Head-up Display Research Articles

Evaluating the Effectiveness of Countermeasures in Improving the Safety of Highway Merging Zones

The merging process from an acceleration lane to the through lane of a motorway is an important aspect at interchanges, both in terms of safety and operating conditions. Several studies have demonstrated that the merging zones of interchanges are the main sources of interference between through and entering vehicles. In this context, incorrectly assessed gaps and headways can lead to severe crashes and traffic delays at interchanges. This study investigates the effectiveness of different countermeasures to improve the safety of merging zones and the ability of through drivers to adopt safe headways to vehicles entering from the motorway on-ramp. Four different countermeasures were tested in a driving simulator study: i) a gap metering signalization, consisting of a Variable Message Sign (VMS) and new pavement markings; ii) a static symbol projected on the vehicle's windshield by means of a Head-Up Display (HUD) that informed the driver to maintain safe headways to the entering vehicle; iii) a dynamic symbol based on Augmented Reality (AR) technology, built into connected vehicle technology, that provided additional visual information to the driver about his current headway to the entering vehicle; iv) the AR system was also tested with an additional audio warning. The driving behavior of forty-four drivers under the four different configurations based on the tested countermeasures was then compared to the driving behavior recorded under a configuration without any countermeasures (baseline condition). The results revealed significant positive effects of all the countermeasures, especially of the AR systems that helped through drivers to adjust their headway to the entering vehicle and thus the most effective solution for improving the safety of the merging zone. This study confirmed the great potential of AR and connected vehicle technologies to improve general safety conditions on the road network, especially in risky situations and difficult maneuvers.

Status and Prospect of Augmented Reality Head-Up Display

Status and Prospect of Augmented Reality Head-Up Display

A Visual Reasoning-based AR-HUD Service Design Approach for Better Driving Experience

Smart traffic or transportation, as a critical part of realizing the smart city, utilizes various smart product-service systems to support it. Among them, the augmented reality head-up display (AR-HUD) system is a typical smart driving solution, which projects vital information about driving situations with pre-defined AR graphics onto the windshield of vehicles. The AR-HUD service design attracts increasing concerns owing to its enhancement of drivers’ situational awareness without glancing down at the instrument cluster. Nevertheless, current in-car AR-HUDs only stack some simple driving information from sensors, such as the driving speed. It's difficult to meet the demand of drivers since they ignore a higher level of drivers’ situation awareness, i.e., driving situation prediction and recommendation of driving advice. To overcome the challenge, this paper proposes a visual reasoning-based service design approach for advancing AR-HUDs’ digital servitization and better user experience. Additionally, drivers are involved in the service design development in a value co-creation manner through human-computer interaction design. With the combination of experiential driving knowledge and cognitive intelligence computing on the driving scene, the proposed novel service design approach enables AR-HUD to percept driving scenarios and infer good strategies accordingly. Finally, an illustrative example is carried out to validate the feasibility of the proposed AR-HUD service design approach.

Physiological Signals as Predictors of Cognitive Load Induced by the Type of Automotive Head-Up Display

Physiological Signals as Predictors of Cognitive Load Induced by the Type of Automotive Head-Up Display

Design and Optimization of Dual-focal Vehicle Head-up Display Optical System Based on Single-optical Machine

Design and Optimization of Dual-focal Vehicle Head-up Display Optical System Based on Single-optical Machine

Vehicle Head-Up Display based on Holographic Combiner and Projection Lens Design

Vehicle Head-Up Display based on Holographic Combiner and Projection Lens Design

Factors and Countermeasures for 3D Image Sickness in 3D Head-up Display

Factors and Countermeasures for 3D Image Sickness in 3D Head-up Display

CALIBRATED HEADS-UP DISPLAY

This article is about heads up displays (HUDs). A heads-up display (HUD) is a technology used in a vehicle’s infotainment display system that allows the driver to view an image on the vehicle’s windshield or a panel just below the driver’s line of sight, or, to put it another way, a display that is wirelessly connected to the internet and shows real-time traffic and navigation information on a car's windshield while driving. The head-up display (HUD), which enables users to simultaneously view a real image and information projected on it without making substantial head movements or eye scans, introduces a new way of displaying information. The head-up display (HUD) invented a brand-new method of information delivery. HUDs have been used for a variety of tasks, including aircraft manipulation, Automobile driving and machinery maintaining to help users better understand their surroundings. The head-up display is far more energy-efficient than the postage-stamp LCD screens used frequently in today's head-up displays for mobile devices. Head-up display screens produce a clean, clear image independent of the lighting conditions outside since they project images directly into the retina. The study above describes how heads- up displays function in real-world situations and specifies three categories of HUD techniques such as head-mounted or ground-referenced, optical see through or video see-through, and single-sided or two- sided types. KEYWORDS: HUD (Heads-Up Display), Vehicle's Infotainment Display, Vehicle's Windshield, Real Image, information delivery, windshield.

Drivers’ gaze patterns when resuming control with a head-up-display: Effects of automation level and time budget

The removal of drivers’ active engagement in driving tasks can lead to erratic gaze patterns in SAE Level 2 (L2) and Level 3 (L3) automation, which has been linked to their subsequential degraded take-over performance. To further address how changes in gaze patterns evolve during the take-over phase, and whether they are influenced by the take-over urgency and the location of the human-machine interface, this driving simulator study used a head-up display (HUD) to relay information about the automation status and conducted take-over driving experiments where the ego car was about to exit the highway with variations in the automation level (L2, L3) and time budget (2 s, 6 s). In L2 automation, drivers were required to monitor the environment, while in L3, they were engaged with a visual non-driving related task. Manual driving was also embodied in the experiments as the baseline. Results showed that, compared to manual driving, drivers in L2 automation focused more on the HUD and Far Road (roadway beyond 2 s time headway ahead), and less on the Near Road (roadway within 2 s time headway ahead); while in L3, drivers’ attention was predominantly allocated on the non-driving related task. After receiving take-over requests (TORs), there was a gradual diversion of attention from the Far Road to the Near Road in L2 take-overs. This trend changed nearly in proportion to the time within the time budget and it exaggerated given a shorter time budget of 2 s. While in L3, drivers’ gaze distribution was similar in the early stage of take-overs for both time budget conditions (2 s vs. 6 s), where they prioritized their early glances to Near Road with a gradual increase in attention towards Far Road. The HUD used in the present study showed the potential to maintain drivers’ attention around the road center during automation and to encourage drivers to glance the road earlier after TORs by reducing glances to the instrument cluster, which might be of significance to take-over safety. These findings were discussed based on an extended conceptual gaze control model, which advances our understanding of gaze patterns around control transitions and their underlying gaze control causations. Implications can be contributed to the design of autonomous vehicles to facilitate the transition of control by guiding drivers’ attention appropriately according to drivers’ attentional state and the take-over urgency.

Measuring workload effects of augmented reality head-up displays using detection response task

Augmented reality (AR) technology could establish direct relationships between displayed information and objects in the real driving environment, e.g. by highlighting relevant objects in the traffic environment. However, it is unclear how these potential benefits of augmentation affect drivers’ distraction from the driving task and their level of workload. Since gaze detection cannot separate glances at AR-elements from glances at the driving scene, new evaluation criteria are needed for measuring the distraction potential of AR-elements. The paper evaluates a methodology based on the Detection Response Task (DRT) for measuring the distraction and workload effects of three different display technologies: Head-Down Display (HDD), Head-Up Display (HUD), and Augmented-Reality HUD (AR-HUD). The DRT measures cognitive and visual workload based on a recognition-response task using the two parameters, reaction time and misses. Two experimental studies using driving simulation (N = 24) and production vehicles in real traffic (N = 24) are reported. As use cases, the subjects had to navigate through an urban traffic environment and pass different intersections. During that they were assisted by navigation systems based on the above mentioned display technologies. The resulting workload is compared to a baseline drive without the need to navigate and a reference drive in which the subjects had to work on a secondary task while driving. The results of the DRT are compared with subjective measures, gaze behaviour, and driving performance. In addition, drivers’ preference and user experience regarding the three display technologies and their feedback concerning the DRT method were assessed. The results of both experimental studies show that drivers prefer the AR-HUD display technology. Reasons are mainly the hedonic aspects of the AR-HUD such as the novelty of the technology. Nevertheless, drivers report that AR-elements could obstruct important areas of the road and distract more from the driving task than HUDs. The subjective and visual-cognitive workload measured by the DRT do not differ between the HMI conditions. An analysis for each type of intersection yields most misses for the AR-HUD condition and fewest misses in the HDD condition. However, visual workload (i.e. eyes-off-road time) is higher for HDD compared to (AR-)HUD. Feedback by participants reveals that the DRT as a method for workload assessment is well accepted. All in all, the two DRT measures, misses and reaction time, allow a valid and objective workload measurement and could serve as a supplement or substitute for gaze analysis. The discussion gives indications for the measurement of workload effects caused by AR-HUD using DRT.

During the COVID-19 Pandemic, Everyone Had a Head-up Display

A head-up display has been part of my daily life for more than two decades, and people often ask why I wear one. This article makes explicit some of the benefits of video conferencing and shows how a head-worn display provides similar benefits when physically co-present in a conversation.

Toward Human-Centered Automated Driving: A Novel Spatiotemporal Vision Transformer-Enabled Head Tracker

Accurate dynamic driver head pose tracking is of great importance for driver–automotive collaboration, intelligent copilot, head-up display (HUD), and other human-centered automated driving applications. To further advance this technology, this article proposes a low-cost and markerless head-tracking system using a deep learning-based dynamic head pose estimation model. The proposed system requires only a red, green, blue (RGB) camera without other hardware or markers. To enhance the accuracy of the driver’s head pose estimation, a spatiotemporal vision transformer (ST-ViT) model, which takes an image pair as the input instead of a single frame, is proposed. Compared to a standard transformer, the ST-ViT contains a spatial–convolutional vision transformer and a temporal transformer, which can improve the model performance. To handle the error fluctuation of the head pose estimation model, this article proposes an adaptive Kalman filter (AKF). By analyzing the error distribution of the estimation model and the user experience of the head tracker, the proposed AKF includes an adaptive observation noise coefficient; this can adaptively moderate the smoothness of the curve. Comprehensive experiments show that the proposed system is feasible and effective, and it achieves a state-of-the-art performance.

Comments on “Scleral Transillumination With Digital Heads-Up Display: A Novel Technique for Visualization During Vitrectomy Surgery”

Comments on “Scleral Transillumination With Digital Heads-Up Display: A Novel Technique for Visualization During Vitrectomy Surgery”

Head-Up Display Graphic Warning to Support Collision Avoidance: Effect of Graphic Animation and Border on Driving Behavior and Eye Movement Pattern

Several studies have determined the positive effect of head-up display (HUD) graphic warning to support collision avoidance. However, the visual complexity of the graphic warning may overburden the driver. Design principles and empirical evidence suggest that animation and border designs may increase visual saliency and reduce the visual complexity of graphic warnings. This study aimed to examine the effects of animation (within-subject factors: animated/non-animated) and border (within-subject factors: with/without border) on drivers’ attention and driving performance. Thirty drivers (19 females) experienced five warning conditions (the no-warning condition was the baseline condition) in simulated driving conditions. The results showed the positive effects of animation and border designs on the visual detection time (animation: 25.64% reduction; border: 6.73% reduction) and attention allocation strategies (animation: 9.53% less dwell time on the warning; border: 6.62% less dwell time on the warning) when compared with the original HUD warning. We only observed a positive effect of borders on brake reaction time (17.14% reduction). However, the positive effect of the border design on brake reaction time was only observed in the non-animated warning condition, and not in the animated warning condition. A limitation of the study is that the results were generated from young and inexperienced drivers, and they should be generalized to other driver groups only with caution. Overall, we recommend using non-animated warnings with borders in conditions that require fast reactions, and animated warnings with borders in conditions that require a superior attention strategy.

Multiplane holographic augmented reality head-up display with a real-virtual dual mode and large eyebox.

We propose a multiplane augmented reality (AR) head-up display (HUD) with a real-virtual dual mode based on holographic optical elements (HOEs). The picture generation unit (PGU) is only a single free-focus projector, and the optical combiner includes a HOE lens (HOEL) for long-distance virtual image display and a HOE diffuser (HOED) for in-plane real image display. A HOED with directional scattering characteristics in the real image mode can significantly increase the size of the eyebox (EB) without increasing the size of the HOE, and a HOEL with a flexible design for the optical focal length in the virtual image mode can be used to achieve a different depth of the AR display. The proposed AR HUD system, which has a compact structure and offers high light transmittance, high energy usage, a multiplane display, and a large EB, is expected to be widely used in the future.

Effects of Volumetric Augmented Reality Displays on Human Depth Judgments

Many driving scenarios involve correctly perceiving road elements in depth and manually responding as appropriate. Of late, augmented reality (AR) head-up displays (HUDs) have been explored to assist drivers in identifying road elements, by using a myriad of AR interface designs that include world-fixed graphics perceptually placed in the forward driving scene. Volumetric AR HUDs purportedly offer increased accuracy of distance perception through natural presentation of oculomotor cues as compared to traditional HUDs. In this article, the authors quantify participant performance matching virtual objects to real-world counterparts at egocentric distances of 7-12 meters while using both volumetric and fixed-focal plane AR HUDs. The authors found the volumetric HUD to be associated with faster and more accurate depth judgements at far distance, and that participants performed depth judgements more quickly as the experiment progressed. The authors observed no differences between the two displays in terms of reported simulator sickness or eye strain.

Examining the Effects of Visibility and Time Headway on the Takeover Risk during Conditionally Automated Driving.

The objective of this study is to examine the effects of visibility and time headway on the takeover performance in L3 automated driving. Both non-critical and critical driving scenarios were considered by changing the acceleration value of the leading vehicle. A driving simulator experiment with 18 driving scenarios was conducted and 30 participants complete the experiment. Based on the data obtained from the experiment, the takeover reaction time, takeover control time, and takeover responses were analyzed. The minimum Time-To-Collision (Min TTC) was used to measure the takeover risk level and a binary logit model for takeover risk levels was estimated. The results indicate that the visibility distance (VD) has no significant effects on the takeover control time, while the time headway (THW) and the acceleration of the leading vehicle (ALV) could affect the takeover control time significantly; most of the participants would push the gas pedal to accelerate the ego vehicle as the takeover response under non-critical scenarios, while braking was the dominant takeover response for participants in critical driving scenarios; decreasing the TCT and taking the appropriate takeover response would reduce the takeover risk significantly, so it is suggested that the automation system should provide the driver with the urgency of the situation ahead and the tips for takeover responses by audio prompts or the head-up display. This study is expected to facilitate the overall understanding of the effects of visibility and time headway on the takeover performance in conditionally automated driving.

Impacts of the Cedar Avenue Driver Assist System on Bus Shoulder Operations

This paper summarizes the first comprehensive evaluation of vehicle assist and automation (VAA) technology in bus revenue service by a U.S. transit agency. The technology in question is a GPS-based technology suite used by the Minnesota Valley Transit Authority for vehicle guidance in the shoulder. Called the Driver Assist System, or DAS, it provides accurate lane position feedback to the driver via a head-up display, virtual mirror, vibrating seat, and actuated steering. The evaluation confirmed that the DAS improved bus operations and reduced driver stress. Drivers stayed in the shoulders 4.3 percent longer, drove 3.5 miles per hour faster, and reduced their side-to-side movement by 4.7 inches when the DAS was activated. The changes in speed and side-to-side movement were statistically significant at the 95% confidence level. In surveys, a majority of the drivers believed the DAS made driving in the shoulder safer and less stressful.

Modeling drivers’ acceptance of augmented reality head-up display in connected environment

Investigating the acceptance of augmented reality head-up displays (AR-HUD) in connected environments is an important research issue as there is great potential to enhance driving safety for both manual and autonomous driving. This study investigated drivers’ acceptance of AR-HUD with the technology acceptance model (TAM) with extended variables, including trust, information service quality, display service quality, perceived distraction, and driving skills. The answers to a survey completed by 388 drivers were analyzed with structural equation modeling (SEM) to verify the reliability and validity of the measurement model and explore the relationships between the observed variables. The results show that all the hypotheses of foundational TAM are supported. Further analysis has indicated that: (1) drivers’ trust has a positive effect on perceived usefulness, attitude, and intention to use; (2) display service quality affects perceived ease of use positively, while information service quality has a positive influence on trust and intention to use; (3) perceived distraction has a significant negative impact on perceived ease of use and trust, while the negative effect on perceived usefulness is not significant; and (4) drivers’ subjective driving skills have a positive influence on perceived ease of use and a negative impact on perceived usefulness. Further analysis attempts to explain the causes of the influence relationship between variables, and suggestions are proposed to improve the drivers’ perceived usefulness and perceived ease of use of AR-HUD. The study provides valuable insights into the factors that significantly affect drivers’ intention to use AR-HUD systems in theory and provides suggestions to AR-HUD designers on system design.

이상 탐지 기반 퍼스널 모빌리티 사용자 인식 및 운전보조 응용

As the number of personal mobility users increases, it is essential to protect both personal mobility users and car drivers. Although existing supervised learning-based object detection methods can recognize personal mobilities, processing for data collection, labeling, and training is time-consuming and expensive whenever new forms of personal mobilities come to the market. Anomaly detection-based methods are proposed to learn normal patterns and detect abnormal patterns without prior training. This study proposes a new approach to personal mobility user recognition using deep learning-based anomaly detection in dynamically changing driving environments. The proposed approach consists of Human detection, Cropping and Anomaly detection modules. The Human detection module detects human regions. The Cropping module removes unnecessary areas and performs a preprocessing. The memory-based Anomaly detection module distinguishes between pedestrians and personal mobility users. Based on the proposed anomaly detection method, augmented reality (AR) visualization in the head-up display (HUD) is proposed for effective driving assistance. Since it is possible to distinguish pedestrians and mobility users effectively, the AR HUD-based visualization can assist the driver to pay more attention to the mobility user.