Browse Topic: Connectivity

Items (831)
North American CAV Performance Data StandardWP-00157/22/2026
As the deployment of connected and automated vehicles (CAVs) expands, the need for a consistent, cross-industry approach to performancerelevant CAV data exchange is becoming more pressing. Vehicle developers, infrastructure owners and operators (IOOs), and technology providers generate and consume data that support safety, mobility, and operational efficiency, yet much of the data remains fragmented, inconsistently formatted, and difficult to reuse across systems. To address these gaps, the Society of Automotive Engineers (SAE) and the Canadian Standards Association (CSA) convened a multi-stakeholder workshop on November 3, 2025, with participants representing original equipment manufacturers (OEMs), automated driving system (ADS) developers, state and local agencies, standards bodies, and technology partners. The workshop focused on identifying challenges, clarifying needs, and outlining a path toward a North American CAV Performance Data Standard. Key themes from the workshop included: -A shared data language is needed to support safe and interoperable CAV operations. -The current ecosystem lacks consistent formatting, labeling, and visibility regarding who produces and consumes data. -A “start small, iterate, and scale” approach is needed, beginning with well-defined use cases such as school zones or baseline work zones. -Progress depends on technical harmonization and governance structures that build trust and support sustained coordination. This white paper summarizes the key findings and outlines a practical approach to developing a Version 0.1 base-layer data standard that can support measurable progress in 2026 and beyond.
Nesheli, Mahmood
Requirements of Interface for Aircraft/Store Electrical Interconnection System (GJB 1188A-99) is the current standard followed by all types of carrier aircraft and stores. This paper designed a 1553B bus remote terminal mode code configuration method that met the requirements of GJB1188A standard, completing the interrupt initialization and data initialization of compulsory mode codes. These comprehensive test results confirm that the proposed mode code configuration method is both reliable and effective, and provides strong portability, which can be used as a reference for the GJB1188A interface software design of other components
Han, BinZhang, KunLiu, XuhanYe, JinhanLi, Zhengmao
Automated Vehicle Marshalling (AVM) is the first functionally safe Level 4 automated driving system. It consists of the wireless control of unoccupied vehicles at low speed in well-defined environments, such as parking facilities or manufacturing plants. The driverless operation in an AVM system is achieved by transmitting control messages between connected vehicles and intelligent infrastructure. Similar to other wireless applications, network reliability poses a major challenge to ensuring safe automated driving. An AVM system must provide uninterrupted communication between the vehicle and the infrastructure at a stable frequency. However, wireless systems usually suffer from varying latencies and network disturbances. In this context, international organizations and automotive industry contributors have defined requirements specifying network performance, communication interfaces, and message formats for different AVM use cases. These requirements cover communication aspects without involving core automated driving functions, such as vehicle motion control, which are also decisive in ensuring the safety of the overall system. Therefore, studying communication factors in combination with vehicle motion control offers better interpretability of system capabilities. In this work, we investigate the trade-off between communication specifications and vehicle lateral control within an AVM framework implemented on a real vehicle. We aim to address the limitations that may arise under real-world AVM driving conditions. First, we revisit the current technical specifications to highlight the specific AVM messages relevant to vehicle lateral control. Then, we propose a testing framework by establishing communication with the test vehicle over a Wi-Fi network using multiple access points deployed across an indoor parking facility and an outdoor test track. Thus, we obtain a quantitative analysis of network factors, such as latency, in different driving environments. In the next step, we present a Model Predictive Control (MPC) approach that uses the AVM control messages to achieve robust vehicle lateral control. By evaluating the control performance under communication conditions, we assess the impact of network latency on vehicle lateral control. This work provides a baseline for exploring the limitations of AVM and deriving potential optimizations.
Mejri, Mohamed AmineMünchhausen, HenrikFlormann, MaximilianSturm, AxelHenze, Roman
Aerospace manufacturing operates within an intricate ecosystem where quality, compliance and traceability are critical to success. Conventional digital thread frameworks provide connectivity but remain largely passive, lacking the intelligence to autonomously manage complex non-conformities across the product lifecycle. This paper introduces an Agentic Digital Thread powered by Agentic AI, designed to transform non-conformity management into an adaptive, self-orchestrating system that actively drives decision-making and corrective actions [1, 4]. The proposed architecture employs a Master Agent to coordinate workflows and maintain end-to-end data continuity, while specialized Agents autonomously manage domain-specific tasks. In the pre-manufacturing phase, these agents proactively validate requirements, material conformity and process planning through integration with PLM, MES, ERP, QMS and supplier systems. In the post-manufacturing phase, the framework extends to concession management, enabling structured workflows for identifying, evaluating and approving deviations during inspection or final assembly. By embedding AI-driven anomaly detection, semantic search of historical concessions, and Generative AI-powered report authoring, the system accelerates resolution and predicts concession acceptance with high confidence. Continuous feedback loops between design, production and quality assurance transform the digital thread from a static data conduit into an intelligent ecosystem that ensures compliance, reduces delays and rework, and fosters continuous improvement. This approach delivers a resilient and adaptive aerospace manufacturing process aligned with the demands of next-generation aircraft production [9, 10].
Veluri, SastryGopala Krishnan, Kannan
As aerospace platforms adopt increasingly interconnected architectures for avionics, telemetry, and predictive diagnostics, lightweight publish–subscribe protocols have become integral to communication efficiency. The Message Queuing Telemetry Transport (MQTT) protocol is widely employed due to its small footprint and low network overhead. The release of MQTT 5.0 introduces new control features—reason codes, session expiry, user properties, topic aliasing, shared subscriptions, and improved error feedback—aimed at enhancing scalability and diagnostic reliability. However, these benefits come with trade-offs in complexity and potential overhead, particularly in real-time and resource-constrained environments typical in aerospace. This paper evaluates MQTT 3.1 and MQTT 5.0 within aerospace IoT contexts using a Raspberry Pi–based experimental framework. The analysis is done using practical throughput benchmarks implemented via popular open-source tools like Eclipse Mosquitto Clients. Realistic aerospace communication scenarios are modeled for inter-module messaging, under varying QoS levels and payload conditions. Comparative throughput, latency, and broker resource utilization benchmarks were conducted under multiple QoS levels and payload sizes to quantify the trade-offs between functionality and efficiency. This research aims to empirically validate the theoretical improvements of MQTT 5.0 on realistic embedded hardware and under controlled network constraints, replicating operational aerospace environments. Results show that MQTT 5.0 provides measurable advantages in complex, multi-tenant environments but introduces moderate processing overhead. Recommendations are proposed for selecting the optimal MQTT version for aerospace deployments and strategies for seamless migration from legacy systems [8].
Bhuyar, PrabhudevM, MeghanaKaniraja, ChristinaThomas, Tinto
Precision agriculture, also known as smart farming, was once reserved for early adopters or large-scale operations, but is now an expectation within the farming industry. Across various regions and farm sizes, smart farming techniques are changing the way crops are planted as well as how they are monitored and harvested. However, farmers today are under increasing pressure to reduce labor, decrease chemical inputs, conserve water and operate in tighter windows. Couple this with factors such as narrow seasonal windows, productivity demands and safety considerations, and the need for smarter decisions becomes imperative. Going one step further, global food demands and environmental pressures are further increasing demand for precise, accurate and intelligent farming solutions.
Love, Jennifer
As the “digital brain” and core foundational support for the development of intelligent transportation and connected vehicles, the performance of data centers directly determines the operational capability of intelligent transportation systems. In the process of advancing the vehicle-road-cloud collaborative architecture, the demand for high-performance computing power in data centers has experienced explosive growth. The substantial increase in computing tasks has posed severe challenges to thermal management, making efficient and reliable cooling systems an indispensable core component. Centrifugal compressor water-cooling units are the mainstream cooling solution for large-capacity scenarios, and their design optimization is crucial for improving the energy efficiency and performance of the entire cooling system. This paper proposes a one-dimensional performance prediction method for centrifugal compressors based on an empirical loss model, and realizes the iterative calculation of parameters in the entire flow path from the impeller inlet to the diffuser outlet through Python programming. A systematic impact assessment was carried out for major loss mechanisms such as surface friction, tip clearance, and wake mixing under standard operating conditions and critical operating conditions. The results show that the original model has high prediction accuracy under standard operating conditions, with isentropic efficiency error not exceeding 5%; however, under critical operating conditions, the efficiency prediction deviation reaches 7.54% due to the neglect of coupling effects between various losses. To address this issue, this paper introduces deviation correction factors related to flow rate, rotational speed, and density, which significantly improve the model’s prediction capability under extreme operating conditions: the efficiency error under critical operating conditions is reduced to 1.54%, and only 0.3% under rated operating conditions. This model provides a reliable tool for compressor performance prediction and extreme operating boundary identification, and has high application value in engineering practice.
Zhu, MinhaoJiang, BinLi, MinZeng, ZihuiGu, Yunhui
The convergence of highly capable edge AI models and advanced commercial-off-the-shelf (COTS) edge AI accelerators is reshaping how computation is deployed across defense, aerospace, and commercial platforms. Mission-critical decisions increasingly must be made at the edge, onboard vehicles, satellites, and infrastructure nodes, where latency, connectivity, and power availability are constrained.
Today's defense operations are defined by mobility, speed and data. Whether coordinating ship-to-shore logistics, maneuvering ground forces, or enabling autonomous and semi-autonomous systems at the tactical edge, reliable communications are no longer a support function - they are mission-critical. Defense forces must operate across fixed and mobile environments while maintaining secure, high-bandwidth connectivity amid interference, jamming, and limited spectrum availability. Legacy approaches, typically optimized for either static infrastructure or limited mobility, struggle to meet these combined requirements.
The scope of this standard is Automated Vehicle Marshalling (AVM) of vehicles to enable remote control functionality for achieving SAE Level 4 (High Driving Automation according to the Surface Vehicle Recommended Practice SAE J3016) driving capabilities at controlled speeds within geofenced private controlled environments utilizing infrastructure-assisted sensing. It specifies a concept of operations which includes a reference-system architecture and use cases, system functional and performance requirements, multiple wireless communications protocols, and associated wireless messages to support AVM. AVM use cases such as plant marshalling, depot marshalling, valet parking, electric vehicle charging, etc. The Automated Vehicle Marshalling Central Server (AVM CS) infrastructure does detect objects, vehicles, vulnerable road users, and any obstructions that help guide the Automated Vehicle (AV) starting from uninitiated, activation, identification, automated control, unavailable and deactivation states of the respective feature lifecycle of AVM use cases by using only two wireless messages named Infrastructure Marshalling Message (IMM) and Vehicle Marshalling Message (VMM). This standard specifies the minimum requirements for the Vehicle-to-Everything (V2X) messages, such as IMM and VMM, and the corresponding data frames and elements which are defined to support Infrastructure guided AVM use cases over Direct (LTE-V2X) and Network-based wireless communications technologies. These messages are utilized to achieve desired interoperability, safety, and data integrity. This standard focuses on an infrastructure-led implementation of an AVM system analogue to a Type 2 Automated Vehicle Parking (AVP) system implementation as described in International Organization for Standardization (ISO) 23374-1; the functional allocations listed below are part of the Type 2 AVP System where AVM CS of infrastructure carries out most of the operation functions including AV Identification and Emergency Stopping: Destination Assignment Route Planning Object and Event Detection and Response (OEDR) AV Localization Path Determination Trajectory Calculation Vehicle Motion Control (VMC) NOTE 1: Functional Safety rating Automotive Safety Integrity Level (ASIL) related requirements are outside the scope of this standard. NOTE 2: This standard could be utilized as a base for human operator assisted AVM. The implementation details from human operator assistance without Infrastructure assistance is outside the scope of this standard. NOTE 3: Unless otherwise marked as Informative, all material in this standard is to be considered normative.
V2X Core Technical Committee
Topology optimization (TO) of dynamic structures has traditionally been constrained to single-body components and simplified harmonic load assumptions. Extending TO to multibody dynamic systems (MBS) remains challenging due to complex coupling between inertia, mass distribution, and joint constraints. This paper presents an inertia-aware topology optimization framework that integrates mass moment of inertia (MMI) constraints within an enhanced Equivalent Static Displacement (ESD) methodology. Building upon the authors’ previously developed ESD framework, the proposed approach — termed Inertia-Augmented Equivalent Static Displacement (IA-ESD) — explicitly incorporates inertial effects arising from accelerations and joint interactions. The approach enables dynamically consistent optimization by coupling design-dependent inertia tensors with equivalent static displacements derived from nonlinear multibody dynamics. Case studies involving an MBB beam and a piston–connecting rod assembly demonstrate that accounting for MMI constraints yields lighter, stiffer, and dynamically balanced multibody topologies. The proposed method establishes a foundation for inertia-aware structural design with applications in aerospace, automotive, and robotics engineering.
Gupta, AakashTovar, Andres
This study investigates factors contributing to autonomous vehicle (AV) accidents and proposes an automated fault determination framework. A total of 563 accident reports from the State of California Department of Motor Vehicles spanning from 2019 to 2024 were analyzed by converting unstructured standardized reports into structured data using custom extraction tools. Analysis of these reports reveals that AVs were not at fault in 69.4% of cases and were fully at fault for 22.6% of cases. The proposed method uses these reports to provide an early indicator of fault likelihood and potentially replaces tedious manual review. Machine Learning (ML) and Natural Language Processing techniques were used to replicate the reported faults, achieving 96% average accuracy across three models: Gradient Boosting, Linear Regression, and Random Forest. Through feature engineering techniques in semantic feature extraction from narrative accident descriptions, quantifiable variables were obtained and aided a robust fault classification performance across diverse collision scenarios with full cross-validation testing. Key contributing factors included the impact location (damage area), vehicle movement, and environmental conditions (weather, road, and lighting conditions). An open-source web-based system is also provided to demonstrate real-time accident report uploads and automated fault analysis, as well as a comprehensive system with source code, enabling scalable analysis of larger datasets for a reproducible, data-driven framework for a full assessment. The framework enables attendees to use existing connected vehicle datasets to provide automated analysis with visualization revealing collision patterns and liability trends for safe AI system validation. This supports early assignment of responsibility in autonomous vehicle collisions as AV deployment accelerates globally; determining fault attributes is essential for establishing public trust, legal precedents, insurance frameworks, and policies.
Rwejuna, Florida PerfectMajid, NishatulGoutham, MithunLoukili, Alae
Achieving full vehicle autonomy is not just about adding sensors or compute - it requires a fundamental shift in how vehicles are architected. Autonomous systems rely on higher-resolution sensors, massive processing power, and the ability to fuse data from multiple sources in real time. Centralized in-vehicle architectures, which consolidate computing and enable sensor fusion, place unprecedented demands on connectivity. Precise time synchronization across systems becomes critical, as does advanced control to ensure safe and reliable operation. Any delay or data loss can impact decision-making, making robust, resilient communication links essential. High-performance connectivity is the backbone of this evolution. It must deliver the highest bandwidth to handle massive streams of sensor data, support long-reach connections across the vehicle, and maintain error-free performance even in the most challenging electromagnetic environments. This combination of speed, reach, and reliability forms the foundation that enables higher-level ADAS and ultimately autonomous driving to move from concept to reality.
Shwartzberg, Daniel
In recent years, the use of software-defined platforms has become increasingly prevalent. As a result, flashing ECUs has become an important factor in ensuring efficiency, quality, and compliance in vehicle production. Conventional approaches, such as final end-of-line flashing, are increasingly unsuitable for the growing amounts of data, complex dependencies, mixed physics and protocols, and traceability requirements. This SAE paper presents the current trends and challenges in ECU flashing. It highlights the impact of the exponential growth in software payloads and the necessary migration to offline and parallel workflows. This can only be achieved through closer integration with automated and robot-assisted production, considering the requirements of cybersecurity and verifiability. It also addresses the shift toward end-to-end flashing ecosystems, where updates are performed consistently from a single source covering the assembly line, warehouses, yards, workshops, and over-the-air updates. By comparing old and new approaches to high-speed flashing and presenting a new flashing strategy for OEMs derived from this, the paper provides a framework for understanding the future of ECU flashing on its way to software-defined mobility.
Böhlen, BorisBudak, OguzWells, Michael
This standard specifies the system requirements for an on-board vehicle-to-vehicle (V2V) safety communications system for light vehicles1, including standards profiles, functional requirements, and performance requirements. The system is capable of transmitting and receiving the SAE J2735-defined basic safety message (BSM) [1] over a dedicated short range communications (DSRC) wireless communications link as defined in the Institute of Electrical and Electronics Engineers (IEEE) 1609 suite and IEEE 802.11 standards [2] to [6].
V2X Core Technical Committee
This document provides vehicle-level data collection, data analysis, and data verification procedures that may be used to verify that an instrument under test (IUT) satisfies the vehicle-level requirements specified in the SAE International (SAE) J2945/1 standard. For the purposes of this recommended practice, “vehicle-level requirements” primarily consist of those requirements which can be verified external to the vehicle. The IUT for these procedures is a configured dedicated short range communications (DSRC) vehicle-to-vehicle (V2V) device as defined in SAE J2945/1 and is installed on a light vehicle. While the IUT is conceptually separated from the vehicle it is installed on, the tests outlined in this document are primarily vehicle-level so the terms “vehicle” and “IUT” can generally be considered interchangeable. Additionally, non-vehicle-level complementary tests, not included in this document, are required to verify that the entire set of requirements specified in SAE J2945/1 is satisfied. This document also includes a traceability matrix to provide traceability between SAE J2945/1 sections and the test procedures. This can be used to ensure thoroughness of testing coverage. The SAE J2945/1 sections that are included in the scope of this revision in this document are indicated in Table 1. SAE J2945/1 major section numbers that are indicated as N/A do not contain any requirements (subsections may include requirements). Sections that are not in scope, such as standards profiles, are expected to be tested and verified as part of device-level certification, prior to vehicle-level testing, which is the primary focus of this document.
V2X Core Technical Committee
This article presents an eco-driving algorithm for electric vehicles featuring multi-speed transmissions. The proposed controller is formulated as a co-optimization problem, simultaneously optimizing both vehicle longitudinal speed and powertrain operation to maximize energy efficiency. Constraints derived from a connected vehicle–based traffic prediction algorithm are used to ensure traffic safety and smooth traffic flow in dynamic environments with multiple signalized intersections and mixed traffic. By simplifying the complex, nonlinear mixed-integer problem, the proposed controller achieves computational efficiency, enabling real-time implementation. To evaluate its performance, traffic scenarios from both Simulation of Urban MObility (SUMO) and real-world road tests are employed. The results demonstrate a notable reduction in energy consumption by up to 11.36% over an 18 km drive.
He, SuiyiSun, Zongxuan
This report analyzes the characteristics of mobile network communication and highlights the technical aspects of using mobile networks to implement V2X applications. This report provides a high-level analysis of architecture, protocols, and performance and is intended to support future implementation guidance and standardization for providing V2X services over mobile networks, also referred to as network V2X.
V2X Core Technical Committee
Modern vehicles require sophisticated, secure communication systems to handle the growing complexity of automotive technology. As in-vehicle networks become more integrated with external wireless services, they face increasing cybersecurity vulnerabilities. This paper introduces a specialized Proxy based security architecture designed specifically for Internet Protocol (IP) based communication within vehicles. The framework utilizes proxy servers as security gatekeepers that mediate data exchanges between Electronic Control Units (ECUs) and outside networks. At its foundation, this architecture implements comprehensive traffic management capabilities including filtering, validation, and encryption to ensure only legitimate data traverses the vehicle's internal systems. By embedding proxies within the automotive middleware layer, the framework enables advanced protective measures such as intrusion detection systems, granular access controls, and protected over-the-air (OTA) update channels. This strategy enhances both data security and system isolation, creating protective boundaries between critical vehicle operations and potential external attacks. The architecture particularly excels in supporting Vehicle-to-Everything (V2X) connectivity, facilitating seamless information exchange between vehicles, roadside infrastructure, and pedestrians. This capability is essential for enhancing roadway safety, optimizing traffic flow, and supporting autonomous driving technologies. The system incorporates dedicated proxy modules for specialized protocols including Trivial File Transfer Protocol (TFTP), Diagnostic Over Internet Protocol (Doip), and Message Queuing Telemetry Transport (MQTT), each fulfilling specific functions in vehicle diagnostics, software updates, and telemetry data management. Performance evaluations will measure latency and throughput metrics to validate the architecture's efficiency and reliability. The framework's modular design aims to provide scalability and adaptability to accommodate both technological advancements and emerging security challenges. The proxy-based security framework presented offers a holistic and forward-looking approach to safeguarding in-vehicle networks. It provides automotive manufacturers with the tools to develop connected vehicles that combine intelligence and efficiency with robust protection against diverse cybersecurity threats.
M, ArvindPraneetha, Appana DurgaRemalli, Ravi Teja
With the rise of AI and other new digital technologies on the horizon, ACT Expo 2026 will be a crucial intersection for industry leaders to map out the route ahead. Since 2011, ACT Expo has served as a meeting point of technology and business discussions for the commercial vehicle industry. The 2026 show in Las Vegas (www.actexpo.com) is shaping up to be another important waypoint for the industry as it continues to grapple with new technologies, regulations and other significant challenges. This year's agenda program builds on ACT Expo's long-established emphasis on clean transportation and places an increased focus on the digital frontier, including AI, autonomy, connectivity and software-defined vehicles. Truck & Off-Highway Enginering interviewed Erik Neandross, president of the Clean Transportation Solutions group at TRC, about what topics are emerging as the main trends heading into 2026 and what he thinks will be some of the most important themes of the upcoming convention.
Wolfe, Matt
SAE International’s Dictionary of ADAS and Connected VehiclesR-5591/20/2026
The convergence of Advanced Driver Assistance Systems (ADAS) and connected vehicle technologies is ushering in a transformative era of automotive innovation—one that is fundamentally enhancing vehicle safety, efficiency, reliability, and the overall driving experience. SAE International’s Dictionary of ADAS and Connected Vehicles stands as the definitive reference for this rapidly evolving domain, meticulously compiled to clarify and standardize the language shaping modern mobility. Inside, readers will find clear, authoritative definitions encompassing the full spectrum of technologies that enable connected and automated driving—including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) communication systems. This comprehensive resource translates complex engineering terminology into accessible language, enabling engineers, researchers, policymakers, educators, and enthusiasts to share a common technical foundation. Reflecting the latest global standards, research, and innovations, this dictionary bridges the gap between theory and application. It fosters interdisciplinary collaboration, supports safer system design, and provides clarity for those engaged in regulatory development or technology deployment. As the pace of mobility innovation accelerates, precise and accessible communication becomes indispensable. SAE International remains committed to advancing global transportation knowledge—empowering professionals to navigate, contribute to, and shape the future of intelligent, connected, and sustainable mobility with confidence and clarity.
Quigley, Jon M.Gulve, AmolKrishnamoorthy, Jayalekshmi
Electric mobility is no longer a distant vision, it is a global imperative in the journey of fight against the climate change and the urban pollution. Yet, despite of explosive growth in the electric vehicle adoptions, a major bottleneck remains which is efficient and convenient charging. The current reliance on physical plug in charging station creates inconvenient, time consuming experience and also faces significant technical and economic challenges those threaten to stall the smooth clean transportation revolution. Without innovation in how we recharge our vehicle the promise of electric mobility appears under threat which is undermined by less efficient, less compatible, and infrastructure hurdles. Wireless charging technology stand out as the game changing breakthrough poised to tackle these all critical problems head on. By enabling the effortless, cable-free charging system across the wide spectrum of electric vehicles, from the personal cars to the public transport fleets and to the micro mobility devices, it offers a more convenient & efficient future in which powering up is as seamless as driving. Still the key challenges such as energy transfer efficiency, infrastructure investments, safety, and interoperability standards must be overcome before this technology can fulfil its transformative potential. The paper embarks on a compelling journey which start with the foundational history of wireless power & navigating through global market dynamics and emerging trends and culminating in a forensic level analysis of ten main wireless EV charging technologies. Each technology is deeply evaluated against the regressive critical criteria which including efficiency, safety, cost and scalability. Ahead a weighted multi criteria hypothesis analysis is done that predicts their future viability and application. This deep, comparative framework demystifies complex trade off and offers clear & actionable guidance for industry leaders, engineers and policymakers. The paper not only highlighting the transformative potential of wireless charging but also providing strategic insights that can reshape our urban mobility and fleet operations. As EV ecosystems evolves toward intelligence, automation and more sustainability, this research becomes indispensable not just for understanding the present but for architecting a smarter, cleaner and electrified future of transportation.
Jain, GauravPremlal, PPathak, RahulGore, Pandurang
Electric Vehicles (EV) are increasingly becoming more and more popular in the markets, especially in the commercial vehicle segments. Amidst this, the need to find new elegant methods to perform charging of EV battery becomes extremely crucial. In areas with high demand and limited power capacity, performing charging for multiple vehicles necessitates efficient usage of charging infrastructure, which can’t be guaranteed by the traditional charging methods. Sequential charging is a new state of art technique for managing the charging of multiple EV’s simultaneously connected to a single charging station. Rather than dividing the available power equally among all connected vehicles or charging them one at a time, this technique dynamically allocates power based on various factors such as charging priority, vehicle needs and available infrastructure capacity. Currently, sequential charging can only be implemented by a particular set of chargers that are interconnected via backend and managed by the respective charge point operator aka CPO. In this case, the CPO has limited information about EV fleets. On the other hand, the EV fleet owner does not have any control over the charge scheduling. In this paper, a solution has been provided to allow fleet operators to perform sequential charging from vehicle side wherein, the fleet operator can schedule the vehicle charging based on battery SOC requirements, departure time, vehicle trip plan and several other factors that are otherwise not available to the charger. This is accomplished via its own cloud, independent of the charger. This provides the fleet operator with a greater degree of freedom to optimize vehicle charging. This method also allows multiple vehicles, under a fleet operator, to be connected to different charging stations while still achieving the sequential charging via fleet backend. This technique can be implemented in vehicles adhering to widely used charging standards such as DIN-61851, ISO-15118-2, ISO-15118-20 etc.
De, AbirBhattacharya, UllashParihar, Aakash
Predictive maintenance is critical to improving reliability, safety and operational efficiency of connected vehicles. However, classic supervised learning methods for fault prediction rely heavily on large-scale labeled data of failures, which are difficult to obtain and maintain a manually built dataset of failure events in real automotives settings. In this paper, we present a novel self-supervised anomaly detection model that makes predictions on the faults without the need for labeled failures by using only the operational data when the systems or robots are healthy. The method relies on self-supervised pretext tasks, like masked signal reconstruction and future telemetry prediction, to extract nominal multi-sensor dynamics (i.e., temperature, pressure, current, vibration) while jointly minimizing the deviation between encoded/decoded signals and normal patterns in the latent space. A unsupervised anomaly detection model is then used to detect when the learned patterns are violated. This in conjunction with data driven predictive allows for early fault detection on key subsystems such as batteries, electric motors, brake systems, and cooling systems. They tested the framework on some public benchmark datasets, and it’s pretty good at catching early anomalies with high accuracy and recall even better than the usual threshold-based methods. The study points out how important it is to use data from normal, healthy systems to build maintenance strategies that can scale well, adapt easily, and save costs, especially for connected vehicle fleets. Plus, the model helps explain what’s going on by identifying which telemetry signals are behind the anomalies, making it easier to take timely and practical maintenance actions. This work basically offers a new, practical way to keep vehicle health in check ahead of time, helping fleets stay up and running longer while cutting down on surprise breakdowns and expensive repairs.
Kumar, PankajDeole, KaushikHivarkar, Umesh
Software-Defined Vehicles (SDVs) are changing the automotive landscape by separating hardware from software and enabling features like over-the-air updates, advanced control strategies, and real-time decision-making. To support this transformation, EV powertrain systems require high-performance computing (HPC) platforms capable of real-time control, data processing, and cross-domain communication. This paper introduces a fully SDV-compatible EV powertrain architecture designed with NXP S32G3 domain controller. This processor supports multiple core having lockstep. It is designed for zonal control and automotive functional safety. The proposed designed uses the automotive Ethernet as an alternate option for CAN based communication to fulfill the bandwidth and timing requirement of today’s SDV applications. Hence it allows gigabit data transfer, Time Sensitive Networking (TSN) and also provides low latency across SDV control domain. Through secure real time interface with the vehicle’s software stack, this work describes how the software defined vehicles can be charged in emergency situations using mobile charging units, over-the-air (OTA) software updates. To confirm the vehicle operation and power restoration in emergency, these emergency energy solutions are merged with the system architecture and controlled by domain controller. The customized hardware HPC board is designed with NXP processor and integrated with vehicle control unit (VCU). The quick control response and smooth data synchronization between powertrain and charging modules have all been showcased by the experimental result of the deployment and validation of the suggested architecture in a real time vehicle environment. This work establishes domain controlled based HPC as a reference architecture for next generation SDV’s.
Pawar, GaneshInamdar, Sumer DeepakKumar, MayankDeosarkar, PankajTayade, NikhilKanse, DattatrayChopade, Vipul
Over-the-Air (OTA) update technology has come forth as a transformative aider in the domain of automotive technology, allowing Original Equipment Manufacturers (OEMs) and Tier-1 suppliers of Electric vehicles (EVs) to frequently make software modifications, enhancements, and bug fixes that are essential to optimize the performance of powertrain components such as the motor controller unit (MCU), Battery Management System (BMS), and Vehicle Control Unit (VCU). This facilitates them to remotely supply updates to the vehicle firmware and software by giving inputs of calibration data without requiring physical access to the vehicle. However, as OTA updates have a direct impact on vehicle’s performance, safety and cybersecurity, a stringent validation methodology is of prime importance prior to deployment process. This paper explores the integration of Hardware-in-Loop (HIL) simulation into the OTA validation pipeline as a means to ensure reliability, safety, and functional correctness of updates before they are applied in the field. We present a structured approach of combining HIL systems with OTA workflows, wherein a virtual vehicle environment is simulated in real time to reproduce a replica of the actual operating conditions for the target ECU under test. The OTA update is injected through a simulated or physical OTA backend and transmitted to the control unit interfaced within the HIL loop. This setup facilitates the emulation of update scenarios including firmware re-flashing, configuration updates, security checks, and rollback mechanisms under controlled, observable conditions. Key advantages include the ability to inject faults, monitor system response, verify communication integrity, and perform automated regression tests without risking physical prototypes or production vehicles. This integration of OTA and HIL not only enhances pre-deployment validation but also lays the foundation for continuous development and in-field update strategies in connected EV platforms. The proposed framework can be scaled to multiple ECUs and integrated with CI/CD pipelines for automated nightly testing. Future work will explore combining this setup with Digital Twin environments and Machine Learning-based anomaly.
Khare, ShivaniKarle, UjjwalaSubramaniam, Anand
The modern vehicle is no longer a mechanical appliance—it has transformed into a software-defined cyber-physical system, integrating OTA updates, cloud-connected diagnostics, V2X services, and telematics-driven personalization. While this evolution promises unprecedented value in consumer experience and fleet operations, it also surfaces a dramatically expanded and evolving attack perimeter, especially across safety-critical ECUs and communication buses. Cyber vulnerabilities have shifted from isolated IT threats to real-time, embedded exploits. Controller area network (CAN), the backbone of vehicle bus systems, remains intrinsically insecure due to its lack of authentication and encryption, making it highly susceptible to message injection and denial-of-service by low-cost tools. Similarly, OEM implementations of BLE-based passive entry systems have proven vulnerable to replay and spoofing attacks with minimal hardware. In the Indian context, the transition to connected mobility is advancing rapidly under national mandates such as FAME II, PM e-DRIVE, and the National Electric Mobility Mission Plan (NEMMP). However, field-level assessments of Indian and international vehicle models—including ICE cars, electric two-wheelers, and fleet EVs—reveal critical gaps in CAN architecture connected to critical ECUs, Cloud API and Endpoints and RF controls. Notably, many of these vulnerabilities materialized after vehicle homologation, propagating through OTA updates or third-party app integrations. This reality underscores the inadequacy of static, pre-market cybersecurity assessments in effectively mitigating operational risk. This paper introduces a novel, scalable methodology that addresses this critical gap by enabling empirical, attack-informed validation, aligned with both Indian priorities and international best practices
Shah, RavindraAwasthi, Vibhu VaibhavKarle, Ujjwala
Ensuring the safety and functionality of sophisticated vehicle technologies has grown more difficult as the automotive industry quickly shifts to intelligent, electric, and connected mobility. Software-defined architectures, electric powertrains, and advanced driver assistance systems (ADAS) all require strong quality assurance (QA) frameworks that can handle the multi domain nature of contemporary vehicle platforms. In order to thoroughly assess the functionality and dependability of next generation automotive systems, this paper proposes an integrated QA methodology that blends conventional testing procedures with model-based validation, digital twin environments, and real-time system monitoring. The suggested framework, which includes hardware-in-the-loop (HIL), software-in-the-loop (SIL), and over-the-air (OTA) testing techniques, concentrates on end-to-end traceability from specifications to validation. Simulating intricate situations for ADAS, electric vehicle battery temperature management, and dynamic system updates in connected platforms are prioritized. This study also outlines the main obstacles to integrating QA methods with changing regulatory environments and draws attention to discrepancies between operational performance in real-world scenarios and compliance benchmarks. Early fault detection, lifecycle validation, and continuous improvement are made possible by the QA process's transition from reactive to proactive through the integration of digital twins and predictive analytics. A strategic roadmap for QA specialists and test engineers to adjust to changing industry demands is presented in the paper's conclusion. In addition to promoting safety and dependability, the suggested framework speeds up time to market, lowers development costs, and increases consumer confidence in cutting-edge automotive technologies.
Komanduri, Arun SrinivasSrivastava, Anuj
With the rapid advancement of connected vehicle technologies, infotainment Electronic Control Units (ECUs) have become central to user interaction and connectivity within modern vehicles. However, this enhanced functionality has introduced new vulnerabilities to cyberattacks. This paper explores the application of Artificial Intelligence (AI) in enhancing the cybersecurity framework of infotainment ECUs. The study introduces AI-powered modules for threat detection and response, presents an integrated architecture, and validates performance through simulation using MATLAB, CANoe, and NS-3. This approach addresses real-time intrusion detection, anomaly analysis, and voice command security. Key benefits include zero-day exploit resistance, scalability, and continuous protection via OTA updates. The paper references real-world automotive cyberattack cases such as OTA vulnerability patches, Connected Drive exploits, and Uconnect hack, emphasizing the critical need for AI-enabled proactive cybersecurity frameworks.
More, ShwetaKulkarni, ShraddhaKumar, PriyanshuGhanwat, HemantJoshi, Vivek
Commercial vehicles form the backbone of global supply chains. In India, the commercial vehicle (CV) industry is at a transformative crossroads, evolving from traditional hardware-centric models to advanced, software-defined architectures. Central to this shift are Software-Defined Vehicles (SDVs) and Automotive Software-as-a-Service (SaaS), catalysing a move toward intelligent, connected, and highly productive mobility solutions. With the Indian CV market surpassing $50 billion in 2024 and witnessing robust growth due to expanding e-commerce, infrastructure projects and regulatory evolution. Indian original equipment manufacturers (OEMs) are spearheading this revolution. This paper presents a comprehensive analysis of the technological enablers, monetization strategies, distinct challenges and opportunities encountered by Indian OEMs during their shift toward SDVs and automotive SaaS based business models. This research also examines the most important technical pillars underpinning next-generation automotive ecosystem creation and these pillars are centralized computing infrastructures, embedded cloud integration, efficient over-the-air (OTA) update engines and enhanced cybersecurity models designed to protect larger numbers of connected vehicles are observed. This work explains, from a financial standpoint, the new and innovative methods in which OEMs and technology providers are leveraging SDVs and SaaS to generate new revenue streams. The prominent strategies being debated are Feature-on-Demand (FoD) services, subscription-based services based on different functionalities and features, the creation of dynamic in-vehicle app ecosystems, data monetization opportunities based on privacy regulations and flexible pay-per-use models. Additionally, the changing paradigm of Mobility-as-a-Service (MaaS) model is comprehensively analysed in terms of its impact on the industry of the future. Yet, this revolutionary process is plagued by a number of challenges. The paper offers a critical analysis of concerns like the necessity of achieving widespread customer acceptance of new service models, the complexities of complying with diverse data privacy regulations.
Saini, GouravJahagirdar, ShwetaKhandekar, Dhiraj Baburao
The proliferation of connectivity features (V2X, OTA updates, diagnostics) in modern two-wheelers significantly expands the attack surface, demanding robust security measures. However, the anticipated arrival of quantum computers threatens to break widely deployed publickey cryptography (RSA, ECC), rendering current security protocols obsolete. This paper addresses the critical need for quantum-resistant security in the automotive domain, specifically focusing on the unique challenges of two-wheeler embedded systems. This work presents an original analytical and experimental evaluation of implementing selected Post-Quantum Cryptography (PQC) algorithms, primarily focusing on NIST PQC standardization candidates (e.g., lattice-based KEMs/signatures like Kyber/Dilithium), on microcontroller platforms representative of those used in two-wheeler Electronic Control Units (ECUs) - typically ARM Cortex-M series devices characterized by limited computational power, memory (RAM/ROM), and strict real-time requirements. Our experimental study involved porting and optimizing PQC reference implementations for these constrained environments. We rigorously benchmarked key performance indicators, including key generation time, encapsulation/decapsulation speeds, signing/verification times, and memory footprint (stack usage, code size). The results demonstrate the feasibility of deploying specific PQC schemes, achieving practical execution times (e.g., key operations completing within tens to hundreds of milliseconds) and manageable memory overhead (fitting within typical MCU constraints) for securing functions like secure boot, firmware updates, and authenticated communication. Performance trade-offs between different PQC algorithms regarding speed, key/signature sizes, and memory consumption are analyzed. The significance of this contribution lies in providing the first quantitative performance data and feasibility analysis for PQC adoption within the specific context of two-wheeler embedded systems. These findings offer crucial insights for OEMs and suppliers planning the transition to quantum-safe security architectures, ensuring the long-term security and trustworthiness of connected two-wheelers against future cryptographic threats.
Mishra, Abhigyan
With the emergence of Software-Defined Vehicles (SDVs), more complex software and connectivity technologies are introduced to support new advanced use cases such as phone as a key, smart parking and vehicle management. However, complex software functionality and external connectivity also increase the attack surface of vehicles and its ecosystem. In this paper, we first perform a classification of recent automotive cybersecurity attacks. We further perform an analysis of these attacks and associated vulnerabilities considering the application of best practices of vulnerability management approaches including Common Vulnerability Scoring System (CVSS), Exploit Prediction Scoring System (EPSS), and Stakeholder-Specific Vulnerability Categorization (SSVC). CVSS is a standardized framework used to assign severity scores to known vulnerabilities and helps organizations prioritize vulnerability remediation based on severity. EPSS is a predictive model that estimates the probability of a vulnerability being exploited in the next 30 days and complements CVSS by focusing on real-world likelihood of exploitation rather than just severity. SSVC is a decision-making framework for vulnerability handling to help organizations make appropriate remediation decisions considering the specific situation based on, e.g., exploitation activity, mission prevalence and public well-being. We discuss the challenges and benefits of using these different vulnerability management approaches to help automotive organizations manage risks and prioritize handling of vulnerabilities. As auto manufacturers are responsible for the cybersecurity during the lifecycle of their fleet of vehicles, we stress the importance of analyzing and assessing vulnerabilities in a systemic way in order to timely address newly detected vulnerabilities with appropriate responses.
Oka, Dennis KengoVadamalu, Raja Sangili
Modern cars have advanced significantly with the rapid growth of connectivity and communication technologies. In the wake of rising cyber attacks and enforcement of regulations, implementation of cybersecurity is imperative to safeguard vehicles. The cybersecurity controls such as secure boot, secure updates, and secure communication require cryptographic primitives (keys/certificates). These security features are largely dependent on robust Key Management System (KMS), as keys are the sensitive assets that must be protected throughout the lifecycle of vehicle. Several security critical applications like over-the-air and car-to-car interaction essentially needs robust KMS to protect the vehicle assets from expanding attack vectors. Traditionally KMS is established centrally in a backend server. The cloud based KMS is becoming complex due to increased number of keys/certificates required to provision in a vehicle. We propose a self-governing in-vehicle key management system for a gateway-based architecture. The solution is derived from core principles of Blockchain technology. Every key or certificate transaction is recorded in a registry, with the first block (genesis block) created during the vehicle manufacturing stage by the gateway. The first stage involves creation of a genesis block, followed by the generation of a PKI blockchain for each ECU during vehicle manufacturing. In the second stage, the established PKI blockchain will be utilized for secure on-road communication during vehicle operations. Key management operations such as key rotation, revocation, addition, and replacement will be performed based on the established blockchain, with the gateway serving as the anchor point. Each key management operation is appended to the chain starting from the genesis block, with updates securely broadcast and replicated across all ECUs ensuring a distributed, tamper-proof key management framework. Several diverse communications like CAN, CAN-FD and Ethernet are comparatively analyzed, against its usage, benefits and complexity in the proposed approach.
Goyal, YogendraSutar, SwapnilJaisingh, Sanjay
This paper presents a comprehensive technical review of the Software-Defined Vehicle (SDV), a paradigm that is fundamentally reshaping the automotive industry. We analyze the architectural evolution from distributed Electronic Control Units (ECUs) to centralized zonal compute platforms, examining the critical role of Service-Oriented Architectures (SOA), the AUTOSAR standard, and virtualization technologies in enabling this shift. A comparative analysis of leading High-Performance Computing (HPC) platforms, including NVIDIA DRIVE, Tesla FSD, and Qualcomm Snapdragon Ride, is conducted to evaluate the silicon foundation of the SDV. The paper further investigates key enabling technologies such as Over- the-Air (OTA) updates, Digital Twins, and the integration of Artificial Intelligence (AI) for applications ranging from predictive maintenance to software-defined battery management. We scrutinize the competing V2X communication standards (DSRC vs. C-V2X) and address the paramount challenges of functional safety (ISO 26262) and cybersecurity (ISO/SAE 21434) in this new landscape. Finally, we identify open research challenges, ethical considerations, and the future trajectory of SDVs toward fully AI-defined, intelligent, and connected mobility.
Ahmad, AqueelHemanth, KhimavathKumar, OmKumar, RajivHaregaonkar, Rushikesh Sambhaji
Automotive Over-the-Air (OTA) software updating has become a cornerstone of the modern connected vehicle, enabling manufacturers to remotely deploy bug fixes, security patches, and new features. However, this convenience comes with significant cybersecurity challenges. This paper provides a detailed examination of automotive OTA update security and the software store (software Applications & services store) mechanisms. I discuss the current industry standards and regulations, notably ISO/SAE 21434 and the United Nations Economic Commission for Europe (UNECE) regulations UN R155 (cybersecurity) and UN R156 (software updates) and explain their relevance to secure OTA and software update management. I then explored the Uptane framework, an open and widely adopted architecture specifically designed to secure automotive OTA updates. Next, OTA-specific threat models are analyzed, detailing potential attack vectors and corresponding mitigation strategies. Real-world case studies are presented to illustrate both the risks and the successful deployment of secure OTA systems in the industry. I conclude with insights into best practices for implementing a robust, compliant OTA update ecosystem, emphasizing a global perspective on regulations and the need for continuous vigilance throughout the vehicle lifecycle.
Kurumbudel, Prashanth Ram
With the increasing connectivity of modern vehicles, cybersecurity threats have become a critical concern. Intrusion Detection Systems (IDS) play a vital role in securing in-vehicle networks and embedded vehicle computers from malicious attacks. This presentation shares about an IDS framework designed specifically for POSIX-based operating systems used in vehicle computers, leveraging system-level monitoring, anomaly detection, and signature-based methods to identify potential security breaches. The proposed IDS integrates lightweight behavioral analysis to ensure minimal computational overhead while effectively detecting unauthorized access, privilege escalation, communication interface monitoring etc. By employing a combination of rule-based and OS datapoints, the system enhances threat detection accuracy without compromising real-time performance. Practical series deployments demonstrate the effectiveness of this approach in mitigating cyber threats in automotive environments, ensuring safer and more resilient vehicle systems.
Shukla, SiddharthChatterjee lng, Avik
Innovation in energy storage and generation system will lead to multiple power train solutions across the vehicle categories in the Automative segment. With various options to the end consumer across different vehicle segments, the complexity associated with E/E Architecture and software engineering will be multi-fold both for the OEMs and Suppliers. Over the air updates shall become mandatory features to manage this complexity and to calibrate the vehicle features in line with changing trends and efficiency plus feature enhancements in post-market release scenarios. These upgrades are more common in digital clusters, in-vehicle entrainment and central digital cockpits. OEMs are introducing the vehicle platforms in multiple power train variants keeping comfort, instrument clusters and in-vehicle entertainment as core features across different power trains. A well-defined and managed comprehensive optimal test strategy and infrastructure will be critical to ensure seamless release of the software solutions to different power trains during the product development phase and post-launch software upgrades. In this novel work, the paper extensively explores the current practices of segregating features through common versus specific powertrains, managing the overall test strategy across varied test types and test infrastructure; then address the advantages and challenges in current practices. The paper would summarize the optimal test strategy in approaching the software development pipeline for a multi-powertrain architecture and focus specifically on early test-driven interventions for robust software deployment on production for both parallel and staggered release pipeline
Rajaram, SaravananVenkata, ParameswaranNaik, Venkatesh
The rapid evolution of modern automotive systems—powered by advancements in autonomous driving and connected vehicle technologies— pose fundamental challenges to design and integration. A specific challenge of these highly interconnected, software-driven systems is in ensuring their safety while avoiding spiralling costs and development times. This challenge 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 —compliance to safety —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 biased or optimistic view of system safety without sufficiently acknowledging areas of unresolved risk. As a result, conventional approaches for developing a safety case may overlook complex interactions, assumptions, and uncertainties that require critical examination, not default acceptance. As opposed to traditional methods of developing safety cases through justification, the dialectic approach emphasizes critical analysis and scrutiny of weak points using open challenges, counterarguments, and alternative perspectives. It encourages a deliberate effort to explore not just what works in a design, but what might fail— anticipating negative aspects, design vulnerabilities, and areas where safety assumptions may fail. Rather than simply validating assumptions, it aims to uncover hidden flaws, inconsistencies, and evidence gaps that could compromise system safety. Constructing a safety case early in a project, and allowing constructive criticism through dialectic argument, transforms the safety case into a living, questioning tool that evolves with improving system understanding—becoming increasingly transparent, robust, and credible. In the paper, we demonstrate 3SK’s practical application of a dialectic methodology for developing safety cases. By this approach, we were able to pick out important safety gaps that would otherwise have gone unnoticed, hence enhancing the completeness, credibility, and robustness of our safety assurance practices.
Kumar, AmrendraBagalwadi, SaurabhMcMurran, Ross
In the development of the automotive electronic control unit (ECU), to keep performance at the desired level, what remains constant is to verify, evaluate, and validate electronic control units. Nowadays, Cars have multiple ECUs even in the range of fifty. Software is validated by a tester using a target ECU, Controller Area network (CAN) communication, and some Input/Output simulation techniques. Also, in some applications, a virtual environment is created for testing. In this paper, the method of Integration testing of Automotive Open System Architecture (AUTOSAR) modules is presented with AUTOSAR software specification as its input. This makes standard test cases as SWS remains the same for AUTOSAR standard release. It enables a platform to efficiently test all layers of AUTOSAR base software (BSW) modules after integration. For the demonstration, TriCore micro controller TC377TX from Infineon is used. Same controllers are usually used in the development of automotive ECUs for various applications. Using winIDEA debugger, setup becomes easier to operate for any new person as it supports both debug and flash mode operation. A simplistic approach is presented in this paper to use the setup while testing. Requirement based testing covers all the implemented features in the software, their connections with higher and lower layers in AUTOSAR architecture. Certain test cases require CAN communication for transferring signals to different ECUs and receive information from them. For those test cases, CANoe Vector hardware is used. Testing using debugging methods is covered in the WinIDEA debugger by checking local and Global variables and putting the breakpoints. For writing test cases and maintaining the defect logs, documentation is prepared and tracked. Requirement traceability is maintained with the test cases to find test case for any requirement easily. This system improvises efficiency and simplifies the testing of AUTOSAR based development. Also, paper demonstrates it as a standard process.
Kelkar, RenuPatil, Vardhman
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 longitudinal gap, which allows the cooperative vehicle to merge the ego’s lane. The negotiation process relies on the bidirectional exchange of MCMs to coordinate the timing and trajectories, ensuring both vehicles complete the manoeuvre safely. Additionally, the Cooperative Awareness Messages (CAMs) allow the vehicles to share real-time information such as position, speed and heading. This connected-enhanced approach extends the capabilities of local perception systems, enabling an improved performance and reaction time to surround traffic participants. The described use case is implemented and validated in a prototype vehicle equipped with V2V communication capabilities and a Highway Pilot (HWP) SAE level 3 driving automation system. Proving ground tests demonstrate that the system can successfully negotiate cut-in manoeuvres in real time, enhancing both safety and traffic flow. The results confirm the feasibility of deploying standardized V2V coordination mechanisms within operational automated driving functions and lay the groundwork for broader integration into future CCAM applications.
Leiva Ricart, GiselaDomingo Mateu, Bernat
The rapid adoption of connected vehicle technologies and advanced driver assistance systems (ADAS) necessitates robust security mechanisms capable of identifying and mitigating sophisticated cyber threats in real-time. Traditional signature-based intrusion detection systems (IDS) are often inadequate in addressing the dynamic and evolving nature of automotive cybersecurity threats, particularly in modern vehicle networks like Controller Area Network (CAN), CAN with Flexible Data-Rate (CAN-FD), and Automotive Ethernet. This research introduces a novel Real-time Intrusion Detection System utilizing advanced Machine Learning (ML) techniques designed specifically for automotive network environments. The proposed IDS framework employs supervised and unsupervised ML algorithms, including anomaly detection, behavioral analytics, and predictive threat modeling, to achieve high accuracy and rapid threat identification capabilities. Through extensive testing in simulated and actual vehicle network scenarios, the developed IDS model demonstrates significant improvements over conventional detection methods, notably in precision, recall, detection latency, and adaptability to zero-day threats. This research further evaluates the proposed system’s alignment with critical regulatory standards such as AIS 189 and UNECE WP.29, ensuring its practical applicability within automotive industry cybersecurity compliance frameworks. The findings highlight the potential for ML-driven IDS solutions to substantially enhance automotive cybersecurity posture, providing OEMs and stakeholders with actionable insights for proactive threat management.
Chaudhary lng, VikashDesai, ManojChatterjee, Avik
There is rapidly increasing advancement in Connectivity, Autonomous, Subscription and Electrification features in vehicles which are being developed. These trends have resulted in an increase in attack surface and security risks on vehicles. To handle these growing risks, it has become important to include passive security systems such as Intrusion detection systems (IDS) which can detect successful or possible attempts of intrusion into vehicle systems compromising their security. In vehicles based on Zonal Architecture, two types of IDS can be implemented, Network based IDS (NIDS) and Host Based IDS (HIDS). The NIDS is implemented in Gateway Electronic Control Unit (ECU) and can monitor multiple networks connected to Gateway, whereas the HIDS usually monitors one single host ECU. Extensive research material is available on NIDS for CAN Networks. For example, the CAN Network in a vehicle is monitored for various abnormal behaviours such as increased busload and invalid signal values. But most of the literature doesn't answer the question, how to ensure the monitoring achieved by NIDS is sufficient? In this paper we try to answer the question by deriving requirements for security monitoring of in-vehicle CAN network using a novel method to guarantee sufficiency in terms of having better coverage of intrusion scenarios. We employ a fusion of (i) Threat Analysis and Risk Assessment approach and (ii) Attack Tree Based approach for deriving security monitoring requirements for the CAN network. We show that security requirements derived by our approach have better coverage of intrusion scenarios, thus enhancing the efficiency in intrusion detection.
E L, Nanda KumarMutagi, MeghaSonnad, PreetiSharma, Dhiraj
The automotive industry is continuously evolving at high pace to meet rising customer expectations, reliability, reduced maintenance, and most relevant, compliance with stringent emission norms. Traditionally, the analysis of vehicle emissions relies heavily on periodic inspections and manual checks. These conventional methods are often time-consuming, prone to human error, and lack the ability to provide real-time insights. Also, identifying failures due to non-manufacturing issues require meticulous physical inspections and historical data reviews, which are not always accurate or timely. Telematics or Connected cars technology being one of the major technological innovations in recent times revolutionizes these processes by enabling real-time data exchange between vehicles and external systems. The current study presents an innovative approach to utilizing telematics data for real-time monitoring of vehicle emissions and pinpointing Catalytic converter failures by analyzing vehicle probe data retrieved from telematics system aimed to identify fuel adulteration events or CNG kit retrofitments that can compromise vehicle performance and longevity. The methodology involves continuous data transmission from telematics devices to the cloud, where the system monitors vehicle emissions in real-time and alerts customers of potential failures. Further to identify the cause of failure, the telematics raw data is processed and aggregated for analysis using statistical models to detect potential fuel tank cleaning due to incorrect or adulterated fuel filling done in the past. This process is validated through a two-level model, ensuring accuracy in detecting fuel adulteration instances. The key advantage of this approach lies in its server-based high-speed processing, which eliminates the resource burden involved during physical inspection and testing of failed parts and enhances detection capabilities compared to existing solutions. This innovative method not only improves vehicle maintenance and customer satisfaction but also ensures compliance with emission norms, thereby contributing to a cleaner and more sustainable environment.
Dev, TriyambakPrasad, Kakaraparti AgamKalkur, VarunModak, SaikatAGARWAL, ShashankChandra, AnimeshPaul, VarshaGarg, AmitSundararaman, VenkataramanBose, Sushant
A more recent focus on driver comfort and the increasing demand for wide range of information availability make automotive Original Equipment Manufacturers (OEMs) provide advanced features such as Head Up Display (HUD) system. Even though HUD projects vital information onto the windshield/glass, its structural integration comes with significant vibration challenges, leading to display instability and haziness. This paper discusses the significant design parameters influencing the functional effectiveness of HUD system. The structure considered for analysis is the HUD assembly and its integration in vehicle. Cross Car Beam (CCB) turns out to be the critical component of the vehicle structure susceptible to road excitations. Although it’s mass dampens the vibrations inherently, due to the low mass of the HUD, relative oscillation between its projector, mirror, and either the windshield or display causes image distortion This paper investigates in detail the role of HUD structural stiffness, eccentric design and material of the display glass and its shaft in achieving optimal HUD functional performance of high definition display. Based on this analysis, the system natural frequency has to be above a particular frequency called Critical Flickering Frequency (CFF) to avoid fuzzy image perception to human eyes . CFF for the HUD discussed is calculated using a structured and controlled subjective study taking care of all the significant parameters affecting it with individuals from all ages and gender. This data is used to build a robust design criteria for the HUD structure for a highly stable display. This research including the novel approach of integrating the concept of CFF in display system vibration development in particular is of significant value to automotive engineers in designing robust functional HUD systems. Addressing the above critical design parameters, this paper paves the way for a seamless in display experience in modern connected vehicles.
Vardhanan K, Aravindha VishnuNaidu, SudhakaraTitave, Uttam
The BioMap system represents a groundbreaking approach to collaborative mapping for autonomous vehicles, drawing inspiration from ant colony behavior and swarm intelligence. It implements a fully decentralized protocol where vehicles use virtual pheromone trails to mark areas of uncertainty, change, or importance, enabling efficient map consensus without centralized coordination. Key innovations include novel pheromone-based compression algorithms and bio-inspired consensus mechanisms that allow real-time adaptation to dynamic environments. In a simulated urban scenario (Town10HD), three vehicles achieved balanced load distribution (±1.8% variance) and comprehensive coverage of a 253.2m × 217.9m × 22.4m area. The final fused map contained 311 chunks with 72,785 particles and required only 10.4 MB of storage. Approximately 49.2% of map particles exceeded the pheromone significance threshold, indicating active importance marking, while no high-uncertainty regions remained. These results demonstrate that BioMap enables natural prioritization of critical navigation areas via virtual pheromones, producing high-confidence maps in real time. Overall, the system achieves its objectives of decentralized mapping, efficient communication, and adaptive coverage through bio-inspired mechanisms, marking a significant advance in multi-vehicle SLAM.
Bhargav, Anirudh SSubbarao, Chitrashree
This paper presents the design, simulation, and evaluation of a low-profile Multiple-Input Multiple-Output (MIMO) antenna configuration, optimized to meet the evolving demands of modernized wireless communication systems, incorporating LTE-Advanced (LTE-A) and emerging 5G Internet of Things (5G-IoT) applications. The antenna’s geometry relies on a novel design comprising staircase-shaped rectangular radiating patches with an integrated stub. This configuration is employed to improve impedance bandwidth and strengthen the isolation between antenna components, which are critical parameters in MIMO system performance. The antenna is fabricated on a Rogers RT/Duroid 5880 substrate, distinguished by its low dielectric loss and high-frequency stability. With a compact physical footprint of 96 × 96 mm2, the proposed design effectively serves the feature of integration into portable and space-constrained wireless devices. The antenna operates effectively across frequency range of 2.13 GHz to 4.2 GHz, covering a broadband that encompasses multiple wireless communication bands, including sub-6 GHz 5G spectrum. Comprehensive performance evaluation was conducted using key MIMO metrics. The design achieves an Envelope Correlation Coefficient (ECC) of less than 0.015, indicating excellent diversity performance. The Mean Effective Gain (MEG) remains below -3 dB for all elements, while the Diversity Gain (DG) reaches up to 10 dB, supporting reliable signal reception in multipath environments. Furthermore, Channel Capacity Loss (CCL) is maintained below 0.12 bps/Hz, confirming the antenna’s proficiency in supporting fast data transmission rates with minimal degradation in channel capacity. Overall, the proposed MIMO monopole antenna exhibits a well-balanced trade-off between compactness, bandwidth, and isolation, making it a strong candidate for next-generation wireless platforms where high-performance, compact antennas are essential.
Gupta, ParulPrasad, Anjay
Edge Artificial Intelligence (AI) is poised to usher in a new era of innovations in automotive and mobility. In concert with the transition towards software-defined vehicle (SDV) architectures, the application of in-vehicle edge AI has the potential to extend well beyond ADAS and AV. Applications such as adaptive energy management, real-time powertrain calibration, predictive diagnostics, and tailored user experiences. By moving AI model execution right into edge, i.e. the vehicle, automakers can significantly reduce data transmission and processing costs, ensure privacy of user data, and ensure timely decision-making, even when connectivity is limited. However, achieving such use of edge AI will require essential cloud and in-vehicle infrastructure, such as automotive-specific MLOps toolchains, along with the proper SDV infrastructure. Elements such as flexible compute environments, deterministic and high-speed networks, seamless access to vehicle-wide data and control functions. This paper outlines examples of edge AI use cases beyond ADAS and AD, the challenges current vehicle electrical/electronic (E/E) architectures pose, and the limitations of general-purpose MLOps tools. It goes on to discuss how the shift to Software-Defined Vehicle (SDV) architectures and MLOps toolchains that are needed to overcome the challenges.
Khatri, SanjaySah, Mohamadali
Software Defined Vehicles (SDV), Software Defined Networks (SDN), Software Defined (Power) Grids (SDG) are just a few examples of how the Software Defined Transformation is unfolding across many industries today (collectively being referred to as Software Defined X – SDX). This paper defines a maturity model for Software Defined Transformation and evaluates different industries including Automotive on their evolution so far. This cross-industry view of SDX helps in analyzing where SDV’s could be headed. A 2020 paper [1] lays out the complexity of the automotive software, with companies pursuing several directions in this transformation. The automotive industry has not yet reached a consensus on the direction it is taking on SDV. While companies like Tesla are already making software centric cars, traditional OEMs like General Motors, Toyota, Ford etc. are making huge investments and redefining their business models, tech stacks and operations to leverage the power of software. There is an opportunity to introduce an overarching framework to compare SDX across industries. We propose a three-step “Define-Evaluate-Forecast” Framework We “Define” a multi-dimensional SDX Maturity Model around three strategic areas: Technical/Product, Operating Model and Business/Financial. Technical areas include State of the Architecture, Reusable Innovation Enablers and level of Autonomy & Abstraction between Hardware and Software. Operational parameters considered are Organization Alignment for Software, Availability/Adoption of Regulations & Standards. Business aspects include Customer Experience, Ecosystem value proposition, Total Cost of Product & Ownership, Return on Investment. We “Evaluate” similar traditional industries like Telecom, Storage, Power Grid and score them per the SDX maturity levels, accounting for their differences in complexity. We “Forecast” the course for Software Defined Vehicle over this decade and the next, based on the observations, analysis and understanding of the journey of the other industries. This paper studies the evolving software defined transformation across several industries and provides a “Define-Evaluate-Forecast” framework to compare them and prognosticate the future of SDV, leveraging the advancements and learnings from other industries.
Mathur, Akshay RajMisra, AmitMakam, Sandeep
With the rapid development of automobile industrialization, the traffic environment is becoming increasingly complex, traffic congestion and road accidents are becoming critical, and the importance of Intelligent Transportation System (ITS) is increasingly prominent. In our research, for the problem of cooperative control of heterogeneous intelligent connected vehicle platoons under ITS considering communication delay. The proposed method integrates the nonlinear Intelligent Driver Model (IDM) and a spacing compensation mechanism, aiming to ensure that the platoon maintains structural stability in the presence of communication disturbances, while also enhancing the comfort and safety of following vehicles. Firstly, construct heterogeneous vehicle platoon system based on the third-order vehicle dynamics model, Predecessor-Leader-Following (PLF) communication topology, and the fixed time-distance strategy, while a nonlinear distributed controller integrating the IDM following behavior and the front-vehicle spacing compensation mechanism is designed to enhance the robustness of the system to delay disturbance. Secondly, leveraging the Lyapunov-Krasovskii functional framework in conjunction with the Moon inequality, an LMI-based stability condition is derived to ensure the uniform asymptotic stability of the system. The corresponding maximum admissible communication delay is then determined, followed by a detailed analysis of the system's string stability. Finally, comparative simulations are conducted on the MATLAB/Simulink platform. Simulation results verify that the proposed controller offers enhanced convergence speed, reduced acceleration variability, and improved suppression of spacing errors under communication delay disturbances. Compared to conventional linear controllers, it demonstrates markedly superior control performance and greater practical applicability. This method provides a valuable reference for the robust design and performance optimization of cooperative control systems for heterogeneous vehicle platoons under communication delay conditions.
Ye, XinKang, Zhongping
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