Browse Topic: Transportation Systems

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Impact of Network Latency on the Lateral Control in Automated Vehicle Marshalling2026-01-0781To be published on 07/01/2026
Automated Vehicle Marshalling (AVM) is the first functionally safe Level 4 automated driving system. It consists of the wireless control of unoccupied vehicles at low speed in well-defined environments, such as parking facilities or manufacturing plants. The driverless operation in an AVM system is achieved by transmitting control messages between connected vehicles and intelligent infrastructure. Similar to other wireless applications, network reliability poses a major challenge to ensuring safe automated driving. An AVM system must provide uninterrupted communication between the vehicle and the infrastructure at a stable frequency. However, wireless systems usually suffer from varying latencies and network disturbances. In this context, international organizations and automotive industry contributors have defined requirements specifying network performance, communication interfaces, and message formats for different AVM use cases. These requirements cover communication aspects
Mejri, Mohamed Amine, Münchhausen, Henrik, Flormann, Maximilian, Sturm, Axel, Henze, Roman
Improved Thermal Management for Operating a Battery Electric City Bus under Real Driving Conditions2026-01-0758To be published on 07/01/2026
The goal of reducing the global CO2 emissions requires actions especially for the transportation sector. To achieve the goal, electric traction motors are frequently implemented in passenger vehicles as well as in commercial vehicles like heavy-duty trucks or buses. Particularly electric city buses have the potential to reduce the local emissions in urban areas and provide local exhaust-emission-free mobility. While their number of registrations rises, research focusses on the improvement of the overall system in order to increase the energy efficiency. High importance is gained by the thermal management of the whole system. This research describes and investigates a simulative approach to improve the thermal management and therefore the energy efficiency of an electric bus system. The different thermal components of an electric city bus like drive system, battery system and heating, ventilation and air conditioning system (HVAC system) are modelled. Their thermal behaviour has been
Schäfer, Henrik, Hellberg, Tobias, Meywerk, Martin
Experimental Characterization and Optimization of a Staggered Rotor System for Heavy-Lift Urban Air Mobility Drones2026-01-0661To be published on 07/01/2026
The increasing adoption of drones in Urban Air Mobility (UAM) applications has intensified the need for compact, high-thrust, and energy-efficient aerial platforms capable of lifting heavy payloads under variable environmental conditions. Conventional coaxial rotor systems, while offering high lift density and reduced footprint, suffer from aerodynamic wake interference that decreases overall thrust efficiency. To address this limitation, a staggered rotor configuration was proposed and experimentally investigated to enhance thrust and stability performance. Controlled thrust stand tests were conducted with lateral rotor spacing varied from 0.25 to 1.0 times the propeller radius, while wind conditions were simulated using a Windshaper system to apply time-dependent gust loads under different payload scenarios. Results demonstrated that the coaxial arrangement exhibited an efficiency loss of about 15%, whereas the staggered configuration reduced this loss to approximately 7%, showing
Santhani, Raju, S, Syam Narayanan, B, Tarun Kumar, Rajalakshmi, P
Simulation and Analysis of Flow and Acoustic Induced Structural Vibration2026-01-0696To be published on 06/20/2026
For analysing flow and acoustic induced structural vibration, a run time fully coupled framework combining a hybrid CFD-CAA approach with a modal response simulation was validated and presented at the ISVNH 2022 (SAE Technical Paper 2022-01-0938). In this contribution we show the application of this method on a production vehicle, simulating the flow and acoustic induced vibration of the bonnet. The simulation setup of this study resolves the geometry of the vehicle including the complex details of the bonnet structure, the frunk and the engine compartment. The FEM model for the modal analysis of the bonnet takes into account the complex boundary conditions at the bearings and provides the eigenmodes relevant for the low frequency range. Simulations are conducted in an undisturbed wind tunnel setup and the resulting vibrations are compared to acceleration measurements under wind tunnel and road conditions. Due to the employed hydrodynamic acoustic split approach, the relevance of the
Schwertfirm, Florian, Ocker, Joerg, Hartmann, Michael
Low-Voltage Electric Vehicle with a Reconfigurable Powertrain Architecture for Urban and Extra-Urban Mobility2026-37-0025To be published on 06/09/2026
The growing penetration of electric mobility in urban environments calls for vehicle solutions that balance accessibility, safety, and operational flexibility. However, high upfront costs and the widespread oversizing of battery systems remain significant barriers to large-scale adoption, particularly for short-range use cases. To address these limitations, this paper presents the Low Voltage Reconfigurable Electric Vehicle (LVREV), a modular vehicle concept designed to bridge the gap between L7e and M1 categories. The proposed architecture integrates a low-voltage electrical system (<60 V), a reconfigurable powertrain, and a modular, swappable battery, enabling mission-adaptive performance while reducing cost, weight, and energy consumption. The vehicle architecture features a dual 52 V inverter system coupled with a six-phase permanent magnet synchronous motor, optimized through model-based design and genetic algorithms. Energy storage consists of two independent swappable battery
Tramacere, Eugenio, Favelli, Stefano, Galluzzi, Renato, Tonoli, Andrea
Autonomous Aircraft Docking: A Vision-Based Approach to Aircraft Parking Systems2026-26-0786To be published on 06/01/2026
Automated aircraft parking systems represent a significant advancement in airport ground operations by enabling precise, autonomous docking of aircraft at gates. These systems aim to reduce turnaround time, minimize human error, and optimize apron space utilization through intelligent control and sensor-guided alignment. By integrating technologies such as real-time object detection, obstacle avoidance, and dynamic path planning, aircraft can be maneuvered safely and efficiently into parking positions without manual intervention. Key challenges include precise steering control during final alignment, maintaining wingtip clearance, and adapting to variable environmental conditions such as low visibility due to rain, fog, or poor lighting. Unlike systems that rely solely on fixed guided paths, the proposed approach dynamically adapts to apron congestion and environmental variability, ensuring consistent performance across diverse conditions. This flexibility enhances operational
Penugonda, Navya Sunaina, Ediga, Venkatadiwakar Goud
Data-Driven Air Traffic Management: Forecasting Demand, Modeling Delay Propagation, and Demand Capacity Balancing2026-26-0772To be published on 06/01/2026
Air traffic management (ATM) systems must accurately estimate flight demand, analyze network delays, and optimize capacity. This research uses a global airline dataset with passenger information, airport characteristics (including geographic markers and continental classifications), flight scheduling data (timing and destination details), crew information, operational status records, and OpenSky Network real-time states data to create an intelligent ATM decision-support framework. Advanced machine learning (ML) methods like gradient boosting and sequential modeling are used to build network models of flight routes between origins and destinations, develop temporal demand patterns for individual airports, analyze delay factors, and predict congestion periods. The research uses mixed-effects statistical models using crew data to measure operational variability and demographic and nationality-based analysis to identify demand trends for improving staffing and security processing during
Senthilkumar, N., S, Gopalakrishnan, Gopinath, S
windpuls simonaero: validation of unsteady aerodynamic effects for single vehicles and vehicle platoons under real-world apparent flow conditions.2026-01-0764To be published on 06/01/2026
Validation of CFD methods against real-world measurements is particularly challenging under unsteady and disturbed flow conditions. In on-road and proving ground scenarios, vehicles are exposed to time-varying apparent wind fields resulting from environmental influences and vehicle interactions. This effect is amplified in vehicle platoons, where the aerodynamic coupling between multiple vehicles leads to complex, unsteady flow regimes. This paper presents an integrated test and validation toolchain designed to correlate steady-state CFD simulations with real-world measurements for single vehicles and vehicle platoons under unsteady apparent flow conditions. The presented use case focuses on proving ground measurements capturing time-resolved apparent flow conditions acting on both vehicles within a platoon. The apparent wind profiles include wind magnitude, incident flow angle, gust index (GI), turbulence intensity (TI), and side wind exposure (SE), derived from synchronized high
Feichtinger, Christoph Simon, Hörschinger, Rene
Preliminary Requirement Elicitation for Urban Air Mobility in India – A Mission-Based Approach2026-26-0777To be published on 06/01/2026
The Real GDP of India has been estimated to grow by 7.8% in Q1 of FY 2025-26, and urbanization is projected to be 50% by 2050. At the same time, India’s population is projected to reach 8.5 billion by 2030, and road traffic congestion is already a crisis in many Indian cities. This only implies a significant increase in demand for Urban Air Mobility (UAM) services in India. While the concept of UAM has been around since the 1940s, much has changed in terms of the stakeholder needs for UAM services; airspace utilization for urban civil and military air operations; the urban infrastructure; adoption of new technologies for reliable and uninterrupted communication, navigation and surveillance; and associated risks. Firstly, this paper explores the India-specific UAM ecosystem along with its boundaries, environments, and constraints. Factors studied include: restrictions in air operations due to India specific climate zones and ionospheric disturbances; usage of Indian airspace zones; and
DE, Manabendra M., Hebbar, Archana, Henry, Devanandham
Prediction of CO 2 Emissions from Light-Duty Vehicles Based on Highway Alignment2026-99-1713To be published on 05/22/2026
In order to allow for the precise prediction of the CO2 emissions of light-duty vehicles during the road design phase and to methodically examine the effect of road alignment on CO2 emissions, this paper classifies the operating conditions of light-duty vehicles according to Vehicle Specific Power (VSP) and the design speed of different road levels. The test vehicle’s environmental data and operational parameters under various road conditions were gathered using a Portable Emission Measurement System (PEMS). The CO2 emissions of the test vehicle under different operating conditions were statistically analyzed. Based on the road’s horizontal and vertical alignment, the road was separated into analytical units, including straight sections, longitudinal slope sections, horizontal curve sections, and curve-slope combination sections. The indicators of each analysis unit were used to anticipate the speed and acceleration of light-duty vehicles in each unit, and a model for forecasting light
Liang, Yao, Wang, Yixuan, Zhao, Xiaoyan, Cheng, Shenzhen, Wu, Bing, Zeng, Weiyi
The Spatial-Temporal Evolution of Coupling Coordination Degree Development of Carbon Emission-Passenger-Freight in Five Northwest Provinces2026-99-1716To be published on 05/22/2026
Taking China’s five northwestern provinces as the study area, this paper investigates the spatial-temporal interactions among carbon emissions, passenger transport, and freight transport from 2010 to 2020. An entropy-weighted composite index is constructed for each system and integrated into a coupling coordination degree model to quantify interaction. It is found that (1) the average annual growth of provincial coupling coordination degree is 4.7%, but the gradient difference between regions is significant, and the extreme difference of coupling coordination degree between east and west reaches 4.5 times in 2020; (2) Spatially, it shows a unipolar leading pattern, with Shaanxi achieving a significant decrease in carbon emission intensity and Qinghai achieving a lesser coupling coordination degree of 23% in Shaanxi due to the high proportion of highway freight transport and single energy structure; (3) the driving mechanism analysis shows that the improvement of transport network
Qian, Yongsheng, Li, Shaoyuan, Zeng, Junwei, He, Qingling
Dynamic Modeling and Optimization of Expressway Weaving Areas Based on Genetic Algorithm and Cell Transmission Model2026-99-1732To be published on 05/22/2026
The traffic situation at urban expressway interchanges is really complicated in daily life. Cars change lanes very often, and problems from cars merging together are obvious. Traditional traffic models aren’t accurate enough when they try to predict what happens in these areas. To solve this, we suggest a better cellular transport model (CTM) that’s improved using genetic algorithms. It can describe and improve traffic conditions in a flexible way. We picked the interchange on Hohhot’s North Second Ring Expressway for our study. To get traffic data during rush hours—7 to 9 in the morning and 5 to 7 in the evening—we used a few methods together. There was video monitoring with tools like YOLOv8 and DeepSORT, people counting cars by hand, and also VISSIM simulation. The data we collected had things like how fast cars were going, how many were packed in an area, and how much traffic was moving through. With this info, we could see how traffic changes in different parts of the interchange
Duan, Xiangyu, Hu, Bing, Yan, Wang
Research on Performance Prediction Methods for Centrifugal Refrigeration Compressors in Data Centers2026-99-0725To be published on 05/15/2026
As the “digital brain” and core foundational support for the development of intelligent transportation and connected vehicles, the performance of data centers directly determines the operational capability of intelligent transportation systems. In the process of advancing the vehicle-road-cloud collaborative architecture, the demand for high-performance computing power in data centers has experienced explosive growth. The substantial increase in computing tasks has posed severe challenges to thermal management, making efficient and reliable cooling systems an indispensable core component. Centrifugal compressor water-cooling units are the mainstream cooling solution for large-capacity scenarios, and their design optimization is crucial for improving the energy efficiency and performance of the entire cooling system. This paper proposes a one-dimensional performance prediction method for centrifugal compressors based on an empirical loss model, and realizes the iterative calculation of
Zhu, Minhao, Jiang, Bin, Li, Min, Zeng, Zihui, Gu, Yunhui
An Energy-Saving Coupling-Decoupling Optimization Strategy for Electric Modular Buses Considering Vehicle to Vehicle Charging2026-99-0730To be published on 05/15/2026
As an emerging innovative mode of public transportation, electric modular buses (EMBs) offer a novel solution to the problems of existing public transportation systems, due to the coupling-decoupling processes. In this paper, we study the energy consumption characteristics of EMBs by joining vehicle-to-vehicle (V2V) charging and reduction in aerodynamic drag due to coupling. For the pursuit of energy economy, ride comfort, and operational efficiency, we constructed an optimization scheme based on the simulated annealing (SA) algorithm to facilitate the coupling-decoupling process. The simulation results show that EMBs can meet 82.5 % of service requests compared with 61.8 % for the benchmark group, and V2V presents a significant contribution to energy efficiency, especially at low battery state of charge (SOC). Additionally, sensitivity analysis is conducted to study the impact of initial SOC, operation interval, and route type. The results provide insights for optimizing EMBs
Liao, Peng, Guo, Jiahe, Ning, Donghong, Li, Sijia, Wang, Tao
A Real-Time Monitoring and Rapid Warning Method for Guardrail Collisions in Intelligent Transportation Systems2026-99-0723To be published on 05/15/2026
This article proposes a method for real-time monitoring and rapid alert for guardrail collisions based on Distributed Acoustic Sensing (DAS). The aim is to enhance traffic safety through continuous analysis of vibration signals. To achieve this, a system architecture that combines both hardware and software design has been developed, enabling the handling of the entire process from signal acquisition and decoding to intelligent event recognition and visualization. To improve signal reliability, an adaptive noise reduction algorithm and a multi-level feature extraction method are introduced, enabling accurate differentiation between collision events and environmental disturbances. Tests at various vehicle speeds show that the DAS-based system detects collisions with over 98% accuracy and cuts false alarms by more than 60% compared to traditional video and point-sensor monitoring. It can locate accidents with an average error of 4.2 meters and respond in under 1 second, demonstrating
Sun, Lang
Design and Research of an Elderly Mobility Assist System Based on Intelligent Power-Assistance Device2026-99-0737To be published on 05/15/2026
The aging of the population has been a key issue worldwide, with mobility and fall of the elderly an important problem to be solved. In this paper, we propose an elderly mobility assist system based on the intelligent power-assisted device consisting of an assistive cane and an intelligent companion. It has the functions of standing support after falling, daily support and on-site rest. The assistive cane adopts a two-stage expansion mechanism of crank and slider structure, which forms a stable triangular support after unfolding, so that the patient can stand safely. The intelligent companion platform is driven by drive wheels, equipped with pushrod motors and vacuum suction devices, it can automatically approach the user and form an stable support column when the cane is in the out-of reach range; the control system is designed by combining microcontroller, camera object recognition, wristband remote control, to realize automatic steering and autonomous navigation at differential
Yu, Chenxi, Wang, Longyi, Zhu, Huayun, Dong, Yan, Mi, Ruixue, Zhu, Lihong
Autonomous Navigation Strategy and Docking Mechanism for Intelligent Chassis Module of Split Flying Vehicle2026-99-0743To be published on 05/15/2026
In response to the problems of urban traffic congestion and the limited expansion of infrastructure, this paper conducts two core research focusing on the intelligent chassis system of split-type flying vehicle. Firstly, an autonomous navigation strategy for the intelligent chassis module is proposed based on chassis module Navigation 2 architecture, which fuses LIDAR and IMU positioning to plan paths using the A* global planning algorithm on a global cost map, and update the local cost map in real time with sensor data. It is orchestrated by the BT Navigator using a behavior tree, with failures handled by the Recovery Server, to achieve autonomous driving across multiple waypoints. In simulation and closed-field experiments, the system can stably reach the preset target points. The positioning accuracy and trajectory tracking performance can meet the design requirements. Secondly, a mechanical slide rail-type docking structure adapted to the split flying vehicle architecture is
Zhao, Wenyu, Shi, Qin, Jiang, Cong, He, Zejia
Multi-Mode Control Strategy for Controllable Vehicle Suspension Systems2026-99-0712To be published on 05/15/2026
The suspension system with variable damping and variable stiffness actuators can realize four-quadrant mechanical output, effectively combining the energy efficiency of the semi-active suspension with the performance levels approaching those of active suspensions. However, the practical effectiveness of this system depends heavily on the ability of the control strategy to adapt to different driving conditions. In order to meet this challenge, this research has developed a multi-mode suspension collaborative control strategy to optimize energy efficiency and ride comfort in various operating scenarios. Based on the four-quadrant characteristics of the actuator, a suspension mode switching framework has been established, and the suspension work is divided into passive, semi-active, pseudo-active and active modes. In order to determine the appropriate switching boundary, first calculate the root mean square (RMS) value of the sprung mass acceleration and suspension dynamic deflection
Li, Zhiying, Li, Jei, Zhu, Anding, Bai, Xianxu, Li, Weihan, Li, Rui
SAE TOMORROW TODAY - Why Curing Motion Sickness Is Key to AV Success135675/7/2026
Did you know that motion sickness affects 1 in 3 people? As AVs gain traction and drivers become passengers, it may be the biggest barrier to the future of autonomous mobility. With a focus on addressing the root cause of motion sickness, Motion Sync offers breakthrough solution: a haptic pre-activation system that helps passengers anticipate vehicle movement with a personalized in-cabin experience that could redefine comfort and productivity. Listen in as we sit down with Dr. Daniel Sousa Schulman, CEO and Founder of Motion Sync, to unpack the science behind motion sickness and why it's a critical issue for EVs, robotaxis, and self-driving cars. You'll learn why passengers get sick, why drivers don't, and how EVs can actually make symptoms worse. As a topic critical to the success of autonomous vehicles, it's a conversation you don't want to miss. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take
Patterson, Lori
The Impact of Transition Window Duration on Nominal Transitions of Control in Conditional Automation09-14-01-00165/4/2026
The objective of this research was to understand the impact of transition window duration on success and performance during nominal transitions from conditional driving automation (SAE level 3). Because the driver can be disengaged from driving when conditional driving automation is engaged, the central challenge is how to safely transition from automated control to human control. Past research from the literature on Level 3 Automated Driving Systems (L3 ADS) has focused on safety-critical event responses (e.g., responding to a hazard) and on automation that operates at high speeds, which is not representative of the systems currently deployed that operate in lower-speed traffic jam situations [4, 5]. This article presents an analysis of data from several transition-of-control studies with conditional driving automation in a high-fidelity driving simulator. A range of transition window durations were compared, and different transition-of-control behaviors were coded from video data
Gaspar, John, Ahmad, Omar, Schwarz, Chris, Fincannon, Thomas, Jerome, Christian
Machine Learning–Driven Safety Analysis of Automated Vehicles in Simulated Mixed Traffic Environments09-14-01-00215/4/2026
Automated Vehicles (AV) pose new challenges in road safety, multimodal interaction, and urban planning, requiring a holistic approach that prioritizes sustainability and protects all road users. The KASSA.AST project addresses this by deploying and evaluating an automated shuttle in southern Austria on three routes. The study area is a Park & Ride zone near a train station, enabling seamless transfers and higher transit use. To assess the safety impacts of the automated shuttle, four Mobility Observation Boxes (MOBs) were deployed. These AI-based systems detect and classify road users, track their trajectories and geospatial coordinates, and identify safety-critical events via Surrogate Safety Measures (SSMs). Over 10 days, a trajectory dataset captured interactions among vehicles and the shuttle. The resulting real-world dataset is a core contribution. This dataset underpins microscopic behavior modeling. Trajectory pairs yield car-following and interaction metrics (relative distance
Losada Arias, Ángel, Rosenkranz, Paul, Hula, Andreas, Aleksa, Michael, Saleh, Peter, Erdelean, Isabela
SAE TOMORROW TODAY - Inside California's Mobility Playbook135654/27/2026
What does it take to move the world's fourth-largest economy every single day? As one of the most complex transportation systems in the world, California is at the forefront of the next generation in mobility. Listen in as we sit down with Toks Omishakin, Secretary of the California State Transportation Agency (CalSTA), for a behind-the-scenes look at how California is preparing for global mega-events like the World Cup and the 2028 Olympics; the latest on high-speed rail projects; and how emerging technologies like automation and electrification have leaders rethinking mobility at scale. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn
Patterson, Lori
Real Turbulent Flow in Vehicle Aerodynamics: Overview of On-Road Measurements and Turbulence Generation in Wind Tunnels2026-01-50294/22/2026
Flow conditions on the road are quite different from the conditions used to develop vehicle aerodynamics. However, a significant amount of statistical data now exists that describes realistic road conditions. Some of these on-road flow characteristics can be replicated in wind tunnels. This paper reviews technical facilities designed to simulate on-road flow characteristics, such as turbulence intensity, turbulent length scales, and flow angle distribution. Reconstruction of a flow field that matches real road conditions is made possible by using active or passive turbulence generators within the wind tunnel. This review provides a comprehensive overview of these facilities, offering readers key insights into the challenges involved in replicating real-world flow conditions in wind tunnels.
Vondruš, Jan, Vančura, Jan
Directional Stiffness Decoupling in Meta-Wheels via Three-Dimensional Discrete Curved Spokes15-19-01-00054/22/2026
Meta-wheels—non-pneumatic wheels whose performance is governed by structural geometry rather than internal pressure—offer new opportunities for directional stiffness control. Yet achieving independent tuning of longitudinal, lateral, and vertical stiffness within a single wheel architecture has remained challenging due to the inherent coupling in conventional radial and planar curved spokes. In this study, we introduce a three-dimensional (3D) discrete curved-spoke design that provides explicit geometric control through two independent parameters: the in-plane curvature angle (α) and the out-of-plane inclination angle (β). Using spoke-level and full-wheel finite-element (FE) simulations, supported by a simplified cantilever-beam analytical model, we show that these two geometric parameters govern stiffness in fundamentally different ways. The curvature angle α serves primarily as a geometric softener, reducing stiffness in all directions while maintaining a high top-loading ratio (TLR
Han, Heeseung, Liu, Zhipeng, Ju, Jaehyung
SAE TOMORROW TODAY - SAE Standards: Building Consensus for Moving Mobility Forward135634/16/2026
Standards aren't flashy ... but they make modern mobility possible by enabling emerging technologies to scale safely. Listen in as we sit down with SAE International experts Christian Thiele, Senior Director of Global Vehicle Ground Standards, and David Franks, Standards Specialist Engineer for Aerospace, for a wide‑ranging conversation on how SAE standards quietly enable trust, interoperability, and scale across automotive and aerospace. This discussion spans EV charging, wireless roads, automated driving, advanced air mobility, hydrogen propulsion, and the growing role of artificial intelligence. Go behind the scenes to learn how these standards are developed, the importance of industry consensus, and why they often exceed regulatory safety requirements. Are you interested in shaping the standards behind next-gen mobility technology? Get involved at sae.org/standards/development. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to
Patterson, Lori
A Dataset for Visual Classification of Flat Tires2026-01-01524/7/2026
Flat tires represent a common yet serious issue in vehicle safety, leading to compromised control, increased braking distance, and potential rim or structural damage when undetected. Conventional tire pressure monitoring systems (TPMS) rely on embedded sensors that can fail, incur high replacement costs, and are not always equipped in older or low-cost vehicles. To address these limitations, this study presents a comprehensive visual dataset for flat-tire classification using computer vision and machine learning techniques. The dataset comprises 600 labeled images—300 flat-tire and 300 non-flat-tire samples—collected from diverse vehicle types, lighting conditions, and viewpoints. This dataset is designed to support the training and benchmarking of lightweight edge-AI models suitable for real-time deployment on embedded platforms. A set of supervised learning models were evaluated. Results demonstrate that visual-based classification provides a cost-effective and scalable pathway
Gunasekaran, Aswin, Govilesh, Vidarshana, Challa, Karthikeya, Maxim, Bruce, Shen, Jie
SAE TOMORROW TODAY - How Kansas is Reshaping Multimodal Transportation135624/7/2026
What does it take to build a multimodal transportation network that actually works for rural communities, growing regions, and everyone in between? In Kansas, that question is critical as the Kansas City Chiefs plan their stadium move and the 2026 FIFA World Cup comes to town. Listen in as we sit down with Matt Messina, Chief of Multimodal Transportation at the Kansas Department of Transportation (KDOT), to explore how Kansas is prioritizing transit solutions for upcoming projects and how community input shapes decisions. It's an insightful journey into the challenges and opportunities of public transit, pedestrian infrastructure, and the future of mobility. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a
Patterson, Lori
Quantifying Highway Flow Improvements via CACC-Based Truck Platooning: A Statistical Modeling Approach2026-01-04634/7/2026
The advancement of Cooperative Adaptive Cruise Control (CACC) technology enables vehicle platooning on public roads, offering significant potential to enhance urban mobility, driving safety, and energy efficiency. Among various applications, truck platooning has become a promising strategy to increase highway flow rates by reducing vehicle headways, improving coordination, and optimizing space utilization. This paper presents a quantitative assessment of a CACC-based truck platooning system, focusing on its effectiveness in enhancing highway mobility under varying traffic conditions. A statistical regression model is developed and calibrated using simulations of real-world highway networks to identify key influencing factors and evaluate the resulting improvements in traffic flow. The analysis considers five primary variables: desired platoon speed, platoon size, space headway, percentage of platooning trucks, and non-platoon traffic flow. The study systematically examines the impact
Karbasi, Amir Hossein, Wang, Jinghui, Yang, Hao
A Study of Tire Grip Performance Evolution under Dry, Wet and Snow Roads with Tire Wear2026-01-05894/7/2026
This study focused on investigating how tire grip performance on dry, wet, and snowy road surfaces varied with the different level of tire wear. New, 50% worn, and end-of-life tires were prepared following worn tire preparation standards. Additionally, worn tires obtained under real driving conditions in the market were used. Tire grip performances on dry, wet and snowy roads were characterized respectively by using an indoor flat belt machine, an outdoor trailer, and a specially designed snow truck. The results demonstrated an evolution of grip performance as a function of tire wear. The study identified differences in impact between worn tire preparation methods —real driving versus artificial—particularly on snowy road surfaces. Furthermore, the effects of tire stiffness, reduced tread depth, and tread surface roughness of worn tires were investigated for each type of road surface. The objective of this study is to enhance the understanding of tire behavior throughout its lifecycle
Kim, Changsu, Saito, Yoshinori
ProGuard: A Real-Time Preemptive Artificial Intelligence-Based Anti-Pinch System for Bus Entryways on Low-Power Edge Devices2026-01-01004/7/2026
This paper proposes ProGuard, a novel approach to preemptive pinch detection systems for buses. ProGuard utilizes state-of-the-art AI object detection algorithms to identify potential pinching events in bus entryways before pinching occurs. Modern conventional anti-pinch systems, such as pressure sensors or hall effect sensors, often rely on mechanical contact before triggering. While these systems are established safety mechanisms, they are reactive and therefore require some level of pinching before triggering. This reactive approach presents numerous safety concerns for passengers, especially when considering children on school buses. Existing preemptive detection methods, such as infrared or ultrasonic sensors, solve the problems presented by these reactive detection systems. However, these systems either lack the range or environmental resilience needed for reliable operation in buses. The critical nature of anti-pinch systems requires a robust and reliable solution that can adapt
Bradley, Hudson, Zadeh, Mehrdad, Tan, Teik-Khoon
An Integrated Methodology for Sustainable Mobility Infrastructure Planning: The Atlanta Aerotropolis Case Study2026-01-00974/7/2026
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
Operational Feasibility of Battery Swapping in U.S. Transit: A Case Study of King County Metro2026-01-04024/7/2026
Battery swapping technology has emerged as a promising alternative to conventional charging for electric bus fleets, offering rapid turnaround times and improved vehicle availability. This paper utilizes existing bus routing information to perform an initial site evaluation for battery swapping stations. A Seattle-based public transit agency—King County Metro, a partner on this project—is used as a case study. Using General Transit Feed Specification (GTFS) data from King County Metro, a MATLAB model was built to reconstruct blocks and layovers, extracts dwell-time opportunities, and performs block-distance and block-time analyses to understand operational rhythms. based bus model was developed that maps route mileage, efficiency, and layover availability for battery swap decisions, using a look-ahead rule that defers battery exchanges whenever the next feasible layover can still be reached while respecting a minimum state-of-charge. The workflow estimates how many swaps each block
Vadlapatla, Taraka Rishi, Jankord, Gregory, D'Arpino, Matilde
Safety-Constrained Control of Nonlinear Mobility Systems Using Reference Governor2026-01-00334/7/2026
Ensuring safe operation and reliable control of mobility systems remains a significant challenge, particularly for nonlinear and high-dimensional applications subject to external disturbances with hard constraints and limited computational resources in real-time implementations. A reference governor (RG) can enforce constraints using an add-on scheme that preserves the pre-stabilizing controller while balancing the need to satisfy other requirements, including reference tracking and disturbance rejection. Thus, in this paper, we exploit RG-based strategies focusing on nonlinear mobility systems. While the method is generalizable to other applications, such as waypoint following for autonomous driving, the flight dynamics of a quadrotor system with twelve states are used as an example. We implement a disturbance rejection RG to satisfy safety constraints and track set points. To handle nonlinearity, we propose an optimal strategy to quantify the maximum deviation between the nonlinear
Dong, Yilong, Li, Huayi
A Comparison of Road Grade Preview Signals from Lidar and Maps2026-01-00264/7/2026
Road grade can impact the energy efficiency, safety, and comfort associated with automated vehicle control systems. Currently, control systems that attempt to compensate for road grade are designed with one of two assumptions. Either the grade is only known once the vehicle is driving over the road segment through proprioception, or complete knowledge of the oncoming road grade is known from a pre-made map. Both assumptions limit the performance of a control system, as not having a preview signal prevents proactive grade compensation, whereas relying only on map data potentially subjects the control system to missing or outdated information. These limits can be avoided by measuring the oncoming grade in real-time using on-board lidar sensors. In this work, we use point returns accumulated during travel to estimate the grade at each waypoint along a path. The estimated grade is defined as the difference in height between the front and rear wheelbase at a given waypoint. Kalman filtering
Schexnaydre, Logan, Poovalappil, Aman, Robinette, Darrell, Bos, Jeremy
Optimization of Road Reference Profiles Based on Highway Design for Connected and Automated Vehicles2026-01-00964/7/2026
Advances in Connected and Automated Vehicles (CAVs) have developed a level in which high-definition maps can be used to improve road safety. Data compactness and robustness on road characterization is essential for the proper handling of vehicles under curves. In this paper, an optimization scheme that relates highway-design road curvature and optimal speed of travel is defined to safely navigate through a given road. The scheme is divided in two main steps. First a nonlinear optimization problem, in which curvature profiles are fitted from a model that based on street design standards as per the American Association of State Highway and Transportation Officials (AASHTO). Secondly, the optimized curvature profile is subject to a secondary optimization problem that uses vehicle dynamics for both constraints and objective function derivation. Guidance reference parameters such as curvature and velocity, at different levels of friction are analyzed. Results show that, even in sparse
Jacome, Ricardo Osmar, Stolle, Cody, Grispos, George
Optimal Control Analysis of Minimum-Time Maneuvers for Vehicles Recovering from Instability2026-01-02184/7/2026
Vehicles may enter highly unstable dynamic states due to lateral collisions, sudden loss of grip, or extreme steering disturbances. When such instability arises in congested road sections where obstacle avoidance is required, the safety risk to both the ego vehicle and surrounding traffic escalates significantly. In such scenarios, the vehicle must not only regain stability but also navigate the roadway in the shortest feasible time to prevent secondary collisions. This paper investigates the minimum-time maneuver of a vehicle starting from an unstable dynamic condition and constrained to travel within prescribed road boundaries. A single-track vehicle model with combined-slip nonlinear tire model is employed to capture the vehicle dynamics under high slip conditions. Phase-plane analysis is conducted to reveal how control inputs reshape the system’s vector field and influence the possibility and speed of stability recovery. An optimal control problem is formulated to compute the
Leng, Jiatong, Yu, Liangyao, Wang, Yongxin, You, Weijie, Li, Ziang, Jin, Zhipeng
Adaptive Eco-Driving Feedback for Battery Electric Vehicles: A Reinforcement Learning Approach Using Context-Aware Policy Optimization2026-01-01654/7/2026
Energy efficiency and range optimization remain critical challenges to the widespread adoption of battery electric vehicles (BEVs). As a result, there is a growing demand for intelligent driver assistance systems that can extend the operating range and reduce range anxiety. This paper presents an adaptive eco-feedback and driver rating system based on proximal policy optimization (PPO) reinforcement learning, designed to support drivers with the target to reduce energy consumption and maximize driving range. The system processes real-time driving data, such as velocity, acceleration and powertrain status. Map data of high quality is used to anticipate traffic events, including but not limited to speed limits, curves, gradients, preceding vehicles and traffic lights. This contextual awareness allows the system to continuously assess driving behavior and provide personalized, context-aware visual feedback alongside a dynamic driving behavior rating. A PPO agent learns optimal feedback
Stocker, Christoph, Hirz, Mario, Martin, Michael, Kreis, Alexander, Stadler, Severin
A Cooperative AFS/DYC Lateral Stability Control for FWDIEV Based on Dissipative Energy Method2026-01-06194/7/2026
To enhance the lateral stability of four-wheel-drive intelligent electric vehicles (FWDIEV) under extreme operating conditions, this paper proposes a cooperative control strategy integrating active front steering (AFS) and direct yaw moment control (DYC) based on dissipative energy method. A nonlinear three-degree-of-freedom vehicle model is established to analyze the evolution of the vehicle state phase trajectory. A quantitative lateral stability index is constructed using dissipative energy to accurately evaluate the vehicle’s lateral dynamics. Utilizing dissipative energy and its gradient information, a time-varying stability boundary is defined under dynamic constraints, and adaptive weighting coordination between the AFS and DYC systems is designed to achieve coordinated control of front steering angle and additional yaw moment. A feedforward–model predictive control (FF-MPC) framework is developed, in which a feedforward module generates compensation based on driver intent to
Zhao, Kun, Zhao, Zhiguo, Wang, Yutao, Xia, Xue, Chen, Xi, Hu, Yingjia
Pre-Impact Seat Rotation for Mitigating Lumbar Injury in Flying Car Crash2026-01-05584/7/2026
Flying cars have already been used in tourism, firefighting, and logistics, and might be soon used for short-distance commute. However, the lumbar spine injury risks in flying car crash accidents have raised safety concerns. This is because the crash load of a flying car is largely aligned with the orientation of the occupant’s spine. This study introduces a countermeasure of actively adjusting seat posture for mitigating lumbar injury in crash events. A flying car crash usually has a few seconds of warning time before collision to ground. The pre-impact warning time is enough to rotate the seat and occupant together using seat motors. Posteriorly rotating seat can alter the angle between the crash load and the spinal axis, thereby reducing lumbar injury risk. Using numerical simulations, the 30g deceleration pulse defined in SAE-AS-8049 was applied to seat of flying car. The THUMS (Total Human Model for Safety) human body model was used to model occupant, sitting in a typical vehicle
Zhuang, Ziao, Puyuan, Tan, Shen, Wenxuan, Zhou, Qing, Gu, Gongyao
Evaluating Cloud Platforms for Real-Time Cooperative Perception Systems Using RSU Cameras: A Comparative Study of Google Colab, Azure, and AWS2026-01-00184/7/2026
This paper presents a comparative study of three widely used cloud platforms, Google Colab, Microsoft Azure, and Amazon Web Services (AWS), for running a real-time cooperative perception system based on roadside unit (RSU) cameras. The goal is to evaluate the performance, scalability, and cost-efficiency of each platform when handling high-volume video data for object detection, a key task in autonomous driving. A unified perception pipeline using the YOLOv8 Small model was deployed on all platforms, with the same dataset and settings to ensure fair comparison. The evaluation focused on key metrics such as latency, frame processing rate, detection accuracy, cost, scalability, and reliability. The results show that Google Colab is a cost-effective starting point but has limitations in uptime and scalability. Azure offers stable performance and balanced cost, making it suitable for medium-scale applications. AWS delivers the best scalability and speed but at a higher cost. This study
Alkharabsheh, Ekhlass, Alawneh, Shadi, Rawashdeh, Osamah
MEDAS: A Multi-Dimensional Framework for Vehicle Detection Model Selection2026-01-00254/7/2026
Recent years have seen a rapid rise in edge-oriented object detection models, including new YOLO variants and transformer-based RT-DETR. Choosing an appropriate model for vehicle detection, however, remains challenged because common metrics such as precision, recall, and mAP capture only part of the trade-off between accuracy and computational cost. To better support model selection, we introduce the Multi-dimensional Equilibrium Detection Assessment Score (MEDAS), which evaluates detectors across four practical dimensions: performance, balance, efficiency, and adaptability. The framework includes a normalization strategy and adjustable weighting so that evaluations can reflect specific deployment needs, especially in resource-limited settings. Experiments on the MS-COCO vehicle dataset show that while RT-DETR models offer competitive accuracy, they require substantially more computation. In contrast, lightweight YOLO variants provide a stronger balance between accuracy and efficiency
Guo, Bin
Generative Agents for High-Fidelity Simulation of Community-Scale Mobility and Energy Behavior2026-01-04654/7/2026
The transition to sustainable mobility and energy systems represents a complex socio-technical challenge, with the success of new technologies and policies critically dependent on their interaction with human behavior. Traditional models frequently struggle to capture the nuanced, heterogeneous, and adaptive characteristics of individual decision-making in mobility choices and energy usage, thereby introducing significant uncertainties into system design and policy evaluation. This paper presents a novel paradigm to bridge this gap: the Hierarchical Generative Agent-based Simulation Framework (HGA-Sim). The framework's core innovations are twofold: 1) It utilizes Large Language Models to generate agents endowed with intrinsic personality traits autonomously, enabling a realistic simulation of diverse, human-like responses to environmental stimuli and personal experiences. 2) It employs a hierarchical "Archetype -Individual" architecture, rendering large-scale community simulations
Chen, Yongjian, Yang, Zhifeng, Ou, Shiqi(Shawn)
An Updated Correlation Study between the Full-Size Wind Tunnels of General Motors, Ford Motor Company, and FCA US LLC2026-01-06124/7/2026
As automotive aerodynamic testing facilities evolve to capture more real-world behavior, updating the correlation between old and new technologies is essential. Recently, the three-member consortium of the United States Council for Automotive Research (USCAR) - General Motors, Ford Motor Company, and FCA US LLC - transitioned from full-size static ground plane facilities to 5-belt moving ground plane wind tunnel facilities. The primary objective of this study was to update the correlation data sets to maintain consistent and robust data sharing among companies, which is the cornerstone of USCAR efforts. To achieve this, a set of updated correlation data sets were calculated to replace the original correlation study results from 2008. Additionally, the methodology for applying correlation equations was revised from using averaged wind tunnel data to employing direct wind tunnel-to-wind tunnel correlation equations. In a two-phase correlation effort conducted in 2022 and 2025, the three
Nastov, Alexander, Lounsberry, Todd, Madin, Trevor, Langmeyer, Gregory, Fadler, Gregory, Skinner, Shaun, Horton, Damien
High Order Control Lyapunov Function - Control Barrier Function - Quadratic Programming Based Autonomous Driving Controller for Bicyclist Safety2026-01-00344/7/2026
Ensuring the safety of Vulnerable Road Users (VRUs) is a critical challenge in the development of advanced autonomous driving systems in smart cities. Among vulnerable road users, bicyclists present unique characteristics that make their safety both critical and also manageable. Vehicles often travel at significantly higher relative speeds when interacting with bicyclists as compared to their interactions with pedestrians which makes collision avoidance system design for bicyclist safety more challenging. Yet, bicyclist movements are generally more predictable and governed by clear traffic rules as compared to the sudden and sometimes erratic pedestrian motion, offering opportunities for model-based control strategies. To address bicyclist safety in complex traffic environments, this study proposes and develops a High-Order Control Lyapunov Function–High-Order Control Barrier Function–Quadratic Programming (HOCLF-HOCBF-QP) control framework. Through this framework, CLFs constraints
Chen, Haochong, Cao, Xincheng, Guvenc, Levent, Aksun Guvenc, Bilin
A Real-Time Spatio-Temporal Configuration Framework for Shared Autonomous Electric Vehicle (SAEV) Fleets Integrating Deep Learning and Multi-Agent Simulation2026-01-00354/7/2026
Shared Autonomous Electric Vehicles (SAEVs) can enhance urban mobility and efficiency. However, their operational performance is often hindered by the spatio-temporal imbalance between vehicle supply and passenger demand, leading to long wait times. This paper develops a novel repositioning framework where a lightweight CNN, informed by computationally intensive multi-agent simulations, enables real-time strategy deployment. The results show that: (1) An optimized repositioning policy, calibrated via multi-agent simulation, effectively cuts the mean passenger waiting time from 12.0 to 3.0 minutes (a 75% reduction). (2) A lightweight CNN surrogate model enables real-time deployment, reducing the policy computation time from ~4 hours to ~5 minutes (>98% faster). (3) The deep learning surrogate achieves this speed with a negligible performance trade-off, increasing the waiting time by only 0.156 minutes (4.9%) compared to the full optimization.
Shang, Kai, Wang, Ning
Real-time Parameter Optimization for Eco-driving Control in Connected and Automated Vehicles Using Reinforcement Learning2026-01-00414/7/2026
This paper introduces a novel methodology to enhance the energy efficiency of eco-driving controllers in Connected and Automated Vehicles (CAVs) by leveraging reinforcement learning (RL) techniques for real-time parameter optimization. Traditional eco-driving strategies rely on fixed control parameters, which limit adaptability across diverse traffic and road conditions. To address this, we apply continuous action space RL algorithms, specifically Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), to dynamically tune four key parameters within a model predictive control framework that is grounded in Pontryagin’s Maximum Principle (PMP). These parameters influence acceleration, braking, cruising, and intersection-approach behaviors, making them critical for achieving optimal eco-driving performance. Our study employs Argonne National Laboratory’s RoadRunner simulator, a Simulink-based environment designed for high-fidelity CAV analysis, incorporating
Zhang, Yaozhong, Ammourah, Rami, Han, Jihun, Moawad, Ayman, Shen, Daliang, Karbowski, Dominik
From Propane to Pi: Affordable AI-Driven Solutions for Sustainable Bus Heating2026-01-01644/7/2026
This paper proposes an intelligent, artificial intelligence (AI) enabled seat heating system for school buses that saves energy by only activating heating elements when a passenger is identified. A custom-trained YOLOv8 deep learning model identifies passengers in real time and opens/closes real-time control of the individual electric seat heaters via a Raspberry Pi 5. The detector achieves around 10 frames-per-second (FPS) of inference on the Raspberry Pi 5 and 80–90 FPS on a laptop with over 92% detection confidence across various illumination conditions. Energy modeling shows the anticipated demand for a 10-kW propane-based heater is approximately 75% lower by implementing a 2.52 kW electric seat-heating system. In a typical operation schedule of 540 hours a year, this results in 4,000–5,000 kWh of annual savings, $465–$579 of annual cost savings and mitigates 0.9–1.3 t CO₂ per bus, annually. When implemented at the fleet level, the energy and cost saving will be in proportion. This
Chikkala, Daney Bhargav, Zadeh, Mehrdad, Tan, Teik-Khoon, Ponnam, Jitin, Batte, Jai Rathan
Data-Driven Study on Chassis Suspension Performance Degradation2026-01-05824/7/2026
The performance of chassis suspension mechanisms critically affects vehicle handling, ride comfort, and safety. Implementing real-time health monitoring for chassis systems contributes to preventing severe consequences such as increased body roll or loss of handling stability caused by shock absorber softening or spring stiffness degradation under deteriorating operating conditions, while circumventing the substantial costs associated with professional facility-based chassis inspections. With the rapid development of sensing and data analytics technologies, data-driven approaches are increasingly used in health monitoring. This study aims to achieve online monitoring of chassis suspension performance degradation using a deep neural network (DNN). First, a half-car model incorporating both vertical and pitch motions was established to simulate bumpy road conditions, with the aim of constructing a dataset that includes key vehicle suspension parameters and vehicle states related to their
Liao, Yinsheng, Lei, Yisong, Su, Ailin, Wang, Zhenfeng, Shi, Shuai, Zhang, Lei, Zhang, Junzhi, Ma, Changye
Aerodynamic Proximity Effects between the DrivAer and AeroSUV Standard Models, Part 1 – Same-Lane Interactions2026-01-06054/7/2026
In this paper, the effects of aerodynamic interactions on the drag of a longitudinally-arranged two-vehicle system are examined by considering the influence of separation distance, cross winds, vehicle size and shape. Testing was undertaken at 30% scale in a large wind tunnel with road-representative freestream turbulence. Separation distances of 0.5, 1.0, and 2.0 vehicle lengths (L) were examined over a range of yaw angles between ±15°. A highlight of the current study is the characterization of platoon drag-reduction benefits for different sizes and shapes of the lead and follower models, by using a DrivAer model and an Aero-SUV model, each with slant-back (Notchback or Fastback) and square-back (Estateback) variants, providing four distinct model pairings. Drag reduction for the lead model appears to be affected mainly by the size of the follower model, while the follower model shows a much greater sensitivity to shape of the lead model. Larger drag reductions were observed at most
McAuliffe, Brian, Ghorbanishohrat, Faegheh
Planning Public Charging Infrastructure Locations for Electric Freight Highway Corridors2026-01-04694/7/2026
Developing efficient fast-charging infrastructure along highway corridors is critical for reducing range anxiety and promoting long-distance electric travel. However, traditional static location approaches often fail to account for the stochastic interactions between continuous traffic flows and the stochastic variability of remaining driving ranges. To address these methodological gaps, this study develops a demand-driven optimization framework that integrates an improved Genetic Algorithm with the flow-capturing location-allocation model (GA-FCLM). Unlike static facility location approaches, the flow-capturing location-allocation component is specifically selected to maximize the interception of continuous traffic flows under strict range constraints, while the genetic algorithm efficiently navigates the high-dimensional discrete search space of simultaneous siting and sizing decisions. By synthesizing segment-level traffic flows with Monte Carlo simulations of state of charge (SOC
Guo, Haifeng, Zhang, Jingzhong, Lian, Jintao
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