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

Tools for Peening Intensity Determination and Verification

J442_202602 (Current)

2/17/2026

This SAE Recommended Practice defines requirements for equipment and supplies to be used in measuring shot peening intensity and other surface enhancement processes. It is intended as a guide toward standard practice and is subject to change to keep pace with experience and technical advances. Guidelines for use of these items can be found in SAE J443 and SAE J2597.

Surface Enhancement Committee

OPEN SAFETY is the shortcut to safer ADAS/AD

26AUTP02_06

2/1/2026

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 Tasks

10-10-02-0014

1/24/2026

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

Realistic LiDAR Data Simulation for Autonomous Systems using Physics-Informed Learning

2026-26-0138

1/16/2026

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

Mileage Accumulation Target Identification for Autonomous Emergency Braking System Validation for Medium & Heavy Trucks Considering Real-World Accidents in India

2026-26-0036

1/16/2026

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

Type Approval for ADS: Considerations for the ODD

2026-26-0245

1/16/2026

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

Towards AI-Driven Automated Driving Systems_Homologations Perspective

2026-26-0133

1/16/2026

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

Determining Absence of Unreasonable Risk: Proposed Criteria for the Evaluation of an Automated Driving System Deployment

12-09-04-0026

1/12/2026

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 Culture

135501/9/2026

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

Automating Concrete Simulation Scenario Generation for Autonomous Driving with Large Language Models

2025-01-7318

12/31/2025

The need for high-quality simulation scenarios to verify the safety of autonomous driving systems is growing, but there are still obstacles to overcome, like the high cost and low efficiency of creating scenario files that satisfy simulation platform standards. To address the issues, this study suggests an automated approach for creating concrete autonomous driving simulation scenarios using a large language model. This approach enables the automated conversion of natural language input into standard scenario file output. The functional scenario generation stage uses the fine-tuned large language model for structured expression and improves the lightweight model deployment efficiency through knowledge distillation; the logical scenario generation stage involves mapping the standard parameter space and introducing constraint rules to ensure rationality; and the concrete scenario generation stage involves generating high-risk key parameters through data mining and generative adversarial

Li, Jiwei, Wang, Runmin

A Framework for Multi-Scenario Performance Evaluation of Modular Autonomous Driving Systems

2025-01-7319

12/31/2025

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

Research on the Influencing Factors of Driver’s Trust in Autonomous Driving System

2025-01-7344

12/31/2025

Although autonomous driving system is being used more frequently, its widespread adoption is still in its infancy. As a result, drivers may perceive the autonomous driving system as unreliable, which hinders the spread of automated driving. The goal of this study is to investigate the major variables influencing drivers’ trust in autonomous driving system. Significant positive correlations between the variables were found using the questionnaire survey, reliability validity test, and factor analysis of the questionnaire data. In order to measure the impact of system performance, user comprehension, system feedback mechanism, individual characteristics, and environmental factors on trust perception, a structural equation modeling (SEM) as an analytical tool. A total of 274 valid data were retained. By modeling and analyzing the recovered data, it showed that the fit are all in the acceptable range, the model construction is reasonable, and therefore the subsequent path analysis can be

Wang, Caiyong, He, Xingmiao, Tang, Yu, Chen, Rong, Li, Chuzhao

3D Point Cloud Object Detection for Autonomous Driving Based on Improved PointPillars

2025-01-7308

12/31/2025

Aiming at the problem of low detection accuracy in 3D object detection for autonomous driving, this paper proposes an improved PointPillars framework that enhances feature representation while reducing computational cost. Accurate perception of surrounding vehicles, pedestrians, and obstacles is critical to ensure the safety and reliability of autonomous driving systems, yet the widely used PointPillars model is often constrained by limited global feature extraction and vulnerability to environmental interference, which restricts its effectiveness in complex real-world scenarios. To address these limitations, the backbone network is reconstructed with a lightweight MobileViTv2 module to strengthen global feature capture and robustness, enabling better modeling of long-range dependencies without significantly increasing model complexity. In addition, a dynamic upsampling strategy is introduced to replace the original upsampling module, which not only improves detection performance but

Ye, Xin, Zhang, Lele, Li, Xiangdong, Cao, Qi, Ye, Ming

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

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

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

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

Deconstructing Driver State in Smart Cockpits: Physiological Validation of Perceived Risk, Trust, and Fatigue during Automated Driving

2025-01-7345

12/31/2025

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

Modeling and Validating Foreseeable Misuse in Safety of the Intended Functionality: A Case Study of Automated Emergency Braking

12-09-03-0017

12/31/2025

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

SAE TOMORROW TODAY UNPLUGGED - I Don't Want to Drive Anymore...Here's Why

1354812/19/2025

What happens when driving becomes a burden? In this Unplugged episode of SAE Tomorrow Today, join host Grayson Brulte for a candid conversation sparked by his own realization: he simply doesn't want to drive anymore. Listen in to discover why more people are finding comfort, calm, and even joy in autonomous driving systems like Tesla's Full Self-Driving (FSD) -- and why traditional driving suddenly feels antiquated. Have your own thoughts on this topic? We'd love to hear from you! Share your comments, questions or ideas for future topics with Grayson on Twitter or send them to podcast@sae.org. Follow SAE on LinkedIn, Instagram, Facebook, Twitter, and YouTube.

Patterson, Lori

2025 International Conference on Transportation Infrastructure and Engineering (ICTIE 2025)

2025-99-0300

12/17/2025

TOC

AVSC Information Report on Automated Driving System-Dedicated Vehicle (ADS-DV) Stopped Conditions

AVSC-I-06-202512/11/2025

AVSC Information Report on Automated Driving System-Dedicated Vehicle (ADS-DV) Stopped Conditions provides a taxonomy of stopped conditions and definitions of key terms and concepts that can support stakeholder discussions around these topics. By offering guidance on appropriate use of terminology related to ADS-DV stops, this report clarifies complex technical concepts and contributes to a more unified understanding across the industry.

Automated Vehicle Safety Consortium

Autonomous Driving Takeover Requests: The Influence of Spatio-Temporal Characteristics of Human-Machine Interface Elements on Driving Trust

2025-99-0440

12/10/2025

Takeover safety in conditional automation depends heavily on effective Takeover Requests (TORs). This study investigated the implication of the temporal distribution of takeover interface elements (temporal distribution: takeover cues appear first/last, spatial distribution: left/center/right) on driving trust in scenarios with different levels of urgency (low: road construction/high: traffic accidents). The results suggest that driver perceptions of the reliability of an automated driving system during control transitions may be influenced by the temporal characteristics of the distribution of human-machine interface elements. Drivers need to supervise the operation status of the autopilot system, and presenting timely information about the system at critical nodes can help improve driver trust. The central spatial distribution contributes to trust in high emergencies, while the right spatial distribution enhances driver trust more in low emergencies. This study informs takeover

Wu, Jianfeng, Li, Zihan

IAA 2025

25AUTP12_01

12/1/2025

One of the biggest splashes at IAA Mobility 2025 in Munich was the debut of the technology-packed iX3, the first of the BMW Neue Klasse line. Inside its shapely contours lie four Superbrains, including the Heart of Joy. These units represent the hometown OEM's big bet on zonal architecture - and on Qualcomm Technologies, Inc. as a co-development partner. That's because Qualcomm co-developed the Snapdragon Ride AD software stack in the Heart of Joy, which uses Qualcomm's Snapdragon Ride Pilot automated driving system, with BMW. The version of Ride Pilot that made its global debut in the iX3 uses a newly developed Snapdragon Ride AD software stack that runs on the Snapdragon Ride Platform.

Blanco, Sebastian

Automated Driving System (ADS) Marker Lamp

J3134_202512 (Current)

12/1/2025

This SAE Recommended Practice provides guidelines for the use, performance, installation, activation, and switching of marking lamps on Automated Driving System (ADS) equipped vehicles.

Signaling and Marking Devices Stds Comm

Full-State Decoupling Motion Control Strategy for Distributed Vehicle via Bayesian Parameter Optimization

10-10-01-0007

11/26/2025

With the rapid development of autonomous driving technology, unmanned ground vehicles (UGVs) are gradually replacing humans to perform tasks such as reconnaissance, target tracking, and search in special scenarios. Omnidirectional mobility based on rapid adjustment of vehicle heading posture enhances the applicability of UGVs in specialized scenarios. Omnidirectional mobility signifies the capability for rapid adjustments to the vehicle’s heading angle, longitudinal velocity, and lateral velocity. Traditional vehicles are constrained by the limitations of under-actuation, which prevents active regulation of lateral movement. Instead, they rely on the coordinated regulation of longitudinal and yaw movements, failing to meet the requirements for omnidirectional mobility. Distributed vehicles featuring steering distributed between the front/rear axles and four-wheel independent drive leverage the over-actuation advantages provided by multi-actuator coordinated control, making them

Chen, Guoying, Dong, Jiahao, Wang, Xinyu, Zhao, Xuanming, Bi, Chenxiao, Gao, Zhenhai, Zhang, Yanping, He, Rong

CARLA Hardware-in-the-Loop Simulation Environment for Autonomous Driving

12-09-02-0015

11/20/2025

This article suggests a validation methodology for autonomous driving. The goal is to validate front camera sensors in advanced driver-assist systems (ADAS) based on virtually generated scenarios. The outcome is the CARLA-based hardware-in-the-loop (HIL) simulation environment (CHASE). It allows the rapid prototyping and validation of the ADAS software. We tested this general approach on a specific experimental application/setup for a vehicle front camera sensor. The setup results were then proven to be comparable to real-world sensor performance. The CARLA simulation environment was used in tandem with a vehicle CAN bus interface. This introduced a significantly improved realism to user-defined test scenarios and their results. The approach benefits from almost unlimited variability of traffic scenarios and the cost-efficient generation of massive testing data.

Cardozo, Shawn Moses, Hlavác, Václav

DriVE-Net: A Multi-Objective Framework for Generating Diverse, Realistic, and Hazard-Aware Driving Scenarios

2025-99-0133

11/11/2025

Xie, Dongxuan, Li, Dongyang, Zhang, Youkang, Zhao, Yingjie, Hong, Baofeng, Wang, Nan

Ensuring Functional Safety and SOTIF for Autonomous Off-Highway Vehicles: Addressing Risks in Complex Systems

2025-28-0315

11/6/2025

The increasing complexity of autonomous off-highway vehicles, particularly in mining, demands robust safety assurance for Electronic/Electrical (E/E) systems. This paper presents an integrated framework combining Functional Safety (FuSa) and Safety of the Intended Functionality (SOTIF) to address risks in autonomous haulage systems. FuSa, based on ISO 19014[1] and IEC 61508[2], mitigates hazards from system failures, while SOTIF, adapted from ISO 21448[3] addresses functional insufficiency and misuse in complex operational environments. We propose a comprehensive verification and validation (V&V) strategy that identifies hazardous scenarios, quantifies risks, and ensures acceptable safety levels. By tailoring automotive SOTIF standards to off-highway applications, this approach enhances safety for autonomous vehicles in unstructured, high-risk settings, providing a foundation for future industry standards.

Kumar, Amrendra, Bagalwadi, Saurabh

A Hybrid Framework for Kinematic Vehicle Prediction: YOLOv11 with Unscented Kalman Filter

12-09-02-0013

10/22/2025

In the context of intelligent transportation systems and applications such as autonomous driving, it is essential to predict a vehicle’s immediate future states to enable precise and timely prediction of vehicles’ movements. This article proposes a hybrid short-term kinematic vehicle prediction framework that integrates a novel object detection model, You Only Look Once version 11 (YOLOv11), with an unscented Kalman filter (UKF), a reliable state estimation technique. This study provides a unique method for real-time detection of vehicles in traffic scenes, tracking and predicting their short-term kinematics. Locating the vehicle accurately and classifying it in a range of dynamic scenarios is achievable by the enhanced detection capabilities of YOLOv11. These detections are used as inputs by the UKF to estimate and predict the future positions of the vehicles while considering measurement noise and dynamic model errors. The focus of this work is on individual vehicle motion prediction

Pahal, Sudesh, Nandal, Priyanka

AVSC Best Practice for Assessment of Automated Driving System-Dedicated Vehicles (ADS-DVs) Safety Case Claims Supported by Evidence (CSE)

AVSC-D-04-202510/14/2025

AVSC Best Practice for Assessment of Automated Driving System-Dedicated Vehicles (ADS-DVs) Safety Case Claims Supported by Evidence (CSE) supports organizations in conducting structured, transparent, and repeatable safety case assessments. It promotes a systems-based approach to reviewing whether the safety arguments presented are credible, well supported, and consistent with industry expectations and regulatory contexts. The document outlines principles, assessment criteria, and illustrative examples to aid practitioners in identifying gaps, evaluating uncertainties, and improving the overall trustworthiness of ADS-DV safety cases.

Automated Vehicle Safety Consortium

Coordinated Strategy of Trajectory Tracking and Stability Control for Autonomous Vehicles with Switched Envelope and Composite Evaluation

10-10-01-0003

10/13/2025

Trajectory tracking and lateral stability under extreme conditions are critical yet conflicting control objectives due to nonlinear tire dynamics and road adhesion limitation, where accurate characterization of vehicle dynamics for each objective is essential to enable coordinated performance. This article proposes a coordinated control strategy based on switched envelope and composite evaluation to improve both tracking accuracy and stability. Unlike previous stability envelope methods that rely solely on the vehicle’s rear tire saturation boundary to prevent instability, the switched envelope approach incorporates both front and rear tire saturation boundaries to simultaneously mitigate steering loss and instability in trajectory tracking. A critical steering angle, derived from tire slip dynamics and phase plane stability analysis, is formulated as the switching criterion. Additionally, a composite stability evaluation is developed by combining a future disturbance resistance index

Shi, Wenbo, Wang, Junlong, Ding, Haitao, Xu, Nan

Identifying Automated Driving Systems-Dedicated Vehicles (ADS-DVs) Passenger Issues for Persons with Disabilities

J3171_202509 (Current)

9/25/2025

It is expected that Level 4 and 5 automated driving systems-dedicated vehicles (ADS-DVs) will eventually enable persons to travel at will who are otherwise unable to obtain a driver’s license for a conventional vehicle, namely, persons with certain visual, cognitive, and/or physical impairments. This information report focuses on these disabilities but also provides guidance for those with other disabilities. This report is limited to fleet-operated, on-demand, shared mobility scenarios, as this is widely considered to be the first way people will be able to interact with ADS-DVs. To be more specific, this report does not address fixed-route transit services or private vehicle ownership. Similarly, this report is focused on motor vehicles (refer to SAE J3016), not scooters, golf carts, etc. Lastly, this report does not address the design of chair lifts, ramps, or securements for persons who use wheeled mobility devices (WHMD) (e.g., wheelchair, electric cart, etc.), as these matters

On-Road Automated Driving (ORAD) Committee

Identification of Vehicle Mass via Experimental Data Analysis under Real Driving Conditions for On-Board Advanced Mobility Technologies Applications

2025-24-0114

9/7/2025

The growing emphasis on road safety and environmental sustainability has spurred the development of technologies to enhance vehicle efficiency. Accurate vehicle mass knowledge is crucial for all vehicles, to optimize advanced driver assistance systems (ADAS) and CCAM (Connected, Cooperative, and Automated Mobility) systems, as well as to improve both safety and energy consumption. Moreover, the continuous need to report precisely on the greenhouse emissions for good transports is becoming a key point to certificate the impact of transportation systems on the environment. Mass influences longitudinal dynamics, affecting parameters such as rolling resistance and inertia, which in turn are critical to adaptive control strategies. Moreover, the knowledge of vehicle mass represents a key challenge and a fundamental aspect for fleet managers of heavy-duty vehicles. Typically, this information is not readily available unless obtained through high-cost weighing systems or estimated

Vicinanza, Matteo, Adinolfi, Ennio Andrea, Pianese, Cesare

SAE TOMORROW TODAY - How the NVIDIA-Magna Collaboration Is Redefining Autonomy

135318/21/2025

From next-gen chipsets to scalable autonomous driving solutions, Magna's groundbreaking partnership with NVIDIA is paving the way for the next generation of software-defined, AI-powered vehicles. By harnessing NVIDIA's new Blackwell GPU architecture to enable ADAS and L2+ through L4 capabilities, Magna is blending its deep automotive expertise with powerful AI compute to redefine what's possible for both in-vehicle experiences and autonomous driving. To learn more, we sat down with Steven Jenkins, Vice President of Technology & Strategy at Magna International, to discuss the company's collaboration with NVIDIA, navigating legacy mindsets, and scaling vehicle intelligence across global markets. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists

Patterson, Lori

Dynamic Driving Task Assessment (DA) Metrics for Automated Driving Systems

J3237_202508 (Current)

8/20/2025

This SAE Recommended Practice provides DA metrics used to quantify the DDT performance of ADS-operated vehicles.3 Here, the primary focus is on the safety-related DDT performance and includes definitions, taxonomy, characteristics, and usage (along with alternatives) for each metric. DDT performance is a subset of overall operational performance of ADS-operated vehicles. Thus, assessments of DDT Fallback [1], cybersecurity, maintenance, interactions with passengers, etc., while important and could have an indirect impact on the DDT, are out of scope for this document. Note that the DA metrics do not specify the actions and/or maneuvers to be executed by the (ADS-operated) subject vehicle (SV). While this document presents a set of individual DA metrics, it is important to note that it is out of the scope of this document to describe how these metrics should be applied in practice. This is because the overall context of the scenario or deployment must be considered during DA metrics

On-Road Automated Driving (ORAD) Committee

SAE TOMORROW TODAY - What Happens After a Crash? The Push for Safer AVs

135237/11/2025

What happens after an AV crashes? That split-second response can make all the difference--and that's exactly what the Automated Vehicle Safety Consortium (AVSC) is focused on. Backed by decades of engineering and mobility expertise, the AVSC brings together top minds in AV development to align on critical safety issues. Their latest work tackles one of the most urgent: how these vehicles should react and communicate after a crash. As AVs expand from test fleets to real-world deployments, the industry needs clear guidance now more than ever. Listen in as we sit down with Jonathan Barentine, Principal Engineer at the AVSC, to discuss the organization's just-released best practices for automated driving systems in the moments following a collision. You'll learn how the AVSC is working with first responders, manufacturers, and policymakers to develop scenario-based protocols that prioritize effective communication, safety management, and a culture of accountability. We'd love to hear from

Patterson, Lori

Exploring the Correlation between Physiological States and Driving Behavior on Highways with Integrated ECG Measurements

2025-01-0292

7/2/2025

Human driver errors, such as distracted driving, inattention, and aggressive driving, are the leading causes of road accidents. Understanding the underlying factors that contribute to these behaviors is critical for improving road safety. Previous studies have shown that physiological states, like raised heart rates due to stress and anxiety, can influence driving behavior, leading to erratic driving and an increased risk of accidents. In this study, we conducted on-road tests using a measurement system based on the Driver-Driven vehicle-Driving environment (3D) method. We collected physiological signals, specially electrocardiography (ECG) data, from human drivers to examine the relationship between physiological states and driving behaviors. The aim was to determine whether ECG can serve as an indicator of potential risky driving behaviors, such as sudden acceleration and frequent steering adjustments. This information enables automated driving (AD) systems to intervene in dangerous

Ji, Dejie, Flormann, Maximilian, Bollmann, Julian, Henze, Roman, Deserno, Thomas M.

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