Does Augmented-Reality Head-Up Display Help? A Preliminary Study on Driving Performance Through a VR-Simulated Eye Movement Analysis

Abstract

Augmented reality heads-up display (AR-HUD) is becoming increasingly popular as a way to keep drivers focusing on roads. By overlaying visuals on the windshield, AR-HUDs improve the drivers' view of the environment outside the car, creating a stronger sense of awareness of the surroundings. However, whether AR-HUD and to what extent different AR-HUD layouts could improve drivers' driving performance are still questionable. Unfortunately, AR-HUD is still at a research stage, not yet fully commercialized. Hence, there are few actual products in the market available for testing. For this reason, this study developed a virtual reality driving simulator to tested drivers' driving performance environment under three scenarios: without AR-HUD, dispersed layout (AR-HUD1), and dense layout (AR-HUD2). Twelve subjects were invited to join the experiment. Their driving performance was measured in various aspects. This study showed that AR-HUD with interfaces that conform to human-computer interaction principles and visual design rules could improve cognitive resource allocation and promote driving safety. Conversely, a poor designed AR-HUD could negatively impact driving safety.

Designing for Human Operations on the Moon: Challenges and Opportunities of Navigational HUD Interfaces

“…For example, Bark et al [11] demonstrated that car-based HUD systems featuring navigation arrows projected on a see-through display in front of the driver help maintain the driver's focus on the road, resulting in improved performance and safety [118]. Similar findings have been reproduced with a 2D minimap projected into the corner of the driver's view [83].…”

Machine learning-based cognitive load prediction model for AR-HUD to improve OSH of professional drivers

“…Lastly, based on the BP neural network between the visual arrangement coding of the head-up display and NASA-TLX, its topological structure is determined, as shown in Figure 6. The meaning of a [1] (x) represents the activation function of the hidden layer in the backpropagation (BP) neural network. A [0] represents the input layer, and A [1] -A [3] represent the hidden layers.…”

“…The meaning of a [1] (x) represents the activation function of the hidden layer in the backpropagation (BP) neural network. A [0] represents the input layer, and A [1] -A [3] represent the hidden layers. A [4] represents the output layer.…”

26‐3: Assessing Vehicle Driver Performance in Dual‐Depth Head‐Up Displays

“…AR HUDs can provide a more intuitive and immersive interface [10], and compared to traditional HUDs, AR HUDs can recognize turn positions earlier [10]. In order to avoid distractions caused by information, it depends on the perceptual form of graphic elements and interfaces on the display [2], [12]. Since vision is a highly complex task, designing information for HUDs must take into account many factors such as color, position, size, light source, and background complexity [9].…”