Browse Topic: Autonomous vehicles

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Artificially Intelligent Rapid Rescue Vehicle System2026-26-0642To be published on 01/16/2026
human lifestyle. The automotive industry plays a crucial role as travel is an integral part of human lifestyle. This indeed has increased the need and demand for automotive domain to step ahead with technology and innovations. Especially, related to ADAS features and AI/ML based algorithms to provide comfort, safety, and many other factors for the consumers. The busy life of human beings has shown increased rate of many health-related issues like stress, anxiety, heart attacks, blood pressure, accidents etc. The existing system in vehicles detects health emergencies and triggers SOS to the emergency service center. However, several catastrophic events occur due to delayed information, thus there is a need for a proactive solution that combines technology and human safety. In this work, we have investigated the different methods which detect the health issues of occupants in a vehicle by monitoring their stress level, heart rate, blood pressure etc., We propose a solution which helps to
Eswarappa, Asha, Nagaraj, Chaitra, Mudassir, Syed
GTSRB-Shield: A Novel Machine Learning Framework for Intrusion Detection in Autonomous Vehicles Leveraging the German Traffic Sign Recognition Benchmark2026-26-0611To be published on 01/16/2026
The escalating dependence of Autonomous Vehicles on Intelligent Transportation Systems (ITS) has highlighted the imperative for comprehensive security protocols to safeguard such vehicles against cyber threats. Intrusion Detection Systems (IDS’s) are pivotal in ensuring the protection of these systems by detecting and alleviating unauthorized access and nefarious activities. The German Traffic Sign Recognition Benchmark (GTSRB) database, which encompasses an extensive compilation of traffic sign imagery, functions as a vital asset for the advancement of machine learning-based IDS. This research elucidates an intrusion detection system (IDS) that employs machine learning algorithms to scrutinize the GTSRB database. The proposed IDS emphasize the preprocessing of the GTSRB dataset to extricate pertinent features that can be employed for the training of machine learning models. Research also focuses on model development with machine learning algorithms to classify traffic signs and
Patil, Kamalesh, Akbar Badusha, A., Jadhav, Savitri, Gunale, Kishanprasad
Swarm-Inspired Cooperative Mapping for Urban Autonomous Navigation2026-26-0635To be published on 01/16/2026
Urban autonomous vehicles today largely rely on individually built maps or centralized cloud services—both rigid under intermittent connectivity and rapid scene changes (construction, pop-up events). Inspired by how ant colonies collectively explore, we introduce a decentralized protocol: each vehicle broadcasts compressed “map particles” embedded with virtual pheromone values that highlight zones of high uncertainty or recent change. Neighboring vehicles incorporate these signals to prioritize sensing and route planning. A lightweight consensus algorithm merges local graphs into a shared dynamic map, resilient to network splits. The result, a bio-inspired SLAM that enables fleets to adapt on the fly to evolving cityscapes, boosting safety, efficiency, and robustness.
Bhargav, Anirudh S, Subbarao, Chitrashree
Enhancing Factory Modelling: Integrating Drone Technology and Automated Post Processing Systems2026-26-0307To be published on 01/16/2026
The growing environmental, economic, and social challenges have spurred a demand for cleaner mobility solutions. In response to the transformative changes in the automotive sector, manufacturers must prioritize digital validation of products, manufacturing processes, and tools prior to mass production. This ensures efficiency, accuracy, and cost-effectiveness. By utilizing 3D modeling of factory layouts, factory planners can digitally validate production line changes, substantially reducing costs when introducing new products. One key innovation involves creating 3D models using point cloud data from factory scans. Traditional factory scanning processes face limitations like blind spots and periodic scanning intervals. This research proposes using drones equipped with LiDAR (Light Detection and Ranging) technology for 3D scanning, enabling real-time mapping, autonomous operation, and efficient data collection. Drones can navigate complex areas, access small spaces, and optimize factory
Narad, Akshay Maruti, C H, Ajheyasimha, Vijayasekaran, Vinothkumar, FASGE, ABHISHEK
Vehicle Self-Soiling from Exterior Cleaning Mechanisms2026-26-0425To be published on 01/16/2026
The automotive industry is rapidly advancing towards autonomous vehicles, making sensors such as cameras, LiDAR, and RADAR critical components for ensuring constant information exchange between the vehicle and its surrounding environment. However, these sensors are vulnerable to harsh environmental conditions like rain, dirt, snow, and bird droppings, which can impair their functionality and disrupt accurate vehicle maneuvers. To maintain sensor performance, cleaning systems are implemented in vehicles. Testing sensor cleaning mechanisms under various weather conditions and vehicle operating situations is essential. One crucial aspect of testing is tracking the trajectory of the cleaning fluid to ensure it does not cause self-soiling of vehicle or affect other field of view, such as the windshield. While wind tunnel tests are valuable, digitalizing this process is vital for making design decisions early in vehicle development. This work presents a digital methodology to test the self
Mane, Suvidya, Makam, Sri Lalith Madhav, Varghese, Rixson, Desu, Harsha
Integrating V2V and V2I Communications for Enhanced Traffic Safety and Efficiency in Connected Vehicular Networks2026-26-0271To be published on 01/16/2026
This paper presents a new approach to improve road safety and traffic flow by combining vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Our study focuses on a system that connects vehicles with each other and with traffic signals to share real-time data about speed, position, and road conditions. Using both analytical models and experimental simulations built in MATLAB/Simulink, we developed a system that predicts and advises the optimal speed for vehicles approaching an intersection. In our design, a Green Light Optimized Speed Advisory (GLOSA) feature suggests an ideal speed for drivers so that they can pass through green lights without unnecessary stopping. This not only improves traffic flow but also reduces fuel consumption and lowers the risk of accidents. We validated our system through a series of simulations and real-time traffic scenarios. The results show that our approach can adapt dynamically to changes in vehicle behavior and traffic density
PINTO, COLIN AUBREY, Shah, Ravindra, Karle, Ujjwala
Advancing Automotive Radar Characterization: OTA HIL Testing for Enhanced ADAS Safety2026-26-0043To be published on 01/16/2026
The increasing demand for reliable Advanced Driver-Assistance Systems (ADAS) and autonomous driving technologies necessitates the precise validation of automotive radar sensors. Traditional on-road testing, while effective, presents challenges such as limited repeatability, high costs, and safety concerns. To address these limitations, this paper introduces an Over-the-Air (OTA) Hardware-in-the-Loop (HIL) methodology for radar characterization, providing a controlled and repeatable lab-based validation approach. This methodology enables dynamic testing of key radar parameters, including range estimation, angle of arrival measurement, Doppler velocity analysis, and object detection accuracy. The proposed setup supports diverse testing scenarios, such as variable target distances, relative speeds, multiple object simulation, and environmental effects, ensuring a comprehensive evaluation of radar behaviour under complex and safety-critical conditions. Through OTA testing, developers can
Jadhav, Tejas, Karle, Ujjwala, Paul, Harshit, SNV, Karthik
Single Radar: A Robust and Cost-Effective Solution for ADAS and Parking functionality2026-26-0049To be published on 01/16/2026
With rapid advancements in Autonomous Driving (AD) & Advanced Driver Assistance Systems (ADAS), numerous sensors are integrated in vehicles to achieve higher and reliable level of autonomy. Due to the growing number of sensors and its fusion creates complex architecture which causes challenges in calibration, cost, and system reliability. Considering the need for further ADAS advancements and addressing the challenges, this paper evaluates a novel solution called One Radar - a single radar system with a wide field of view enabled by advanced antenna design. Placing the One Radar at the rear of the vehicle eliminates the need for corner radars and ultrasonic sensors used for parking assistance. With rigorous real-world testing in different urban and low-speed scenarios, the One Radar solution showed comparable accuracy in object detection with warning and parking assistance to the conventional combination of corner radars and ultrasonic sensors. The simple single sensor-based
Anandan, Ram, Sharma, Akash
Automotive Cybersecurity Incident Response (CSIR) Framework and Its Integration into OEM Processes2026-26-0613To be published on 01/16/2026
The exponential growth of connected and autonomous vehicles has significantly escalated cybersecurity threats, compelling automotive Original Equipment Manufacturers (OEMs) to adopt robust and structured Cybersecurity Incident Response (CSIR) capabilities. Current automotive cybersecurity regulations, such as AIS 189 in India and UNECE WP.29 globally, mandate precise frameworks for proactive threat detection, timely response, and comprehensive incident documentation. This research presents an innovative, comprehensive CSIR framework specifically tailored to integrate seamlessly into OEM cybersecurity management processes. Leveraging a combination of real-time monitoring systems, structured threat categorization methodologies, and integrated escalation and communication protocols, the proposed CSIR framework ensures efficient incident handling aligned with stringent regulatory compliance. The framework encompasses advanced methodologies including Vehicle Security Operations Center (VSOC
chaudhary lng, VIKASH, Desai, Manoj, Chatterjee, Avik, Chatterjee lng, Avik
Smart Charge Port Housing with Integrated Single Shaft Self-Locking Mechanism for Electric Vehicles2026-26-0593To be published on 01/16/2026
This research presents the development of an advanced Smart Charge Port Housing system for electric vehicles that incorporates intelligent actuation and an integrated single shaft self-locking mechanism. Conventional charge port systems rely on communication with the vehicle’s Body Control Module or Vehicle Control Unit, limiting functionality when the vehicle is in an ignition-off state. The proposed Charge Port Housing addresses this limitation through a microcontroller-based rotary actuator capable of autonomous decision-making, storing the last known position of the lid and executing operations independently of the vehicle’s electronic control architecture. The actuator operates via wired input signals and supports seamless communication over the Local Interconnect Network protocol during ignition-on state. A key innovation lies in the mechanical design: a single shaft self-locking mechanism that combines actuation and locking into a compact, unified assembly, eliminating the need
Mohunta, Sanjay, Khadake, Sagar
Mapless Yet Accurate: Trajectory Prediction for traffic agents using online HD Map Reconstruction for Autonomous Driving2026-26-0039To be published on 01/16/2026
Accurate trajectory prediction of traffic agents is vital for enabling safer and more reliable autonomous driving. High-definition (HD) and standard-definition (SD) maps play a critical role in this process by providing detailed lane topology and directional cues essential for forecasting the movement of surrounding traffic agents. However, creating HD maps is both expensive and resource-intensive, often relying on complex SLAM systems and specialized sensors, while SD maps, though more readily available, lack the precision needed for accurate navigation in autonomous driving. In this work, we present a novel framework for trajectory prediction that uses online reconstruction of HD maps using images from vehicle-mounted cameras, offering a more scalable and cost-effective solution. Our method achieves notable improvements in prediction accuracy, especially in scenarios lacking access to pre-built maps. Additionally, we propose a new evaluation metric that emphasizes safety by
Upreti, Minali, Girijal, Rahul, B A, NaveenKumar, Thontepu, Phani, Ghosh, Shankhanil, Chakraborty, Bodhisattwa
Revolutionizing Mass Mobility: Sustainable, Safe, and Smart Transport Ecosystems2026-26-0360To be published on 01/16/2026
Mass Mobility Systems are critical for a sustainable and progressive society. As the world confronts the serious challenges of global warming and urban traffic congestion, efficient mass mobility solutions become critical in reducing carbon footprints and enabling equitable access. Advancement in mass mobility is not limited to electric buses alone but also includes innovations across conventional ICE vehicles, autonomous vehicles, Trains and other integrated transport networks. Safety, and accessibility for users remain critical to the sustainability of future mass mobility concepts. The COVID-19 pandemic exposed vulnerabilities in public transportation, highlighting the urgent need for safer and more resilient systems. Road safety, passenger well-being, and hygienic standards must be deeply embedded into future mobility solutions. Furthermore, strong last-mile connectivity will be essential to ensure that mass mobility truly meets the needs of all citizens. An effective Mass Mobility
Vasudevan, M, Kumar S, Ashok, Sridevi, M, Kumar, Rajiv, Kumar, Om
Reducing Risk in Safety Critical Systems through Early Safety Case Development and Dialectic Arguments2026-26-0044To be published on 01/16/2026
The rapid evolution of modern automotive systems—powered by advancements in autonomous driving, and connected vehicle technologies— pose fundamental challenges to design and integration. Ensuring the safety of these highly interconnected, software-driven systems, with the widespread reuse of components across platforms. This growing complexity calls for a more structured and rigorous approach to safety assurance than traditional methods. Traditional safety cases tend to take a linear, justification-focused approach that mainly focuses on positive assertions —statements of compliance or success—while giving limited attention to potential weaknesses, or gaps in supporting evidence. This practice may lead to criticism that such arguments are “too positive,” portraying an overly optimistic view of system safety without sufficiently acknowledging areas of unresolved risk. As a result, conventional approaches for developing safety case may overlook complex interactions, assumptions, and
Kumar, Amrendra, Bagalwadi, Saurabh, McMurran, Ross
Curvature-Continuous Trajectory Refinement for Autonomous Vehicles Using a Pure Pursuit Based Path Smoothing Framework2026-26-0135To be published on 01/16/2026
Path planning is a key element of autonomous vehicle navigation, allowing vehicles to calculate feasible paths in challenging environments. Algorithms such as Hybrid A*, Reeds-Shepp, and Dubins paths effectively generate collision-free paths but tend to create curvature discontinuities. These discontinuities result in sudden steering transitions, which create control instabilities, higher mechanical stress, and lower passenger comfort. To overcome these issues, this paper suggests a path-smoothing technique based on the pure-pursuit algorithm to produce curvature-continuous, distortion-free paths appropriate for real-world driving. This method utilizes the practical approach of the original path but removes sudden transitions that destabilize control. By ensuring smooth curvature, the vehicle undergoes fewer jerky steering actions, enhanced energy efficiency, less actuator wear, and improved high-speed tracking. Experimental verifications, such as real-time tests on autonomous vehicles
S, Shriniyathi, A, Josana, Edwin J, Joel, T, Akshayaa, M, Senthil Vel, Kumar PhD, Vimal
Regulatory Divergence in Autonomous Vehicle Deployment A Comparative Study of the US, EU, and China2026-26-0243To be published on 01/16/2026
Autonomous vehicle (AV) regulatory frameworks vary significantly across global regions, with the United States, European Union (EU), and China exemplifying distinct approaches. The US adopts a decentralized model, allowing state-level regulation with federal guidance, fostering testing and commercial deployment of Level 4 automation. The EU enforces a harmonized, safety-focused framework under legislation like Regulation (EU) 2019/2144 and (EU) 2022/1426, emphasizing structured validation within defined operational domains. China employs a centralized regulatory hierarchy, integrating national standards with localized pilot programs and connected infrastructure. While the US leads in commercial deployment and China advances through coordinated efforts, the EU’s cautious framework is often perceived as a barrier to rapid AV adoption. This paper critically analyzes these regulatory models, emphasizing the need for a robust, harmonized framework that ensures safety and public trust
Lujan Tutusaus, Carlos, Hidalgo, Justin, Flix, Oriol
Research on Off-Road Performance Optimization Strategy for Agricultural Smart Vehicles Based on Traction Control System2025-99-011811/11/2025
(TC)The paper presents a designed and evaluated optimal traction control (TC) strategy for unmanned agriculture vehicle, where onboard sensors acquire various real-time information about wheel speed, load sharing, and terrain characteristics to achieve the precise control of the powertrain by establishing an optimal control command; moreover, the developed AMT-adaptive SMC combines the AMT adaptive control algorithm and the SMC to implement the dynamic gear shifting, torque output, and driving mode switching to obtain an optimal power distribution according to different speed demand and harvest load. Based on the establishment of models of the autonomous agriculture vehicle and corresponding tire model, a MATLAB/Simulink method based on dynamic simulation is adopted to simulate the unmanned agricultural vehicle traversing different terrains conditions. The results from comparison show that the energy saving reaches 19.0%, rising from 2. 1 kWh/km to 1. 7 kWh/km, an increase in
Feng, Zhenghao, Lu, Yunfan, Gao, Duan, An, Yi, Zhou, Chuanbo
AI-Empowered Lightweight In-Vehicle Network Security Mechanisms: From Cryptographic Algorithms to Collaborative Defense Architectures2025-99-013211/11/2025
With the rapid development of Internet of Vehicles (IoV) and cyber-physical systems (CPS), connected autonomous vehicles (CAVs) have also developed rapidly. However, at the same time, in-vehicle networks also face more security challenges, mainly in terms of resource constraints, dynamic attacks, protocol heterogeneity, and high real-time requirements. Firstly, the trade-offs between lightweight encryption primitives and their software and hardware collaborative design in terms of performance, resource overhead, and security strength are analyzed. Secondly, the resource efficiency of AI-based intrusion detection system (IDS) is evaluated at the edge. Finally, we propose a dynamic adaptive collaborative defense framework (DACDF), which integrates federated learning with dynamic weight distillation, blockchain authentication with lightweight verifiable delay function (Light-VDF) and cross-domain IDS with hierarchical attention feature fusion to deal with collaborative attacks in resource
Zhou, You, Zhang, Jigui, Ding, Kani, Yang, Guozhi
Research on Low-Power Control of Offshore Wind Sail Stabilization Devices Based on Single Pendulum Control Simulation2025-99-013111/11/2025
To tackle persistent operational instability and excessive energy consumption in marine observation platforms under wave-induced disturbances, this paper introduces a novel ultra-low-power stabilization system based on pendulum dynamics. The system employs an innovative mechanical configuration to deliberately decouple the rotation axis from the center of mass, creating controlled dynamic asymmetry. In this behavior, the fixed axis serves as a virtual suspension pivot while the camera payload functions as a concentrated mass block. This configuration generates intrinsic gravitational restoring torque, enabling passive disturbance attenuation. And its passive foundation is synergistically integrated with an actively controlled brushless DC motor system. During platform oscillation, embedded algorithms detect angular motion reversals. In addition, their detection triggers an instantaneous transition from motor drive to regenerative braking mode, and transition facilitates bidirectional
Zhang, Tianlin, Liu, Shixuan, Xu, Yuzhe
SAE TOMORROW TODAY - Your Order Has Landed: The Power of Drone Delivery1354211/10/2025
From crispy French fries to critical medical supplies, one company is transforming how goods move around the world with autonomous drone delivery technology. In 2016, Zipline, began with life-saving deliveries of blood and medical products in Rwanda and has since evolved into a global network expanding access to healthcare, consumer goods, and food. Operating across four continents and completing a delivery every 60 seconds, Zipline now serves more than 5,000 hospitals, retailers, and restaurants. Listen in as we sit down with Keenan Wyrobek, Co-Founder and CTO, to explore how Zipline's pioneering approach to drone logistics is saving lives, reducing emissions, and opening new doors for economic opportunity -- one autonomous flight at a time. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility
Patterson, Lori
Functional Safety of Electric or Hybrid Off-Highway Vehicles – An Embedded Software Viewpoint2025-28-021711/06/2025
To provide growing needs of food, clothing and infrastructure for growing population of the world, off-highway vehicles such as those in construction, agriculture and commercial landscaping are moving towards electrification for enhanced precision, productivity, efficiency and sustainability. It has also paved a way to adopt autonomy of these vehicles to address challenges like skilled labor shortage for timely and efficient execution. Despite the tremendous advantages of electrification, be it through completely replacing engines in vehicles or efficiency improvements using hybrid architecture for powertrain and auxiliary power demands, safety remains a significant challenge and critical requirement for off-highway electric vehicles. This paper explains the concept and importance of functional safety in electric off-highway vehicles, and shows how different standards like ISO 26262, ISO 25119, ISO 13849 can be utilized to achieve state of the art in functional safety for different off
Mujumdar, Chaitanya Gajanan, Bachhav, Kiran, Deshpande, Chinmay
Ensuring Functional Safety and SOTIF for Autonomous Off-Highway Vehicles: Addressing Risks in Complex Systems2025-28-031511/06/2025
The increasing complexity of autonomous off-highway vehicles, particularly in mining, demands robust safety assurance for Electronic/Electrical (E/E) systems. This paper presents an integrated framework combining Functional Safety (FuSa) and Safety of the Intended Functionality (SOTIF) to address risks in autonomous haulage systems. FuSa, based on ISO 19014[1] and IEC 61508[2], mitigates hazards from system failures, while SOTIF, adapted from ISO 21448[3] addresses functional insufficiency and misuse in complex operational environments. We propose a comprehensive verification and validation (V&V) strategy that identifies hazardous scenarios, quantifies risks, and ensures acceptable safety levels. By tailoring automotive SOTIF standards to off-highway applications, this approach enhances safety for autonomous vehicles in unstructured, high-risk settings, providing a foundation for future industry standards.
Kumar, Amrendra, Bagalwadi, Saurabh
Cybersecurity: Applying Threat Modeling to Sensor-Control Architectures in Autonomous Off-Highway Vehicles2025-28-031611/06/2025
The rapid evolution of autonomy in Off-Highway Vehicles (OHVs)—spanning agriculture, mining, and construction—demands robust cybersecurity strategies. Sensor-control systems, the cognitive core of autonomous OHVs, operate in harsh, connectivity-limited environments. This paper presents a structured approach to applying threat modeling to these architectures, ensuring secure-by-design systems that uphold safety, resilience, and operational integrity.
Kotal, Amit
Off-Highway Electrification: A Case Study beyond Propulsion and Emission2025-28-022311/06/2025
To provide needs of food, clothing and infrastructure for growing population of the world, off-highway vehicles such as those in construction, agriculture and commercial landscaping are moving towards electrification for enhanced precision, productivity, efficiency and sustainability. It has also paved way to adopt autonomy of these vehicles to address challenges like skilled labour shortage for timely and efficient execution. There are many challenges and opportunities of electrification in off-highway domain, be it through completely replacing engine in vehicles or efficiency improvements using hybrid architecture for powertrain and auxiliary power demands, electrification being key enabler precision and speed of the complex operations, automation of complex operation. This paper explains the need of electrification in electric off-highway vehicles and shows how the electrification solves the current challenges faced by off-highway heroes like farmers, construction site owners and
Deshpande, Chinmay Vasudev, Mujumdar, Chaitanya, Bachhav, Kiran
A Theoretical Approach to Secure Protocols for Ultra-Reliable Low-Latency Communication (URLLC) in V2X Systems2025-28-032611/06/2025
Ensuring secure and ultra-reliable low-latency communication (URLLC) is critical for Vehicle-to-Everything (V2X) systems, which form the backbone of autonomous transportation. This paper presents a theoretical framework for designing secure communication protocols tailored for V2X systems with stringent latency and reliability requirements. The proposed framework incorporates dynamic message prioritization, adaptive encryption, and lightweight authentication to address the unique challenges of V2X networks. The study provides mathematical models to predict latency and security performance under varying network conditions, with a focus on scalability and efficiency. This work aims to contribute a foundational approach for future advancements in URLLC protocols in autonomous vehicle ecosystems.
Imran, Shaik Moinuddin
Leveraging AI for Autonomous Vehicle Simulations in SIL and HIL Frameworks2025-28-027111/06/2025
This paper offers recent ideas and its implementation on leveraging AI for off highway Autonomous vehicle Simulations in SIL and HIL frameworks. Our objective is to enhance software quality and reliability while reducing costs and efforts through advanced simulation techniques. We employed multiple innovative solutions to build a System of Systems Simulation. Physics based models are a prerequisite for detailed and accurate representation of the real-world system, but it poses challenges due to its computational complexity and storage requirements. Machine learning algorithms were used to create surrogate/reduced order models to optimize by preserving the expected fidelity of models. It helped to speed up simulation and compile model code for SIL & HIL Targets. Built AI driven interfaces to bridge windows, Linux and Mobile Operating systems. Time synchronization was the key challenge as multiple environments were needed for end-to-end solutions. This was resolved by reinforcement
Karegaonkar, Rohit P., Aole, Sumit, Dasnurkar, Swapnil, Singh, Vishwajeet, Saha, Soumyadeep
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
Development of Capacitive Pressure Sensor for Automotives2025-28-038710/30/2025
Measurement plays a crucial role in the precise and accurate management of automotive subsystems to enhance efficiency and performance. Sensors are essential for achieving high levels of accuracy and precision in control applications. Rapid technical advancements have transformed the automobile industry in recent years, and a wide range of novel sensor devices are being released to the market to speed up the development of autonomous vehicle technology. Nonetheless, stricter regulations for reliable pressure sensors in automobiles have resulted from growing legal pressures from regulatory bodies. This work proposes and investigates a tribo electric nano sensor that is affected by a changing parameter of the separation distance between the device's primary electrode and dielectric layers. The system is being modeled using the COMSOL multiphysics of electrostatics and the tribo-electric effect. Open circuit electric potential and short circuit surface charge density are two of the
P, Geetha, K, Neelima, Sudarmani, R, C, Venkataramanan, Satyam, Satyam, Nagarajan, Sudarson
Automatic Brake Failure Detection with Electromagnetic Braking System2025-28-037310/30/2025
Brake failures in the vehicles can cause hazardous accidents so having a better monitoring and emergency braking system is very important. So, this project consists of an autonomous brake failure detector integrated with Automatic Braking using Electromagnetic coil braking which detects the braking failure at the time and applied the combinations of the brakes, to overcome this kind of accidents. So, here the system comprises of IR sensor circuit, control unit and electromagnetic braking system. How it works: The IR sensor monitors the brake wire, and if the wire is broken, the control unit activates the electromagnetic brakes, stopping the vehicle in a safe manner. This system enhances vehicle safety by ensuring immediate braking action without driver intervention. Key advantages include real-time brake monitoring, reduced mechanical wear, quick response time, and an automatic failsafe mechanism. The system’s minimal reliance on hydraulic components also makes it suitable for harsh or
Raja, Selvakumar, John, Godwin, Siddarth, J P, Senthilkumar, Akash, Mathew, Abhay, R. S., Nakandhrakumar, Nandagopal, Sasikumar, Arumugam, Sivasankar
A Hybrid Framework for Kinematic Vehicle Prediction: YOLOv11 with Unscented Kalman Filter12-09-02-001310/22/2025
In the context of intelligent transportation systems and applications such as autonomous driving, it is essential to predict a vehicle’s immediate future states to enable precise and timely prediction of vehicles’ movements. This article proposes a hybrid short-term kinematic vehicle prediction framework that integrates a novel object detection model, You Only Look Once version 11 (YOLOv11), with an unscented Kalman filter (UKF), a reliable state estimation technique. This study provides a unique method for real-time detection of vehicles in traffic scenes, tracking and predicting their short-term kinematics. Locating the vehicle accurately and classifying it in a range of dynamic scenarios is achievable by the enhanced detection capabilities of YOLOv11. These detections are used as inputs by the UKF to estimate and predict the future positions of the vehicles while considering measurement noise and dynamic model errors. The focus of this work is on individual vehicle motion prediction
Pahal, Sudesh, Nandal, Priyanka
The Morning Commute Problem with Carpool and Dynamic Toll Scheme2025-99-002210/17/2025
This study extends the bottleneck model to analyze dynamic user equilibrium (UE) in carpooling during the morning peak commute. It is assumed that the carpooling platform offers both traditional human-driven vehicles and novel shared autonomous vehicles. First, we analyze the traffic distribution on a two-lane road. We find that traffic distribution is influenced by the additional cost of carpooling behavior. A corresponding functional relationship is established and visualized. Second, we derive the critical fare threshold for carpooling. Carpooling occurs only when the fare is below this threshold. Third, we obtain the user equilibrium (UE) solution under a specified case, including flow distribution, equilibrium cost, and total number of vehicle. Furthermore, a system-optimal dynamic tolling scheme is proposed to minimize total system cost while maintaining commuter UE. By equating the system marginal cost to the equilibrium cost, we derive the analytical expression for the lane
Zheng, XiaoLong, Zhong, RenXin
A Distributed V2X Hardware-in-the-loop Test System Based on the PanoSim for Intelligent Connected Vehicles2025-99-004110/17/2025
Based on the similarity analysis of Intelligent Connected Vehicles (ICVs), a distributed V2X hardware-in-the-loop test system for ICVs is designed, including the PanoSim autonomous driving simulation engine, GNSS simulator, V2X simulator, and management and cooperative control software. The system integrates the major technologies of distributed interaction, including operation management, time synchronization, coordinate conversion, and data preprocessing, and realizes the spatial and temporal consistency of each simulation node. 89 V2X first-stage application scenarios (e.g., FCW, RLVW) and 5 V2X second-stage application scenarios (e.g., CLC) use case experimental results have proved the reliability of the system. The FCW use case experiment results show that its simulation results pass with high confidence. The study emphasizes the value of the system in reducing development costs, improving safety, and accelerating the deployment of V2X applications, while identifying future
Gao, Tianfang, Zhang, XingHui, Chen, Liang, Huang, Zhichen, Ni, Dong
A Review of Current Research and Future Developments in Field Modelling for Traffic Risk Analysis2025-99-005810/17/2025
Discovering the trend of risk changes and formulating risk prevention and control measures are important links in achieving proactive risk prevention and control. Constructing and analyzing field models can visualize the distribution and change of risks and formulate effective risk prevention and control measures. Based on the current situation and trend of field model research, this paper discusses its application in risk identification, aiming to improve the accuracy of risk avoidance. Firstly, different types of field models are classified, and their respective characteristics and application scenarios are introduced. Secondly, the shortcomings in the development of field models are summarised. Finally, in the field of autonomous driving and intelligent traffic management, it is proposed that the accuracy of the model can be improved by multi-scene data fusion, the dynamic response enhances the efficiency of risk avoidance, and the aspect of risk classification in complex
Song, Yulian, Yue, Lihong, Wang, Chunxiao
Experimental Evaluation of Behavior Perception and Prediction Methods in Automated Port Logistics Operations2025-99-006010/17/2025
With the rapid development of autonomous driving technologies, intelligent ports, particularly autonomous logistics, have become the focus of industry attention. Ensuring safe and efficient operations require port management systems to perceive and predict the behaviors of people and vehicles. In the filed of behavior perception, research efforts have primarily focused on the detection and tracking of vehicles, pedestrians, and obstacles under various sensor configurations. Common approaches include vision-based, LiDAR-based, and multi-sensor fusion methods. In terms of behavior prediction, existing approaches can be broadly categorized into four paradigms: model-driven, data-driven, environment-assisted, and anomaly prediction methods. Model-driven approaches rely on physical and motion models, while data-driven approaches utilize deep learning techniques. Environment-assisted approaches integrate prior knowledge such as maps, while anomaly prediction focus on identifying unexpected
Lu, Zhiyong, Wang, Xiyuan, Liu, Shiqu, Yang, Zheng, Li, Hao, He, Xiaofei
Motion Planning Algorithm for Autonomous Driving Based on Trajectory Prediction2025-99-004710/17/2025
To solve the problems of trajectory prediction and obstacle avoidance of self-vehicles in autonomous driving, a obstacle avoidance algorithm that combines trajectory prediction and vehicle motion planning is proposed. Firstly, in this paper, Unscented Kalman filter and constant acceleration model, namely UKF + CA, as well as Hidden Markov model, namely HMM, are combined together. Predict the trajectory of the vehicle in front and integrate the prediction results obtained by these two methods, which can improve the accuracy of the prediction. Then, in the Frenet coordinate system, this paper adopts the methods of dynamic programming and quadratic programming to generate the planning trajectory of the self-aircraft. After that, this paper conducts collision detection between the fusion trajectory of the preceding vehicle and the planning trajectory of the self-vehicle. If there is a risk of collision, a virtual obstacle will be generated and the path will be re-planned to avoid the
Cao, Zheng, Shen, Yong-Feng, Hu, Hao-Dong, Ouyang, Le-Wen
Coordinated Strategy of Trajectory Tracking and Stability Control for Autonomous Vehicles with Switched Envelope and Composite Evaluation10-10-01-000310/13/2025
Trajectory tracking and lateral stability under extreme conditions are critical yet conflicting control objectives due to nonlinear tire dynamics and road adhesion limitation, where accurate characterization of vehicle dynamics for each objective is essential to enable coordinated performance. This article proposes a coordinated control strategy based on switched envelope and composite evaluation to improve both tracking accuracy and stability. Unlike previous stability envelope methods that rely solely on the vehicle’s rear tire saturation boundary to prevent instability, the switched envelope approach incorporates both front and rear tire saturation boundaries to simultaneously mitigate steering loss and instability in trajectory tracking. A critical steering angle, derived from tire slip dynamics and phase plane stability analysis, is formulated as the switching criterion. Additionally, a composite stability evaluation is developed by combining a future disturbance resistance index
Shi, Wenbo, Wang, Junlong, Ding, Haitao, Xu, Nan
Review of Bio-Inspired Actuators and Their Potential for Adaptive Vehicle Control12-09-02-001110/10/2025
Adaptive vehicle control systems are crucial for enhancing safety, performance, and efficiency in modern transportation, particularly as vehicles become increasingly automated and responsive to dynamic environments. This review explores the advancements in bio-inspired actuators and their potential applications in adaptive vehicle control systems. Bio-inspired actuators, which mimic natural mechanisms such as muscle movement and plant tropism, offer unique advantages such as flexibility, adaptability, and energy efficiency. The article categorizes these actuators based on their mechanisms, including shape memory alloys, dielectric elastomers, ionic polymer–metal composites, and soft pneumatic actuators. The review highlights the properties, operating principles, technical maturity, and potential applications for each mechanism in automotive systems. Additionally, it investigates current uses of these actuators in adaptive suspension, active steering, braking systems, and human–machine
Mittal, Vikram, Shah, Rajesh, Roshan, Mathew
Deep Reinforcement Learning with Behavioral Cloning for Path Tracking of Four-Wheel Steering Vehicles10-10-01-000110/09/2025
When the vehicle system performs trajectory tracking control, it presents relatively complex nonlinear coupling dynamics characteristics. The traditional coordination algorithm relying on a simplified linear model is mostly unable to deal well with the actual nonlinear dynamic behaviors. In contrast, reinforcement learning (RL) method will derive the optimal strategy by means of interaction with the environment. This eliminates the need for accurate vehicle modeling. These methods use all of the nonlinear approximation capabilities of deep neural networks and can effectively reflect the complex relationship between vehicle state and control actions. The framework itself supports multidimensional input processing and continuous operation space optimization because of the development of parallel processing architectures. In order to reduce the motion jitter caused by the direct generation of front and rear wheel angles by the network, this article uses steering angle increments as
Ren, Gaotian, Wang, Yangyang
Aerospace & Defense Technology: October 202525AERP1010/02/2025
Nadcap at 35: Still Defining Excellence in Aerospace and Defense Manufacturing High Performance Enclosure Systems for Aerospace and Defense Applications: Preparing for What’s Next Selecting the Best Plastic for Spacecraft Applications 2025 Rising Star Awards Aerospace and Defense Winner – Keshika Warnakula Managing Thermal Reliability at the Edge: The Critical Challenge DoD Isn’t Talking About Analyzing Aircraft Antenna Placement and Performance with Unified Modeling and Simulation U.S. Army Tests Innovative Autonomous Tactical Vehicle Prototype Researchers Discover New Materials That Could Enhance Fighter Jet Efficiency Could Metasurfaces Be The Next Quantum Information Processors?
Automotive Engineering: October 202525AUTP1010/02/2025
Engineering EV, charger safety and security From a quick access port to help firefighters fight EV battery fires faster than otherwise possible to preventing public charger vandalism, here are some developments that haven't made the big headlines. Electronic stability control builds the foundation for safer roads How a mechanically simple idea has kept cars stable for decades, and why it can still evolve for an autonomous future. The factory's hidden power grid Why smart electrical distribution is the new frontier in sustainable manufacturing. Safety by design: a next-gen blueprint for EV batteries The EV industry can - and should - stop pushing decades-old battery architecture well past its limits. Vehicle fire safety with Erbis Biscarri Author turns classroom quest into a tome for anyone who wants to engineer safer cars. Editorial IAA Mobility: A new class of auto show Supplier Eye The new supplier strategy playbook BMW brings EV motor production to Steyr in Austria Ford unveils its
Lifelike simulation accelerates MIDDLE-MILE AUTONOMY25TOFHP10_0110/01/2025
The Gatik Arena platform integrates NVIDIA Cosmos models to create closed-loop, ultra-realistic digital environments that address real-world limitations. Gatik Arena is a next-generation simulation platform designed to accelerate the development and validation of autonomous vehicle (AV) systems. Gatik, which targets autonomous middle-mile logistics, built and fine-tuned Arena in-house to meet specific operational and technical needs. Unveiled in July 2025, the platform is said to produce photorealistic, structured and controllable synthetic data that addresses the limitations of traditional real-world data collection. Founded in 2017, Gatik plans to scale its freight-only, driverless operations in 2025, and the Arena platform is central to this endeavor. Gatik collaborated with NVIDIA to integrate its Cosmos world foundation models (WFMs), which enable the creation of ultra-high-fidelity, physics-informed digital environments for robust AV training and validation, said Norm Marks, VP
Gehm, Ryan
2025 RISING STAR AWARDS Aerospace and Defense Winner - Keshika Warnakula25AERP10_0410/01/2025
Keshika Warnakula is a Senior Flight Mechanics Engineer at Syos Aerospace Limited and the winner of the 2025 Rising Stars Award Aerospace and Defense category. Syos Aerospace is based in Mount Maunganui, New Zealand, specializing in robotics engineering and the development of autonomous air, land, and sea vehicles. The company also has an office located in Fareham, UK, and was recently named New Zealand's “Hi-Tech Company Of the Year.”
Fault Tree Analysis Generation Using Generative Artificial Intelligence with an Autonomy Sensor Usecase12-09-02-001009/25/2025
Functional safety forms an important aspect in the design of systems. Its emphasis on the automotive industry has evolved significantly over the years. Till date many methods have been developed to get appropriate fault tree analysis (FTA) for various scenarios and features pertaining to autonomous driving. This article is an attempt to explore the scope of using generative artificial intelligence (GenAI) FTA with the use case of malfunction for the LIDAR sensor in mind. We explore various available open source large language models (LLM) models and then dive deep into one of them to study its responses and provide our analysis. Although the article does not solve the entire problem but has given some guidance or thoughts/results to explore the possibility to train existing LLM through prompt engineering for FTA for any autonomy use case aided with PlantUML tool.
Shetiya, Sneha Sudhir, Garikapati, Divya, Sohoni, Veeraja
Sustainable Circular Future Mobility: Environmental Impact of Next-gen VehiclesEPR202501509/22/2025
The next generation of mobility, driven by shared, driverless, connected, and electrified vehicles, holds strong potential to advance sustainability through lower emissions and improved resource efficiency. However, critical questions remain regarding their true environmental impact, including battery lifecycle management, material consumption, and circular manufacturing practices. Sustainable Circular Future Mobility: Environmental Impact of Next-gen Vehicles explores these unresolved issues, focusing on the shift from internal combustion to electric vehicles, supply chain challenges, regulatory gaps, and the operational realities of sustainable productization. It also critically examines the risks of greenwashing, the need for consistent standards, and the role of intersectoral collaboration—with energy, urban planning, information and communications technologies, and waste management sectors—in building resilient, scalable solutions. The report provides strategic recommendations and
Abdul Hamid, Umar Zakir
SAE TOMORROW TODAY - AVs in Europe: Balancing Innovation and Regulation1353509/18/2025
What does it take to bring safe, reliable autonomous vehicles to Europe's roads? From shifting to performance-based regulations for L3 and L4 systems to the role of human oversight, the European Commission Joint Research Centre is using technology, policy, and collaboration to drive the future of autonomy. Listen in as we sit down with members Akos Kriston, Officer, Scientific Research for ADAS, and Espedito Rusciano, Programme Manager, Scientific Research for Connected, Automated and Smart Mobility, to discuss the EU's evolving approach to semi and fully autonomous driving. You'll learn how projects like HIDDEN are pushing the boundaries of safety in complex urban environments, how the Open Regulatory Framework could shape the next decade of mobility, and why international collaboration is key to success. 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
Patterson, Lori
Developing Digital Twins at the Subsystem Level Using Heterogenous Modeling and Simulation (M&S) for Development and Testing of Artifical Intelligence/Machine Learning (AI/ML) and Autonomous Ground Systems2025-01-047409/16/2025
To achieve Army modernization plans, advanced approaches for testing and evaluation of autonomous ground systems and their integration with human operators should be utilized. This paper presents a framework for developing digital twins at the subsystem level using heterogeneous modeling and simulation (M&S) to address the challenges of manned-unmanned teaming (MUM-T) in operational environments. Focusing on the interplay between robotic combat vehicles (RCVs) and human operations, the framework enables evaluation of soldiers’ cognitive loads while managing tasks such as maneuvering robotic systems, interacting with aided target detection, and engaging simulated adversaries. By employing subsystem-level digital twins, we aim to isolate and control key variables, enabling a detailed assessment of both systems’ performance and operator effectiveness. Through realistic operational scenarios and human-machine interface testing, our approach may help identify optimal solutions for soldier
Van Emden, Kristin, Strickland, Jared, Whitt, John, Flint, Benjamin, Ma, Lein, McDonnell, Joseph, Bergin, Dennis, Huynh, Kevin, Nolta, Lukas, Song, Jae, Weber, Kody, Gates, Burhman, Bounker, Paul, Madak, Joseph T.
Simulating Off-Road Autonomous Navigation: Fast Simulation of Vegetation Override2025-01-045209/16/2025
Navigation in off-road terrains is a well-studied problem for self-driving and autonomous vehicles. Frequently cited concerns include features like soft soil, rough terrain, and steep slopes. In this paper, we present the important but less studied aspect of negotiating vegetation in off-road terrain. Using recent field measurements, we develop a fast running model for the resistance on a ground vehicle overriding both small vegetation like grass and larger vegetation like bamboo and trees. We implement of our override model into a 3D simulation environment, the MSU Autonomous Vehicle Simulator (MAVS), and demonstrate how this model can be incorporated into real-time simulation of autonomous ground vehicles (AGV) operating in off-road terrain. Finally, we show how this model can be used to simulate autonomous navigation through a variety of vegetation with a PID speed controller and measuring the effect of navigation through vegetation on the vehicle speed.
Goodin, Christopher, Moore, Marc N., Hudson, Christopher R., Carruth, Daniel W., Salmon, Ethan, Cole, Michael P., Jayakumar, Paramsothy, English, Brittney
The Sotif Meta-Algorithm: Quantiative Analyses of the Safety of AI2025-01-043709/16/2025
This paper presents updates to a “meta-algorithm” for achieving safer AI driven systems by integrating systems theoretic process analysis, quantitative fault tree analysis, structured generation of safety metrics, and statistical hypothesis testing of metrics between simulation and reality. This paper presents updates to the meta-algorithm after its application in use cases involving commercial autonomous vehicle deployment.
Wagner, Michael, Carlson, Noah, Dwyer, Chris
MOSA C2 for Autonomous Vehicles to Counter UAS, SWARM, and Ground-Based Robotic Threats2025-01-043509/16/2025
A Modular Open Systems Approach (MOSA) for command and control (C2) of autonomous vehicles equipped with sensor and defeat mechanisms enhances force protection against unmanned aerial systems (UAS), swarm, and ground-based robotic threats with current technology while providing an adaptable framework able to accommodate technological advances. This approach emphasizes modularity, which allows for independent upgrades and maintenance; interoperability, which ensures seamless integration with other systems; and scalability, which enables the system to grow and adapt to increasing threats and new technologies – all of which are essential for managing complex, dynamic, and evolving operational threats from UAS, swarm, and ground-based robots. The proposed systems approach is designed around component-based modules with standardized interfaces, ensuring ease of integration, maintenance, and upgrades. The integration of diverse sensors through plug-and-play capabilities and multi-sensor
Davidson, Jeremy, Drewes, Peter, Graham, Roger, Haider, Eric, Phillips, Michael
LIPO: Lidar Inertial Odometry for ICP Comparison2025-01-043909/16/2025
We introduce a LiDAR inertial odometry (LIO) framework, called LiPO, that enables direct comparisons of different iterative closest point (ICP) point cloud registration methods. The two common ICP methods we compare are point-to-point (P2P) and point-to-feature (P2F). In our experience, within the context of LIO, P2F-ICP results in less drift and improved mapping accuracy when robots move aggressively through challenging environments when compared to P2P-ICP. However, P2F-ICP methods require more hand-tuned hyper-parameters that make P2F-ICP less general across all environments and motions. In real-world field robotics applications where robots are used across different environments, more general P2P-ICP methods may be preferred despite increased drift. In this paper, we seek to better quantify the trade-off between P2P-ICP and P2F-ICP to help inform when each method should be used. To explore this trade-off, we use LiPO to directly compare ICP methods and test on relevant benchmark
Mick, Darwin, Pool, Taylor, Nagaraju, Madankumar Sathenahally, Kaess, Michael, Choset, Howie, Travers, Matthew
Planning for Employment of Robotic and Autonomous Systems (RAS)2025-01-043209/16/2025
Employment of Robotic and Autonomous Systems requires a different paradigm of mission planning, one which considers not only the tasks to be performed by the RAS themselves but regards the flow of information to support the observability of the RAS by the operator. GTRI has developed an initial capability for mission planning of mixed motive, heterogeneous, autonomous systems for management of macro level metrics that support the decision making of the operator or user during employment. The work is ongoing, extensible to additional capability sets, and modular to support integration of other autonomous capabilities.
Spratley, Michael, Schooley, Andrew, Dickerhoff, Trey
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