Browse Topic: Vehicles and equipment

Items (44,039)
Find
Benchmarking Driver Responses in Head-On Conflicts: Toward an Objective Protocol for Human and AV Evaluation2026-01-0534To be published on 04/07/2026
Head-on emergency conflicts present a unique challenge for evaluating driver and automated vehicle (AV) performance. Unlike most emergency scenarios that follow a direct stimulus–response sequence, head-on encounters are characterized by a stimulus–wait–response pattern in which time to contact (TTC) strongly shapes driver behavior. Drawing on naturalistic and simulator data, this study quantified human responses to head-on conflicts. When the opposing vehicle crossed the centerline with more than 2 seconds of TTC, drivers delayed braking until an average of 1.3 seconds (SD = 0.8) before impact, regardless of the timing of the intrusion. By contrast, when TTC was less than 2 seconds, braking was initiated only 0 to 1.0 second before impact. As a result, many drivers applied the brakes less than half a second before collision. These findings provide a meta-analytically grounded benchmark for comparing human behavior to AV decision-making processes and demonstrate the need for conflict
Muttart, Jeffrey, Dinakar, Swaroop, Maloney, Tim, Adikhari, Bikram, Gernhard-Macha, Suntasty
Forensic Acquisition and Analysis of Unsupported Lexus Infotainment Modules via CMD42 Bypass and Data Carving2026-01-0540To be published on 04/07/2026
Crashes involving passenger vehicles increasingly include models equipped with infotainment modules that are unsupported by commercial vehicle system forensics hardware and software. Examiners facing these systems must overcome challenges in acquiring and analyzing user data, requiring an understanding of both digital forensics principles and the proprietary characteristics of the modules. This paper presents a novel methodology for acquiring data from previously unsupported Lexus infotainment modules, including techniques to bypass CMD42 security locks on SD cards and extract data in a forensically sound manner. Once acquired, the paper outlines methods for analyzing user data through data carving techniques, enabling recovery of information from binary images even when the full file system cannot be reconstructed. Emphasis is placed on maintaining the integrity of the evidence and validating findings through controlled testing. These validation procedures ensure that the recovered
Burgess, Shanon
Advanced Tire Looseness Identification through Algorithmic Gear Error Analysis and Probabilistic Assessment2026-01-0590To be published on 04/07/2026
At present, tire failures directly affect road safety, and the number of incidents caused by them is gradually increasing. Examining tire looseness on time is vital for vehicle safety. Tire-related incidents not only put people in peril but also have a detrimental effect on the economy. Therefore, the goal of this research is to develop a new and effective method for identifying tire looseness. A novel gear error reduction approach, distinct from traditional methods, combines advanced computing and probabilistic analysis. This paper involves three key components: extracting looseness eigenvalues, calculating ring gear errors, and computing the tire loosen probabilities. Gear errors derived from the Kalman filter and adjusted for speed, eigenvalues were calculated, and a tire loosening probability analysis was performed. Real-car trials across speeds and roads confirm its accuracy and reliability. This technology can improve automotive safety and maintenance, reducing accidents, claims
Zhao, Gaoming, Zhang, Zhijie, Liu, Jianjian, Ma, Guangtao, Wang, Zhenfeng, Zhao, Binggen
Research on Fault-Tolerant Control Strategy for Drive System Failure of Corner Module-by-Wire Chassis Vehicles2026-01-0620To be published on 04/07/2026
As an emerging research focus, corner module-by-wire chassis vehicles address the pain points of traditional chassis in flexibility, cost, and development efficiency, and have become one of the key infrastructures in the autonomous driving era. However, their large number of actuators significantly increases the risk of failures. This paper analyzes the characteristics of such vehicles and studies the fault-tolerant control for their drive system failures. Firstly, a full-vehicle dynamic model was established, with mathematical modeling conducted for the vehicle body, motor, tire, and corner module system models. Secondly, a hierarchical yaw stability control strategy was designed for the non-faulty actuator scenario: the decision-making and control layer adopted both Sliding Mode Control (SMC) and fuzzy PID control, selecting the method with better performance to output the additional yaw moment; the control allocation layer used a quadratic programming algorithm based on tire load
Zheng, Hongyu, Zhang, Tianhao, Zhang, Yuzhou
Wind-Tunnel Study of the Traffic-Wake Impacts for Road-Vehicle Aerodynamics2026-01-0613To be published on 04/07/2026
When driving in traffic, the wakes of leading vehicles reduce the wind speed experienced by a following vehicle, lowering its drag relative to isolated driving. These wake effects can persist to large intervehicle distances, on the order of hundreds of meters, while lateral convection due to cross winds can influence vehicles in adjacent lanes. Wind tunnel testing was conducted at 30% scale for light- and heavy-duty-vehicle models in a large wind tunnel with a traffic-wake simulation system, expanding upon a previous study that examined only heavy vehicles. Three variants of the DrivAer model, four variants of the AeroSUV model, and three variants of a zero-emission heavy-duty-truck model were tested with a range of simulated wake conditions that varied the type, forward distance, and lane position of the wake-source vehicle(s), for a range of yaw angles up to 11deg. Results show drag reductions of up to about 10% for the heavy-truck model, and up to about 20% for the passenger-vehicle
McAuliffe, Brian, Ghorbanishohrat, Faegheh, Barber, Hali
Mathematical Modeling of Trust Calibration for Human–Automation Safety2026-01-0530To be published on 04/07/2026
Trust between humans and automated vehicles is increasingly recognized as a pivotal factor in achieving safety in advanced vehicles and mobility environments. Poorly calibrated trust—either over-trust leading to complacency or under-trust leading to disengagement—has been identified in numerous incidents involving advanced driver assistance systems and autonomous functions. Yet, most current approaches to managing trust rely on surveys, heuristics, or qualitative assessments, providing little predictive capability for safety engineers and system designers. This paper presents a formal mathematical framework for trust calibration, modeling trust as a dynamic, evolving construct grounded in probabilistic reasoning. We represent trust as a Beta-distributed belief that updates over time through Bayesian inference, with parameters adjusted by observed successes and failures, contextual cues, and system feedback signals. The model also incorporates forgetting and drift functions, allowing
Wen, He, Mounir, Adil
Introduction to Catesby Aero Research Facility, CARF, the coast-down tunnel proving ground.2026-01-0611To be published on 04/07/2026
This paper reports on the Catesby Aero Research Facility (CARF), which began commercial use in 2019, and provides an overview of the facility and related technologies, particularly details on the vehicle-mounted component force measurement devices. CARF is a proving ground that was renovated from a former railway tunnel approximately 2.74km long and is now covered with high-quality tarmac. The road surface quality was designed to be at least as good as that of SUBARU's proving ground, and the standards were achieved using British construction technology. The course is almost straight, but there is a slight incline. The course specifications are: blockage 40m2, road width 6m (maximum 8.4m), flatness σ<0.5mm, and gradient 0.57%.In particular, compared to outdoor coastdown trucks, its overall length allows for continuous measurement right up to the stop, giving it a significant advantage in repeatability.At the same time, Subaru's newly developed six-component force sensor built into the
Shimoyama, Hiroshi
A Simple Analytical Model for Predicting Cooperative Ratio in Agreement-Seeking Cooperative Driving Automation with C-V2X Communication2026-01-0078To be published on 04/07/2026
Cooperative Driving Automation (CDA) has emerged as an active research area in recent years, categorized into four classes of operations with varying levels of cooperation as defined in the SAE J3216 standard. Among these, Class C CDA, referred to as Agreement-Seeking Cooperation (ASC), has received limited attention in literature. Unlike Cooperative Adaptive Cruise Control (CACC), which typically engages when lead vehicles are identified as cooperative and disagree under manual override or safety-critical conditions, ASC requires agents to exchange messages interactively to reach consensus on a proposed plan and its implementation. This necessitates more sophisticated communication and control designs, which in turn influences customized ASC efficiency. Previous work has examined, through simulation, the impact of three key parameters on ASC system performance: CDA message transmission frequency, Packet Drop Ratio (PDR), and Cooperation Duration Length (CDL). In this paper, we extend
Zhan, Lu, Hyeon, Eunjeong, JEONG, Jongryeol, Misra, Priyash, Di Russo, Miriam, Das, DEBASHIS, Stutenberg, Kevin
Integrated Design and Validation of a Robust Lane Centering Controller for Automated Driving.2026-01-0037To be published on 04/07/2026
Lane centering is a critical active safety feature whose effectiveness depends on robust design and validation across diverse driving conditions. This paper presents the development of a Lane Centering Controller (LCC) using a structured model-based design workflow in MATLAB and Simulink. A kinematic bicycle model was employed to simulate vehicle dynamics and evaluate an angle based steering controller integrating both feedforward and feedback control paths. The controller was tested across multiple road geometries and speeds up to 65 mph to ensure tracking consistency and stability under nominal and perturbed conditions. Perception noise models for lane curvature and curvature rate were extracted from onboard camera data under controlled conditions, revealing Gaussian characteristics. No filtering was applied, allowing direct evaluation of the controller’s inherent robustness to raw signal variability. The LCC maintained a peak lateral offset within ±0.35 m and lateral jerk within ±9
Bijinepalli, Ravi Teja, Tambolkar, Pooja, Midlam-Mohler, Shawn
StreamPETR-TKD: Temporal Knowledge Distillation for Efficient 3D Object Detection in Object-Centric Streaming Models2026-01-0023To be published on 04/07/2026
Bird's-Eye-View (BEV) perception has become pivotal in autonomous driving for 3D object detection, enabling the transformation of multi-view image features into a unified BEV representation. However, purely visual methods still struggle with depth estimation and temporal consistency, particularly in modeling long-term dependencies and avoiding trajectory confusion in dynamic scenes. Knowledge distillation (KD) offers a promising solution by transferring rich knowledge from a powerful teacher model to a efficient student model, enhancing performance without incurring significant computational overhead. This paper aims to enhance the temporal reasoning and geometric understanding of the StreamPETR model—an object-centric, streaming-based 3D detector—via a multi-level distillation strategy. 1.Temporal Relation Distillation: The self-attention mechanisms of the teacher model are distilled to impart temporal relational knowledge, enabling the student to associate object states across time
Yan, Yixiong
Hydrogen Propulsion for Heavy Commercial vehicle Applications in India : A Quality-Driven Approach from Tata Motors2026-01-0144To be published on 04/07/2026
As the global commercial vehicle sector searches for innovation in propulsion technologies that deliver sustainability and zero tail pipe emissions, hydrogen (H₂) mobility is gaining momentum—especially in heavy duty segments where battery electric drivetrains still face constraints. Tata Motors, recognized in India for its pioneering work in commercial vehicles, has launched an ambitious programme to engineer hydrogen powered solutions custom built for the nation’s heavy trucks and buses that underpin logistics and infrastructure. This paper outlines Tata Motors’ structured approach on ‘Failure mode avoidance’ for a safe and reliable H₂ propulsion, anchored in the ‘Quality by Design’ approach through Design Failure Mode and Effects Analysis (DFMEA) at a very early stage. Every critical vehicle system and sub-system—storage, fuel delivery, control and the propulsion unit itself—is subjected to DFMEA at the concept stage so that potential risks are detected and addressed before vehicle
Giri, Debabrata, Biswas, Kaushik, Saji, Amal
Embedded Real-Time Control of a Distributed Trailer Propulsion System with Regenerative Braking for Net Neutral Load Towing2026-01-0065To be published on 04/07/2026
Towing significantly degrades vehicle efficiency, reducing battery-electric vehicle (BEV) range by up to 50% and increasing internal-combustion engine (ICE) fuel consumption by ~30%. To address this challenge, this paper presents a real-time embedded control architecture for a distributed trailer propulsion system that actively regulates drawbar force to achieve net neutral load towing while enabling regenerative braking. Unlike prior e-trailer concepts, this platform demonstrates a cost-effective, fully networked, model-based control implementation using commercial off-the-shelf components. The control system integrates dual Raspberry Pi controllers running ROS 2: one samples a hitch-mounted load cell at 100 Hz and the other processes OBD-II/CAN messages. Controllers designed in MATLAB/Simulink are deployed to a 5 kWh LiFePO₄ pack and a 5 kW traction motor, utilising embedded Linux for real-time scheduling and onboard data logging. Two operating modes are implemented: (i) PI
Joshi, Gaurav, Adelman, Ian, Liu, Jun, Donnaway, Ruthie
Methodology to Quantify the Petroleum Equivalent Fuel Economy Impact of BEV Thermal System Energy Consumption2026-01-0132To be published on 04/07/2026
This paper will present research and 1D modeling results to support a proposed methodology to determine a fleet ambient weighted average Petroleum Equivalent Fuel Economy (PEFE) for the energy consumed by the thermal system of a BEV, including a proposal of how factor in the thermal system PEFE result into an OEM’s overall CAFE determination on an incentivized (not punitive) basis, within the existing regulatory framework. The topic is particularly important because EPA and NHTSA final rules issued in early 2024 eliminated A/C Efficiency Credits for BEVs that are currently in place for all classes of light duty vehicles. However, without some sort of regulatory incentivization, market forces will dictate thermal comfort system solutions that consume more energy – and with that more negative secondary effects such as higher greenhouse gas emissions and vehicle mass - than would otherwise be the case. Eliminating incentivization for BEVs is particularly concerning because the thermal
Taylor, Dwayne
High Order Control Lyapunov Function - Control Barrier Function - Quadratic Programming Based Autonomous Driving Controller for Bicyclist Safety2026-01-0034To be published on 04/07/2026
Ensuring the safety of vulnerable road users is a critical challenge in the development of advanced autonomous driving systems. Among vulnerable road users, bicyclists present unique characteristics that make their safety both critical and manageable. Compared to pedestrians, vehicles often travel at significantly higher relative speeds when interacting with bicyclists, yet bicyclist motion is generally more predictable and governed by clear traffic rules, offering opportunities for model-based control strategies. To address bicyclist safety in complex traffic environments, this study proposes a High-Order Control Lyapunov Function–High-Order Control Barrier Function–Quadratic Programming (HOCLF-HOCBF-QP) control framework. By using this approach, CLFs enforce system stability, ensuring the vehicle follows a desired trajectory, while CBFs define safety boundaries, preventing collisions between the ego-vehicle and other road users. The optimal control input is then obtained by solving a
Chen, Haochong, Cao, Xincheng, Guvenc, Levent, Aksun Guvenc, Bilin
The Impact of Extreme Ambient Temperatures on Electric Vehicle Performance in the Kingdom of Saudi Arabia2026-01-0133To be published on 04/07/2026
The rapid expansion of Saudi Arabia’s electric vehicle sector driven by manufacturers such as CEER Lucid Tesla and BYD necessitates evaluating vehicle performance under extreme thermal conditions. This study investigates the impact of high ambient temperatures on electric vehicle operation by analyzing a 60 kWh NMC battery pack under representative Riyadh summer conditions of up to 47 °C using MATLAB Simulink simulations. The results show that elevated temperatures accelerate state of charge (SOC) depletion and increase battery internal resistance. To maintain safe operating limits the BTMS must operate continuously consuming up to 20 percent of the available energy. Cooling demand increases nonlinearly with temperature exceeding 40 kW at 50 °C which leads to significant parasitic losses and a 15 to 20 percent reduction in driving range. At the system level this increased energy demand raises charging frequency and intensifies electricity load during peak summer periods thereby
Bedywi, Lama Mohammed A, AlJuhani, Haneen, Duan, Chen, AbdulNour, Bashar
Securing the AI-Driven Vehicle: A Hardware-Based Security Framework for Scalable, Trustworthy Autonomy2026-01-0085To be published on 04/07/2026
Autonomous driving technology in modern vehicles marks a pivotal evolution in transportation, but it may also introduce system-level vulnerabilities that span from tampering with sensors and interfaces to compromising compute units and communication links. This paper proposes a unified, layered hardware security architecture for AI-equipped automated vehicles. Based on current automotive Ethernet and zonal architectures, it provides end-to-end trust using hardware interface security, accelerated-cryptography, and SRAM PUF-based key provisioning. All security primitives are anchored to a hardware root of trust, delivering cryptographic identity, secure boot enforcement, and trusted key storage across the entire vehicle lifecycle. Our architectural assumptions follow the direction ISO 26262, ISO/SAE 21434 and AI development mandates for next-generation SAE Level 3+ vehicles using zonal Ethernet backbones, Time-Sensitive Networking (TSN), and heterogeneous compute islands (e.g., vision
C Suriyanarayanan, Pav, Iacob, Radu
Data-driven feedforward compensation-based trajectory tracking control2026-01-0028To be published on 04/07/2026
Advanced autonomous driving is a critical component in the intelligent development of new-generation electric vehicles. Research on reliable chassis control algorithms ensures the safety and stability of autonomous vehicles during operation. To enhance the control performance of autonomous vehicles and improve the accuracy of trajectory tracking, this paper proposes a data-driven feedforward compensation trajectory tracking control approach. By optimizing the design of the feedforward compensation loop, systematic errors and latency in the vehicle’s steering system are mitigated, thereby enhancing the precision and robustness of the control algorithm. Initially, the paper analyzes the control errors present when the vehicle responds to controller commands. Subsequently, the paper focuses on the steering angle errors in trajectory tracking, identifying and analyzing the most relevant factors. A time-delay neural network (TDNN) based on data-driven principles is designed to model and
Yang, Yijin, Yuan, Yin, Su, Ailin, Zhang, Zhijie, Wang, Zhenfeng
Optimizing Virtual Scenario Testing for Autonomous Vehicles with Large-Scale Scenario Generation.2026-01-0050To be published on 04/07/2026
The validation of Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) Systems, especially at higher automation levels such as SAE Level 3 or 4, demands the testing of a vast array of scenario variants far exceeding the scope of standard safety specifications like Euro NCAP (The European New Car Assessment Programme). Autonomous vehicles require thorough real-world testing to ensure automotive safety. However, public road tests are costly and risky. Instead, virtual scenarios - digital twins of real environments - offer a safe, cost-effective testing alternative. Exhaustive simulation across this high-dimensional scenario space, which includes variations in actor behavior, environmental conditions, and event characteristics, is computationally infeasible. We propose a constraint-solving approach to address this challenge that leverages mathematical and geometric techniques to analytically assess the existence and validity of scenario variants prior to simulation. Two
Karve, Omkar, Saurav, Saket, Purwar, Prabhanshu
Embedded Coding Agent: Natural-Language Driven Generation of AUTOSAR & Non-AUTOSAR Embedded C/C++ code2026-01-0105To be published on 04/07/2026
Embedded software development in the automotive sector is becoming more demanding as engineers balance performance, safety, and certification requirements. Traditional approaches such as manual coding or model-based workflows require significant effort in writing boilerplate code, ensuring AUTOSAR compliance, and preparing verification artifacts for audits. Commercial tools like MathWorks Simulink Embedded Coder already provide model-to-code generation capabilities, but these solutions are often costly and may not be viable for budget-sensitive domains such as two-wheelers and three-wheelers. This creates a strong need for alternative, more accessible approaches that can deliver efficiency without compromising on quality. This paper introduces an embedded coding agent designed to address this gap. The agent accepts natural language or structured prompts and generates AUTOSAR-compatible ARXML as well as embedded C and C++ implementations. Unlike general-purpose coding assistants, it is
Daware, Kartik
Adaptive Torque Vector Control for Distributed Drive Intelligent Electric Vehicles Using Model and Data Driven Approaches2026-01-0628To be published on 04/07/2026
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 roughness and adhesion is proposed, which optimizes the comprehensive performance of vehicle chassis. First, combined with the characteristic of the unbalance dynamic force caused by the air gap between the stator and
Wang, Zhenfeng, Zhao, Gaoming, Zhang, Zhijie
Machine Learning-Based Prediction of Wind-Induced Interior Noise in Ground Vehicles2026-01-0599To be published on 04/07/2026
In response to increasing customer demand for enhanced passenger comfort and perceived vehicle quality, OEMs in automotive and commercial vehicles are placing significant emphasis on reducing the interior cabin noise. At highway speeds, wind noise is a primary contributor to the overall noise within the vehicle cabin. Conventional approaches to predict vehicle wind noise rely on physical testing, which can only be conducted in the later stages of the design process once a physical prototype is available. Increased adoption of established computational fluid dynamics (CFD) methods has enabled earlier assessment. However, such simulations require several hours to complete, posing a challenge in the context of rapid design iteration cycles. With the growing adoption of artificial intelligence in engineering, machine learning (ML) approaches have been proposed to predict a vehicle’s aerodynamics performance. Nevertheless, development of ML techniques in the context of aeroacoustics
Higgins, John, Fougere, Nicolas, Sondak, David, Senthooran, Sivapalan, Moron, Philippe, Jantzen, Andreas, Bi, Jing, Oancea, Victor
Camera-Based Method for Surrounding Light Intensity Calculation2026-01-0015To be published on 04/07/2026
Accurate and dynamic evaluation of ambient light intensity surrounding a vehicle is needed for the robust operation of vehicle, autonomous driving functions, and adaptive lighting control. This paper introduces a novel image-sensing method for assessing the vehicular surrounding light environment. The proposed approach integrates sensor fusion techniques with advanced computer vision applications to provide a comprehensive and real-time understanding of ambient illumination. We detail the methodology, which processes visual data from onboard cameras in conjunction with other relevant sensor inputs to derive nuanced light intensity measurements. The discussion will encompass the significant challenges inherent in accurately perceiving ambient light across diverse conditions, including rapid changes in illumination, glare, shadows, and varying weather phenomena. Furthermore, we present the evaluation of critical parameters essential for the system's performance, such as sensor
Schroeter, Robert, Shaik, Faizan, Ghannam, Mahmoud
Architecture-Driven Fixed-Point Scaling for AUTOSAR-based ECUs: A Production-Feasible Architecture for Centralizing Scaling Semantics2026-01-0069To be published on 04/07/2026
Floating-point arithmetic is widely used in automotive embedded software to scale Controller Area Network signals and calibration parameters with fractional factors such as 0.1. However, floating-point operations, even on microcontrollers equipped with floating-point units, can increase execution time and CPU load. In AUTOSAR architectures, converting floating-point scaling to fixed-point is not trivial because scaling semantics must be integrated consistently across components, yet AUTOSAR platform toolchains offer only limited automation at the Application Data Type level. Although CompuMethod definitions can express scaling, integration typically remains manual and distributed across application software components, reducing consistency and reusability. This study presents an architecture-driven methodology that formalizes fixed-point scaling as a centralized architectural service, realized through a parser-driven fixed-point macro generation pipeline. Standardized CAN DBC and
Lee, Hoseok, Ko, Donggun
A Study on TSC(Technical Safety Concept) design and verification in EMB(Brake By Wire) Systems2026-01-0095To be published on 04/07/2026
The Electro-Mechanical Brake (EMB) system, a dry-type Brake-by-Wire technology, represents a significant advancement in eco-friendly and intelligent vehicle braking systems. Unlike conventional hydraulic brakes, EMB eliminates fluid-based components by directly controlling friction braking through electrical actuators installed at each wheel. This not only reduces environmental impact but also enhances system responsiveness and integration with advanced driver assistance systems (ADAS). The EMB architecture comprises six controllers: a Main Center Control Unit, a Backup Center Control Unit for redundancy, and four Wheel Control Units. These controllers communicate seamlessly using multiple vehicle communication protocols, including CAN, Ethernet, and FlexRay. This distributed control structure is essential for achieving compliance with ISO 26262, the international standard for functional safety in road vehicles. As EMB systems become more complex, they introduce new safety risks that
KIM, Dokun
Camera Monitor System Soiling Correlation for a Class-8 Truck Using Lattice Boltzmann Method2026-01-0596To be published on 04/07/2026
As Camera Monitoring Systems (CMS) become an integrated part of the driving experience for current automotive and heavy vehicles, keeping the CMS clean from water, dirt, sand, snow and ice is a main focus of the design process in order to avoid safety issues due to obscured visibility. On-road soiling prevention becomes an important feature when designing these camera and sensor systems. Computational Fluid Dynamics (CFD) analysis can be used to facilitate the design process, to provide important information of the cause of the problems and design mitigation mechanism to prevent the visibility issues. There are many published papers for soiling studies for the automotive application but very limited publication available for heavy trucks, this paper is targeted to fill this space. In these current studies during a design process, road tests were performed in Alaska by the testing department. Correlation was done between those road tests and the CFD analysis, performed in-house. This
He, Wei, Dasarathan, Devaraj, Linden, Tom, Park, Jeongbin
Attention and Intrinsic Curiosity-Enhanced Deep Reinforcement Learning for Path Planning in Dynamic Environments2026-01-0044To be published on 04/07/2026
Efficient and reliable path planning remains a core challenge for autonomous vehicles operating in dynamic and crowded environments. Although Deep Reinforcement Learning (DRL) has shown considerable potential in autonomous decision-making, it still faces challenges such as insufficient feature extraction, sparse rewards, and low obstacle avoidance efficiency in complex scenarios. To address these issues, this paper proposes an end-to-end path planning framework, PPO-ICM-Attn. Built upon the Proximal Policy Optimization (PPO) algorithm, the framework incorporates a dual-channel attention convolutional neural network module (Attention-CNN) to enhance spatial and semantic understanding of dynamic obstacles, and introduces an Intrinsic Curiosity Module (ICM) to promote active exploration in sparse reward settings. Furthermore, a reactive avoidance reward function based on velocity obstacle theory is designed and embedded to achieve real-time proactive collision avoidance in highly dynamic
Shen, Shiquan, Liu, Jiahao, Chen, Zheng, Li, Zongdian, Zhao, Yuting, Wu, Minggong, Zhao, Jie, Qin, Zongquan, Wang, Yanfeng
Autonomous Racing Control Using Reinforcement Learning with Racing Line Guidance2026-01-0031To be published on 04/07/2026
In recent years, with the emergence of autonomous racing competitions such as Roborace, autonomous racing cars have become a prominent research focus and have undergone rapid development. This paper establishes a reinforcement learning-based decision-making and control framework for autonomous racing cars, achieving cooperative optimization between high-speed racing and safety control under extreme operating conditions. By integrating a Control Barrier Function constrained reward function for dynamic stability and a racing line-guided path optimization, the proposed framework achieves a synergistic balance between minimum lap time and operational safety. Dynamic stability domain constraints based on a two-degree-of-freedom single-mass rigid-body model and racing line optimization based on track geometric modeling are established. The phase plane stability boundary is constructed using the yaw rate method and double-line method, upon which Control Barrier Function constraints are
peng, tao, Zhang, Weiqi, Li, Zengwen, sudong, jiang
Navigation Integrated End-to-end Path Planning Development and Vehicle Testing2026-01-0032To be published on 04/07/2026
In order to achieve fully autonomous driving, point to point autonomous navigation is the most important task. Most existing end-to-end models output a short-horizon path which makes the decision process hard to interpret and unreliable at intersections and complex driving scenarios. In this research, we build a navigation-integrated end-to-end path planner on top of an openpilot open source model. We created a navigation branch that encodes route polyline geometry, distance-to-next-maneuver, and high-level instructions and combines with path plan branch using residual blocks and feed-forward layers. By adding minimal parameters, new model keeps the original openpilot tasks unchanged and have the path output based on the navigation information. The model is trained on diverse urban scenes and lighting conditions, and it shows improved route adherence in vehicle testing. The proposed model is validated in a comma 3x device installed on a 2025 Nissan Leaf test vehicle. The road test
Wang, Hanchen, Li, Taozhe, Hajnorouzali, Yasaman, Burch, Collin, li, Victoria, Tan, Lin, Arjmanzdadeh, Ziba, Xu, Bin
Effect of housing design on oil splash lubrication in an E-drive Unit through Computational Fluid Dynamics Simulation2026-01-0116To be published on 04/07/2026
Proper lubrication of gears and supporting bearings is crucial for the optimal functioning of an e-drive unit, particularly under high-speed and high-load conditions. Understanding the causes of bearing failure requires an analysis of the oil splash pattern in a testbed with a transparent casing. Additionally, modifying the gearbox and housing designs can be both time-consuming and costly to achieve performance goals. As an alternative, Computational Fluid Dynamics (CFD) modeling provides a virtual testing environment that allows for quicker design evaluations and reduces product development time. This study focuses on using CFD to assess a housing design aimed at enhancing lubrication for the rear and front drive units (RDU/FDU) in electric vehicles. Multiphase flow simulations were conducted using the volume of fluid (VOF) method in the commercial CFD software Simerics-MP+. This software generates an unstructured Cartesian mesh and manages complex mesh movements through a volume
Kumar, P. Madhan, Motin, Abdul, Pasunurthi, Shyam Sundar, Ganamet, Alain, Maiti, Dipak, Taghizadeh, Salar, Mohapatra, Chinmoy K.
Advanced Damping Force Modeling Using Machine Learning for Next-Generation Electric Vehicle Suspensions2026-01-0586To be published on 04/07/2026
The rapid evolution of electric vehicles (EVs) has necessitated innovative approaches to optimize ride comfort, handling, and overall suspension performance. Unlike conventional internal combustion engine vehicles, EVs introduce unique challenges due to their distinct weight distribution, powertrain dynamics, and noise characteristics. This paper presents an advanced damping force modeling methodology leveraging machine learning (ML) techniques to enhance the suspension design process for next-generation EVs. The study employs data-driven ML algorithms, such as Gradient Boosting, Random Forest, and Neural Networks, to model the nonlinear and frequency-dependent behavior of dampers under various operational conditions. A comprehensive dataset, generated through simulation and experimental testing, captures the effects of road profiles, vehicle dynamics, and damping settings. The proposed ML-based approach predicts damping forces with high accuracy, outperforming traditional analytical
Hazra, Sandip, Tangadpalliwar, Sonali, Khan, Arkadip
Innovative Smart Switch Architecture Catering to 12 Volts to 48 Volts Battery Bus Requirements2026-01-0064To be published on 04/07/2026
Electronics is rapidly entering all automotive subsystems, performing control and monitoring tasks apart from making the entire vehicle intelligent.. Interface with the external automotive ecosystem needs careful attention during the system design. It defines how seamlessly the electronic unit interacts with the rest of the vehicle. It needs to do so in an effective manner without compromising on cost and other automotive application constraints. This paper focuses on the “ smart switch building block” that forms the heart of an automotive output interface echo system. Its importance stems from the fact that a smart switch is an indispensable building block for any electronic control system driving external loads. As various novel electrical and electronics architectures are entering various vehicle segments need for a single reusable solution that will cater to 12 Volts to 48 Volts battery buses is increasingly being felt. However, no prevalent solution meets this requirement. Even
Vaidya, Vishwas Manohar
Climate Heat Recovery Energy Study in Electric Vehicles2026-01-0129To be published on 04/07/2026
Climate control systems in Battery Electric Vehicles (BEVs), Plug-in Hybrid Electric Vehicles (PHEVs), and Extended Range Electric Vehicles (EREVs) rely on electrical energy to provide cabin heating. In winter conditions, the absence of waste heat from internal combustion engines necessitates increased energy consumption for thermal comfort, which directly impacts vehicle range. Conventional HVAC systems typically operate with a mixture of cold ambient air and recirculated cabin air. However, the proportion of recirculated air is limited due to windshield fogging risks, constraining energy-saving potential. To address this, MAHLE has developed modular heat recovery technologies that utilize the thermal energy of exhaust cabin air, that would normally leave the passenger compartment through vehicle body vents, to precondition incoming fresh air, thereby reducing the heating load. These solutions are engineered for scalable integration into existing HVAC architectures, allowing
Wolfe, Edward, Wochele, Kerstin, Reid, Bailey
Simulating Cryptographic Primitives in Model Based Systems Engineering Projects2026-01-0083To be published on 04/07/2026
Security has become an increasingly critical concern for large off-road vehicle manufacturers due to the growing threats posed by hackers, heightened awareness around data privacy, the implementation of anti-tamper technologies, and stringent emissions regulations. These pressures necessitate the development of robust security measures that can effectively safeguard vehicle systems while promoting responsible and sustainable innovation. To address these challenges, cybersecurity teams and engineers can leverage tools such as Model-Based Systems Engineering (MBSE), which facilitates the rapid and cost-effective development of comprehensive security mechanisms. This paper explores the application of systems engineering techniques to create a solution for ensuring integrity and authenticity in Controller Area Network (CAN) systems, which are commonly utilized in heavy-duty diesel engines. The deployment of solutions to secure the CAN bus will likely depend on cryptography. However
DiValentin, Maria, Daily, Jeremy
Development of a virtual test rig to evaluate thermal management of a battery-electric vehicle under battery heating-cooling modes2026-01-0416To be published on 04/07/2026
A battery-electric vehicle (BEV) has multiple powertrain components (battery, inverter, e-motor), a thermal management system (compressor, heat exchanger, cabin heating, ventilation, and air-conditioning), and a vehicle body, among others. Vehicle testing is time-consuming, and also changing powertrain components during the testing and design process is costly. Simulation models (aka virtual or simulation test rig) have been widely used for efficient vehicle design. This work presents a systematic approach to developing a virtual test rig to evaluate the thermal performance of battery-electric vehicles. A Tesla Model Y is tested in a chassis dynamometer, and the measured vehicle performance data are used as boundary conditions for the complete vehicle model. The detailed lithium-ion battery (LIB) pack model, including its cooling system, was developed and calibrated using various transient driving cycle data. The HVAC model uses a simplified controller to maintain the cabin temperature
Sok, Ratnak, Kusaka, Jin
OFEODM: An Onboard Fast and Efficient Object Detection and Distance Prediction Model2026-01-0017To be published on 04/07/2026
Object detection and distance prediction have advanced significantly in recent years. The YOLO series has released its 11th version, along with numerous variants that have been applied across various fields. Meanwhile, the Detection Transformer (DETR) has repeatedly set new state-of-the-art (SOTA) records in the field of object detection. Depth Anything also released its second version last year, further pushing the boundaries of distance detection. Although these models achieve impressive performance, they often require substantial computational resources. However, for algorithms intended for real-world applications and deployment on onboard devices, computational resources are limited and valuable. Inference time per frame is a critical factor in ensuring an algorithm's reliability and feasibility. Designing a model that operates in real time without sacrificing accuracy remains an extremely challenging problem, and extensive research is ongoing in this area. To address this
Li, Taozhe, Wang, Hanchen, Hajnorouzali, Yasaman, Xu, Bin
Standalone Battery Pack Thermal Behavior Simulation and Experimental Correlation in a Controlled Environment2026-01-0125To be published on 04/07/2026
Battery thermal management is crucial for ensuring the safety, efficiency, and longevity of lithium-ion battery packs, particularly in electric vehicles (EVs). The primary purpose of a lithium-ion battery in an electric vehicle is to store and provide electrical energy for vehicle propulsion while maintaining safety under different operating conditions. This work proposes a thermal correlation between 1D CFD simulation and experimental test data under passive environmental heat exchange conditions without active coolant flow of a battery pack comprising four modules. An environmental exchange test was conducted using a 50% state of charge (SOC) battery pack, which is stabilized at 25°C to assess passive heat dissipation, thermal soak behavior, temperature distribution, and potential thermal runaway risks. The simulation predictions correlate well within a 1.5°C range compared to test results using ambient temperature and flow inputs, which confirms the reliability of the modeling
Nayaka, Sateesh Kumar, Dixit, Manish, Gudiyella, Soumya
An Integrated Methodology for Sustainable Mobility Infrastructure Planning: The Atlanta Aerotropolis Case Study2026-01-0097To be published on 04/07/2026
By the early 2020s, more than 4.5 billion people have been living in urban areas worldwide, compared to just 1 billion in 1960. Rising growth in urban populations present challenges to infrastructure and transportation systems. Higher traffic levels and reliance on conventional vehicles have contributed to heightened greenhouse gas (GHG) emissions, rising global temperatures, and irreversible environmental degradation. In response, emerging transportation solutions—including intelligent ridesharing, autonomous vehicles, zero-tailpipe-emission transport, and urban air mobility—offer opportunities for safer and more sustainable transportation ecosystems. However, their widespread adoption depends not only on technological performance and efficiency, but also on integration with current infrastructure, safety, resilience to unexpected disruptions, and economic viability. A dynamic, agent-based System-of-Systems (SoS) transportation model is developed to simulate vehicle traffic and human
Rana, Vishva, Balchanos, Michael, Mavris, Dimitri, Valenzuela Del Rio, Jose
Development of technology to distinguish contaminants using LiDAR near-field point cloud based on real-field data2026-01-0020To be published on 04/07/2026
Sensing technologies are essential to autonomous driving, enabling environmental perception and supporting navigation and its decision-making. However, most publicly available datasets are collected under perfect weather conditions and fail to capture the challenges of real-world environments. To address this gap, we built a large-scale dataset from extensive driving campaigns across diverse regions of the United States, covering conditions such as rain, snow, dust, clear weather and anti-ice material affects. Data were acquired through a multimodal sensing devices - including perception cameras, LiDAR, infrared sensors, and external monitoring systems - providing a comprehensive representation of the vehicle's operational environment. Building on this dataset, our study focuses on LiDAR data, emphasizing contaminant classification as a critical requirement for sensor-cleaning strategies essential to reliable autonomous driving. Using raw LiDAR signals collected over 22,000km of
KIM, HUNJAE
Energy-Efficient Predictive Cruise Control in Heavy-Duty Electric Trucks with Optimization of the Penalty Factor as a Function of Payload2026-01-0042To be published on 04/07/2026
Heavy-duty Class 8 battery electric trucks not only offer a significant reduction in greenhouse gas (GHG) emissions compared to conventional diesel trucks but can also provide significant savings in fuel costs. To further enhance the energy and freight efficiency, Predictive Cruise Control (PCC) algorithms can be developed that generate optimal acceleration profiles for the vehicle by minimizing a cost function which combines both energy consumption and deviation from the desired velocity. A critical component of the cost function is the penalty factor, which governs the trade-off between energy use and travel time, which are two conflicting objectives in freight logistics. Selecting an appropriate penalty factor is essential, as freight deliveries are time-sensitive but minimizing energy consumption remains a priority. Moreover, variations in payload significantly affect vehicle dynamics and energy usage, making it critical to adapt the penalty factor to different payload conditions
Safder, Ahmad Hussain, Villani, Manfredi, Wang, Eric, Khuntia, Satvik, Nelson, James, Meijer, Maarten, Ahmed, Qadeer
Model-based Development of a Heavy-Duty H2-ICE and Integrated, Turbogeneration, Electrification, and Supercharging (ITES) System2026-01-0426To be published on 04/07/2026
Changing global economic conditions and efforts to reduce greenhouse gas emissions to reduce their impact on climate and public health is driving the need to develop alternative technologies to meet the demands of heavy-duty vehicles and increase propulsion system efficiency. This study combines a Hydrogen internal combustion engine (H2-ICE) for its near zero CO2 emissions capability, with electrically assisted turbocharging, exhaust energy recovery, and mild hybridization to maximize propulsion system efficiency in real-world driving. The focus is on model-based integration and optimization of a technology package termed as the Integrated, Turbogeneration, Electrification, and Supercharging (ITES) system to demonstrate its potential for low emissions, high efficiency, and reduced cost will be demonstrated. In a previous study, a H2-ICE and aftertreatment concept for a MY2027 7.7L medium heavy-duty on-road engine was developed and evaluated through 1D simulation. The concept was to
Bustamante, Oscar, Correia Garcia, Bruno, Joshi, Satyum, Franke, Michael
Ambient and Initial Temperature Effects on Battery Electric Vehicle (BEV) Range and Energy Consumption Rate Modeled in FASTSim2026-01-0277To be published on 04/07/2026
Ambient and initial temperatures have a significant impact on the energy consumption rate of battery electric vehicles (BEVs) due to auxiliary loads and the dependence of battery efficiency on temperature. Quantifying these impacts can help BEV owners, communities, and researchers understand and prepare for the impact of temperature on BEV vehicles, especially as adoption increases. We used NREL's FASTSim 3 to investigate the impacts of a two-dimensional design-of-experiment that swept ambient and initial temperature in simulating a compact BEV hatchback over UDDS (city) and HWFET (highway) US EPA drive cycles. Compared to a 22 ° C ("cozy") initial / ambient temperature baseline in UDDS, a cold initial / cold ambient temperature (-7 ° C) resulted in a 221% increase in the energy consumption rate, a cozy initial / cold ambient temperature resulted in a 94% increase, a hot initial (45 ° C) / hot ambient (40 ° C) temperature resulted in a 40% increase, and a cozy initial / hot ambient
Baker, Chad, Steuteville, Robin, Holden, Jake, Gonder, Jeffrey, Carow, Kyle
Cradle-to-grave assessment of Austrian passenger car traffic based on actual vehicle movements and derivation of future forecasts for minimising greenhouse gas emissions2026-01-0457To be published on 04/07/2026
As part of the decarbonisation process for passenger car fleet in Austria, battery electric cars in particular have been subsidised in recent years, as these vehicles are considered to be largely emission-free during use and are expected to reduce emissions in future. However, in order to sustainably reduce the global greenhouse gas emissions of Austrian passenger car traffic, taking into account all types of propulsion systems, it is necessary to apply a cradle-to-gate approach, as is commonly done in comparable analyses in the literature, which evaluates the emissions of the entire vehicle life cycle. The most important phase in the life cycle assessment remains the well-to-wheel phase, which includes emissions from energy supply and vehicle use. Due to the large number of influencing factors, highly simplified models are usually used for this phase in the literature. As part of this work, a methodology was developed that, allows an in-depth analysis of entire vehicle fleets by
Lischka, Gregor, Tober, Werner
The Necessity of a Holistic Safety Evaluation Framework for AI-Based Automation Features2026-01-0522To be published on 04/07/2026
The intersection of Safety of Intended Functionality (SOTIF) and Functional Safety (FuSa) analysis for driving automation features has traditionally excluded Quality Management (QM) components from rigorous safety impact evaluations. Historically, QM components have not been classified as safety-relevant, and therefore their potential influence on overall system safety has often been overlooked. However, recent advancements in artificial intelligence (AI) integration within automated driving systems have revealed that these components can, under certain conditions, contribute to SOTIF-related hazardous risks. This emerging reality challenges long-standing assumptions and underscores the need for a more inclusive approach to safety analysis. Furthermore, compliance with evolving AI safety standards, such as ISO/PAS 8800, necessitates a re-evaluation of safety considerations for components previously deemed outside the scope of FuSa and SOTIF frameworks. This paper examines the necessity
Abbaspour, Ali Reza, Mahadevan, Shabin, Zwirglmaier, Kilian, Stafford, Jeff
Planning Charging Infrastructure for Electric Freight Corridors2026-01-0469To be published on 04/07/2026
With the rapid development of electric vehicles, electric freight transportation is gradually increasing. However, the construction of charging infrastructure on expressways has lagged far behind, becoming a key bottleneck in the development of new energy freight transportation. This paper proposes an optimization system for the layout of charging stations in the rest areas of the Beijing–Hong Kong–Macao Expressway, based on freight logistics information. The system combines the Flow Capturing Location-Allocation Model (FCLM) with an improved genetic algorithm to dynamically analyze traffic flow, service area distribution, and charging demand, thereby achieving an optimized layout of charging stations. By simulating traffic flow variations under different scenarios, the study analyzes the spatiotemporal distribution of charging demand and optimizes the selection of charging station sites and charger allocation. Experimental results demonstrate that the proposed optimization method
Guo, Haifeng, Zhang, Jingzhong, Lian, Jintao
Simulation of Conjugate Heat Transfer using the Lattice Boltzmann Method2026-01-0135To be published on 04/07/2026
Flow simulation with conjugate heat transfer, which involves fluid flow, conduction, and radiation in solid components, is a critical capability that helps engineers design and evaluate cooling systems for heat-generating components such as brakes, powertrains, batteries, and power electronics for both gasoline and electric vehicles. For this study, we utilize PowerFLOW™, featuring its novel native thermal solver, which models both fluid and solid domains within a single model. For the flow domain, we use the Lattice Boltzmann Method with VLES turbulence modeling based on the RNG k-epsilon method. And for the solid domain, the finite volume method with second-order accuracy for thermal conduction and a surface-to-surface radiation for the surfaces. This approach not only leads to a more integrated and streamlined workflow but also enables an accurate modeling of conduction and radiation. In this study, we demonstrate the accuracy of the conjugate heat transfer simulation methodology
Mukutmoni, Devadatta, Shock, Richard, Li, Han, Wanderer, John
Research on Reliability Test Methods for Electric Drive Axle Differentials2026-01-0007To be published on 04/07/2026
With the rapid socio-economic development, global issues such as the energy crisis and environmental pollution have become increasingly severe. Electric vehicles have garnered growing attention due to their environmental friendliness, simple structure, and high energy efficiency. Electric drive technology is regarded as the most ideal universal driving solution for pure electric vehicles, hybrid electric vehicles, and fuel cell vehicles. The electric drive axle, as a new type of integrated mechatronic transmission system, is being increasingly adopted owing to its advantages such as high drive efficiency, flexible spatial arrangement, and facilitation of digital and automated chassis control. This study focuses on reliability testing methods for the differential in electric drive axles, mainly including the extraction of reliability test conditions and feasibility analysis of the test scheme. The main research contents are as follows: First, based on specific parameters of an electric
Zheng, Hongyu, Li, Ziyu, Wang, Dajiang, tian, kai
A Proposed Methodology for Human Operator Modeling and Development of a Virtual Operator Model for Control of a Simulated Compact Track Loader2026-01-0146To be published on 04/07/2026
A methodology for performing Human Operator Modeling (HOM) using a Caterpillar Model 299D3 XE Compact Track Loader (CTL) is presented. The proposed method uses task analysis techniques to decompose a material excavation and moving task into smaller, individual tasks presented in a task list. A method for verifying and refining the task list is presented, along with a procedure for identifying relevant human operator sensory information and analyzing human decision making in the context of CTL operation. This methodology is then partially verified through the analysis of a non-expert human operator in Vortex Studio, a realistic construction equipment simulator. A modified test course is executed by a non-expert human operator in the simulation environment, and the recorded data is used to create a quantitative Human Operator Model. From this, a Virtual Operator Model (VOM) feedback controller simulating the performance of the human operator is developed. The VOM is implemented using a
Wang, Orson R., Norris, William R., Patterson, Albert E., Soylemezoglu, Ahmet, Nottage, Dustin S.
Simulink DevOps Evolution: Bridging Interactive Platforms for Accelerated Model-Based Workflows2026-01-0080To be published on 04/07/2026
This paper builds on last year’s paper presenting DevOps automation in the context of model-based development. Following that paper, we interviewed Simulink users in passenger automotive, motorsports, commercial vehicles, aviation, rocketry, and industrial automation. We discovered that much of the benefit of DevOps platforms to reduce product development cycle time relies on their interactive features. We prototyped new tools to bridge interactive DevOps Git-based platforms with model-based development workflows, and then gathered reactions from another round of interviews. Here we present these interactive DevOps workflows with the feedback from these interviews to contextualize how engineering teams could adopt them to accelerate their own model-based workflows.
Mathews, Jon, Ferrero, Sergio, Tamrawi, Ahmed, Sauceda, Jeremias
A System of System Design Framework for Multi-Domain Vehicles2026-01-0263To be published on 04/07/2026
A System of System Design Framework for Multi-Domain Vehicles - U.S. Army OPSEC approval has been received. Please find the approved Abstract for your consideration below : Traditionally, ground vehicle design is based on identifying engineering solutions that fulfil the requirements and specifications put forth by the stakeholders. Although a vehicle is a single entity, it is composed of many subsystems and thousands of parts that must operate together in unison to meet all design goals. A System of Systems (SoS) design approach enables the consideration of sub-system performance within a framework of overall system operation which includes possible trade-offs. This collaborative approach to subsystem and primary system design draws upon modelling, optimization, tradespace analysis and virtual studies. In this paper, a system of system design approach will be investigated for a collection of multi domain vehicles assembled to undertake coordinated search and rescue operations on land
Somanchi, Anang, Abeynayake, Chandima, deshmukh, Mrunal, Suresh, Johir, Ramnath, Satchit, Turner, Cameron, Schmid, Matthias, Castanier, Matthew P., Rapp, Stephen, Jaczkowski, Jeffery, Wagner, John
Agile Safety Case Assessments for Autonomous Vehicle Fleets2026-01-0521To be published on 04/07/2026
The large-scale deployment of autonomous vehicles (AVs) demands not only robust safety cases but also systematic approaches for their assessment. A safety case is a structured argument, supported by evidence, that a system is acceptably safe within its intended domain. While many AV developers are now adopting safety cases as a prerequisite for launch readiness, effective assessment methods have not kept pace with the field’s rapid progress. This paper presents a three-stage methodology for safety case assessment—framework, solution, and implementation assessment—developed through nearly fifty evaluations across commercial and defense AV programs. Framework assessment benefits from leveraging safety case frameworks, which accelerate development and prevent argument defects. Solution assessment focuses on defining clear evidence criteria, while implementation assessment evaluates the sufficiency of complete evidence packages. Together, these stages support iterative improvement and
Wagner, Michael
Items per page:
1 – 50 of 44039