A Driving Simulation to Analysis and Quantitative Comparison of Driving Behavior of Guide Signs at Intersections

Guide signs are an important source for drivers to obtain road information. However, the evaluation methods for the effectiveness of guide signs are not unified. The quantitative model for evaluating guide signs needs to be constructed to unify the current system of guide signs. This study aims to take the commonly used guide signs in China as the research object to explore the evaluation method of guide signs at intersections. Eight kinds of guide signs were designed and made based on the common layout (layout 1 and layout 2) and the amount of information on signs (3–6). Thirty-four drivers were recruited to organize a driving simulation based on the visual cognitive tasks. Drivers’ legibility time and driver behavior were obtained by using the driving simulator and E-Prime program. A comprehensive quantitative evaluation model of guide signs was established based on the factor analysis method and grey correlation analysis method from the perspective of safe driving. The results show that there is no significant difference in the SD of speed and the SD of acceleration under the influence of various guide signs. The average vehicle speed and acceleration decrease, and the lateral offset distance of the vehicle increases with the amount of information on guide signs increasing. The quantitative evaluation results of guide signs show that the visual security decreases with the increase of the amount of information on guide signs. And layout 2 has better performance than layout 1 when the amount of information on guide signs is the same. This study not only explores the change rule of driving behavior under the influence of guide signs, but also provides a reference for the selection of guide signs.

Get Ready for Take-Overs: Using Head-Up Display for Drivers to Engage in Non–Driving-Related Tasks in Automated Vehicles

Objective The study aims to investigate the potential of using HUD (head-up display) as an approach for drivers to engage in non–driving-related tasks (NDRTs) during automated driving, and examine the impacts on driver state and take-over performance in comparison to the traditional mobile phone. Background Advances in automated vehicle technology have the potential to relieve drivers from driving tasks so that they can engage in NDRTs freely. However, drivers will still need to take-over control under certain circumstances. Method A driving simulation experiment was conducted using an Advanced Driving Simulator and real-world driving videos. Forty-six participants completed three drives in three display conditions, respectively (HUD, mobile phone and baseline without NDRT). The HUD was integrated with the vehicle in displaying NDRTs while the mobile phone was not. Drivers’ visual (e.g. gaze, blink) and physiological (e.g. ECG, EDA) data were collected to measure driver state. Two take-over reaction times (hand and foot) were used to measure take-over performance. Results The HUD significantly shortened the take-over reaction times compared to the mobile phone condition. Compared to the baseline condition, drivers in the HUD condition also experienced lower cognitive workload and physiological arousal. Drivers’ take-over reaction times were significantly correlated with their visual and electrodermal activities during automated driving prior to the take-over request. Conclusion HUDs can improve driver performance and lower workload when used as an NDRT interface. Application The study sheds light on a promising approach for drivers to engage in NDRTs in future AVs.

Comparing Young and Old Adults’ Night Hazard Detection With Driving Simulation and on Road

While there is validity of using driving simulation as a proxy for on-road performance, few studies have examined hazard detection at night. Night driving is a self-restricting practice with little evidence demonstrating the need with healthy older adults. This study’s objective was to analyze night driving using eye-tracking technology examining differences between on-road/simulated drives and older/younger adults. A 2 (old, young) x 2 (simulator, on-road) repeated-measures design measured three roadway “hazards” of pedestrains looking at their cell phone while posed to cross the roadway. Pupil glances were recorded using outcome measures of total fixation duration, number of fixations, and time-to-first fixation for the pedestrains on-road and on a specifically designed scenario matching the on-road route. Thirty-three healthy, community-living drivers age 65+ years (N=16) and drivers age 20-40 years (N=17) completed both drives. Using non-parametric statistics, results demonstrated that night hazard detection was similar across driving conditions except for time-to-first fixation, which was faster on-road for both age groups (p<.001). At some hazard locations, there were significant differences between the two age groups, with older adults taking longer to initially see hazards. Results suggest, older adults detected hazards similarly to younger adults, especially during on-road performance, suggesting avoidance of night driving may not be necessary. Results also support using driving simulation as a proxy for on-road with night driving needing to be incorporated. Additionally, eye-tracking has the potential for research in hazard detection with emphasis on the time-to-first fixation outcomes when considering driving analysis.

Overview of Intelligent Vehicle Infrastructure Cooperative Simulation Technology for IoV and Automatic Driving

This paper reviews the development of vehicle road collaborative simulation in the new era, and summarizes the simulation characteristics of two core technologies in the field of transportation after entering the era of Intelligent Networking: Internet of Vehicles technology and automatic driving technology. This paper analyzes and compares the mainstream Internet of Vehicles (IoV) simulation and automatic driving simulation platforms on the market, deeply analyzes the model-based IoV simulation, and explores a new mode of IoV simulation in the era of big data. According to the latest classification standard of automatic driving in 2020, we summarize the simulation process of automatic driving. Finally, we offer suggestions on the development directions of intelligent network-connected vehicle simulation.

Driving and Flying Simulators: A Review on Relevant Considerations and Trends

Flying and driving simulation has encouraged an enormous and growing community in a wide variety of areas such as research centers, driver or pilot training academies, vehicle-testing facilities, amusement parks, and even at home by household enthusiasts, providing carefully integrated visual and perceptual illusions of driving or flying real vehicles. The global research on this subject is explored during the period 2000 to 2019 from an interdisciplinary perspective based on a systematic methodology, providing both new and experienced researchers with broad guidance toward key aspects for further investigations and developments. Emphasis is given to the analysis of the findings and in particular to their applicability, to an extent not attempted earlier, by considering both human and machine aspects.

Driving Safety Assurance Method in Work Zone Crossovers of Highway Reconstruction and Expansion Project

Work zone crossover is an important area in highway reconstruction and expansion projects because it profoundly impacts the traffic safety and efficiency of the construction sites. This research sets the different median opening widths in the driving simulation experiment, collects the vehicle control signal parameters during entrance by-pass and exit by-pass, and analyzes the driving characteristics in these sections. Comparison of the driving features between the simulation experiment and the actual driving under the same median width has been also made. We should set the median width separately because the results show that driving behaviors significantly differ between entrance by-pass and exit by-pass. When the median opening width is 70 m, the driving simulation experiment and actual driving characteristics are quite different. However, both show that driving factors of the entrance and exit by-pass are not the same. When there are two lanes in the traffic control zone and the speed limit is 60 km/h, we should set the median width at 90 m to ensure transportation safety.

FencingEstimate: A 3D Game-Based Interactive Driving Simulation Training System using Artificial Intelligence and Computer Vision

Training for fencing during the pandemic has changed from what it was beforehand. Students have been taking lessons online, and instead of fencing with peers, students now train by watching and analyzing fencing videos. Learning fencing from watching videos of world class fencers is an effective way of learning. However, sabre fencing is so fast that many inexperienced fencers are unable to capture the important information by watching short clips. Therefore, they are unable to learn techniques such as sabre fencing just from watching videos. This paper traces the development of an application that can utilize computer vision and pose estimation to analyze fencing video clips and output accurate scored points as well as the techniques used within the given clips. We applied our application to help less experienced fencers improve their ability to recognize points and conduct qualitative evaluations of different fencing techniques.

Evaluation of Mental Load of Drivers in Long Highway Tunnel Based on Electroencephalograph

In recent years, the mileage of the tunnels has substantially increased with the rapid highway construction that led to increasing highway tunnels. Most studies on tunnel accidents have mainly focused on the external environments, such as tunnel structure, traffic volume, and lighting. In addition, although many studies on mental load of drivers have been conducted for public roads, such studies for highway tunnels have been limited. In this study, three scenarios with different front vehicle speeds (60, 45, and 30 km/h) in a two-lane long tunnel (one lane in each travel direction) were evaluated using a driving simulator. The experiment involved 24 participants (14 men and 10 women) with an average age of 25.8 years and an average experience of 3.2 years. The electroencephalogram (EEG) technology was used to collect the leading EEG indicators during the driving simulation of the scenarios: α, β, and θ waves and the wave ratio, (α + θ)/β. According to the β-wave energy measurements, the alertness of drivers was the lowest at 45 km/h after adapting to the tunnel environment, indicating that the drivers were more comfortable at this speed. This preliminary finding should help in determining the speed limit in this type of tunnel.