Browse Topic: Automation

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Vehicle-to-Vehicle Negotiation Via Manoeuvre Coordination Messages for Cooperative Cut-in Scenarios2026-26-027401/16/2026
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
Mileage Accumulation Target Identification for Autonomous Emergency Braking System Validation for Medium & Heavy Trucks Considering Real-World Accidents in India2026-26-003601/16/2026
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
Integration and Proving Ground Evaluation of a Connected Highway Pilot System2026-26-013601/16/2026
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 Perspective2026-26-013301/16/2026
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
Realistic LiDAR Data Simulation for Autonomous Systems using Physics-Informed Learning2026-26-013801/16/2026
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
A Case Study of Emission Optimization using Machine Learning2026-26-022901/16/2026
The Exhaust Emission Control is a vital part of automotive development aimed at ensuring effective control of pollutants such as NOx, CO, and HC. The traditional method of calibrating emission control strategies is a highly time-consuming process, which requires extensive vehicle testing under a variety of operating conditions. The frequent updates in emission legislation requires a high-efficiency process to achieve a faster time-to-market. The use of Machine Learning (ML) in the domain of emission calibration is the need of the hour to proactively improve the process efficiency and achieve a faster time-to-market. This paper attempts to explores emerging trend of Machine Learning (ML) based data analysis that have improved the overall process efficiency of emission control calibration. The data generated by automated programs could be used directly in data analysis with minimal or no need for data cleaning. The Machine Learning (ML) models could be trained by historical data from
Dhayanidhi, Hukumdeen, Balasubramanian, Karthick, A, Akash
Transforming Manufacturing: Increasing Throughput and Reducing Production Complexity with Digital Twins2026-26-044901/16/2026
This paper presents a bidirectional digital twin developed for the Fischertechnik Smart Factory Kit, enabling real-time simulation and validation of production line modifications prior to actual deployment. The digital twin integrates with a Siemens Programmable Logic Controller (PLC) to mirror real-world operations, capturing live production data and visualizing key factory parameters, such as product, process, and resource metrics within a 3D environment. Engineers can test various optimization scenarios by adjusting robot speed and path, conveyor speeds, part & process sequences, and modifying equipment layout sizes to enhance efficiency. Based on the optimization scenarios, the best-performing configurations are identified using metrics such as throughput, cycle time, and resource utilization. Once validated, these changes are directly deployed to the PLC, ensuring seamless implementation. Beyond capacity optimization, this solution enhances overall production efficiency by
Kumar, Rahul, Singh, Randhir
Vision-Based Framework for Automated Testing of Automotive HMI Systems Using Deep Learning Techniques2026-26-057101/16/2026
Nowadays, digital instrument clusters and modern infotainment systems are crucial parts of cars that improve the user experience and offer vital information. It is essential to guarantee the quality and dependability of these systems, particularly in light of safety regulations such as ISO 26262. Nevertheless, current testing approaches frequently depend on manual labor, which is laborious, prone to mistakes, and challenging to scale, particularly in agile development settings. This study presents a two-phase framework that uses machine learning (ML), computer vision (CV), and image processing techniques to automate the testing of infotainment and digital cluster systems. The NVIDIA Jetson Orin Nano Developer Kit and high-resolution cameras are used in Phase 1's open loop testing setup to record visual data from infotainment and instrument cluster displays. Without requiring input from the system being tested, this phase concentrates on both static and dynamic user interface analysis
Lad, Rakesh Pramod, Mehrotra, Soumya, Mishra, Arvind
Type Approval for ADS: Considerations for the ODD2026-26-024501/16/2026
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
Digital Control of Synchronous Rectification and Dead Time Configuration for Improving Efficiency and Longevity of DC Motors2026-26-008901/16/2026
This paper elucidates the implementation of software-controlled synchronous rectification and dead time configuration for bi-directional controlled DC motors. These motors are extensively utilized in applications such as robotics and automotive systems to prolong their operational lifespan. Synchronous rectification mitigates large current spikes in the H-bridge, reducing conduction losses and improving efficiency [1]. Dead time configuration prevents shoot-through conditions, enhancing motor efficiency and longevity. Experimental results demonstrate significant improvements in motor performance, including reduced thermal stress, decreased power consumption, and increased reliability [2]. The reduction in power consumption helps to minimize thermal stress, thereby enhancing the overall efficiency and longevity of the motor.
Patil, Vinod, Kulkarni, Malhar, Soni, Asheesh Kumar
At-Home Robotic RehabilitationTBMG-5445001/13/2026
It all started when Owen Kent and Todd Roberts became roommates at the University of California Berkeley. Owen has muscular dystrophy and had recently acquired a robotic arm, which he noticed he was using to do range of motion. Todd had come to Berkeley to study mechanical engineering with a focus on biomechanics, and both were enrolled in Designing for the Human Body, a biomechanics course taught by Mechanical Engineering Professor Grace O’Connell.
Determining Absence of Unreasonable Risk: Proposed Criteria for the Evaluation of an Automated Driving System Deployment12-09-04-002601/12/2026
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 Culture1355001/09/2026
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
5Ws of Unloading RobotsTBMG-5442901/02/2026
Electronic Fiber Provides Stretchable SensingTBMG-5442101/01/2026
EPFL researchers have engineered a fiber-based electronic sensor that remains functional even when stretched to over 10 times its original length. The device holds promise for smart textiles, physical rehabilitation devices, and soft robotics.
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
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
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
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
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
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
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
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
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
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
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
Commercial Vehicles: Vol. 2EPRCOMPV05202512/29/2025
Hsu, Tsung-Ming, Muelaner, Jody Emlyn
Infrastructure Enablers for Automation: Vol. 4EPRCOMPV02202512/29/2025
Coyner, Kelley, Bittner, Jason, Ercisli, Safak, Razdan, Rahul
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
Analysis and Prioritization for Automation of Administrative Processes: Application in a Shared Service Center2025-36-023312/18/2025
This research aimed to develop a method for identifying and prioritizing the feasibility of automation in administrative processes, using as an example an application in a Shared Services Center (SSC) of a Brazilian multinational in the auto parts sector. The study considers the use of various automation technologies, including Robotic Process Automation (RPA), Decision Rules, Extract, Transform, Load (ETL), Analytics, and Workflow, with the goal of optimizing operational efficiency and reducing costs. The methodological approach adopted is based on Design Science Research (DSR), allowing for the creation and validation of an innovative artifact that, through a questionnaire applied to each process, assists in identifying the administrative processes most suitable for automation. Using the questionnaire responses, an indicator is calculated related to the percentage of automation feasibility (Paut) of the processes. The results obtained demonstrate an artifact that makes the
Junior, Osvaldo Vicente Jardim, Campos, Renato de, Franco, Bruno Chaves
Current Challenges and Technological Opportunities for Implementing Autonomous Vehicles in Urban Public Transport2025-36-008512/18/2025
Driven by technological advances in artificial intelligence, sensors, connectivity and sustainable mobility, autonomous buses are a reality in many contexts where their application is viable and efficient. The potential of the technology is a clear theme and has been widely discussed over the last two decades, due to various factors such as reducing accidents, increasing operating cost efficiency, improving the efficiency of public transport, reducing environmental impact and offering mobility solutions for increasingly congested urban areas. Due to the implementation of the General Safety Regulation (GSR II) in the European Union, with the aim of reducing traffic accidents and paving the way for fully autonomous vehicles, autonomous vehicles are getting closer to becoming a viable reality on the streets and highways of developed countries [1]. In order to guarantee the necessary safety in autonomous systems, data reliability is fundamental. To this end, it is essential to implement
Gameiro, João, Pirocchi, Amanda, Matias, Brenda, Paterlini, Bruno, Souza, Kerylli de, Angelone, Luca, Gama, Ulisses
2025 International Conference on Transportation Infrastructure and Engineering (ICTIE 2025)2025-99-030012/17/2025
TOC
The Design and Simulation of Absolute Priority Bus Lane Incorporating Clearance Distance2025-99-036512/17/2025
To alleviate the congestion in general-purpose lanes while exclusive bus lanes remain idle, this paper proposes absolute-priority bus lane design with clearance distance. By establishing specific clearance distances and lane-changing rules, the proposed design method not only enhances overall road utilization efficiency but also ensures unimpaired bus speeds, thereby maintaining bus priority. The simulation is performed based on cellular automaton (CA) model and the results demonstrate that this design is effective when general-purpose lane traffic density ranges between 0-50 vehicles/km/lane, with greater improvements in other non-public vehicle speeds under longer bus dispatch intervals. These results provide a theoretical basis and practical guidance for future bus lane management.
Wei, Liying, Yang, Nan, Gao, Chang
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
Bosch Rexroth adds HS linear motion, 6D floating prototype to multi-axis systems25AUTP12_1312/01/2025
Bosch Rexroth displayed new battery automation solutions at The Battery Show 2025 in Detroit in October, focusing on making batteries in a more efficient manner. The company's customizable conveyance system can now add the ctrlX Flow HS linear motion system and the ctrlX Flow 6D contactless transport system to move components, promising new options in an era when everyone and their suppliers are looking for ways to make better EVs. Andreas Letsch, director of the Battery Factory Automation Center of Competence at Bosch Rexroth AG, told SAE Media that the new additions can make a modern battery plant more efficient as its footprint shrinks. Used with the company's Cartesian multi-axis systems, the new systems offer improved ways to position components and handle tasks. The multi-axis cobots can effectively shift workspace vertically - they need less floor space than traditional 6-axis robot arms - providing another important factor in an efficient facility.
Blanco, Sebastian
IAA 202525AUTP12_0112/01/2025
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
Robotic Skin Allows Tiny Robots to Navigate Complex, Fragile EnvironmentsTBMG-5428512/01/2025
Researchers at the University of California San Diego have developed a soft robotic skin that enables vine robots that are just a few millimeters wide to navigate convoluted paths and fragile environments. To accomplish this, the researchers integrated a very thin layer of actuators made of liquid crystal elastomer at strategic locations in the soft skin. The robot is steered by controlling the pressure inside its body and temperature of the actuators.
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
AR-Enhanced Rescue Robot for Disaster ResponseTBMG-5428612/01/2025
Robots may soon have a new way to communicate with people. Not through words or screens, but with light and images projected directly onto the world around them.
New AI-Driven Design Tool Optimizes Performance and Functionality of RobotsTBMG-5428312/01/2025
Like octopi squeezing through a tiny sea cave, metatruss robots can adapt to demanding environments by changing their shape. These mighty morphing robots are made of trusses composed of hundreds of beams and joints that rotate and twist, enabling astonishing volumetric transformations.
Programming Robots with Rubber BandsTBMG-5428412/01/2025
From sorting objects in a warehouse to navigating furniture while vacuuming, robots today use sensors, software control systems, and moving parts to perform tasks. The harder the task or more complex the environment, the more cumbersome and expensive the electronic components.
Robotic System for Assembly and Maintenance of Lightweight Reconfigurable StructuresTBMG-5428212/01/2025
Missions to the moon and other planets will require large-scale infrastructure that would benefit from autonomous assembly by robots without on-site human intervention. Modular and reconfigurable structures, such as those built from lattice-based building blocks, are reusable and easy to manufacture. Furthermore, reconfigurable systems have the potential to outperform traditional, fixed infrastructure in applications that require high levels of flexibility in addition to structural strength and rigidity. NASA Ames Research Center has developed a novel and efficient mobile bipedal robot system to construct low-mass, high precision, and largescale infrastructure.
Trebro's automated sod harvester deploys motion control on multiple levels25TOFHP12_0812/01/2025
Automating harvesters started out as a necessary solution to a severe labor shortage in 1990, Trebro Manufacturing states on its website. The Billings, Montana-based manufacturer has been producing turf harvesting machines since 1999, and its automated sod harvesters and entire harvesting process feature self-driving, automated-control functions. The company's tag line, “The Future of Turf Harvesting,” refers to its position of being the first in the industry to offer automated turf harvesting products. Trebro's AutoStack 3 harvester is an automated combine for turf that steers itself while an operator monitors and performs quality control actions when needed. The harvesting process combines several automated control processes.
Komatsu augments intelligent machine control in new excavator25TOFHP12_0612/01/2025
Komatsu has launched a new excavator, the PC220LCi-12, that features its latest intelligent machine control technology. IMC 3.0 incorporates automation enhancements and a reported “construction-industry first” technology - factory-integrated 3D boundary control - designed to boost operator productivity. The intelligent machine, displayed previously at Bauma 2025 in Munich, Germany, has many of the same features as the new PC220LC-12 excavator, including a cab that is 28% larger, with 30% more legroom and 50% improved visibility compared to the PC210LC-11 model. Other advantages the new machines offer are up to a 20% increase in fuel efficiency thanks to a new electrohydraulic system and 129-kW (173-hp) next-generation engine, and up to a 20% reduction in maintenance costs due to longer replacement intervals for hydraulic oil and oil filters and longer cleaning intervals for the particulate filter.
Gehm, Ryan
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
Open-Source Software for 3D Printing with Multiple MaterialsTBMG-5419111/13/2025
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
Design for Welding Automation for Construction Equipment2025-28-024811/06/2025
Over the past 25 years, the heavy fabrication and construction equipment industry has experienced significant transformation. Driven by a global surge in demand for construction machinery, manufacturers are under increasing pressure to deliver higher volumes within shorter timelines and at competitive costs. This demand surge has been compounded by workforce-related challenges, including a declining interest among the new generation in acquiring traditional manufacturing skills such as welding, heat treatment, and painting. Furthermore, the industry faces difficulties in staffing third-shift operations, which are essential to meet production targets. The adoption of automation technologies in heavy fabrication and construction equipment manufacturing has been gradual and often hindered by legacy product designs that were optimized for conventional manufacturing methods. As the industry transitions toward smart, connected manufacturing environments under the industry 4.0 paradigm, it
Saseendran, Unnikrishnan, Bhorge, Pankaj
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