Driving Simulator Research Articles

Improving the Catalytic Performance of BaMn0.7Cu0.3O3 Perovskite for CO Oxidation in Simulated Cars Exhaust Conditions by Partial Substitution of Ba.

The sol-gel method, adapted to aqueous media, was used for the synthesis of BaMn0.7Cu0.3O3 (BMC) and Ba0.9A0.1Mn0.7Cu0.3O3 (BMC-A, A = Ce, La or Mg) perovskite-type mixed oxides. These samples were fully characterized by ICP-OES, XRD, XPS, H2-TPR, BET, and O2-TPD and, subsequently, they were evaluated as catalysts for CO oxidation under different conditions simulating that found in cars exhaust. The characterization results show that after the partial replacement of Ba by A metal in BMC perovskite: (i) a fraction of the polytype structure was converted to the hexagonal BaMnO3 perovskite structure, (ii) A metal used as dopant was incorporated into the lattice of the perovskite, (iii) oxygen vacancies existed on the surface of samples, and iv) Mn(IV) and Mn(III) coexisted on the surface and in the bulk, with Mn(IV) being the main oxidation state on the surface. In the three reactant atmospheres used, all samples catalysed the CO to CO2 oxidation reaction, showing better performances after the addition of A metal and for reactant mixtures with low CO/O2 ratios. BMC-Ce was the most active catalyst because it combined the highest reducibility and oxygen mobility, the presence of copper and of oxygen vacancies on the surface, the contribution of the Ce(IV)/Ce(III) redox pair, and a high proportion of surface and bulk Mn(IV). At 200 °C and in the 0.1% CO + 10% O2 reactant gas mixture, the CO conversion using BMC-Ce was very similar to the achieved with a 1% Pt/Al2O3 (Pt-Al) reference catalyst.

Algorithm Optimization of Computer Simulation Vehicle Driving Simulation System Based on Virtual Reality Technology

The traditional automobile driving simulation system has the problems of low calculation efficiency and lack of realism. The traditional automobile driving simulation system with low computational efficiency and lack of realism limits the learning effect. Through virtual reality technology, vehicle driving can be simulated. By optimizing the algorithm of simulating vehicle driving simulation system, the preference of testers for driving style is investigated and the driver's preference style is determined. Finally, through the automatic driving simulation test method based on genetic algorithm, the key scenes can be divided into 11 different types, and the Euclidean distance of these 11 types is analyzed. Most drivers prefer a more conservative autonomous driving style. When analyzing 11 key scenario types, the Euclidean distance between scenario 2 and scenario 3 is the smallest, which is 33 m, and the maximum Euclidean distance between scenario 6 and scenario 11 is 91 m. The difference between scene 2 and scene 3 is the smallest, while the difference between scene 6 and scene 11 is the largest, and there are differences between each scene. Through virtual reality technology and algorithm optimization, the performance and user experience of driving simulation system are improved.

Vehicle Driving Safety of Underground Interchanges Using a Driving Simulator and Data Mining Analysis

In the process of driving in an underground interchange, drivers are faced with many challenges, such as being in a closed space, visual changes alternating between light and dark conditions, complex road conditions in the confluence section, and dense signage, which directly affect the safety and comfort of drivers in an underground interchange. Thus, driving simulation, building information modeling (BIM), and data mining were used to analyze the impact of underground interchange safety facilities on driving safety and comfort. Acceleration disturbance and steering wheel comfort loss values were used to assist the comfort analysis. The CART algorithm, classification decision trees, and neural networks were used for data mining, which uses a dichotomous recursive partitioning technique where multiple layers of neurons are superimposed to fit and replace very complex nonlinear mapping relationships. Ten different scenarios were designed for comparison. Multiple linear regression combined with ANOVA was used to calculate the significance of the control variables for each scenario on the evaluation index. The results show that appropriately reducing the length of the deceleration section can improve driving comfort, setting reasonable reminder signs at the merge junction can improve driving safety, and an appropriate wall color can reduce speed oscillation. This study indicates that the placement of traffic safety facilities significantly influences the safety and comfort of driving in underground interchanges. This study may provide support for the optimization of the design of underground interchange construction and internal traffic safety facilities.

Enhancing Overt and Covert Attention Using a Real-Time Neurofeedback Game With Consumer-Grade EEG

Neurofeedback training is emerging as a promising tool for cognitive enhancement in healthy as well as cognitive-deficit patients. In this paper, we propose the design of a real-time neurofeedback game using a consumer-grade wireless Electroencephalography (EEG) system and examine its efficacy in enhancing overt and covert attention abilities for healthy individuals. The game uses a simulated car navigation task, in which a car is navigated using the attention level of the player. A total of twenty-three healthy subjects participated in four sessions of neurofeedback training. The effectiveness of the proposed neurofeedback game is evaluated using attention scores based on the Sample entropy of EEG signals, beta-to-(alpha + theta) ratio, and the time taken for completing the game. The variations in covert attention are assessed by introducing visual distractions when the player navigates the car and quantified using changes in the attention score and the time taken for restoring the initial attention level. The attention score significantly improved from the first session to the final session by 22.64% and 21.43% for overt attention and covert attention tasks, respectively. The experimental results demonstrate that the proposed neurofeedback game is an effective tool for enhancing overt and covert attention skills in healthy individuals.

Autonomous Driving Control for Passing Unsignalized Intersections Using the Semantic Segmentation Technique

Autonomous driving in urban areas is challenging because it requires understanding vehicle movements, traffic rules, map topologies and unknown environments in the highly complex driving environment, and thus typical urban traffic scenarios include various potentially hazardous situations. Therefore, training self-driving cars by using traditional deep learning models not only requires the labelling of numerous datasets but also takes a large amount of time. Because of this, it is important to find better alternatives for effectively training self-driving cars to handle vehicle behavior and complex road shapes in dynamic environments and to follow line guidance information. In this paper, we propose a method for training a self-driving car in simulated urban traffic scenarios to be able to judge the road conditions on its own for crossing an unsignalized intersection. In order to identify the behavior of traffic flow at the intersection, we use the CARLA (CAR Learning to Act) self-driving car simulator to build the intersection environment and simulate the process of traffic operation. Moreover, we attempt to use the DDPG (Deep Deterministic Policy Gradient) and RDPG (Recurrent Deterministic Policy Gradient) learning algorithms of the DRL (Deep Reinforcement Learning) technology to train models based on the CNN (Convolutional Neural Network) architecture. Specifically, the observation image of the semantic segmentation camera installed on the self-driving car and the vehicle speed are used as the model input. Moreover, we design an appropriate reward mechanism for performing training according to the current situation of the self-driving car judged from sensing data of the obstacle sensor, collision sensor and lane invasion detector. Doing so can improve the convergence speed of the model to achieve the purpose of the self-driving car autonomously judging the driving paths so as to accomplish accurate and stable autonomous driving control.

Analysis of Driver Aggression’s Effects on Safety-Related Microscopic Traffic Behaviors Using Driving Simulator and Surrogate Measures

The main objective of this paper is to propose a methodology to simulate the traffic conflicts caused by aggressive driving behaviors in real-world driving environments. A research gap was identified in which driving simulators and microscopic simulation software co-simulation were not well-researched for traffic safety studies. The driving simulator and microscopic simulation software have been used to study autonomous vehicles (AV) and connected automated vehicles (CAVs) driving algorithms, behavior, and sensor use case research. However, little research directly utilizes co-simulation to replicate or study driving behaviors and road safety impacts in a non-AV or non-CAV environment. In this paper, two example driving simulator interfaces are examined for their co-simulation interface, capabilities to simulate a real-world driving environment, and their suitability for driving behavior or safety studies. A co-simulation pair would be used to mimic the aggressive driving behaviors that would appear in real-world driving conditions, and the microscopic simulation parameters and vehicle trajectories would be recorded for each vehicle. The trajectories are then analyzed using surrogate safety measures to determine if the change in driving aggression in the simulation resulted in significant changes in traffic conflicts, a surrogate of traffic accidents.

A Novel Approach to Quantify Microsleep in Drivers With Obstructive Sleep Apnea by Concurrent Analysis of EEG Patterns and Driving Attributes.

Accurate quantification of microsleep (MS) in drivers is crucial for preventing real-time accidents. We propose one-to-one correlation between events of high-fidelity driving simulator (DS) and corresponding brain patterns, unlike previous studies focusing general impact of MS on driving performance. Fifty professional drivers with obstructive sleep apnea (OSA) participated in a 50-minute driving simulation, wearing six-channel Electroencephalography (EEG) electrodes. 970 out-of-road OOR (microsleep) events (wheel and boundary contact ≥1 s), and 1020 on-road OR (wakefulness) events (wheel and boundary disconnection ≥1 s), were recorded. Power spectrum density, computed using discrete wavelet transform, analyzed power in different frequency bands and theta/alpha ratios were calculated for each event. We classified OOR (microsleep) events with higher theta/alpha ratio compared to neighboring OR (wakefulness) episodes as true MS and those with lower ratio as false MS. Comparative analysis, focusing on frontal brain, matched 791 of 970 OOR (microsleep) events with true MS episodes, outperforming other brain regions, and suggested that some unmatched instances were due to driving performance, not sleepiness. Combining frontal channels F3 and F4 yielded increased sensitivity in detecting MS, achieving 83.7% combined mean identification rate (CMIR), surpassing individual channel's MIR, highlighting potential for further improvement with additional frontal channels. We quantified MS duration, with 95% of total episodes lasting between 1 to 15 seconds, and pioneered a robust correlation (r = 0.8913, p<0.001) between maximum drowsiness level and MS density. Validating simulator's signals with EEG patterns by establishing a direct correlation improves reliability of MS identification for assessing fitness-to-drive of OSA-afflicted adults.

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

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

Human Merging Behavior in a Coupled Driving Simulator: How Do We Resolve Conflicts?

Human Merging Behavior in a Coupled Driving Simulator: How Do We Resolve Conflicts?

High-Fidelity Real-Time Radar Modeling with Consideration of False and Missed Alarms for Autonomous Driving Simulation

High-Fidelity Real-Time Radar Modeling with Consideration of False and Missed Alarms for Autonomous 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

Steering Feedback in Dynamic Driving Simulators: The Influence of Steering Wheel Vibration and Vehicle Motion Frequency

Steering Feedback in Dynamic Driving Simulators: The Influence of Steering Wheel Vibration and Vehicle Motion Frequency

Exploring Safe Overtaking Behavior on Two‐Lane Two‐Way Road Using Multiagent Driving Simulators and Traffic Simulation

Safety and efficiency of autonomous driving behavior are a tradeoff. Behaviors that are too focused on safety can reduce road operation efficiency, while those that are too efficient can compromise passengers’ safety beyond their tolerance. Therefore, it is important to understand people’s characteristics and maintain a balance between safety and efficiency. Overtaking, which involves passing the preceding vehicle and improving road capacity, requires complex interaction as collisions with opposing vehicles must be avoided on a two‐lane, two‐way road. Overtaking to increase road capacity can induce unnecessary deceleration in oncoming vehicles, harming oncoming traffic flow. To address these concerns, a diverse dataset of natural overtaking behavior is a priority. We conduct experiments using a network connection between two multiagent driving simulators to collect a human behavior‐based overtaking dataset and develop driving behavior models engaged in overtaking situations using the Extra Trees model. The behavior models are embedded in microsimulation to generate human behavior‐based datasets under different conditions using a dynamic link library and component object model interfaces. To understand the interaction in an overtaking scenario by the generated datasets, we used a K‐means clustering technique to analyze the different reaction behaviors between the oncoming and overtaking vehicles. The threshold for achieving a balanced combination of safety and efficiency is established using XGboost. Finally, safe overtaking behavior is analyzed using a combination of the classified driving styles and thresholds. The results show that the overtaking vehicle can safely start overtaking without endangering oncoming vehicles when both speed and distance conditions are met simultaneously; the speed is lower than 44.29 km/h and it is 407 m away from oncoming vehicles.

Research on Specific Scenario Generation Methods for Autonomous Driving Simulation Tests

In this paper, we propose a method for the generation of simulated test scenarios for autonomous driving. Based on the requirements of standard regulatory test scenarios, we can generate virtually simulated scenarios and functional scenario libraries for autonomous driving, which can be used for the simulated verification of different ADAS functions. Firstly, the operational design domain (ODD) of a functional scenario is selected, and the weight values of the ODD elements are calculated. Then, a combination test algorithm based on parameter weights is improved to generate virtually simulated autonomous driving test cases for the ODD elements, which can effectively reduce the number of generated test cases compared with the traditional combination test algorithm. Then, the traffic participant elements in each test case are sampled and clustered so as to obtain hazard-specific scenarios. Then, the values of the subelements under the traffic participant element in each test case are sampled and clustered to obtain hazard-specific scenarios. Finally, the specific scenarios are applied to the automatic emergency braking (AEB) system on the model-in-the-loop (MIL) testbed to verify the effectiveness of this scenario generation method.

Design, fabrication and dynamic performance analysis of Magneto-Rheological damper suitable for vehicle application

In the present scenario, automobile sector has witnessed ample amount research and development to provide a greater comfort zone to the passenger. The term ‘comfort’ includes not only economical meaning but also a pleasant feeling through the journey by mitigating road disturbances reaching the passenger. In the present work, an effort has been dedicated to design, fabricate and characterize a magneto-rheological (MR) damper suitable for automobile application. MR damper is filled with magneto-rheological fluid the attribute of which can be quickly deviated with variation in externally applied magnetic field. Damper characterization has been performed for different amplitudes, frequencies and currents. These rheological results have been used to mathematically model the damper characteristics. Later, using the mathematical model, an attempt has been made to analyze the dynamic mannerism of the MR damper in a quarter car simulation model. For the given road input, the suspension system with MR damper has shown its ability in suppressing the road disturbance in a greater manner.

Model Predictive Control based Motion Cueing Algorithm for Driving Simulator

Model Predictive Control based Motion Cueing Algorithm for Driving Simulator

Developing Character Height Model for Tibetan–Chinese Bilingual Guide Signs Using Driving Simulation

Developing Character Height Model for Tibetan–Chinese Bilingual Guide Signs Using Driving Simulation

Indonesian users' active sound design preference for armored electric SUV interior

This paper explores the potential of Active Sound Design (ASD) in the Indonesian electric vehicle (EV) market, where there are a projected 2 million EV users by 2030. While ASD is a crucial aspect in addressing the lack of engine sound in EV, it is still developing and mainly focused on certain regions. The ASD approach includes active noise control (ANC) to reduce unwanted noise inside the cabin and engine sound enhancement by inserting additional engine sound through the speakers. The study evaluates the original EV stimulus, ANC stimulus, and two ASD stimuli (ICE and futuristic) of an armored electric SUV using an audiovisual car cabin soundscape simulator. The results show that the ANC technology (improved EV with noise filter) is the most preferred stimulus with the highest comfort value perceived by the participants. These insights can help EV manufacturers optimize their sound design solutions for future EV productions in Indonesia.

Experimental and simulation study of an automobile cooling system: Performance improvement using passive flow control

The performance of an automobile cooling system has substantial impacts on the engine's efficiency, fuel consumption, and CO2 emission. In this novel experimental and numerical investigation, a combination of full three-dimensional car simulation, and one-dimensional modeling of the radiator along with the results of road tests on two car models, namely Vehicle A (the base car) and Vehicle B, was used to evaluate and compare the performance of the vehicles' cooling systems. The effects of implementing a passive flow control as well as changing the angle and geometry of the air deflectors on the cars' performance were investigated. The simulation utilized the K-epsilon (k-ε) turbulence model, while the experimental section involved standard road cooling tests. The results show that modifying the air deflectors in Vehicle B enables quick cooling of the system by controlling the air flow rate moving toward the radiator, which increased the air mass flow rate through the condenser and the radiator by 13% and 21%, respectively. Also, the radiator inlet water temperature decreased by about 1.9 °C, and the fuel consumption was reduced by approximately 0.5% under the New European Driving Cycle (NEDC) conditions, which decreased the greenhouse gas emissions, namely CO2 by about 0.9%.

Research and Development on Noise, Vibration, and Harshness of Road Vehicles Using Driving Simulators—A Review

&lt;div&gt;Noise, vibration, and harshness (NVH) is a key aspect in the vehicle development. Reducing noise and vibration to create a comfortable environment is one of the main objectives in vehicle design. In the literature, many theoretical and experimental methods have been presented for improving the NVH performances of vehicles. However, in the great majority of situations, physical prototypes are still required as NVH is highly dependent on subjective human perception and a pure computational approach often does not suffice. In this article, driving simulators are discussed as a tool to reduce the need of physical prototypes allowing a reduction in development time while providing a deep understanding of vehicle NVH characteristics. The present article provides a review of the current development of driving simulator focused on problems, challenges, and solutions for NVH applications. Starting from the definition of the human response to noise and vibration, this article describes the different driving simulator technologies to tackle all the involved perception aspects. The different available technologies are discussed and compared as to provide design engineers with a complete picture of the current possibilities and future trends.&lt;/div&gt;