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[ARCHIVED] Attn.: System maintenance @ 6 PM ET, April 15, 2025

Last updated: April 16, 2025, 09.11 AM ET Date posted (original message): April 15, 2025, 12.10 PM ET UPDATE: The maintenance work has been completed and the website is back up and running again. Thank you for your patience. ORIGINAL MESSAGE: Please note that this evening beginning at 6pm ET, we will be performing network maintenance on pieces of the core network infrastructure. We expect this work to last for 1 hour. During this period, access to Mobilus may be impacted and the site may be unreachable during this time. We will update once the maintenance activities have been completed. - The Mobilus team

[ARCHIVED] Experience WCX for Free—Expo Pass + Keynote Insights!

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[Resolved] Service notice - System issues

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Last updated: November 11, 2024 [LATEST UPDATE] The issue has been fixed and Counter reports are now accessible through the portal. Usage data until the previous month (i.e. October 2024) can be pulled in reports. Useful links: FAQs: https://saemobilus.sae.org/help/counter-usage-reports Mobilus Minute video on Counter: https://www.youtube.com/watch?v=45s8S3ixURk Thank you for your patience and understanding. - The Mobilus team -- [PREVIOUS MESSAGE HISTORY] Original announcement: Please note that Counter Database Reports and Title Reports are currently experiencing technical issues, and our team is working on restoring all functionality as soon as possible. We have disabled usage data extraction, while we're looking into the issue. If you need immediate access to usage reporting, please reach out to your SAE Mobilus sales representative. Thank you for your patience, and we apologize for the inconvenience.

[ARCHIVED] Important Notice: Sunset of Mobilus 3.0

[LATEST UPDATE] The legacy version of Mobilus (Mobilus 3.0) has now been officially sunset. Thank you for your valuable feedback and continued support in helping to improve Mobilus. - The Mobilus team -- [PREVIOUS MESSAGE HISTORY] IMPORTANT UPDATE: Sunset of Mobilus 3.0 (legacy version) We would like to inform you of an important update regarding SAE Mobilus - As part of our ongoing commitment to enhancing your experience, the legacy version of the platform (Mobilus 3.0) will be phased out on October 31, 2024. The current website with the newly redesigned interface introduced back in April, featuring an intuitive interface, improved search capabilities, advanced search, and in-depth browsing capabilities, will continue to remain operational going forward to support your needs. What You Need to Know: Last Day of Service (Mobilus 3.0): October 31, 2024 Content Access: You will retain access to all subscribed content through your institutional subscription. Personal Preferences Migration: While most settings tied to your Dasboard in Mobilus 3.0 (alerts, annotations, folders) will transfer over to the new platform, saved searches will not be retained and will need to be recreated in the new platform through your complimentary My Library account. Access the New Platform: Your institutional preferences around login (IP authentication, SSO, username/password) will be retained. We appreciate your understanding during this transition. Thank you for your continued support. If you have any questions, please don’t hesitate to contact us. Answers to common questions / FAQs are accessible here. Best regards, The Mobilus Team SAE International

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Recent SAE Edge™ Research Reports

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Wing-in-Ground Effect Vehicles: From Modern Military and Commercial Development to Global Disaster Response

EPR202501711/24/2025

The wing-in-ground effect (WIG) vehicle represents a significant advancement in aerodynamics and vehicle design, leveraging the ground effect phenomenon to enhance lift and reduce drag when flying close to the surface. This unique capability allows WIG vehicles to achieve higher payloads, longer range, and greater fuel efficiency compared to traditional aircraft, making them an attractive option for modern military and global disaster response applications. Wing-in-Ground Effect Vehicles: From Modern Military and Commercial Development to Global Disaster Response discusses future disaster response, logistics, and military applications for WIG vehicles, including the ongoing development of aerospace and transportation technology. Relavant advancements in materials and propulsion systems holds promise for further enhancing WIG performance and operational range. Additionally, cost-effective and powerful flight computers with various types of mission-enabling sensor suites from the

Next-gen AI for Aerospace Engineering

EPR202501609/22/2025

Despite growing investments, the widespread adoption and scalable deployment of generative artificial intelligence (AI) remains a challenge due to data trustworthiness, regulatory uncertainty, interpretability, and ethical governance. The need to accelerate automation and maintain the human-in-the-loop demonstrates broader questions of responsibility and transparency. Next-gen AI for Aerospace Engineering investigates the transformative role of GenAI within aerospace engineering, examining its shift from conventional workflows toward more AI-driven solutions in design, manufacturing, and maintenance. It emphasizes GenAI’s emerging ability to automate repetitive mundane tasks, reduce design complexity, and optimize engineering pipelines. The report underscores the need for validation methods that must align AI-generated outputs with physics-informed models, integration with legacy engineering tools (e.g., computational fluid dynamics, finite element analysis, digital twins), and

Sustainable Circular Future Mobility: Environmental Impact of Next-gen Vehicles

EPR202501509/22/2025

The next generation of mobility, driven by shared, driverless, connected, and electrified vehicles, holds strong potential to advance sustainability through lower emissions and improved resource efficiency. However, critical questions remain regarding their true environmental impact, including battery lifecycle management, material consumption, and circular manufacturing practices. Sustainable Circular Future Mobility: Environmental Impact of Next-gen Vehicles explores these unresolved issues, focusing on the shift from internal combustion to electric vehicles, supply chain challenges, regulatory gaps, and the operational realities of sustainable productization. It also critically examines the risks of greenwashing, the need for consistent standards, and the role of intersectoral collaboration—with energy, urban planning, information and communications technologies, and waste management sectors—in building resilient, scalable solutions. The report provides strategic recommendations and

Small, Mobile, and Autonomous Agricultural Robots

EPR202501208/13/2025

The automation of labor-intensive picking and planting operations is having an immediate impact in the agricultural indutry. In its simplest form, robotic automation can reduce the labor and soil disturbance while enabling organic soil cover and increasing species diversification through precision approaches to planting, weeding, and spraying. With this, pesticides and fertilizers can be applied in a more targeted way, and with machinery visiting fields more frequently, earlier and more targeted intervention can occur before pests become established. Small, Mobile, and Autonomous Agricultural Robots identifies issues that need to be resolved fo for this technology to thrive, including improving methods of acquiring and labeling training data to facilitate more accurate models for specific applications. It also discusses concepts such as general-purpose mechanical platforms for use as carriers of agricultural automation systems with high stability, positional accuracy, and variable

Safety Impacts of Active Safety and Driving Automation Features

EPR202501308/13/2025

Advanced technologies that assist the human driver or reduce (or even eliminate) the human driver’s role are becoming increasingly prevalent in new light-duty vehicles used by the general public. These technologies are divided between Active Safety features that monitor the human driver and vehicle motion and act intermittently to mitigate and avoid crashes, and Driving Automation features that assume some or all of the dynamic driving task from the human driver. Both types of technologies have the potential to reduce injuries and save lives by reducing the frequency and/or severity of crashes. Safety Impacts of Active Safety and Driving Automation Features addresses the current capabilities and future potential for Active Safety and Driving Automation features to reduce crash frequency and severity and provides an overview of the state of the industry for both types of features, including current deployments, trends, and anticipated rollouts. Gaps in knowledge, unsettled issues, and

Hybrid Data- and Physical Model-driven Safe and Intelligent Motion Planning and Control for Autonomous Vehicles

EPR202501407/18/2025

Autonomous vehicle motion planning and control are vital components of next-generation intelligent transportation systems. Recent advances in both data- and physical model-driven methods have improved driving performance, yet current technologies still fall short of achieving human-level driving in complex, dynamic traffic scenarios. Key challenges include developing safe, efficient, and human-like motion planning strategies that can adapt to unpredictable environments. Data-driven approaches leverage deep neural networks to learn from extensive datasets, offering promising avenues for intelligent decision-making. However, these methods face issues such as covariate shift in imitation learning and difficulties in designing robust reward functions. In contrast, conventional physical model-driven techniques use rigorous mathematical formulations to generate optimal trajectories and handle dynamic constraints. Hybrid Data- and Physical Model-Driven Safe and Intelligent Motion Planning and

Product Assurance in the Age of Artificial Intelligence

EPR202501105/21/2025

Driven by the vast consumer marketplace, the electronics megatrend has reshaped nearly every sector of society. The advancements in semiconductors and software, originally built to serve consumer demand, are now delivering significant value to non-consumer industries. Today, electronics are making inroads into traditionally conservative, safety-critical sectors such as automotive and aerospace. In doing so, electronics—now further propelled by artificial intelligence—are disrupting the functional safety architectures of these cyber-physical systems. Electronics have created the world of cyber-physical systems, raising broader concerns about the broader category of product assurance. Product Assurance in the Age of Artificial Intelligence continues the work of previous SAE Edge Research Reports in examining open research challenges arising from this shift, particularly in automotive systems, as core electronic technologies (e.g., the combination of software and communications) have even

Edge Artificial Intelligence in Connected, Cooperative and Automated Mobility

EPR202500905/20/2025

With many stakeholders involved, and major investments supporting it, the advancements in automated driving (AD) are undoubtedly there. Generally speaking, the motivation for advancing AD is driver convenience and road safety. Regarding the development of AD, original equipment manufacturers, technology start-ups, and AD systems developers have taken different approaches for automated vehicles (AVs). Some manufacturers are on the path toward stand-alone vehicles, mostly relying on onboard sensors and intelligence. On the other hand, the connected, cooperative, and automated mobility (CCAM) approach relies on additional communication and information exchange to ensure safe and secure operation. CCAM holds great potential to improve traffic management, road safety, equity, and convenience. In both approaches, there are increasingly large amounts of data generated and used for AD functions in perception, situational awareness, path prediction, and decision-making. The use of artificial

Leveraging Advanced Data Technologies for Smart Traffic Management

EPR202500805/20/2025

The transportation industry is transforming with the integration of advanced data technologies, edge devices, and artificial intelligence (AI). Intelligent transportation systems (ITS) are pivotal in optimizing traffic flow and safety. Central to this are transportation management centers, which manage transportation systems, traffic flow, and incident responses. Leveraging Advanced Data Technologies for Smart Traffic Management explores emerging trends in transportation data, focusing on data collection, aggregation, and sharing. Effective data management, AI application, and secure data sharing are crucial for optimizing operations. Integrating edge devices with existing systems presents challenges impacting security, cost, and efficiency. Ultimately, AI in transportation offers significant opportunities to predict and manage traffic conditions. AI-driven tools analyze historical data and current conditions to forecast future events. The importance of multidisciplinary approaches and

Automated Vehicles and Infrastructure Enablers: Dedicated Lanes

EPR202500704/24/2025

Dedicated lanes provide a simpler operating environment for ADS-equipped vehicles than those shared with other roadway users including human drivers, pedestrians, and bicycles. This final report in the Automation and Infrastructure series discusses how and when various types of lanes whether general purpose, managed, or specialty lanes might be temporarily or permanently reserved for ADS-equipped vehicles. Though simulations and economic analysis suggest that widespread use of dedicated lanes will not be warranted until market penetration is much higher, some US states and cities are developing such dedicated lanes now for limited use cases and other countries are planning more extensive deployment of dedicated lanes. Automated Vehicles and Infrastructure: Dedicated Lanes includes a review of practices across the US as well as case studies from the EU and UK, the Near East, Japan, Singapore, and Canada. Click here to access the full SAE EDGETM Research Report portfolio.

Latest Journal Issues

Browse All 16

Passenger Vehicle Systems

Electrified Vehicles

Advances and Current Practices in Mobility

Sustainable Transportation, Energy, Environment, & Policy

Connected and Automated Vehicles

Transportation Cybersecurity and Privacy

Vehicle Dynamics, Stability, and NVH

Transportation Safety

Alternative Powertrains

Commercial Vehicles

Engines

Materials and Manufacturing

Fuels and Lubricants

Aerospace

Passenger Cars - Mechanical Systems

Recent Books

Browse All 718

Electrification: Vol. 3

EPRCOMPV04202512/29/2025

Safety, ADAS, and Cybersecurity: Vol. 4

EPRCOMPV01202512/29/2025

Commercial Vehicles: Vol. 2

EPRCOMPV05202512/29/2025

Infrastructure Enablers for Automation: Vol. 4

EPRCOMPV02202512/29/2025

Automated Vehicles: Development, Testing, and Validation: Vol. 4

EPRCOMPV03202512/29/2025

Manufacturing and Industry 4.0 : Vol. 2

EPRCOMPV06202512/29/2025

Aerospace Developments: Vol. 7

EPRCOMPV07202512/29/2025

The Racing & High-Performance Tire

R-59412/17/2025

The Racing & High-Performance Tire originally published in 2003 and continues to be a best-seller. Following up on his first two books, Inside Racing Technology and Inside Racing: A Season with the PacWest CART Team, Paul Haney presents a new book revealing the complexities of tires, how they influence vehicle dynamics, and how to use tires in the engineering of racing and high-performance cars. More than 150 photos and drawings help the reader learn the colorful history and unique characteristics that make the pneumatic tire one of the world's most complex and useful products.

Manufacturing Reliability Growth for Automotive Engineering Volume III

R-55112/11/2025

As the third installment in SAE International's DOE for Product Reliability Growth series, this volume explores the critical intersection of manufacturing processes, reliability, and profitability in automotive engineering. With a sharp focus on innovation, cost-efficiency, and customer satisfaction, this book demonstrates how design of experiments (DOE) methodologies can transform product quality through smarter manufacturing decisions. From structural assemblies to additive manufacturing, Volume III offers hands-on, data-driven guidance for engineers and professionals. Key topics include: • Structural Joints: Analyze and optimize bonded and mechanical joints-bolts, rivets, welds, adhesives, and more-using real-world DOE case studies to ensure robust assembly. • Mechanical Fabrication: Improve precision and consistency in processes like injection molding, drilling, heat treatment, and surface finishing through targeted DOE applications. • Electronic Fabrication: Harness DOE to enhance

Pedestrian Accident Reconstruction

R-59010/30/2025

As technology advances, so too must the methods we use to investigate and understand pedestrian collisions. Pedestrian Accident Reconstruction provides a much-needed update to the field—one that reflects the dramatic rise in digital evidence and the growing role of simulation in modern reconstructions. While earlier texts laid the foundation for understanding pedestrian impacts, they were written before the widespread use of video footage, onboard vehicle sensors, and powerful simulation software. This book addresses that shift. It introduces updated methods that incorporate event data recorders, surveillance video, dash cams, and advanced simulation tools like the PC-Crash multibody pedestrian model. These technologies allow for more precise, repeatable, and illustrative reconstructions than ever before. The book highlights when and how simulation can enhance an investigation—especially in complex scenarios such as roof vaults, sideswipes, or collisions that defy conventional

Recently Published

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A Study on Combustion Process in Hydrogen/Hydrotreated Vegetable Oil Dual-Fuel Operation Using Hydroxyl Radical Chemiluminescence

03-18-08-0050

01/07/2026

This study investigated the combustion processes in hydrogen dual-fuel operation using hydrotreated vegetable oil (HVO) and diesel fuel as pilot fuels. The visualizations of hydrogen dual-fuel combustion processes were conducted using hydroxyl radical (OH*) chemiluminescence imaging in an optically accessible rapid compression and expansion machine (RCEM), which can simulate a compression and expansion stroke of a diesel engine. Pilot injection pressures of 40 and 80 MPa and injection quantities of 3, 6 mm3 for diesel fuel and to match the injected energy, 3.14, 6.27 mm3 of HVO were tested. The total excess air ratio was kept constant at 3.0. The RCEM was operated at a constant speed of 900 rpm, with in-cylinder pressure at top dead center (TDC) set to approximately 5.0 MPa. Results demonstrated that using HVO as pilot fuel, compared to diesel fuel, led to shorter ignition delay and combustion duration. OH* chemiluminescence imaging revealed that longer ignition delays observed with

Mukhtar, Ghazian Amin, Une, Naoto, Horibe, Naoto, Hayashi, Jun, Kawanabe, Hiroshi, Hiraoka, Kenji, Koda, Kazuyuki

Architecting ATA 28 with Model-Based Systems Engineering: Center of Gravity Balance and System Performance

01-18-03-0015

01/07/2026

The fuel management system for a fixed-wing aircraft has been developed and explored with the model-based systems engineering (MBSE) methodology for maintaining the center of gravity (CoG) and analyzing flight safety. The system incorporates high-level modeling abstractions that exploit a mix of behaviors and physical detail resembling real-world components. This approach enables analysis for a multitude of system requirements, verification, and failure scenarios at high simulation speed, which is necessary during system definition. Initially, the CoG is maintained by directly accessing the flight deck valves and pumps in both wings and controlling them through the bang-bang control law. In the refinement phase of the fuel system controller, the manual and individual controls of the valves and pumps are replaced with an autonomous fuel transfer scheme. The autonomous scheme achieves no more than a 20 kg difference in fuel between the wings during normal conditions. In the event of

Zaidi, Yaseen, Michalek, Ota

Weaving Data Fabric with AI

TBMG-54430

01/06/2026

One of the biggest goals for companies in the field of artificial intelligence (AI) is developing “agentic” systems. These metaphorical agents can perform tasks without a guiding human hand. This parallels the goals of the emerging urban air mobility industry, which hopes to bring autonomous flying vehicles to cities around the world. One company wants to do both and got a head start with some help from NASA.

Graphite Coating, Thin Lubricating Film, Impingement Applied

AMS2525F (Current)

01/05/2026

This specification covers a coating consisting of finely powdered graphite in a heat-resistant inorganic binder applied to parts.

AMS B Finishes Processes and Fluids Committee

5Ws of Unloading Robots

TBMG-54429

01/02/2026

SAE TOMORROW TODAY - An Inside Look at the 2026 Detroit Auto Show

1354901/02/2026

From surprise unveilings to one-of-a-kind experiences, excitement is building for the 2026 Detroit Auto Show. But it's about more than just horsepower. This year, SAE International is co-sponsoring Media and Industry Days, where OEMs, suppliers, tech leaders, and students come together for vehicle debuts, industry insights, and future-focused conversations. Listen in as Sam Klemet, Executive Director of the Detroit Auto Dealers Association, discusses what visitors can expect this year, including new STEM-driven, career-focused activities designed to inspire future engineers. He also highlights the show's multi-million-dollar philanthropic and economic impact, and how its growing emphasis on education, talent development, and innovation continues to reflect the true spirit of the Motor City. 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

Patterson, Lori

On Optima Selection Strategies of Adversarial Swarm Learning for Energy Management of PHEVs

2025-01-7320

12/31/2025

Developing robust optimization and learning methods is necessary for intelligent vehicles since an increasing number of critical control functions will be handled by artificial intelligence. This paper proposes an adversary swarm learning (ASL) system and an optima selection strategy for robust energy management of plug-in hybrid electric vehicles (PHEVs). The proposed ASL system comprises an attacking swarm and a defending swarm, which compete against each other iteratively to derive the most robust equivalent consumption minimization strategy (ECMS) for PHEV energy management. During the attacking rounds, the ECMS settings are fixed by the defender. Meanwhile, the attacker generates worst-case driving conditions by training a model in order to Maximize the equivalent energy consumption. During the defending rounds, the ECMS settings are optimized by the defender based on the driving scenarios generated by the attacker. The settings of robust ECMS are derived by introducing the

Zhong, Danyang, Yu, Zhuoping, Xiong, Lu, Zhou, Quan

Ideal Skyhook-Groundhook Model Informed Wheelbase-Preview Controller for Semi-Active Suspension System

2025-01-7325

12/31/2025

The suspension system, as a critical component of vehicle chassis, connects body frame and wheels, therefore affecting the ride comfort and handing stability of vehicles. To prevent high-frequency oscillations from large control increments of traditional algorithms, an ideal reference model is introduced to ensure a more smooth and efficient suspension responses that align with actual physical characteristics. The ideal skyhook, ideal groundhook, and ideal skyhook-groundhook models are evaluated with respect to their frequency response. As a result, the optimal configuration-ideal skyhook-groundhook model, exhibits the best overall performance and is incorporated with wheelbase preview mechanism as reference model (WP-SHGH). Further, a wheelbase-preview controller based on MPC framework (WPMPC-SHGH) is developed to regulate the responses of semi-active suspension. The Adams/Car-Simulink co-simulation platform is built for validation and comparison on the impact and ISO-B random roads

Yang, Li, Wang, Qingyun, Tan, Kanlun, Chen, Hao, Zhang, Zhifei

Research and Implementation of a Neural Radiance Fields-Based 3D Reconstruction Method for Autonomous Driving Scenes

2025-01-7310

12/31/2025

This paper addresses the scarcity of training and testing data in autonomous driving scenarios. We propose a 3D reconstruction framework for autonomous driving scenes based on Neural Radiance Fields (NeRF). Compared to traditional multi-view geometry methods, NeRF offers superior scene representation and novel view synthesis capabilities but suffers from low training efficiency and limited generalization. To overcome these limitations, we integrate existing NeRF optimization techniques and introduce a scene-specific data reuse strategy tailored for autonomous driving, enabling continuous 3D reconstruction directly from 2D images without requiring elaborate calibration. This approach significantly improves reconstruction efficiency, achieving reliable reconstruction and real-time visualization in complex traffic environments. Furthermore, we develop an interactive scene editing plugin in Unreal Engine 5, supporting the addition, removal, and adjustment of static objects. This extension

Pan, Deng, Zou, Jie, Chen, Yuhan, Meng, Zhangjie, Li, Jie, Li, Guofa

Modeling on Self-Learning Driver Model under Extreme Driving Conditions

2025-01-7315

12/31/2025

Ensuring the safe and stable operation of autonomous vehicles under extreme driving conditions requires the capability to approach the vehicle’s dynamic limits. Inspired by the adaptability and trial and error learning ability of expert human drivers, this study proposes a Self-Learning Driver Model (SLDM) that integrates trajectory planning and path tracking control. The framework consists of two core modules: In the trajectory planning stage, an iterative trajectory planning method based on vehicle dynamics constraints is employed to generate dynamically feasible limit trajectories while reducing sensitivity to initial conditions; In the control stage, a neural network enhanced nonlinear model predictive controller (NN-NMPC) is designed, which incorporates a self-learning mechanism to continuously update the internal vehicle model using trial-and-error data on top of mechanistic physical constraints, thereby improving predictive accuracy and path-tracking performance. By combining

Zhang, Xinjie, Xu, Long, Guo, Konghui, Zhuang, Ye, Hu, Tiegang, Mao, JingGang, Zeng, Qingqiang

STGT-Gen: Spatio-Temporal Graph Transformer for Multi-Vehicle Traffic Scenario Generation

2025-01-7316

12/31/2025

In the testing and validation of autonomous driving systems, scenario-based simulation is crucial to address the high costs and insufficient scene coverage of real-road testing. However, existing simulators rely on handcrafted rules to generate traffic scenarios, failing to capture the complexity of multi-agent interactions and physical rationality in real traffic. This paper proposes STGT-Gen, a data-driven Spatio-Temporal Graph Transformer framework, to generate realistic and diverse multi-vehicle traffic scenarios by integrating spatio-temporal interaction modeling, physical constraints, and high-definition (HD) map information.STGT-Gen adopts an encoder-decoder architecture: The encoder captures temporal dependencies of vehicle trajectories and spatial interactions via a Temporal Transformer and a Spatial Graph Transformer, respectively, while a hierarchical map encoding module fuses lane topologies and traffic rules. The decoder ensures physical feasibility during long-term

Qin, Xupeng, Lu, Chao, Wei, Yangyang, Fan, Sizhe, Song, Ze, Gong, Jianwei

RainSense: An Autonomous Driving Environmental Perception Dataset with Rain Intensity Annotations

2025-01-7311

12/31/2025

Rainfall, as a common trigger condition in the Safety of the Intended Functionality (SOTIF) framework, can impair autonomous driving perception systems, leading to unexpected functional failures. However, studies focusing on sensor performance degradation under natural rainfall conditions are limited, primarily due to the lack of datasets with detailed rainfall information. To address this gap, this study present RainSense, a multi-sensor autonomous driving dataset collected under natural rainfall conditions, featuring fine-grained rainfall intensity annotations. RainSense was recorded at nine representative intersection scenarios in the campus, where a single dummy target was placed at various distances as a detection target. A laser-optical disdrometer was deployed to continuously measure rainfall intensity (mm/h), while camera images, lidar point clouds, and 4D radar data were synchronously collected under different rainfall levels. In total, the dataset comprises 728 cases

Xia, Tian, Yang, Xingbo, Chen, Tianrui, Zhang, Longgao, Ye, Shaolingfen, Chen, Junyi

Design and Implementation of a Multimodal Driver Data Collection and Processing Apparatus for Naturalistic Driving Scenarios

2025-01-7346

12/31/2025

As intelligent cockpit technology continues to evolve, the ways in which information is presented and interacted with within vehicle systems are becoming increasingly diverse, driving the development of driver-machine interaction toward multi-modal integration, proactive sensing, and personalized responses. As the core perception object of the intelligent cockpit, the accuracy of driver state recognition directly impacts the intelligence level of cockpit interaction and driving safety. In response to the increasing trend of task diversity and behavioral response complexity in natural driving scenarios, there is an urgent need to develop a driver multimodal data collection and processing tool with high timeliness, non-intrusiveness, and multi-source synchronization capabilities, serving as the key foundation for driver state modeling and intelligent interaction support. Based on multiple resource theory (MRT) and driver status perception mechanisms, this study designs and develops a

Chen, Ke, Li, Xinyi, Cheng, Jiahao, Guo, Gang, Li, Wenbo

Distributed Robust Control of Heterogeneous Vehicular Platoons Based on IDM Following Model under Communication Delay

2025-01-7350

12/31/2025

With the rapid development of automobile industrialization, the traffic environment is becoming increasingly complex, traffic congestion and road accidents are becoming critical, and the importance of Intelligent Transportation System (ITS) is increasingly prominent. In our research, for the problem of cooperative control of heterogeneous intelligent connected vehicle platoons under ITS considering communication delay. The proposed method integrates the nonlinear Intelligent Driver Model (IDM) and a spacing compensation mechanism, aiming to ensure that the platoon maintains structural stability in the presence of communication disturbances, while also enhancing the comfort and safety of following vehicles. Firstly, construct heterogeneous vehicle platoon system based on the third-order vehicle dynamics model, Predecessor-Leader-Following (PLF) communication topology, and the fixed time-distance strategy, while a nonlinear distributed controller integrating the IDM following behavior

Ye, Xin, Kang, Zhongping

A Unified Layer-by-Layer Progressive Framework for Sensorless Control of Brake-By-Wire Systems

2025-01-7328

12/31/2025

Conventional control of Brake-by-Wire (BBW) systems, including electro-hydraulic brake(EHB) and electro-mechanical brake(EMB), relys on pressure sensors, the errors of which usually resulted inaccurate braking force tracking bringing a lot of safety hazards, e.g., wheel locking and slipping. To address challenges of accurate braking force control under the circumstance of the system nonliearities (such as friction) and uncertainties (such as stiffness characteristics) for a sensorless BBW system, this paper proposes a unified Layer-by-Layer Progressive (LLP) control framework to enable fast and precise brake control. The work has been conducted with three new contributions in the three cascaded stages within the control framework: in the coarse compensation stage, a load-adaptive LuGre friction model is proposed to handle modellable nonlinearities; in the fine compensation stage, an Adaptive Extended Disturbance Observer (AEDO) is developed to estimate and compensate for parameter

Zhou, Quan, Lv, Zongyu, Han, Wei, Li, Congcong, Zhao, Xinyu, Xiong, Lu, Shu, Qiang

Kinematics-Based Differential In-Situ Steering Control for Distributed-Drive Electric Vehicles

2025-01-7333

12/31/2025

In-situ steering can significantly improve the vehicle's maneuverability in narrow spaces, especially suitable for extreme scenarios such as off-road driving and professional operations. For distributed drive electric vehicles, kinematics-based left and right wheel differential control and dynamics-based vehicle yaw control can achieve in-situ steering, however, the two methods have different effects on in-situ steering performance. This paper proposes a kinematics-based distributed drive electric vehicle differential in-situ steering control method, which first establishes the functional relationship between the drive pedal and the expected yaw rate, so that the driver can adjust the steering speed. The initial reference wheel speed is calculated from the expected yaw rate, and the reference wheel speed is adjusted by feedback from the actual and expected yaw rate errors to improve the tracking accuracy. On this basis, the sliding mode control algorithm is used to calculate the

Chen, Jingxu, Li, Yang, Zhang, Yi, Zhao, Hongwang, Qiao, Miaomiao, Wang, Beibei, Wu, Dongmei

Dynamic Switching Control of Vehicle Platoon with Kalman Filter Compensator Considering Communication Delays and Packet Dropouts

2025-01-7341

12/31/2025

This paper presents a dynamic switching control strategy for vehicle platoons to address communication delays and packet dropouts in connected and autonomous vehicle systems. The proposed strategy combines adaptive cruise control (ACC), cooperative adaptive cruise control (CACC), and a Kalman filter to compensate for time-varying delays, while employing an equidistant spacing policy to support reliable information flow within the platoon. A switching mechanism based on an acceleration threshold enables seamless transition between ACC, which depends on onboard sensor data, and CACC, which relies on vehicle-to-vehicle (V2V) communication. This design reduces dependence on V2V communication, thereby lowering the risk of packet dropouts and improving platoon stability. The control architecture adopts a hierarchical structure: an upper-level sliding mode controller generates desired acceleration commands, and a lower-level PID controller converts them into throttle and brake actions. A

Pan, Deng, Yao, Zhiyong, Wang, Chang, Ji, Jie, Zhang, Bohan

Thin-Walled Panel Noise Suppression for EV Car through Acoustic Black Hole-Induced Energy Localization

2025-01-7342

12/31/2025

Under vehicle lightweighting constraints, acoustic black hole (ABH) structures offer novel vibration and noise control through bending wave manipulation. This study investigates non-ideal ABH plates with truncations, analyzing their energy-trapping efficacy and damping performance. A hybrid FE-SEA model evaluates ABH-embedded electric vehicles, revealing critical insights: Through-hole truncations concentrate energy at tips (increasing fracture risk), while smaller circular-platform radii significantly enhance energy trapping and damping. For noise reduction, peak effectiveness occurs at 300–800 Hz, achieving 3.7 dB attenuation at 500 Hz (front) and 2.8 dB at 700 Hz (rear) with 4 ABHs. Increasing ABH count improves suppression by ≤3 dB. This work establishes a predictive framework for optimizing ABH-enhanced NVH performance in electric vehicles.

Zhang, Yunfei, Wang, Huixuan, Long, Yifan, Wang, Jing, Yang, Shuai

LWMOcc: Lightweight Monocular 3D Occupancy Prediction Method

2025-01-7312

12/31/2025

Environmental perception is the base of autonomous driving systems, and it directly affects both operational safety and intelligent decision-making capability. Among the emerging technologies, vision-based 3D occupancy prediction is gaining more attention because of its high cost-effectiveness and high-resolution scene understanding capability. However, existing methods often have too much model complexity and limited inference efficiency, which makes deployment on resource-constrained embedded platforms difficult. To address the limitations, we propose LWMOcc, a lightweight monocular 3D occupancy prediction framework. The main component of LWMOcc is the lightweight Encoder-Decoder module, which is a lightweight fine-grained scene perception module that combines a simplified backbone with an efficient decoding strategy. By performing structural simplification and parameter compression, LWMOcc effectively reduces computational overhead, while retaining high predictive accuracy

Chen, Feiyang, Li, Jihao, Fu, Pengyu, Hu, Jincheng, Liu, Ming, Liu, Chengjun, Hong, Yinuo, Cazorla, Miguel, González Serrano, Germán, Zhang, Yuanjian, Cadini, Francesco

Corner Scenario Generation Method Based on Feature-Optimized Combinations for Automated Driving Systems

2025-01-7317

12/31/2025

With the advancement of automated driving system levels, corner scenarios characterized by low probability and high risk have become critical for the safety validation of automated vehicles. However, due to the typical long-tail distribution of such scenarios, data-driven mining approaches face significant challenges in achieving efficient generation. To address this issue, this study proposes a feature-optimized combination-based method for generating corner scenarios in automated driving systems. Key scenario features related to functional failures are first identified using a combined approach of system theoretic process analysis (STPA) and hazard and operability analysis (HAZOP). Based on these features, an adaptive genetic algorithm is employed to optimize feature combinations and generate large numbers of corner scenario types that meet specified constraints. The proposed method is validated using cut-in and pedestrian-crossing scenarios as baseline cases. The results show that

Zhou, Shiying, Zhang, Dongbo, Zhao, Deyin, Zhu, Bing, Zhang, Peixing

STO-VINS: A Robust Monocular Visual-Inertial SLAM Based on a Fusion Feature Extraction Algorithm Combining Shi-Tomasi and ORB

2025-01-7306

12/31/2025

The VINS-Mono algorithm, which is based on a visual-inertial SLAM framework, faces challenges in extracting feature points in regions with weak or repetitive textures and struggles to achieve accurate localization under unstable lighting conditions. This paper proposes STO-VINS, a robust monocular visual-inertial SLAM algorithm that introduces several key innovations in feature extraction. Key innovations of STO-VINS include: (1) an adaptive multi-scale image preprocessing pipeline that combines image scaling, CLAHE enhancement, and Gaussian filtering, reducing computational complexity by 64% while maintaining feature quality; (2) bidirectional Lucas-Kanade optical flow consistency verification with geometric constraint validation, which significantly reduces false tracking rates by 30-40%; (3) a grid-based multi-feature fusion detection strategy combining Shi-Tomasi corner detection and ORB feature extraction, ensuring uniform spatial distribution of features and feature diversity; (4

Li, Jing, Wu, Jing, Liu, Bo, Gong, Zeyuan, Zhang, Guofang

CANet: A Lightweight Cross-Attention Guided Network for Unstructured Road Segmentation

2025-01-7309

12/31/2025

To address the issues of multiple background interferences and blurred road boundaries in unstructured scene road segmentation tasks, a lightweight and precise unstructured road segmentation model based on cross-attention (CANet) is proposed. This model constructs an encoder using the lightweight neural network MobileNetV2. By doing so, it ensures light weight while enhancing the feature discrimination ability of unstructured roads, thus achieving efficient feature extraction. The decoder integrates the cross-attention mechanism and a low-level feature fusion branch. The attention mechanism improves the model’s perception of road boundaries by capturing long-distance context information in the feature map, thereby solving the problem of blurred edges. The low-level feature fusion branch enhances the detail accuracy and edge continuity of the segmentation results by incorporating high-resolution information from shallow features. Experimental results show that the proposed model attains

Wang, Xuewei, Cao, Guangyuan, Liang, Xiao, Li, Shaohua

Centralized Control of Distributed Drive Steer-by-Wire Chassis Considering Tire Force Zoning Constraints

2025-01-7329

12/31/2025

Distributed drive steer-by-wire chassis has significant potential for enhancing vehicle dynamics performance, while also presenting great challenges to vehicle dynamics control. To address the coordination among multiple chassis subsystems and the coupled control allocation of longitudinal and lateral tire forces, this paper proposes a centralized control framework based on optimal yaw moment control. By analyzing the impact of longitudinal and lateral tire forces on vehicle yaw moments, a method for allocating longitudinal and lateral forces with maximum yaw moment as the objective is proposed. On this basis, a hierarchical control architecture is designed, including the driver control layer, motion control layer, tire force allocation layer, and actuator execution layer, to achieve centralized domain control of longitudinal and lateral dynamics in distributed drive steer-by-wire chassis. Finally, the proposed centralized controller is validated using offline simulation and real-time

Wu, Dongmei, Guo, Chunzhi, Liu, Changsheng, Xia, Xin, Li, Miao, Liu, Wei

Mode Identification and Collaborative Switch for the Front-Centralized and Rear-Distributed Electric Drive Vehicle Based on BP Neural Network

2025-01-7335

12/31/2025

Distributed-drive electric vehicles (DDEVs) significantly enhance off-road maneuverability but suffer from compromised high-speed stability and robustness. This research introduces a front-centralized and rear-distributed (FCRD) architecture that synergistically leverages the advantages of each configuration. The electric-drive-wheel (EDW) on the rear suspension can provide three working modes: (a) Drive-connected mode, (b) Drive-disconnected mode, (c) Brake mode. It is the key actuator for vehicle mode-switching, which supports the vehicle with three driving modes: (a) DDEV, (b) front-wheel drive (FWD), (c) all-wheel drive (AWD). A hierarchical control architecture employs the upper-layer controller with Back Propagation Neural Network (BPNN) for mode identification and decision-making. The lower-layer controller enables the intelligent torque distribution and collaborative control of the motors. The control strategy is pre-trained in the VCU (vehicle control unit) with off-line data

Ding, Xiaoyu, Chen, Xinbo, Wang, Wei, Zhang, Jiantao, Kong, Aijing

Driver Personalized Lane Change Behavior Analysis Based on Attention Deep Embedding Clustering

2025-01-7347

12/31/2025

The characteristic representation and in-depth understanding of driver personalized driving behavior are fundamental to achieving human-like autonomous driving, enhancing the rationality of autonomous driving decisions, and meeting passengers’ personalized needs. [ADDED]Personalized driving behavior refers to individual-specific patterns in vehicle operation that emerge from drivers’ unique combinations of skills, risk tolerance, and habitual responses.However, current research lacks consideration of cluster analysis in the feature representation stage and ignores the time-varying contribution degree of time series values to low-dimensional features, which inhibits further utilization and development. This study adopts deep embedding clustering method and introduces attention mechanism to investigate driver personalized high-speed lane change behavior.[ADDED] Using a comprehensive driving simulator platform, we collected 15-channel time series data from 12 drivers performing 216 lane

Dong, Haomin, Wang, Wei, Wang, Yue, Li, Lun, Yue, Yi, Tian, Jiaxiao, Han, Jiayi

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