Browse Topic: Comfort

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Optimization of the Light Environment for the Remote Control Tower Humancomputer Interaction Interface Based on Ergonomics2026-99-18477/17/2026
The development of remote tower systems in aviation and the resurgence of multi-display interfaces and virtual environments have dramatically influenced ATC, increasing both controllers’ visual demands and their ergonomic needs. This study uses the Visual Ergonomics to study the impact of screen luminance level, along with color temperature, on trainees’ visual performance, fatigue, and physical discomfort in the control rooms of the Remote Tower. By combining a simulated remote control system with spectrometer measurements, PVT alertness tests, VMT (Visual Memory Test) measurements, and subjective evaluations, COST B21 can build up a multi-dimensional ergonomic assessment framework. Eight levels of display luminance (and color temperature) were tested, including two illuminance levels (300 lx and 400 lx) and four color temperature ranges (6000 K–9000 K). Using the Analytic Hierarchy Process (AHP), these parameters were assigned weights to derive a Visual Ergonomics (VE) scoring model, and the ideal visual performance was observed at 400 lx illuminance and 8000 K CCT. The results clearly illustrate the significant impact of display parameters on operational performance in remote tower systems and provide both practical data and a theoretical basis for the human factors design and fatigue reduction research on RTSs.
Zhong, Linfeng, Hu, Ruohui, Luo, Peilin, Zuo, Qinghai, Zhong, Qingwei, Ai, Yi
Evaluation of Civil Aircraft Options Based on Grey Analytic Hierarchy Process2026-99-18087/17/2026
Product options are an important means for civil aircraft manufacturers to meet market demand, increase revenue, and enhance competitiveness. How to achieve a customized configuration of civil aircraft options is the focus of attention for aircraft manufacturers. In order to reduce manufacturing costs and cover more target markets, it is necessary to pay attention to the customized detail design of aircraft products in the early stages of design. At present, academic research on product selection is relatively limited and lacks quantitative evaluation methods. This article selects four elements to form an evaluation indicator system, namely comfort, competitiveness, cost investment, and maintainability; establishes a civil aircraft option evaluation model based on grey correlation analysis, quantifies the degree of correlation between product options and customer needs, and uses the analytic hierarchy process to reflect the weight differences of evaluation indicators. Taking the option list of a wide-body aircraft as an example, the model was used to evaluate and rank the options, verifying the rationality of the model and providing a reference for aircraft manufacturers to make provisions in advance.
Lu, Meihua
Analysis and Improvement of Rattle Noise in Manual Transmission2026-99-05277/10/2026
In response to the problem of manual transmission rattle noise in the acceleration process of a truck, the mechanism of the problem is analysed, and the scheme is developed and verified from two aspects: reducing the torsional vibration of the system and reducing the response of the transmission gear. The results show that, on the one hand, reducing the clutch stiffness and optimizing the torsional vibration of the system can reduce the rattle noise of the transmission; On the other hand, it can also reduce the rattle noise of transmission gears by improving the engagement precision of transmission gears and reducing the gear clearance. Considering the improvement effect, cost, and influence on other performance of the two schemes, the appropriate engineering scheme is selected to effectively solve the problem and improve the riding comfort of the product.
Yang, Zhijie, Xu, Binghua
Context-Dependent Assessment of Driving Dynamics and User Perception in Automated Driving2026-01-07577/1/2026
As automation advances and occupants transition from active drivers to passive passengers, understanding how automated driving behavior is evaluated becomes increasingly important. While longitudinal and lateral vehicle dynamics are known to influence perceived comfort and safety, it remains unclear to what extent motion–perception relationships remain stable across urban traffic contexts. This study compares two real-world investigations of automated driving: a left-turn maneuver at a signalized intersection on a test track and a roundabout maneuver with a shuttle in public traffic. Both datasets include high-resolution vehicle dynamics and structured subjective ratings. A consistent objectification approach was applied to examine the transferability of motion–perception relationships across contexts. However, differences in vehicle platform, automation level, trajectory characteristics, and study design limit direct comparability and require cautious interpretation. Despite partially overlapping ranges in selected peak-based dynamic parameters, such as longitudinal acceleration, subjective comfort and safety ratings were consistently higher in the roundabout scenario. Furthermore, strong associations were observed between motion parameters and subjective evaluations in the intersection context (adj. R2 up to 0.891), whereas objective parameters showed only limited explanatory power in the roundabout scenario (adj. R2 ≤ 0.06). The results indicate that motion–perception relationships derived within a specific context may not be directly transferable across different traffic scenarios. The findings highlight limitations of globally derived motion-based evaluation models and underline the importance of validating objectification approaches across diverse operational environments.
Panzer, Anna, Strenge, Emma, Iatropoulos, Jannes, Henze, Roman
Methodological Framework for the Derivation of Objective Criteria for Ride Comfort Assessment2026-01-07657/1/2026
In recent years, the automotive industry has faced increasing pressure to accelerate development cycles and reduce costs. Simultaneously, ride comfort standards have risen due to the ongoing integration of autonomous driving functionalities. Consequently, it has become essential to ensure that ride comfort attains a high degree of maturity at the very early stages of the automotive development process. This necessitates the establishment of objective criteria that enable the reliable estimation of subjective ride comfort, utilizing simulation-based assessment methods. This study introduces a methodological framework designed to systematically translate the manufacturer specific subjective perception and assessment of ride comfort into objective descriptions using a dynamic driving simulator. The framework is conceived as a generic approach, enabling the comprehensive application to a wide spectrum of subjective ride comfort phenomena, while being specifically optimized for the challenges of the automotive industry. Employing this framework facilitates the derivation of highly detailed, objective descriptions of subjective ride comfort evaluations, which promotes the achievement of advanced ride comfort maturity for new vehicles in early development phases and supports the overall enhancement of ride comfort. The exemplary application of the framework to a transient, one-dimensional ride comfort phenomenon demonstrates its capability to derive robust objective models from subjective evaluations conducted with professional test drivers in a dynamic driving simulator environment.
Stroesser, Simon, Zwosta, Tobias, Angrick, Christian, Neubeck, Jens, Wagner, Andreas
BO-NRPPO: A Bayesian Optimization–Tuned Proximal Policy Optimization Framework for Semi-Active Suspension Control10-10-03-00256/25/2026
Semi-active suspension systems enhance ride comfort and handling performance by adaptively modulating damping characteristics. However, conventional model-based controllers often fail to maintain optimal performance under uncertain and time-varying vehicle conditions. This article proposes Bayesian Optimization–Tuned Proximal Policy Optimization with Non-Parametric Rewards (BO-NRPPO), a novel reinforcement learning (RL) framework that integrates Bayesian Optimization (BO) with Proximal Policy Optimization (PPO) and a non-parametric reward function (NRF). The proposed approach enables adaptive self-tuning, data-driven reward shaping, and uncertainty-aware policy learning. Moreover, a Trapezoidal Simple Moving Average (TSMA)–based reward normalization scheme is introduced to accelerate convergence and stabilize training. Simulation results across diverse driving scenarios demonstrate that BO-NRPPO outperforms the passive suspension, the classical Linear Quadratic Regulator (LQR), and PPO with parametric rewards. Specifically, compared to the passive suspension and the LQR baseline, BO-NRPPO achieves up to 6.63% and 5.14% improvements in handling stability, respectively. Concurrently, it delivers maximum enhancements of 46.96% and 42.55% in ride comfort over these two baselines. For real-world vehicle applications, this adaptive self-tuning capability significantly reduces the time-consuming manual calibration efforts typically required in chassis development. Furthermore, Hardware-in-the-loop (HiL) validation confirms its real-time applicability and robustness under uncertain driving conditions, highlighting its immense potential as a scalable intelligent suspension control solution.
Chen, Guoying, Wang, Xinyu, Wang, Jiaqi, Zhan, Xinwang, Bi, Chenxiao, Cong, Shiqi, Hua, Min, Sun, Tianjun, Gao, Zhenhai
Vibro-Acoustic Simulation for Vehicle Audio Design2026-01-06706/20/2026
Acoustic user interfaces and audio experiences are among the leading comfort factors in new vehicle interior designs. OEMs are more and more focusing on loudspeaker design and positioning, to provide the most immersive experience to the customers. The industrial target is to be able to predict the performance of an audio system in early design phases. This paper presents an integrated vibro-acoustic methodology enabling early-stage prediction of loudspeaker performance in real vehicle conditions. The approach combines electromechanical characterization, a hybrid loudspeaker calibrated model valid across the audible range and coupled FEM/BEM/SEA simulations to capture the loudspeaker response in the vehicle’s cabin considering door-installation effects and cabin acoustics. The method is validated experimentally on a rear-door loudspeaker installed in a production vehicle, showing strong correlation with measured SPL. A final application case demonstrates its capability to assess the impact of alternative speaker mounting positions during the design phase.
Zerrad, Mehdi, Errico, Fabrizio, Mordillat, Philippe
Ergonomic Assessment of Glove-Box Design in Passenger Vehicles for the Indian Scenario15-19-02-00096/10/2026
Passenger vehicles experience severe packaging constraints around the instrument panel, rendering glove-box operation a critical yet ergonomically underexplored interaction. Although glove-box interaction occurs frequently during routine vehicle use, its potential implications for ergonomic risk remain largely unexamined in existing automotive research. To isolate the influence of driver-side packaging constraints from component-level design effects, this study adopts a comparative evaluation of driver and co-driver glove-box interaction as a built-in control condition. This study introduces a discomfort-based evaluation framework that integrates Digital Human Modeling with India-specific anthropometric datasets. A composite loss-function scoring model is developed to quantify functional usability differences across four glove-box configurations, defined by variations in latch placement (center or side) and storage-bin mechanisms (fixed or rotating). Indians are utilized to assess reachability and visibility during glove-box interaction. Ergonomic performance is analyzed through reach and visibility metrics for both latch actuation and storage-access tasks. For the co-driver, all configurations exhibit 0% loss, confirming that usability remains unaffected. In contrast, the driver assessment reveals pronounced limitations. Center-mounted latches prove inaccessible from a neutral seated posture, reflecting an approximate loss function of 55%. Among the side-latch alternatives, the rotating-bin configuration achieves the lowest discomfort score (41%), supported by more favorable access posture and smoother hand-entry alignment. The findings specify that ergonomic limitations stem primarily from driver-side packaging constraints rather than inherent flaws in the glove box unit. Based on the reach and visibility loss values obtained through the developed framework, the Side-Latch + Rotating-Bin configuration emerges as the most suitable design option for passenger-vehicle layout. The proposed methodology offers a practical decision-support tool for early stage ergonomic evaluation of glove-box configurations in passenger vehicles.
Jujjavarapu, Sreeram, Kota, Srinivas, Kotkunde, Nitin, Jasti, Naga Vamsi Krishna
Adaptive HVAC Strategies for Enhanced Cabin Thermal Comfort, Air Quality and Energy Efficiency2026-26-07916/1/2026
Passenger comfort within vehicles and aerospace cabins relies on finely tuned management of temperature, air quality, and energy use. This paper proposes an integrated HVAC framework that combines zonal climate control, intelligent airflow distribution, and real-time sensor data to maintain thermal balance across different cabin zones. Leveraging predictive thermal load modelling and machine learning, the system anticipates environmental changes—such as sudden shifts in external temperature or passenger load—and proactively adjusts heating and cooling outputs. Simultaneously, air quality is enhanced through a multistage filtration system, active air purification technologies, and dynamic CO₂ concentration monitoring. Comfort assessment integrates PMV (Predicted Mean Vote) and PPD (Predicted Percentage Dissatisfied) indices to adapting environmental conditions. Simulations and early-stage prototypes improve energy savings and improve occupant comfort and air quality. The proposed HVAC approach is a promising avenue for enhancing passenger experience and operational efficiency in both ground and air mobility platforms.
Mudavath, Lehitha Sai, Patil, Ashish, Saha, Sudipta
A GenAI-Based Approach for Real-Time Multilingual In-Flight Communication Systems2026-26-07846/1/2026
In today’s global aviation industry, passenger experience is strongly influenced by effective communication. In-flight announcements, often limited to English and a single local language, can create confusion and stress for international travelers who may not be fluent in either. This communication gap not only impacts passenger comfort but also poses potential risks in conveying time-sensitive or safety-critical information. Recent advances in Generative Artificial Intelligence (GenAI), particularly in speech recognition, neural machine translation, and naturalistic text-to-speech, provide a pathway to overcome these challenges. This paper explores the concept of real-time multilingual in-flight announcements delivered in each passenger’s preferred language through connected headphones or personal devices. The proposed system architecture integrates speech-to-text conversion, language translation, and speech synthesis with aircraft infotainment platforms. Potential applications range from pre-generated multilingual safety messages to long-term visions of fully personalized, real-time translations with minimal latency. Benefits include improved inclusivity, accessibility for hearing-impaired passengers, and enhanced brand differentiation for airlines. Challenges such as regulatory certification, translation accuracy, latency constraints, and hardware integration must be addressed. Beyond aerospace, this capability has cross-domain relevance in automotive, railways, and public services, making it a promising area for future customer experience innovations.
Mishra, Ashwini, Kature, Kartik, Patil, Ashish
Hitting the road IN THE ALL-NEW VNR26TOFHP06_016/1/2026
Volvo Trucks' revised VNR brings updated safety tech, improved fuel economy and driver comfort features to the regional haul segment. Volvo Trucks has continued its rollout of new models for every sector of the commercial truck market. The redesigned VNR is the latest model to see the spotlight. The new VNR naturally carries all of Volvo's latest safety tech, but also prioritizes maneuverability, fuel efficiency and configurability for a wide variety of fleet uses. “The VNR is an incredibly versatile truck,” said Maddie Sullivan, product marketing manager. “There are so many different configurations to meet our customer's needs. We offer four different cab sizes, three different axle configurations and two different chassis configurations.”
Wolfe, Matt
Kenworth's new C580 clocks in for work26TOFHP06_116/1/2026
Kenworth's new C580 vocational truck made its debut at CONEXPO 2026. The C580 is the replacement for the long-serving C500 and aims to build on that truck's legacy thanks to new tech, more muscle and improved interior amenities. According to Kenworth, the C580 rides on the C500 platform, but has been endowed with Kenworth's latest cab, which brings modern comfort and technology features. Truck & Off-Highway Engineering was in attendance for Kenworth's introductory press conference for the C580 in Las Vegas.
Wolfe, Matt
Energy Saving and Personalized Thermal Comfort Control Based on Reinforcement Learning and Decision Tree2026-99-17145/22/2026
Indoor thermal comfort is closely related to people’s health and work efficiency. Control systems typically consume a large amount of energy to maintain a comfortable thermal environment. Currently, reinforcement learning is widely applied to optimize thermal comfort control systems. However, existing research mainly adopts universal thermal comfort evaluation models that aim to satisfy the majority of people, which makes it difficult to quickly and accurately reflect the specific thermal comfort needs of individuals. As a result, the hot environment is neither comfortable nor energy-efficient in practical use. Therefore, this paper proposes an energy-saving personalized thermal comfort control method based on decision trees and reinforcement learning. First, decision tree learning is used to obtain an individual thermal comfort evaluation model from a small amount of historical data. Then, this individual comfort model is combined with energy consumption to form a reward function, which is used in reinforcement learning to derive personalized thermal comfort control strategies. The experiments show that, compared to traditional methods, this approach can improve user thermal comfort by 43.8% and achieve an energy-saving effect of 30.7%.
Li, Xianying
Toward Human-Centric Autonomous Vehicle Control: A Systematic Model-Based Approach to Motion Sickness Mitigation and Path Tracking10-10-03-00235/22/2026
Autonomous Vehicles (AVs) offer unprecedented opportunities to design control strategies that could be able to simultaneously enhance safety, performance, user experience, time efficiency, and the environmental impact of mobility. However, as automation levels increase, a paradigm shift becomes not only necessary but imperative: the integration of human needs into mobility objectives. This includes not only traditional comfort considerations but also minimizing Motion Sickness (MS), a largely under-explored challenge in control strategy design. In recent literature, several methodologies for modeling and mitigating MS have been proposed, yet their integration into vehicle control logics remains limited, often restricted to isolated and specific case studies, with the research area largely unexplored, particularly with respect to the generalization of the proposed methods. This work introduces a theoretically grounded multi-objective Nonlinear Model Predictive Control (NMPC) framework for coupled vehicle–passenger systems, featuring a novel prediction horizon optimization methodology and adaptive conflict resolution strategies for heterogeneous performance metrics to mitigate motion-induced discomfort while ensuring accurate path tracking. Human-centric control design is pursued by embedding increasingly complex vehicle models and MS metrics, further addressing the trade-off between model fidelity and computational feasibility, and introducing a methodological standpoint for selecting the optimal prediction horizon in the presence of heterogeneous and conflicting control objectives, an aspect often overlooked in current literature. An experimental campaign supports model calibration and validation, while multi-scenario simulations demonstrate the framework’s ability to balance tracking performance, computational efficiency, and passenger comfort.
Ponticelli, Lorenzo, Bottiglione, Francesco, Rini, Gabriele, Timpone, Francesco, Sakhnevych, Aleksandr
Multi-Mode Control Strategy for Controllable Vehicle Suspension Systems2026-99-07125/15/2026
The suspension system with variable damping and variable stiffness actuators can realize four-quadrant mechanical output, effectively combining the energy efficiency of the semi-active suspension with the performance levels approaching those of active suspensions. However, the practical effectiveness of this system depends heavily on the ability of the control strategy to adapt to different driving conditions. In order to meet this challenge, this research has developed a multi-mode suspension collaborative control strategy to optimize energy efficiency and ride comfort in various operating scenarios. Based on the four-quadrant characteristics of the actuator, a suspension mode switching framework has been established, and the suspension work is divided into passive, semi-active, pseudo-active and active modes. In order to determine the appropriate switching boundary, first calculate the root mean square (RMS) value of the sprung mass acceleration and suspension dynamic deflection under passive conditions. With the existing human comfort sensitivity as a reference, the switching threshold of sprung mass acceleration is 0.527 m/s2, and the switching threshold of suspension dynamic deflection is 8.31×10−3m, and the corresponding conversion rules are formulated. Then, the LQR controller optimized by the genetic algorithm is used to allocate the control force adaptively according to the suspension mode to realize cooperative multi-mode operation. The simulation results on B-D composite road surfaces show that compared with traditional passive suspension, this method can reduce the sprung mass acceleration, suspension dynamic deflection and tire dynamic load by 10.59%, 16.65% and 32.9% respectively. These results confirm that the collaborative control strategy significantly improves the ride comfort, vehicle adaptability and overall performance in complex road conditions.
Li, Zhiying, Li, Jei, Zhu, Anding, Bai, Xianxu, Li, Weihan, Li, Rui
SAE TOMORROW TODAY - Why Curing Motion Sickness Is Key to AV Success135675/11/2026
Did you know that motion sickness affects 1 in 3 people? As AVs gain traction and drivers become passengers, it may be the biggest barrier to the future of autonomous mobility. With a focus on addressing the root cause of motion sickness, Motion Sync offers breakthrough solution: a haptic pre-activation system that helps passengers anticipate vehicle movement with a personalized in-cabin experience that could redefine comfort and productivity. Listen in as we sit down with Dr. Daniel Sousa Schulman, CEO and Founder of Motion Sync, to unpack the science behind motion sickness and why it's a critical issue for EVs, robotaxis, and self-driving cars. You'll learn why passengers get sick, why drivers don't, and how EVs can actually make symptoms worse. As a topic critical to the success of autonomous vehicles, it's a conversation you don't want to miss. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.
Patterson, Lori
An Integrated Workflow for Structural Dynamics, Comfort, and Airbag Deployment for Virtual Seat DesignF-0082-2026-03295/5/2026
This paper presents an integrated simulation workflow for aircraft seat development that combines (i) structural dynamics and certification load cases, (ii) occupant comfort and living-space assessment using finite-element digital humans, and (iii) airbag folding, deployment, and calibration using a coupled gas-dynamics solver suited to early-time transients. The workflow is built around a single manufacturing-aware, as-built seat model that is reused across comfort, certification, and restraint-system studies, allowing design iterations to move upstream before design freeze. Each stage is paired with validation or industrial case examples, and the airbag-calibration process is accelerated through reduced-order modeling (ROM) of parameter identification. The result is a practical virtual-seat-development methodology that is sufficiently predictive to de-risk physical testing while remaining fast enough for concept iteration and late-stage compliance support.
Dwarampudi, Ramesh, Vaz, Ignatius
Optimizing Ride Comfort and Stability in In-Wheel Motor Drive Electric Vehicles: A Correlated Semi-Active Suspension System with Kalman Filter and Spring-Type Analysis10-10-04-00304/24/2026
This research provides a unique contribution to the field of in-wheel motor drive (IWMD) electric vehicles (EVs) by addressing the challenges associated with the use of permanent magnet synchronous motors (PMSMs) for traction. These motors, integrated into the unsprung masses, increase the wheels’ rotational inertia, reducing ride smoothness on uneven roads. To mitigate this issue, we present an optimal Kalman filter for a magnetorheological (MR) control suspension system that correlates road inputs between the front and rear wheels. This filter significantly improves the estimation accuracy of state variables by incorporating the motor’s vertical motion, along with potential enhancements from wheelbase preview. To determine the most suitable coil spring types for use with MR dampers, we used the WDW-600 computer-controlled electronic universal testing machine to evaluate three coil spring types: constant-pitch (model A), variable-pitch (model B), and conical (model C). To assess the impact of controlled vibration on dynamic performance, we compared the dynamic characteristics of IWMD EVs equipped with passive, uncorrelated, and correlated suspension systems, all of which have controlled inverters integrated into their design. The results indicate that motor vertical acceleration and dynamic tire load are the primary factors influencing the dynamic behavior of EVs. Additionally, the vehicle’s vibration performance metrics are negatively impacted by the in-wheel motor driving system in both passive and uncorrelated suspension systems. However, the MR-controlled suspension system with a conical spring significantly enhances ride comfort and dynamic stability by addressing complex stiffness and evaluating the effects of different coil spring types on the structural response of EVs. This analysis is based on a correlated-suspension-system scenario.
Gad, Ahmed Shehata, Jabeen, Syeda Darakhshan, El-Zomor, Haytham M., Tolba, Mohamed, Elamy, Mamdouh I.
Hierarchical Control Strategy Evaluation and Migration for a Four-Degree-of-Freedom Quarter Commercial Vehicle02-19-03-00144/9/2026
In order to improve the comfort performance in commercial vehicles, this study proposes a hierarchical control strategy that integrates the evaluation and migration of control algorithms. First, a quarter-vehicle model with four-degree-of-freedom (4-DOF) is constructed, incorporating the dynamics of the wheel, frame, driver’s cab, and seat. The key modal characteristics of the model are then verified through amplitude–frequency analysis, confirming their consistency with the typical vibration patterns observed in actual commercial vehicles, which provides the foundation for subsequent control strategy evaluation and migration. Then, based on a standard two-degree-of-freedom (2-DOF) suspension model, a weighted comprehensive evaluation function is developed to account for comfort, structural safety, handling stability, and both time- and frequency-domain performance indicators. Using this evaluation function, various control algorithms—including Skyhook control (SH), acceleration-based damping control (ADD), and proportional–integral–derivative control (PID)—are systematically assessed. The control algorithm is migrated to the 4-DOF model to carry out the hierarchical collaborative control. The results show that this method can effectively inhibit vibration transmission to enhance ride comfort and improve structural safety at the same time, while maintaining an acceptable level of handling performance. The transferability and applicability of the hierarchical control method are validated for the considered vertical dynamics scenarios. This article provides a new theoretical method and technical pathway for the comfort-oriented performance optimization of commercial vehicles.
Pan, Ting, Pang, Jianzhong, Wu, Jinglai, Zhang, Jiuxiang, Kang, Gong, Zhang, Yunqing
Impact of User-Adjustable Vehicle-Dynamics Tuning on Vehicle Durability2026-01-01944/7/2026
Software-defined vehicles offer customers a greater degree of customization of vehicle controls and driving experience. One such feature is user-adjustable tuning of vehicle ride and handling, where customers can vary ride height, damper stiffness, front-rear torque balance, and other aspects of vehicle dynamics. While promising a great customer experience, such a feature can expose the vehicle to a wider range of structural loads than those in the nominal design condition, particularly when such tuning is extended to cover spirited “sport” mode driving, off-road driving, etc. In this paper we present a novel methodology combining Road Load Data Acquisition (RLDA) data and real-world telemetry data to estimate the impact of user-adjustable vehicle-dynamics tuning on structural durability. In doing so, the method combines the physics of damage accumulation (from RLDA data) with user behavior (from telemetry data) to present an accurate assessment of the impact on durability, moving beyond traditional durability methods that do not model a range of real-world usage behavior. The study has been conducted using one instrumented vehicle (RLDA) and de-identified telemetry data from over 20,000 Rivian customer vehicles. The study analyzes the impact of variations in ride height, damper stiffness of active dampers, and roll stiffness of the suspension on vehicle structural durability. By combining usage frequency of the different settings with the damage accrued in these settings, the methodology estimates the high-cycle fatigue pseudo-damage variation for a wide range of customers and compares real world damage risk with the damage accounted for in the baseline durability testing. Through the analysis, we recommend a way to optimize the Accelerated Duty Cycle (ADC) for Over the Road (OTR) testing to minimize real-world risk, while keeping the duty cycle simple and practical for testing, i.e., test for an optimized combination of a few dominant settings and not a wide range of settings. The approach also suggests a path to a real-time fleet monitoring system to identify high-durability-risk customers and develop mitigation strategies.
Demiri, Albion, Ramakrishnan, Sankaran, White, Dylan, Khapane, Prashant, Borton, Zackery
Adaptive Torque Vector Control for Distributed Drive Intelligent Electric Vehicles Using Model and Data Driven Approaches2026-01-06284/7/2026
To effectively improve the performance of chassis control of distributed drive intelligent electric vehicles (EVs) under difference road conditions, especially in combing road information and chassis control for improving road handling and ride comfort, is a challenging task for the distributed drive intelligent EVs. Simultaneously, inaccurate chassis control and uncertainty with system input, are always existing, e.g., varying road input or control parameters. Due to the higher fatality rate caused by variable factors, how to precisely chose and enforce the reasonable chassis control strategy of distributed drive intelligent EVs become a hot topic in both academia and industry. To issue the above mentioned, an adaptive torque vector hierarchical controller based on road level and adhesion is proposed, which optimizes the comprehensive. First, combined with the characteristic of the unbalance dynamic force caused by the air gap between the stator and the rotor of the in-wheel motor, a nonlinear vehicle model based on motor unbalanced electromagnetic force is developed. Then, using the deep neural network, an algorithm for road level and adhesion recognition based on system response data is designed. Meanwhile, an adaptive torque vector controller based on road information is designed to improve the driving safety and handling stability of chassis. Finally, the proposed algorithm is validated on the full-car test rig platform, results show that the proposed algorithm can improve chassis performance under double lane-change test. The research achievements develop a reasonable algorithm to apply to the improving road handling and ride comfort performance for distributed drive intelligent EVs.
Wang, Zhenfeng, Zhao, Gaoming, Zhang, Zhijie, Zhou, Zitao, Huang, Taishuo, Ma, Changye
From Prediction to Design: Using Context-Aware Graph Neural Networks and Explainable AI to Anticipate Transfer-of-Control2026-01-00494/7/2026
Drivers often interact with partial automation (SAE Level 2) systems, initiating transfer of control (TOC) either by handing control over to the automation or by taking it back. Accurately predicting these interactions may inform the design of future automation systems that adapt proactively to the operating context, enhance comfort, and ultimately may improve safety. We present a context-aware framework that generates a unified driver–vehicle–environment representation by fusing data from in-cabin video of the driver and of the forward roadway with vehicle kinematics, driver glance, and hands-on-wheel behaviors. This representation was encoded in a hierarchical Graph Neural Network that classified driver-initiated TOCs to: (i) Manual-to-automation and (ii) Automation-to-manual transitions and predicted time-to-TOC. Shapley-based explainable AI was used to quantify how the importance of behavioral, contextual, and kinematic cues evolved in the seconds preceding a TOC. Analysis of a naturalistic dataset of 1,565 driver-initiated TOCs from 16 experienced drivers revealed distinct patterns. Manual-to-automation transitions were preceded by lane count increases, acceleration, and spikes in glances to the instrument-cluster. In contrast, Automation-to-manual transitions were associated with lane count reductions, higher surrounding-vehicle density, deceleration, reduction in secondary-task engagement, and higher steering wheel control. Together, these patterns highlight key cues for predicting the TOC type and time-to-TOC. Using environment-only features, the classifier achieved 78% accuracy; adding vehicle kinematics increased accuracy to 84%, and incorporating driver behavior features further improved prediction to 90%. Across prediction horizons, the Manual-to-automation TOC was consistently predicted more accurately than the automation-to-manual TOC. Shapley analyses underscore that driver behavior provided the strongest cues for predicting TOCs, highlighting the value of fusing driving context with information obtained from monitoring the driver behavior to anticipate the type of driver-automation interaction and its timing.
Zhao, Zhouqiao, Gershon, Pnina
Multi-Objective Reinforcement Learning Framework for Transmission Shift Schedule Optimization2026-01-01624/7/2026
Building upon previous work that successfully employed a Reinforcement Learning (RL) agent for the autonomous optimization of transmission shift programs to enhance fuel efficiency, this paper addresses a critical limitation of that approach: the neglect of human-centric factors. While the prior methodology achieved substantial fuel consumption reductions by training an RL agent in a Software-in-the-Loop (SiL) environment, it did not explicitly account for aspects such as driver comfort and preferences, which are paramount for real-world user acceptance and drivability. This work presents a multi-objective optimization framework extending the artificial calibrator to simultaneously maximize fuel efficiency and enhance driver comfort. The method introduces a modified RL reward function that penalizes undesirable shift behavior to ensure a smooth driving experience (drivability). This new methodology also incorporates a mechanism to capture and integrate driver preferences, moving beyond a purely quantitative fuel-economy-driven objective to a holistic, user-focused calibration. Experimental evaluation demonstrates that the extended framework successfully generates a shift strategy that achieves a favorable trade-off between fuel efficiency and drivability, resulting in a more balanced and practical calibration. The ability to integrate these qualitative factors into an automated, data-driven process represents a significant step forward, promising to accelerate the development of powertrain control systems that are both highly efficient and aligned with the expectations of human drivers. This work lays the foundation for future RL-based calibration tools that are capable of addressing the full spectrum of development objectives, from fuel economy to the subtleties of vehicle drivability.
Kengne Dzegou, Thierry Junior, Schober, Florian, Rebesberger, Ron, Henze, Roman, Sturm, Axel
A Frequency-Aware Reinforcement Learning Framework for Suspension Control with Integrated Temporal Features and Expert Guidance2026-01-02094/7/2026
Active suspension systems play a crucial role in improving vehicle ride comfort and handling stability. However, most existing studies focus on the low-frequency range below 20 Hz, leaving the suppression of high-frequency vibrations within 50–500 Hz largely unexplored, even though these vibrations strongly affect in-cabin noise and ride quality. To address this gap, this study introduces a quarter-car suspension model incorporating both bushing dynamics and a rigid-ring tire within a reinforcement learning (RL) framework. A major challenge for RL-based suspension control is its degradation in high-frequency performance. To overcome this issue, we design an innovative training framework that integrates multiple synergistic strategies. First, frequency-domain rewards are incorporated as auxiliary signals to explicitly guide policy optimization in the high-frequency band. Second, long short-term memory (LSTM) networks are embedded in both the Actor and Critic to capture the sequential dependencies of time-domain suspension signals, thereby enhancing temporal feature extraction. Finally, model predictive control (MPC) expert knowledge is injected through reward shaping, which accelerates convergence and stabilizes the learned policy. This combination allows the proposed controller to effectively exploit both data-driven learning and model-based insights for full-band suspension optimization. Simulation results show that the method achieves a 29.67% reduction in body acceleration RMS in the 0–20 Hz range compared with a passive suspension, and further achieves a 62.65% reduction in the 50–500 Hz range relative to a baseline RL controller. By explicitly targeting vibration responses in the in-cabin acoustic control band (20–500 Hz), this study establishes a foundation for integrated suspension-acoustic optimization, offering new insights into ride comfort and NVH enhancement in intelligent vehicles.
zhu, Zhehui, Zhang, Lijun, Meng, Dejian, Hu, Xingyu
Advanced Integrated Chassis Control for Improved Energy Efficiency and Driving Experience in Electric Vehicle Applications2026-01-06434/7/2026
Complexity of modern ground vehicles grows constantly, since car manufacturers want to provide functionality, while customers are expecting innovation and recent technologies to be integrated into the latest models released to the market. Recent advances in hard- and software opened the gates for new means of vehicle control and operation. Especially the transition to electric propulsion systems and decoupled chassis actuators offer completely new opportunities of dynamics control and manipulation. This paper presents an approach for integrated chassis and vehicle motion control in (battery) electric vehicle applications by using new and innovative controllers as well as mechatronic chassis systems. In several experiments on public roads with a fully instrumented vehicle demonstrator, that features in-wheel based rear-wheel drive and a hybrid brake-by-wire-system, the proposed control is tested under real environmental and traffic conditions with respect to aspects like energy efficiency and driving comfort. The improvements are evaluated by objective performance indicators. In particular, it was found that the controller recovers more kinetic energy during braking maneuvers and lowers driver stress by up to over 90 % fewer mandatory pedal changes compared to already industrialized approaches.
Heydrich, Marius, Mitsching, Thomas, Ivanov, Valentin
Research on Driving Performance of Vehicle Body Control Using Vehicle Height Adjustment System2026-01-06264/7/2026
In recent years, premium vehicles have increasingly incorporated suspension systems capable of adjusting ride height. The primary function of these systems is to enable the vehicle to traverse uneven terrain by elevating the chassis, thereby preventing contact between the underbody and the road surface. Notably, air spring-based mechanisms enhance ride comfort by modulating the wheel rate. The system proposed in this study achieves ride height adjustment through vertical displacement of the spring’s lower seat. By constructing a detailed mechanical topology model using a dynamic simulation tool, this research aims to evaluate the feasibility of improving driving performance not only through height regulation but also by actively controlling the vehicle’s posture during motion.
Park, Jaeyong, Sang Hoon, Lee, Jong Min, Kim, Choi, Jang Han
Data-Driven Study on Chassis Suspension Performance Degradation2026-01-05824/7/2026
The performance of chassis suspension mechanisms critically affects vehicle handling, ride comfort, and safety. Implementing real-time health monitoring for chassis systems contributes to preventing severe consequences such as increased body roll or loss of handling stability caused by shock absorber softening or spring stiffness degradation under deteriorating operating conditions, while circumventing the substantial costs associated with professional facility-based chassis inspections. With the rapid development of sensing and data analytics technologies, data-driven approaches are increasingly used in health monitoring. This study aims to achieve online monitoring of chassis suspension performance degradation using a deep neural network (DNN). First, a half-car model incorporating both vertical and pitch motions was established to simulate bumpy road conditions, with the aim of constructing a dataset that includes key vehicle suspension parameters and vehicle states related to their degradation characteristics. Subsequently, a DNN model comprising three hidden layers is developed to assess suspension performance degradation. To optimize model performance, the effects of different numbers of neurons and hidden layers on model accuracy are explored. Experimental results show that the maximum absolute percentage errors of the DNN model in predicting suspension stiffness and damping coefficients are less than 0.13% and 0.17%, respectively, with average absolute percentage errors below 0.046% and 0.06%. The coefficients of determination (R2) exceed 0.999. The proposed method accurately predicts the trend of key suspension parameters, providing robust data support for health management and maintenance decision-making. This is expected to reduce safety risks and maintenance costs while enhancing overall vehicle performance and reliability.
Liao, Yinsheng, Lei, Yisong, Su, Ailin, Wang, Zhenfeng, Shi, Shuai, Zhang, Lei, Zhang, Junzhi, Ma, Changye
A Dynamic Driver Comfort Index for Stabilizing Mixed Traffic Behavior2026-01-00994/7/2026
Despite remarkable advances in vehicle technology - enhancing comfort, safety, and automation – productivity of transportation over the road continues to decline. Stop-and-go driving remains one of the most persistent inefficiencies in modern mobility systems, leading to greater travel delays, energy waste, emissions, and accident risk. As vehicle volumes rise, these effects compound into systemic challenges, including driver frustration, unstable flow dynamics, and elevated greenhouse gas (GHG) emissions. To address these issues, an extensive data-driven evaluation was performed characterizing the underlying causes of traffic instability and uncovering hidden behavioral parameters influencing traffic flow. This research led to the identification of a previously unrecognized metric - the Driver Comfort Index (DCI) - which quantifies an inter-vehicle spacing behavior that reflects intrinsic human driving behavior. Building on this discovery, mixed traffic is explored to identify its phenomena, where human-driven and machine-controlled vehicles coexist and share the road. It appears that adaptive cruise control (ACC) and connected autonomous vehicles (CAV) are controlled by a non-intrinsic parameter so that traffic mix suffers from a mismatch of vehicle dynamics. This mismatch is explored, and it is proposed to harmonize traffic dynamics by adopting the natural DCI parameter as the single control mechanism. Analytical studies demonstrate that DCI-based traffic flow orchestration, applied integrally to human- and machine-controlled vehicles, enhances traffic flow stability, mitigates stop-and-go oscillations, and significantly improves network efficiency, safety, and environmental performance.
Schlueter, Georg J.
Acoustic Improvements to a Passenger Service Unit Panel with Core Filler and Facesheet Patches using Topology Optimization2026-01-05114/7/2026
Passenger comfort is becoming the forefront of luxury private jets where noise needs to be kept to a minimum. One source of structure-borne noise is the vibration of the Passenger Service Unit (PSU) panel. These vibrations originate from the outer skin, excited by turbulent boundary layer, and are transmitted through the fuselage frame to the PSU panel. This panel resides overhead of passenger seating, it is composed of a corrugated honeycomb core sandwiched between thin face-sheets. This paper presents a systematic approach to improve the vibro-acoustic performance of a honeycomb core sandwich structure by employing core filler and facesheet patches. Topology Optimization (TO) is used to determine the optimal layouts of these design modifications. The vibro-acoustic performance of the PSU panel with facesheet patches and core filler is evaluated using a frequency response analysis in the commercial finite element solver OptiStruct. The effectiveness of vibration reduction will be quantified by using the Dynamic Stiffness (DS) and velocity response of the PSU panel measured using the Frequency Response Function (FRF). Validation used excitations from 500 to 3000 Hz and indicated that using the TO interpreted layout of core filler and facesheet patches, separately improved the DS of the panel by 402% and 198%, and the velocity response by -35% and -18%, while increasing the weight by 0.1% and 4.7% respectively. Their combined effect has also been tested and found to provide an additional 445% improvement to DS and a -39% improvement in velocity response with 4.8% of additional material, though overall behavior varies depending on frequency range.
Russo, Connor, Whetstone, Isobel, Patel, Anuj, Wotten, Erik, Kim, Il Yong
Use Inertial Sliding of Reclined Seat to Enhance Occupant Retention under Rear-End Crash2026-01-05634/7/2026
Autonomous vehicles may attract more passengers to recline their seat for comfort. However, under severe rear-end crashes and large reclining angle, the backward inertia could completely throw occupant out of seat. Even if the occupant body can be restrained by seatbelt, the occupant’s head could slide out of the head restraint area. Any of these situations may cause severe injuries. To address this safety concern, we developed a sliding seat system designed to enhance occupant retention. Activated by impact inertia of rear-end collision, the system allows the seat sliding backward along its track in a controlled manner, and the sliding stroke is accompanied by a restraint force and absorbs some amount of kinetic energy during the sliding. Thus, occupant retention can be enhanced, and injury risks of head and neck can be reduced. To demonstrate this concept, we built a MADYMO model and conducted a parametric analysis. The model includes a 50th percentile human model, a vehicle seat, and a seat-mounted three-point seatbelt. Under 50 km/h rear-impact load, we evaluated occupant kinematics and critical injury metrics of 45o reclined posture. The relative displacement between occupant pelvis and seatback was used to measure the distance that occupant slides backward, which is a metric for occupant retention. The results have shown that seat sliding distance is the most critical factor for occupant retention, and the longer the sliding distance, the greater the retention effect and the lower the injury risk. In a typical scenario when 200 mm of sliding distance is available for sliding, compared to traditional fixed seat (no sliding allowed), the occupant displacement is reduced by 45%, the Head Injury Criterion value is reduced by 55%, and the Neck Injury Criterion value is decreased by 66%. For vehicle seat design, using the sliding seat system may help off-load the burden of enhancing recliner stiffness, a critical component for maintaining seatback stiffness level in rear-end collisions.
Dai, Rui, Zhou, Qing, Puyuan, Tan, Shen, Wenxuan
Aircraft Cabin Pressurization System CriteriaARP1270C (Current)1/29/2026
This report, in conjunction with other referenced SAE documents, provides recommendations for development of aircraft cabin pressure control systems and equipment, with particular emphasis on performance objectives, requirements definition, operational scenarios, design practices, safety processes, and verification methods. The objective of a Cabin Pressure Control System (CPCS) is to regulate aircraft cabin pressure throughout the operational flight envelope, in order to ensure occupant safety, aircraft safety, and passenger comfort. The system should comply with all relevant certification and safety requirements, particularly in the areas of: Maintaining a breathable environment within occupied compartments Protecting the fuselage structure against excessive positive and negative differential pressure loads Supporting cabin egress on ground The system should have the capability to schedule cabin pressure at rates of change that are comfortable to crew and passengers. Careful consideration should be given to external system interfaces and the role of CPCS in providing supporting functions. The system should be fault tolerant and reliable, support crew awareness of key system parameters and failure conditions, and support efficient fault isolation and resolution by maintenance crews. If applicable, the system design should provision for high altitude airport operation or application on a freighter configuration aircraft. The system architecture and design should minimize aircraft fuel burn through optimized weight. To this end, the complexity and level of automation of the system should be carefully evaluated within the context of a functional hazard assessment and the overall impact to system reliability, maintainability, and cost of ownership. This recommended practice is applicable to pressurized aircraft, both civil and military, regardless of the number of passengers or crew.
AC-9 Aircraft Environmental Systems Committee
Enhancing Thermal Comfort and Vehicle Energy Efficiency: A Simulation Study Using GT ISE + TAITherm2026-26-04581/16/2026
The automotive industry is encountering difficulties in balancing occupant thermal comfort with HVAC system energy efficiency, particularly under the hot Indian conditions, to meet user expectations and address range anxiety in electric vehicles. Front-loaded comfort-based approach simulations during the development stages have the potential to increase energy savings compared to the stages required at the end of product design. The focus of the current research targets HVAC energy consumers, such as blower flow rates, temperatures, and Cabin heaters, and investigates how these factors influence occupant overall comfort. Additionally, design elements like glass properties and the impact of solar radiation on human comfort are studied at the early concept stages to adopt an energy-based approach for comfort optimization. Simulations are conducted using GT-SUITE and GT-TAITherm software, integrated with CFD field maps platforms to obtain exact flow field predictions. The simulation results are validated with test results obtained from climatic wind tunnel experiments. Key parameters, such as relative humidity (RH), are analyzed to understand their effect on the comfort index and control strategies to maintain vent temperatures that meet comfort requirements with minimal energy consumption. The impact of solar glass properties on comfort indices is studied. To evaluate thermal comfort comprehensively, the Berkeley model provides localized insights into physiological comfort by accounting for variations in temperature and airflow, while the Fanger model assesses overall comfort parameters using predictive indices. We identified the optimal RH levels that can reduce HVAC load while focusing on localized comfort indices for occupants. This helps to go deeper into occupant comfort under multiple scenarios, including extreme temperatures, and evaluates their physiological aspects. This exercise has helped find possible areas for front-loading comfort-based vehicle development processes and pinpoint opportunities for reducing energy consumption. Furthermore, this study reduces reliance on costly physical prototype testing and accelerates the design and development of sustainable automotive solutions, addressing critical challenges in the transition to sustainable mobility.
Bavrisetti, Sai Sampath Kumar, Chothave, Abhijeet, Gummadi, Gopakishore, Khan, Parvej, Thiyagarajan, Rajesh, Raju, Kumar, A Sr, Mahesh
Improve the Electric Vehicle Range by Using the Climate Control Setting in Economy Mode at Hot Climatic Conditions2026-26-01721/16/2026
Electric vehicles (EVs) have surged in popularity in recent years due to their environmental benefits. The influence of range on air conditioning (AC) power consumption is a critical concern for electric vehicle (EV) owners, particularly in warmer climates. Overcoming obstacles such as a limited vehicle range is necessary for the increased use of electric-powered automobiles. Cabin heating and cooling demand for climate control consumes more energy from the main battery and has been revealed to significantly reduce vehicle range. During peak cooling or heating, the overall power consumption of the AC system takes almost 50% of the energy used for traction. The average reduction in driving range caused by air conditioning (heating and cooling) approximates 33%. The energy usage of an electric vehicle can be considerably decreased by switching the climate control setting to economy mode. The AC system will operate more effectively, enabling the vehicle to save energy and extend its range. In hot areas, the air conditioning unit can rapidly deplete the battery, therefore, adopting economy mode can mitigate this problem. There are other ways to increase the range of electric vehicles in hot weather besides utilizing economy mode on the climate control setting. Effective strategies for minimizing energy and maximizing driving range include parking the car in a shaded spot, and pre-cooling the interior while the car is still plugged in to a charger. The current study uses a variety of economy mode strategies for optimizing HVAC power consumption in hot climate conditions, which significantly increases the EV vehicle's overall range without compromising cabin comfort. An experimental test was carried out with the vehicle in higher ambient conditions following the implementation of several econ mode strategies, and the results demonstrated the significant benefit in range without affecting the cabin comfort.
Mulamalla, Sarveshwar Reddy, Anugu, Anil, E A, Muhammed, Ummiti, Kumar, M, Nisshok, Choudhary, Ankit
Refinement of Road Induced Steering Wheel Vibration in Electric Vehicle2026-26-03371/16/2026
Nowadays, customers expect excellent cabin insulation and superior ride comfort in electric vehicles. OEMs focus on fine tuning the suspension system in electric vehicle to isolate the road induced shocks which finally offers superior ride quality. This paper focuses on enhancing the ride comfort by reducing the road excitation which originates mainly due to road inputs. Higher steering wheel vibration is perceived on the test vehicle on rough road surfaces. To determine the predominant force transfer path, Multi reference Transfer Path Analysis (MTPA) is performed on the front and rear suspension. Based on the finding from MTPA, various recommendations are explored and the effect of each modification is discussed. Apart from this, Operational Deflection Shape (ODS) analysis is used to determine the deflection shape on the entire steering system . Based on ODS findings, recommendations like dynamic stiffness improvements on the steering column and steering wheel are explored and the impact on the steering wheel vibration is discussed. With all the counter measures proposed, steering wheel vibration levels are reduced by ~ 7 dB . Component level modal targets are proposed to avoid the vibration concern due to road excitation.
S, Nataraja Moorthy, Rao, Manchi, Selvam, Ebinezer, Raghavendran, Prasath
A Scientific Approach to Identify the Source of Intermittent Steering Rattle Noise and Implementing the Design Solutions2026-26-03461/16/2026
Body-on-frame vehicles are well-regarded for their durability and off-road capabilities, but their structural design often makes them more vulnerable to noise, vibration, and harshness (NVH) issues. Vibrations originating from uneven roads are transmitted through the suspension and steering assemblies, sometimes resulting in rattles or other disturbances. These vibrations can be amplified by the inherent flexibility in the body-to-frame mounting system. In such vehicles, the steering system plays a critical role in driver comfort and is highly sensitive to vibrational inputs from the road surface, especially on coarse or uneven terrain. Occasionally, these inputs result in subtle rattle noises that are perceptible only to the driver and may not be detected under controlled testing environments. This poses a challenge for engineers trying to isolate and resolve such intermittent NVH phenomena. Identifying the source requires a combination of real-world driving evaluations, structural analysis, and vibration measurement techniques. This paper presents a case study of an intermittent steering-related rattle noise in a body-on-frame D-SUV with a column EPS steering system. A systematic investigation using on-road testing and accelerometer-based diagnostics was conducted. Through targeted design enhancements focused on improving system stiffness and connection integrity, the issue was resolved effectively. The approach outlined offers a replicable methodology for diagnosing and mitigating similar NVH concerns in other vehicle platforms, thereby contributing to improved driver comfort and product refinement.
Ramesh Chand, Karan Kumar, Gopinathan, Haridoss, Kabdal, Amit
Experimental Evaluation of Tyre Vertical Dynamic Stiffness and Damping for Ride Model Applications Using Flat Trac2026-26-06071/16/2026
The vertical dynamic stiffness and damping of a tyre are critical to ride comfort and overall dynamics, particularly for low-frequency excitations in urban and highway driving. As the tyres are the primary interface between the vehicle and the road, absorbing surface irregularities before the suspension engagement, precise tyre parametrization is essential for accurate ride models. This study investigates an experimental methodology characterizing the vertical dynamic behavior of pneumatic tyres using a Flat Trac test machine. Contrary to the conventional approaches that depend on intricate shaker rigs or frequency dependence function models, the proposed technique uses a realistic force displacement loop-based methodology which is appropriate for ride models. Dynamic stiffness is computed from slope of a linear regression fitted to force and displacements during vertical sinusoidal excitation. Damping is derived from hysteresis energy loss per cycle. The tests were conducted under various conditions by varying vertical loads, inflation pressures (IP), excitation frequencies, and deflection amplitudes (4–8 mm). The generated stiffness and damping curves from the test results can be directly applied in quarter-car models and could potentially be extended to the full-vehicle ride simulations for ride characteristics assessment studies. Research indicates that the dynamic stiffness of a non-rolling tyre is consistently higher than that of a rolling tyre. Under rolling conditions, dynamic stiffness increases with test speed due to excitation frequency effects. Additionally, vertical dynamic stiffness correlates positively with inflation pressure (IP); increasing it from 216 to 264 kPa yields a 12–14% rise in stiffness for both rolling and non-rolling condition. The proposed framework facilitates the integration of realistic tyre vertical dynamics into vehicle ride models while maintaining minimal complexity, thereby improving simulation fidelity and supporting better design and evaluation of ride quality in early stage of vehicle development.
Duryodhana, Dasari, Sethumadhavan, Arjun, Tomer, Avinash, Ghosh, Prasenjit, Mukhopadhyay, Rabindra
Optimizer Sunshade Packaging with Half Aluminum Cassette and Metal Pull Bar2026-26-05971/16/2026
The automotive industry is advancing rapidly with the integration of cutting-edge technology, aesthetics, and performance. One area that has remained relatively underexplored in the pursuit of sleek, minimalistic interiors is the packaging of Sunshade in door trim system. Traditional sunshade design, often bulky and increasingly incompatible with the trend towards compact design and packaging. The car sunshade is a shield that is placed on a car side window and used for regulating the amount of light entering from the car window and helps improve the passenger comfort inside the cabin. Car Interior components, specifically plastic and seats are based on thermal stress properties. When we expose these parts to direct contact with sunlight, humidity and ambient temperature above threshold limit, the interior plastic parts can start to soften and melt. Due to this, they start emitting harmful chemicals which cause anemia and poor immune systems. So, the Sunshade, in addition to protecting passengers’ comfort inside car, it also protects passenger from harmful radiation and enhances overall visual appeal of the vehicle. The main objective of this paper is to address the following: An innovative approach to the design of sunshade for Door trim Meeting shoulder room target Focusing on enhancing aesthetics, Low weight impact, robust design, and assembly, Managing sunshade quality as per regular standard.
Palyal, Nikita, D, Gowtham, Bhaskararao, Pathivada, Bornare, Harshad, Ritesh, Kakade
Adaptability Survey and Analysis of Camera Monitoring Systems (CMS) on Various Driving Population for Passenger Vehicles in Indian Driving Scenario2026-26-06011/16/2026
This paper presents a comprehensive survey and data collection study on the adaptability of Camera Monitoring Systems (CMS) for passenger vehicles. With the growing demand for enhanced safety, automation, and driver assistance technologies, Camera Monitoring Systems (CMS) has emerged as a key component in modern automotive design. This study aims to explore the current state of camera-based monitoring in passenger vehicles, focusing on their adaptability through survey data collection of various driving population and analysis. This paper evaluates the acceptance of CMS configurations in replacement to conventional rear-view mirrors through Position of Monitor, Clarity, CMS Adaptiveness to eyes, Comfort while turning, Merging into moving traffic, Monitoring Rear Traffic, while Getting Out of Car, while Overtaking, Coverage Area and Overall Acceptance. The findings offer valuable insights for manufacturers, engineers, and researchers working toward the evolution of intelligent vehicle systems.
Sinha, Ankit, Tambolkar, Sonali Ameya, Belavadi Venkataramaiah, Shamsundara, Kauffmann, Maximilian
New Generation Natural Rubber-Based Bump Stopper with Improved NVH Characteristics2026-26-02921/16/2026
In automotive suspension systems, components like bump stoppers and jounce bumpers play critical roles in controlling suspension travel and enhancing ride comfort. Material selection for these components is driven by functional demands and performance criteria. Traditionally, Natural rubber (NR) has traditionally been favored for bump stopper applications due to its excellent vibration absorption, tear resistance, cost-effectiveness, and biodegradability. However, in more demanding environments, it has been largely replaced by microcellular polyurethane (PU) elastomers, which offer superior durability, environmental resistance, and enhanced noise, vibration, and harshness (NVH) performance. This study revisits NR with the goal of re-establishing its viability by enhancing its performance to match or surpass that of PU. Through compound optimization and advanced material processing techniques, significant improvements have been achieved in NR’s mechanical strength, compression set resistance, and environmental durability. Also a convolute bump stopper design was explored to enhance energy absorption and packaging efficiency. Compared to traditional solid profiles, the convoluted geometry provided progressive stiffness characteristics, improved deformation control, and optimized ride comfort under dynamic loading conditions. Traditional NR design and formulation were compared against PU and next-generation NR in terms of Aging Durability Factor, stiffness, fatigue durability, vehicle-level buzz, squeak, and rattle (BSR), as well as ride and handling performance. A comparative assessment of carbon emissions between PU and NR was also conducted to evaluate environmental impact. The result is a next-generation NR formulation that delivers performance comparable to PU while retaining the ecological and economic advantages of natural rubber. This research demonstrates a sustainable pathway toward high-performance elastomeric materials, bridging the gap between conventional and advanced solutions in modern engineering applications.
Murugesan, Annarajan, Hingalaje, Abhijeet, Perumal, Mathavan, Pawar, Rohit
High-Fidelity Modeling of Vehicle Dynamics for Control-Oriented Stability Enhancement2026-26-00931/16/2026
This study presents an integrated vehicle dynamics framework combining a 12-degree-of-freedom full vehicle model with advanced control strategies to enhance both ride comfort and handling stability. Unlike simplified models, it incorporates linear and nonlinear tire characteristics to simulate real-world dynamic behavior with higher accuracy. An active roll control system using rear suspension actuators is developed to mitigate excessive body roll and yaw instability during cornering and maneuvers. A co-simulation environment is established by coupling MATLAB/Simulink-based control algorithms with high-fidelity multibody dynamics modeled in ADAMS Car, enabling precise, real-time interaction between control logic and vehicle response. The model is calibrated and validated against data from an instrumented test vehicle, ensuring practical relevance. Simulation results show significant reductions in roll angle, yaw rate deviation, and lateral acceleration, highlighting the effectiveness of the proposed approach. Overall, the framework offers a scalable and robust foundation for developing adaptive stability control systems in modern four-wheeled vehicles
Duraikannu, Dinesh, Dumpala, Gangi Reddi
Comprehensive Evaluation of Bolster Behaviour: Field Data Insights for Design Modifications2026-26-04971/16/2026
Bogie suspension systems are becoming increasingly popular in tipper vehicles to enhance their performance and durability, especially in demanding environments like construction and mining areas [1]. Bolsters contribute significantly to the overall performance and durability of the bogie suspension systems of tipper vehicles by evenly distributing the loads across the whole suspension system. They act as shock absorbers and negate the impact caused by the rough terrains and heavy loads, thereby reducing stress on individual components and maintaining the structural integrity of the vehicle. Bolsters also help in improving the ride comfort and to maintain the position of the suspension system [2]. This study focuses on the comprehensive testing and evaluation of bolsters to understand their modes and displacement data derived from field data. The primary objective is to analyse the performance and behaviour of bolsters under various operational conditions. Critical manners of deformation and displacement patterns were identified by methodically examining the collected data from the field. The purpose of these acumens is to inform and guide the consequent design modifications, to which they will be of utmost importance. The result of these evolutions in the design of bolsters will eventually lead to more effectual and durable bolsters which in turn will improve their trustworthiness and efficiency in real-world applications. Due to a number of aspects measuring bolster displacement and modes data in the field is a challenging task. Because the bolster may move unpredictably in jagged and rough terrain, it is more difficult to measure displacement and modes precisely. Precise data collection depends on the sensor’s placement. It can be difficult to identify the best places for sensors in the field that prevent interference and produce accurate data. It is crucial to make sure that every measurement tool is accurately attuned both before and during data collection activity. Sensor drift due to field conditions may demand frequent recalibration due to the complex and multidirectional movement of bolsters in tipper vehicles. It takes sophisticated algorithms and analytical methods to accurately capture these movements and realize their modes. Due to its placement within the vehicle’s suspension system, the is challenging to reach for measurement. This may curb the kinds of sensors and techniques that are available for use. In general, an assortment of environmental, technical and practical obstacles must be overcome in order to measure bolster displacement and modes data in the field. Careful planning, sturdy tools and pioneering analytical techniques are needed to handle these complications and guarantee accurate and reliable data collection.
V Dhage, Yogesh, Kolage, Vikas
Experimental Study to Investigate and Control Image Blurriness due to IRVM Vibration2026-26-03211/16/2026
Vibration is one of the prominent factors that determine the quality & comfort level of a vehicle. Moreover, if vibration occurs in areas that are almost entirely within customer touchpoints, it could become a critical factor behind vehicle comfort and affects the brand image within the market negatively. The interior rear-view mirror (IRVM) is one of the important components inside passenger cabin, providing drivers with a clear view of the rear traffic. However, vibrations induced by engine operation, road irregularities, and aerodynamic forces can cause the IRVM to oscillate, leading to image blurriness and compromised visibility and safety. This paper investigates the underlying causes of IRVM vibration and its impact on rear visibility. Through experimental analysis we identify key factors contributing to mirror instability. The findings indicate the specific frequencies of vibration, particularly those resonating with the mirror's natural frequency, significantly exacerbating image blurriness. This paper presents the design modifications and damping solutions to mitigate these vibrations, enhancing the overall safety and driving experience. The study results provide valuable insights for automotive engineers and designers aiming to reduce IRVM vibration and enhance the driver/ passenger safety.
Khan, Aamir Naved, Saraswat, Vivek, Jha, Kartik, Singh, Hemendra, Seenivasan, Gokulram, Khan, Nafees
Cabin Heat-Up Thermal Assessment for Human Comfort in Passenger Car2026-26-04081/16/2026
The HVAC (Heating, Ventilation, and Air conditioning) system is designed to fulfil the thermal comfort requirement inside a vehicle cabin. Human thermal comfort primarily depends upon an occupant’s physiological and environmental condition. Vehicle AC performance is evaluated by mapping air velocity and local air temperature at various places inside the cabin. There is a need to have simulation methodology for cabin heating applications for cold climate to assess ventilation system effectiveness considering thermal comfort. Thermal comfort modelling involves human manikin modeling, cabin thermal model considering material details and environmental conditions using transient CAE simulation. Present study employed with LBM (Lattice-Boltzmann Method) based PowerFLOW solver coupled with finite element based PowerTHERM solver to simulate the cabin heat up. Human thermal comfort needs physiological modelling; thus, the in-built Berkeley human comfort library is used in simulation. Human thermal modelling includes metabolic rate of heat production with effects of clothing in external ambient conditions. Once human thermal modelling in a controlled environment stabilized, LBM-based solver used to predict the convective heat transfer phenomenon. Thereafter, conduction and radiation effects were solved using a coupling approach in PowerTHERM. Physical tests conducted in a controlled environment of climate chambers. Simulation results obtained correlated with experimental data. Occupants’ thermal comfort evaluated using the Berkeley comfort model. The current process further highlights the impact of heater capacity variation on in-cabin air temperature and passenger comfort level. The proposed method is helpful in thermal comfort prediction for passenger vehicles at cold ambient comfort requirements, heater capacity, and airflow delivery system effectiveness. Current process is found more effective where heater capacity and thermal comfort balance prediction are sensitive to two heaters, discussed in this paper.
Baghel, Devesh Kumar, Kandekar, Ambadas, Kumar, Ravi, Dimble, Nilesh
A Robustness Approach to Reducing Variability in Suspension System Performance2026-26-03811/16/2026
Determination of part tolerances for reduced variation in suspension level performance by using Multi-objective Robust Design Optimization (MORDO) The car industry is very competitive, and companies need to satisfy their customers to keep or grow their market share. It’s important for car makers to build affordable cars that provide a good driving experience, comfort for passengers, and safety for everyone. Suspension systems are very important for how a vehicle rides, handles, and stays stable, and they directly affect how driving feels. If parts are not positioned correctly, it can really impact how well a vehicle works. As a result, suggested limits for where suspension parts are placed are given to prevent issues with Kinematics and Compliance (K&C) properties. So, designing parts with the right tolerances is very important in making vehicles. It helps lower production costs and keeps the vehicle's performance consistent. This paper shows a step-by-step method to find the strongest solution that meets all the requirements for suspension performance. Multi-objective Robust Design Optimization (MORDO) and Reverse Multi-objective Robust Design Optimization (R-MORDO) have been used to find strong solutions while keeping K&C parameters intact. K&C analysis is done with ADAMS/CAR software, and Multi-Objective Robust Design optimization is done using Mode Frontier.
Pathak, Jugal, Ganesh, Lingadalu
Development of a GUI-Based Pitch Sequence Optimization Tool for Tire Noise Reduction2026-26-03301/16/2026
Tire noise reduction is important for improving ride comfort, especially in electric vehicle due to lack of engine noise and majority of the noise generated in-cabin is from tire-road interaction. Therefore, the tire tread pattern contribution is one of the important criteria for NVH performance apart from other structurally generated noise and vibration. In this work a GUI-based pitch sequence optimization tool is developed to support tire design engineers in generating acoustically optimized tread sequences. The tool operates in two modes: without constraints, where the pitch sequence is optimized freely to reduce tonal noise levels; and with constraints, where specific design rules are applied to preserve pattern consistency and manufacturability. The key point to be considered in this pitch sequence is that it should be reducing the tonal sound and equally spread i.e., the same pitch cannot be concentrated on one side which may lead to non-uniformity. So, the restriction is that the highest and lowest pitch types cannot occur adjacent to one another. This design rule helps in reducing undesirable pattern non-uniformity and improves both acoustic and structural performance. This tool helps in faster design iteration and integration with downstream development processes. This tool is also validated in current OE projects showing promising improvements in tire noise behavior while maintaining realistic design feasibility.
Sampathraghavan, Lakshmi, Ramarathnam, Krishna Kumar, Mantripragada PhD, Krishna Teja, Ramachandran, Neeraj
Addressing Safety Belt Discomfort among Female Driving Population through Data Collection and Simulation2026-26-00161/16/2026
The number of female drivers in India is increasing alongside the rapid growth of the Indian automotive industry. A driving comfort survey conducted among female drivers revealed that many of them experienced discomfort when wearing safety belts—while driving and as front-seat passengers. This discomfort is primarily due to a phenomenon referred to as “neck cutting.” The root cause of neck cutting is likely related to vehicle design, which is traditionally based on Anthropometric Test Devices (ATD’s) representing the 5th, 50th & 95th percentile (%tile) of the global population. However, a literature review indicated that the anthropometric dimensions of the Indian populations are generally smaller than those of the global for the respective candidate. To validate the neck-cutting issue, various female candidates were asked to sit in the Driver’s seat for physical measurements trials. Accordingly, methodology was developed to quantify neck cutting parameters objectively. A correlation study was performed to align virtual simulation results with physical trials outcomes, to fine-tune the virtual methodology. Based on the findings, few recommendations were suggested which were evaluated against its effect on existing relevant standards.
Kulkarni, Nachiket A, Chitodkar, Vivek V, Eknath Chopade, Santosh, Mahajan, Rahul, Yamgar, Babasaheb S
Curvature-Continuous Trajectory Refinement for Autonomous Vehicles Using a Pure Pursuit Based Path Smoothing Framework2026-26-01351/16/2026
Path planning is a key element of autonomous vehicle navigation, allowing vehicles to calculate feasible paths in challenging environments for applications like automated parking and low speed autonomous driving. Algorithms such as Hybrid A*, Reeds-Shepp, and Dubins paths are widely used and can generate collision-free paths but tend to create curvature discontinuities. These discontinuities result in sudden steering transitions, which create control instabilities, higher mechanical stress, and lower passenger comfort. To overcome these issues, this paper suggests a path-smoothing technique based on the pure-pursuit algorithm to produce smoothed curve paths appropriate for real-world driving. This method utilizes the practical approach of the original path, but removes sudden transitions that destabilize control. By ensuring smooth curvature, the vehicle undergoes fewer jerky steering actions, improved energy efficiency, less actuator wear, and improved high-speed tracking. This paper provides a valuable approach to usual limitations of discrete path planning, on the contribution of control algorithms such as pure pursuit to bridging the gap between planning and execution towards more adaptable autonomous driving particularly automated parking systems.
S, Shriniyathi, A, Josana, Anto Edwin J, Joel, T, Akshayaa, M, Senthil Vel, Kumar, Vimal
Effect of Correlated Colour Temperature of Automotive LED Headlamps on Visibility and Safety2026-26-01301/16/2026
The objective of this study was to examine the effect of Correlated Colour Temperature (CCT) of automotive LED headlamps on driver’s visibility and comfort during night driving. The experiment was conducted on different headlamps having different correlated colour temperatures ranging from 5000K to 6500K in laboratory. Further study was conducted involving participants of different age group and genders for understanding their perception to identify objects when observed in light of different LED headlamps with different CCTs. Studies have shown that both Correlated Colour Temperature and illumination level affect driver’s alertness and performance. Further study required on headlamps with automatically varying CCT to get better solution on driver’s visibility and safety.
Patil, Mahendra G., Kirve, Jyoti, Parlikar, Padmakumar
Modelling and Simulation of Key Vehicle Dynamics Parameters for a Conceptual Race Car2026-26-00851/16/2026
Vehicle dynamics is a vital area of automotive engineering that focuses on analyzing how a vehicle responds to driver inputs and external factors like road conditions and environmental influences. Achieving optimal performance, safety, and ride comfort requires a detailed understanding of longitudinal, lateral, and vertical dynamic behavior. The objective of this paper is to develop and validate the model of a concept Race car and evaluate its vehicle dynamics behavior using IPG CarMaker, a high-fidelity virtual testing environment widely used in industry. The model incorporates a range of vehicle parameters, including suspension parameters like spring and damper characteristics, mass distribution, tire properties and powertrain parameters. The performance evaluation is done as per standard guidelines, including Constant Radius turn test, Sine Steer test and other standard tests like Acceleration, Braking along with Ride and Comfort classification. The key parameters that are calculated and validated are vehicle accelerations in the principal axes, stopping distance, yaw velocity and yaw velocity gain, vehicle roll characteristics, steering parameters, ride and driver comfort metrics. Validation of simulation outputs is achieved through comparison with empirical data obtained from literature and mathematical calculations based on vehicle dynamics principles. The test results show a close correlation between mathematical and simulated values, therefore accurately predicting vehicle behavior.
Agrewale, Mohammad Rafiq B., Vaish, Ujjwal
Game Theory-Based Coordinated Control Strategy for Multiple Types of Vehicles in Ramp Concourse Area2025-01-734812/31/2025
Decision modeling based on game theory provides an effective means to achieve safe and efficient ramp merging. However, there are some limitations in the current research, such as previous ramp merge control only studied the interaction problem of networked autonomous vehicles, ignoring the diversity of vehicle types, which is a non-negligible problem in real life. To solve this problem, this study proposes to use different game approaches to address the merging challenge. First, a static game is used to deal with the merging problem of networked self-driving vehicles, and then a belief pool with non-cooperative game approach is used to deal with the problem of human driver’s driving style with the merging problem of self-driving vehicles with human-driven vehicles with unknown information. The simulation results show that the efficiency of on-ramp merging can be significantly improved when networked self-driving cars interact with each other; in the case of merging self-driving cars with human-driven cars, the self-driving cars can recognize the driving styles of the opposite cars and make accurate decisions, which improves the driving efficiency, ensures driving safety and maximizes passenger comfort to the greatest extent.
Gao, Zhenyu, Dong, Jiuyun, Zhang, Lu, Guo, Ge
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