Browse Topic: Simulators

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Simulation based optimization of the NVH characteristics in modern high speed engine during development phase2026-26-0326To be published on 01/16/2026
Engine noise mitigation is paramount in powertrain development for enhanced performance and occupant comfort. Identifying NVH problems at the prototype stage leads to costly and time-consuming redesigns and modifications, potentially delaying the product launch. NVH simulations facilitate identification of noise and vibration sources, informing design modifications prior to physical prototyping. Early detection and resolution of NVH problems through simulation can significantly shorten the overall development cycle and multiple physical prototypes and costly redesigns. During NVH simulations, predicting and optimizing valvetrain and timing drive noise necessitates transfer of bearing, valve spring, and contact forces to NVH simulation models. Traditional simulations, involved continuous force data export and NVH model evaluation for each design variant, pose efficiency challenges. In this paper, an approach for preliminary assessment of dB level reductions across design iterations is
Rai, Ankur, Deshpande, Ajay Mahadeo, YADAV, RAKESH
Adoptive safety solutions in Road Simulators for commercial vehicle structural validation2026-26-0533To be published on 01/16/2026
Road Simulators used to carry out accelerated structural durability validation of a vehicle. As a commercial vehicle manufacturer, for our commercial vehicles structural validation, we are using 8 poster road simulators. We use road load data, torture track data, synthetic profiles or events as the input test data. From a mini 4 wheeler trucks to high capacity 8 wheeler truck, and any bus variant is being tested at road simulator. All the vehicle variants are tested with prescribed road and load conditions for the pre-determined life. Each wheel of the vehicle is positioned on the wheel pan of the hydraulic actuators so that each actuator excites the corresponding vibration data. The vehicle is being restrained as per the manufacturers recommendation. Manufacturer recommendations widely addresses the risks associated with the test rigs. In addition to that there are risks associated with the vehicle running, vehicle handling, vehicle positioning. For example, when durability test
Arumugam, Paramasivam, N, Gopi Kannan, N, Mahendra, Muthu kumar, Pandurangan, Singh, laxman, Tiwari, Manish, V, Subash
A Frequency-Based correlation adjustment method for translating Proving Ground Test Tracks to 4-DOF Road Simulator Rig Testing of Electric Two wheelers2026-26-0556To be published on 01/16/2026
Real-world usage subjects two-wheelers to complex and varying dynamic loads, necessitating early-stage durability validation to ensure robust product development. Conducting a full life-cycle durability testing on proving grounds is time-consuming, extremely difficult for the riders involved, and costly, which is why accelerated testing using rigs such as the road simulator system have become a preferred approach. The use of road simulators necessitates, accurately measured inputs and precise simulation to ensure proper actuation of the rig, thereby enabling realistic representation of road undulations. Road Load Data Acquisition (RLDA) is used to measure various inputs such as displacement, acceleration, strain, and predicted forces from the road which are then used to simulate real-world conditions in a road simulator. This paper covers two important aspects essential for achieving an accurate and clear representation of road simulation in a 4-DOF road simulator, encompassing both
Ganju, Shubham, V, Vijayamirtharaj, Prasad, Sathish, R S, Mahenthran
Simulation of EV range Estimation for different payload of vehicle.2026-26-0389To be published on 01/16/2026
The electrification of transportation is reshaping the automotive and logistics industries, with electric vehicles (EVs) playing an increasingly central role in passenger mobility and commercial operations. As EV adoption grows, the need for accurate range estimation becomes critical, particularly under varying operational conditions such as increased payload. A key business challenge lies in the significant variability of EV range due to changes in vehicle load, which can affect performance, operational efficiency, and cost-effectiveness—especially for fleet-based services. This study focuses on addressing the technical gap in predicting EV range with respect to different payload conditions. Traditional range estimation methods often fail to account for real-world variables like additional cargo weight, leading to suboptimal route planning, increased energy consumption, and unanticipated charging requirements. Payload-induced range degradation can result in up to some deviation in
Khatal, Swaraj, Gupta, Anjali, Krishna, Thallapaka
Testing and Validation of Future Mobility Vehicles using Realistic Digital Twin of Road Corridors and Tires2026-26-0580To be published on 01/16/2026
Recent development in Real Time Modelling & Driving simulator technology has made it possible to real time evaluation of vehicle/ tire performance like NVH, Ride-comfort, HMI and ADAS etc. objectively and subjectively using driving simulator. Many OEMs worldwide are adapting this technology for speeding up their tire and vehicle development by reducing decision making time and optimization of the performance by suitable design changes in initial phase of conceptualization. In order to use this technology, there is requirement of good digital twin of the vehicle, tire, road surfaces Test tracks & realistic Road Corridors. Focus of the current paper is on Digital Twin of Road Corridors. Digital Twin of road corridor comprises of various elements viz. the digitized road profile 3D (X-Y-Z) and road infrastructure elements that includes basic elements like road, barricades, signal, signage etc. along with infrastructure furniture like building, bus stops, flyovers etc. Digital twin of road
Joshi, Omkar Prakash, Pawar, Prashant R
Evaluation of Waste Heat Recovery technology in FCEV on different drive-cycles2026-26-0259To be published on 01/16/2026
Growing global warming and the associated climate change have expedited the need for adoption of carbon-neutral technologies. The transportation sector accounts for ~ 25 % of total carbon emissions. Hydrogen (H2) is widely explored as an alternative for decarbonizing the transport sector. The application of H2 through PEM Fuel Cells is one of the options available for the trucking industry, due to their relatively higher efficiency (~50%) and power density. However, at present the cost of an FCEV truck is considerably higher than its diesel equivalent. Hence, new technologies either enabling cost reduction or efficiency improvement for FCEVs are imperative for their widespread adoption. FCEVs have a system efficiency in the range of 40-60% implying that around half of the input energy is lost to the atmosphere as waste heat. However, recapturing this significant amount of low-grade waste heat into useful work is a challenge. This paper discusses the feasibility of waste heat recovery
P V, NAVANEETH
Conceptual Crash Analysis in Automotive Applications with Reduced Computational Effort2026-26-0451To be published on 01/16/2026
In the realm of automotive safety engineering, the demand for efficient and accurate crash simulations is ever-increasing. As finite element (FE) modeling of components becomes increasingly detailed and the availability of advanced material models improves, crash simulations for full vehicles can become time-consuming. Evaluating the crash performance of any vehicle subsystem requires full vehicle simulations at the industry level, which are complex and time-consuming. This paper introduces a novel methodology for replicating full vehicle crash simulations using Principal Component Test (PCT) cut-model level. Adapting PCT simulations, a significant reduction in computational time and resource usage is achieved, thereby optimizing CPU cluster performance. The proposed method has been successfully implemented across various crash load cases, including frontal 100 percent overlap, 40 percent overlap, small overlap crash, side impact, rear impact, underbody impact and low speed impact
Moncayo, David, Malipatil, Anand, Prasad, Rakesh, kunnath, Allwin
Simulation Driven Virtual Road Load and Accelerated Duty Cycle Generation for 3-Speed eAxle Systems2026-26-0401To be published on 01/16/2026
The transition from Internal Combustion Engine (ICE) vehicles to Battery Electric Vehicles (BEVs) introduces significant challenges in drivetrain development, particularly when historical road load data (RLDA) is unavailable. This research proposes a methodology for virtually generating and processing RLDA to evaluate the durability of a novel 3-speed electric axle (eAxle) system before physical prototype construction. By employing AVL Route Studio, we simulated diverse global driving conditions across urban, highway, and mixed terrains to generate high-frequency torque and speed data. Since the resulting millions of data points are impractical for direct fatigue assessment, we utilised Romax software to condense the virtual RLDA into an accelerated duty cycle while maintaining the original fatigue damage characteristics. Unlike traditional binning techniques that often mischaracterise load severity, our damage-matching approach ensures the condensed cycle accurately replicates gear
LIGADE, PRATIK, KHAN, Dr. NURUZZAMA MEHADI, Koona, Rammohan Rao
Prediction of REESS factor for SOC correction in Hybrid Vehicles2026-26-0471To be published on 01/16/2026
The regulatory mechanisms to measure emissions from automobiles have evolved drastically over the years. Certification of CO2 emissions is one of them. It is not only critical for environmental protection but can also invite heavy fines to OEMs, if not complied with. In homologation test of a Hybrid Vehicle, it is necessary to correct the measured CO2 to account for deviations in measurement from failed Start-Stop phase and difference between start and end State of Charge (SOC) of battery. The correction methodology is also applicable for vehicle simulation in Software-in-Loop environment. The focus of this paper is on the correction of CO2 derived from SOC delta in Homologation cycle. The battery energy delta due to difference in SOC between start and end of drive cycle should be converted to corresponding fuel energy/CO2 expended from Internal Combustion Engine. The resulting correction factor is known as Rechargeable Electrical Energy Storage System (REESS) factor. To provide this
Gopinath, Shravanthi Poorigali, Khatod, Krishna
NN FMU: Deep Learning Models for Next-Gen xiL Vehicle Simulation2026-26-0452To be published on 01/16/2026
Functional Mock-up Units (FMUs) have become a standard for enabling co-simulation and model exchange in vehicle system development. However, traditional FMUs derived from physics-based models can be computationally intensive, especially in scenarios requiring real-time performance. This paper presents a Python-based approach for developing a Neural Network (NN) based FMU using deep learning techniques, aimed at accelerating vehicle simulation while ensuring high fidelity. The neural network was trained on vehicle simulation data and developed using Python frameworks such as TensorFlow. The trained model was then exported into FMU, enabling seamless integration with FMI-compliant platforms. The NN FMU replicates thermal behavior of vehicle with high accuracy while offering a significant reduction in computational load. Benchmark comparisons with Physical Thermal Model demonstrate that the proposed solution provides both efficiency and reliability across various driving conditions
Srinivasan, Rangarajan, ASHOK BHARDE, Pooja, MHETRAS, Mayur, CHEHIRE, Marc
Simulation driven design of robust Crank-train systems for elevated In-cylinder combustion pressure2026-26-0400To be published on 01/16/2026
The need for increased in-cylinder pressure to meet the higher engine rating and stringent emissions norms requires special attention on the Main moving components torsional behaviour and the bearing performance. The work presented in this paper utilizes the 1D and 3D simulation potential of AVL EXCITE for the assessment of design changes required in Crank-Train system for the increased in-cylinder pressure from 170bar to 220bar. The key feature of the executed work was that all the design changes were accommodated with minimal changes in packaging / layout interfaces. The given work was executed on 6-Cylinder Turbocharged In-cylinder with Type V valve-train configurations. Initial brain storming & 0d calculation helped in understanding the design optimization required in each and every crank-train parts. Considering the guidelines available in terms of L/d ratio for the bearing, connecting rod length, the overall linkage specification was set up for starting with design work for
Khandelwal, Meha
Boosting the development of a BEV Rear Twist Beam suspension through a pioneering approach: ADAMS Real Time on Driving simulator2026-26-0397To be published on 01/16/2026
Twist beam suspensions are widely utilised in passenger vehicles because of their simplicity and cost-efficiency, yet they provide engineers with a complex challenge as their performance depends entirely upon the structural properties of the beam itself. Traditional methodologies rely on the generation of Modal Neutral Files (MNF) based upon vehicle dynamics requirements and packaging constraints, which is a highly time-consuming process that starts failing to fulfil the demands of a market where development times are being exponentially reduced. Besides this, part of flexible body’s real behaviour might be lost in the process of converting multibody models into parametric ones, which, in turn, presents difficulties in modifying compliant-related items. Thanks to a novel approach followed jointly by Mahindra and IDIADA, quick identification and optimisation of key tuneable items is achieved by employing a hybrid solution that combines full flexible and FE elements in ADAMS Car
Osorio, Alejandro García, Prabhakara Rao, Vageesh, Asthana, Shivam, Rasal, Shraddhesh
Driver-in-the-Loop Optimization of Tire-Vehicle Dynamics using Virtual Tire Development approach through CD Tire Models2026-26-0099To be published on 01/16/2026
This study provides a virtual development process using VI-Grade Driving Simulator and Adams simulation integrating CD tire models to modify all-terrain tire performance for an altered and new proposed version of an existing production vehicle. The proposed alteration introduces ride height changes, weight distribution changes, and centre of gravity changes from existing vehicle design. The proposed new vehicle variant also introduces tire change from highway terrain type to all-terrain type as it was intended to deliver some off-roading capabilities, thereby vehicle dynamics and kinematics recalibration & tuning required. The conventional development cycles through physical prototypes are time-consuming and expensive. Alternatively, this solution combines high-fidelity tire simulation, driver-in-the-loop (DIL) simulation, and multi-body dynamics analysis to reduce development time and prototype cycles. The proposed method begins with baseline correlation, augmenting Adams vehicle
Shrivastava, Apoorv, Asthana, Shivam
Correlation of Power Rating for Battery Electric Vehicles Simulation vs. Test Validation in Accordance with regulatory compliance2026-26-0575To be published on 01/16/2026
Accurate power and energy assessment in Battery Electric Vehicles (BEVs) requires high-fidelity simulation models that reflect real-world performance. This study presents a power rating correlation of the Stellantis Dodge model in compliance with the GTR 21 standard, validating the simulation’s accuracy against physical test data. Simulations conducted using STLAsim, Stellantis’ proprietary tool, estimate EDM power at the inverter input and axle output under standardized conditions. The operating parameters were shared with the testing team to ensure consistency; however, the initial test results deviated by approximately 18 kW from the simulation target. To address this delta, EDM speed and vehicle speed were optimized, achieving a close alignment with the simulation and meeting the GTR 21 power rating criteria. The results demonstrate a strong correlation between the simulation and test data, confirming the model’s accuracy and reliability. This study underscores the effectiveness of
Mahajan, Prasad, ali, Shoaib
Implementation of new educational technologies for training personnel for the transport industry2026-26-0241To be published on 01/16/2026
Today's challenges affect the system of training highly qualified personnel for the transport industry, which is actively transforming through the introduction of innovative educational technologies. Key areas have become digitalization of the educational process, integration of virtual simulators, augmented reality technologies (AR/VR) and platform solutions for managing educational programs. Today, transport education in Russia is focused on advanced training of specialists capable of working with autonomous transport and neural network solutions. Development prospects are associated with further integration of adaptive educational trajectories and global network platforms to ensure the competitiveness of graduates in the digital economy. Taking into account the specifics of the modern Russian transport industry, using an analytical method, the article examines solutions to some priority problems, namely: • Methods of modernizing educational programs through designing «professions of
SHISHANOV, Sergei, Kurmaev, Rinat, revenok, Svetlana
Optimizing System Design performance and its accuracy using Feature Importance2026-26-0421To be published on 01/16/2026
With continuous improvements in model accuracy and computational performance, system simulation can be seamless integrated into development tools and methodologies as a "virtual test bed" in synergy with experimental tests. However, virtual vehicle and powertrain thermal models still face significant obstacles in realizing their full benefits. One common challenge is modeling accuracy and its anomalies, particularly in models provided by internal automotive companies and external suppliers. The models developed for vehicle thermal management and its systems have been created using specific software, making it difficult to verify the accuracy of the models across different systems and their performance. Integrating different systems in a Co-simulation environment has affected the overall accuracy of the Vehicle Simulation Platform model. This situation significantly reduces the return on investment in model development. To address these issues, we propose deploying feature importance
Srinivasan, Rangarajan, SARAPALLI RAMACHANDRAN, Raghuveeran, ASHOK BHARDE, Pooja, SARAVANAN, Vivek
Study on Motorcycle Rider Model Using Reinforcement Learning - Learning Examples Including Following Target Velocity and Basic Research on Rider Proficiency2024-32-002804/18/2025
In this study, an initial approach using deep reinforcement learning to replicate the complex behaviors of motorcycle riders was presented. Three learning examples were demonstrated: following a target velocity, maintaining stability at low speeds, and following a target trajectory. These examples serve as a starting point for further research. Additionally, the proficiency of the constructed models was examined using rider proficiency evaluation methods developed in previous studies. Initial results indicated that the models have the potential to mimic real rider behaviors; however, challenges such as differences between the model’s output and what humans can produce were also identified for future work.
Mitsuhashi, Yasuhiro, Momiyama, Yoshitaka, Yabe, Noboru
Feasibility of Road Longitudinal Profiles for Automated Vehicles Based on Vehicle Dynamics2025-01-715003/19/2025
With the development and maturity of new generation digital technologies such as artificial intelligence, Internet of Things, and 5G mobile communication, their integration with physical products is becoming increasingly seamless. Automobiles serve as a prime example in this regard. In recent years, automated vehicle (AV) technologies have emerged as a prominent focal point, witnessing an escalating acceptance in the market and a growing number of self-driving vehicles on the roads, existing roads are primarily designed for traditional human-driven vehicles (HVs). Due to the differences in perception between automated systems and human drivers, it is essential to assess AVs' feasibility to current road infrastructure. This paper analyzes the safety and comfort of automated vehicles equipped with adaptive cruise control systems (ACC-AVs) on longitudinal road profiles from the perspective of vehicle dynamics. Firstly, a co-simulation platform integrating PreScan, CarSim, and Simulink
Li, Zezhou, Cai, Mingmao, Gu, Tianqi, Yu, Bin
Research and Simulation Implementation of Self-Driving Vehicle Trajectory Optimization in Networked Intersection Environment2025-01-719402/21/2025
Nowadays, the rapidly developing Connected and Autonomous Vehicle (CAV) provides a new mode of intersection vehicle cooperative control, which can optimize vehicle trajectories and signal phases in real time and reduce intersection delays through the advantages of vehicle-road cooperative information interaction and the high controllability of CAV. In this paper, the intersection of Jintong West Road and Guanghua Road in Beijing is taken as the research object, and the vehicle trajectory constraints, acceleration constraints, speed constraints, safe driving constraints, signal switch constraints and traffic signal control constraints are set up with the minimization of traffic delay as the objective function. The DQN deep reinforcement learning network is constructed based on vehicle states, vehicle actions, reward functions, and update rules, and starts learning and updating to generate the target network. Then, SUMO software is used to simulate and test and compare the trajectory
Xu, Yuting, Zhang, Yong, Wu, Xianyu
Vehicle-Level Control Systems Framework for Use in Create-GV Vehicle Dynamics Simulations2024-01-410709/16/2024
Abstract This paper presents a software framework developed for the simulation of vehicle-level control systems for modern (existing or conceptual) ground vehicles, targeted for high-performance platforms (Linux clusters). The framework augmented existing ground vehicle simulation environments (such as CREATE-GV MERCURY or other object-oriented software packages) making it possible to perform a comprehensive evaluation of a ground vehicle’s performance when equipped with vehicle level controllers to determine the effectiveness of the control systems on the vehicle. The framework, implemented as part of the PACE (Powertrain Analysis Computational Environment), was comprised of software components (a C++ objects library) simulating various vehicle-level controllers, an Application Programming Interface for the development of new components to be used within the framework, and C++ code for integrating these components into simulations of control systems within a ground vehicle simulation
Jelinek, Bohumir, Henley, Greg, Card, Angela, Hannis, Tyler, Gibson, Michael, Priddy, Jody, Boyle, Sara, Figueroa-Santos, Miriam, Mange, Jeremy
Gearshift Simulator – Perceive Gearshift Feel at Early Development Stage2024-26-029201/16/2024
One of the very first customer touchpoint in a vehicle is quality of gear shifting. Gearshift quality is perceived as a symbol of refinement, robustness of a vehicle. Globally, lot of efforts are taken to refine the gearshift quality. Design improvements in internal components of transmission, cable and shifter assembly, knob design iterations are carried out to arrive at optimum gearshift quality at the vehicle level. Current practice for this activity includes processes such as design modification, manufacturing of proto components, assembly of components and fitment in the vehicle. This vehicle is then instrumented with sensors and data acquisition units to capture the parameters which determine the gearshift quality. This is an iterative process which goes on until necessary refinement/improvement is achieved. This process requires investment of lot of time, efforts and the budget. This paper describes a virtual approach to arrive at optimum design of components. This paper gives
Tongaonkar, Yogesh Manohar, Patel, Hiral, Thambala, Prashanth, halingale, Amol
A Multi-Disciplinary Optimization Approach for Lightweighting and Performance Improvement of Electric Light Commercial Vehicle2024-26-025201/16/2024
Electric Light Commercial vehicles (eLCVs) are growing rapidly to meet the business and user needs. Electric vehicles are seen as a way to reduce the impact of emissions due to transportation in urban areas. Due to the growth of e-commerce, commercial transportation, and particularly last-mile delivery, is anticipated to increase. In this context, eLCVs have the potential to be a promising solution by tackling the emission impacts, ensuring faster delivery along with ideal running costs and payload capacity. To increase the range of electric vehicle, it has to be designed for lighter weight with optimal performance in order to meet the user requirements. Cargo capacity and payload have to be taken into account while design & validating the vehicle structure under static and dynamic conditions. Simulation driven product development will help the design team to account the possible design failure cases at system and vehicle level. However, all these cases are multi-disciplinary and
Kukkillaya U, Saketh, Gumma, Muralidhar, Potarlanka, Srinivasarao, Durgam, Padmaja
System Validation with Battery-in-the-Loop Configuration using a Virtual Testing Toolchain2024-26-011601/16/2024
Challenge Today, the battery development process for automotive applications is relatively decoupled from the vehicle integration and system design validation phase. Battery module design targets are very much linked to vehicle-level requirements, such as performance, lifetime and cost but are often disregarded at very early design phases. Converging these requirements to more specific battery pack criteria is not straightforward and often based on previous vehicle generation data or cell testing measurements usually performed after prototyping and via artificial charge and discharge patterns Solution To reduce time to market, AVL proposes here a methodology guided by virtual testing techniques to consider vehicle-level validation steps at the very early phase of battery testing. In a first step, AVL utilises a vehicle system simulation supported with real-world test scenarios and high-fidelity battery models. To generate such realistic scenarios, AVL uses an online digital map and
Kolar, Ales, Santiago, Prabhu, Kural, Emre
Generation of tire digital twin for virtual MBD simulation of vehicles for durability, NVH and handling evaluation2024-26-030101/16/2024
With the recent development in virtual modelling and vehicle simulation technology, many OEM's worldwide are using digital road profiles in virtual environment for vehicle durability load prediction and virtual design evaluation. For precise simulation results, it is important to have the tire digital twin which is the realistic representation of tire in the virtual environment. The study comprises of discussion about different types of tire models such as empirical, solid model, rigid ring model and flexural ring models such as Pacejka, MF Swift, CD tire, F tire etc. and also the complexity involved in development of these tire models. Generation of virtual tire model requires highly sophisticated test rigs as well as vehicle level testing with Wheel Force transducers and other vehicle dynamics sensors. The large number of data points generated with testing are converted in standard TYDEX format to be further processed in various software tool for virtual model generation. Thus, a
Bakal, Nikhil
Simulation of in-vehicle tire noise performance from single-tire test stand2025-01-006105/05/2023
A test and signal processing strategy was developed to allow a tire manufacturer to predict vehicle-level interior response based on component-level testing of a single tire. The approach leveraged time-domain Source-Path-Contribution (SPC) techniques to build an experimental model of an existing single tire tested on a dynamometer and substitute into a simulator vehicle to predict vehicle-level performance. The component-level single tire was characterized by its acoustic source strength and structural forces estimated by means of virtual point transformation and a matrix inversion approach. These source strengths and forces were then inserted into a simulator vehicle model to predict the acoustic signature, in time-domain, at the passenger’s ears. This approach was validated by comparing the vehicle-level prediction to vehicle-level measured response. The experimental model building procedure can then be adopted as a standard procedure to aid in vehicle development programs.
Nashio, Hiroshi, Kajiwara, Kohei, Rinaldi, Giovanni, Sakamoto, Yumiko
Model Based NVH Simulator for Virtual Development2025-01-005905/05/2023
For mature virtual development, enlarging coverage of performances and driving conditions comparable with physical prototype is important. The subjective evaluation on various driving conditions to find abnormal or nonlinear phenomenon as well as objective evaluation becomes indispensable even in virtual development stage. From the previous research, the road noise had been successfully predicted and replayed from the synthesis of system models. In this study, model based NVH simulator dedicated to virtual development have been implemented. At first, in addition to road noise, motor noise was predicted from experimental models such as blocked force and transfer function of motor, mount and body according to various vehicle conditions such as speed and torque. Next, to convert driver’s inputs such as acceleration and brake pedal, mode selection button and steering wheel to vehicle condition, 1-D performance model was generated and calibrated. Finally, the audio and visual feedback
Park, Sangyoung, Dirickx, Tom, Kang, Yeon June, Nam, Jeong Min
Leveraging user feedback to optimize the tuning of EVSE models2025-01-002005/05/2023
The implementation of active sound design models in vehicles requires precise tuning of synthetic sounds to harmonize with existing interior noise, driving conditions, and driver preferences. This tuning process is often time-consuming and intricate, especially facing various driving styles and preferences of target customers. Incorporating user feedback into the tuning process of Electric Vehicle Sound Enhancement (EVSE) offers a solution. A user-focused empirical test drive approach can be assessed, providing a comprehensive understanding of the EVSE characteristics and highlighting areas for improvement. Although effective, the process includes many manual tasks, such as transcribing driver comments, classifying feedback, and identifying clusters. By integrating driving simulator technology to the test drive assessment method and employing machine learning algorithms for evaluation, the EVSE workflow can be more seamlessly integrated. But do the simulated test drive results
Hank, Stefan, Kamp, Fabian, Gomes Lobato, Thiago Henrique
Development and Evaluation of Comfort Assessment Approaches for Passengers in Autonomous Vehicles2023-01-078804/11/2023
Passenger comfort is a critical factor in user acceptance of autonomous vehicles (AVs). Despite existing methods for passenger comfort assessment, new approaches to assessing passenger comfort in AVs may be valuable to the automotive industry. In this paper, continuous pressing-based and discrete smartphone-based approaches for comfort assessment were designed and implemented in a user study. Participants used the two approaches to evaluate their comfort levels in an experimental study based on a high-fidelity autonomous driving simulator. Performances of the two approaches in assessing comfort levels were analyzed and compared. In general, the discrete approach showed better measurement repeatability and lower measurement bias than the continuous approach. The performance gap of the continuous approach could be reduced with proper post-processing measures. Discussions on the potential uses of the approaches were also raised.
Su, Haotian, Brooks, Johnell, Jia, Yunyi
682 Guidance for Air Transport Service Bulletins (TEST POPST CT and APPSEARCH)ARINC682 (Current)09/21/2022
This document provides industry guidance to supplement ATA standards and airframe requirements, addressing gaps and vague areas to move suppliers/vendors and airframers toward a common standard for format and wording in Vendor Service Bulletins (VSB) for Air Transport components. This document supplements the VSB guidance of iSpec 2200, providing clarity of requirements and resulting in a common standard wording and format of VSBs from all suppliers on all aircraft types. The guidance in this document is intended to be applied to VSBs created and released subsequent to the release of the guidance document and will not require the retroactive update of VSBs released prior to the creation and release of the guidance.
Enhancing Meta Model of the Brake Pad Friction Coefficient using the Explainable Machine Learning2022-01-117509/19/2022
As increasing system complexity and various customer demands result in the need for highly efficient vehicle development processes. Once the brake torque is predicted accurately during the driving scenario in the earlier stage, it will be able to prevent the changing the vehicle or brake system design to satisfy the legal regulation and customer requirement. As brake torque performance target allocate brake pad friction coefficient level and characteristic, the accurate friction coefficient prediction should be preceded for accurate prediction for brake torque. Generally, the friction coefficient of the brake pad is known to vary nonlinearly depending on the physical properties of the disc and the pad, as well as the rotational speed of the brake disc, the disc temperature, and the hydraulic pressure. Furthermore, it varies depending on the driving scenario even the same condition. Therefore, there was a need for a new method : machine learning to solve this problem. A Mixed Effect
Cho, Sunghyun, Bang, Sunghoon, Jang, Jiwoo, Kim, Youngjae
SAE TOMORROW TODAY: Using AI to Scale Autonomous Vehicles1322906/03/2022
A driving school for self-driving vehicles? It's here and proving to be a game-changer. Waabi World is the most scalable, highest fidelity, closed-loop simulator for autonomous vehicles. Powered by AI, it's an immersive and reactive environment that can design tests, assess skills, and teach the self-driving "brain" to learn to drive on its own. This technology significantly reduces the need to test drive in the real world resulting in a safer, more affordable solution. When AI trailblazer & CEO Raquel Urtasun founded Waabi, she set out to scale one of the most important technologies of our generation. By taking an AI-first approach, Raquel and her team are unlocking the potential of self-driving technology and accelerating the commercial deployment of autonomous vehicles. And their work is changing the world for the better.
Hineman, Marcie
Virtual EET for Combat Robotic Vehicles: Integrating Qualitative and Quantitative Analyses in Autonomous Vehicle Simulation2022-01-043203/29/2022
Spurred on by the constraints of the COVID-19 pandemic, virtual testing is becoming an increasingly essential method for validation and verification of autonomous ground vehicle simulation tools. The Mobility Systems Branch (MSB) of the US Army Corps of Engineers Engineering Research and Development Center (ERDC) Geotechnical and Structures Laboratory (GSL) has developed a new approach in physics-based virtual testing of autonomous ground vehicle systems through the incorporation of both qualitative and quantitative data in congruency with MSB's Simulation-in-the-Loop laboratory. Virtual testing of autonomous vehicles combines simulation tools consisting of vehicle and sensor models represented in a virtual scene, performer observations, and modeling and simulation observations. The first iteration of a Virtual Engineering Evaluation Test (V-EET) for Simulation Tools for Combat Robotic Vehicles took place in 2021 at the ERDC GSL in Vicksburg, Mississippi. Virtual testing took place
Lyons, Jessica W., Jackson, Rebekah, Richards, James, Gates, Burhman, Fairley, Josh, Price, Stephanie
A constant equivalence ratio multi-zone approach for a detailed and fast prediction of performances and emissions in CI engines2022-01-045503/29/2022
The paper illustrates and validates a novel predictive combustion model for the estimation of in-cylinder performances and pollutant production in CI engines. The numerical methodology was developed by the authors for near real-time and / or control-oriented applications, while aiming at an accurate description of the air mixing process by means of a multi-zone approach of the air-fuel mass. Charge stratification is estimated via a 2D representation of the fuel spray distribution that is numerically derived by an axial one-dimensional control-volume description of the direct injection. The radial coordinate of each control volume is reconstructed a posteriori by means of a local distribution function. Fuel mass clustered in each zone is further split in ‘liquid’, ‘unburnt’ and ‘burnt’ sub-zones, given the local properties of the fuel spray control volumes with respect to space-time location of modelled ignition delay, liquid length, and flame lift-off. Multiple injections are described
Tamborski, Matteo, D'Errico, Gianluca, Lucchini, Tommaso, Cerri, Tarcisio, Onorati, Angelo
Numerical simulation of Vehicle Dynamics performance of a Formula 1 Car based on 2022 regulations with integrated Power Unit2022-01-044503/29/2022
The Formula One World Championship gathers the best of engineering to produce the safest and the fastest cars in straight lines and corners. The new FIA budget restrictions and the cost of testing push the teams to use numerical simulations for the development of their car, aiming to reproduce the behaviours of the vehicle on different racetracks. Current published models use a rigid body representation of the chassis to describe the motion of the car. This omits the influence of the powertrain, in particular by not fully representing the action of the Motor Generator Unit-Kinetic (MGUK) system acting on braking. The aim of this work is to predict the vehicle dynamics performance of a Formula One car from a powertrain perspective on three circuits of the Formula One Championship race Calendar. The vehicle model developed on GT-Suite and which complies with the FIA 2022 technical regulations, includes the main elements for predicting vehicle dynamics, such as the vehicle body, wheels
Praneuf, Valentin, Samuel, Stephen
Construction of Personalized Driver Models based on LSTM using Driving Simulator.2022-01-098103/29/2022
The purpose of this study is to construct personalized driver models using Long Short-Term Memory (LSTM). The driver model is used for automated driving systems and driver assistance systems and is expected to control longitudinal and lateral directions of vehicles. For example, driver models including the individual driving characteristics adapt the control of systems, which can reduce discomfort and nuisance to drivers. The LSTM models in this study enable the time series data processing. In some previous studies, LSTM models have been used to investigate pedestrian behaviors. However, there are few studies of driver behavior models based on LSTM. In this study, we measured the data of the driver's driving operation using a Driving Simulator (DS). DS consists of a visual display and a motion display, of which motion functions consist of a 6-axis motion device and a turntable. DS has a cockpit using an actual body of a vehicle, and a visual information projected on a cylindrical
Hamada, Ayaka, Oikawa PhD, Shoko, Hirose, Toshiya
Mixed Reality Driving Simulator as a Training Tool for Autonomous Vehicles2022-01-097303/29/2022
While manufacturers have invested billions of dollars in the self-driving car technology, little has been done to prepare drivers to this paradigm shift in transportation. According to a recent survey published by PAVE (Partners for Automotive Vehicle Education, 2020) of 1,200 adult drivers, 48% of American drivers say they “would never get into a taxi or ride-share vehicle that was driven autonomously”. To overcome these issues and to address this problem, we developed a highly immersive driving simulator which provides a safe platform for drivers of all ages and abilities to get a first hand experience of a self-driving vehicle. The proposed system provides a highly innovative use of mixed reality and can be easily retrofitted to any vehicle to provide mechanical immersion. Visual immersion is obtained through a mixed reality experience delivered through a Head Mounted Display. In this paper we will discuss the various mixed reality techniques that were implemented during the
Loeb, Helen S.
Effects of combined Phone use and Alcohol-impaired Driving Behaviours among Female Drivers; A Driving Simulator Study2022-01-097203/29/2022
Driver behavior and passenger safety remain one of the foremost axes developed for investigative research in driving simulator studies. However, a plethora of studies on driver’s behaviour using driving simulators has seen the underrepresentation of female participants whose characteristics have scarcely been investigated. More tellingly, the responsibility of female drivers for road traffic accidents with respect to cell phone use and alcohol-impaired behaviours is overlooked. Using a driving simulator experiment, this study thematically modelled and analysed the combined upshot of cell phone use and alcohol-impaired tasks among 15 female driver participants. The experiment was carried out in an urban simulated environment to extract discrete responses from these intoxicated drivers at four different levels of blood alcohol concentration (BAC) (0%, 0.02%, 0.05%, and 0.08%). Vehicle telematic variables such as acceleration (m/s2), vehicle speed (m/s), angular velocity (rad/s2) and RPM
Frimpong, Justice Alex, Prince PhD, Owusu-Ansah, Agyeman, Philip, Arhin, Ebenezer, Fayzullayevich, Jamshid
A Novel Method For Displaying Systems Datums Based On "Cognitive Load"2022-01-096903/29/2022
Operator attention has been a focus of human factors research. This research has defined how electronic devices and other stimuli can become distractions when included in the operator environment. Distracted driving occurs when a stimulus interrupts the essential cognitive processes of operating a vehicle. Attention is the most often mentioned of this loosely ordered sequence of processes that also includes perception, memory, cognition, and planful behaviors. These processes are the “cognitive demands” of vehicle operation. There is another issue, similar to “distracted driving,” that may cause an operator to fail to respond appropriately. That issue is the “cognitive load” of human-machine interface devices, including instrument clusters. The present paper explores the effects of cognitive load on the speed of operator responses. It describes a novel method for displaying systems datums that is designed to manage cognitive load. The paper describes a pilot study where participants
Havins, William
High-Fidelity Heavy-Duty Vehicle Modeling Using Sparse Telematics Data 2022-01-063503/29/2022
Heavy-duty commercial vehicles tend to have poor energy efficiency due to relatively high mass, poor aerodynamic characteristics, and high auxiliary power loads. These factors lead directly to high fuel cost per mile, perpetuate domestic dependence on imported oil, and contribute to climate change and environmental damage in the form of air pollution. The optimization of energy efficiency in these vehicles presents a way to reduce industry expenses while also taking action on the growing issue of climate change. Vehicle modeling provides a path for energy efficiency improvement by allowing rapid experimentation of different vehicle characteristics on fuel consumption. This research focused on creating higher fidelity models using real-world, sparsely collected telematics data from a large fleet of heavy-duty vehicles. The largest challenge encountered in this research was the use of sparse data, where samples are sporadically collected, leading to infrequent sampling rate and
Carow, Kyle, Cantwell, Nathaniel, Ivanco, Andrej, Holden, Jacob, Baker, Chad, Miller, Eric, Asher, Zachary
Comfort Improvement for Autonomous Vehicles using Reinforcement Learning with In-situ Human Feedback 2022-01-097403/29/2022
Autonomous vehicles have become a driving force to transform our existing transportation system in the near future and will help improve a lot of driving performance such as driving safety, congestion, emissions. Despite that passengers’ comfort in autonomous vehicle (AV) has been an essential topic, there are insufficient methods to significantly increase AVs’ ride comfort or adapt humans to accept AVs. Ride comfort in AVs is dramatically different from that in a traditional vehicle. Traditionally, researchers have investigated ergonomic factors such as seat vibrations and noise. However, the introduction of automated driving functions would lead researchers to shift toward vehicle control factors. In order to make AVs comfortable for humans, we need to know human’s expectations on AVs’ comfort. General approaches to model human expected driving behaviors can be categorized into analytical approaches and heuristic approaches. However, all existing approaches require humans to drive
Xiang, Jun, Guo, Longxiang
A study of driving performance prediction using integrated objective and subjective index for concept design stage2022-01-110403/29/2022
Over the recent years with a strong focus on optimizing its early performance prediction capabilities, vehicle manufacturers around the world are reviewing their virtual development processes with the intent to address the vehicle attributes that are most important to its customers during the early stages of development. However, reliable performance prediction through simulation in the concept development phase for new technologies and new entries in different vehicle segments is a very challenging task. Furthermore, the strong need for increased vehicle energy efficiency requires a precise balancing with the driving performance. Therefore, in order to respond quickly to a rapidly changing market and customer preferences, the development of a robust process to reliably evaluate vehicle concepts is absolutely mandatory. This paper is focused on the prediction of vehicle driving attributes (more specifically handling, and lap time) through the application of various objective and
Jeong, Pilyoung, Lee, Jaekil, OSWALD, MARIO, Ecker, Joachim, KELLNER, STEFAN, Kogler, Manfred
A Geographically Distributed Simulation Framework for the Analysis of Mixed Traffic Scenarios Involving Conventional and Autonomous Vehicles2022-01-105403/29/2022
We report on a technical project that interfaced the National Advanced Driving Simulator (NADS) with SynChrono, a module of the Project Chrono open source platform, to enable real-time, physics-based simulation of multiple autonomous vehicles (AVs). In this setup, a driver at NADS, at the University of Iowa, participates in a traffic scenario that involves AVs that run at the University of Wisconsin-Madison on a cluster supercomputer. The NADS simulator is a driving simulator giving the “most realistic driving simulation experience in the country”. Thanks to its actuators, it can move across its 64-foot by 64-foot bay, rotate and tilt, to emulate vehicle movement and vibrations. In addition, the human driver drives in a full-size cab, surrounded by LED monitors, resulting in an immersive, high fidelity driving simulation experience. SynChrono draws on the Chrono::Vehicle module for detailed vehicle dynamics simulation, providing simulation of vehicle subsystems and tire models
Benatti, Simone, Schwarz, Chris, Young, Aaron, Elmquist, Asher, Serban, Radu, Negrut, Dan
Controller design for path tracking of autonomous vehicle incorporating four-wheel steering system2022-01-034603/29/2022
This research aims to model and assess autonomous vehicle controller while including a four-wheel steering and longitudinal speed control. Such a modeling process simulates human driver behavior with consideration of real vehicle dynamics’ characteristics during standard maneuvers. However, a four-wheel steering control improves vehicle stability and maneuverability as well. A two-degree of freedom bicycle model, lateral deviation and yaw angle, is constructed to describe vehicle dynamics’ behavior. Moreover, a comprehensive traction model is implemented which includes an engine, automatic transmission, and non-linear magic formula tire model for simulation of vehicle longitudinal dynamics. A combination of PID longitudinal controller and fuzzy lateral controller are implemented simultaneously to track the desired vehicle path while minimizing lateral deviation and yaw angle errors. Then, A LQR based rear steering controller is introduced to represent a performance improvement over
Gafar, Ibrahim, Oraby, Walid, Aly, Mahmoud Atef
Development of an Integrated Co-Simulation Environment to Model and Test Advanced DAT Features Utilizing MIL and HIL2022-01-010303/29/2022
The widespread deployment of Connected and Automated Vehicles (CAVs) in the future will bring major changes in the automotive industry and the vehicle features it will offer. Currently, technology and infrastructure are not ready to test and develop CAV features fully in real traffic. Simulators are becoming popular to develop CAVs and assisted automated driving features for all levels of automation to overcome the infrastructural needs of the automotive industry. Simulators allow researchers to design CAV algorithms safely, quickly, and efficiently, and test these algorithms for various metrics. A co-simulation environment, where a vehicle simulator like CarSim and a traffic simulator like Simulation of Urban Mobility (SUMO) feed into each other, is an invaluable tool, allowing CAV features to be tested in a realistic traffic environment. This paper presents a co-simulation environment, where the vehicle simulator CarSim and the traffic simulator SUMO share data. The paper follows the
Kavas-Torris, Ozgenur, cai, Xiaolin, Joshi, Adit, Mittal, Archak, Djavadian, Shadi, Fishelson, James
A driving simulator for UNECE 157 homologation activities2022-01-010803/29/2022
UNECE 157 Regulation is devoted to the homologation of SAE Level 3 automated vehicles. The research and development activities related with the Regulation may require an extensive usage of driving simulators. Developing a sickness-free driving simulator is crucial for extensive tests to be performed with ordinary drivers. The paper presents the technical features of the new driving simulator of the Politecnico di Milano. A number of biometric set of signals is recorded, namely, forces and moments at each single hand at the steering wheel, heart rate variability, skin resistance potential, eye tracking. A matching of the objective monitoring of the driver is compared with the subjective psychological status. An initial population of 30 subjects has been investigated. The new driving simulator has shown a good acceptance by ordinary drivers. Both objective and psychologic subjective assessment agree to state that the new driving simulator may be used to test ordinary driver behavior in a
Gobbi, Massimiliano, Mastinu, Giampiero, Melzi, Stefano, Previati, Giorgio, Sabbioni, Edoardo, Ronconi, Luca
Synthetic LiDAR Point Cloud Data Generation Tool and Deep Learning Validation2022-01-017703/29/2022
LiDAR sensors are set to become common in automated driving due to their high accuracy. However, LiDAR processing algorithm development suffers from lack of training data, partly due to the sensors' high cost. The few public datasets (e.g. KITTI) offer poor coverage of edge case scenarios, whereas these are essential to address to support safe self-driving. We address the unmet need for abundant, high-quality LiDAR data with the development of an artificial LiDAR point cloud generation tool and validate this tool's performance against the PIXOR model, which was trained on the KITTI dataset. The tool makes use of a single camera raycasting process to generate synthetic data and is used in conjunction with filtering techniques to create segmented and filtered object class-specific datasets . The tool has the potential to support the low-cost generation of accurate bulk data in support of training advanced self-driving algorithms, with configurability to simulate commercial and yet-to-be
Karur, Karthik, Pappas, Georgios, Siegel, Joshua, Zhang, Mi
Pedestrian Safety Performance Prediction Using Machine-Learning Techniques2021-26-002609/22/2021
As per WHO 2018 report, pedestrian fatalities accounts for 23% of world road accident fatalities. Every day 850 pedestrians lose their lives in the world. As per MoRTH 2018 report, 16% of road accident fatalities are of pedestrians in India. Everyday 64 pedestrians lose their lives in India. Based on accident data, one of the most common reason for the pedestrian fatality is head injury due to primary contact from vehicle front-end structure. Pedestrian head injury performance is highly depended on front-end styling, bonnet stiffness, clearance with aggregates underneath the bonnet and hard contact points. During concept stage of vehicle development, safety recommendation on front-end design is provided based on geometric assessment of the class A surface, as the maturity of the geometry is insufficient to carryout CAE simulations. This paper presents the novel approach of using machine-learning algorithms to predict the head injury performance at the early stage of the vehicle design
Kaushik, Bharat, Daphal, Pratap, Khare, Pratyush, Koralla, Sivaprasad, Bera, Satadru
Reward Function Design via Human Knowledge Graph and Inverse Reinforcement Learning for Intelligent Driving2021-01-018004/06/2021
Motivated by applying artificial intelligence technology to the automobile industry, reinforcement learning is becoming more and more popular in the community of intelligent driving research. The reward function is one of the critical factors which affecting reinforcement learning. Its design principle is highly dependent on the features of the agent. The agent studied in this paper can do perception, decision-making, and motion-control, which aims to be the assistant or substitute for human driving in the latest future. Therefore, this paper analyzes the characteristics of excellent human driving behavior based on the six-layer model of driving scenarios and constructs it into a human knowledge graph. Furthermore, for highway pilot driving, the expert demo data is created, and the reward function is self-learned via inverse reinforcement learning. The reward function design method proposed in this paper has been verified in the Unity ML-Agent environment. The result shows that
Guo, Rong, Hong, Ze, Xue, Xiang
Driver Identification Using Driving Behavior, Habits and Driver Characteristics2021-01-018504/06/2021
In this paper, a driver identification scheme is studied using general driver inputs such as accelerating, braking and steering behavior, in addition to the settings related to driver’s physical characteristics, such as driver’s seat position. Several drivers are selected with various ages, genders and driving skills to participate in the study. Their driving data is collected using the same test vehicle, and while driving on the same routes. This helps eliminate the inherent vehicle to vehicle variations and the impact of the route differences and enables the identification algorithm to focus on the driving behavior. The driving routes are broken down into shorter segments where the driving features are calculated and populated in these segments. To reduce the identification bias towards certain rare events in the ride, the features are reset at the beginning of each trip segment. This additionally helps to ensure that there is no spill of feature values across the segments. Trip
Jalaliyazdi, Milad, Sheikh, Tooba, Bani Milhim, Alaeddin, Dixon, Regan, CHIM, Huong
Assessment of Squeak and Rattle Noise of a Car Seat Using 3D Sound Intensity Measurements2020-01-155706/03/2020
Assessment of squeak and rattle noise of a car seat using 3D sound intensity measurements Squeak and Rattle (S&R) noises are transient sound events occurring when adjacent parts come into contact, either impacting or sliding. All components and sub-systems integrated in a vehicle may produce noise when excited with certain vibro-acoustic load. S&R noise can be linked to the perceived build quality, durability and even discomfort or annoyance. As a result, car manufacturers have strict regulations to prevent noise issues. Current vibro-acoustic validation tests can vary in complexity from full vehicle simulation to component level tests. Additionally, subjective assessments are often required to locate problematic areas and quantify their relevance. In this paper, S&R noise of a car seat is investigated using 3D sound intensity measurements. A multi-axial shaker is used to drive the seat with a short time-stationary excitation extracted from a road profile. Results show that both active
Fernandez Comesana, Daniel, Gonzalez, Damian, Storani, Tales, Meng PhD, Fanyu
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