The Effects on Driving Behavior When Using a Head-mounted Display in a Dynamic Driving Simulator

Driving simulators are established tools used during automotive development and research. Most simulators use either monitors or projectors as their primary display system. However, the emergence of a new generation of head-mounted displays has triggered interest in using these as the primary display type. The general benefits and drawbacks of head-mounted displays are well researched, but their effect on driving behavior in a simulator has not been sufficiently quantified. This article presents a study of driving behavior differences between projector-based graphics and head-mounted display in a large dynamic driving simulator. This study has selected five specific driving maneuvers suspected of affecting driving behavior differently depending on the choice of display technology. Some of these maneuvers were chosen to reveal changes in lateral and longitudinal driving behavior. Others were picked for their ability to highlight the benefits and drawbacks of head-mounted displays in a driving context. The results show minor changes in lateral and longitudinal driver behavior changes when comparing projectors and a head-mounted display. The most noticeable difference in favor of projectors was seen when the display resolution is critical to the driving task. The choice of display type did not affect simulator sickness nor the realism rated by the subjects.

Influence of Landscape Design on Driving Behavior Based on Slope Illusion

In a continuous downhill section of a mountain highway, factors such as road alignment, roadside environment, and other visual characteristics will impact the slope illusion drivers experience and engage in unsafe driving behaviors. To improve the negative consequences of slope illusion and driving safety in continuous downhill sections, the effects of plant spacing, height, roadside distance, and color on driving behavior were all studied by simulating the plant landscape in a virtual environment. A driving simulator and UC-win/road software were used to conduct an indoor driving simulation experiment, and parameters such as speed and lateral position offset were used as the evaluation indices of driving stability to reflect the driver’s speed perception ability with subjective equivalent speeds. The results show that a plant landscape with appropriate plant spacing, height, roadside separation, and color is conducive to improving driving stability. Furthermore, a landscape with a height of 3 m, spacing of 10 m, roadside spacing of 0.75 m, and appropriate color matching can enhance the slope perception ability and speed perception ability of drivers, which is conducive to improving the driving safety of continuous downhill sections.

Mitigating Motion Sickness in Automated Vehicles with Vibration Cue System

<p>Motion sickness is very common in road transport. To guarantee ride comfort and user experience, there is an urgent need for effective solutions to motion sickness mitigation in semi- and fully-automated vehicles. Considering both effectiveness and user-friendliness, a vibration cue system is proposed to inform passengers of the upcoming vehicle movement through tactile stimulation. By integrating the motion planning results from automated driving algorithms, the vibration cueing timing and patterns are optimized with the theory of motion anticipation. Using a cushion-based prototype of vibration cue system, 20 participants were invited to evaluate this solution in two conditions of driving simulator experiments. Results show that with the proposed vibration cue system, it could also help participants to comprehend the cues and to generate motion anticipation. The participants’ motion sickness degrees were significantly lowered. This research may serve as one foundation for the detailed system development in practical applications.</p><p>(This article has been accepted for publication in <i>Ergonomics</i>, published by Taylor & Francis.)</p><br>

Effectiveness Analysis on Human-Machine Information Interaction of Intelligent Highway

Different human-machine collaboration modes and driving simulation tests with the orthogonal method considered are designed for a series of typical intelligent highway landscapes. The feedback of drivers under different interaction modes is evaluated through NASA-LTX questionnaire, driving simulator, eye tracker, and electroencephalograph (EEG). This optimal interaction mode (including voice form, broadcasting timing, and frequency) of each driving assistance scene in CVI (Cooperative Vehicle Infrastructure) environment under the conditions of high and low traffic is determined from subjective and objective perspectives. In accordance with feedback of these subjects on each set scene, the voice information structure of each assistance mode plays the most important role on drivers followed by the broadcasting timing and frequency. These broadcasts which provide good effects include scenarios such as various assistance scenes at curves and an early warning timing at a long-distance trip as well as a high early warning frequency; in addition, as for an exit-tip assistance scenario, a voice mode assistance is preferred; and for various speed assistance scenes, the beep mode is better. Furthermore, it is found that, at a higher traffic level but a short-distance trip, an early warning timing is favored generally for various scenes while under a low traffic level, a long-distance early warning timing is better.

Algebraic Driver Steering Model Parameter Identification

Modeling driver steering behavior plays an ever-important role in nowadays automotive dynamics and control applications. Especially, understanding individuals' steering characteristics enables the advanced driver assistance systems (ADAS) to adapt to particular drivers, which provides enhanced protection while mitigating human-machine conflict. Driver-adaptive ADAS requires identifying the parameters inside a driver steering model in real-time to account for driving characteristics variations caused by weather, lighting, road, or driver physiological conditions. Usually, Recursive Least Squares (RLS) and Kalman Filter (KF) are employed to update the driver steering model parameters online. However, because of their asymptotical nature, the convergence speed of the identified parameters could be slow. In contrast, this paper adopts a purely algebraic perspective to identify parameters of a driver steering model, which can achieve parameter identification within a short period. To demonstrate the effectiveness of the proposed method, we first apply synthetic driver steering data from simulation to show its superior performance over an RLS identifier in identifying constant model parameters, including feedback steering gain, feedforward steering gain, preview time, and first-order neuromuscular lag. Then, we utilize real measurement data from human subject driving simulator experiments to illustrate how the time-varying feedback and feedforward steering gains can be updated online via the algebraic method.