Browse Topic: Automated vehicles

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Unlocking Edge AI in Software-Defined Vehicles: Balancing model complexity, compute resources, and data access2026-26-0686To be published on 01/16/2026
The potential of Edge AI in automotive goes beyond ADAS and automated driving. AI-driven optimizations in driving efficiency, in-cabin personalization, predictive maintenance, and enhanced safety require intelligent processing at the vehicle edge. However, integrating AI into current vehicle electrical/electronic architectures (EEAs) presents significant challenges. This paper examines how AI models can run on general-purpose ECUs, gateways, and domain controllers while balancing model complexity, compute resources, and data availability. We explore trade-offs, architectural choices, and the need for software configurability to ensure AI models evolve with new data and vehicle parameters. We conclude by providing a glimpse into Sonatus’ latest AI product that encapsulates a holistic approach optimized for vehicle platforms—spanning cloud-based model development, optimization, and validation to in-vehicle inference—that unlocks the potential of Edge AI and SDVs for the automotive
Sah, Mohamadali, Khatri, Sanjay
From UN R79 to UN R171: NATM Multi-Pillar Framework in DCAS Regulation2026-26-0137To be published on 01/16/2026
The introduction of Connected and Automated Vehicles (CAVs) represents a paradigm shift in transportation, promising enhanced safety, efficiency, and accessibility. However, the deployment of these technologies requires a robust regulatory framework to ensure their safe integration into existing transportation systems. This paper examines the role of UN Regulation 171 on Driver Control Assistance Systems (DCAS) in shaping the future of CAVs. As the automotive industry progresses towards higher levels of automation, from the current SAE Level 2 vehicles to the aspired Level 5 fully autonomous cars, the importance of clear regulatory guidelines becomes mandatory. This regulation provides a standardized approach to DCAS, ensuring consistency across different manufacturers and countries, but also to Approval Authorities and Technical Services. UN R171 aims to cover the gap between current ADAS systems already covered by UN Regulations, and CAV systems, under development within the
Flix, Oriol, Lujan Tutusaus, Carlos, Hidalgo, Justin
Automotive Cybersecurity Regulatory Framework2026-26-0625To be published on 01/16/2026
During the last few years, cybersecurity has become one of the key aspects in the automotive industry to ensure the safety and security of vehicles throughout their lifecycle. The implementation of new technologies and the goal of deploying fully automated vehicles in the not-so-distant future have created new needs regarding vehicle safety. As the automotive industry shifts toward automated and connected vehicles, the possibility of cyberattacks emerges as a significant concern. To address these challenges, different regulatory bodies and governments worldwide have developed or are developing a new set of regulations and standards with varying approaches to legislate or standardize the minimum criteria that manufacturers must follow to ensure security against potential attacks, adding requirements to ensure the cybersecurity of the vehicles at all levels. At the United Nations level, UN Regulation 155 was introduced in 2021, establishing requirements for manufacturers' cybersecurity
Flix, Oriol, Lujan Tutusaus, Carlos, Hidalgo, Justin
A Dynamic Cybersecurity Benchmarking Framework for Automotive Cyber-Physical Systems2026-26-0612To be published on 01/16/2026
As the automotive industry accelerates towards greater connectivity and automation, ensuring robust cybersecurity has become a critical priority, especially in emerging markets like India. This paper introduces a comprehensive framework designed to assess and enhance the cybersecurity of production-ready vehicles, with a particular focus on addressing the unique challenges posed by both domestic and international markets. The proposed system combines advanced threat modeling, vulnerability assessment, and dynamic evaluation techniques to continuously monitor and quantify a vehicle’s cybersecurity posture throughout its lifecycle. In the Indian context, where the automotive market is rapidly evolving and the adoption of connected and automated vehicles is on the rise, the need for tailored cybersecurity measures is particularly pressing. By evaluating the impact of cyber threats on safety, data privacy, financial risks, and vehicle functionality, the system generates a detailed
Shah, Ravindra, Awasthi, Vibhu Vaibhav, Karle, Ujjwala
Adaptability Survey and analysis of Camera Monitoring Systems (CMS) on Various Driving Population for Passenger Vehicles in Indian Driving Scenario2026-26-0601To be published on 01/16/2026
This paper presents a comprehensive survey and data collection study on the adaptability of Camera Monitoring Systems (CMS) for passenger vehicles. With the growing demand for enhanced safety, automation, and driver assistance technologies, Camera Monitoring Systems (CMS) has emerged as a key component in modern automotive design. This study aims to explore the current state of camera-based monitoring in passenger vehicles, focusing on their adaptability through survey data collection of various driving population and analysis. This paper evaluates the acceptance of CMS configurations in replacement to conventional rear view mirrors through Position of Monitor, Clarity, CMS Adaptiveness to eyes, Comfort while turning, Merging into moving traffic, Monitoring Rear Traffic, while Getting Out of Car, while Overtaking, Coverage Area and Overall Acceptance. The findings offer valuable insights for manufacturers, engineers, and researchers working toward the evolution of intelligent vehicle
Sinha, Ankit, Tambolkar, Sonali Ameya, Belavadi Venkataramaiah, Shamsundara, Kauffmann, Maximilian
Development of a High-Fidelity Vehicle Dynamics Model with Rear-Wheel Active Roll Control for Improved Stability2026-26-0093To be published on 01/16/2026
This work introduces a unified vehicle dynamics framework that combines detailed modeling and active control to enhance ride comfort and handling stability. A full 12-degree-of-freedom vehicle model is developed to simulate dynamic responses across a range of driving conditions. Unlike conventional approaches, this model incorporates both linear and nonlinear tire behavior, enabling more accurate prediction of vehicle motion. To further improve performance, an active roll control mechanism is proposed, utilizing rear-wheel suspension actuators to counteract undesired body roll and yaw instabilities. A novel co-simulation environment is established by coupling MATLAB/Simulink-based control strategies with high-fidelity multibody dynamics from ADAMS Car. The model is calibrated and validated using experimental data from an instrumented test vehicle. Results show that the integrated control system significantly reduces critical parameters such as roll angle, yaw rate deviation, and
Duraikannu, Dinesh, DUMPALA, GANGI REDDY
Artificial Intelligence in Passenger Cars: Unlocking Intelligent Mobility and Human-Centric Vehicle Innovation2026-26-0672To be published on 01/16/2026
Artificial Intelligence (AI) is fundamentally reshaping the landscape of automotive engineering, particularly in passenger vehicles where comfort, safety, and customization are paramount. This paper presents a comprehensive review of current and emerging AI-driven applications in passenger cars, including intelligent vehicle insurance, real-time traffic optimization, personalized driving experiences, emotion recognition, and advanced EV battery management. The integration of AI technologies—ranging from machine learning and computer vision to natural language processing and deep neural networks—enables predictive maintenance, adaptive driver-assist systems, and next-generation human-machine interfaces. AI’s role in vehicle insurance is also explored through behaviour-based risk scoring and claims automation. Furthermore, emotion-aware cabin systems are discussed, which aim to detect driver fatigue, stress, or distraction to improve road safety and mental wellness. The paper also
Hazra, Sandip, Tangadpalliwar, Sonali, Khan, Arkadip
Vehicle-to-Vehicle negotiation via Manoeuvre Coordination Messages for Cooperative Cut-In scenarios2026-26-0274To be published on 01/16/2026
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
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
Hybrid Safety Framework for Autonomous Off-Highway Vehicles2025-28-032511/06/2025
The evolution of Autonomous off-highway vehicles (OHVs) has transformed mining, construction, and agriculture industries by significantly improving efficiency and safety. These vehicles operate in high dust, uneven terrain, and potential communication failures, where safety is challenged. To guarantee vehicle safety in such situations, a robust architecture that combines AI-driven perception, fail-safe mechanisms, and conformance to many ISO standards is required. In unstructured environments, AI-driven perception, decision-making, and fail-safe mechanisms are not fully addressed by traditional safety standards like ISO26262 (road vehicles), ISO19014 (earth-moving machinery and it is replacing withdrawn ISO 15998), ISO12100 (Safety of machinery) and ISO25119 (agriculture), ISO 18497 (safety of highly automated agricultural machinery), and ISO/CD 24882 (cybersecurity for machinery).These standards mainly concentrate on the reliability of mechanical and electric/electronic systems
Muthusamy, Sugantha
Vehicle to Vehicle Recharge and Power Sharing for Modular Public Transportation Systems: Design and Testing of the UNIFI fleet at ENEA CASACCIA Research Center2025-32-009511/03/2025
The synergistic adoption of automated driving technologies and the electrification of the vehicle power train offers the possibility of proposing new and innovative solutions for public transportation systems. In particular, an interesting solution is represented by modular systems in which multiple autonomous vehicles/transportation modules can be aggregated to form reconfigurable compositions according to desired transportation demand. In this work, a configurable connection between vehicles is adopted, as convoying ensures the possibility of power sharing between vehicles, allowing coordinated power management throughout the composition. Connected vehicles can also share power between batteries for battery recharge that is performed using a custom solution from a tram-like catenary. In this work, the authors design a demonstrator to investigate the feasibility of the proposed solution. Once designed, the proposed system has been assembled and tested at the ENEA Casaccia Research
Alessandrini, Adriano, Berzi, Lorenzo, Fabbri, Marco, Franci, Michael, Gulino, Michelangelo Santo, Pugi, Luca, Ortenzi, Fernando, Vitiello, Francesco
Research on The Architecture of Vehicle Front Collision Warning Function2025-99-006110/17/2025
The rapid development of the automobile industry has put forward higher requirements for safe travel, especially in today’s vigorous development of new energy vehicle technology, faster driving speed and more intelligent vehicle configuration, which makes the automobile safety technology become a key research direction. If you can judge the current driving state of the vehicle and provide early warning information to the driver, the occurrence of traffic accidents can be avoided to a large extent. However, in the current field of vehicle safety technology, vehicle collision warning systems are rarely involved. Therefore, this paper proposes a system functional architecture for vehicle front collision warning, which can provide the driver with collision warning according to the current state of the vehicle. If the driver fails to take effective actions within a specified time, the vehicle will automatically brake. This function can effectively avoid the occurrence of traffic accidents.
Shao, Youlin, Zhao, Guochu, Li, Xiaoqing, Shi, Ling, Cheng, Zhiqing
Robust Object Tracking Method Based on Multi-Level Association Matching2025-99-002010/17/2025
Target tracking is an important component of intelligent vehicle perception systems, which has outstanding significance for the safety and efficiency of intelligent vehicle driving. With the continuous improvement of technologies such as computer vision and deep learning, detection based tracking has gradually become the mainstream target tracking framework in the field of intelligent vehicles, and target detection performance is the key factor determining its tracking performance. Although remarkable progress has been made in current 3D object detection networks, a single network still struggles to provide stable detection for distant and occluded targets. Besides, traditional tracking methods are based on single-stage association matching, which can easily lead to identity jumps and target loss in case of missed detections, resulting in poor overall stability of the tracking algorithm. To solve the above problem, a hierarchical association matching method using a dual object
Wu, Shaobin, Chu, Yunfeng, Li, Yixuan, Su, Shengjie, Liu, Zhaofeng, Li, Xiaoan, Si, Lingrui
Investigation of Traffic Oscillation Suppression by Periodic-Configuration Vehicular Platoon12-09-02-001210/11/2025
To mitigate traffic oscillation in mixed traffic flow environments, which reduces road capacity and may lead to traffic accidents, this article innovatively proposes a periodic-configuration vehicular platoon to enhance traffic stability, inspired by the vibration attenuation properties of periodic structures. First, the vehicular platoon model is developed based on the periodic structure principle, and the lumped mass method is applied to derive the platoon spacing transfer matrix. Second, the band gap range is calculated based on the common traffic oscillation frequency by appropriately designing the period parameters in the periodic-configuration vehicular platoon. Additionally, the influence of these period parameters on the band gap range is analyzed. Finally, simulation experiments are conducted to analyze the propagation characteristics of traffic oscillations within the platoon, and the relative position diagrams of vehicles in the platoon are obtained. To validate the
Yang, Xiujian, Zhuang, Qingyuan, Wang, Shenyi
Safety Impacts of Active Safety and Driving Automation FeaturesEPR202501308/13/2025
Advanced technologies that assist the human driver or reduce (or even eliminate) the human driver’s role are becoming increasingly prevalent in new light-duty vehicles used by the general public. These technologies are divided between Active Safety features that monitor the human driver and vehicle motion and act intermittently to mitigate and avoid crashes, and Driving Automation features that assume some or all of the dynamic driving task from the human driver. Both types of technologies have the potential to reduce injuries and save lives by reducing the frequency and/or severity of crashes. Safety Impacts of Active Safety and Driving Automation Features addresses the current capabilities and future potential for Active Safety and Driving Automation features to reduce crash frequency and severity and provides an overview of the state of the industry for both types of features, including current deployments, trends, and anticipated rollouts. Gaps in knowledge, unsettled issues, and
Wishart, Jeffrey
Enhanced Cooperative Perception through Asynchronous Vehicle-to-Infrastructure Framework with Delay Mitigation for Connected and Automated Vehicles12-09-01-000808/08/2025
Perception is a key component of automated vehicles (AVs). However, sensors mounted to the AVs often encounter blind spots due to obstructions from other vehicles, infrastructure, or objects in the surrounding area. While recent advancements in planning and control algorithms help AVs react to sudden object appearances from blind spots at low speeds and less complex scenarios, challenges remain at high speeds and complex intersections. Vehicle-to-infrastructure (V2I) technology promises to enhance scene representation for connected and automated vehicles (CAVs) in complex intersections, providing sufficient time and distance to react to adversary vehicles violating traffic rules. Most existing methods for infrastructure-based vehicle detection and tracking rely on LIDAR, RADAR, or sensor fusion methods, such as LIDAR–camera and RADAR–camera. Although LIDAR and RADAR provide accurate spatial information, the sparsity of point cloud data limits their ability to capture detailed object
Saravanan, Nithish Kumar, Jammula, Varun Chandra, Yang, Yezhou, Wishart, Jeffrey, Zhao, Junfeng
SAE TOMORROW TODAY - How Buses Are Leading the Self-Driving Revolution1352908/08/2025
Thanks to their fixed routes and predictable environments, commercial vehicles like airport shuttles and public transit buses are proving to be the perfect match for SAE Level 4 automation. One company at the forefront of this transformation is Fusion Processing, a global leader in automated driving, situational awareness, and control systems. Fusion has logged over 90,000 kilometers of on-highway autonomous driving -- more than any other AV company in Europe. Their innovative technologies, including CAVstar and CycleEye, are already delivering tangible benefits in real-world commercial vehicle fleets, from freight to passenger services. To learn more, we sat down with Jim Hutchinson, Co-Founder and CEO, to discuss the future of transit and how Fusion is working to scale autonomous buses globally. 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
Patterson, Lori
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.
Data-Driven Analysis of Vehicle Lateral Control in the Country Road Domain2025-01-026807/02/2025
The optimization and further development of automated driving functions offer significant potential for reducing the driver's workload and increasing road safety. Among these functions, vehicle lateral control plays a critical role, especially with regard to its acceptance by end customers. Significant development efforts are required to ensure the effectiveness and reliability of this aspect in real-world conditions. This work focuses on analyzing lateral vehicle control using extensive measurement data collected from a dedicated vehicle fleet at the Institute of Automotive Engineering at the Technical University of Braunschweig. Equipped with state-of-the-art measurement technology, the fleet has driven several hundred thousand kilometers, allowing for the collection of detailed information on vehicle trajectories under various driving conditions. A total of 93 participants, aged between 20 and 43 years, contributed to the dataset. These measurements have been classified into
Iatropoulos, Jannes, Panzer, Anna, Arntz, Martin, Prueggler, Adrian, Henze, Roman
A Modular Framework for Lateral Movement Modeling2025-01-026907/02/2025
The lateral movement of vehicles within their lane determines occurring occlusions, and thus decides on the range of vision of vehicle sensors used by automated driving functions, and the objects detected by down-stream algorithms. As simulations play an integral role in the validation of automated driving functions, their ability to realistically model the lateral movement is crucial. However, currently applied methods such as microscopic traffic simulations, and scenario-based testing making use of maneuver-based scenario descriptions simplify or neglect the lateral movement of vehicles. For that reason, a two-level stochastic model has been introduced in earlier work. It consists of a Markov model for the systematic coarse movement, and a noise model for the residual fine movement. In follow-up publications, several advancements for both model components have been presented. These follow a modular structure, thus, can be flexibly combined. This paper for the first time gives an
Neis, Nicole, Beyerer, Jürgen
Applying Real World Traffic Data on Machine Learning Methods for Graph Based Cooperation Strategies of Automated Vehicles2025-01-028607/02/2025
With the increasing distribution of smart mobility systems, automated & connected vehicles are more and more interacting with each other and with smart infrastructure using V2X-communication. Hereby, the vehicles’ position, driving dynamics data, or driving intention are exchanged. Previous research has explored graph-based cooperation strategies for automated vehicles in mixed traffic environments based on current V2X-communication standards. Thereby, the focus is set on cooperation optimization and maneuver negotiation. These strategies can be implemented through both centralized and decentralized computational approaches and are conflict-free by design. To enhance these previously established cooperation models, real-world traffic data is used to derive vehicle trajectories, providing a more accurate representation of actual traffic scenarios in order to enhance the practical application of the described methodology. Additionally, machine learning algorithms are employed to train
Flormann, Maximilian, Meyer, Felix, Henze, Roman
Conception and Experience of Non-Driving Related Activities in Semi-Autonomous Vehicles2025-01-026007/02/2025
While semi-autonomous driving (SAE level 3 & 4) is already partially a reality, the driver still needs to take over driving upon notice. Hence, the cockpit cannot be designed freely to accommodate spaces for non-driving related activities. In the following use case, a mobile workplace is created by integrating a translucent acrylic glass pane into the cockpit and introducing joystick steering of the car. By using the technology Virtual Desktop 1, which is a software layer, any desktop application can be represented freely transformable on arbitrary physical and virtual surfaces. Thus, a complete Windows environment can be distributed across all curved and flat surfaces of an interior. The concept is further enhanced by a voice-driven generative AI which helps to summarize documents. A physical and a virtual demonstrator are created to experience and assess the mobile workspace, the well-being of the driver, external influences, and psychological aspects. The physical demonstrator is a
Beutenmüller, Frank, Reining, Nine, Rosenstiel, Reto, Schmidt, Maximilian, Layer, Selina, Bues, Matthias, Mendonca, Daisy
Edge Artificial Intelligence in Connected, Cooperative and Automated MobilityEPR202500905/20/2025
With many stakeholders involved, and major investments supporting it, the advancements in automated driving (AD) are undoubtedly there. Generally speaking, the motivation for advancing AD is driver convenience and road safety. Regarding the development of AD, original equipment manufacturers, technology start-ups, and AD systems developers have taken different approaches for automated vehicles (AVs). Some manufacturers are on the path toward stand-alone vehicles, mostly relying on onboard sensors and intelligence. On the other hand, the connected, cooperative, and automated mobility (CCAM) approach relies on additional communication and information exchange to ensure safe and secure operation. CCAM holds great potential to improve traffic management, road safety, equity, and convenience. In both approaches, there are increasingly large amounts of data generated and used for AD functions in perception, situational awareness, path prediction, and decision-making. The use of artificial
Van Schijndel-de Nooij, Margriet, Beiker, Sven
Variable Speed Limit Control Based on Deep Deterministic Policy Gradient under Mixed Traffic Environment12-09-01-000405/06/2025
The existing variable speed limit (VSL) control strategies rely on variable message signs, leading to slow response times and sensitivity to driver compliance. These methods struggle to adapt to environments where both connected automated vehicles (CAVs) and manual vehicles coexist. This article proposes a VSL control strategy using the deep deterministic policy gradient (DDPG) algorithm to optimize travel time, reduce collision risks, and minimize energy consumption. The algorithm leverages real-time traffic data and prior speed limits to generate new control actions. A reward function is designed within a DDPG-based actor-critic framework to determine optimal speed limits. The proposed strategy was tested in two scenarios and compared against no-control, rule-based control, and DDQN-based control methods. The simulation results indicate that the proposed control strategy outperforms existing approaches in terms of improving TTS (total time spent), enhancing the throughput efficiency
Ding, Xibin, Zhang, Zhaolei, Liu, Zhizhen, Tang, Feng
Simulation and Experimental: Enhanced Stability Control of Electric Vehicle Based on Phase Plane Boundary Analysis10-09-02-001705/05/2025
To optimize vehicle chassis handling stability and ride safety, a layered joint control algorithm based on phase plane stability domain is proposed to promote chassis performance under complicated driving conditions. First, combining two degrees-of-freedom vehicle dynamics model considering tire nonlinearity with phase plane theory, a yaw rate and side slip angle phase plane stability domain boundary is drew in real time. Then based on the real-time stability domain and hierarchical control theory, an integrated control system with active front steering (AFS) and direct yaw moment control (DYC) is designed, and the stability of the controller is validated by Lyapunov theory. Finally, the lateral stability of the vehicle is validated by Simulink and CarSim simulations, real car data, and driving simulators under moose test and pylon course slalom test. The experimental results confirm that the algorithm can enhance the maneuverability and ride safety for intelligent vehicles.
Liao, Yinsheng, Zhang, Zhijie, Su, Ailin, Zhao, Binggen, Wang, Zhenfeng
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
Data Dictionary for Quantities Used in Cyber-physical SystemsAS6969C (Current)04/30/2025
This data dictionary provides definitions for quantities, measurement units, reference systems, measurands, measurements, and quantity modalities commonly used in the command and control of cyber-physical systems. A cyber-physical system is an engineered system that is built from, and depends upon, the seamless integration of computational algorithms and physical components. Cyber-physical systems are often interconnected via data links and networks. The term encompasses intelligent vehicles and devices that operate in any environment, including robotic and autonomous systems.
AS-4 Unmanned Systems Committee
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
Traffic Flow Prediction Based on the Optuna–CNN–BiLSTM–GBRT Model12-09-01-000104/24/2025
Traffic flow prediction is very important in traffic-related fields, and increasing prediction accuracy is the primary goal of traffic prediction research. This study proposes a new traffic flow prediction method, which uses the CNN–BiLSTM model to extract features from traffic data, further models these features through GBRT, and uses Optuna to tune important hyperparameters of the overall model. The main contribution of this study is to propose a new combination model with better performance. The model integrates two deep learning models that are widely used in this field and creatively uses GBRT to process the output features of the front-end model. On this basis, the optimal hyperparameters and the robustness of the model are deeply explored, providing an effective and feasible solution to the difficult problems in traffic flow prediction. This model is experimentally studied using three different data transformation methods (original data, wavelet transform, Fourier transform
Ma, Changxi, Jin, Renzhe
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
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
Eco-Approach and Departure Algorithm for Connected and Automated PHEVs: Simulation and in-Vehicle Results2025-01-838404/01/2025
Plug-in Hybrid Electric Vehicles (PHEVs) combine the benefits of electric propulsion and storage with the extended range of conventional internal combustion engines to reduce fuel consumption and greenhouse gas emissions. However, optimizing the efficiency of PHEVs in real-world driving conditions remains a challenge due to the uncertainties of environmental and driving conditions. Connectivity and automation technologies can offer a unique opportunity to enhance the efficiency of PHEVs by enabling real-time interaction with surrounding vehicles and infrastructure. By leveraging these technologies, significant reductions in energy consumption for PHEVs can be achieved. However, most existing works primarily rely on simulation-based analyses to evaluate energy savings offered by connected and automated PHEVs. This study advances the understanding of the energy-saving potential of connected and automated PHEVs by incorporating experimental validation alongside simulation-based analyses
Kibalama, Dennis, Ozkan, Mehmet Fatih, Stockar, Stephanie, Canova, Marcello, Rizzoni, Giorgio
The Model Predictive Control of Dual-Chamber Active Air Suspension Based on Road Preview2025-01-878904/01/2025
Adverse weather conditions such as rain and snow, as well as heavy load transportation, can cause varying degrees of damage to road surfaces, and untimely road maintenance often results in potholes. Perception sensors equipped on intelligent vehicles can identify road surface conditions in advance, allowing each wheel’s suspension to actively adjust based on the road information. This paper presents an active suspension control strategy based on road preview information, utilizing a newly designed dual-chamber active air suspension system. It addresses the issue of point cloud stratification caused by vehicle body vibrations in onboard LiDAR data. The point cloud is processed through segmentation, filtering, and registration to extract real-time road roughness information, which serves as preview information for the suspension control system. The MPC algorithm is applied to actively adjust the nonlinear stiffness and damping of the suspension’s dual-chamber air springs, enhancing
Dong, Fuxin, Shen, Yanhua, Wang, Kaidi, Liu, Zuyang, Qian, Shuo
Testing Complex Circuits with Radio-Frequency Reflectometry2025-01-825104/01/2025
Driving automation systems rely heavily on sophisticated electronics to function effectively, and economic pressure poses new challenges in manufacturing. Tightly integrated sensors, processors, and communication modules monitor and control the vehicle's operation at any time. Size, weight, power, and cost constraints put pressure on manufactures to reduce stack electronics, miniaturize boards, and innovate over the traditional sequential assemble/test cycle. Consequently, designers and manufacturers reduce access to boards, remove test points, co-locate RF with other components, and break the sequential SMT line. Radio-frequency (RF) reflectometry is a mature and reliable technology essential for characterizing materials, components, and analog circuits. It provides precise insights into electromagnetic properties like impedance and permittivity, crucial for optimizing RF and microwave designs. Widely used in fields from material science to quantum computing, RF reflectometry is key
Moreno, Carlos, Sharma, Rakshit, Pabbi, Srijan, Fischmeister, Sebastian
Tube-MPC Based Path Tracking for I Vehicle Considering Data-Driven Stable Region and External Disturbances2025-01-826204/01/2025
Path tracking is a key function of intelligent vehicles, which is the basis for the development and realization of advanced autonomous driving. However, the imprecision of the control model and external disturbances such as wind and sudden road conditions will affect the path tracking effect and even lead to accidents. This paper proposes an intelligent vehicle path tracking strategy based on Tube-MPC and data-driven stable region to enhance vehicle stability and path tracking performance in the presence of external interference. Using BP-NN combined with the state-of-the-art energy valley optimization algorithm, the five eigenvalues of the stable region of the vehicle β−β̇ phase plane are obtained, which are used as constraints for the Tube-MPC controller and converted into quadratic forms for easy calculation. In the calculation of Tube invariant sets, reachable sets are used instead of robust positive invariant sets to reduce the calculation. Simulation results demonstrates that the
Zhang, Haosen, Li, Yihang, Wu, Guangqiang
The Effect of Directional Tactile Guidance on Takeover Performance in Level 3 Automated Driving2025-01-804904/01/2025
Automated driving is an important development direction of the current automotive industry. Level 3 automated driving allows the driver to perform non-driving related tasks (NDRTs) during automated driving, however, once the operating conditions exceed the designed operating domain, the driver is still required to take over. Therefore, it is important to rationally design takeover requests (TORs) in Level 3 conditional automated driving. This paper investigates the effect of directional tactile guidance on driver takeover performance in emergency obstacle avoidance scenarios during the transfer of control from automated driving mode to manual driving. 18 participants drove a Level 3 conditional automated driving vehicle in a driving simulator on a two-way four-lane urban road, performed a takeover, and avoided obstacles while performing non-driving related tasks. The driver's takeover performance during the takeover process was measured and subjective driver evaluation data was
Liang, Xinying, Liang, Yunhan, Ma, Xiaoyuan, Wang, Luyao, Chen, Guoying, Hu, Hongyu
Artificial intelligence being schooled for mining applications25TOFHP04_0604/01/2025
A battery-electric Honda midsize SUV entering production in early 2026 will use Helm.ai's artificial intelligence to facilitate conditional automated driving. The start-up firm's AI technology could soon see its first off-highway application. “Different driving environments look pretty much the same from an engineering perspective, so the lessons we've learned from [passenger vehicle] autonomous driving can be brought to the mining space in a fairly seamless fashion,” Vladislav Voroninski, cofounder and CEO of Helm.ai, said in an interview with SAE Media.
Buchholz, Kami
Machine Learning-Based Lane Detection and Lateral Offset Estimation Model for Vehicle Following Applications2025-01-802004/01/2025
Precisely understanding the driving environment and determining the vehicle’s accurate position is crucial for a safe automated maneuver. vehicle following systems that offer higher energy efficiency by precisely following a lead vehicle, the relative position of the ego vehicle to lane center is a key measure to a safe automated speed and steering control. This article presents a novel Enhanced Lane Detection technique with centimeter-level accuracy in estimating the vehicle offset from the lane center using the front-facing camera. Leveraging state-of-the-art computer vision models, the Enhanced Lane Detection technique utilizes YOLOv8 image segmentation, trained on a diverse world driving scenarios dataset, to detect the driving lane. To measure the vehicle lateral offset, our model introduces a novel calibration method using nine reference markers aligned with the vehicle perspective and converts the lane offset from image coordinates to world measurements. This design minimizes
Karuppiah Loganathan, Nirmal Raja, Poovalappil, Aman, Naber, Jeffrey, Robinette, Darrell, Bahramgiri, Mojtaba
Safe Cooperative Control Framework for Highway On-Ramp Merging2025-01-802404/01/2025
The development of connected and automated vehicles (CAVs) is rapidly increasing in the next generation and the automotive industry is dedicated to enhancing the safety and efficiency of CAVs. A cooperative control strategy helps CAVs to collaborate and share information among the neighboring CAVs, improving efficiency, optimizing traffic flow, and enhancing safety. This research proposes a safe cooperative control framework for CAVs designed for highway merging applications. In the urban transportation system, highway merging scenarios are high-risk collision zone, and the CAVs on the main and merging lanes should collaborate to avoid potential accidents. In the proposed framework, the on-ramp CAVs merge at 40 mph within the same and opposite directions to the main lane CAVs. The proposed framework includes the consensus controller, safety filter, and quadratic programming (QP) optimization method. The consensus controller incorporates the communication between CAVs and designs the
Chang, PeiYu, Bhatti, Sidra, Javed, Nur Uddin, Ahmed, Qadeer
On-Road Investigation of Energy Saving Opportunity for Autonomous Light-Duty Vehicles through Automated Vehicle-Following in Safe Distance Scenarios2025-01-802904/01/2025
Reducing aerodynamic drag through Vehicle-Following is one of the energy reduction methods for connected and automated vehicles with advanced perception systems. This paper presents the results of an investigation aimed at assessing energy reduction in light-duty vehicles through on-road tests of reducing the aerodynamic drag by Vehicle-Following. This study provides insights into the effects of lateral positioning in addition to intervehicle distance and vehicle speed, and the profile of the lead vehicle. A series of tests were conducted to analyze the impact of these factors, conducted under realistic driving conditions. The research encompasses various light-duty vehicle models and configurations, with advanced instrumentation and data collection techniques employed to quantify energy-saving potential. The study featured two sets of L4 capable light duty vehicles, including the Stellantis Pacifica PHEV minivan and Stellantis RAM Truck, examined in various lead and following vehicle
Poovalappil, Aman, Robare, Andrew, Schexnaydre, Logan, Santhosh, Pruthwiraj, Bahramgiri, Mojtaba, Bos, Jeremy P., Chen, Bo, Naber, Jeffrey, Robinette, Darrell
Objective and Subjective Evaluation of the Interaction of Automated Vehicles and Human Drivers in Take-Over Scenarios2025-01-804804/01/2025
The research activity aims at defining specific Operational Design Domains (ODDs) representative of Italian traffic environments. The paper focuses on the human-machine interaction in Automated Driving (AD), with a focus on take-over scenarios. The study, part of the European/Italian project “Interaction of Humans with Level 4 AVs in an Italian Environment - HL4IT”, describes suitable methods to investigate the effect of the Take-Over Request (TOR) on the human driver’s psychophysiological response. The DriSMI dynamic driving simulator at Politecnico di Milano has been used to analyse three different take-over situations. Participants are required to regain control of the vehicle, after a take-over request, and to navigate through a urban, suburban and highway scenario. The psychophysiological characterization of the drivers, through psychological questionnaires and physiological measures, allows for analyzing human factors in automated vehicles interactions and for contributing to
Gobbi, Massimiliano, Boscaro, Linda, De Guglielmo, Veronica, Fossati, Andrea, Galbiati, Andrea, Mastinu, Leda, Ponti, Marco, Mastinu, Gianpiero, Previati, Giorgio, Sabbioni, Edoardo, Signorini, Maria Gabriella, Somma, Antonella, Subitoni, Luca, Uccello, Lorenzo
Impact of Connected and Automated Vehicles on Longitudinal and Lateral Performance of Heterogeneous Traffic Flow in Shared Autonomy on Two-Lane Highways2025-01-809804/01/2025
Intelligent transportation systems and connected and automated vehicles (CAVs) are advancing rapidly, though not yet fully widespread. Consequently, traditional human-driven vehicles (HDVs), CAVs, and human-driven connected and automated vehicles (HD-CAVs) will coexist on roads for the foreseeable future. Simultaneously, car-following behaviors in equilibrium and discretionary lane-changing behaviors make up the most common highway operations, which seriously affect traffic stability, efficiency and safety. Therefore, it’s necessary to analyze the impact of CAV technologies on both longitudinal and lateral performance of heterogeneous traffic flow. This paper extends longitudinal car-following models based on the intelligent driver model and lateral lane-changing models using the quintic polynomial curve to account for different vehicle types, considering human factors and cooperative adaptive cruise control. Then, this paper incorporates CAV penetration rates, shared autonomy rates
Wang, Tianyi, Guo, Qiyuan, He, Chong, Li, Hao, Xu, Yiming, Wang, Yangyang, Jiao, Junfeng
Improving Vehicle Localization Confidence under Different Road Geometries2025-01-804304/01/2025
A significant challenge to the scalability of automated driving systems is the potential unavailability of GPS information for localization. To address this issue, a methodology using a static 2D map of road and lane geometry and vehicle on board sensors data is proposed to ensure reliable localization and navigation for automated vehicles in GPS-denied situations. In this study, a dead reckoning system based on vehicle kinematics is implemented by using onboard sensor data from the vehicle's Controller Area Network (CAN). However, the kinematic dead reckoning estimate has error accumulation, the drift in the dead reckoning position estimate is eliminated by using an arc-length based map matching approach. This innovative approach was tested and validated at various safety-critical intersection scenarios, including four-way intersection, roundabout, slip-lane intersection, and curved road. This approach ensures the continuous and reliable localization of automated vehicles, thereby
Javed, Nur Uddin, Singh, Yuvraj, Tan, Shengzhe, Ahmed, Qadeer
Portable Track-Based Connected Intersection Testing System for Connected and Automated Vehicles2025-01-806204/01/2025
Connected and automated vehicle (CAV) technology is a rapidly growing area of research as more automakers strive towards safer and greener roads through its adoption. The addition of sensor suites and vehicle-to-everything (V2X) connectivity gives CAVs an edge on predicting lead vehicle and connected intersection states, allowing them to adjust trajectory and make more fuel-efficient decisions. Optimizing the energy consumption of longitudinal control strategies is a key area of research in the CAV field as a mechanism to reduce the overall energy consumption of vehicles on the road. One such CAV feature is autonomous intersection navigation (AIN) with eco-approach and departure through signalized intersections using vehicle-to-infrastructure (V2I) connectivity. Much existing work on AIN has been tested using model-in-loop (MIL) simulation due to being safer and more accessible than on-vehicle options. To fully validate the functionality and performance of the feature, additional
Hamilton, Kayla, Misra, Priyashraba, Ord, David, Goberville, Nick, Crain, Trevor, Marwadi, Shreekant
Incorporating the Scenario-Based Framework Underpinned by Operational Design Domain (ODD) for Automated Driving Stack (ADS) Evaluation2025-01-806704/01/2025
The rapid development of open-source Automated Driving System (ADS) stacks has created a pressing need for clear guidance on their evaluation and selection for specific use cases. This paper introduces a scenario-based evaluation framework combined with a modular simulation framework, offering a scalable methodology for assessing and benchmarking ADS solutions, including but not limited to off-the-shelf designs. The study highlights the lack of clear Operational Design Domain (ODD) descriptions in such systems. Without a common understanding, users must rely on subjective assumptions, which hinders the process of accurate system selection. To address this gap, the study proposes adopting a standardised ISO 34503 ODD description format within the ADS stacks. The application of the proposed framework is showcased through a case study evaluating two open-source systems, Autoware and Apollo. By first defining the assumed system’s ODD, then selecting a relevant scenario, and establishing
Chodowiec, Emil, Zhang, Xizhe, Mitchell, Joe, Baker, Peter, Khastgir, Siddartha, Jennings, Paul
Integrated Multimodal System for Real-Time Driver Fatigue Detection and Cognitive Load Assessment2025-01-807604/01/2025
As human drivers' roles diminish with higher levels of driving automation (SAE L2-L4), understanding driver engagement and fatigue is crucial for improving safety. We developed an integrated hardware and software system to analyze driver interaction with automated vehicles, with a particular focus on cognitive load and fatigue assessment. The system includes three submodules; namely the Driver Behavior Measurement (DBM), Vehicle Dynamics Measurement (VDM), and the Driver Physiological Measurement (DPM). The DBM module uses electro-optical (EO) and infrared (IR) camera to track a number of facial features such as eye aspect ratio (EAR), mouth aspect ratio (MAR), pupil circularity (PUC), and mouth to eye aspect ratio (MOE). Although determining these metrics from images of the driver’s face in conditions such as low light or with sunglasses is challenging, the paper showed that fusion of EO and IR image analysis produces robust performance. The VDM module utilizes an Inertial Measurement
Jirjees, Abdullah, Rahman, Taufiq, Farhani, Ghazal, Singh, Daniel, Charlebois, Dominique
Reproduction of Real-World Scenarios in CARLA: An Extension of CARLA Functionality2025-01-805904/01/2025
Reproducing driving scenarios involving near-collisions and collisions in a simulator can be useful in the development and testing of autonomous vehicles, as it provides a safe environment to explore detailed vehicular behavior during these critical events. CARLA, an open-source driving simulator, has been widely used for reproducing driving scenarios. CARLA allows for both manual control and traffic manager control (the module that controls vehicles in autopilot manner in the simulation). However, current versions of CARLA are limited to setting the start and destination points for vehicles that are controlled by traffic manager, and are unable to replay precise waypoint paths that are collected from real-world collision and near-collision scenarios, due to the fact that the collision-free pathfinding modules are built into the system. This paper presents an extension to CARLA’s source code, enabling the replay of exact vehicle trajectories, irrespective of safety implications
Ai, Yan, Adhikari, Bikram, Park, Chung-Kyu, Kan, Cing-Dao, Wijesekera, Duminda
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