Browse Topic: Automated driving systems

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Mileage Accumulation Target Identification for Autonomous Emergency Braking System Validation for Medium & Heavy Trucks Considering Real-World Accidents in India2026-26-0036To be published on 01/16/2026
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
Towards AI-Driven Automated Driving Systems_Homologations Perspective2026-26-0133To be published on 01/16/2026
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
Type Approval for ADS: Considerations for the ODD2026-26-0245To be published on 01/16/2026
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
Realistic LiDAR Data Simulation for Autonomous Systems using Physics-Informed Learning2026-26-0138To be published on 01/16/2026
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
SAE TOMORROW TODAY - How AI and Software-Defined Vehicles Are Transforming Automotive Culture1355001/08/2026
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
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
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
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
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
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
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
Automating Concrete Simulation Scenario Generation for Autonomous Driving with Large Language Models2025-01-731812/31/2025
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
3D Point Cloud Object Detection for Autonomous Driving Based on Improved PointPillars2025-01-730812/31/2025
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
Research on the Influencing Factors of Driver’s Trust in Autonomous Driving System2025-01-734412/31/2025
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
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
SAE TOMORROW TODAY UNPLUGGED - I Don't Want to Drive Anymore...Here's Why1354812/19/2025
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)2025-99-030012/17/2025
TOC
AVSC Information Report on Automated Driving System-Dedicated Vehicle (ADS-DV) Stopped ConditionsAVSC-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 Trust2025-99-044012/10/2025
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
Automated Driving System (ADS) Marker LampJ3134_202512 (Current)12/01/2025
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 Optimization10-10-01-000711/26/2025
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 Driving12-09-02-001511/20/2025
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 Scenarios2025-99-013311/11/2025
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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 Systems2025-28-031511/06/2025
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 Filter12-09-02-001310/22/2025
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 Evaluation10-10-01-000310/13/2025
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 DisabilitiesJ3171_202509 (Current)09/25/2025
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 Applications2025-24-011409/07/2025
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 Autonomy1353108/21/2025
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 SystemsJ3237_202508 (Current)08/20/2025
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 AVs1352307/11/2025
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 Measurements2025-01-029207/02/2025
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.
A Generalized Formulation for Safe Obstacle Avoidance Maneuvers in Autonomous Vehicles2025-01-028507/02/2025
This paper deals with autonomous vehicle trajectory planning for avoidance maneuver. It introduces a trajectory planning approach that allows for static obstacle avoidance maneuvers. Specifically, this study proposes a generalized geometric formulation based on Sigmoid functions in order to generate a smooth path that guides the vehicle on a lateral deviation and returns to the initial lane. In addition, the method considers various geometrical and dynamic constraints to ensure vehicle stability while taking into account a safety area around the obstacle. The algorithm validation is conducted on the professional CarMaker simulator by associating the path generation module with a robust lateral tracking controller. The results demonstrate the effectiveness of the proposed planning method in the field of autonomous driving vehicle control.
Vigne, Benoit, Giuliani, Pio Michele, Orjuela, Rodolfo, Basset, Michel
Development of a Cost Model for ADAS and AD Functions2025-01-026107/02/2025
Computer-aided synthesis and development tools are essential for discovering and optimizing innovative concepts. Evaluating different concepts and making informed decisions relies heavily on accurate assessments of system properties. Estimating these properties in the early stages of vehicle development is challenging due to the depth of modelling required. In order to enable a cost prognosis for driving assistance and automated driving functions including software and hardware properties a cost model was developed at the Institute of Automotive Engineering. The methodology and cost model focuses on multiple combined approaches. This includes a bottom-up approach for the hardware. The costs of the software components are integrated into the model with the help of existing literature data and an exponential regression. For a comprehensive view of the total costs, the model is the model is also supplemented by a top-down approach for estimating the costs of other hardware components. The
Sturm, Axel, Hichri, Bassem, Rohde García, Álvaro, Henze, Roman
Letter from the Guest Editors12-08-02-001105/06/2025
Letter from the Guest Editors
Liang, Ci, Törngren, Martin
Legal Issues and Policy: Vol. 3EPRCOMPV05202404/30/2025
Abdul Hamid, Umar Zakir, Eastman, Brittany
Infrastructure Enablers for Automation: Vol. 3EPRCOMPV04202404/30/2025
Coyner, Kelley, Bittner, Jason
Automated Vehicles: Development, Testing, and Validation: Vol. 3EPRCOMPV03202404/30/2025
Beiker, Sven, Kolodziejczyk, Bart
A Systematic Literature Review on Safety of the Intended Functionality for Automated Driving Systems2025-01-503004/28/2025
In the automobile industry, ensuring the safety of automated vehicles equipped with the automated driving system (ADS) is becoming a significant focus due to the increasing development and deployment of automated driving. Automated driving depends on sensing both the external and internal environments of a vehicle, utilizing perception sensors and algorithms, and electrical/electronic (E/E) systems for situational awareness and response. ISO 21448 is the standard for Safety of the Intended Functionality (SOTIF) that aims to ensure that the ADS operate safely within their intended functionality. SOTIF focuses on preventing or mitigating potential hazards that may arise from the limitations or failures of the ADS, including hazards due to insufficiencies of specification, or performance insufficiencies, as well as foreseeable misuse of the intended functionality. However, the challenge lies in ensuring the safety of vehicles despite the limited availability of extensive and systematic
Patel, Milin, Jung, Rolf, Khatun, Marzana
Automated Vehicles and Infrastructure Enablers: Dedicated LanesEPR202500704/24/2025
Dedicated lanes provide a simpler operating environment for ADS-equipped vehicles than those shared with other roadway users including human drivers, pedestrians, and bicycles. This final report in the Automation and Infrastructure series discusses how and when various types of lanes whether general purpose, managed, or specialty lanes might be temporarily or permanently reserved for ADS-equipped vehicles. Though simulations and economic analysis suggest that widespread use of dedicated lanes will not be warranted until market penetration is much higher, some US states and cities are developing such dedicated lanes now for limited use cases and other countries are planning more extensive deployment of dedicated lanes. Automated Vehicles and Infrastructure: Dedicated Lanes includes a review of practices across the US as well as case studies from the EU and UK, the Near East, Japan, Singapore, and Canada. Click here to access the full SAE EDGETM Research Report portfolio.
Coyner, Kelley, Bittner, Jason
Cross-Scene Traffic Target Detection and Recognition Based on Improver YOLOv5s12-09-01-000304/24/2025
Aiming at the problem of insufficient cross-scene detection performance of current traffic target detection and recognition algorithms in automatic driving, we proposed an improved cross-scene traffic target detection and recognition algorithm based on YOLOv5s. First, the loss function CIoU of insufficient penalty term in the YOLOv5s algorithm is adjusted to the more effective EIoU. Then, the context enhancement module (CAM) replaces the original SPPF module to improve feature detection and extraction. Finally, the global attention mechanism GCB is integrated with the traditional C3 module to become a new C3GCB module, which works cooperatively with the CAM module. The improved YOLOv5s algorithm was verified in KITTI, BDD100K, and self-built datasets. The results show that the average accuracy of mAP@0.5 is divided into 95.1%, 72.2%, and 97.5%, respectively, which are 0.6%, 2.1%, and 0.6% higher than that of YOLOv5s. Therefore, it shows that the improved algorithm has better detection
Ning, Qianjia, Zhang, Huanhuan, Cheng, Kehan
Learned Human Eye Gaze for Object Tracking: A Preliminary Study09-13-01-000404/15/2025
Visual object tracking technology is the core foundation of intelligent driving, video surveillance, human–computer interaction, and the like. Inspired by the mechanism of human eye gaze, a new correlation filter (CF) tracking algorithm, named human eye gaze (HEG) tracking algorithm, was proposed in this study. The HEG tracking algorithm expanded the tracking detection idea from the traditional detection-tracking to detection-judging-tracking by adding a judging module to check the initial and retrack the unreliable tracking result. In addition, the detection module was further integrated into the edge contour feature on the basis of the HOG (histogram of oriented gradients) extracting feature and the color histogram to reduce the sensitivity of the algorithm to factors such as deformation and illumination changes. The comparison conducted on the OTB-2015 dataset showed that the overall overlap precision, distance precision, and center location error of the HEG tracking algorithm were
Jiang, Yejie, Jiang, Binhui, Chou, Clifford C.
Taxonomy and Definitions for Truck and Bus Automated Vehicle SystemsJ3150_202504 (Current)04/14/2025
This document provides definitions, terminology, and classifications for automated truck and bus vehicle applications. Vehicles covered by this document are those with a GVWR of more than 10000 pounds and where each vehicle utilizes driving automation systems that perform part or all of the driving task on a sustained basis and that range in level from some driving automation to full driving automation. The document also provides levels of driving automation that apply to the driving automation feature engaged in any given instance of operation of an equipped vehicle. A vehicle may be equipped with a driving automation system that is capable of delivering multiple driving automation features that perform at different levels; the level of driving automation exhibited in any given instance is determined by the feature(s) that are engaged. This document provides guidance for the elements of the dynamic driving task (DDT) for a truck or bus equipped with an Automated Driving System (ADS).
Truck and Bus Automation Safety Committee
Avoiding the Crash: A Vision-Language Model Evaluation of Critical Traffic Scenarios2025-01-821304/01/2025
Autonomous Vehicles (AVs) have transformed transportation by reducing human error and enhancing traffic efficiency, driven by deep neural network (DNN) models that power image classification and object detection. However, to maintain optimal performance, these models require periodic re-training; failure to do so can result in malfunctions that may lead to accidents. Recently, Vision-Language Models (VLMs), such as LLaVA-7B and MoE-LLaVA, have emerged as powerful alternatives, capable of correlating visual and textual data with a high degree of accuracy. These models’ robustness and ability to generalize across diverse environments make them especially suited to analyzing complex driving scenarios like crashes. To evaluate the decision-making capabilities of these models across common crash scenarios, a set of real-world crash incident videos was collected. By decomposing these videos into frame-by-frame images, we task the VLMs to determine the appropriate driving action at each frame
Fernandez, David, MohajerAnsari, Pedram, Salarpour, Amir, Pesé, Mert D.
Prognostic Techniques in Automated Driving System (ADS) Vehicle Safety2025-01-809404/01/2025
The recent advancements in fields such as sensors, AI, and IoT are majorly impacting the automotive industry. Automated Driving Systems (ADS) are developing rapidly, meaning that SAE J3016 Level 3 and above vehicles are quickly becoming a reality. As a result, maintenance of such systems becomes essential to ensure their safe and efficient operation. Prognostic techniques in particular are crucial to monitor the state of health and predicting the end of life for components. Prognostics engineering is being applied in many industries and for conventional automotive applications, but ADS is new technology, and the prognostics for these systems are still being developed and adapted. In this paper, we first present a review of the most used prognostic techniques across different safety-critical domains such as aerospace, power, and manufacturing. Then, we summarize the main challenges that must be faced to successfully develop novel approaches for prognostics of ADS components and provide
Merola, Francesco, Hanif, Athar, Lami, Giuseppe, Ahmed, Qadeer, Monohon, Mark
Developing a Safety Management System for the Automated Vehicle Industry2025-01-867304/01/2025
Safety Management Systems (SMSs) have been used in many safety-critical industries and are now being developed and deployed in the automated driving system (ADS)-equipped vehicle (AV) sector. Industries with decades of SMS deployment have established frameworks tailored to their specific context. Several frameworks for an AV industry SMS have been proposed or are currently under development. These frameworks borrow heavily from the aviation industry although the AV and aviation industries differ in many significant ways. In this context, there is a need to review the approach to develop an SMS that is tailored to the AV industry, building on generalized lessons learned from other safety-sensitive industries. A harmonized AV-industry SMS framework would establish a single set of SMS practices to address management of broad safety risks in an integrated manner and advance the establishment of a more mature regulatory framework. This paper outlines a proposed SMS framework for the AV
Wichner, David, Wishart, Jeffrey, Sergent, Jason, Swaminathan, Sunder
The Rain Test Domain: An Outdoor Testbed for Automotive Sensor Evaluation in Adverse Weather Conditions2025-01-805804/01/2025
The rapid development of autonomous vehicles necessitates rigorous testing under diverse environmental conditions to ensure their reliability and safety. One of the most challenging scenarios for both human and machine vision is navigating through rain. This study introduces the Digitrans Rain Testbed, an innovative outdoor rain facility specifically designed to test and evaluate automotive sensors under realistic and controlled rain conditions. The rain plant features a wetted area of 600 square meters and a sprinkled rain volume of 600 cubic meters, providing a comprehensive environment to rigorously assess the performance of autonomous vehicle sensors. Rain poses a significant challenge due to the complex interaction of light with raindrops, leading to phenomena such as scattering, absorption, and reflection, which can severely impair sensor performance. Our facility replicates various rain intensities and conditions, enabling comprehensive testing of Radar, Lidar, and Camera
Feichtinger, Christoph Simon
Efficient Sidewalk Route Planning on Aerial Images with Cost-Based A* and Mini-Max Objective Function2025-01-803204/01/2025
With the growing diversification of modern urban transportation options, such as delivery robots, patrol robots, service robots, E-bikes, and E-scooters, sidewalks have gained newfound importance as critical features of High-Definition (HD) Maps. Since these emerging modes of transportation are designed to operate on sidewalks to ensure public safety, there is an urgent need for efficient and optimal sidewalk routing plans for autonomous driving systems. This paper proposed a sidewalk route planning method using a cost-based A* algorithm and a mini-max-based objective function for optimal routes. The proposed cost-based A* route planning algorithm can generate different routes based on the costs of different terrains (sidewalks and crosswalks), and the objective function can produce an efficient route for different routing scenarios or preferences while considering both travelling distance and safety levels. This paper’s work is meant to fill the gap in efficient route planning for
Bao, Zhibin, Lang, Haoxiang, Lin, Xianke
Path Tracking of Autonomous Vehicles with Model Predictive Control (MPC) Optimized with Particle Swarm Optimization (PSO)2025-01-803604/01/2025
This paper presents a comparative study between many control techniques to investigate the efficiency of the path tracking in various driving scenarios. In this study the Model predictive control (MPC), the adaptive model predictive control (AMPC) and the Stanley controller are employed to ensure that the vehicle follows reference paths accurately and robustly under varying environmental and vehicular conditions. Two driving scenarios are utilized S-road and Curved-road with MATLAB/Simulink under three different vehicle speeds to investigate vehicle performance employing the root mean square error (RMSE) as the performance evaluation function. Particle swarm optimization (PSO) utilized for optimizing the six parameters of the MPC prediction horizon (P), Control horizon(m), manipulated variable rates, manipulated variables weights and two output variables weights. Four objective functions are employed with PSO and compared to each other in terms of the time domain regarding the RMSE of
Eldesouky, Dina M.Mustafa, Abdelaziz, Taha Helmy, Mohamed, Amr.E
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