scholarly journals Validation of Vehicle Driving Simulator from Perspective of Velocity and Trajectory Based Driving Behavior under Curve Conditions

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8429
Author(s):  
Liang Chen ◽  
Jiming Xie ◽  
Simin Wu ◽  
Fengxiang Guo ◽  
Zheng Chen ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Short Term Memory ◽  
Road Traffic ◽  
Lateral Displacement ◽  
Safety Evaluation ◽  
Driving Behavior ◽  
Steering Wheel ◽  
Vehicle Speed ◽  
Driving Simulators ◽  
Convenient Setting

With their advantages of high experimental safety, convenient setting of scenes, and easy extraction of control parameters, driving simulators play an increasingly important role in scientific research, such as in road traffic environment safety evaluation and driving behavior characteristics research. Meanwhile, the demand for the validation of driving simulators is increasing as its applications are promoted. In order to validate a driving simulator in a complex environment, curve road conditions with different radii are considered as experimental evaluation scenarios. To attain this, this paper analyzes the reliability and accuracy of the experimental vehicle speed of a driving simulator. Then, qualitative and quantitative analysis of the lateral deviation of the vehicle trajectory is carried out, applying the cosine similarity method. Furthermore, a data-driven method was adopted which takes the longitudinal displacement, lateral displacement, vehicle speed and steering wheel angle of the vehicle as inputs and the lateral offset as the output. Thus, a curve trajectory planning model, a more comprehensive and human-like operation, is established. Based on directional long short-term memory (Bi–LSTM) and a recurrent neural network (RNN), a multiple Bi–LSTM (Mul–Bi–LSTM) is proposed. The prediction performance of LSTM, MLP model and Mul–Bi–LSTM are compared in detail on the validation set and testing set. The results show that the Mul–Bi–LSTM model can generate a trajectory which is very similar to the driver’s curve driving and have a preferable generalization performance. Therefore, this method can solve problems which cannot be realized in real complex scenes in the simulator validation. Selecting the trajectory as the validation parameter can more comprehensively and intuitively reflect the simulator’s curve driving state. Using a speed model and trajectory model instead of a real car experiment can improve the efficiency of simulator validation and lay a foundation for the standardization of simulator validation.

Training Effectiveness: How Does Driving Simulator Fidelity Influence Driver Performance?

2005 ◽  
Vol 49 (25) ◽  
pp. 2201-2205 ◽  
Author(s):  
George D. Park ◽  
R. Wade Allen ◽  
Theodore J. Rosenthal ◽  
Dary Fiorentino
Keyword(s):  
Graduation Rates ◽  
Analysis Of Variance ◽  
Performance Measures ◽  
Training Trial ◽  
Driving Simulator ◽  
Steering Wheel ◽  
Vehicle Speed ◽  
Driver Training ◽  
Driver Performance ◽  
The Difference

Driver performance effects were compared between two configuration types: 1) a low-cost, three-monitor, 135 degree field-of-view (FOV), PC desktop with PC gaming steering wheel controls and 2) a medium-cost, fixed-based, projected image, 135 degree FOV, instrumented vehicle cab. The experiment was part of a larger novice driver training experiment with teenage drivers who had yet to receive their license to drive (Allen, Park, et al. 2003). Participants drove a minimum of six training trial runs on either the three-monitor configuration (N = 180) or the vehicle cab configuration (N = 143). A 2 times 6 (configuration type x training trial runs) analysis of variance was performed for a variety of performance measures as well as a one-way analysis of variance to assess the graduation rates between the two configurations. Significant differences were found for certain performance measures suggesting that handling behaviors (i.e. braking and steering) were largely affected by the difference in controls while lane position, vehicle speed, time-to-collision, and simulator sickness ratings were largely affected by the difference in graphical display. However, non-significant differences in certain performance measures (e.g. total accidents and graduation rates) suggested that the three-monitor configuration may be as useful of a tool for driver training, assessment, and research as a higher fidelity vehicle cab.

scholarly journals Research on the classification for road traffic visibility based on the characteristics of driving behaviour – a driving simulator experiment

2020 ◽  
Vol 3 (1) ◽  
pp. 30-36
Author(s):  
Kun Wang ◽  
Weihua Zhang ◽  
Zhongxiang Feng ◽  
Cheng Wang
Keyword(s):  
Traffic Safety ◽  
Driving Simulator ◽  
Road Traffic ◽  
Driving Behavior ◽  
Control Measures ◽  
Content Type ◽  
The Road ◽  
Road Traffic Safety ◽  
Fine Classification

Purpose The purpose of this paper is to perform fine classification of road traffic visibility based on the characteristics of driving behavior under different visibility conditions. Design/methodology/approach A driving simulator experiment was conducted to collect data of speed and lane position. ANOVA was used to explore the difference in driving behavior under different visibility conditions. Findings The results show that only average speed is significantly different under different visibility conditions. With the visibility reducing, the average vehicle speed decreases. The road visibility conditions in a straight segment can be divided into five levels: less than 20, 20-30, 35-60, 60-140 and more than 140 m. The road visibility conditions in a curve segment can be also divided into four levels: less than 20, 20-30, 35-60 and more than 60 m. Originality/value A fine classification of road traffic visibility has been performed, and these classifications help to establish more accurate control measures to ensure road traffic safety under low-visibility conditions.

scholarly journals Sound Effects on Physiological State and Behavior of Drivers in a Highway Tunnel

2021 ◽  
Vol 12 ◽  
Author(s):  
Yanqun Yang ◽  
Yang Feng ◽  
Said M. Easa ◽  
Xiujing Yang ◽  
Jiang Liu ◽  
...  
Keyword(s):  
Physiological State ◽  
Driving Behavior ◽  
Driving Safety ◽  
Steering Wheel ◽  
Vehicle Speed ◽  
Accelerator Pedal ◽  
Acoustic Environment ◽  
Highway Tunnel ◽  
Significant Difference ◽  
Sound Environment

Driving behavior in a highway tunnel could be affected by external environmental factors like light, traffic flow, and acoustic environments, significantly when these factors suddenly change at the moment before and after entering a tunnel. It will cause tremendous physiological pressure on drivers because of the reduction of information and the narrow environment. The risks in driving behavior will increase, making drivers more vulnerable than driving on the regular highways. This research focuses on the usually neglected acoustic environment and its effect on drivers' physiological state and driving behavior. Based on the SIMLAB driving simulation platform of a highway tunnel, 45 drivers participated in the experiment. Five different sound scenarios were tested: original highway tunnel sound and a mix of it with four other sounds (slow music, fast music, voice prompt, and siren, respectively). The subjects' physiological state and driving behavior data were collected through heart rate variability (HRV) and electroencephalography (EEG). Also, vehicle operational data, including vehicle speed, steering wheel angle, brake pedal depth, and accelerator pedal depth, were collected. The results indicated that different sound scenarios in the highway tunnel showed significant differences in vehicle speed (p = 0.000, η2 = 0.167) and steering wheel angle (p = 0.007, η2 = 0.126). At the same time, they had no significant difference in HRV and EEG indicators. According to the results, slow music was the best kind of sound related to driving comfort, while the siren sound produced the strongest driver reaction in terms of mental alertness and stress level. The voice-prompt sound most likely caused driver fatigue and overload, but it was the most effective sound affecting safety. The subjective opinion of the drivers indicated that the best sound scenario for the overall experience was slow music (63%), followed by fast music (21%), original highway tunnel sound environment (13%), and voice-prompt sound (3%). The findings of this study will be valuable in improving acoustic environment quality and driving safety in highway tunnels.

Effectiveness of Driver Compensation to Avoid Vehicle Collision under Visual Field Contraction

2016 ◽  
Vol 60 (1) ◽  
pp. 1911-1915 ◽  
Author(s):  
Jieun Lee ◽  
Makoto Itoh ◽  
Toshiyuki Inagaki
Keyword(s):  
Visual Field ◽  
Traffic Accidents ◽  
Driving Simulator ◽  
Road Traffic ◽  
Field Of View ◽  
Vehicle Speed ◽  
Experimental Conditions ◽  
Vehicle Collision ◽  
Hazardous Object ◽  
Set Up

Visual field contraction is an important contributing factor to road traffic accidents. Visually impaired drivers may compensate for the adverse effects of the visual field contraction. This study investigated the effectiveness of two types of compensation: (1) reducing vehicle speed and (2) looking around more frequently. Furthermore, we focused on a hazardous event, where a hazardous object comes into driver’s field of view again after passing out of sight. We conducted an experiment by using a driving simulator and special eyeglasses that reduce healthy people’s field of view to approximately 10 degrees. We set up 3 experimental conditions: driving without contraction, driving with contraction following two different instructions: reduce speed or look around more frequently. Statistically, reducing speed was effective in reducing the risk of collision compared to looking around frequently. However, it was difficult to determine whether the drivers recognized the hazard or not based on driver’s behavioral data or an interview that carried out to check whether participants recognize the hazard after collision.

scholarly journals Identifying Driver Behavior in Preturning Maneuvers Using In-Vehicle CANbus Signals

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
M. Zardosht ◽  
S. S. Beauchemin ◽  
M. A. Bauer
Keyword(s):  
Data Streams ◽  
Driver Behavior ◽  
Driving Behavior ◽  
Steering Wheel ◽  
Vehicle Speed ◽  
Statistical Features ◽  
Instrumented Vehicle ◽  
Using Data ◽  
Near Future ◽  
Driving Period

Our objective in this contribution is to categorize driver behavior in terms of preturning maneuvers. We analyze driving behavior in an urban environment prior to turns using data obtained from the CANbus of an instrumented vehicle during a one-hour driving period for 12 different individuals. CANbus data streams such as vehicle speed, gas pedal pressure, brake pedal pressure, steering wheel angle, and acceleration are collected and analyzed for 5, 10, and 15 seconds of driving prior to each turn. We consider all turns for each driver and extract statistical features from the signals and use cluster analysis to categorize drivers into groups reflecting different driving styles. The results show that using this approach we can effectively cluster drivers into two groups. The results show consistency in the membership within a cluster throughout the different timeframes. We conclude that driver behavior classification from such data streams is possible and we hope in the near future to devise driver descriptors that include additional maneuvers.

scholarly journals Promoting eco-driving behavior through multisensory stimulation: a preliminary study on the use of visual and haptic feedback in a virtual reality driving simulator

2021 ◽  
Author(s):  
Andrea Pietra ◽  
Marina Vazquez Rull ◽  
Roberta Etzi ◽  
Alberto Gallace ◽  
Giulia Wally Scurati ◽  
...  
Keyword(s):  
Virtual Reality ◽  
High Speed ◽  
Haptic Feedback ◽  
Driving Simulator ◽  
Visual Stimulation ◽  
Physiological State ◽  
Driving Behavior ◽  
Steering Wheel ◽  
Mean Power ◽  
Accelerator Pedal

AbstractThis paper describes the design and preliminary test of a virtual reality driving simulator capable of conveying haptic and visual messages to promote eco-sustainable driving behavior. The driving simulator was implemented through the Unity game engine; a large street environment, including high-speed and urban sections, was created to examine different driving behaviors. The hardware setup included a gaming driving seat, equipped with a steering wheel and pedals; the virtual scenarios were displayed through an Oculus Rift headset to guarantee an immersive experience. Haptic stimulation (i.e., vibrations) was delivered to the driver through the accelerator pedal, while visual stimuli (i.e., icons and colors) were shown on a virtual head-up display. The sensory feedbacks were presented both alone and in combination, providing information about excessive acceleration and speed. Four different virtual scenarios, each one including a distracting element (i.e., navigator, rain, call, and traffic), were also created. Ten participants tested the simulator. Fuel consumption was evaluated by calculating a mean power index (MPI) in reference to the sensory feedback presentation; physiological reactions and responses to a usability survey were also collected. The results revealed that the haptic and visuo-haptic feedback were responsible for an MPI reduction, respectively, for 14% and 11% compared with a condition of no feedback presentation; while visual feedback alone resulted in an MPI increase of 11%. The efficacy of haptic feedback was also accompanied by a more relaxing physiological state of the users, compared with the visual stimulation. The system’s usability was adequate, although haptic stimuli were rated slightly more intrusive than the visual ones. Overall, these preliminary results highlight how promising the use of the haptic channel can be in communicating and guiding the driver toward a more eco-sustainable behavior.

The Validity of Three New Driving Simulator Scenarios: Detecting Differences in Driving Performance by Difficulty and Driver Gender and Age

2020 ◽  
pp. 001872082093752
Author(s):  
Hillary Maxwell ◽  
Bruce Weaver ◽  
Sylvain Gagnon ◽  
Shawn Marshall ◽  
Michel Bédard
Keyword(s):  
Discriminant Validity ◽  
Driving Simulator ◽  
Age Groups ◽  
Driving Behavior ◽  
Driving Simulation ◽  
Driving Simulators ◽  
Safe Driving ◽  
Convergent And Discriminant Validity ◽  
Gender And Age ◽  
Driver Assessment

Objective We explored the convergent and discriminant validity of three driving simulation scenarios by comparing behaviors across gender and age groups, considering what we know about on-road driving. Background Driving simulators offer a number of benefits, yet their use in real-world driver assessment is rare. More evidence is needed to support their use. Method A total of 104 participants completed a series of increasingly difficult driving simulation scenarios. Linear mixed models were estimated to determine if behaviors changed with increasing difficulty and whether outcomes varied by age and gender, thereby demonstrating convergent and discriminant validity, respectively. Results Drivers adapted velocity, steering, acceleration, and gap acceptance according to difficulty, and the degree of adaptation differed by gender and age for some outcomes. For example, in a construction zone scenario, drivers reduced their mean velocities as congestion increased; males drove an average of 2.30 km/hr faster than females, and older participants drove more slowly than young (5.26 km/hr) and middle-aged drivers (6.59 km/hr). There was also an interaction between age and difficulty; older drivers did not reduce their velocities with increased difficulty. Conclusion This study provides further support for the ability of driving simulators to elicit behaviors similar to those seen in on-road driving and to differentiate between groups, suggesting that simulators could serve a supportive role in fitness-to-drive evaluations. Application Simulators have the potential to support driver assessment. However, this depends on the development of valid scenarios to benchmark safe driving behavior, and thereby identify deviations from safe driving behavior. The information gained through simulation may supplement other forms of assessment and possibly eliminate the need for on-road testing in some situations.

scholarly journals Examination of Realism in a High-Fidelity Tractor Driving Simulator

2020 ◽  
Vol 26 (4) ◽  
pp. 123-137
Author(s):  
Kayla Faust ◽  
Carri Casteel ◽  
Daniel V. McGehee ◽  
Marizen Ramirez ◽  
Diane S. Rohlman ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Cost Effective ◽  
Steering Wheel ◽  
High Fidelity ◽  
List Type ◽  
Driving Simulators ◽  
Simulated Driving ◽  
Perceived Realism ◽  
Farm Equipment ◽  
Equipment Operator

HighlightsDescribes the creation of a new high-fidelity tractor driving simulator.Describes the perceived realism of a tractor driving simulator among 99 Midwestern farm equipment operators.Examines how farm equipment operator characteristics affect perceived realism of a tractor driving simulator.Discusses potential improvements for future generations of tractor driving simulators.Abstract. Transportation-related incidents are the leading cause of occupational fatalities for all industries in the U.S., including the agricultural industry, which suffers thousands of crashes involving farm equipment each year. Simulated driving studies offer a safe and cost-effective way to conduct driving research that would not be feasible in the real world. A tractor driving miniSim was developed and then evaluated for realism at the University of Iowa among 99 Midwestern farm equipment operators. It is important for driving simulators to have a high degree of realism for their results to be applicable to non-simulated driving operations. High-fidelity driving simulators facilitate extrapolations made by driving research but should be re-tested for realism when changes are made to the design of the simulator. The simulator used in this study emulated a tractor cab with realistic controls, three high-resolution screens, and high-fidelity sound. After completing a 10-minute drive, farm equipment operators completed a survey and scored four specific domains assessing specific characteristics (i.e., appearance, user interface, control, and sound) of the tractor simulator’s realism using a seven-point Likert scale (from 0 = not at all realistic to 6 = completely realistic). An overall realism score and domain scores were calculated. Farm equipment operators were also asked to provide recommendations for improving the tractor miniSim. Overall, farm equipment operators rated the simulator’s realism favorably (i.e., >3 on a scale from 0 to 6) for all individual items and domains. The appearance domain received the highest average realism score (mean = 4.58, SD = 1.03), and the sound domain received the lowest average realism score (mean = 3.86, SD = 1.57). We found no significant differences in realism scores across farm equipment operator characteristics. The most frequently suggested improvements were to tighten the steering wheel (27%), make the front tires visible (19%), and that no improvements were needed to improve the simulator realism (18%). This study demonstrates that the new tractor miniSim is a viable approach to studying farm equipment operations and events that can lead to tractor-related crashes. Future studies should incorporate the suggested improvements and seek to validate the simulator as a research and outreach instrument. Keywords: Driving simulator, Farm equipment operators, Realism, Tractors.

scholarly journals Glaucomatous visual fields and neurocognitive function are independently associated with poor lane maintenance during driving simulation

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
David E. Anderson ◽  
John P. Bader ◽  
Emily A. Boes ◽  
Meghal Gagrani ◽  
Lynette M. Smith ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Vehicle Control ◽  
Driving Behavior ◽  
Driving Performance ◽  
Driving Simulation ◽  
Sensor Data ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Neurocognitive Performance ◽  
Loss Severity

Abstract Background Driving simulators are a safe alternative to on-road vehicles for studying driving behavior in glaucoma drivers. Visual field (VF) loss severity is associated with higher driving simulator crash risk, though mechanisms explaining this relationship remain unknown. Furthermore, associations between driving behavior and neurocognitive performance in glaucoma are unexplored. Here, we evaluated the hypothesis that VF loss severity and neurocognitive performance interact to influence simulated vehicle control in glaucoma drivers. Methods Glaucoma patients (n = 25) and suspects (n = 18) were recruited into the study. All had > 20/40 corrected visual acuity in each eye and were experienced field takers with at least three stable (reliability > 20%) fields over the last 2 years. Diagnosis of neurological disorder or cognitive impairment were exclusion criteria. Binocular VFs were derived from monocular Humphrey VFs to estimate a binocular VF index (OU-VFI). Montreal Cognitive Assessment (MoCA) was administered to assess global and sub-domain neurocognitive performance. National Eye Institute Visual Function Questionnaire (NEI-VFQ) was administered to assess peripheral vision and driving difficulties sub-scores. Driving performance was evaluated using a driving simulator with a 290° panoramic field of view constructed around a full-sized automotive cab. Vehicle control metrics, such as lateral acceleration variability and steering wheel variability, were calculated from vehicle sensor data while patients drove on a straight two-lane rural road. Linear mixed models were constructed to evaluate associations between driving performance and clinical characteristics. Results Patients were 9.5 years older than suspects (p = 0.015). OU-VFI in the glaucoma group ranged from 24 to 98% (85.6 ± 18.3; M ± SD). OU-VFI (p = .0066) was associated with MoCA total (p = .0066) and visuo-spatial and executive function sub-domain scores (p = .012). During driving simulation, patients showed greater steering wheel variability (p = 0.0001) and lateral acceleration variability (p < .0001) relative to suspects. Greater steering wheel variability was independently associated with OU-VFI (p = .0069), MoCA total scores (p = 0.028), and VFQ driving sub-scores (p = 0.0087), but not age (p = 0.61). Conclusions Poor vehicle control was independently associated with greater VF loss and worse neurocognitive performance, suggesting both factors contribute to information processing models of driving performance in glaucoma. Future research must demonstrate the external validity of current findings to on-road performance in glaucoma.

Experimental modeling of the effect of hurricane wind forces on driving behavior and vehicle performance

2015 ◽  
Vol 13 (2) ◽  
pp. 159 ◽  
Author(s):  
Jose M. Rodriguez, MS ◽  
Julius Codjoe ◽  
Osama Osman ◽  
Sherif Ishak, PhD ◽  
Brian Wolshon, PhD
Keyword(s):  
Mathematical Models ◽  
Driving Simulator ◽  
Planning Process ◽  
Lateral Displacement ◽  
Weather Conditions ◽  
Driving Behavior ◽  
Hurricane Evacuation ◽  
Vehicle Performance ◽  
Hurricane Wind ◽  
Lateral Displacements

While traffic planning is important for developing a hurricane evacuation plan, vehicle performance on the roads during extreme weather conditions is critical to the success of the planning process. This novel study investigates the effect of gusty hurricane wind forces on the driving behavior and vehicle performance. The study explores how the parameters of a driving simulator could be modified to reproduce wind loadings experienced by three vehicle types (passenger car, ambulance, and bus) during gusty hurricane winds, through manipulation of appropriate software. Thirty participants were then tested on the modified driving simulator under five wind conditions (ranging from normal to hurricane category 4). The driving performance measures used were heading error and lateral displacement. The results showed that higher wind forces resulted in more varied and greater heading error and lateral displacement. The ambulance had the greatest heading errors and lateral displacements, which were attributed to its large lateral surface area and light weight. Two mathematical models were developed to estimate the heading error and lateral displacements for each of the vehicle types for a given change in lateral wind force. Through a questionnaire, participants felt the different characteristics while driving each vehicle type. The findings of this study demonstrate the valuable use of a driving simulator to model the behavior of different vehicle types and to develop mathematical models to estimate and quantify driving behavior and vehicle performance under hurricane wind conditions.

Sign in / Sign up

Export Citation Format

Share Document