scholarly journals Driving Simulator Validity of Driving Behavior in Work Zones

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yanning Zhang ◽  
Zhongyin Guo ◽  
Zhi Sun
Keyword(s):  
Delay Time ◽  
Driving Simulator ◽  
Driving Behavior ◽  
Controlled Environment ◽  
Analysis Method ◽  
Work Zones ◽  
Relative Validity ◽  
Car Following ◽  
Driving Behaviors ◽  
Driving Scenario

Driving simulation is an efficient, safe, and data-collection-friendly method to examine driving behavior in a controlled environment. However, the validity of a driving simulator is inconsistent when the type of the driving simulator or the driving scenario is different. The purpose of this research is to verify driving simulator validity in driving behavior research in work zones. A field experiment and a corresponding simulation experiment were conducted to collect behavioral data. Indicators such as speed, car-following distance, and reaction delay time were chosen to examine the absolute and relative validity of the driving simulator. In particular, a survival analysis method was proposed in this research to examine the validity of reaction delay time. The result indicates the following: (1) most indicators are valid in driving behavior research in the work zone. For example, spot speed, car-following distance, headway, and reaction delay time show absolute validity. (2) Standard deviation of the car-following distance shows relative validity. Consistent with previous researches, some driving behaviors appear to be more aggressive in the simulation environment.

Autonomous Vehicles’ Car-Following Drivability Evaluation Based on Driving Behavior Spectrum Reference Model

2021 ◽  
pp. 036119812199485
Author(s):  
Xiao Qi ◽  
Ying Ni ◽  
Yiming Xu ◽  
Ye Tian ◽  
Junhua Wang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Reference Model ◽  
Numerical Test ◽  
Safety Evaluation ◽  
Driving Behavior ◽  
Car Following ◽  
Driving Behaviors ◽  
Human Driver ◽  
Normal Human ◽  
Naturalistic Driving

A large portion of the accidents involving autonomous vehicles (AVs) are not caused by the functionality of AV, but rather because of human intervention, since AVs’ driving behavior was not properly understood by human drivers. Such misunderstanding leads to dangerous situations during interaction between AV and human-driven vehicle (HV). However, few researches considered HV-AV interaction safety in AV safety evaluation processes. One of the solutions is to let AV mimic a normal HV’s driving behavior so as to avoid misunderstanding to the most extent. Therefore, to evaluate the differences of driving behaviors between existing AV and HV is necessary. DRIVABILITY is defined in this study to characterize the similarity between AV’s driving behaviors and expected behaviors by human drivers. A driving behavior spectrum reference model built based on human drivers’ behaviors is proposed to evaluate AVs’ car-following drivability. The indicator of the desired reaction time (DRT) is proposed to characterize the car-following drivability. Relative entropy between the DRT distribution of AV and that of the entire human driver population are used to quantify the differences between driving behaviors. A human driver behavior spectrum was configured based on naturalistic driving data by human drivers collected in Shanghai, China. It is observed in the numerical test that amongst all three types of preset AVs in the well-received simulation package VTD, the brisk AV emulates a normal human driver to the most extent (ranking at 55th percentile), while the default AV and the comfortable AV rank at 35th and 8th percentile, respectively.

scholarly journals Driving Performance Evaluation Correlated to Age and Visual Acuities Based on VR Technologies

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Sooncheon Hwang ◽  
Sunhoon Kim ◽  
Dongmin Lee
Keyword(s):  
Visual Acuity ◽  
Driving Simulator ◽  
Driving Behavior ◽  
Driving Performance ◽  
Present System ◽  
The South ◽  
Dynamic Visual Acuity ◽  
South Korean ◽  
Driving Behaviors ◽  
License System

There is currently much debate regarding the effectiveness of the driver license system in South Korea, due to the numerous traffic crashes caused by drivers who are suspected of having insufficient physical and mental abilities. Through the present system, it is quite difficult to identify such drivers indirectly through physical tests, such as visual acuity tests, since the correlation of such results with driving performance remains unclear. The objective of this study was to investigate the relationship between driving performance and visual acuities for improving the South Korean driver license system. In this study, two investigations were conducted: static and dynamic visual acuity examinations and driving performance tests based on a virtual reality (VR) system. The driving performance was evaluated with a driving simulator, based on driving behaviors in different experimental scenarios, including daytime and nighttime driving on a rural highway, and unexpected incident situations. Here, we produce statistically significant evidence that reduced visual acuity impairs driving performance, and driving behaviors differ significantly among groups with different vision capabilities, especially dynamic vision. Visual acuities, typically dynamic visual acuity, greatly influenced driving behavior, as measured by the standard deviation of speeds and vehicle LPs, and this was especially notable in curved road segments in daytime experiment. These experimental results revealed that the driving performance of participants with impaired dynamic visual acuity was deficient and unsafe. This confirmed that dynamic visual acuity levels are significant determinants of driving behavior, and they well explain driver performance levels. These findings suggest that the South Korean driver license system should include a test of dynamic visual acuity to create better and safer driving.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tianzheng Wei ◽  
Tong Zhu ◽  
Chenxin Li ◽  
Haoxue Liu
Keyword(s):  
Quantitative Evaluation ◽  
Evaluation Method ◽  
Driving Simulator ◽  
Current System ◽  
Driving Behavior ◽  
Driving Simulation ◽  
Analysis Method ◽  
Safe Driving ◽  
Amount Of Information ◽  
Significant Difference

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.

Car-Following Model Calibration Based on Driving Simulator Data to Study Driver Characteristics and to Investigate Model Validity in Extreme Traffic Situations

2021 ◽  
pp. 036119812110326
Author(s):  
Moritz Berghaus ◽  
Eszter Kallo ◽  
Markus Oeser
Keyword(s):  
Driving Simulator ◽  
Driver Behavior ◽  
Field Studies ◽  
Driving Behavior ◽  
Model Parameters ◽  
Prediction Errors ◽  
Trajectory Data ◽  
Car Following ◽  
Model Validity ◽  
Simulator Study

In this paper we use traffic data from a driving simulator study to calibrate four different car-following models. We also present two applications for which the calibration results can be used. The first application relied on the advantage that driving simulator data also contain information on driver characteristics, for example, age, gender, or the self-assessment of driver behavior. By calibrating the models for each driver individually, the resulting model parameters could be used to analyze the influence of driver characteristics on driver behavior. The analysis revealed that certain characteristics, for example, self-identification as an aggressive driver, were reflected in the model parameters. The second application was based on the capability to simulate dangerous situations that require extreme driving behavior, which is often not included in datasets from real traffic and cannot be provoked in field studies. The model validity in these situations was analyzed by comparing the prediction errors of normal and extreme driving behavior. The results showed that all four car-following models underestimated the deceleration in an emergency braking scenario in which the drivers were momentarily shocked. The driving simulator study was validated by comparing the calibration results with those obtained from real trajectory data. We concluded that driving simulator data were suitable for the two proposed applications, although the validity of driving simulator studies must always be regarded.

scholarly journals Increased visual and cognitive demands emphasize the importance of meeting visual needs at all distances while driving

2020 ◽  
Author(s):  
Amigale Patoine ◽  
Laura Mikula ◽  
Sergio Mejía-Romero ◽  
Jesse Michaels ◽  
Océane Keruzore ◽  
...  
Keyword(s):  
Visual Acuity ◽  
Cognitive Load ◽  
Driving Simulator ◽  
Negative Impact ◽  
Visual Quality ◽  
Driving Behavior ◽  
Driving Safety ◽  
Quality Degradation ◽  
Driving Scenario ◽  
The Impact

ABSTRACTHaving an optimal quality of vision as well as adequate cognitive capacities is known to be essential for driving safety. However, the interaction between vision and cognitive mechanisms while driving remains unclear. We hypothesized that, in a context of high cognitive load, reduced visual acuity would have a negative impact on driving behavior, even when the acuity corresponds to the legal threshold for obtaining a driving license in Canada, and that the impact observed on driving performance would be greater with the increase in the threshold of degradation of visual acuity. In order to investigate this relationship, we examined driving behavior in a driving simulator under optimal and reduced vision conditions through two scenarios involving different levels of cognitive demand. These were: 1. a simple rural driving scenario with some pre-programmed events and 2. a highway driving scenario accompanied by a concurrent task involving the use of a navigation device. Two groups of visual quality degradation (lower/ higher) were evaluated according to their driving behavior. The results support the hypothesis: Driving behavior was less stable under reduced visual quality in the context of a high cognitive load and this effect was exacerbated when visual quality was more severely altered. These results support the idea that visual quality degradation impacts driving behavior when combined with a high mental workload driving environment while specifying that this impact is not present in the context of low cognitive load driving condition.

scholarly journals Car Following and Microscopic Traffic Simulation Under Distracted Driving

2021 ◽  
pp. 036119812110003
Author(s):  
Sunbola Zatmeh-Kanj ◽  
Tomer Toledo
Keyword(s):  
Traffic Flow ◽  
Coefficient Of Variation ◽  
Driving Simulator ◽  
Traffic Simulation ◽  
Driving Behavior ◽  
Transportation Systems ◽  
Microscopic Simulation ◽  
Microscopic Traffic Simulation ◽  
Car Following ◽  
The Impact

Microscopic simulation models have been widely used as tools to investigate the operation of traffic systems and different intelligent transportation systems applications. The fidelity of microscopic simulation tools depends on the driving behavior models that they implement. However, current models commonly do not consider human-related factors, such as distraction. The potential for distraction while driving has increased rapidly with the availability of smartphones and other connected and infotainment devices. Thus, an understanding of the impact of distraction on driving behavior is essential to improve the realism of microscopic traffic tools and support safety and other applications that are sensitive to it. This study focuses on car-following behavior in the context of distracting activities. The parameters of the well-known GM and intelligent driver models are estimated under various distraction scenarios using data collected with an experiment conducted in a driving simulator. The estimation results show that drivers are less sensitive to their leaders while talking on the phone and especially while texting. The estimated models are implemented in a microscopic traffic simulation model. The average speed, coefficient of variation of speed, acceleration noise and acceleration and deceleration time fractions were used as measures of performance indicating traffic flow and safety implications. The simulation results show deterioration of traffic flow with texting and to some extent talking on the phone: average speeds are lower and the coefficient of variation of speeds are higher. Further experimentation with varying fractions of texting drivers showed similar trends.

scholarly journals Distraction “Hangover”: Characterization of the Delayed Return to Baseline Driving Risk After Distracting Behaviors

2021 ◽  
pp. 001872082110122
Author(s):  
Joseph Snider ◽  
Ryan J. Spence ◽  
Anne-Marie Engler ◽  
Ryan Moran ◽  
Sarah Hacker ◽  
...  
Keyword(s):  
Correct Identification ◽  
Simulated Driving ◽  
Car Following ◽  
Driving Behaviors ◽  
Peripheral Cues ◽  
Driving Risk ◽  
Driving Scenario ◽  
Smartphone Use ◽  
Cognitive Cost

Objective We measured how long distraction by a smartphone affects simulated driving behaviors after the tasks are completed (i.e., the distraction hangover). Background Most drivers know that smartphones distract. Trying to limit distraction, drivers can use hands-free devices, where they only briefly glance at the smartphone. However, the cognitive cost of switching tasks from driving to communicating and back to driving adds an underappreciated, potentially long period to the total distraction time. Method Ninety-seven 21- to 78-year-old individuals who self-identified as active drivers and smartphone users engaged in a simulated driving scenario that included smartphone distractions. Peripheral-cue and car-following tasks were used to assess driving behavior, along with synchronized eye tracking. Results The participants’ lateral speed was larger than baseline for 15 s after the end of a voice distraction and for up to 25 s after a text distraction. Correct identification of peripheral cues dropped about 5% per decade of age, and participants from the 71+ age group missed seeing about 50% of peripheral cues within 4 s of the distraction. During distraction, coherence with the lead car in a following task dropped from 0.54 to 0.045, and seven participants rear-ended the lead car. Breadth of scanning contracted by 50% after distraction. Conclusion Simulated driving performance drops dramatically after smartphone distraction for all ages and for both voice and texting. Application Public education should include the dangers of any smartphone use during driving, including hands-free.

scholarly journals Longitudinal Effects of Acute Cannabis Exposure on Automobile Driving Behavior in a Naturalistic Simulated Environment

2021 ◽  
Author(s):  
Shashwath Meda ◽  
Erwin Boer ◽  
Nicolas Ward ◽  
Gregory Book ◽  
Michael Stevens ◽  
...  
Keyword(s):  
Drug Exposure ◽  
Mixed Model ◽  
Driving Simulator ◽  
Real Life ◽  
Driving Behavior ◽  
High Dose ◽  
Model Framework ◽  
Double Blind ◽  
Car Following ◽  
Simulated Environment

Background: Driving is a complex day-to-day activity that employs a variety of cognitive and psychomotor functions in harmony, many of which are known to be affected acutely by CNB intoxication which could in turn pose a significant public health risk. The recent legalization of both recreational and/or medicinal marijuana in several states has thus created an urgent need to better understand the effects of CNB on such functions in the context of driving. The present study employs a longitudinal, double-blind, placebo- 2 active dose study to investigate the effects of CNB on a variety of driving-related behaviors in a controlled, naturalistic simulated environment. Methods: The current study employed N=37 subjects (N=25 male, frequent cannabis users, mean age 24.25+7.01), each exposed to a placebo, low and high dose of CNB on three separate days. On each day, following a single acute inhaled 0.5 g dose of either 0%, 3% or 5-7% of THC via a desktop vaporizer, subjects drove a virtual driving simulator (RTI SimVehicle platform) three times inside an MRI scanner and once out of scanner, randomized, and dispersed throughout an eight hour daily period. During each driving session three distinct real time behavioral tasks corresponding to lane-keeping following simulated wind gusts (operational), lead car following (tactical) and safe overtaking (strategic) were assessed and corresponding behavioral data were computed using custom Matlab scripts. Data were analyzed using a mixed model framework in SPSS v24 which included dose, session, instrument (desktop v MRI), dose*session, dose*instrument and session*instrument as primary factors, covarying for age and sex. Results: Intoxicated subjects made significantly fewer gas pedal corrections (p<0.02) during the car following task and similarly fewer corrections to the steering reversal rate (p<0.02) during the lane weaving task, suggesting reduced awareness under the influence of cannabis. In addition we found that several variables showed significant differences in terms of estimates captured throughout the day suggesting that overall risk taking lessened as the day progressed and CNB effects wore off. Also, data trends suggested that under the high dose subjects took longer to return to baseline from their ‘impaired’ driving patterns. Key metrics that showed such significant daily effects included mean headway (p<0.001) and time to collision (p=0.02) from the car following task, deviation of lane position (p=0.03) from the lane weaving task, median gap (p=0.02) and overtaking speed (p=0.02) from the overtaking task. Although many driving measurements differed depending on whether driving was done in MRI or at a desktop setting, these differences had no relationship to different drug dose levels. Conclusion: In summary, key driving functions affected under higher doses of CNB largely agreed current cross sectional literature. Generally, largest impairments in driving behavior seemed to occur within 1-4 hours after drug exposure, which might have important implications for real life driving situations. Our preliminary analyses yield numerous metrics that changed throughout the day, suggesting broad-based impairment on many metrics commonly used to quantify driving performance and risk.

scholarly journals Influence of Landscape Design on Driving Behavior Based on Slope Illusion

2022 ◽  
Vol 2022 ◽  
pp. 1-7
Author(s):  
Hao Li ◽  
Yueyang Zhang
Keyword(s):  
Simulation Experiment ◽  
Driving Simulator ◽  
Lateral Position ◽  
Driving Behavior ◽  
Driving Safety ◽  
Plant Spacing ◽  
Negative Consequences ◽  
Driving Behaviors ◽  
Speed Perception ◽  
Driving Stability

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.

scholarly journals Increased visual and cognitive demands emphasize the importance of meeting visual needs at all distances while driving

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247254
Author(s):  
Amigale Patoine ◽  
Laura Mikula ◽  
Sergio Mejía-Romero ◽  
Jesse Michaels ◽  
Océane Keruzoré ◽  
...  
Keyword(s):  
Visual Acuity ◽  
Cognitive Load ◽  
Dual Task ◽  
Driving Simulator ◽  
Negative Impact ◽  
Visual Quality ◽  
Driving Behavior ◽  
Quality Degradation ◽  
Driving Scenario ◽  
The Impact

Having an optimal quality of vision as well as adequate cognitive capacities is known to be essential for driving safety. However, the interaction between vision and cognitive mechanisms while driving remains unclear. We hypothesized that, in a context of high cognitive load, reduced visual acuity would have a negative impact on driving behavior, even when the acuity corresponds to the legal threshold for obtaining a driving license in Canada, and that the impact observed on driving performance would be greater with the increase in the threshold of degradation of visual acuity. In order to investigate this relationship, we examined driving behavior in a driving simulator under optimal and reduced vision conditions through two scenarios involving different levels of cognitive demand. These were: 1. a simple rural driving scenario with some pre-programmed events and 2. a highway driving scenario accompanied by a concurrent task involving the use of a navigation device. Two groups of visual quality degradation (lower/ higher) were evaluated according to their driving behavior. The results support the hypothesis: A dual task effect was indeed observed provoking less stable driving behavior, but in addition to this, by statistically controlling the impact of cognitive load, the effect of visual load emerged in this dual task context. These results support the idea that visual quality degradation impacts driving behavior when combined with a high mental workload driving environment while specifying that this impact is not present in the context of low cognitive load driving condition.

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