Global Heads-Up Display (HUD) Market Research Report 2019 is a market research report available at US $2900 for a Single User PDF License from RnR Market Research Reports Library.
This study was conducted to examine the usage effects of an augmented reality head‐up display (AR‐HUD) system on the risk perception and psychological changes of drivers. To do so, we conducted an experiment to collect the driver response times for vehicles and pedestrians as their risk perception behavior, and used a driving behavior determinants questionnaire consisting of Problem Evading, Benefits/Sensation Seeking, Anti‐Personal Anxiety, Anti‐Personal Angry, and Aggression factors for collecting the psychological characteristics of the drivers. Thirty drivers were randomly assigned into an in‐vehicle AR‐HUD using group and a control group. As a result, the Anti‐Personal Anxiety and Anti‐Personal Angry factors were negatively correlated with the response time for the control group. In contrast, these results were not shown for the in‐vehicle AR‐HUD system using group. These results indicate that the in‐vehicle AR‐HUD system may partially induce a relaxation of tension or stress for drivers with a high level of interpersonal anxiety.
Therefore, the in‐vehicle AR‐HUD system might contribute to not only the visual safety driving behaviors of drivers, but also to their psychological driving safety with specific characteristics. IntroductionScience and technology have improved the safety of motor vehicles, enabling additional behaviors such the operation of navigation devices or smartphones. In addition, motor vehicles have become an indispensable part of our lives.Although motor vehicles and traffic roads are becoming safer than in the past, traffic accidents owing to driver distractions and additional behaviors still occur. There are large numbers of vehicles and pedestrians on the roads, and drivers are therefore occasionally under stress.Studies on driver stress have therefore been conducted. One such study reported meaningful results on the relationship between the physiological changes in drivers and their driving behaviors, such as steering‐wheel angle corrections, velocity changes, and time responses under incremental stress conditions.
In another study, the use of an interactive prototype that displays the emotions of the driver indicated that driver stress is reduced when compared to the driver's original state. In, speech modeling of the drivers for the classification of stress, including the short speech utterances of the drivers, was studied. In addition, another study attempted to recognize human emotions using physiological signals.The National Highway Traffic Safety Administration reported that driver‐distraction accounted for 10% of all fatal crashes, 18% of injury crashes, and 16% of all motor vehicle traffic crashes in 2012.
An adaptive multimodal in‐vehicle information system that can be used to estimate driver distractions was recently proposed. Effects of a Head‐up Display on Driver BehaviorsDrivers acquire more than 90% of their driving‐related information from their visual perception. Unlike other perception types, visual perception can be used to accurately obtain the driving information over a long distance, as well as for the movement direction, trajectory, and speed of the vehicle. Other types of perception can be used to obtain the driving information for only a close distance or narrow range of area.For a visual interface that concentrates the driver's attention on the road ahead, one possible solution is the use of an HUD. In most cases, a driver's visual and cognitive driving‐workload can be improved through clever human factor engineering combined with an HUD and obstacle detection technologies. In particular, to deal with obstacles, new approaches for vehicle tracking and nighttime vehicle detection were proposed to improve the stability and robustness,. In addition, a two‐step pedestrian detection was used to reduce the computation time of the algorithm, and an iterative particle repropagation method was proposed to enhance its tracking accuracy.An HUD shows the driving information exactly where the drivers need it–directly in their line of sight.
Therefore, drivers can receive all of their important information, such as the vehicle speed, warning signals, and indicator arrows for navigation, without looking down at the instrument panel or a secondary display.Since General Motors first began developing automobiles equipped with an HUD in 1999, many other companies (for example, BMW, PSA Group, and Cadillac) have also developed vehicles with an installed HUD. The development of HUD technologies has made it possible to provide information within the driver's field of view.The sensitivity of visual cues leads a person to conduct their current task, which is known as selective visual attention. In addition, according to the visual design, searching based on color has been shown to be the fastest search method when compared to size, brightness, or geometric shape.
Effects of Augmented Reality Technology on Driver BehaviorsSafe driving requires the driver's attention, visibility, view, optimal traffic signs, a smooth ride, and a number of different available roads.AR‐HUD differs from a normal windshield HUD because the reflected information appears to be part of the driving situation itself. Therefore, the drivers will become aware of a critical situation even faster than without the use of an AR‐HUD. In addition, it supplements the exterior view of the traffic conditions in front of the vehicle through augmented virtual information provided to the driver.AR can improve an awareness of other road vehicles and pedestrians surrounding the driver.
In addition, it can be used to increase the saliency of important elements in the driver's view and has the potential to enhance the driver's situation awareness. All drivers, regardless of age, can benefit through a trickle‐down effect.First, the drivers respond more quickly to lane change information when it is directly augmented into their perspective over the road surface, as compared to being displayed through 2D icons.Second, in the context of safety‐related applications, augmenting a realistic human‐machine interaction view provides drivers with a higher perceived level of safety than with a conventional HMI visualization style, despite the higher visual complexity. In addition, one study found that the more complex the safety recommendation that the human‐machine interaction has to communicate, the more the drivers perceive an augmented realistic HMI visualization as a valuable support.In addition, an AR‐based navigation system causes drivers to spend more attention looking ahead on the road than does a non‐AR system.During a non‐augmented traffic situation, information such as lane markings and road signs has proven to be very useful for indicating static traffic aspects. In contrast, AR can be used to provide dynamic markings that can adapt to changing contextual traffic situations to better regulate a driver's traffic behaviors.In this way, automotive AR can potentially enhance a driver's experience by providing a new visual modality that can overlay information over the driver's field of view. Relationship between Driver Characteristics, Risk Perception, and Driving BehaviorsIn relation to driving behavior, risk perception refers to “the subjective experience of risk in potential traffic hazards.” Inexperienced drivers who have low levels of altruism and high levels of anxiety and anger tend to underestimate objective risk factors in a presented traffic environment. This is related to risky behaviors such as speeding. In addition, drivers with low adaptive capability in a physical/social environment and high levels of anxiety have experienced a relatively greater number of traffic accidents.For many drivers, trying to follow digital route guidance in an unfamiliar place during heavy rush‐hour traffic can cause sensory overload and high anxiety, and can compromise safety.
A new HUD technology is aimed at mitigating and even preventing such scenarios. If we focus on driver behaviors, accidents are likely to occur when the drivers make mistakes or drive improperly when misjudging driving risks.
Such driving behaviors can be caused by four main factors: speeding, drunk driving, fatigue, or distractions.However, the driver response time is not only affected by the traffic situation that the drivers experience, but also by their age, psychological state, personality, and attitude.One's state of anxiety is interactively determined by their personal traits and situational stress. It has often been found that anxiety impairs performance, especially when the task being performed is complex and demands attention. In addition, stress directly elicited by driving can be used to predict lapses and violations within a driving environment.Moreover, anxiety and diverse factors such as a lack of experience, emotions, day‐dreaming, tension, fatigue, lack of sleep, weather changes, frost, heat, biorhythms, aggression, a bad mood, conflicts, stress (workload), alcohol, drugs, smoking, and pain affect one's level of concentration.However, anxiety is not necessarily bad.
Anxious personality types have shown faster reaction times to threatening situations, and at the same time, can quickly process more threatening circumstances.Therefore, this study aims to determine the effect of an in‐vehicle AR‐HUD system on a driver's response behavior and psychological characteristics. Additionally, we tried to confirm the difference in effectiveness of an in‐vehicle AR‐HUD system between normal and risky drivers. ParticipantsThirty male drivers and one female driver participated in this experimental study. These participants were randomly assigned into two groups: an in‐vehicle AR‐HUD system user group (16 participants) and a control group (15 participants).However, we excluded the driver data of both the female (in‐vehicle AR‐HUD system user group) and two of the male (control group) drivers from the final statistical analysis because the female driver had less than one year of driving experience, and the two male drivers showed a response of less than 50% to the total experimental stimuli.The average age and driving experience of the participants are shown in. There was no statistically significant difference between the two experimental groups. However, for the risk of a collision with a cutoff vehicle (E4, E8), it seems that a dangerous situation was not properly induced to the participants.
In addition, the characteristics of the preceding vehicle stimulus and the pedestrian stimulus remained from the beginning until the end when the drivers completely responded. However, in this experiment, the cutoff vehicle stimulus had both its own characteristics and those of the preceding vehicle stimuli. In addition, the cutoff vehicle initially operated as the cutoff vehicle near an adjacent lane, and after it moved into the same lane, its characteristics changed to those of a preceding vehicle. We found that the response time for the cutoff vehicle stimuli of the participants had two mixed response times for the cutoff and preceding vehicles.
The response behavior of the participant was different from our initial experimental purpose of providing cutoff vehicle stimulus. We therefore determined that the mixed responses of the participants were not an accurate response to an accurate stimulus.
According to this judgment, we excluded data on the cutoff vehicle stimuli from our final analysis.The drivers from the in‐vehicle AR‐HUD system using group watched a video clip made by simply adding AR information to an original clip. Danger information regarding detected vehicles and pedestrians was displayed based on the TTC levels as AR technology through an HUD, as shown in. The original video clip was used for the control group.
AR‐HUD system presentation information: risk of (a) rear‐end collision with a preceding vehicle, (b) pedestrian on a crosswalk, and (c) collision with a cutoff vehicle.In addition, we made a training video clip that allowed portions of the video that did not reflect the intention of our experiment to be cut out. The purpose of the training video clip was to make sure that the participants became familiar with the driving speed and distance presented through a kiosk‐HUD device.We conducted the experiment on an indoor test bed that included kiosk HUD devices. The indoor‐test bed consisted of a 180‐in screen, a beam projector, an HUD device, a life‐sized windshield, and a PC for collecting the driver response data and for running the integrated SW, as shown in. Indoor location test‐bed including a kiosk HUD.B. Driving Behaviors: Driver Response TimesWe collected the response times of the participants under both precautionary and risky situations, including vehicles and pedestrians suddenly appearing in front of them. To do so, we explained to the participants that they should press the spacebar of the keyboard when they wanted to brake to avoid a collision with a vehicle or pedestrian.
In addition, we calculated their response time under both the objective precautionary and risky situations during which AR information was displayed in a different color according to each TTC level.C. Driving Behavior DeterminantsA driving behavior determinants (DBD) questionnaire was used in this study to collect the psychological information of all of the drivers, including their driving characteristics and attitudes.The questionnaire was developed based on the risky behaviors, human factors, psychological characteristics, and general attitudes of Korean drivers.The DBD was divided into two levels: a DBD level and a driver's reckless driving level. The DBD level consists of five factors: Problem Evading, Benefits/Sensation Seeking, Anti‐Personal Anxiety, Anti‐Personal Angry, and Aggression. The meaning of each factor of the DBD level is as follows. First, a high score for Problem Evading means that the respondent has a tendency to avoid or give up rather than try to solve a problem they are facing. Second, a respondent with a high score for Benefit/Sensation Seeking has a tendency to seek a sense of thrill from dangerous behavior and to benefit from a situation in which a conflict occurs in terms of what they wish to pursue and the societal regulations that they have violated.
The respondents who score high on the Anti‐Personal Anxiety factor are likely to be reluctant to come forward in front of others and have difficulty in forming new relationships. The Anti‐Personal Angry factor is similar to the Anti‐Personal Anxiety factor in terms of the inadaptability of interpersonal relationships; however, a distinction in this factor is the expression of negative aspects in an aggressive form. Finally, the Aggression factor has intrinsic characteristics and does not have a specific cause or object.
The range of reliability of the DBD level showed a Cronbach's α value of 0.71 to 0.86. In addition, the level uses a seven point Likert scale (strongly disagree to strongly agree).In addition, the reckless driving level consists of Speeding, Inexperienced Coping, Wild Driving, Drunken Driving, and Distraction. The results of the correlation between two levels indicate that an inappropriate level of DBD is highly correlated with dangerous driving behaviors and the strong possibility of a traffic accident.Based on this, the authors proposed a discriminant function that can be used to discriminate drivers according to the DBD level and predict their reckless driving behaviors through a standardization procedure.Therefore, in this study, we collected the DBD level data of the participants simply to determine whether they are a risky driver or not.D. Experimental ProcedureFirst, we described the purpose of our study to the participants, and then played them the training video clip. The participants watched the training video clip until they became familiar with the speed and distance of the video scene, which was presented using the indoor kiosk HUD test bed. Next, the participants were instructed regarding the response form and allowed to ask questions about the experiment.
We provided a range of answers to avoid affecting their response behaviors. When the above procedure was finished, the participants conducted the main experiment. Finally, they filled in a questionnaire set composed of the DBD (37 questions) and basic information (age, driving experience, and so on).E.
AR‐HUDControlObjective risky situationPreceding vehicle0.41 (1.26)0.58 (0.90)Pedestrian−0.60 (0.66)−0.76 (0.66)Objective precautionary situationPreceding vehicle1.94 (1.31)2.11 (0.96)Pedestrian0.05 (0.49)−0.05 (0.49)DBD3.30 (0.68)3.07 (0.78)Factor 1. Problem Evading2.83 (0.79)2.60 (1.01)Factor 2. Benefits/Sensation Seeking3.04 (1.24)2.61 (0.85)Factor 3. Anti‐Personal Anxiety3.53 (0.86)3.50 (1.10)Factor 4.
Anti‐Personal Angry3.94 (0.70)3.68 (1.00)Factor 5. Aggression3.37 (1.10)3.24 (1.05). As a result, during a precautionary situation for the in‐vehicle AR‐HUD system using group, only the Benefit/Sensation Seeking factor was positively correlated with the response time for the preceding vehicle stimulus (, ). The Anti‐Personal Anxiety factor was only negatively correlated with the response time to the preceding vehicle and the pedestrian stimulus for the control group (a risky situation for a vehicle, and, and for a pedestrian, and; and a precautionary situation for a vehicle, and, and for a pedestrian, and ). In addition, both the DBD level and Anti‐Personal Angry factor were only negatively correlated with the response time for the pedestrian stimulus (,; and, respectively).Further, we also conducted a regression analysis to confirm how the psychological characteristics of the driver affected their response behavior.As a result, we found that only the DBD level of the drivers had a significant impact on their response time for the control group (, and adjusted ). In detail, among the five factors, only Anti‐Personal Anxiety had a significant impact on the response time (, adjusted, and ).B. Results of Response Behavior between Risky and Normal DriversWe tried to confirm the difference in effectiveness of the in‐vehicle AR‐HUD system between normal and risky drivers.
To do so, we divided the participants into two groups, a normal driver group and a risky driver group, for both the in‐vehicle AR‐HUD group and the control group. We also applied the discriminant function proposed in.The data on the drivers’ response time and DBD levels are shown in.
We conducted an ANOVA based on these data.
Continental Heads Up Display
Growing demand for avionics in military aviation and rising adoption of HUDs in civil and commercial aircraft has collectively spurred the demand for head-up displays in the aviation segment. In addition, escalating demand from automotive sector has further fueled market momentum. Strong growth in the premium and luxury car segment has provided the much needed impetus to the market growth. Government regulations promoting vehicle safety are also expected to support the market growth in the coming years. These factors are expected to contribute towards a compounded annual growth rate (CAGR) of 21.9% during the forecast period 2015 – 2022. Although high upfront cost and lack of awareness have collectively weighed down the market growth, the penetration of the head-up display technology is expected to gradually increase as the technology unearths applications across multiple verticals. Report Scope and Description1.2.
Research Methodology2. Executive Summary2.1. Head-Up Display Market Industry Snapshot3. Global Head-Up Display Market Analysis3.1. Head-Up Display Market Overview3.2. Market Inclination Insights3.3.
Market Dynamics3.3.1. Market Drivers3.3.2. Opportunity Matrix3.4.
See-Saw Analysis3.5. Attractive Investment Proposition3.6.
Market Positioning of Key Head-Up Display Vendors3.6.1. Key Strategies Adopted3.6.2.
Analyst Recommendations4. Global Head-Up Display Market Revenue, By End-use, 2013 – 2022 (US$ Mn)4.1.
Doughnut Analysis4.2. Defense and Civil Aviation4.3. Others (Sports, etc.)5.
Global Automotive Head-Up Display Market Revenue, By Product, 2013 – 2022 (US$ Mn)5.1. Pac-Man Analysis5.2. Windshield Projected HUD5.3. Combiner Projected HUD6. Global Automotive Head-Up Display Market Revenue, By End-use, 2013 – 2022 (US$ Mn)6.1. Doughnut Analysis6.2. Premium and Luxury Cars6.3.
Sports Car6.4. Mid-segment and Basic-segment Cars7. Global Automotive Head-Up Display Market Revenue, By Distribution Channel, 2013 – 2022 (US$ Mn)7.1.
Pac-Man Analysis7.2. North America Head-Up Display Market Revenue, 2013 – 2022 (US$ Mn)8.1. North America Head-Up Display Market, By End-use, 2013 – 2022 (US$ Mn)8.2.1. Doughnut Analysis8.2.2. Defense and Civil Aviation8.2.3. Others (Sports, etc.)8.3. North America Head-Up Display Market, By Country 2013 – 2022 (US$ Mn)8.3.1.
Rest of North America8.4. North America Automotive Head-Up Display Market, By Product, 2013 – 2022 (US$ Mn)8.4.1. Pac-Man Analysis8.4.2. Windshield Projected HUD8.4.3. Combine Projected HUD8.5. North America Automotive Head-Up Display Market, By End-use, 2013 – 2022 (US$ Mn)8.5.1. Pac-Man Analysis8.5.2.
Premium and Luxury Cars8.5.3. Sports Car8.5.4. Mid-segment and Basic-segment Cars8.6. North America Automotive Head-Up Display Market, By Distribution Channel, 2013 – 2022 (US$ Mn)8.6.1. Pac-Man Analysis8.6.2. Europe Head-Up Display Market Revenue, 2013 – 2022 (US$ Mn)9.1. Europe Head-Up Display Market, By End-use, 2013 – 2022 (US$ Mn)9.2.1.
Doughnut Analysis9.2.2. Defense and Civil Aviation9.2.3.
Others (Sports, etc.)9.3. Europe Head-Up Display Market, By Country 2013 – 2022 (US$ Mn)9.3.1. EU7 (France, Germany, Spin, U.K., Italy, Netherlands, Belgium)9.3.2. Rest of Europe9.4. Europe Automotive Head-Up Display Market, By Product, 2013 – 2022 (US$ Mn)9.4.1. Pac-Man Analysis9.4.2.
Windshield Projected HUD9.4.3. Combine Projected HUD9.5.
Europe Automotive Head-Up Display Market, By End-use, 2013 – 2022 (US$ Mn)9.5.1. Pac-Man Analysis9.5.2. Premium and Luxury Cars9.5.3. Sports Car9.5.4.
Mid-segment and Basic-segment Cars9.6. Europe Automotive Head-Up Display Market, By Distribution Channel, 2013 – 2022 (US$ Mn)9.6.1. Pac-Man Analysis9.6.2. Asia Pacific Head-Up Display Market Revenue, 2013 – 2022 (US$ Mn)10.1.
Asia Pacific Head-Up Display Market, By End-use, 2013 – 2022 (US$ Mn)10.2.1. Doughnut Analysis10.2.2. Defense and Civil Aviation10.2.3. Others (Sports, etc.)10.3. Asia Pacific Head-Up Display Market, By Country 2013 – 2022 (US$ Mn)10.3.1. Rest of Asia Pacific10.4. Asia Pacific Automotive Head-Up Display Market, By Product, 2013 – 2022 (US$ Mn)10.4.1.
Pac-Man Analysis10.4.2. Windshield Projected HUD10.4.3. Combine Projected HUD10.5.
Asia Pacific Automotive Head-Up Display Market, By End-use, 2013 – 2022 (US$ Mn)10.5.1. Pac-Man Analysis10.5.2. Premium and Luxury Cars10.5.3. Sports Car10.5.4. Mid-segment and Basic-segment Cars10.6.
Asia Pacific Automotive Head-Up Display Market, By Distribution Channel, 2013 – 2022 (US$ Mn)10.6.1. Pac-Man Analysis10.6.2. Rest of World Head-Up Display Market Revenue, 2013 – 2022 (US$ Mn)11.1. Rest of World Head-Up Display Market, By End-use, 2013 – 2022 (US$ Mn)11.2.1. Doughnut Analysis11.2.2. Defense and Civil Aviation11.2.3. Others (Sports, etc.)11.3.
Ford Heads Up Display
Rest of World Head-Up Display Market, By Country 2013 – 2022 (US$ Mn)11.3.1. Latin America11.3.2. Middle-East and Africa11.4. Rest of World Automotive Head-Up Display Market, By Product, 2013 – 2022 (US$ Mn)11.4.1. Pac-Man Analysis11.4.2. Windshield Projected HUD11.4.3.
Combine Projected HUD11.5. Rest of World Automotive Head-Up Display Market, By End-use, 2013 – 2022 (US$ Mn)11.5.1. Pac-Man Analysis11.5.2. Premium and Luxury Cars11.5.3.
Sports Car11.5.4. Mid-segment and Basic-segment Cars11.6. Rest of World Automotive Head-Up Display Market, By Distribution Channel, 2013 – 2022 (US$ Mn)11.6.1. Pac-Man Analysis11.6.2. Company Profiles12.1. Nippon Seiki12.2.
Yazaki Corporation12.3. Johnson Controls, Inc.12.4. Continental AG12.5. Denso Corporation12.6. Delphi Automotive Plc12.7. Rockwell Collins12.8.
BAE Systems Plc12.9. Garmin Ltd.12.10. MicroVision, Inc.12.11. Elbit Systems Ltd.12.12. Thales Group. End-use Segmentation AnalysisOn the basis of end-use, the automotive head-up display market is categorized into following verticals:.
Premium and Luxury Cars. Sports Cars. Mid-segment and Basic CarsTheir contribution to the global automotive head-up display market in 2014 and 2022 is as shown in the figure below.Premium and Luxury car segment was the largest end-use segment in the global automotive head-up display market, accounting for over 2/3rd of the market revenue in 2014. Rising demand for smart cars is driving the demand for advanced car infotainment systems, which, in turn has had positive impact on the overall demand for head-up displays in luxury and premium car segment. End-use Segmentation AnalysisOn the basis of end-use, the head-up display market is categorized into following verticals:. Aviation (Defense and Commercial). Automotive.
Premium and Luxury Cars. Sports Cars. Mid-segment and Basic Cars. Others (Sports, etc.)Their contribution to the global head-up display market in 2014 and 2022 is as shown in the figure belowIn 2014, the aviation segment comprising both defense and commercial aviation was the largest application segment in the global head-up display market.
Head-Up Displays are majorly used to raise situational alertness among flight crew/aircrew while carrying out different operations including take-off and landing. In addition, head-up displays are also used in aircrafts for wide range of activities such as event detection and ground navigation. Greater adoption of avionics for tracking and navigation applications is expected to boost the demand for head-up displays, especially in the defense sub-division.
Over the forecast period 2015 – 2022, head-up displays are poised to witness alarming adoption among automotive segment. The advancements in technologies have resulted in the development of low-cost interactive and mobile HUDs, extending their demand beyond primary and luxury car segments. In addition, rising demand for smart and safer vehicles is expected to stimulate the demand for head up displays in the automotive segment. Hismation: Head-Up DisplayA Head-Up Display (HUD) is a display system that presents critical flight information and navigation details either on a windshield or a transparent screen called combiner. It helps aircrew, car drivers, and other profession users to get the requisite details and information without requiring them to look elsewhere from their usual viewpoints.
A head-up display system has following components:. Combiner- It is the surface on which the data is projected. Video generation computer- It generates the image. Projector unit- It puts out the image to the userThe concept of head-up display traces its roots to the first half of the 20th century. Head-up displays were first employed during the World War II in military aviation.The Blackburn Buccaneer built for the British Royal Air Force and Royal Navy was the first aircraft with a built-in heads-up display.
Ar Heads Up Display Refocus Research Paper
It was prototyped in 1958 and flew for the British Royal Air Force and Royal Navy from 1968 until 1994. Pilots gained more accuracy in viewing routes and tracking objects using HUDs as compared to other conventional instruments. As a result, head-up displays which were initially introduced only for the military aviation, gained acceptance among private and commercial aviation. While the earliest use of HUDs in commercial planes were in the 1970s, the technology did not receive wider acceptance until it was incorporated in Boeing 737 in the 1990s. Today, the HUD technology is employed in several CanadaAir and Airbus planes. It helps commercial pilots to take-off or land in severe weather environment and displays information including height from the sea level, airspeed, and flight path among others.In 1988, General Motors introduced the first heads-up display in a car.
These systems were initially used only to display speed, tachometer, and other primary readings from the car’s dashboard. However, with the advancements in technology, head-up displays have evolved to offer additional information including navigation details, alerts and warnings, and infotainment information among others. With the advent of the advanced augmented reality (AR) technology, the head-up displays supporting AR capability and capable of being integrated with infrared cameras, GPS systems, Bluetooth device, and other infotainment products have enabled drivers to get all the requisite information in front of their eyes.In the beginning of the 21st century, several head-up display models were introduced for sports and consumer space.
Since then, number of HUD prototypes have been developed that display the requisite details on the inside of a scuba divers mask or swimmer's goggles.