Browse Topic: Driving automation

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Automated Design Sustainability Evaluation Framework: a CAD-Integrated Tool for Real-Time Costing and LCA in Automotive Engineering2026-37-0044To be published on 06/09/2026
As the automotive industry faces increasingly rigorous environmental regulations and an approaching obligation for Digital Product Passports (DPPs), incorporating sustainability metrics into the early design phase has become a necessity. Traditionally, Life Cycle Assessment (LCA) and manufacturing cost estimation are performed during or after the design phase using specific methods and tools, resulting in costly iterations and delayed decision-making. This paper introduces a preliminary computational tool that combines 3D CAD and spreadsheet software via VBA integration. The framework automates the generation of an “Extended Bill of Materials” by extracting geometric and manufacturing data directly from CAD models. This tool’s classification logic is a key innovation that intelligently processes CAD features to identify component categories, such as sheet metal, machined parts, or plastic injections. This automated recognition allows the framework to implement specific algorithmic
Guadagno, Maurizio, Cecconi, Leonardo, Berzi, Lorenzo, Delogu, Massimo
SAE TOMORROW TODAY - Building Trust in AV Safety135644/17/2026
As AVs continue to grow in popularity, one question remains top of mind: How do we know autonomous driving systems are genuinely safe? The Automated Vehicle Safety Consortium (AVSC) is an industry collaboration group focused on improving the safe development and deployment of automated driving systems (ADS). By bringing together automakers, technology companies, suppliers, mobility providers, and government stakeholders, the AVSC develops voluntary best practices and technical guidance that fosters public trust and delivers consistent AV safety standards. Listen in as we sit down with Darcyne Foldenauer, Executive Director, AVSC, and Erin McCurry, Principal Engineer, AVSC, to explore two new publications: Best Practice for ADS-DV Assessment of Safety Claims, and the Information Report on ADS-DV Stopped Conditions. From the difference between minimal risk maneuvers and minimal risk conditions, to when it's actually safer for a vehicle to stay stopped in lane, this conversation sheds
Patterson, Lori
Automated Vehicle Marshalling SystemJ3292_202604 (Current)4/13/2026
The scope of this standard is Automated Vehicle Marshalling (AVM) of vehicles to enable remote control functionality for achieving SAE Level 4 (High Driving Automation according to the Surface Vehicle Recommended Practice SAE J3016) driving capabilities at controlled speeds within geofenced private controlled environments utilizing infrastructure-assisted sensing. It specifies a concept of operations which includes a reference-system architecture and use cases, system functional and performance requirements, multiple wireless communications protocols, and associated wireless messages to support AVM. AVM use cases such as plant marshalling, depot marshalling, valet parking, electric vehicle charging, etc. The Automated Vehicle Marshalling Central Server (AVM CS) infrastructure does detect objects, vehicles, vulnerable road users, and any obstructions that help guide the Automated Vehicle (AV) starting from uninitiated, activation, identification, automated control, unavailable and
V2X Core Technical Committee
The ML FMEA in Action: Lessons from Applications of Machine Learning Safety2026-01-00794/7/2026
Introducing machine learning (ML) into safety-critical systems presents a fundamental challenge, as traditional safety analysis techniques often struggle to capture the dynamic, data-driven, and non-deterministic behavior of learning-enabled components. To address this gap, the Machine Learning Failure Mode and Effects Analysis (ML FMEA) methodology was developed as an open-source framework tailored to ML-specific risks. This paper reports on the maturation of ML FMEA from an initial conceptual framework to a proven, practice-driven methodology. We make four primary contributions. First, we extend the ML FMEA pipeline with two new stages: a “Step Zero” for problem definition and system-level hazard analysis, and a “Step 5” for constructing ground truth or reward signals. Autonomous vehicle and humanoid robot applications are presented to illustrate the practical application and safety benefits of these additions. Second, we introduce tailored Severity, Occurrence, and Detection
Schmitt, Paul, Shinde, Chaitanya, Diemert, Simon, Pennar, Krzysztof, Seifert, Bodo, Poh, Justin, Lopez, Jerry, Mannan, Fahim, Mohammed, Majed, Chalana, Akshay, Wadhvana, Neil, Wagner, Michael
AI Model Creation and System Verification of “Telemotion” Infrastructure-Cooperative Autonomous Driving System Using Digital Twin2026-01-02554/7/2026
The concept of the vehicle has changed as a result of many innovations over the last decade in the fields of connected, autonomous/automated, shared, and electric (CASE) technologies. At the same time, labor shortages in Japan are becoming more serious due to a decline in the working population. To help resolve these issues, a remote-controlled autonomous vehicle driving system called Telemotion has been developed that automates the movement of vehicles in production plants. This system is an autonomous driving and transportation system in which the recognition, judgment, and operation functions of driving are handled by a control system outside the vehicle that communicates wirelessly with the vehicle. This system utilizes artificial intelligence (AI) and other advanced technologies to realize safe unmanned autonomous driving, and is already in operation in production plants. Currently, efforts are under way to build a digital twin environment and conduct AI learning using computer
Hatano, Yasuyoshi, Iwazaki, Noritsugu, Nagafuchi, Yuhei, Iwahori, Kento, Tanaka, Atsushi, Uezu, Satoru, Kanou, Takeshi, Inoue, Go, Okamoto, Yuki, Oka, Yuhei, Kakuma, Daisuke, Chiba, Hiroya, Egashira, Kazuki, Ishikuro, Megumi, Sawano, Takuro
Assessing a Safety Case: Bottom-up Guidance for Claims and Evidence Evaluation2026-01-05234/7/2026
As Automated Driving Systems (ADS) technology advances, ensuring safety and public trust requires robust assurance frameworks, with safety cases emerging as a critical tool toward such a goal. This paper explores an approach to assess how a safety case is supported by its claims and evidence, toward establishing credibility for the overall case. Starting from a description of the building blocks of a safety case (claims, evidence, and optional format-dependent entries), this paper delves into the assessment of support of each claim through the provided evidence. Two domains of assessment are outlined for each claim: procedural support (formalizing process specification) and implementation support (demonstrating process application). Additionally, an assessment of evidence status is also undertaken, independently from the claims support. Scoring strategies and evaluation guidelines are provided, including detailed scoring tables for claim support and evidence status assessment. The
Schnelle, Scott, Favaro, Francesca, Fraade-Blanar, Laura, Broce, Holland, Miranda, Justin, Wichner, David, Shrivastava, Mohit
Safety Monitor for Off-Road Planning with Uncertainty Bounded Bekker Costs2026-01-01414/7/2026
Reliable off-road autonomy requires operational constraints so that behavior stays predictable and safe when soil strength is uncertain. This paper presents a runtime assurance safety monitor that collaborates with any planner and uses a Bekker-based cost model with bounded uncertainty. The monitor builds an upper confidence traversal cost from a lightweight pressure sinkage model identified in field tests and checks each planned motion against two limits: maximum sinkage and rollover margin. If the risk of crossing either limit is too high, the monitor switches to a certified fallback that reduces vehicle speed, increases standoff from soft ground, or stops on firmer soil. This separation lets the planner focus on efficiency while the monitor keeps the vehicle within clear safety limits on board. Wheel geometry, wheel load estimate, and a soil raster serve as inputs, which tie safety directly to vehicle design and let the monitor set clear limits on speed, curvature, and stopping at
Naik, Akshay, Norris, William, Sreenivas, Ramavarapu S., Soylemezoglu, Ahmet, Nottage, Dustin S., Patterson, Albert
Collision Avoidance Testing of the Waymo Automated Driving System2026-01-05194/7/2026
This paper describes Waymo's Collision Avoidance Testing (CAT) methodology: a scenario-based testing method that evaluates the safety of the Waymo Driver Automated Driving Systems' (ADS) intended functionality in conflict situations initiated by other road users that require urgent evasive maneuvers. Because SAE Level 4 ADS are responsible for the dynamic driving task (DDT), when engaged, without immediate human intervention, evaluating a Level 4 ADS using scenario-based testing is difficult due to the potentially infinite number of operational scenarios in which hazardous situations may unfold. To that end, in this paper we first describe the safety test objectives for the CAT methodology, including the collision and serious injury metrics and the reference behavior model representing a non-impaired eyes on conflict human driver used to form an acceptance criterion. Afterward, we introduce the process for identifying potentially hazardous situations from a combination of human data
Kusano, Kristofer, Beatty, Kurt, Schnelle, Scott, Favaro, Francesca, Crary, Cam, Victor, Trent
Collision Avoidance Effectiveness of an Automated Driving System Using a Human Driver Behavior Reference Model in Reconstructed Fatal Collisions2026-01-05204/7/2026
Avoiding and mitigating any potential collision is dependent on (1) road user ability to avoid entering into a conflict (conflict avoidance effect) and (2) road user response should a conflict be entered (collision avoidance effect). This study examined the collision avoidance effect of the Waymo Driver, a currently deployed SAE level 4 automated driving system (ADS), using a human behavior reference model, designed to be representative of a human driver that is non-impaired, with eyes on the conflict (NIEON). Reliable performance benchmarking methodologies for assessing ADS performance are an essential component of determining system readiness. This consistently performing, always-attentive driver does not exist in the human population. Counterfactual simulations were run on responder collision scenarios based on reconstructions from a 10-year period of human fatal crashes from the Operational Design Domain of the Waymo ADS in Chandler, Arizona. Of 16 simulated conflicts entered, 12
Scanlon, John M., Kusano, Kristofer D., Engstrom, Johan, Victor, Trent
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
Development of a Model-Aware Data-Driven Control Algorithm for a Drive-by-Wire System for a Level 4 Automated Vehicle2026-01-00434/7/2026
Precision control in Level 4 Automated Vehicles is essential for enhancing operational efficiency, accuracy, and safety. This work, conducted as part of ARPA-E’s NEXTCAR program, focuses on developing a robust hardware and software control solution to enable drive-by-wire functionality. A previous publication by the authors presented the hardware solutions for overtaking stock vehicle controls. This paper focuses on a model-based and data-driven control algorithm to enable drive-by-wire functionality for longitudinal and lateral motion control for a 2021 Honda Clarity Plug-In Hybrid Electric Vehicle. This vehicle was equipped with a set of sensors and an onboard processing unit to enable Level 4 automation. For lateral controls, an algorithm was developed to command steering torque to the electronic power steering module, ensuring the vehicle could attain the desired steering angle position at varying speeds. The system leveraged feedforward and feedback mechanisms. Feedback controller
Adsule, Kartik, Bhagdikar, Piyush, Drallmeier, Joseph, Alden, Joshua, Gankov, Stanislav
Understanding Adversarial Transferability in Vision-Language Models for Autonomous Driving: A Cross-Architecture Analysis2026-01-01704/7/2026
Vision-language models (VLMs) are increasingly used in autonomous driving because they combine visual perception with language-based reasoning, supporting more interpretable decision-making, yet their robustness to physical adversarial attacks, especially whether such attacks transfer across different VLM architectures, is not well understood and poses a practical risk when attackers do not know which model a vehicle uses. We address this gap with a systematic cross-architecture study of adversarial transferability in VLM-based driving, evaluating three representative architectures (Dolphins, OmniDrive, and LeapVAD) using physically realizable patches placed on roadside infrastructure in both crosswalk and highway scenarios. Our transfer-matrix evaluation shows high cross-architecture effectiveness, with transfer rates of 73–91% (mean TR = 0.815 for crosswalk and 0.833 for highway) and sustained frame-level manipulation over 64.7–79.4% of the critical decision window even when patches
Fernandez, David, MohajerAnsari, Pedram, Salarpour, Amir, Pese, Mert D.
The Necessity of a Holistic Safety Evaluation Framework for AI-Based Automation Features2026-01-05224/7/2026
The intersection of Safety of Intended Functionality (SOTIF) and Functional Safety (FuSa) analysis of driving automation features has traditionally excluded Quality Management (QM) components from rigorous safety impact evaluations. While QM components are not typically classified as safety-relevant, recent developments in artificial intelligence (AI) integration reveal that such components can contribute to SOTIF-related hazardous risks. Compliance with emerging AI safety standards, such as ISO/PAS 8800, necessitates re-evaluating safety considerations for these components. This paper examines the necessity of conducting holistic safety analysis and risk assessment on AI components, emphasizing their potential to introduce hazards with the capacity to violate risk acceptance criteria when deployed in safety-critical driving systems, particularly in perception algorithms. Using case studies, we demonstrate how deficiencies in AI-driven perception systems can emerge even in QM
Abbaspour, Ali Reza, Mahadevan, Shabin, Zwirglmaier, Kilian, Stafford, Jeff
From Prediction to Design: Using Context-Aware Graph Neural Networks and Explainable AI to Anticipate Transfer-of-Control2026-01-00494/7/2026
Drivers often interact with partial automation (SAE Level 2) systems, initiating transfer of control (TOC) either by handing control over to the automation or by taking it back. Accurately predicting these interactions may inform the design of future automation systems that adapt proactively to the operating context, enhance comfort, and ultimately may improve safety. We present a context-aware framework that generates a unified driver–vehicle–environment representation by fusing data from in-cabin video of the driver and of the forward roadway with vehicle kinematics, driver glance, and hands-on-wheel behaviors. This representation was encoded in a hierarchical Graph Neural Network that classified driver-initiated TOCs to: (i) Manual-to-automation and (ii) Automation-to-manual transitions and predicted time-to-TOC. Shapley-based explainable AI was used to quantify how the importance of behavioral, contextual, and kinematic cues evolved in the seconds preceding a TOC. Analysis of a
Zhao, Zhouqiao, Gershon, Pnina
A Simple Analytical Model for Predicting Cooperative Ratio in Agreement-Seeking Cooperative Driving Automation with C-V2X Communication2026-01-00784/7/2026
Cooperative Driving Automation (CDA) has emerged as an active research area in recent years, categorized into four classes of operations with varying levels of cooperation as defined in the SAE J3216 standard. Among these, Class C CDA, referred to as Agreement-Seeking Cooperation (ASC), has received limited attention in literature. Unlike Cooperative Adaptive Cruise Control (CACC), which typically engages when lead vehicles are identified as cooperative and disagree under manual override or safety-critical conditions, ASC requires agents to exchange messages interactively to reach consensus on a proposed plan and its implementation. This necessitates more sophisticated communication and control designs, which in turn influences customized ASC efficiency. Previous work has examined, through simulation, the impact of three key parameters on ASC system performance: CDA message transmission frequency, Packet Drop Ratio (PDR), and Cooperation Duration Length (CDL). In this paper, we extend
Zhan, Lu, Di Russo, Miriam, Das, Debashis, Stutenberg, Kevin, Misra, Priyash, Jeong, Jongryeol, Hyeon, Eunjeong
Agile Safety Case Assessments for Autonomous Vehicle Fleets2026-01-05214/7/2026
Rapidly upcoming deployment of autonomous vehicles (AVs), including robotaxis and trucks, has intensified the need for rigorous safety assessment of complex AI-driven systems. While considerable effort has been invested in constructing safety cases for AVs, systematic approaches for evaluating these safety cases remain underdeveloped. This paper presents a three-stage methodology for assessing AV safety cases. A process for assessing argumentation is presented that involves traceability to pre-reviewed and peer-reviewed safety cases such as the Open Autonomy Safety Case (OASC). Next, we present a structured process for evaluating the quality of evidence supporting these arguments. We applied this methodology to evaluate safety cases from multiple AV developers, enabling iterative refinement throughout the development lifecycle. Our agile approach supports efficient assessments by establishing clear traceability to industry standards and enabling early identification of potential gaps
Wagner, Michael
Driving with Influence: Exploring Crash Factors of Automated Systems in Different Roadway Contexts2026-01-05264/7/2026
Although SAE Level 2 Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) have been shown to provide some safety benefits, they have largely been constrained to specific driving contexts, namely motorways for ADAS and lower speed roadways for ADS. As more advanced systems are entering the roadways and their operating conditions are expanding, it remains an ongoing challenge to assess the safe operation of vehicles with automation in different roadway contexts and leverage lessons learned from real-world incidents to create safer and more robust systems. As of August 2025, NHTSA’s Standing General Order on Crash Reporting offers systematic data on such incidents, providing at least a cursory overview of where and how they occur. From this source, a total of 1,375 crash records were extracted, 657 for ADAS systems and 715 for ADS systems. Through the application of association rule mining and a novel metric termed influence, patterns in ADAS- and ADS-related
Astle, W. Abram, Haus, Samantha
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
Developing Safety Case for Automated Driving following UL 4600 & ISO 50832026-01-05274/7/2026
Automated Driving Systems (ADS) rely on AI algorithms, machine learning, and sensor fusion to perform autonomous driving tasks. Safety challenges arise due to the probabilistic behavior of AI/ML algorithms and the need to ensure safety within defined Operational Design Domains (ODDs). Traditional standards such as ISO 26262[3] (Functional Safety) and ISO 21448[4] (SOTIF) address hardware and software failures or functional deficiencies but are insufficient for higher-level autonomous systems (SAE Levels 3–5). To close this gap, additional standards such as UL 4600[1] and ISO 5083[2] provide complementary frameworks for ADS safety assurance. UL 4600[1] establishes a claim-based safety case encompassing the vehicle, infrastructure, and processes, emphasizing structured arguments supported by evidence and reasoning. It offers guidance on autonomy functions, V & V, tool qualification, dependability, and safety culture. ISO 5083[2] focuses on design, verification, and validation of ADS
Mudunuri, Venkateswara Raju, Almasri, Hossam, Fan, Hsing-Hua
Development of Technology to Distinguish Contaminants using LiDAR Near-Field Point Cloud based on Real-Field Data2026-01-00204/7/2026
Reliable environmental perception under adverse and contaminated conditions is a critical requirement for autonomous driving systems. Although LiDAR sensors play a central role in such perception, their performance is significantly degraded by surface contamination caused by environmental factors such as rain, snow, dust, anti-icing materials, and bug splatter impacts. However, most existing public datasets and prior studies rely on simulated or laboratory-generated contamination scenarios, which limit their applicability to real-world autonomous driving. To address this gap, we construct a large-scale real-world dataset collected from approximately 22,000 km of on-road driving across diverse regions of the United States, covering a wide range of naturally occurring environmental contamination conditions. The dataset was acquired using a multimodal sensing platform integrating LiDAR, perception RGB cameras, infrared camera sensors, and external monitoring systems, enabling
Kim, Hunjae
Optimizing Virtual Scenario Testing for Autonomous Vehicles with Large-Scale Scenario Generation2026-01-00504/7/2026
The validation of Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) Systems, especially at higher automation levels such as SAE Level 3 or 4, demands the testing of a vast array of scenario variants far exceeding the scope of standard safety specifications like Euro NCAP (The European New Car Assessment Programme). Autonomous vehicles require thorough real-world testing to ensure automotive safety. However, public road tests are costly and risky. Instead, virtual scenarios - digital twins of real environments - offer a safe, cost-effective testing alternative. Exhaustive simulation across this high-dimensional scenario space, which includes variations in actor behavior, environmental conditions, and event characteristics, is computationally infeasible. We propose a constraint-solving approach to address this challenge that leverages mathematical and geometric techniques to analytically assess the existence and validity of scenario variants prior to simulation. Two
Karve, Omkar, Saurav, Saket, Purwar, Prabhanshu
Integration and Validation of Adaptive Cruise Control across Different Modes2026-01-00764/7/2026
This paper presents the integration and validation of Adaptive Cruise Control (ACC) algorithms on a student-team-developed vehicle as part of the U.S. Department of Energy EcoCAR EV Challenge. The competition provided each team with a 2023 Cadillac Lyriq, which was modified to an all-wheel-drive configuration and re-architected to support the development of SAE Level 3 autonomous features including Adaptive Cruise Control (ACC), Automatic Intersection Navigation (AIN), Lane Centering Control (LCC), and Automatic Parking (AP). The scope of this paper, however, is limited to the development, implementation, and validation of a Level 2 longitudinal ADAS function. Higher-level automation requirements such as Operational Design Domain (ODD) definition and Driver Monitoring System (DMS) enforcement are addressed at the vehicle architecture and competition level but are not the focus of this work. The major contribution of this work is the development of ACC with Vehicle-to-Infrastructure
Gupta, Ishika, Estrada, Tyler, Tambolkar, Pooja, Midlam-Mohler, Shawn
Maintaining a Healthy Safety Culture in Autonomous Vehicle Development and Operations2026-01-05284/7/2026
Safety isn’t just the absence of accidents - it’s the presence of trust, empowerment, and accountability at every level. The result is a high-trust culture where process becomes practice and safety is a shared achievement. When people closest to the work feel supported to act on what they see, safety becomes the standard. Thus, the deployment of autonomous driving systems (ADSs) requires not only technical rigor but also a resilient organizational safety culture that supports continuous learning, accountability, and transparent communication. This paper examines how safety culture can be operationalized in ADS development and operations by integrating guidance from standards such as UL 4600 and best practices from SAE AVSC. UL 4600’s requirements for systematic hazard analysis, safety case maintenance, and safety performance indicators (SPIs) are used as a foundation for quantifying organizational behavior within a Just Culture framework. This work draws on Human and Organizational
Wagner, Michael, Gittleman, Michele
Monocular 3D Perception and Lane-Aware Bird’s-Eye-View Mapping for Autonomous Driving2026-01-00124/7/2026
Accurate perception of the surrounding environment is fundamental and essential to safe and reliable autonomous driving. This work presents an integrated vision-based framework that com bines object detection, 3D spatial localization, and lane segmentation to construct a unified bird’s-eye-view (BEV) representation of the driving scene. The pipeline provides geometric information on object position and orientation by employing Omni3D to infer 3D bounding boxes of objects from monocular camera frames. Detections are subsequently projected onto a 2D BEV canvas, where object instances are represented with respect to the ground plane for enhanced interpretability. To complement the object-level perception, we utilized YOLOPv2 to perform lane segmentation, producing both lane masks and lane line masks in the image domain for future coordinate transformation. By adopting a pinhole camera model, the coordinate transformation of these masks from the perspective image plane into the BEV canvas
Tan, Lin, Arjmanzdadeh, Ziba, Wang, Hanchen, Li, Taozhe, Hajnorouzali, Yasaman, Burch, Collin, Lee, Victoria, Xu, Bin
Characterizing the Impact of C-V2X PC5 Radio on Cooperative Driving Automation in Hardware-in-the-Loop Experiments2026-01-00774/7/2026
The SAE J3216 standard defines Cooperative Driving Automation (CDA), which has received increasing attention in recent years as an umbrella framework encompassing a wide range of automated vehicle applications enabled by Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) technologies. Despite this growing interest, limited research has investigated the impact of Cellular Vehicle-to-Everything (C-V2X) on CDA applications, particularly with respect to agreement-seeking operations. This work presents a hardware-in-the-loop (HIL) experimental study designed to evaluate an Argonne National Laboratory designed CDA controller under different message configurations and varying C-V2X PC5 radio transmission frequencies. A three-vehicle car-following scenario was implemented in the Argonne-developed Roadrunner simulator, incorporating CDA agreement-seeking logic, vehicle powertrain models, and V2V communication modules. CDA messages were exchanged through two physical C-V2X PC5 radios
Zhan, Lu, Di Russo, Miriam, Das, Debashis, Stutenberg, Kevin, Misra, Priyash, Jeong, Jongryeol, Hyeon, Eunjeong
From Stoplights to On-Ramps: A Comprehensive Set of Crash Rate Benchmarks for Freeway and Surface Street ADS Evaluation09-14-02-00033/31/2026
This paper presents crash rate benchmarks for evaluating US-based automated driving systems (ADSs) for multiple urban areas, distinguishing between freeway and surface street crash rates, and breaking them down by crash severity and type. The purpose of this study was to extend prior benchmarks focused only on surface streets to additionally capture freeway crash risk for future ADS safety performance assessments. Using publicly available police-reported crash and vehicle miles traveled (VMT) data from Arizona, California, Georgia, and Texas, the methodology details the isolation of in-transport passenger vehicles, road type classification, and crash typology. Key findings revealed that freeway crash rates exhibit large geographic dependence variations with any-injury-reported crash rates being approximately three times higher in Atlanta (2.3 IPMM; the highest) when compared to San Diego (0.7 IPMM; the lowest). The results show the critical need for location-specific benchmarks to
Scanlon, John M., McMurry, Timothy L., Chen, Yin-Hsiu, Kusano, Kristofer D., Victor, Trent
Automotive Engineering: March 202626AUTP033/12/2026
Mercedes unveils S-Class amidst celebrating 140 years Classic design with the latest tech. Faraday Future says new robot business not a pivot, but a plus At NADA, Faraday Future introduced the FF Futurist, FF Master and FX Aegis, robots that it hopes to sell with the help of, among other things, auto dealerships. How higher-quality gasoline keeps modern engines clean and efficient Gasoline direct injection (GDI) engines are the most common technology on American roadways in 2025, and soon, an industrywide gasoline quality standard will better reflect their unique operational needs. Mobility for All with Christopher Borroni-Bird How can we make vehicles more sustainable for those who can afford to buy a new car, and how can we make mobility more affordable for the remaining 90%? Editorial Politics hits engineering, but harder Supplier Eye Feast, then famine Riding Along with Mercedes and its in-city driver assistance system Microvision acquires Luminar, plans relationship restoration
SAE TOMORROW TODAY - Building an End-to-End Autonomous Driving System135573/2/2026
Join us as we sit down with Richard Chelminski, Senior Vice President, Head of SDV Platform, at 42dot, an autonomous driving software and mobility platform development startup from Hyundai Motor Group. We discuss how the company is pushing boundaries with its end-to-end autonomous driving system, Atria AI, which is focused on Level 4 autonomy and designed for seamless integration into consumer vehicles -- no LIDAR required! You'll also learn why camera-first and radar technologies are reshaping the industry, how software is now at the heart of vehicle development, and what the future holds for Transportation as a Service (TaaS). If you're curious about the next wave of smart, software-defined vehicles and autonomous driving innovation, this episode is a must-listen. 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
Patterson, Lori
Eco-Driving Control for Electric Vehicles with Multi-Speed Transmission: Optimizing Vehicle Speed and Powertrain Operation in Dynamic Environments12-09-03-00212/28/2026
This article presents an eco-driving algorithm for electric vehicles featuring multi-speed transmissions. The proposed controller is formulated as a co-optimization problem, simultaneously optimizing both vehicle longitudinal speed and powertrain operation to maximize energy efficiency. Constraints derived from a connected vehicle–based traffic prediction algorithm are used to ensure traffic safety and smooth traffic flow in dynamic environments with multiple signalized intersections and mixed traffic. By simplifying the complex, nonlinear mixed-integer problem, the proposed controller achieves computational efficiency, enabling real-time implementation. To evaluate its performance, traffic scenarios from both Simulation of Urban MObility (SUMO) and real-world road tests are employed. The results demonstrate a notable reduction in energy consumption by up to 11.36% over an 18 km drive.
He, Suiyi, Sun, Zongxuan
Recommendations for ADS-DVs to Accommodate the Needs of People with DisabilitiesJ3261_202602 (Current)2/17/2026
ADS-DVs promise to expand transportation options for individuals who have been historically underserved in personal transportation. However, for this to be truly realized, the unique needs of persons with disabilities (PWDs; including those who are deaf and hard of hearing, blind, have low vision, have upper body limitations, have lower body limitations, are wheelchair users, and have cognitive disabilities) should be understood at the design stage of vehicle development. This document presents a list of recommendations for use in the design and development of ADS-DVs based on the identified needs of PWDs. It considers the accessibility of services used to interact with the ADS-DV before the trip and the complete trip (including planning the trip and requesting the vehicle, determining a pickup location, finding the vehicle, authenticating the user, entering the vehicle, interacting with the vehicle while inside, determining a drop-off location, exiting the vehicle, and finding the
On-Road Automated Driving (ORAD) Committee
Automotive Engineering: February 202626AUTP022/5/2026
Qualcomm expands partnerships for more Snapdragons Bosch is ready to bring AI to your vehicle, likely to still be ICE-powered in 2035 Etching for a greener future How chemical etching is helping enable next-gen automotive technologies. Rewriting the engineer's playbook: What OEMs must do to spin the AI flywheel The automotive industry's future hinges on a new AI-native engineering workflow that accelerates iteration, strengthens system thinking, and preserves human judgment. From redundancy to resilience: building smarter safety systems through sensor collaboration ADAS sensor fusion can provide improved and required safety technologies by rethinking the best strategy for allowing a car to sense the world. Open Safety is the shortcut to safer ADAS/AD An open safety stack, shared scenarios, benchmarks, and core validation tools can speed certification, reduce duplicated V&V and build public trust while preserving vendor differentiation. Editorial Robots, physical AI shift the focus at
OPEN SAFETY is the shortcut to safer ADAS/AD26AUTP02_062/1/2026
Treat foundational AV safety like seatbelts - make it non-proprietary and universal. An open safety stack, shared scenarios, benchmarks, and core validation tools can speed certification, reduce duplicated V&V and build public trust while preserving vendor differentiation. The bottleneck isn't compute - it's verification. Autonomous features are shipping in more vehicles and markets, but the gating factor is no longer raw compute. It's whether developers and regulators can verify systems against requirements and validate them against real-world operating design domains (ODDs) with confidence and repeatability. Today, many safety-critical components, from scenario libraries to pass/fail criteria, live in proprietary silos. That fragmentation slows regression testing, complicates regulator audits across regions, and duplicates effort across the industry. The result is an expensive, bespoke path to certification for every program and geography.
Musa, Mohammad, Khawaja, Muhammad Zain
RDAS: A Shared Control Strategy Considering Real-Time Driving Ability in Driver Takeover Tasks10-10-02-00141/24/2026
Autonomous vehicles require drivers to assume control of the vehicle in situations where the vehicle control system cannot perform its intended task. A shared control-based approach to driving authority transfer can effectively mitigate the driving risks associated with diminished driver capability due to prolonged disengagement, but it may readily precipitate human–machine conflicts—oscillatory steering behavior, excessive driver workload, and unstable control during weight transitions. Addressing the characteristics of driver capability variations during takeover tasks, a shared control strategy incorporating real-time driving ability, termed the real-time driving ability strategy (RDAS), is proposed. Initially, a real-time capability assessment strategy based on an expected steering angle model is developed. By collecting driving data under conditions of adequate driver capability to train an adaptive neuro-fuzzy inference system (ANFIS) neural network, the expected steering angle
Qi, Zhenliang, Liu, Ping, Duan, Haotian, Zhou, Zilong, Huang, Haibo
Vehicle-to-Vehicle Negotiation Via Manoeuvre Coordination Messages for Cooperative Cut-in Scenarios2026-26-02741/16/2026
The automotive industry is rapidly extending the capabilities of automated systems by incorporating connectivity and cooperation features that enable real-time information exchange between vehicles and road infrastructure. Within the Connected, Cooperative, and Automated Mobility (CCAM) framework, Vehicle-to-Vehicle (V2V) communication is expected to play a key role in improving road safety, traffic efficiency, and driving comfort. This work addresses a practical implementation of the standardized Manoeuvre Coordination Messages (MCMs), as defined in the ongoing ETSI standard (ETSI TS 103 561). The proposed approach is demonstrated through a cooperative cut-in use case in which two vehicles negotiate a lane change manoeuvre. In the considered scenario, the ego vehicle, driven by a Highway Pilot (HWP) system, receives the intention to cut-in from a neighbouring cooperative vehicle through an MCM. In response, the ego vehicle adapts its behaviour by decelerating to generate a safe
Leiva Ricart, Gisela, Domingo Mateu, Bernat
Realistic LiDAR Data Simulation for Autonomous Systems using Physics-Informed Learning2026-26-01381/16/2026
Accurate and realistic simulation of LiDAR data is critical for the development and validation of autonomous driving systems. However, existing simulation approaches often suffer from a significant sim-to-real gap due to oversimplified modelling of physical interactions and environmental factors. In this work, we present a physics-informed deep learning framework that bridges this gap by enhancing the realism of simulated LiDAR data using generative adversarial networks guided by domain-specific physical constraints for LiDAR intensity. Our method incorporates key physical factors such as range, surface material properties, angle of incidence, and environmental conditions along with their underlying physical relationships as constraints into the Cycle-Consistent GAN architecture, enabling it to learn realistic transformations from synthetic to real-world LiDAR intensity data without requiring paired samples. We demonstrate the effectiveness of our approach across multiple datasets
Anand, Vivek, Yadav, Sourav, Limba, Mohit, Pandey, Gaurav, Lohani, Bharat
Integration and Proving Ground Evaluation of a Connected Highway Pilot System2026-26-01361/16/2026
Highway Pilot (HWP) systems, classified as SAE Level 3 Automated Driving Systems (ADS), represent a potential advancement for safer and more efficient highway drives. In this work, the development of a connected HWP prototype is presented. The HWP system is deployed in a real test vehicle and designed to operate autonomously in highway environments. The implementation presented in this paper covers the complete setup of the vehicle platform, including sensor selection and placement, hardware integration and communication interfaces for both autonomous functionality and Vehicle-to-Everything (V2X) connectivity. The software architecture follows a modular design, composed of modules for perception, decision-making and motion control to operate in real-time. The prototype integrates Vehicle-to-Vehicle (V2V) communication, such as Cooperative Awareness Messages (CAM), to enhance situational awareness and improve the overall system behaviour. The modular structure allows new functionalities
Domingo Mateu, Bernat, Leiva Ricart, Gisela, Facerias Pelegri, Marc, Perez, Marc
Towards AI-Driven Automated Driving Systems_Homologations Perspective2026-26-01331/16/2026
This paper examines the challenges and opportunities in homologating AI-driven Automated Driving Systems (ADS). As AI introduces dynamic learning and adaptability to vehicles, traditional static homologation frameworks are becoming inadequate. The study analyzes existing methodologies, such as the New Assessment/Test Methodology (NATM), and how various institutions address AI incorporation into ADS certification. Key challenges identified include managing continuous learning, addressing the "black-box" nature of AI models, and ensuring robust data management. The paper proposes a harmonized roadmap for AI in ADS homologation, integrating safety standards like ISO/TR 4804 and ISO 21448 with AI-specific considerations. It emphasizes the need for explainability, robustness, transparency, and enhanced data management in certification processes. The study concludes that a unified, global approach to AI homologation is crucial, balancing innovation with safety while addressing ethical
Lujan Tutusaus, Carlos, Hidalgo, Justin
Vehicle Self-Soiling from Exterior Cleaning Mechanisms2026-26-04251/16/2026
The automotive industry is rapidly advancing towards autonomous vehicles, making sensors such as Cameras, LiDAR, and RADAR critical components for ensuring constant information exchange between the vehicle and its surrounding environment. However, these sensors are vulnerable to harsh environmental conditions like rain, dirt, snow, and bird droppings, which can impair their functionality and disrupt accurate vehicle maneuvers. To ensure all sensors operate effectively, dedicated cleaning is implemented, particularly for Level 3 and higher autonomous vehicles. It is important to test sensor cleaning mechanisms across different weather conditions and vehicle operating scenarios to ensure reliability and performance. One crucial aspect of testing is tracking the trajectory of the cleaning fluid to ensure it does not cause self-soiling of vehicles and affects the field of view or visibility zones of other components like the windshield. While wind tunnel tests are valuable, digitalizing
Mane, Suvidya, Makam, Sri Lalith Madhav, Varghese, Rixson, Desu, Harsha
Type Approval for ADS: Considerations for the ODD2026-26-02451/16/2026
The rapid introduction of new Automated Driving Systems (ADS) in the last years has led to an urge for robust methodologies for the type approval of vehicles equipped with such technologies. As a result, different Regulations addressing this field have been adopted. These Regulations are mainly based in the New Assessment and Testing Methodology (NATM) developed within the World Forum for the Harmonisation of Vehicle Regulations (WP29). However, the complexity of the regulatory ecosystem extends beyond type approval. This complexity requires a thorough analysis in order to avoid any possible gap which may jeopardise the feasibility of Automated Driving Vehicles deployment. This paper analyses the possible mismatches among the different regulations currently in place or under development and proposes a holistic approach, where the concept of the Operational Design Domain (ODD) takes a relevant role.
Lujan Tutusaus, Carlos, Hidalgo, Justin, Flix, Oriol
Mileage Accumulation Target Identification for Autonomous Emergency Braking System Validation for Medium & Heavy Trucks Considering Real-World Accidents in India2026-26-00361/16/2026
Robust validation of Advanced Driver Assistance Systems (ADAS) considering real-world conditions is a vital for ensuring safety. Mileage accumulation is a one of the validation method for ensuring ADAS system robustness. By subjecting systems to diverse real-world driving environments and edge-case scenarios, engineers can evaluate performance, reliability, and safety under realistic conditions. In accordance with ISO 21448 (SOTIF), known hazardous scenarios are explicitly tested during robustness validation in combination of virtual and physical testing at component, sub system and vehicle level, while unknown hazards may emerge through extended mileage by running vehicles on roads, allowing them to be identified and classified. However, defining a mileage target that ensures comprehensive safety remains a significant engineering challenge. This paper proposes a data-driven approach to define mileage accumulation targets for validating Autonomous Emergency Braking Systems (AEBS
Koralla, Sivaprasad, Ravjani, Amin, Tatikonda, Vijay, Gadekar, Ganesh
Determining Absence of Unreasonable Risk: Proposed Criteria for the Evaluation of an Automated Driving System Deployment12-09-04-00261/12/2026
This article provides an overview of how the determination of absence of unreasonable risk can be operationalized. It complements previous theoretical work published by existing developers of automated driving systems (ADS) on the overall engineering practices and methodologies for readiness determination. Readiness determination is, at its core, a risk assessment process. It is aimed at evaluating the residual risk associated with a new ADS deployment. The article proposes methodological criteria to ground the readiness review process for an ADS release. Specifically, it lists 12 readiness criteria connected with system safety, cybersecurity, verification and validation, collision avoidance testing, predicted collision risks, impeded progress, rules of the road compliance, vulnerable road users interactions, high-severity assessment, conservative estimate of severity, risk management, and field safety. The criteria presented are agnostic of any specific ADS technological solution and
Favaro, Francesca Margherita, Schnelle, Scott, Fraade-Blanar, Laura, Victor, Trent, Peña, Mauricio, Webb, Nick, Broce, Holland, Paterson, Craig, Smith, Daniel
SAE TOMORROW TODAY - How AI and Software-Defined Vehicles Are Transforming Automotive Culture135501/9/2026
The automotive industry is undergoing a major shift -- not just toward AI-powered, software-defined vehicles, but also toward a new culture that challenges traditional ways of working. Listen in as we sit down with Amol Gulve, Software-Defined Vehicle and AI Expert, for a candid discussion on why the pace of innovation is both exhilarating and challenging for legacy automakers. Amol breaks down the traditional seven-year vehicle development cycle and why it's incompatible with an AI-driven world, challenging OEMs to rethink everything from architecture and procurement to culture and org structure. From the future of AI in vehicles to the cultural shifts inside OEMs, you'll get an inside look at the questions defining the next decade of mobility -- and why trust, transparency, and smarter standards will shape the road ahead. 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
Patterson, Lori
Modeling and Validating Foreseeable Misuse in Safety of the Intended Functionality: A Case Study of Automated Emergency Braking12-09-03-001712/31/2025
This study presents a structured evaluation framework for reasonably foreseeable misuse in automated driving systems (ADS), grounded in the ISO 21448 Safety of the Intended Functionality (SOTIF) lifecycle. Although SOTIF emphasizes risks that arise from system limitations and user behavior, the standard lacks concrete guidance for validating misuse scenarios in practice. To address this gap, we propose an end-to-end methodology that integrates four components: (1) hazard modeling via system–theoretic process analysis (STPA), (2) probabilistic risk quantification through numerical simulation, (3) verification using high-fidelity simulation, and (4) empirical validation via driver-in-the-loop system (DILS) experiments. Each component is aligned with specific SOTIF clauses to ensure lifecycle compliance. We apply this framework to a case of driver overreliance on automated emergency braking (AEB) at high speeds—a condition where system intervention is intentionally suppressed. Initial
Kang, Do Wook, Kim, Woojin, Jang, Eun Hye, Chang, Mi, Yoon, Daesub, Jang, Youn-Seon
STGT-Gen: Spatio-Temporal Graph Transformer for Multi-Vehicle Traffic Scenario Generation2025-01-731612/31/2025
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
Corner Scenario Generation Method Based on Feature-Optimized Combinations for Automated Driving Systems2025-01-731712/31/2025
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
LWMOcc: Lightweight Monocular 3D Occupancy Prediction Method2025-01-731212/31/2025
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
Dynamic Switching Control of Vehicle Platoon with Kalman Filter Compensator Considering Communication Delays and Packet Dropouts2025-01-734112/31/2025
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
Deconstructing Driver State in Smart Cockpits: Physiological Validation of Perceived Risk, Trust, and Fatigue during Automated Driving2025-01-734512/31/2025
For driver-automation collaborative driving, accurately monitoring driver state in smart cockpits is crucial for enhancing safety, comfort, and human-computer interactions. However, existing research lacks clarity regarding the relationships among driver states, and there is no consensus on the optimal physiological channels to reliably capture these states. This study examined three critical psychological constructs (i.e., perceived risk, trust in the automated driving system, and driver fatigue) using a 37-participant driving simulation experiment. We manipulated multiple factors to induce distinct driver states among participants and recorded subjective scale ratings, heart rate variability, galvanic skin response, and eye movement data. Subjective scale ratings were adopted as the ground truth to examine the corresponding measurement relationships between different physiological signals and the three targeted dimensions of driver states. Our results proved that perceived risk
Wang, Zhenyuan, Li, Qingkun, Wang, Wenjun, Liu, Weimin, Sun, Zhaocong, Cheng, Bo
CANet: A Lightweight Cross-Attention Guided Network for Unstructured Road Segmentation2025-01-730912/31/2025
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
A Framework for Multi-Scenario Performance Evaluation of Modular Autonomous Driving Systems2025-01-731912/31/2025
Currently, we face the challenge that ensuring ADS safety remains the primary bottleneck to large-scale commercial deployment—while benchmarks such as the CARLA Leaderboard have spurred progress, their coarse evaluation granularity, inability to quantify procedural risks, and lack of differentiation among algorithms in complex scenarios make in-depth diagnostics and functional safety validation exceedingly difficult. To address these challenges, we propose EvalDrive, a framework that seems to offer a more comprehensive approach to multi-scenario performance evaluation for modular autonomous driving systems. Within this broader analytical framework, EvalDrive appears to provide what seems to be three key contributions. (1) It constructs what appears to represent a structured and extensible scenario library, comprising a majority of 44 interactive scenarios, 23 weather conditions, and 12 town environments, which are then systematically expanded through parameterized variations. (2) Our
Jia, Chunyu, Kong, Yan, Ma, Yao, Pei, Xiaofei
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