Head-up Display Image Research Articles

Low complexity local dimming algorithm for high quality head up displays in automotive vehicles

We present a high image quality head up display (HUD) based on the local dimming liquid crystal display (LCD). The proposed HUD provides high white luminance to improve day-time visibility and low black luminance to cope with the postcard effect during the night by the local dimming technique. To reduce the computational complexity, the pixel-based boosting values for the pixel compensation are computed by simple bi-linear interpolation, complex power functions are implemented by look-up tables, some multiplications are replaced with the sums of shifted values, and the numbers of look-up tables and multipliers are reduced by sequential process. The simulation results of the proposed local dimming algorithm for 640×1280 LCD panel and 10×22 LED backlight ensure higher peak signal-to-noise ratio than 40 dB and larger power reduction performance than 73% over 7 HUD images. In addition, the postcard effect improvement is verified over images on LCD panel as well as windshield. Maximum luminance and contrast ratio on the windshield of the prototype HUD system are measured as 16,088 cd/m 2 and 342 × 10 6 :1.

54‐5: Consideration of Image Distance on Cognitive Tunneling with Augmented Reality Head Up Displays

Previous studies have shown that cognitive tunneling can be reduced when the Head‐up Display (HUD) image is not placed directly in the central vision area but rather a few degrees off center. The theory is that angular eye movement acts as a trigger to allow the brain to separate from the information that the driver does not want to focus on at that time. This paper tests an ancillary hypothesis that locating the HUD image at a different focal distance than the road scene has a similar effect, i.e., changes in eye focus can have a similar trigger affect. This study compares 12 subjects ranging in age from 17 to 49. A video of a car from the passenger's perspective is used to simulate the external environment. While viewing the scene, the subjects must simultaneously monitor speed and detect objects of interest on the road. Speed is located at various image distance to identify performance differences related to focal distances.

Saliency difference based objective evaluation method for a superimposed screen of the HUD with various background

The head-up display (HUD) is an emerging device that can project information on a transparent screen. This device is used in airplanes and vehicles and is usually placed in the operator’s field of view. In the case of a vehicle, the driver can visualize not only a wide variety of information on the HUD, but also the background (driving environment) through the HUD. However, the projected information on the display may interfere with the colors in the background because the device is transparent. For example, a text message in red color on the HUD has a lower contrast when there the background is also red, such as the red brake lights of a vehicle. As a first step in solving this issue, it is necessary to evaluate the mutual interference between the information on the HUD and the background. In this regard, a method to evaluate mutual interference based on saliency is proposed. This information can be evaluated by comparing the HUD component in a saliency map of a measured image with the HUD image.

47.3: Invited Paper: World Fixed Augmented‐Reality HUD for Smart Notifications

In this paper, we present a Head‐Up Display (HUD) system that displays perspective correct surrounding information for driver safety. In our implementation, we display contrast enhanced ego lane information on the HUD during driving conditions with poor visibility. The use of HUD for Augmented Reality (AR) needs an accurate model of the picture generation process for proper visualization of the 3D content that is perspective accurate from the user view point. The system uses multiple computer vision techniques for the accurate visualization.First, we use a camera calibration algorithm that uses view independent spatial geometry and view dependent perspective transformation that compensates for the distortion of the optics as well as the combiner. The calibration is done by capturing the test patterns from the front facing camera located close to the rear view mirror and a camera from the driver's point of view. Further registration of the HUD image is done through a head tracker that uses the camera mounted for the driver monitoring on the steering wheel. The second module detects the lane markings on the road and returns the location of the polygon in the camera co‐ordinate system. The image data bounded by this polygon is further enhanced to reduce the effect of haze, rain and insufficient illumination and blended with the camera input from the front facing camera. The HUD then projects the synthesized image that is perspective correct rendered in the vehicle co‐ordinate system.

Change in Driver Sensitivity to Moving Objects in the Forward Direction Caused by Head-Up Display Images

Change in Driver Sensitivity to Moving Objects in the Forward Direction Caused by Head-Up Display Images

HUD Image Vibration Detection on Improved Edge Detection and Corner Extraction

HUD (Head Up Display) image vibration detection requires the real-time performance. In this paper, after analyzing different algorithms for the movement estimation of the HUD, a new vibration detection algorithm is studied based on feature extraction. In the algorithm, after image smoothing, an improved Canny operator is proposed for the edge detection, then a proposed corner detection method is applied to extract the feature points on edges. The testing results prove that the studied algorithm works in real-time and with high precision.

Visual perception of HUD (Head‐up display) image with small angle of depression in practical automotive use

Although HUDs (head-up displays) were originally developed for aircraft use, in the past decade they have found application in automobiles. The angle of depression of the displayed image is one of the most important parameters for designing automotive HUDs. Generally, drivers can perceive more information displayed by HUDs when the angle of depression is smaller. However, it has been observed in experiments that decreasing the angle in an actual automotive HUD, where the distance to the displayed image is not great, can result in misperception of the displayed information. This article describes how the angle of depression affects the driver's perception of HUD images and the forward view. It also discusses why misperception occurs, what causes it, and how to design HUDs to avoid it, based on experimental results derived from an analysis of drivers' eye movements. ©1999 Scripta Technica, Electron Comm Jpn Pt 3, 82(4): 1–9, 1999

Visual perception of HUD (Head-up display) image with small angle of depression in practical automotive use

Although HUDs (head-up displays) were originally developed for aircraft use, in the past decade they have found application in automobiles. The angle of depression of the displayed image is one of the most important parameters for designing automotive HUDs. Generally, drivers can perceive more information displayed by HUDs when the angle of depression is smaller. However, it has been observed in experiments that decreasing the angle in an actual automotive HUD, where the distance to the displayed image is not great, can result in misperception of the displayed information. This article describes how the angle of depression affects the driver's perception of HUD images and the forward view. It also discusses why misperception occurs, what causes it, and how to design HUDs to avoid it, based on experimental results derived from an analysis of drivers' eye movements. ©1999 Scripta Technica, Electron Comm Jpn Pt 3, 82(4): 1–9, 1999

The influence of cognition and age on accommodation, detection rate and response times when using a car head-up display (HUD)

Car head-up displays (HUDs), which portray information in the form of a virtual image reflected off a combiner, allow the viewing of complex information, such as route guidance, without the need for the driver to look away from the road ahead. The cognitive demand required by the HUD task may distract drivers from the outside world scene and cause reaction times to slow. Cognitive capacity also decreases with age. The aim of the present study was to determine the effect of cognitive demand and age on the use of a car HUD. Subjects (young, middle or older age drivers, each group n=8) performed a HUD-assisted driving task, with three levels of cognitive demand, whilst accommodation was simultaneously measured using a dynamically recording infra-red optometer. Response times to, and the detection rate of, changes in both the HUD image and the outside world scene were analysed. Increasing the cognitive demand associated with the HUD image increased over-accommodation (greatest in the young, 0.34±0.05 D, p<0.001). Response times to and detection of changes in the HUD image and outside world scene were significantly worse with increased cognitive demand. Response times to and detection of changes in the HUD image and outside world scene also increased with age. The implication of the results with respect to car HUD design and safety are discussed.

The influence of cognition and age on accommodation, detection rate and response times when using a car head‐up display (HUD)

Car head-up displays (HUDs), which portray information in the form of a virtual image reflected off a combiner, allow the viewing of complex information, such as route guidance, without the need for the driver to look away from the road ahead. The cognitive demand required by the HUD task may distract drivers from the outside world scene and cause reaction times to slow. Cognitive capacity also decreases with age. The aim of the present study was to determine the effect of cognitive demand and age on the use of a car HUD. Subjects (young, middle or older age drivers, each group n = 8) performed a HUD-assisted driving task, with three levels of cognitive demand, whilst accommodation was simultaneously measured using a dynamically recording infra-red optometer. Response times to, and the detection rate of, changes in both the HUD image and the outside world scene were analysed. Increasing the cognitive demand associated with the HUD image increased over-accommodation (greatest in the young, 0.34 +/- 0.05 D, p < 0.001). Response times to and detection of changes in the HUD image and outside world scene were significantly worse with increased cognitive demand. Response times to and detection of changes in the HUD image and outside world scene also increased with age. The implication of the results with respect to car HUD design and safety are discussed.

The effects of viewing modality and positioning of a car head‐up display (HUD) on response times

Purpose: Car HUDs which, unlike those in aircraft, do not usually overlay the outside world, have been positioned in a variety of locations. With the likely increase of HUDs in cars, it is important to establish the optimum location to place the virtual HUD image. The effects of presenting the virtual HUD image to just one eye, or both eyes, while the outside world (traffic lights) is viewed binocularly, were also assessed.Methods: Six subjects responded to changes in simulated traffic lights and a HUD image. Response times and the number of occasions a change was missed were recorded. The HUD was presented at a range of eccentricities of up to 10° horizontally and vertically, relative to the traffic lights. The outside world was always seen in both eyes, with the HUD image being viewed monocularly and binocularly for each of the eccentricities tested.Results: Response times to changes in the outside world view (traffic lights) are significantly faster (P &lt; 0.01) than to changes in the HUD image. Response times to changes in the HUD virtual image and the amount of information missed increased with increasing eccentricity. More information was missed and response times were slower with a monocular HUD than a binocular HUD.Conclusion: The optimum location based on response times and information gathering was found to be approximately 5° diagonally down from the driver's line of sight. A virtual HUD image seen in both eyes is preferable. It is of concern that gross outside world changes are missed when drivers attend to the virtual HUD image and the outside world.

自動車用ヘッドアップデイスプレイによる前景情報と表示情報の認識についてIII

自動車用ヘッドアップデイスプレイによる前景情報と表示情報の認識についてIII

自動車用ヘッドアップデイスプレイによる前景情報と表示情報の認識についてIV

自動車用ヘッドアップデイスプレイによる前景情報と表示情報の認識についてIV

Driver’s Ability to Recognize the Forward View and Head-Up Display Images in Automobiles

Although originally developed for aircraft use, head-up displays (HUDs) have found application in automobiles recently. A system for evaluating automotive HUDs has been developed which is based on the double task method and takes into account driving conditions. Seven kinds of correct response rates (CRRs) are defined as the evaluation standards. Using various kinds of HUDs, evaluations are made focussing on the relationship between the fineness of the image and the distance from the driver’s eyes to the image. It can be concluded that the task of reading the HUD image has little effect on recognition of the forward view and is closely related to peripheral vision. This paper also discusses a new merit index derived from the CRRs, which can be used as a measure of HUD performance. The results suggest there is an optimal image fineness and distance to the HUD image.