Driving Simulator Research Articles

Predicting Motion Incongruence Ratings in Closed- and Open-Loop Urban Driving Simulation

Predicting Motion Incongruence Ratings in Closed- and Open-Loop Urban Driving Simulation

Eco-Driving of Electric Vehicles: Objective and Subjective Evaluation of Passenger Comfort by a Dynamic Driving Simulator

Eco-Driving of Electric Vehicles: Objective and Subjective Evaluation of Passenger Comfort by a Dynamic Driving Simulator

Accelerating Awareness: Hidden Equity, Diversity, and Inclusion Learning Outcomes within an Immersive Classroom Activity

This work addresses the challenge of meaningfully introducing engineering students to equity, diversity, and inclusion (EDI) principles. Traditionally, tertiary education introduces these concepts through online modules and/or workshops, often ‘down-selling’ them compared to technical courses. This paper proposes a more impactful experiential approach targeting the affective learning domain. In collaboration with the Centre for Student Wellness, instructors designed a lesson integrating EDI principles into an interactive car simulation game. Students form teams and customize their cars based on survey responses, with rewards and penalties reflecting real-world inequalities. For instance, certain car colors or gender characteristics may result in penalties, mirroring observed racism and gender biases in STEM fields. This innovative approach aims to foster critical thinking about the role of personal privilege in decision-making, offering a dynamic learning experience beyond conventional methods.

Toward Heterogeneous Graph-based Imitation Learning for Autonomous Driving Simulation: Interaction Awareness and Hierarchical Explainability

Toward Heterogeneous Graph-based Imitation Learning for Autonomous Driving Simulation: Interaction Awareness and Hierarchical Explainability

A reinforcement learning model for autonomous vehicles with realistic car simulation in urban using Unity

Abstract Simulator training with reinforcement learning (RL) for autonomous vehicles (AVs) offers advantages over supervised learning. However, transferring the learned behaviours to the real world is a challenging task due to the inconsistencies between the data captured by the vehicle's sensors in the simulated environment and the real world. Additionally, some of the sensors that the AVs rely on are sensitive to weather and lighting conditions. Our proposed model addresses the challenges of sensor data disparity and environmental variation. It utilizes three sensing components which are radio detection and ranging (RADAR), inertial measurement units (IMUs), and global positioning systems (GPSs) to overcome the addressed drawbacks. The proposed model incorporates a carefully designed reward system and prioritizes computational efficiency by using fewer number of sensors and ensures safe and efficient driving. The chosen sensors enable easier knowledge transfer from the simulator to the real-world due to their consistent data representation. The model leverages the Unity engine and ML agent to train AVs for both urban and highway environments. The experimental results show that our suggested model effectively trained AVs to navigate through complex urban areas without collisions while keeping them in their lanes. The demonstration video is provided in the following link: https://youtu.be/YCOjli7lrCM

Reliability and Models of Subjective Motion Incongruence Ratings in Urban Driving Simulations

Reliability and Models of Subjective Motion Incongruence Ratings in Urban Driving Simulations

Driving Fatigue Onset and Visual Attention: An Electroencephalography-Driven Analysis of Ocular Behavior in a Driving Simulation Task.

Attentional deficits have tragic consequences on road safety. These deficits are not solely caused by distraction, since they can also arise from other mental impairments such as, most frequently, mental fatigue. Fatigue is among the most prevalent impairing conditions while driving, degrading drivers' cognitive and physical abilities. This issue is particularly relevant for professional drivers, who spend most of their time behind the wheel. While scientific literature already documented the behavioral effects of driving fatigue, most studies have focused on drivers under sleep deprivation or anyhow at severe fatigue degrees, since it is difficult to recognize the onset of fatigue. The present study employed an EEG-driven approach to detect early signs of fatigue in professional drivers during a simulated task, with the aim of studying visual attention as fatigue begins to set in. Short-range and long-range professional drivers were recruited to take part in a 45-min-long simulated driving experiment. Questionnaires were used to validate the experimental protocol. A previously validated EEG index, the MDrow, was adopted as the benchmark measure for identifying the "fatigued" spans. Results of the eye-tracking analysis showed that, when fatigued, professional drivers tended to focus on non-informative portions of the driving environment. This paper presents evidence that an EEG-driven approach can be used to detect the onset of fatigue while driving and to study the related visual attention patterns. It was found that the onset of fatigue did not differentially impact drivers depending on their professional activity (short- vs. long-range delivery).

S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Driving

As artificial intelligence (AI) technology advances, ensuring the robustness and safety of AI-driven systems has become paramount. However, varying perceptions of robustness among AI developers create misaligned evaluation metrics, complicating the assessment and certification of safety-critical and complex AI systems such as autonomous driving (AD) agents. To address this challenge, we introduce Simulation-Based Robustness Assessment Framework (S-RAF) for autonomous driving. S-RAF leverages the CARLA Driving simulator to rigorously assess AD agents across diverse conditions, including faulty sensors, environmental changes, and complex traffic situations. By quantifying robustness and its relationship with other safety-critical factors, such as carbon emissions, S-RAF aids developers and stakeholders in building safe and responsible driving agents, and streamlining safety certification processes. Furthermore, S-RAF offers significant advantages, such as reduced testing costs, and the ability to explore edge cases that may be unsafe to test in the real world.

Reconstructing Road Roughness Profiles Using ANNs and Dynamic Vehicle Accelerations

Road networks are crucial infrastructures that play a significant role in the progress and advancement of societies. However, roads deteriorate over time due to regular use and external environmental factors. This deterioration leads to discomfort for road users as well as the generation of noise and vibrations, which negatively impact nearby structures. Therefore, it is essential to regularly maintain and monitor road networks. The International Roughness Index (IRI) is commonly used to quantify road roughness and serves as a key indicator for assessing road condition. Traditionally, obtaining the IRI involves manual or automated methods that can be time-consuming and expensive. This study explores the potential of using artificial neural networks (ANNs) and dynamic vehicle accelerations from two simulated car models to reconstruct road roughness profiles. These models include a simplified quarter-car (QC) model with two degrees of freedom, valued for its computational efficiency, and a more intricate full-car (FC) model with seven degrees of freedom, which replicates real-life vehicle behavior. This study also examines the ability of ANNs to predict the mechanical properties of the FC model from dynamic vehicle responses to obstacles. We compare the accuracy and computational efficiency of the two models and find that the QC model is almost 10 times faster than the FC model in reconstructing the road roughness profile whilst achieving higher accuracy.

Development and Validation of a Driving Simulator for Comfort Assessment

The passenger’s comfort is of vital importance in today’s car, even more so in the future autonomous car. Its thorough analysis requires the use of objective measures, but the subjectivity of the passenger must also be considered. The relationship between both aspects is an uncommon research avenue, and could greatly benefit from the use of a vehicle simulator. This study use a simulator to replicate a journey with diverse characteristics that numerous passengers may experience (including engaging in different activities). The appropriate variables for a proper comfort analysis are determined in this study, resulting in a comprehensive database for the same purpose. Additionally, a passenger comfort based questionnaire is developed and applied to obtain the subjective assessment of different passengers. The results of these questionnaires help not only in the identification and study of the self perception of comfort by the passengers but also in the use of the simulator for comfort experiments. The detailed comparison between both the objective approach of signal analysis and the subjective approach of questionnaires that is performed thanks to the simulator creates a foundation for future research and narrows the existing research gap.

Driving Abilities and Wearing-Off in Parkinson's Disease: A Driving Simulation Study.

Driving abilities require the synchronized activity of cerebral networks associated with sensorimotor integration, motricity, and executive functions. Drivers with Parkinson's disease (DwP) have impaired driving ability, but little is known about the impact of "wearing-off" and therapies in addition to L-DOPA on driving capacities. This study aimed to (i) compare driving performance between DwP during different motor states and healthy controls and (ii) assess the impact of add-on therapies on driving abilities. DwP (n = 26) were enrolled as individuals experiencing wearing-off symptoms and treated (within 6 months before the enrollment) with add-on therapies to L-DOPA, including MAO inhibitors for DwP-A (n = 12) or opicapone for DwP-B (n = 14). Age- and sex-matched controls (CON, n = 12) were also enrolled. DwP received two driving assessments in a driving simulator during their "best-on" time and during their wearing-off time on different days. An anamnestic driving questionnaire was collected with the assistance of partners. A Virtual Driving Rating Scale (VDRS) was calculated, as well as learning curves (LCs) for driving items calculated in minutes. DwP reported worse driving performance than CON at the driving questionnaire. In line with this, DwP showed worse VDRS (p < 0.01) and LC (p = 0.021) than CON. Lower VDRS was associated with wearing-off (p < 0.01), but DwP-B had better driving performance while in their "best-on" time (p = 0.037) and more items improving with LCs (7 vs. 3) than DwP-A. DwP demonstrated impaired driving compared to controls. Wearing-off symptoms can also affect driving ability, but therapies (opicapone more so than MAO inhibitors) may play a role in preserving specific driving skills, possibly through maintaining learning abilities.

A Theoretical and Experimental Investigation of the Effects of Inverted Wings Modifications on the Stability and Aerodynamic Performance of a Sedan Car at Cornering

ABSTRACTThis research examines the impact of cornering on the aerodynamic forces and stability of a Nissan Versa (Almera) passenger sedan car by introducing novel modifications. These modifications included single inverted wings with end plates as a front spoiler, double‐element inverted wings with end plates as a rear spoiler, and incorporating the ground as a diffuser under the car trunk. The goal is to enhance the performance and stability of conventional passenger cars. To ensure the accuracy of the numerical data, the study utilized multiple methodologies to model the turbulence model, ultimately selecting the most suitable option. This involved comparing numerical data with wind tunnel experimental data using force balance and pressure distribution. Once validated, the computational fluid dynamics (CFD) was employed to analyze the vehicle's aerodynamic performance relative to the straight‐line condition under cornering conditions. The car simulation in a cornering condition was conducted at a representative Reynolds number based on the vehicle length of about 1.3 × 107. The study discovered that asymmetry was a recurring theme regarding surface pressure distribution, with greater prominence under cornering conditions. All modified models exhibited a more favorable lift‐to‐drag ratio than the baseline, indicating improved aerodynamic efficiency. The underbody double‐element diffuser proved most effective for enhancing fuel efficiency and stability. Mesh refinement with a polyhedral algorithm consisting of 11.27 million elements and a computational domain with a frontal area of 91.8 m2 and a curved length of 31 m (˜7 times car length) was crucial for achieving accurate and repeatable results. The study employed multiple turbulence models within the CFD framework. The realizable k‐ε model was chosen due to its balance between accuracy and computational cost for all Nissan Versa models. These findings are limited to the selected parameters and wind tunnel conditions, and further investigations might be needed for extreme driving scenarios.

Exploring Deep Q-Network for Autonomous Driving Simulation Across Different Driving Modes

The rapid growth in vehicle ownership has led to increased traffic congestion, making the need for autonomous driving solutions more urgent. Autonomous Vehicles (AVs) offer a promising solution to improve road safety and reduce traffic accidents by adapting to various driving conditions without human intervention. This research focuses on implementing Deep Q-Network (DQN) to enhance AV performance in different driving modes: safe, normal, and aggressive. DQN was selected for its ability to handle complex, dynamic environments through experience replay, asynchronous training, and epsilon-greedy exploration. We designed a simulation environment using the Highway-env platform and evaluated the DQN model under varying traffic densities. The performance of the AV was assessed based on two key metrics: success rate and total reward. Our findings show that the DQN model achieved a success rate of 90.75%, 94.625%, and 95.875% in safe, normal, and aggressive modes, respectively. Although the success rate increased with traffic intensity, the total reward remained lower in aggressive driving scenarios, indicating room for optimization in decision-making processes under highly dynamic conditions. This study demonstrates that DQN can adapt effectively to different driving needs, but further optimization is needed to enhance performance in more challenging environments. Future work will focus on improving the DQN algorithm to maximize both success rate and reward in high-traffic scenarios and testing the model in more diverse and complex environments.

Effect of multi-modal input to the perception of sound quality

The perception of sound quality of car interior noise is treated. The decisive factor of sound quality is car interior noise itself but our percetion varies also by the symultaneous exposure of seat and steenring wheel vibrations and the moving seanary from the window screen. We have conducted subjective experiments on the perception of car interior noise unsing car simulator located inside the sound proof room to see the effect of seat and steering wheel vibration and moving sceenary to our perception on sound quality such as pleasantness, booming sensation and powefulness. As the results, the addition of moving sceenary and seat-floor vibration together with the steering wheel viration have significant effect to our perception of sound quality.

Synthetic Training Dataset Generation Using a Digital Twin-based Autonomous Driving Simulator

Synthetic Training Dataset Generation Using a Digital Twin-based Autonomous Driving Simulator

Verifying Motion Sickness Induction in Automated Vehicles Using Motion-Based Driving Simulators

Motion sickness in passengers of automated vehicles could prevent users from taking advantage of the automation features, as well as lead to unsafe takeover conditions. To conduct motion sickness research, the first step requires motion sickness induction, and its verification. In the current study, twenty-nine participants experienced being a passenger in a fully self-driving vehicle on a motion-based driving simulator. A Control route designed to mimic typical driving conditions on a driving simulator, and a Treatment route that produced large lateral accelerations were tested. The Treatment route was also tested with a non-driving related task (NDRT). Results showed that exposure duration and increased lateral accelerations significantly increased motion sickness. Addition of the cognitively demanding NDRT led to a reduction in motion sickness, suggesting the possibility of a masking effect when engaged in an NDRT in an automated vehicle. Overall, the designed Treatment route was verified to successfully induce motion sickness.

Multibody Dynamics and Terramechanics-based modeling and simulation of Quarter-Car with Suspension

Abstract While tires only compress when acted upon by a load in on-road scenarios, in off-road conditions, on the other hand, the tires not only undergo compression but also sinkage into the ground, in a manner that depends on the terrain. The tire-ground contact, in turn, affects the forces acting on the suspension system. Hence, it is necessary to study the vertical dynamic response of a system in off-road conditions by accounting for the terramechanics phenomenon (tire-terrain interface). The forces acting at the tire and terrain interface in off-road conditions have been studied in terramechanics separately; however, their integration with multibody systems is relatively less explored. In this paper, a multibody dynamic-based modeling and simulation of a quarter car with a linear spring-damper suspension system for off-road vehicles is presented. The generalized external forces are obtained using the Bekker-Wong based soil pressure-sinkage approach for two different kind of multibody systems interacting with sandy loam terrain. This model is integrated with the Tangent Space Ordinary Differential Equations of the quarter car and suspension system and solved using numerical integration techniques. Thus, by accounting for the terramechanics in the multibody system, the technique provides a more realistic response of the system in off-road scenarios.

Sex and Age Differences in Virtual Reality (VR) Sickness Susceptibility in Forklift Driving Simulation

Traditional forklift training methods are limited by their time-consuming nature and high costs. Virtual reality (VR) simulators have emerged as a cost-effective and safe alternative for forklift driving training. However, previous research revealed a substantial challenge associated with VR experiences, specifically concerning motion sickness during forklift training. Motion sickness poses a significant challenge to the effectiveness and user experience in VR forklift simulations. The primary objective of this research is to examine the influence of sex and age on motion sickness susceptibility within a virtual reality forklift simulation. Twenty individuals (nine females, eleven males) participated in the study and completed several driving tasks in a VR forklift simulator. Survival analysis (Kaplan-Meier) and Cox Proportional Hazards models examined sex and age differences in VR sickness. Age was the only significant predictor. The effects of age on motion sickness susceptibility highlight the need for tailored approaches in VR development and training.

Exploring the Relationship Between Drivers' Stationary Gaze Entropy and Situation Awareness in a Level-3 Automation Driving Simulation.

The transition period from automation to manual, known as the takeover process, presents challenges for drivers due to the deficiency in collecting requisite contextual information. The current study collected drivers' eye movement in a simulated takeover experiment, and their Situation Awareness (SA) was assessed using the Situation Awareness Global Assessment Technique (SAGAT) method. The drivers' Stationary Gaze Entropy (SGE) was calculated based on the percentages of time they spent on six pre-defined Areas of Interests (AOIs). Three critical time windows were extracted by using the takeover alert time spot and the hazard perceived time spot. The result indicated that drivers with higher SAGAT scores would spread their attention among multiple AOIs. Also, drivers' SGE and SA have a linear relationship only at the last time window (hazard perceived to the end) wherein SGE potentially functions as an evaluative metric for assessing SA in the future.

Developing a Driving Simulator Protocol to Build the Driving Knowledge, Skills, &amp; Abilities of Novice Drivers With Autism Spectrum Disorder

Abstract Date Presented 03/22/24 Driving, as a complex instrumental activity of daily livng, uses integrated skills from all performance skills and client factors. As a gap in service, protocols for training novice drivers with autism spectrum disorder (ASD) on the DriveSafety Simulator is described along with results from a pretest-posttest. Primary Author and Speaker: Alexandra Woolverton Additional Authors and Speakers: Kaitlin Warren Contributing Authors: Anne E. Dickerson