttons. Psychological parameters, functionality, usability and, the ergonomics of In-Vehicle Information Systems (IVIS) were evaluated using a specific questionnaire, a system usability scale (SUS), workload assessment (NASA-TLX), and a physiological sensor system. The measurements are based on a wearable eye-tracker that provides fixation points of the driver’s gaze in order to detect distraction. The closed road used for the naturalistic driving study was provided by the ZalaZONE Test Track, Zalaegerszeg, Hungary. Objective and subjective results of the pilot study indicate tha...
CLW 2016: The Sixth Workshop on Cognitive Load and In-Vehicle Human-Machine Interaction
Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2016
Interactions with in-vehicle electronic devices can interfere with the primary task of driving and increase crash risk. Interactions with in-vehicle interfaces draw upon visual, auditory, psychomotor, and cognitive resources. Researchers often investigate how these resources interfere with performance through the use of different measurement techniques, particularly doing so in applied settings such as automotive scenarios. The goal of this workshop is to share knowledge with the community regarding the theoretical underpinnings, collection, and filtering of eye tracking data, particularly focusing on gaze distribution (percent on fixations on road center and time/number of gazes on visual field), blink rate, height of gaze in visual field, and pupil size measures of cognitive load within the scope of automotive research. The workshop will describe and demonstrate the theory behind these measures, approaches and issues in regards to collection, and successful methods of filtering da...
Design and Elimination of Driving Distraction
IJAEM , 2023
Driving is already a complex task that requires varying degrees of cognitive and physical stress. With the advancement of technology, the automobile has become the work place of media consumption, communication center and interconnection. The car's futures have also increased. As a result, the user interaction in the car becomes crowded and complicated. This increases the number of distracted driving and increases the number of traffic accidents caused by distracted driving. This paper focuses on two main aspects of the current automobile environment, multi-modal interaction (MMI) and advanced driver assistance system (ADAS) to reduce interference. It also provides indepth market research for the future trend of smart car technology. After careful analysis, it has been found that a fun filled with many underlying picture information screen, one with a large number of small button at the center of the stack, and terrible voice recognition (VR) led to a high cognitive load, and these are the cause of driver distraction. While VR has become a standard technology, the current state of technology focuses on functional design and sales driven approaches. Most automakers have focused on the virtual reality better, but perfect in the VR is not the answer, as there are inherent challenges and limitations in respect to the in-car environment and cognitive load.
A Cascaded Multimodal Natural User Interface to Reduce Driver Distraction
IEEE Access, 2020
Natural user interfaces (NUI) have been used to reduce driver distraction while using in-vehicle infotainment systems (IVIS), and multimodal interfaces have been applied to compensate for the shortcomings of a single modality in NUIs. These multimodal NUIs have variable effects on different types of driver distraction and on different stages of drivers' secondary tasks. However, current studies provide a limited understanding of NUIs. The design of multimodal NUIs is typically based on evaluation of the strengths of a single modality. Furthermore, studies of multimodal NUIs are not based on equivalent comparison conditions. To address this gap, we compared five single modalities commonly used for NUIs (touch, mid-air gesture, speech, gaze, and physical buttons located in a steering wheel) during a lane change task (LCT) to provide a more holistic view of driver distraction. Our findings suggest that the best approach is a combined cascaded multimodal interface that accounts for the characteristics of a single modality. We compared several combinations of cascaded multimodalities by considering the characteristics of each modality in the sequential phase of the command input process. Our results show that the combinations speech + button, speech + touch, and gaze + button represent the best cascaded multimodal interfaces to reduce driver distraction for IVIS. INDEX TERMS Cascaded multimodal interface, driver distraction, head-up display (HUD), humancomputer interaction (HCI), in-vehicle infotainment system (IVIS), learning effect, natural user interface (NUI).
Improving Drivers’ Hazard Perception and Performance Using a Less Visually-Demanding Interface
Frontiers in Psychology, 2020
In-vehicle devices and infotainment systems occasionally lead to driver distraction, and as a result, increase the risk of missing on-road information. In the current study, a novel multi-touch interface for an in-vehicle infotainment system was evaluated, which potentially requires less visual attention and thus may reduce distraction and increase safety. The interface was compared with a functionally similar control interface in terms of hazard perception metrics and mental workload. Twenty-two participants drove a simulated route once with each system. During each drive, which included eight potentially-hazardous scenarios, participants were instructed to interact with one of the in-vehicle interfaces to perform phone calls or to navigate to specified destinations. Eye-gaze data were collected throughout the drive to evaluate whether participants detected the hazards while interacting with the in-vehicle interface, how much time they needed to identify them, and for how long they engaged with the secondary task. Additionally, after each drive, participants completed a NASA R-TLX questionnaire to evaluate their subjective workload during their engagement with the secondary tasks. Participants using the multi-touch interface needed less time to complete each secondary task and were quicker at identifying potential hazards around them. However, the probability of detecting hazards was similar for both interfaces. Finally, when using the multi-touch interface, participants reported lower subjective workload. The use of a multi-touch interface was found to improve drivers' performance in terms of identifying hazards quicker than the control condition. The road safety and driver distraction implications of this novel interface are discussed.
Driver Distraction/Overload Research and Engineering: Problems and Solutions
Driver distraction is a topic of considerable interest, with the public debate centering on the use of cell phones and texting while driving. However, the driver distraction/overload issue is really much larger. It concerns specific tasks such as entering destinations on navigation systems, retrieving songs on MP3 players, accessing web pages, checking stocks, editing spreadsheets, and performing other tasks on smart phones, as well as, more generally, using in-vehicle information systems. Five major problems related to distraction/overload research and engineering and their solutions are addressed in this paper. Problems include (1) the misuse of the term distraction (and possible misdirection of effort), (2) driving performance measures and statistics that are either undefined or poorly defined (to be resolved by an SAE practice), (3) the workload of the driving task is not quantified (for which an equation is proposed), (4) the demand characteristics of in-vehicle tasks are not quantified (for which a scheme is proposed), and (5) too often, standards specify only measurement methods, not compliance criteria (which must be developed).