In-vehicle displays: Driving information prioritization and visualization

Olaverri-Monreal, Cristina; Lehsing, Christian; Trübswetter, Nicole; Schepp, Cheree Anne; Bengler, Klaus

doi:10.1109/ivs.2013.6629542

Cited by 21 publications

(7 citation statements)

References 5 publications

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“…Based on research on autonomous or non-autonomous cars, on different communication technologies, on decentralized and centralized connectivity [45,46], we have concluded that there is a rich cluster of information and interaction produced between vehicles and other road entities. In the Solution section, we are going to explore a dynamic visualization [47] of road information in terms of predictive weather conditions [48], visibility [49,50] and outdoor illumination [51,52] and potential sun glares [53,54] at the future moment of passing through that area, with markings on possible hazards [55,56], for a better trip planning. We chose this kind of information compared to the better-known real-time crowd sourced [57] traffic data, as an example of a novel cluster of visualized road information.…”

Section: Connected Cars and Smart Transport Infrastructurementioning

confidence: 99%

Ubiquitous Computing: Driving in the Intelligent Environment

et al. 2021

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In the context of hyper-connected cars and a growing heterogeneous digital ecosystem, we wish to make the most of the data available from the various sensors, devices and services that compose the ecosystem, in order to propose a proof of concept in-vehicle system that enhances the driving experience. We focus on improving the driving experience along three main directions, namely: (1) driving and trip planning, (2) health and well-being and (3) social and online activities. We approached the in-vehicle space as a smart interface to the intelligent driving environment. The digital data-producers in the ecosystem of the connected car are sources of raw data of various categories, such as data from the outside world, gathered from sensors or online services, data from the car itself and data from the driver gathered with various mobile and wearable devices, by means of observing his state and by means of his social media and online activity. Data is later processed into three information categories—driving, wellness, and social—and used to provide multi-modal interaction, namely visual, audio and gesture. The system is implemented to act in response to the trafficked information on different levels of autonomy, either in a reactive manner, by simple monitoring, or in a proactive manner. The system is designed to provide an in-vehicle system that assists the driver with planning the travel (Drive panel), by providing a comfortable environment for the driver while monitoring him (Wellness panel), and by adaptively managing interactions with their phone and the digital environment (Social panel). Heuristic evaluation of the system is performed, with respect to guidelines formulated for automated vehicles, and a SWOT analysis of the system is also presented in the paper.

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Section: Connected Cars and Smart Transport Infrastructurementioning

confidence: 99%

Ubiquitous Computing: Driving in the Intelligent Environment

et al. 2021

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“…Several studies agree that multimodal messages consisting of a combination between acoustic sounds, images, and vibrotactile messages are more effective in TOR situations [36,60], but there are still many open questions about how this should be achieved and which is the message that has to be conveyed. To this end, several works have investigated information prioritization and functionality clustering for different modules in Driver Information Systems (DIS) and Advanced Driver Assistance Systems (ADAS) [41,[84][85][86][87] to ascertain where the increasing amount of vehicle information should be located within the vehicle to reduce the drivers' eye time off the road when looking for it. To illustrate several design concepts, Figure 3 shows HMI examples that have been implemented in TOR related studies.…”

Section: Drivermentioning

confidence: 99%

Automated Driving: A Literature Review of the Take over Request in Conditional Automation

Morales-Alvarez

Sipele

Léberon³

et al. 2020

Electronics

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In conditional automation (level 3), human drivers can hand over the Driving Dynamic Task (DDT) to the Automated Driving System (ADS) and only be ready to resume control in emergency situations, allowing them to be engaged in non-driving related tasks (NDRT) whilst the vehicle operates within its Operational Design Domain (ODD). Outside the ODD, a safe transition process from the ADS engaged mode to manual driving should be initiated by the system through the issue of an appropriate Take Over Request (TOR). In this case, the driver’s state plays a fundamental role, as a low attention level might increase driver reaction time to take over control of the vehicle. This paper summarizes and analyzes previously published works in the field of conditional automation and the TOR process. It introduces the topic in the appropriate context describing as well a variety of concerns that are associated with the TOR. It also provides theoretical foundations on implemented designs, and report on concrete examples that are targeted towards designers and the general public. Moreover, it compiles guidelines and standards related to automation in driving and highlights the research gaps that need to be addressed in future research, discussing also approaches and limitations and providing conclusions.

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“…• We designed a card-sorting experiment to determine preferences of display location and functional integration in the vehicle while driving from a sample of 45 persons, extending, therefore, the sample from the approach that we originally presented in [12]. • We performed a validation process in a high-fidelity driving simulator to study the driving performance when interacting with the preferred layout and find out if it differed from the performance with current layouts.…”

Section: Impact Of In-vehicle Displays Location Preferences On Drivermentioning

confidence: 99%

“…From our previous analysis in [12], we learned that selected positions for functions that can be presented in both analog and digital forms did not differ. Thus, we merged in common category functions that only differed in the specific name but had the same functionality (i.e., news and ntv).…”

Section: A Experimental Designmentioning

confidence: 99%

Impact of In-Vehicle Displays Location Preferences on Drivers' Performance and Gaze

Olaverri-Monreal

Hasan

Bulut

et al. 2014

IEEE Trans. Intell. Transport. Syst.

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Advanced driver assistance systems (ADAS) and driver information systems (DIS) do not always comply with the intended driver safety enhancement. Even if they aim to augment the driver's awareness of the surrounding environment, perceiving this information requires the occasional attention diversion from the road, which could lead to a loss of vehicle control if the total eyes-off-road time exceeds the National Highway Traffic Safety Administration (NHTSA) recommendation for glances away from the roadway. Additionally, technologies that can be found in other mobile environments, smartphones, and tablets are increasingly being integrated into cars, providing a necessary facet of study and continued research in their effects. We addressed this question by analyzing differential preferences for the layout of DIS and ADAS compared with existing ones through a card-sorting experiment. To validate our data, we additionally studied the drivers' performance and gaze with the preferred locations for in-vehicle information through gaze location and speed metrics measurements.Our validation process showed that the time the drivers needed to find the conveyed information in the preferred layout was within the recommended time of the NHTSA Guidelines. Drivers' preferences with regard to the functional layout of current DIS and ADAS compared with existing ones did not essentially differ from the layouts that are currently on the market. However, including mobile applications and social media in a vehicular context was not considered necessary.Index Terms-Information visualization, in-vehicle displays, vehicular user interfaces.

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In-vehicle displays: Driving information prioritization and visualization

Cited by 21 publications

References 5 publications

Ubiquitous Computing: Driving in the Intelligent Environment

Ubiquitous Computing: Driving in the Intelligent Environment

Automated Driving: A Literature Review of the Take over Request in Conditional Automation

Impact of In-Vehicle Displays Location Preferences on Drivers' Performance and Gaze

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