Browse Topic: Vehicle occupants

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Smart Fleet Management for Mechanical Tillage Implements2025-28-0312To be published on 11/06/2025
Tillage, a fundamental agricultural practice involving soil preparation for planting, has traditionally relied on mechanical implements with limited real-time data collection or adjustment capabilities. The lack of real-time data and implement statistics results in fleet managers struggling to track performance, driver behavior, and operational efficiency of the implements. Lack of data on vehicle performance can result in unexpected breakdowns and higher maintenance costs, ensuring compliance with regulations is challenging without proper data tracking, potentially leading to fines and legal issues. Bluetooth-enabled mechanical implements for tillage operations represent an emerging frontier in precision agriculture, combining traditional soil preparation techniques with modern wireless technology. Implement mounted battery powered BLE (Bluetooth Low Energy) modules operated by solar panel based rechargeable batteries to power microcontroller. When Implement is operational turns
Kaniche, Onkar, Rajurkar, Kartik, Gokhale, Sourabha, VADNERE, Mohan
AI in Off-Highway Fleet Management: Driving Towards a Smarter Future2025-28-0335To be published on 11/06/2025
AI in Off-Highway Fleet Management: Driving Towards a Smarter Future Managing fleets of off-highway vehicles presents unique challenges that significantly impact operational efficiency, safety, and sustainability. These vehicles, often used in industries such as mining, construction, and agriculture, face several issues like maintenance and repair, reduced fuel efficiency, fleet tracking and monitoring. Tackling these challenges is crucial for enhancing the efficiency, safety, and sustainability of off-highway vehicle fleet management, and this paper presents an AI-driven solution to address these issues. AI systems leverage real-time data from telematics devices, sensors, and GPS tracking to optimize various aspects of fleet management. Key areas of impact include: Predictive Maintenance: AI algorithms analyse vehicle data to predict potential failures before they occur, reducing downtime and maintenance costs. By monitoring indicators such as engine performance and battery health
Selvaraj, Ajith Kumar, Nashipudi, Sandesh, Ramanathan, Vadivel, Durairaj, Manikandan
Strategies for Improving Energy Consumption for Electric Vehicles.2025-32-0092To be published on 11/03/2025
The global transition toward sustainability and decarburization has positioned Electric Vehicles (EVs) at the forefront of future mobility. While EVs inherently offer lower emissions compared to internal combustion engine (ICE) vehicles, optimizing their energy efficiency remains a critical challenge to ensure their viability, affordability, and widespread adoption. With global EV sales surpassing 10 million units in 2022 representing over 14% of the global vehicle market. Energy efficiency has emerged as a cornerstone in addressing key concerns such as range anxiety, charging infrastructure, operational costs and overall environmental impact. This paper embarks on a comprehensive journey, beginning with the historical evolution of EVs and identifying the multifaceted energy losses that occur during vehicle operation. By mapping the energy distribution within a typical EV, the study lays a foundational understanding of where and how inefficiencies arise. It then explores current
Jain, Gaurav, PREMLAL, P, Pathak, Rahul, Gore, Pandurang
An SVM-based approach to develop a new control module for H2-powered engine using driver behaviour classification2025-32-0042To be published on 11/03/2025
In the next years, the global hydrogen vehicle market is expected to grow at a very high rate. Consequently, it is necessary for scholars and professionals to study and test specific components in order to rise motor efficiency leveraging the new features of connectivity available in smart roads. In particular, our research is focused on the developement of an engine control module driven by evaluation of usage characteristics (e.g., driving style) and "connected-to-x" scenarios using the standard engine control approach. Moreover, the module proposed enables the implementation of "fast running" models to improve the response of vehicle and make the best possible use of H2-powered engine characteristics. That said, in this paper is proposed a new approach to implement the control module, using Support Vector Machine (SVM) as the machine learning algorithm to detect driving style, and consequently modify the parameters of the engine. We choose SVM because i) it is less prone to
Mastroianni, Michele, Merola, Simona, Irimescu, Adrian, De Santis, Marco, Esposito, Christian, Aversano, Lerina
Automatic Brake failure detection with Electromagnetic Braking system2025-28-0373To be published on 10/30/2025
Brake failure is a major cause of road accidents, contributing significantly to vehicle-related fatalities and injuries across the globe. To address this issue, this project focuses on the development of an automatic brake failure detection and integration of electromagnetic braking system to enhance vehicle safety and reliability. The system continuously monitors brake functionality by detecting anomalies in pedal free play and oil pressure levels within the braking system. If an irregularity is detected, a signal is transmitted to a microcontroller, which triggers a dashboard warning light to alert the driver, once the driver sees this light he must pull towards the service lane. If correct action is not taken within five seconds, the auxiliary electromagnetic braking system engages automatically, ensuring a controlled deceleration of the vehicle. The integration of electromagnetic brakes provides an additional layer of safety by acting as a backup braking mechanism in case of
Raja, Selvakumar
Risk Assessment of Delayed MAC Compressor Engagement on Occupant Thermal Comfort2025-28-0399To be published on 10/30/2025
Mobile air conditioning (MAC) systems play a critical role in ensuring occupant thermal comfort, particularly under extreme ambient conditions. Any delay in compressor engagement directly affects cabin cooldown performance, impacting both perceived and measured comfort levels. This study assesses the thermal comfort risks associated with compressor engagement delays of 6.5 seconds and 13 seconds under varying ambient conditions. A comprehensive frontloading approach was employed, integrating 1D CAE simulations with objective and subjective experimental testing. Initial simulations provided insights into transient cabin heat load behavior and air distribution effectiveness, enabling efficient test case selection. Physical testing was conducted in a controlled climatic chamber under severe (>40°C) ambient condition, replicating real-world scenario. Objective metrics, including cabin air temperature, vent temperature and cooldown rates, were measured to quantify thermal performance
Kulkarni, Shridhar, Deshmukh, Ganesh, Joshi, Gaurav, Jaybhay, Sambhaji, Shah, Geet
Numerical study to predict the Impact of Geometric Modification on Aero-Acoustic behaviour of a Commercial Vehicle2025-28-0426To be published on 10/30/2025
To address the growing concern of increasing noise levels in urban areas, modern automotive vehicles need improved engineering solutions. The need for automotive vehicles to have a low acoustic signature is further emphasized by local regulatory requirements, such as the EU's regulation 540/2014, which sets sound level limits for commercial vehicles at 82 dB(A). Moreover, external noise can propagate inside the cabin, reducing the overall comfort of the driver, which can have adverse impact on the driving behavior, making it imperative to mitigate the high noise levels. This study explores the phenomenon of change in acoustic behaviour of external tonal noise with minor geometrical changes to the A-pillar turning vane (APTV), identified as the source for the tonal noise generation. An incompressible transient approach with one way coupled Acoustics Wave solver was evaluated, for both the baseline and variant geometries. Comparison of CFD results between baseline and variant showed
Pawar, Sourabh, Sharma, Shantanu, singh, Ramanand
Real Driving Database for Automotive Drive System Development2025-01-0378To be published on 10/07/2025
Knowledge of real-world driving behavior is fundamental to the development of drive systems. An application example would be the derivation of representative requirements or driving cycles based on information about the use case-specific vehicle use. The profiles are additionally enriched with underlying information such as distance, standstill times, average accelerations or a customer driving style estimation. This information could be used, for example, for drive system design. In addition, the real-world data can be used for regulatory purposes such as the definition of utility factors or the definition of real driving emission cycles. In a project funded by FVV e.V., we have developed an universal database software including data storage, user interface and general data plausibility functions for real driving data. The database contains on component and vehicle level detailed time series measurement data such as torque and speed of electric motors and internal combustion engines
Sander, Marcel, Sturm, Axel Wolfgang, Martínez Medina, Óscar, Henze, Roman, Kühne, Ulf, Eilts, Peter
The impact of inter-vehicular distance on CO₂ emissions in on-road driving hybrid vehicles2025-01-0404To be published on 10/07/2025
A major challenge for internal combustion engine vehicles is reduction of CO₂ emissions. Hybrid vehicle demand has recently increased as a countermeasure. However, in hybrid vehicles, the frequency of motor and engine usage varies depending on the driver's driving style, even when driving the same vehicle on the same route. As a result, CO₂ emissions can differ significantly between drivers. Analyzing the impact of driving characteristics on CO₂ emissions can contribute to improving the efficiency of engine and motor control in vehicles, leading to further reductions in CO₂ emissions. Therefore, this paper examines the impact of different driver behaviors on CO₂ emissions in hybrid vehicles. In this study, two drivers were asked to follow a preceding vehicle on real roads, and various data were collected during these drives. Conducting the experiments on actual roads allowed us to obtain results that closely reflect real-world driving conditions, thereby enhancing the relevance of the
Hosogi, Takafumi, Imamura, Kotaro, Sato, Susumu
Adapted Intelligent Driver Model for Improved Vehicle Following and Assessment of Energy Impacts2025-01-0387To be published on 10/07/2025
Central to predicting the impacts of individual vehicle operations within microscopic traffic simulation is the driver model. A driver model determines a vehicle’s velocity profile in various driving scenarios and interactions with other vehicles. Characteristics including driver behavior and interactions with stop signs, traffic signals, and interactions with a lead vehicle can be modeled and assessed with a representative driver model. This paper presents the application of an existing intelligent driver model (IDM) with an adaptation for vehicle following dynamics and the interaction with the lead vehicle to be more representative of driver assist systems concerning the relative distance between the lead and simulated ego vehicle. The method uses an additional control term to augment the existing IDM and reduce inter-vehicle distance to the time gap. The impact on vehicle dynamics is compared and validated with real-world ego vehicle data recorded through driver assist systems. The
Udipi, Anirudh, Jadhav, Shreeprasad, Bhure, Mayur, Hegde, Bharatkumar, Poovalappil, Aman, Robare, Andrew, Apostol, Peter, Bahramgiri, Mojtaba, Naber, Jeffrey
Influence of MPC based Driver Types on the Energy Consumption of Battery Electric Vehicles2025-24-011309/07/2025
Electric vehicles are increasingly important to reducing emissions and promoting sustainable mobility. Despite their advantages over conventional vehicles, the energy consumption of electric vehicles is heavily influenced by various factors such as driving behavior, elevation profile, and environmental conditions. In particular, the driving style plays a crucial role in determining range and energy consumption. This influence is also observed in the context of the Interreg project FreeE-Bus. This project focuses on the development of optimized charging management for electric buses in the public transport system of the Lake Constance region. Due to strict data protection regulations that prevent a detailed analysis of driver data, assessing the impact of driving styles is difficult. This paper addresses this issue by developing an innovative driver model that simulates different driver types and analyzes their effects on energy consumption. The driver model employs a Model Predictive
Konzept, Anja, Reick, Benedikt, Miller, Marius, Rautenberg, Philip, Störzer, Martin
Reducing Range Prediction Uncertainty in Heavy-Duty Electric Vehicles Using a Route-Informed Transformer-Based Model2025-24-012209/07/2025
Electrification of heavy-duty on-road trucks used for regional freight transportation is a viable option for fleets to reduce operational and maintenance costs and lower their carbon footprint. However, there is considerable uncertainty in projecting their daily range because highly variable payload mass, among other factors, confounds battery state of charge (SOC) prediction algorithms. Previous work by the authors proposed an electric vehicle range prediction model based on two parallel recurrent neural networks (RNNs). The first RNN used mean-variance estimation to output a predicted mean and variance, and the second used bounded interval estimation to provide bounds on the SOC required to complete a trip. The dual RNN approach resulted in estimating the remaining range and error bands of the SOC over the route. The previous work was limited because it did not incorporate driving conditions, like road type and ambient temperature, that affect driver behavior and energy consumption
Jayaprakash, Bharat, Eagon, Matthew, Northrop, William F.
Design of a Reconfigurable Winding Switch for an In-Wheel Automotive Motor2025-24-013009/07/2025
A design is presented for a mechanical switchgear, intended for reconfiguring the windings of an electric machine whilst in operation. The design is a result of the EM-TECH project funded under Horizon Europe, and includes integration onto an in-wheel automotive motor. The motor features 6 phase fractions, which can be reconfigured by the switchgear between series-star or parallel-star arrangements, thereby doubling the torque or speed range of the electric machine. The effect on the operating envelope of the machine is similar to the addition of a 2-speed transmission, but with an added mass of only 1.5kg – around one tenth of the equivalent transmission. As well as the extended operating envelope, the reconfigurable winding motor offers benefits in efficiency and power density. Previous studies have shown the technical feasibility of such devices using semiconductor switches. Although these studies have confirmed the performance of the technology, such semiconductor solutions have
Vagg, Christopher, Thomas, Luke, Pickering, Simon, Herzog, Matic, Trinchuk, Danylo, Romih, Jaka
Use of Model-Based Datasets and Neural-Networks to support Real-Time Driving Suggestions System for Components Preservation in Battery Electric Vehicles2025-24-011509/07/2025
Nowadays, Battery Electric Vehicles (BEVs) are considered an attractive solution to support the transition towards more sustainable transportation systems. Although their well-known advantages in terms of overall propulsion efficiency and exhaust emissions, the diffusion of BEVs on the market is still reduced by some technical bottlenecks. Among those, the uncertainty about the expected durability of the vehicle's onboard battery packs plays a key role in affecting customer choice. In this context, this paper proposes the use of model-based datasets for training a driving support system based on machine learning techniques to be installed on board. The objective of this system is to acquire vehicle, environmental, and traffic information from sensor’ networks and provide real-time smart suggestions to the driver to preserve the remaining useful life of vehicle components, with particular reference to the battery pack and brakes. For the generation of the training dataset, first, a set
Bernardi, Mario Luca, Capasso, Clemente, Iannucci, Luigi, Sequino, Luigi
A New Methodology to Evaluate In-Service Emissions of Used Vehicles at a Large Scale using a SEMS and Digital Tools2025-24-009009/07/2025
Air quality is an increasing concern, particularly in densely populated urban areas. Indeed, large European cities have seen pollutant concentrations exceed World Health Organization thresholds, with a significant portion of NOx emissions originating from road transportation. Studies have shown that less than five percent of the vehicle fleet, often including vehicles with defective after-treatment systems, is responsible for a disproportionate share of these emissions. This highlights the importance of not solely relying on the gradual renewal of vehicle fleets to mitigate health risks associated with air pollution. This research, funded by the French Agency for the Ecological Transition (ADEME), introduces an experimental methodology aimed at controlling emissions from vehicles already in circulation. Aramis Group, a European specialist of refurbishment and online sales of used cars, provided several refurbished used vehicles for testing, directly taken from its workflow. These
Carlos Da Silva, Daniel, Kermani, Joseph, Farcot, Fabrice, Gaie, Fabien
S.I.S.T.E.R.: an integrated approach to non-exhaust emissions2025-24-009109/07/2025
As electric mobility spreads and evolves, non-exhaust Particulate Matter sources are gaining more attention for total vehicular emissions. A holistic approach for studying the involved phenomena is necessary to identify the parameters that have the greatest impact on this portion of emissions. To achieve this, it is needed to develop a new platform capable of both creating testing methodologies for future regulations and enabling the parallel development of advanced tires and brakes that meet these standards, by correlating vehicle dynamics, driving style, tire and brake characteristics, and the resulting emissions. Here the authors present the Sustainable Integrated System for Total non-Exhaust Reduction (S.I.S.T.E.R.) project, funded by the Italian Centro Nazionale per la Mobilità Sostenibile (MOST), that aims to develop an integrated approach to study tire/brake-related emissions from the initial stages of compound development to outdoor vehicle tests, allowing actions to be taken
Genovese, Andrea, De Robbio, Roberta, Lenzi, Emanuele, Caiazza, Antonio, Lippiello, Felice, Costagliola, Maria Antonietta, Marchitto, Luca, Serra, Antonio, Arimondi, Marco, Bardini, Perla
Safety Impacts of Active Safety and Driving Automation FeaturesEPR202501308/13/2025
Advanced technologies that assist the human driver or reduce (or even eliminate) the human driver’s role are becoming increasingly prevalent in new light-duty vehicles used by the general public. These technologies are divided between Active Safety features that monitor the human driver and vehicle motion and act intermittently to mitigate and avoid crashes, and Driving Automation features that assume some or all of the dynamic driving task from the human driver. Both types of technologies have the potential to reduce injuries and save lives by reducing the frequency and/or severity of crashes. Safety Impacts of Active Safety and Driving Automation Features addresses the current capabilities and future potential for Active Safety and Driving Automation features to reduce crash frequency and severity and provides an overview of the state of the industry for both types of features, including current deployments, trends, and anticipated rollouts. Gaps in knowledge, unsettled issues, and
Wishart, Jeffrey
SAE TOMORROW TODAY - How Simulation is Shaping the Future of Motorsports1352707/24/2025
From driver training to vehicle engineering and strategy development, simulation is no longer just a tool -- it's a competitive advantage. Top racing teams are using simulators to optimize car setups, dramatically cutting down on testing time and maximizing on-track performance. But the impact of simulation goes far beyond the racetrack. With cutting-edge realism and high-bandwidth systems, Dynisma is helping elite drivers stay sharp while accelerating product development for automotive manufacturers. Dynisma Motion Generators (DMGs) create detailed 3-D environments that include driver behavior and environmental factors, and accurately model the physical characteristics of vehicles, such as tire performance and surface interactions. To learn more, we sat down with Simon Holloway, Commercial Director, to discuss how Dynisma's data-driven engineering is revolutionizing simulation in both the motorsport and automotive sectors to enhance performance, safety, and efficiency. We'd love to
Patterson, Lori
Data-Driven Analysis of Vehicle Lateral Control in the Country Road Domain2025-01-026807/02/2025
The optimization and further development of automated driving functions offer significant potential for reducing the driver's workload and increasing road safety. Among these functions, vehicle lateral control plays a critical role, especially with regard to its acceptance by end customers. Significant development efforts are required to ensure the effectiveness and reliability of this aspect in real-world conditions. This work focuses on analyzing lateral vehicle control using extensive measurement data collected from a dedicated vehicle fleet at the Institute of Automotive Engineering at the Technical University of Braunschweig. Equipped with state-of-the-art measurement technology, the fleet has driven several hundred thousand kilometers, allowing for the collection of detailed information on vehicle trajectories under various driving conditions. A total of 93 participants, aged between 20 and 43 years, contributed to the dataset. These measurements have been classified into
Iatropoulos, Jannes, Panzer, Anna, Arntz, Martin, Prueggler, Adrian, Henze, Roman
Study on Driver-Dependent Stabilization Capabilities for Safe Chassis Control with Steer By Wire2025-01-027607/02/2025
Steer-by-wire actuators represent a transformative advancement in chassis control, opening up new potential for optimizing driving behavior across the entire range of driving dynamics - including driver-dependent automatic counter steering in critical driving situations. However, from a functional safety perspective, the increased potential also introduces new risks with respect to possible system failures. To mitigate these risks, sophisticated monitoring functions are essential to ensure vehicle controllability at all times. Current research approaches for monitoring functions use safe driving envelopes. This set of safe driving states is often found by open-loop simulations, which provide a phase portrait of the nonlinear system under control and from which stability limits can be derived. However, it remains open how these open-loop stability limits correspond to the stabilization capability of a real human driver in the loop. And secondly, how these closed-loop stability limits
Birkemeyer, Janick, Naidu P.M, Tarun, Borkowski, Lukas, Müller, Steffen
Longitudinal Dynamic Metrics for Describing and Developing the Deceleration Behavior of Vehicles in the Concept Phase2025-01-026407/02/2025
The brake system is a critical safety component in motor vehicles. Advances in the electrification of the powertrain and the rise of autonomous driving technologies are significantly impacting the brake system, which allows innovative approaches and necessitating the development of new brake concepts and new deceleration strategies. A major technological advance is the decoupling of the driver from the brake system through Brake-by-wire technology. A crucial attribute of Brake-by-wire systems is the attainment of a concept-independent deceleration behavior. To establish a consistent and brand-specific deceleration behavior in the early development phase, objective metrics and perceptual thresholds are required to describe the desired subjective braking behavior. Moreover, objective metrics are indispensable for the virtual phase of the vehicle development process. This article focuses on deceleration from a straight-ahead drive. To identify objective metrics and perceptual thresholds
Biller, Ralph, Udovicic, Matej, Ketzmerick, Erik, Kirch, Sebastian, Mayr, Stefan, Prokop, Günther, Wagner, Andreas
Exploring the Correlation between Physiological States and Driving Behavior on Highways with Integrated ECG Measurements2025-01-029207/02/2025
Human driver errors, such as distracted driving, inattention, and aggressive driving, are the leading causes of road accidents. Understanding the underlying factors that contribute to these behaviors is critical for improving road safety. Previous studies have shown that physiological states, like raised heart rates due to stress and anxiety, can influence driving behavior, leading to erratic driving and an increased risk of accidents. In this study, we conducted on-road tests using a measurement system based on the Driver-Driven vehicle-Driving environment (3D) method. We collected physiological signals, specially electrocardiography (ECG) data, from human drivers to examine the relationship between physiological states and driving behaviors. The aim was to determine whether ECG can serve as an indicator of potential risky driving behaviors, such as sudden acceleration and frequent steering adjustments. This information enables automated driving (AD) systems to intervene in dangerous
Ji, Dejie, Flormann, Maximilian, Bollmann, Julian, Henze, Roman, Deserno, Thomas M.
Systematic Derivation of Requirements for the Perception Task of Free Space Driving Assistance Function Applied to Trucks2025-01-027907/02/2025
The larger size and expanded blind spots of heavy-duty trucks in comparison to passenger cars, create unique challenges for truck drivers navigating narrow roads, such as in urban scenarios. For this reason, the detection of free space around the vehicle is of critical importance, as it has the potential to save lives and reduce operating costs due to less maintenance and downtime. Despite the existence of numerous approaches to free space detection in the literature, few of these have been applied to the trucking sector, disregarding important aspects for these kinds of vehicles such as the altitude at which obstacles are located. This paper aims to present the initial results of our research, a “Not Free Space Warner”, a driving assistance function intended for implementation in series trucks. A methodology is followed to define the characteristics that the perception component of this function shall fulfill. To this end, an analysis of the most critical accidents and common driving
Martinez, Cristian, Peters, Steven
Early Detection of the Loss of Control of Motorsport Vehicles10-09-03-002906/30/2025
The article investigates how to detect as quickly as possible whether the driver will lose control of a vehicle, after a disturbance has occurred. Typical disturbances refer to wind gusts, obstacle avoidance, a sudden steer, traversing a pothole, a kick by another vehicle, and so on. The driver may be either human or non-human. Focus will be devoted to human drivers, but the extension to automated or autonomous cars is straightforward. Since the dynamic behavior of vehicle and driver is described by a saddle-type limit cycle, a proper theory is developed to use the limit cycle as a reference trajectory to forecast the loss of control. The Floquet theory has been used to compute a scalar index to forecast stable or unstable motion. The scalar index, named degree of stability (DoS), is computed very early, in the best case, in a few milliseconds after the disturbance has ended. Investigations have been performed at a dynamic driving simulator. A 14 DoF vehicle model, virtually driven by
Della Rossa, Fabio, Fontana, Matteo, Giacintucci, Samuele, Gobbi, Massimiliano, Mastinu, Giampiero, Previati, Giorgio
Yaw Motion of Motor Vehicles2025-01-504306/20/2025
Vehicular accident reconstruction is intended to explain the stages of a collision. This also includes the description of the driving trajectories of vehicles. Stored driving data is now often available for accident reconstruction, increasingly including gyroscopic sensor readings. Driving dynamics parameters such as lateral acceleration in various driving situations are already well studied, but angular rates such as those around the yaw axis are little described in the literature. This study attempts to reduce this gap somewhat by evaluating high-frequency measurement data from real, daily driving operations in the field. 813 driving maneuvers, captured by accident data recorders, were analyzed in detail and statistically evaluated. These devices also make it possible to record events without an accident. The key findings show the average yaw rates as a function of driving speed as well as the ratio between mean and associated peak yaw rate. Beyond that, considerably lower yaw rates
Fuerbeth, Uwe
Enhanced Equivalent Consumotion Minimization Strategy Based on Bayesian Optimization2025-01-019406/16/2025
The Equivalent Consumption Minimization Strategy (ECMS) is an effective approach for managing energy flow in hybrid electric vehicles (HEVs), balancing the use of electric energy and fuel consumption. The strategy’s performance depends heavily on the Equivalent Factor (EF), which governs this trade-off. However, the optimal EF varies under different driving conditions and is influenced by the inherent randomness in factors such as traffic, road gradients, and driving behavior, making it challenging to determine through traditional methods. This paper introduces Bayesian Optimization (BO) as a solution to address the stochastic nature of the EF parameter tuning process. By using a probabilistic model, BO efficiently navigates the complex, uncertain performance landscape to find the optimal EF parameters that minimize fuel consumption and emissions across variable conditions. Simulation results under WLTP cycles show that the proposed method reduces fuel consumption by 0.9% and improves
Zhang, Cetengfei, Zhou, Quan, Jia, Yiqi, Xiong, Lu
Comprehensive Management of Water Ingress in the Torque Overlay Steering System for Enhanced Reliability in Smart Trucks02-18-03-001606/02/2025
The reliability and performance of steering systems in commercial vehicles are paramount, given their direct impact on reducing hazardous driving and improving operational efficiency. The torque overlay system is designed to enhance driver control, feedback, and reduce driver fatigue. However, vulnerabilities such as water ingress under certain environmental conditions have raised significant reliability requirements. This article discusses the systematic investigation into how radial bearing sideloading led to the input shaft seal failing to contact the input shaft. Water was allowed a path to enter the TOS module, affecting the electronic sensor, and faulting out the ADAS functionality. Improvement to the bearing support and sealing design culminated to an enhanced TOS module package able to withstand testing procedures that mimic the environmental and use case situation which caused the ingress.
Bari, Praful Rajendra, Kintner, Jason
Methods to Reduce Transmission of Airborne Viruses in a Bus Cabin02-18-04-002105/19/2025
Public buses can be high-risk environments for the transmission of airborne viruses due to the confined space and high passenger density. However, advanced cabin air control systems and other measures can mitigate this risk. This research was conducted to explore various strategies aimed at reducing airborne particle transmission in bus cabins by using retrofit accessories and a redesigned parallel ventilation system. Public transit buses were used for stationary and on-road testing. Air exchange rates (ACH) were calculated using CO2 gas decay rates measured by low-cost sensors throughout each cabin. An aerosol generator (AG) was placed at various locations inside the bus and particle concentrations were measured for various experiments and ventilation configurations. The use of two standalone HEPA air filters lowered overall concentrations of particles inside the bus cabin by a factor of three. The effect of using plastic “barriers” independently showed faster particle arrival times
Lopez, Brenda, Swanson, Jacob, Dover, Kevin, Renck, Evan, Chang, M.-C. Oliver, Jung, Heejung
Driving Level Skill CertificationJ3300_202505 (Current)05/07/2025
This SAE Recommended Practice is intended to establish a procedure to certify the fundamental driving skill levels of professional drivers. This certification can be used by the individual driver to qualify their skills when seeking employment or other professional activity. These certification levels may also be used by test facilities or other organizations when seeking test or professional drivers of various skills. The associated family of documents listed below establish driving skill criteria for various specific categories. SAE J3300: Driving level SAE J3300/1: Low mu/winter driving SAE J3300/2: Trailer towing SAE J3300/3: Automated driving Additional certifications to be added as appropriate. This main document provides: (1) common definitions and general guidance for using this family of documents, (2) directions for obtaining certification through Probitas Authentication®1, and (3) driving level examination requirements.
Driving Skills Standards Committee
Variable Speed Limit Control Based on Deep Deterministic Policy Gradient under Mixed Traffic Environment12-09-01-000405/06/2025
The existing variable speed limit (VSL) control strategies rely on variable message signs, leading to slow response times and sensitivity to driver compliance. These methods struggle to adapt to environments where both connected automated vehicles (CAVs) and manual vehicles coexist. This article proposes a VSL control strategy using the deep deterministic policy gradient (DDPG) algorithm to optimize travel time, reduce collision risks, and minimize energy consumption. The algorithm leverages real-time traffic data and prior speed limits to generate new control actions. A reward function is designed within a DDPG-based actor-critic framework to determine optimal speed limits. The proposed strategy was tested in two scenarios and compared against no-control, rule-based control, and DDQN-based control methods. The simulation results indicate that the proposed control strategy outperforms existing approaches in terms of improving TTS (total time spent), enhancing the throughput efficiency
Ding, Xibin, Zhang, Zhaolei, Liu, Zhizhen, Tang, Feng
Leveraging User Feedback to Optimize the Tuning of EVSE Models2025-01-002005/05/2025
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
Global Vehicle Vibration Enhancement Using Circular Force Technology2025-01-001505/05/2025
In addition to providing safety advantages, sound and vibration are being utilized to enhance the driver experience in Battery Electric Vehicles (BEVs). There's growing interest and investment in using both interior and exterior sounds for pedestrian safety, driver awareness, and unique brand recognition. Several automakers are also using audio to simulate virtual gear shifting of automatic and manual transmissions in BEVs. According to several automotive industry articles and market research, the audio enhancements alone, without the vibration that drivers are accustomed to when operating combustion engine vehicles, are not sufficient to meet the engagement, excitement, and emotion that driving enthusiasts expect. In this paper, we introduce the use of new automotive, high-force, compact, light-weight circular force generators for providing the vibration element that is lacking in BEVs. The technology was developed originally for vibration reduction/control in aerospace applications
Norris, Mark A., Orzechowski, Jeffrey, Sanderson, Brad, Swanson, Douglas, Vantimmeren, Andrew
Acoustic Characterization of AC Motor Using Sound Intensity in Non-Anechoic Laboratory Environments2025-01-001005/05/2025
Sound power is a commonly used metric to quantify acoustic sources like AC motor in electrified powertrain. Testing for sound power determination is often performed in an anechoic environment to create free-field conditions around the unit. To eliminate the influence of extraneous noise sources, the anechoic facilities must be further isolated from driver and absorber dynamometers. These dynamometers are needed for running the AC motors in the desired speed and load conditions. For early detection of potential issues, it is advantageous to have the capability for engineers to conduct acoustic tests in standard laboratory environments. These may include non-acoustically treated rooms, presence of extraneous noise sources (e.g., driver and absorber dynos), etc. In such environments, sound intensity-based sound power determination methods could be utilized. The sound intensity-based approach is covered in ISO 9614 standard. The norm is to sweep an intensity probe on a sound source in
Kumar, Aditya, Ippili, Rajani
Fast Noise Prediction of an Electric Powertrain Integrated in Various Vehicles2025-01-007605/05/2025
As the automotive industry moves toward electrification, new challenges emerge in keeping pleasant acoustics inside vehicles and their surroundings. This paper proposes a method for anticipating the main sound sources at driver’s ear for custom driving scenarios. Different categories of Road and Wind noise were created from a dataset of multiple vehicles. Using innovative sound synthesis techniques, it enables Valeo to make early predictions of the emergence of an electric axle powertrain (ePWT) once it is combined with this masking noise. Realistic signals could be generated and compared with actual acoustic measurements to validate the method.
Redon, Milan, Dendievel, Clement, Pluton, Matthias
Subjective Development of EV Propulsion Noise to Drive Product Differentiation2025-01-006705/05/2025
The frequency and amplitude content of powertrain noise is motor torque and speed dependent and tends to influence the driver’s subjective perception of the vehicle. This provides manufacturers with an opportunity to drive product differentiation through consideration of powertrain noise in early stages of the development cycles for electric vehicles (EVs). This paper focuses on the evaluation of customer preference and perception of acoustic feedback from different powertrain design options based on targeted powertrain orders and expected wind and road masking during high acceleration maneuvers. A jury study is used to explore customer feedback to a two-stage gearbox design with AC permanent magnet motor order combinations. The subjective influence of order spacing, dominant frequency content and the number of audible orders is studied to understand aural perspective product differentiation opportunities.
Joodi, Benjamin, Jayakumar, Vignesh, Conklin, Chris, Pilz, Fernando, Iyengar, Shashank, Weilnau, Kelby, Hodgkins, Jeffrey
Sound Design for Electric Vehicles: Enhancing Safety and User Experience Through Acoustic Vehicle Alerting System (AVAS)2025-01-006805/05/2025
Silent motors are an excellent strategy to combat noise pollution. Still, they can pose risks for pedestrians who rely on auditory cues for safety and reduce driver awareness due to the absence of the familiar sounds of combustion engines. Sound design for silent motors not only tackles the above issues but goes beyond safety standards towards a user-centered approach by considering how users perceive and interpret sounds. This paper examines the evolving field of sound design for electric vehicles (EVs), focusing on Acoustic Vehicle Alerting Systems (AVAS). The study analyzes existing AVAS, classifying them into different groups according to their design characteristics, from technical concerns and approaches to aesthetic properties. Based on the proposed classification, an (adaptive) sound design methodology, and concept for AVAS are proposed based on state-of-the-art technologies and tools (APIs), like Wwise Automotive, and integration through a functional prototype within a virtual
Rodrigues Ferraz Esteves, Ana Raquel, Campos Magalhães, Eduardo Miguel, Bernardes de Almeida, Gilberto
Study of Sound and Cabin Touchpoint Contributions to Subjective Perception of Secondary Ride2025-01-006005/05/2025
Subjective perception of vehicle secondary ride is dependent on simultaneous touchpoint vibrations and audible inputs to the occupants. Standards such as ISO 2361 provide guidelines for objective assessments of human body thresholds to vibration [1]. However, when a human experiences vibration inputs at multiple touchpoints, as well as aural inputs, it becomes complicated to judge each individual contribution to the overall subjective perception [2]. Additional factors, such as ambient conditions, ergonomics, age, gender etc. also play a role. Secondary ride, which is defined as energy in the 10-30 Hz frequency range, is one such event that affects the customers’ perception of ride comfort and quality. The goal of this work is to develop a sound and vibration simulator model and execute a secondary ride jury study of vehicle driving over cleats. The aim of the study is to rank the contributions of each touch point vibration input, as well as sound to the overall subjective perception
Jayakumar, Vignesh, Joodi, Benjamin, Geissler, Christian, Pilz, Fernando, Lynch, Luke, Conklin, Chris, Weilnau, Kelby, Hodgkins, Jeffrey
Snap-Fits Evaluation for Minimizing the Squeak & Rattle in Automotives2025-01-009105/05/2025
The world of plastic products has been growing due to its versatile properties and has become an intrinsic and fundamental part of engineering for new products. The most important aspects contributing to this spectacular growth are the design and assembly, making sure that plastic parts are designed optimally. The safety requirements have been increased due to the safety ratings and thus interior parts must provide more absorption and protection to occupants. The main connection types used in the plastic parts are heat stakes and snap fits. The purpose of a good snap fit is not only to have a high retention effort but also to present ergonomic characteristics with optimal insertion and extraction effort because each part requires a different function. With the time-dependent loading, the material will redistribute its internal energy thereby performing a time-related flow leading to reduced pretension thus decreasing stiffness. This paper presents an analytical and numerical method for
Michael Stephan, Navin Estac Raja, C M, Mithun, Mohammed, Riyazuddin, R, Prasath
SAE TOMORROW TODAY - Scalable AV Deployment Starts with Fewer Close Calls1351305/01/2025
The hallmark of exceptional autonomous driving technology isn't just how it reacts in a crisis but how it avoids one altogether. That's the vision behind May Mobility: a world where self-driving cars confidently navigate busy intersections, unexpected detours, and pedestrian-filled crosswalks with the instincts of a seasoned human driver. At the core of May Mobility's technology platform is its patented Multi-Policy Decision Making (MPDM) system. This breakthrough technology uses real-time, in-situ AI to interpret data, continuously learning and adapting to new, complex, and unpredictable driving conditions. By learning on the fly--much like a human driver--May Mobility's AVs can be deployed faster and more cost-effectively than traditional systems. To explore how May Mobility is scaling its AV technology, we spoke with Ed Olson, CEO and Founder, about the company's city-wide AV deployments, strategic partnerships with Toyota and NTT, and its entrance into the rideshare market through
Hineman, Marcie
Trailer Towing Driving Skill CertificationJ3300/2_202504 (Current)04/28/2025
This Recommended Practice is intended to establish a procedure to certify the trailer towing driving skill levels of professional drivers. This certification can be used by the individual driver to qualify their skills when seeking employment or other professional activity. These certification levels may also be used by test facilities or other organizations when seeking test or professional drivers of various skills. This document provides directions for obtaining certification through Probitas Authentication®1 and associated trailer towing driving skill examination requirements. This document is a supplement to SAE J3300, providing information specific to the trailer towing driving skill certification and clarifying the application of the rules set forth in SAE J3300 to the trailer towing certification. While the references, definitions, rules, and guidelines presented in SAE J3300 Sections 1 through 5 apply to the trailer towing certification, they are not repeated in this document.
Driving Skills Standards Committee
Development of an Imperceptible Brake Failure Control Technology and Real Vehicle Application10-09-02-001504/26/2025
With the advancement of control technology in the automotive field, there is a possibility of cross-system redundant control between various actuators. As for the braking system, current brake-by-wire system often uses mechanical backup braking methods to give the vehicle a certain braking capacity after failure. However, in the mechanical backup braking mode, the brake master cylinder is connected to the supporting wheel cylinder, and the brake assist is lost, which leads to an increase in brake pressure and makes it difficult for the driver to step on the brake pedal. Meanwhile, due to the limitation of the brake master cylinder stroke, the maximum braking deceleration of the vehicle is only 3 m/s2 after the driver fully presses the brake pedal. The above two defects greatly affect the safety of the vehicle during backup braking. To solve the above problems, this article takes electric vehicles as the research object, designs a new type of hydraulic circuit for the braking system
Tian, Boshi, Li, Liang, Liao, Yinsheng, Lv, Haijun, Hu, Zhiming, Sun, Yue, Qu, Wenying
Automated Vehicles and Infrastructure Enablers: Dedicated LanesEPR202500704/24/2025
Dedicated lanes provide a simpler operating environment for ADS-equipped vehicles than those shared with other roadway users including human drivers, pedestrians, and bicycles. This final report in the Automation and Infrastructure series discusses how and when various types of lanes whether general purpose, managed, or specialty lanes might be temporarily or permanently reserved for ADS-equipped vehicles. Though simulations and economic analysis suggest that widespread use of dedicated lanes will not be warranted until market penetration is much higher, some US states and cities are developing such dedicated lanes now for limited use cases and other countries are planning more extensive deployment of dedicated lanes. Automated Vehicles and Infrastructure: Dedicated Lanes includes a review of practices across the US as well as case studies from the EU and UK, the Near East, Japan, Singapore, and Canada. Click here to access the full SAE EDGETM Research Report portfolio.
Coyner, Kelley, Bittner, Jason
Operator PrecautionsJ153_202504 (Current)04/14/2025
These general operator precautions apply to off-road work machines as defined in SAE J1116. These should not be considered as all-inclusive for all specific uses and unique features of each particular machine. Other more specific operator precautions not mentioned herein should be covered by users of this recommended practice for each particular machine application.
OPTC1, Personnel Protection (General)
Driver Seating Accommodation and Steering Wheel Location ModelsJ3099_202504 (Current)04/14/2025
This practice presents methods for establishing the driver workspace. Methods are presented for: Establishing accelerator reference points, including the equation for calculating the shoe plane angle Locating the SgRP as a function of seat height (H30) Establishing seat track dimensions using the seating accommodation model Establishing a steering wheel position Application of this document is limited to Class-A Vehicles (Passenger Cars, Multipurpose Passenger Vehicles, and Light Trucks) as defined in SAE J1100.
Human Accom and Design Devices Stds Comm
The Effect of Directional Tactile Guidance on Takeover Performance in Level 3 Automated Driving2025-01-804904/01/2025
Automated driving is an important development direction of the current automotive industry. Level 3 automated driving allows the driver to perform non-driving related tasks (NDRTs) during automated driving, however, once the operating conditions exceed the designed operating domain, the driver is still required to take over. Therefore, it is important to rationally design takeover requests (TORs) in Level 3 conditional automated driving. This paper investigates the effect of directional tactile guidance on driver takeover performance in emergency obstacle avoidance scenarios during the transfer of control from automated driving mode to manual driving. 18 participants drove a Level 3 conditional automated driving vehicle in a driving simulator on a two-way four-lane urban road, performed a takeover, and avoided obstacles while performing non-driving related tasks. The driver's takeover performance during the takeover process was measured and subjective driver evaluation data was
Liang, Xinying, Liang, Yunhan, Ma, Xiaoyuan, Wang, Luyao, Chen, Guoying, Hu, Hongyu
A Path Tracking Control Method Considering Vehicle Stability and Road Curvature2025-01-803804/01/2025
The current research landscape in path tracking control predominantly focuses on enhancing tracking accuracy, often overlooking the critical aspect of passenger comfort. To address this gap, we propose a novel path tracking control method that integrates vehicle stability indicators and road curvature variations to elevate passenger comfort. The core contributions are threefold: firstly, we conduct comprehensive vehicle dynamics modeling and analysis to identify key parameters that significantly impact ride comfort. By integrating human comfort metrics with vehicle maneuverability indices, we determine the optimal range of dynamics parameters for maximizing passenger comfort during driving. Secondly, inspired by human driving behavior, we design a path tracking controller that incorporates an anti-saturation algorithm to stabilize tracking errors and a curvature optimization algorithm to mimic human driving patterns, thereby enhancing comfort. Lastly, comparative simulations with two
Lu, Jun, Zeng, Dequan, Hu, Yiming, Wang, Xiaoliang, Liu, Dengcheng, Jiang, Zhiqiang
A Deep Learning Framework for Time Series Prediction of Passenger Motion Sickness Based on Vehicle Dynamics Data2025-01-810404/01/2025
Technology development for enhancing passenger experience has gained attention in the field of autonomous vehicle (AV) development. A new possibility for occupants of AVs is performing productive tasks as they are relieved from the task of driving. However, passengers who execute non-driving-related tasks are more prone to experiencing motion sickness (MS). To understand the factors that cause MS, a tool that can predict the occurrence and intensity of MS can be advantageous. However, there is currently a lack of computational tools that predict passenger's MS state. Furthermore, the lack of real-time physiological data from vehicle occupants limits the types of sensory data that can be used for estimation under realistic implementations. To address this, a computational model was developed to predict the MS score for passengers in real time solely based on the vehicle's dynamic state. The model leverages self-reported MS scores and vehicle dynamics time series data from a previous
Kolachalama, Srikanth, Sousa Schulman, Daniel, Kerr, Bradley, Yin, Siyuan, Wachsman, Michael Ben, Pienkny, Jedidiah Ethan Shapiro, Jalgaonkar, Nishant M., Awtar, Shorya
Active and Passive Security Generalization Algorithm and Typical Case Analysis2025-01-874404/01/2025
In order to effectively predict the vehicle safety performance and reduce the cost of enterprise safety tests, a generalized simulation model for active and passive vehicle safety was proposed. The frontal driver-side collision model under the intervention of the Autonomous Emergency Braking (AEB) was created by using the MADYMO software. The collision acceleration obtained from the sled test was taken as the original input of the model to conduct simulation for the working conditions under different sitting postures of the human body. The injury values of various parts of the Hybrid III 50th dummy were read. Based on the correlation between the two, an active and passive simulation model was established through the Back Propagation (BP) neural network. The input of the model was the inclination angle centered on the dummy's waist, and the output was the acceleration of the dummy's head. The results showed that the comprehensive prediction accuracy rate exceeded 80%. Therefore, the
Ge, Wang, fengyao, LV
Recommendation of Adaptive Safe Speed Limits for Different Driver Profiles Using Deep Learning2025-01-810604/01/2025
Personalization is a growing topic in the automotive space, where Artificial Intelligence can be used to deliver a customized experience in features like seat positioning and climate control. Considering that the leading cause of accidents is driving at an inappropriate speed, personalizing the speed limit for a driver can greatly improve vehicle safety. Current speed limits apply to all drivers, irrespective of skill, including special speed limits when there are adverse weather conditions. As these speed limits do not consider an individual’s skill and capabilities, the limit could still be inappropriate for a given driver in that specific driving context. Therefore, we propose a system that can profile the driver’s style to recommend a personalized speed limit, based on both the environmental context and their skill in that environment. The system uses a neural network to classify the driver’s behavior in specific environments by monitoring the vehicle data and the environmental
Perumal, Rathapriya, Chouhan, Madhvendra, Rangarajan, Rishi
Integrated Multimodal System for Real-Time Driver Fatigue Detection and Cognitive Load Assessment2025-01-807604/01/2025
As human drivers' roles diminish with higher levels of driving automation (SAE L2-L4), understanding driver engagement and fatigue is crucial for improving safety. We developed an integrated hardware and software system to analyze driver interaction with automated vehicles, with a particular focus on cognitive load and fatigue assessment. The system includes three submodules; namely the Driver Behavior Measurement (DBM), Vehicle Dynamics Measurement (VDM), and the Driver Physiological Measurement (DPM). The DBM module uses electro-optical (EO) and infrared (IR) camera to track a number of facial features such as eye aspect ratio (EAR), mouth aspect ratio (MAR), pupil circularity (PUC), and mouth to eye aspect ratio (MOE). Although determining these metrics from images of the driver’s face in conditions such as low light or with sunglasses is challenging, the paper showed that fusion of EO and IR image analysis produces robust performance. The VDM module utilizes an Inertial Measurement
Jirjees, Abdullah, Rahman, Taufiq, Farhani, Ghazal, Singh, Daniel, Charlebois, Dominique
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