car dashboard lcd screen free sample

Artificial intelligence driverless safety system with hud interface in cockpit of autonomous car vehicle interior driverless car driver assistance system acc adaptive cruise control

Confident and beautiful. rear view of attractive young woman in casual wear looking over her shoulder while driving a car. girl holding hand on wheel to handle the car, safety concept.

We all know that traditional electromechanical instruments are relentlessly being replaced with LCD screens. Generally, this can be a good thing, as the screens weigh less and can display much more and more varied types of information. But there"s one catch: they must be rectangular. Not anymore.

It"s funny, but when I wrote that article up there I was pretty sure no LCD company would bother trying to develop non-rectangular LCDs. Boy was I wrong. I underestimated both the importance of the automotive market to LCD manufacturers (think about how many more screens they can get us to buy once we decided that every car has to have them) and the intensity of longing of designers for non-rectangular screens.

The examples Sharp is showing are clearly targeted at the automotive sector, with three round gauges shaping the screen, or a nice gradual arc for the instrument binnacle, or holes in the screen to accommodate knobs and buttons.

They"re accomplishing this by moving the minuscule pixel-driver chips from the edges of the screen to spaces between the pixels. I"m not certain yet if this will affect dot pitch or not, but from the images we"re seeing from Sharp"s booth at Japan"s Ceatac expo, it doesn"t look like it. An added benefit is that the pixels can now almost reach the edges of the screen nearly eliminating any bezel area.

These will initially cost a premium I"m sure, but that price will come down, and this tech will trickle down from the premiums into lower segments, just as we"ve seen with conventional LCDs previously.

I"m more excited about the idea of having multiple sizes of round screens to replace gauges on vintage cars, with software tools to both emulate the look of the original gauges or design your own gauge faces. That would be awesome.

The application is geared to dashboards in high-end vehicles, but I could envision a version of this being applied in applications like retail merchandising and wayfinding.

The idea is to use a printed foil as the top layer of the digital displays that are expected to take over from analog information displays in cars – so a dash that has a uniform woodgrain finish when the car is parked gets gauges, controls and maps when it is fired up, visible through the semi-transparent foil layer.

Continental calls its solution the ShyTech display – “a screen that appears only when it is needed …” The company plans to launch the display for cars next year, presumably in BMWs and Audis long before Kias and Buicks. The company makes no mention of doing retail merchandising or anything else. That’s just me speculating and imagineering a bit.

With the ShyTech Display, Continental is serving the increasing demand for large screen solutions, which are increasingly becoming the standard, especially in the upscale vehicle segments (upper middle class, luxury class, lifestyle models). If necessary, the ShyTech display can occupy the entire width of the dashboard. In view of this trend towards so-called mega screens, the Continental developers asked themselves two crucial questions: How can sensory overload of the vehicle occupants be prevented despite very large monitors? And: How can display areas that are not used in a given situation be optically improved? The solution from the innovators at Continental: ShyTech Displays. These are only visible when they are needed. This is made possible by a semi-transparent surface with which screens can be seamlessly integrated into the surrounding surface – visually as well as haptically. If the display is not needed, it is practically invisible to the human eye. At the same time, Continental is pursuing the approach of a puristic interior design reduced to the essentials.

Specifically, this means that even if the dashboard appears to be “as if made from one piece”, navigation or communication information, for example, as well as the menu on the touchscreen are always available. However, the displays and controls of the screen are only activated when a hand approaches. It is also possible to activate the display via voice control or by briefly tapping the screen surface.

With ShyTech technology, Continental has also solved the design requirement that screens that are switched off appear as visually unappealing, black empty areas. The display surface is designed to imitate the look of the dashboard. Depending on the configuration, this can be, for example, that of wooden panels, carbon panels or a leather-covered surface. Furthermore, it not only looks like the original material, but it also feels like it. Thanks to ShyTech innovations, screens can be placed practically anywhere in the interior. “This enables a calm and tidy interior design in which a wealth of new functions can be integrated at the same time – without overwhelming the occupants,” says Ulrich Lüders, Head of Strategy and Portfolio in the Human Machine Interface business unit at Continental. In addition, disturbing light reflections, for example when the sun is low, are reduced. “ShyTech thus enables a holistic, positive user experience with a breathtaking design, which at the same time reduces potential sources of distraction in the vehicle,” says Lüders.

A system concept developed by Continental ensures seamless integration of the ShyTech display even when it is activated. The system consists, among other things, of special backlighting, which, in special coordination with the LCD panel and the decorative surface, allows the display content to appear in the ideal brightness and sharpness in every light situation – from glaring sunlight to cloudy rainy weather. The razor-sharp indications of the display also impress with their high contrast values. Continental will launch the ShyTech Display in 2023.

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Because time is limited, salespeople need to have information that quickly shows a holistic view of their deals with the ability to easily dive deeper into any data point—without requiring much time or effort. Sellers spend the majority of their time in a CRM tool because it contains information they need to close their deals. However, account details often get buried on multiple screens, and having an accurate, overall understanding of an account is difficult.

Sales dashboards allow sellers to focus their time on selling and less time on administrative tasks or searching for data they need. Sales analytics teams have the challenging task of cleaning up CRM data and analyzing it; exporting data from a CRM source system is highly manual and time-consuming, with ad hoc analysis of an account being nearly impossible. Data exported from a CRM tool often has a limited scope of information and immediately becomes static and outdated, left in an unviewed spreadsheet.

The case for sales analytics teams to create centralized, self-service dashboards is compelling. Having centralized sales dashboards means that everyone will be using the same data to make decisions—with these reports serving as single sources of truth. Sellers, sales managers, and senior leaders would speak the same language and work toward common sales KPIs. Using single sources of truth across departments means that critical decisions are made with data and not just gut feeling.

Not to be confused with Android Auto or Apple CarPlay, the built-in navigation system does not require the use of any mobile phone device. Two ways to confirm your car has navigation - 1) look for a “MAP” hard key or 2) go to the system info page on your display screen (usually under hard key “SETUP”, then “System” or “Sys Info” on the touchscreen). If your car has navigation, there will be a “map version” number displayed.

Although a map update can provide the latest software, there could be many other unaddressed issues related to why an owner may be experiencing issues. Map updates provide the latest roads, points of interest and (depending on model) updated software. AC/AA issues should be addressed to Hyundai or Genesis customer care.

For SD cards, the SD card slot in the vehicle is generally located just below or above the display screen and labeled “SD Map”. The slot can also be located above and behind the display screen, near the dashboard. You can also contact your dealer for assistance.

That error message can appear when the SD card is first inserted because the vehicle software has not been updated yet. Ignore the message and go to the system info page and press “Update”. Refer to the vehicle installation manual for instructions.

For those of us who learned how to drive a few decades ago, driving a modern car is a completely different experience. New cars come with rear-view cameras, obstacle sensors, parking assistance, lane-change assistance, adaptive cruise controls, autopilot driving, and even web browsers. Many of these features should make driving a safer and more comfortable activity. And they will — but only if car designers understand the most basic fact about human attention: it’s limited. And if they design these sophisticated car features so that they don’t take away cognitive resources from the basic task, which is driving.

Tesla’s Model S has few physical controls — all placed on or very close to the steering wheel. Driving-related functions like cruise control, autopilot, wipers, and lights are all accessible through these controls. But most of the “secondary” features (including rear-view camera, cell phone, media player, and climate control) do not have dedicated physical controls. Instead, the main way to select them is through the 17” touchscreen display placed on the dashboard, between the driver and the passenger seats.  While this is a big screen (three times the area of an iPad), it can’t show everything — so,   making matters even more complicated, in Version 9 of Tesla’s operating system, some features are placed inside an expandable menu.

Tesla Model S touchscreen: The secondary car controls — applications such as car climate, media player, or the rear-view camera — can be accessed from the menu bar at the bottom of the car’s 17” touchscreen display.

While touchscreen dashboards offer more flexibility than real dashboards, they have one big disadvantage: no haptic feedback. In order to reliably touch these buttons, people must look at them. Whereas with a physical button we can learn its location and acquire it without directing much, if any, attention to it (and hence we can play the piano while reading the score or we can  touchtype on a real keyboard), locating a soft button requires us to visually confirm its position.

When soft buttons are hidden under menus, selecting them involves multiple touchscreen interactions, and thus even more time and attention. And, in a car, time spent with the UI is time spent ignoring the road.

When my generation learned to drive, the standard hand position was 2–10 (corresponding to numbers 2 and 10 on the clock). With airbags and smaller steering wheels becoming common, National Highway Traffic Safety Administration (NHTSA) has changed its recommendation and now 3–9 has become the safest position.  For both these hand placements, the optimal control positioning would be on the middle left edge of the screen, close to the driver’s right hand. Yet, Model S’s controls are placed at the very bottom of the 17” screen — an area that is next worst possible (after the right edge of the screen).

Having the controls at the bottom of the screen also means more time for the eyes to move from the windshield to the menu area, and also less of a chance that people will be able to use their peripheral vision to attend to unexpected stimuli that appear on the road when they are busy interacting with (and looking at) the touchscreen.

Within a given menu, the most commonly used options should be the easiest to access. Yet, Tesla’s decisions with respect to the ordering of the controls in the menu are at best dubious. The first option is the access to all the car’s settings and customizations — something that is unlikely to be used often while driving.  The rear-view camera (an essential feature in a car whose rear-windshield view is partially blocked by the backseat headrests) is available under the arrow menu, as is the cell phone. And these arguably frequently used options are not even given priority in that menu: instead of being immediately above the arrow icon (to minimize finger travel time), the Calendar, Energy, and Web (for the web browser) are listed first. (How often will you use these during driving compared to the rear-view camera and the phone?)

The seat warmer can be accidentally triggered when the user is swiping vertically on the app icon to launch the most recent app (as shown in this screenshot — the dotted red line represents the trajectory of the finger). The same seat icon can be tapped by mistake when the user is trying to adjust the driver-side temperature.

To allow people to quickly get the information that they need from the screen and then move on, text on car dashboards should be easy to read in a variety of light conditions. Text that is too small, appears on a busy background, or has low contrast with the background does not usually satisfy these requirements.

Yet, Tesla’s map app is always displayed in the background of all apps.  As a result, the status bar at the very top of the screen can be hard to read, as it blends in with the map text. Moreover, it can be quite irrelevant (and potentially distracting) to a see a brightly colored street appearing between two application windows, like in the screenshot below. Plus, the fact that no application can be brought at the very top of the screen not only takes away user control (and goes against one of the 10 usability heuristics) but is also wasteful, forcing users to see at the top a small part of the map that carries very little information.

The map in the background also makes the interaction with the other apps more error prone: if you’re trying to increase the size of an app window, you will have to drag from the top edge of the app’s view. But if you’re not careful and position your finger above the window handle, nothing will happen — because the OS will assume that you are swiping the map instead of maximizing the map window.

The rear-view camera (In previous versions of the Tesla OS, you could pin that at the top of the main touchscreen.  Now it’s never at the very top, so, to glance at it, your eyes need to move from the road down to the touchscreen.)

The instrument panel shows whether the car can safely change lanes (top) or not (bottom). Only the lanes immediately adjacent to the vehicle are shown.

The problem is that none of these information sources are complete (for example, the rear-mirror view is partially blocked by headrests), and by the time you check them all, you’ve already reached your destination. So, what most people end up doing is taking a shortcut to save interaction cost and relying on just one of these sources of information. Which one do you think they’re most likely to pick? The one with the lowest interaction cost: the lane assist displayed on the secondary dashboard, which always appears when the turn signal is engaged.

So, if people end up doing the same task faster, it’s good, right? Well, it would be if this solution was guaranteed to work correctly in all circumstances. In fact, Tesla warns against relying solely on lane assist for lane changes. Even barring sensor errors (which are more common than one would expect), it can be challenging to interpret the displayed information correctly. The problem is that the user may not recognize to what lane the lane-assist cues apply. To understand why, imagine that you are driving on a three-lane road and you want to switch lanes from the rightmost lane to the middle lane. When you engage the turn signal, the car might show that the lane is free. But, as soon as you reach the middle lane, the car will show information about switching into the left lane since your turn signal is still engaged. Yet, users are not always perfectly aware of the location of their car — so as they may check the lane-assist display, engage in a lane switch, then check again the display, notice the red alert (which this time would pertain to switching into the next lane), misinterpret that as referring to their current goal, and panic, swerving the car back into its original lane.

This example points out an important dilemma that car designers and manufacturers face today. New features such as autopilot (or self-driving), lane assist, collision detection and so on have the potential to replace well-learned, traditional driver behaviors such as looking over the shoulder or checking the mirrors that we used to rely on. If these features are functional and using them is are easier than performing the gestures and actions that we learned in driving school, then they will replace those actions (we are creatures of minimum effort: we always take the solution that requires least work — not because we’re lazy, but because we’re efficient). That’s why we hear in the news about people putting on makeup, playing games, or even sleeping at the wheel — because modern cars make it seem safe to do so. Yet, it is our responsibility as designers to create the right mental model and give people the right understanding of how our systems work, in order to make sure that by displacing these old-fashioned behaviors we’re not making driving more dangerous.

The big screen is a plus: it offers the ability to see multiple application windows side by side. Switching apps can be a pain (as it does involve multiple taps and looking at the screen), but the ability to see several of them (e.g., the rear-view camera and the map or the media player) simultaneously makes things easier for the driver.

Driving is a situation where people do need to access multiple sources of information simultaneously (e.g., the map and the rear-view camera) and seeing all those windows at the same time significantly decreases the user’s working-memory load. (In contrast, even on the biggest tablets, people rarely split the screen to show multiple windows at the same time, although this feature has been around for a while.)

Tesla is always connected to the internet and can receive updates through the air. This feature can be viewed both as a plus and as a minus. The good is that bugs can be fixed quickly and deployed to cars in the same way in which a website can change colors from one day to the next. Analytic data about the drivers can be easily connected, bugs can be reported, and problems fixed. Unfortunately, a continuously changing interface can also create potentially fatal issues if users’ expectations formed in prior versions are now suddenly violated. (Plus, one must trust that the car manufacturer will only deploy safe, well-tested versions of the operating system.)

Modern cars are powerful computers. They can augment drivers’ cognitive and physical abilities with information collected from a variety of sensors; they can also enhance the driving experience with a plethora of convenience features that are one tap away. Yet none of these will truly happen until car designers take into account the decades of designing computer interfaces and follow well-known principles of usability and human psychology.

Carsharing scale has been increasing rapidly with sharing economy. However, many users are reluctant to rent cars any longer due to the low-quality of interactive experience and usability, especially in terms of the dashboard design. This challenge should be urgently addressed in order to maintain the sustainable development of car-sharing industry and its environmental benefits. This study aims to investigate the relationship between users" driving activities (e.g., searching time, reading time, eye movement, heart rate) and dashboard layout. This study was conducted based on the experimental investigation among 58 respondents who were required to complete driving tasks in four types of cars with different dashboard layouts. Afterwards, a prediction model was developed to predict users heart rate (HR) based on the long short-term memory model, and logistic models were used to examine the relationship between the occurrence probability of minimum HR and dashboard reading. The results showed that the system usability of a dashboard was related to the drivers" eye movement characteristics including fixation duration, fixation times and pupil diameter. Most indicators had significant effects (p < 0.05) on the system usability score of corresponding dashboard. The long short-term memory model network (RMSE = 1.105, MAE = 0.009) was capable of predicting heart rate (HR) that happened in the process of instrument reading, which presented a periodic pattern rather than a continuous increase or decrease. It reflected that the network could better fit the non-linear and time-sequential laws of HR data. Furthermore, the probability of the lowest heart rate occurrence during the interaction with four dashboards was influenced by the average searching time, reading time and reading accuracy that were related to a specific layout. Overall, this study provided a theoretical reference for uncovering users" adaptive behaviors with the central control screen and for the optimal choice of a suitable dashboard layout in interface design.

Sharing economy has promoted automobile industry development, along with which car-sharing scale has been expanding dramatically. It is estimated that there have been more than five million carsharing users all over the world (1). However, many users are unwilling to use shared cars any longer, whilst the business modes (e.g., business to customer, peer-to-peer) are mature and friendly and carsharing has a series of environmental benefits (the reduction of vehicle ownership and emissions, the increase of flexibility of transit and the increase in land effectiveness) (2). To uncover the reason making these people abandon carsharing service is urgent and meaningful to maintain carsharing industry and promote its environmental benefits for sustainable development. Using behavior difference from the private cars is one of the key factors, where users can only own the right to use the car for a temporarily short term so that people will feel difficult in adapting themselves fully to shared cars, similar with the driving experience of a newly purchased car. Besides, car rental needs the users to go through processes of car ordering, car searching in the parking lot and getting in the car. When the user accesses to the car, it is necessary to judge whether the car is consistent with that displayed on the ordering APP interface and whether there are damaged or missing parts. These early cognitive activities would enhance users" sense of tension and fatigue, which is different from private cars that can be driven directly. It indicates that carsharing needs to be additionally compensated in terms of drivers" cognition to make the industry obtain more acceptance. In addition, for both business to customer car sharing or time-sharing car rental, human machine interface (HMI) features exclusive to an individual automobile brand are not suited for car sharing and will cut down the flexibility of users when interacting with the cars (3).

During the use of shared cars, drivers are required to read necessary information from dashboards to master running state of cars. This process indicates the significance of dashboard design for improving people"s interactive experience with cars. In particular, the development of dashboard design has experienced many stages such as pure machinery, liquid crystal display (LCD) combined with machinery, and digital instrument. These different dashboard features may lead users to be exposed to different types of automobiles and a variety of dashboards in shared cars. As a consequence, the adaptability of dashboard needs to be revealed. No matter how advanced the dashboard display technology is, if the information recognition and visual interaction with the interface are improper, there will be increasing possibilities of safety issues. In particular, it is found that an off-road glance of more than 2 s would greatly increase the incidence of driving risks (4).

A digital instrument panel integrates more driving information into the interface, displaying navigation information, running status and vehicle driving control through an LCD screen. Drivers can get an overall description of the car intuitively from the instrument panel (5). However, the mode that drivers receive the feedback from cars, such as the visual feedback and auditory feedback, through digital instrument panels should be concerned, in particular when drivers should be informed appropriate feedback according to actual driving scenes. Nevertheless, the cognitive process of the use of shared cars are different from those of private cars (6). It is difficult for users to quickly understand and master the information through an unfamiliar interaction scheme. In the context of time-sharing rental cars, reading efficiency and mental load induced by different dashboard layouts are of great value to improve driving safety and user experience.

A driving simulator was therefore used to simulate acceleration, uniform velocity running and deceleration behaviors in this paper, in order to study the usability of different dashboard layouts. In particular, reading efficiency and driver"s mental load of tested users were investigated with the requirements of reporting their readings on digital dashboard during driving. Afterwards, this study develops the prediction models of heart rates of drivers based both the long short-term memory model (LSTM) and logistic model in order to reveal the relationship between user heart rate and reading efficiency. Overall, this study is of significance to understand people"s physiological behaviors toward environmental design in the shared cars and to promote the optimization of dashboard design.

The HMI research works provided theoretical support for ergonomic assessment and interface evaluation of mechanical equipment and medical devices (7–9). The study on distraction and inattention caused by the HMI of in-vehicle information system (IVIS) was also a hot spot. For drivers, more than 90% information was obtained through visual channels when perceiving the external environment (10). In recent years, there were a growing number of driver assistance systems (DAS) in the automobile market. These functions, such as self-adaptive cruise and lane keeping system, reduced the measurement and control tasks of drivers (11), but put forward greater demands for the layout design works of dashboards and the recognition efficiency of drivers, especially in the era of aging. For example, from the perspective of take-over performance in intelligent vehicles, the average reacting time of older drivers was at least 1.2 s longer than that of young drivers (12). The in-vehicle HMI dimensions had a significant impact on drivers" task completion time (13), but a reasonable layout of the dashboard also played an important role in the driver"s recognition efficiency. However, at present, people tended to just study the shape and character encoding of instrument panels (14). There are few researches on how these specific design factors are organized together to affect the driver, which may cause the influence of the design factors to be explained vaguely.

The display interface had an important effect on the visual load, work performance and subjective reaction in the use of automobiles (15). In the transition period to intelligent vehicles, new technologies drove the design works of automobile interactive experience to users" pleasure, integrated touch panel research, multi-channel interaction mode and so on (16). The emotion of car sharing users during driving was an important embodiment of the quality of in-vehicle display design. For example, drivers" satisfaction was related to the character lines of the dashboard with a 30% further converging point (17). For the automobile instrument panels, users could identify the images of dashboards according to the dimensions of visual acceptability, emotion and evaluation by the PAD scale, and there was a correlation between the arousal and the evaluation when reading the dashboard (18). A research using a virtual prototype also showed that a relationship existed between user impression and size, color, number of items, character/graphic size and gauge size of the dashboard (19). The results of these case studies illustrated that interaction performance of the dashboard could be adequately explained by design. However, in addition to impression and emotion, the effect of the designed dashboard layout on human physiological senses were not fully explained.

Automobile dashboards could be mainly divided into two types: center-locational ones and driver-orientational ones (20), and the visual ergonomic performance during interaction with the two types of panels was divergent. The dashboard system might produce different effects on the driver"s psychological burden and feelings, thus affecting the information acquisition and operation behaviors of the driver in the cabin (21). The visual recognition efficiency of the form (circle or linear), indicators (pointers or bar graphs), and direction (horizontal or vertical) of the gauges on various instrument panels was different. Among these elements, the efficiency of linear instrument form with a pointer as the indicator and horizontal direction display was higher, while the drivers preferred round instruments subjectively (5).

Physiological control system maintained the balance between the system and the internal and external environment through the interaction and feedback among multiple variables. Among them, heart rate (HR), respiration (RESP), blood pressure (BP), and other important physiological variables could show complex variation patterns in different time scales (27). In transportation, such as cycling, electrocardiogram (ECG) could be used to estimate the real-time blood pressure as a feedback of the physiological signal changes during the process of behavior completion (28). For a driver who was stimulated by a nervous or intellectual signal while other conditions were relatively unchanged, the HR varied with the strength of the signal (29). On the part of car driving, the alteration of information input would give rise to the variation of stress level and then the fluctuation of HR, which was the physiological connection between HR and stress (30). Literatures (31–33) all applied ECG signals to analyze human-vehicle interaction behaviors. The measurement and analysis of driver ECG is a common research method from the field of neuropsychology, but from the perspective of driver visual attention, few studies combined ECG signal with eye movement. In this study, ECG data were put into practice to combine with the eye movement law and explore the subject"s visual arousal degree during the dashboard reading experiment.

The study was oriented to typical dashboard layouts and used a simple driving simulator to conduct experiments (Figure 1). The simulator was commonly used in driving schools in China, and existing research showed that it could effectively collect people"s driving behaviors (13). Interaction efficiency, eye movement characteristics, mental stress changes and system usability were collected.

Digital dashboards of different automobile brands present various information element layouts, which are based on the distinctions of the instrument information architectures. Conventional functions of the dashboards are composed of relatively fixed parts, which can be summarized as: tachometer, speedometer, water temperature gauge, fuel gauge, gear information, signal light, air temperature, time, multimedia information and other driving related information. According to our investigation to the market and the conclusion of literatures (42, 43), the current layouts of automobile dashboards could be divided into four types (Figure 2).

Interface prototypes of four kinds of dashboard layouts as the stimuli were adopted in this paper. These prototypes were saved as GIF images and the pointers could rotate at a certain speed to simulate the readings of the dashboard. The dashboard size, line width, division value, scale and pointers were unified. The colors of the background, pointers and scale lines of the instrument panels were set to black, red and white, respectively. In order to avoid the difference of scale line density on arcuate meters from affecting the subjects" cognition, the instruments in the four interface prototypes were all circular. An iPad 2 (1024 * 768dpi) was used to present the prototypes, which was attached in front of the dashboard position of the driving simulator"s screen (21.5 inches, 1920*1080dpi). The size of the prototypes on the iPad was the same as the dashboard in the simulator display, and other information on the screen would not be sheltered. The subjects were asked to use the driving simulator to complete the driving tasks.

(2) At the beginning of the experiment, the subjects needed to accelerate to the speed of 60 km/h [the high speed on ordinary roads in China (44)] and drove at this speed. After that, the staff placed the iPad with the dashboard prototypes in front of the simulator screen and started recording. Each round of driving tasks lasted about 135 s. The driving environment was on urban roads (Figure 4). The driving tasks included: (1) straight line driving; (2) turning left; (3) turning right; (4) shifting gears according to the prompt information; (5) crossing an intersection; (6) crossing a crosswalk; (7) turning around; (8) parking on the side. The subjects needed to perform four rounds of driving tasks totally and the whole experiment lasted about 9 min. Each time the subject completed one round of tasks and returned to the starting point of the route, the dashboard prototype on the iPad would be switched to the next one. In the process of driving, the subjects should observe the prototype according to the voice prompt, and report the reading of the tachometer and speedometer every 20 s. There was no time limit for reading the dashboard so that the subjects should finish reading in their normal cognitive state. If the subjects felt tired, they could apply to terminate the test and start again after a rest so as to eliminate the error caused by fatigue effect.

In the experiment, only the reading of tachometer and speedometer was studied. Other information, such as gear position, navigation prompt, emergency alarm, time, and temperature, were not included. Besides, considering the universality in the context of sharing, the study focused on the functional layout of the dashboards. And personal preference settings such as the font, dynamic effect, visual style of graphics, color, and brightness were taken as controlled variables.

Based on the car sharing application situation, the experiment mainly evaluated the interaction efficiency, eye movement characteristics and mental stress changes of the drivers (Table 1). When users accessed a shared car and faced with unfamiliar equipment and environment, the three indicators were important feedback of user experience. In addition, the subjective evaluation was collected by SUS to analyze the relationship between eye movement characteristics of the subjects and usability of the dashboard. The scale being made up of 10 items had gained widespread application in the field of usability research (45). Respondents were asked to score the items with an integer value from 1 to 5 according to their perception after using an interface system (45). The scale was applicable to appraise the HMI of passenger cars (20, 44). Large sample researches on the scale presented a reliability coefficient of 0.91, which showed a good internal consistency reliability (46, 47).

Fifty-eight subjects were recruited to participate in the experiment, aged 23–36. The subjects consisted of postgraduates, Ph. D candidates and young teachers who majored in ergonomics or industrial design. All the subjects had more than 3 years of driving experience and were familiar with the information on the digital instruments. All of them had used rental cars and among them, 38 (about 65.5% of the subjects) had rented both basic and high-end cars and could adapt to a variety of dashboard layouts. The car sharing platforms that the subjects had used included GoFun, Yidu, Morefun, CAR (China Auto Rental), EVCARD, Urcar, and GreenGo. Basic information of the subjects is listed in Table 2.

HR, which means the frequency of heart contraction, is an important factor in cardiac work and one of the important mechanisms of increased cardiac output. HR related indicators could effectively describe the driver"s psychological state and mental stress changes (30). In studies of dashboard recognition performance, it is needed to consider the driver"s HR variation patterns during the experiment. The driver"s ECG is physiological data collected according to the time series, which contains abundant characteristic information in time sequence. In this study, an LSTM network was used to identify and predict the mean HR of the subjects in the process of dashboard interaction, and to judge its time series characteristics.

Binomial and multinomial logit models played an important role in studies of traffic demands and vehicle safety (53, 54). In this experiment, the subjects needed to interact with the four dashboard prototypes to complete the tasks of reading. In order to analyze the relationship between users" interaction efficiency and their HR variation during the interface interaction, an unordered multinomial logistic regression model was introduced in the study. The predicted variable was the type of the dashboard layouts, which could be divided into four categories (Figure 2). Therefore, the logistic regression model was expressed as follows:

where j = 1, 2, 3, 4 was the type of the dashboard layouts, p(yi = j) represented the probability that the lowest HR occurred in the interaction process with a certain dashboard in the 4-stage experiment. xk(k = 1, 2, 3) meant the kth explanatory variable that could predict the type of dashboard. The explanatory variables included average searching time, average reading time and total reading accuracy. βjk was the regression coefficient vector of the model. Taking J as the reference type, the ratio (p(y=j|x)p(y=J|x)) of the probability of the lowest HR in the 4-stage experiment occurring on other types of dashboards to the probability of occurring on the type J was the odds ratio value (OR). We chose type A dashboard as the reference type, and the following three logistic models were set up:

In order to understand the user experience of different dashboard layouts, we analyzed the task completion time under the four layouts. The searching time and reading time under each layout were obtained by averaging the data of the multiple oral reports in each round of reading tasks. The results were shown in Figure 6.

The average searching time and reading time of each type could be used to explore the influencing factors of dashboard recognition efficiency. In the analysis of the differences among the four groups, we chose paired sample t-test because the intergroup standard deviation of the two indicators was much larger than the between-group standard deviation. The results showed that the differences in the average searching time and reading time among the dashboards were significant (Tables 3, ​,44).

The results of significance tests showed that most eye movement indicators were related to system usability of the dashboards. Except for FT of type B and D instruments (X2B and X2D) and FD of type C (X1C), other indicators had significant effects on the SUS score of corresponding instruments (p < 0.05). Among them, FD and FT both had positive effects on the usability of A-type dashboard. In addition, for type B and D, FD had a positive effect. And for type C, the effect of FT was positive. In contrast, PD had a significant negative effect on the SUS scores of all the four instruments, indicating that during the reading process, the smaller the PD got, the higher the dashboard"s usability was. In conclusion, the system usability of the dashboards was in connection with visual ergonomics. And among the four dashboard layouts, the SUS score of type A was the highest (A_SUS = 84.612), while that of type C was the lowest (C_SUS = 78.578).

In addition to task completion time and system usability, the change of drivers" mental stress was also an important indicator to further reflect the user experience brought by different dashboard layouts. In this study, the subjects" ECG signals in instrument reading tasks were analyzed. Time domain signals were collected in the experiment. In Figure 7, the ECG waveforms (a) of one of the subjects during driving in a straight section of the road (from the 14th to 30th second of the experiment) and the waveforms (b) while turning along a large radius curve (in the period from the 135th to 145th second) were illustrated.

In the experiment, the average HR of the subjects fluctuated regularly with time, which showed that the ECG during a driving task would change with the time going on and the subjects" attention switching between the HMI and the road. This series of HR variation when reading the instruments was consistent with the subjects" nervous mood. Literature (55) also discovered that when the driver was in the emotional situation of appreciation or mental focus (during simple tasks), the HR would show fluctuations similar to Figure 8A. The HR during the interaction with a dashboard presented a periodic pattern rather than a continuous increase or decrease. Therefore, it was unreasonable to evaluate the quality of an interface design and its level of information transmission only by the users" HR variation. It was also necessary to consider the influence of searching time and reading time on the HR.

To sum up, in the process of instrument reading interaction, which kind of interface layout the lowest mental stress occurred on could be explained by these three variables to a certain extent. The users" average searching time, reading time and reading accuracy brought about by different layouts were important for the context of car sharing, and needed to be taken into consideration in usability tests of dashboard design schemes.

For any interface design, a reasonable information architecture is a key factor to build user experience. When it comes to automobile digital instrument panels, the layouts should be convenient for drivers to quickly identify and organize a variety of information types. According to the existing literatures, at present, researchers tended to study the shape and character encoding of instrument panels (14), or different properties of the gauges, such as the form (circle or linear), indicator styles (pointers or bar graphs) and direction (horizontal or vertical) (5). However, there is a shortage of researches for a specific layout. This will lead to an unclear effect of information architecture and inadequate research on dashboard interaction. A dashboard with a clear display layout design should methodically feed back the information to the tenants who do not know the vehicle very well.

In this study, from the perspective of recognition efficiency in human-computer interaction, we found a suitable dashboard layout for shared cars. The results showed that when users accessed unfamiliar dashboards, different layouts would bring about significant distinctions in interactive performance. Besides, the HR during driving and reading the instruments presented a certain periodicity, which should be considered in design works for shared cars. Currently, the particularity of user experience in shared cars compared with that in private cars was a hot issue. For example, the stated preference (SP) approach (56) was frequently used to determine the characteristics of car-sharing users. The usability and ease of use of human-computer interaction was very important for shared car users when being transmitted to a specific vehicle. Kuemmerling et al. had studied the efficiency of human-vehicle-interfaces in shared vehicles when setting driver assistant functions on the dashboard, so as to improve the safety, comfort and usability (3). This study further clarified the problems about instrument layouts that shared cars should pay attention to when considering dashboard recognition.

Regression models were established to explore the effects of fixation duration, fixation times and pupil diameter on the usability of the four dashboard layouts. The results showed that the usability of a dashboard system had a bearing on visual ergonomics, which was consistent with existing research conclusions. Some eye movement characteristics during driving, such as fixations and glances, could reflect the driver"s workload (44) and information demands under a certain dashboard, which related to in-vehicle display and traffic light situation (57). Therefore, the study further analyzed the impact of visual task completion time and reading efficiency on drivers" mental stress under various dashboard layouts, which provided a theoretical basis for HMI design of shared cars.

Existing researches showed that HR was significantly affected by speed [F(3, 184) = 43.076; p < 0.001], but not by interface [F(1, 184) = 1.726; p = 0.191] (44). However, this study found that the drivers" HR in the process of interface interaction presented characteristics in time series. Thus, it was necessary to take the influence of interaction features on HR brought by different interfaces into consideration. The study indicated that type A layout was the most conducive to the overall reading of an instrument to some extent because the searching time and reading time under this layout were both the shortest. And compared with type A instruments, the shorter the searching time under type B was, or the shorter the reading time under type C and D was, the more drivers would present lower HR. This meant that the differences in task completion time caused by the dashboard layout design had an impact on drivers" mental stress. Existing researches had also shown that when drivers interacted with a cockpit, the panel components would influence the quality of information delivery and the accomplishment of visual tasks (58, 59). Meanwhile, in design works of layouts, it was inappropriate to pursue a shorter time only. For example, compared with type A layout, a lower HR under type D was accompanied by a lower reading accuracy (p < 0.05), which was not good for driving safety. Therefore, for dashboard layout design, it was necessary to consider both reading time and reading accuracy according to the results in Table 6.

Entropy weight method can be used in future research. Based on the characteristics of the tachometer and speedometer studied in this study, the indicators of other dashboard information under the four layouts, such as gear position, navigation prompt, emergency alarm, time and temperature, can be further supplemented. Through the original data matrix and the entropy weight of each indicator, the comprehensive scores of the layouts can be calculated, so as to sort the importance of each layout. Based on the optimal scheme, visual elements such as color, texture and dynamic effect can be added, which will help to achieve a reasonable interface design that is more in line with the driver"s eye movement and favorable to adjusting their heart rate.

Although the specific implementation of car sharing varied with business models, these implementation schemes shared common goals in reducing total travel volume and distance, diversifying travel modes and improving efficiency (60, 61). When consumers decided to adopt peer-to-peer car sharing services instead of business-to-customer services, public concerns about sustainable solutions would play a certain role (62). However, the willingness of current consumers to continue using shared cars was not high. By optimizing the user experience, this willingness could be strengthened to contribute to the promotion of shared cars. In this way the green level of the urban environment and sustainable utilization of resources could be improved.

One limitation came from the immersion degree of the experiment. In order to reduce the subjects" sense of strangeness, the simulator we chose was a simple one commonly used in driving school training. The simulator and display screen were separate and not integrated into one workspace, which led to a lower immersion. In the experiment, the subjects had a subconscious feeling that they were observed and was more tense than driving in normal times. In future, we plan to use an integrated driving simulator to carry out experiments with a higher immersion degree, and collect ECG signals in a more natural state. Besides, in the process we found that the total experiment duration about 9 min was a little short. Some subjects failed to fully enter the natural driving state, while the experiment had been over. In order to get truer HR data, the experiment time should be lengthened.

In this study, we reported an experiment about dashboard recognition of 58 subjects, which compared the reading efficiency, system usability and drivers" mental stress of four typical dashboard layouts under the context of car sharing. The main conclusions were as follows:

(1) A statistical analysis presented that among the four types of dashboards, the reading time and task completion time of type A were the shortest, but the searching time was longer than that of type B. Besides, the average fixation time of type A was the shortest (Mean = 1.029), and the system usability of this type was the highest correspondingly (SUS = 84.612). On the other side, the average fixation times of type B was the lowest (Mean = 7.364). By linear regression models, it could be found that most of the eye movement indicators under the four dashboards had significant impacts on system usability. Therefore, optimizing the visual ergonomics of a dashboard through the interface layout design helps to improve the usability of the system. And it will affect the task completion time, and raise the efficiency of reading.

(2) The prediction effect of the LSTM model was good with an RMSE = 1.105 and an MAE = 0.009. It confirmed that the network could effectively fit the non-linear, abrupt and periodic laws of HR data after parameter debugging. Better prediction performance was achieved from the perspective of algorithm, which contributed to analyzing the HR changes in the process of dashboard interaction.

(3) Multinomial logistic regression models indicated that the searching time, reading time and reading accuracy could explain what kind of dashboard layout a low mental stress was prone to occur on. For example, compared with type A layout, the higher the reading accuracy under type D was, the higher the HR would be (p < 0.05). Thus, in design works for shared car dashboard layouts, it was unreasonable to appraise the interaction performance of a design scheme only by temporal indicators such as searching time and reading time. The reading accuracy was also crucial.

2. Amatuni L, Ottelin J, Steubing B, José MM. Does car sharing reduce greenhouse gas emissions? Assessing the modal shift and lifetime shift rebound effects from a life cycle perspective. J Clean Prod. (2020) 266:121869. 10.1016/j.jclepro.2020.121869 [CrossRef]

4. Dingus T, Klauer, S, Neale, V, Petersen, A, Lee, S, Sudweeks, J, . The 100-Car Naturalistic Driving Study Phase II - Results of the 100-Car Field Experiment. U.S. Department of Transportation, National Highway Traffic Safety Administration,Washington, DC (2006). Available online at: https://rosap.ntl.bts.gov/view/dot/37370

6. Sioui L, Morency C, Trépanier M. How carsharing affects the travel behavior of households: a case study of Montréal, Canada. Int J Sustain Transport. (2013) 7:52–69. 10.1080/15568318.2012.660109 [CrossRef]

9. Akyeampong J, Udoka S, Caruso G, Bordegoni M. Evaluation of hydraulic excavator human-machine interface concepts using NASA TLX. Int J Indus Ergon. (2014) 44:374–82. 10.1016/j.ergon.2013.12.002 [CrossRef]

13. Yang H, Wang Y, Jia R. Dimensional evolution of intelligent cars human-machine interface considering take-over performance and drivers" perception on urban roads. Complexity. (2020) 2020:1–13. 10.1155/2020/6519236 [CrossRef]

14. Iqbal BM, Suzianti A, Nurtjahyo B. Military vehicle dashboard design using semantics method in cognitive ergonomics framework. In: Harris D, editor. Engineering Psychology and Cognitive Ergonomics, Lecture Notes in Computer Science, Vol. 9174. Cham; Los Angeles, CA: Springer (2015). p. 152–163.

15. Pitts MJ, Burnett G, Skrypchuk L, Wellings T, Attridge A, Williams MA. Visual-haptic feedback interaction in automotive touchscreens. Displays. (2012) 33:7–16. 10.1016/j.displa.2011.09.002 [CrossRef]

20. Yang H, Zhao Y, Wang Y. Identifying modeling forms of instrument panel system in intelligent shared cars: a study for perceptual preference and in-vehicle behaviors. Environ Sci Pollution Res. (2020) 27:1009–1023. 10.1007/s11356-019-07001-0 [PubMed] [CrossRef]

21. Ren H, Tan YP, Zhang NN. Research on form design of automotive dashboard based on Kansei Engineering. In: 2019 7th International Forum on Industrial Design. Bristol: Iop Publishing Ltd. (2019).

24. Hu Z, Xin X, Xu W, Sun Y, Jiang Z, Wang X, et al.. A literature review of the research on interaction mode of self-driving cars in the 21st HCI international conference. In: Marcus A, Wang W, editors. Design, User Experience, and Usability: Application Domains. Cham: Springer; (2015). p. 29–40.

28. Ghosh S, Banerjee A, Ray N, Wood PW, Padwal R. Continuous blood pressure prediction from pulse transit time using ECG and PPG signals. In: Healthcare Innovation Point-of-care Technologies Conference. Cancun: IEEE; (2016).

29. Napoletano P, Rossi S. Combining heart and breathing rate for car driver stress recognition. In: Moeller R, Ciabattoni, editors. 2018 IEEE 8th International Conference on Consumer Electronics. Berlin: IEEE; (2018).

42. Sun GL, Li Q, Fu PW, Ran LH. Analysis and optimization of information coding for automobile dashboard based on human factors engineering. China Safety Sci J. (2018) 28:68–74. 10.16265/j.cnki.issn1003-3033.2018.08.012 [CrossRef]

43. Sun GL, Li Q, Meng YH, Ran LH, Fu PW. Design of car dashboard based on eye movement analysis. Packaging Eng. (2020) 41:148–60. 10.19554/j.cnki.1001-3563.2020.02.022 [CrossRef]

59. Wang YY, Liu QF, Lou WL, Xiong DQ, Bai Y, Du J, et al.. Ergonomics evaluation of large screen display in cockpit based on eye-tracking technology. In: Long S, Dhillon BS, editors. Man-Machine-Environment System Engineering, MMESE 2018. (2019). p. 347–56.

60. Rabbitt N, Ghosh B. A study of feasibility and potential benefits of organised car sharing in Ireland. Transport Res Part D Transport Environ. (2013) 25:49–58. 10.1016/j.trd.2013.07.004 [CrossRef]

62. Hartl B, Sabitzer T, Hofmann E, Penz E. Sustainability is a nice bonus the role of sustainability in carsharing from a consumer perspective. J. Clean. Prod. (2018) 202:88–100. 10.1016/j.jclepro.2018.08.138 [CrossRef]

In a world where companies now use dozens (upon dozens) of different tools to track business performance, data dashboards have become critical for quickly conveying the most important numbers to teams within your company as well as to clients.

Much like the dashboard in your vehicle, data dashboards display real-time key metrics and performance indicators that are essential for guiding decisions and better navigating the surrounding landscape.

But in order for you to do just that, you need the know-how on how data dashboards work and how to create them so as to get the most out of your dashboard reporting.

Simply put, a data dashboard can be defined as an information management tool that visually tracks, analyzes, and displays key performance indicators (KPI), metrics, as well as key data points, allowing you to monitor the current state of your business, department, team, or specific process.

Keep in mind that with a data dashboard, it’s easier to draw parallels between different but related metrics. You can also identify trends and predict potential challenges concealed in an organization’s data.

The best part is that you can access your data dashboards through smartphones, tablets, and other mobile technology and just as easily share them with your company, team, or clients.

Data visualization is best defined as a graphical representation of information and data. Data visualization tools, like dashboards, use visual elements like charts, graphs, and maps, to enable users to easily see and understand trends and patterns in data.

In the world of Big Data, having a dashboard software to help you visualize a large amount of data and easily convey the results is essential. Data visualization is more intuitive and meaningful, and it is very important to use appropriate charts to visualize data.

We use different types of data visualization in order to better present and interpret collected data in our dashboards. Here are some of the main dashboard examples:

Number Visualization – is best for showing a simple Metric or to draw attention to one key number. It is compact clear and precise and is great for highlighting progress in your Dashboard.

This is where the benefits of data dashboards show their true values. They use raw data from all the sources that you currently use along with spreadsheets and databases to create tables, line charts, bar charts. All of these find their place on your business data dashboards where you can quickly find and understand the key metrics you are looking for.

Simply put, data dashboards can significantly simplify weekly, monthly, and yearly reporting by allowing you to communicate information at any time without hours of preparation and analysis. That way your team, management, and clients can have a clear idea of the progress you have made.

Most business users focus on two methods of sharing data – the report and the dashboard. As we explain how each works, it will become clear why one is far more efficient than the other.

There is a great way of understanding what makes dashboards more time-efficient, so the name for this type of reporting came from the car dashboard that we all have.

While reports can be time-consuming and often confusing, using data dashboards to visualize data is cleaner and faster. More importantly, you will get the KPIs and numbers you need within the moment and anyone requesting will easily access it.

This where a data dashboard comes in, you can connect KPIs and Metrics from multiple sources in one place. That way all the important data will be in one clear and well-visualized dashboard. It will save you time and allow you to monitor your performance in real-time.

Like most marketers and marketing managers, you want to know how well your efforts are translating into results each month. How much traffic and new contact conversions do you get? How many new contacts do you get from organic sessions? How are your email campaigns performing? How well are your landing pages converting? You might have to scramble to put all of this together in a single report, but now you can have it all at your fingertips in a single Databox dashboard.

Now you can benefit from the experience of our Google Analytics and HubSpot Marketing experts, who have put together a plug-and-play Databox template that contains all the essential metrics for monitoring your leads. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!

In order to motivate your team and have far greater performance transparency, you should make goal-tracking a team sport. And dashboards make it a lot easier.

By creating a Goal-Tracking Dashboard you can easily show your team where you are currently if you are on track to meet your weekly, monthly, quarterly, or yearly goal. But more importantly, you can make adjustments to your efforts in order to reach the desired goals.

When running a business you, naturally, need to get well-acquainted with the importance of implementing analytical tools, building reports on data, and creating dashboards. However, it is just a part of having a data-driven company. Most only have certain departments where numbers play a role. This, of course, is usually accounting or marketing.

So where do data dashboards come in? Well, it is simple they help pull in the data you have compiled in one place and make it visible/accessible to your whole team as well as across departments all the way to management.

Waiting for your monthly marketing report in order to see if your marketing plan has been successful and if you have reached your goals is no longer a valid option. You will lose both time and resources. And that is exactly why having a marketing dashboard where all your important KPIs are being tracked along with your goals is vital.

You can check the data daily, and take immediate action to adjust your campaign or ads so that you hit the marks you have set. With a marketing dashboard software, your team will be able to spot trends quickly and make adjustments when they matter instead of waiting until