Browse Topic: Embedded software

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Enhancing SDV Architecture by Rethinking In-Vehicle Connectivity2026-28-021011/5/2026
The rapid evolution of automotive electronics has pushed traditional vehicle electrical and electronic (E/E) architectures to their limits. Conventional designs, characterized by point-to-point wiring harnesses, distributed domain-specific ECUs, and communication buses such as LIN, CAN, and Ethernet, impose significant drawbacks in terms of vehicle weight, integration complexity, and long-term scalability. As the industry moves toward the Software-Defined Vehicle (SDV) paradigm, there is a compelling need to rethink the foundational principles governing in-vehicle connectivity. This paper proposes a wireless-centric SDV architecture in which non-functional-safety-relevant sensors and actuators communicate over standardized wireless protocols, eliminating the need for their associated physical wiring. By decoupling these peripheral nodes from the wired backbone, the proposed architecture reduces harness bulk, lowers vehicle mass, minimizes redundant compute hardware, and significantly reduces integration cost. Safety-critical systems including those governing powertrain, braking, and steering are explicitly retained on hardwired, deterministic communication channels in compliance with ISO 26262 functional safety requirements. This paper additionally defines explicit assumptions and fallback behavior governing that wired/wireless boundary. The architectural framework is evaluated against key SDV design objectives such as modularity, over-the-air (OTA) updateability, and feature scalability, assessed qualitatively against published engineering precedent, with a structured validation roadmap defined to quantify latency, reliability, and coexistence performance in follow-on work. The analysis demonstrates that a hybrid wired-wireless topology can substantially simplify vehicle E/E architecture while maintaining the reliability and latency guarantees demanded by automotive-grade systems. This work contributes to a structured transition roadmap for OEMs and Tier-1 suppliers seeking to adopt SDV principles without compromising vehicle safety or regulatory compliance, together with an explicit protocol-selection framework and a functional-safety argument for the wired/wireless boundary.
Hebbale Ramkumar, Ramya, Sarkar, Dibas
An Agent Driven Approach for Test Specification Generation for Software Defined Vehicle Features2026-28-021211/5/2026
Automotive industry is currently experiencing a major transition towards Software-Defined Vehicles (SDV) to facilitate seamless new feature integration via over the air (OTA) update. SDVs integrate more than 50 + ECUs with more than 400+ software functions, leading to significant software complexity and a substantial need for comprehensive bench level testing to prevent software issues at vehicle level. To address these challenges, a robust and edge case focused test specification is essential, ensuring maximum requirement coverage across all scenarios while incorporating insights from past bugs and field issues. Currently, the automotive industry faces challenges in producing high-quality test specifications mainly focusing on functional testing (both positive and negative scenario), fault injection testing, ISO standards testing, network management testing which often exceed 2,000 + test cases for SDV features in parallel with maintaining consistency over time, ensuring maximum system-requirement coverage, and meeting project milestones. This paper introduces a new method to overcome above said problems. We provide a comparative study involving our proposed method with other methods to show the efficacy of the proposed method.
Arumugam, Jayalakshmi
Achieving Deterministic and Safety-Certified Operation in Software-Defined Vehicles through Hardware–Software Co-Design2026-28-020911/5/2026
The transition toward Software-Defined Vehicles (SDVs) [1] is driving a fundamental shift in automotive electrical and electronic architectures [1], where centralized high-performance computing platforms must support mixed-critical workloads under strict requirements for determinism, performance isolation [2], and functional safety. Consolidating safety-critical and non-critical applications onto shared System-on-Chip (SoC) platforms [3] introduces significant challenges in ensuring predictable execution behavior, bounded latency, and compliance with safety standards [4, 15]. Addressing these challenges is essential for enabling scalable and reliable next-generation automotive systems [1] [5]. This paper presents a hardware–software co-design approach to achieve deterministic and safety-certified operation in SDV [1] architectures. Unlike traditional implementations that rely on software hypervisors or hardware isolation independently, the novel core contribution of this work is an integrated, dual-layer hardware-software orchestration model. It dynamically coordinates static Stage-2 translation with hardware Extended Resource Domain Controllers (XRDC) and Memory Partitioning and Monitoring (MPAM) to enforce end-to-end spatial, temporal, and interconnect-level bus isolation. The proposed solution combines hardware-assisted resource isolation mechanisms, including Hardware-enforced resource isolation and Memory Partitioning and Monitoring (MPAM) [6], with a lightweight partitioning hypervisor and Virtual Machine Monitor (VMM). XRDC [7] provides fine-grained access control for memory and peripheral resources, while MPAM [6] enables memory bandwidth allocation and monitoring to minimize resource contention across competing workloads. On the software side, the partitioning hypervisor [8] enforces strong temporal and spatial isolation between Virtual Electronic Control Units (vECUs), allowing mixed critical applications to coexist safely on a shared platform. VMM supports efficient virtualization [9, 10] and lifecycle management of virtual machines while maintaining deterministic execution characteristics. The proposed architecture aligns with the requirements of ISO 26262 [4], enabling the certification of safety critical applications alongside non-critical services. By integrating hardware-enforced isolation with software-level partitioning, the approach ensures predictable system behavior and reduces interference between workloads. The results demonstrate that a coordinated hardware–software strategy is essential to bridge the gap between high-performance computing and functional safety, thereby enabling scalable, deterministic, and safety-compliant SDV [1] platforms for future automotive applications. System-level validation conducted on an NXP S32G3 [7] [10] vehicle network processor platform demonstrates the efficacy of the proposed co-design. Under artificial memory-bus contention generated by background non-critical (QM) Linux traffic, the hardware-enforced MPAM bandwidth regulation reduced maximum worst-case execution time (WCET) jitter for an ASIL real-time control loop from 142.6 μs down to 8.4 μs—achieving a 94.1% reduction in latency variation. Paravirtualized hypercall operations achieved a 40.2% latency reduction compared to standard Trap-and-Emulate approaches.
Khan, Misbah Ullah
A System and Method for Software Computation in a Software Defined Vehicle2026-28-021411/5/2026
Modern day vehicles are equipped with multiple features to enhance user experience, increased safety and continuous over the air updates for upgradability and updatability of vehicles over lifetime. The implementation of every embedded automotive software feature consumes microcontroller resources (like RAM and ROM) which are highly critical and need careful handling. In an SDV architecture, where continuous software upgrades and updates are enabled, optimal feature handling becomes necessary. The microcontroller resources chosen during the development phase often reaches the threshold limit even before series production. In such scenarios, software feature enhancement/updates are challenging. When the memory is limited and becomes a constraint for installing newer features / upgrades, OEM has minimal provision to enhance or introduce new features. The paper presents framework to OEM to get a summary of how to manage functionality in a vehicle variant depending on specific demographic location and user preferences. The summary of the functionality usage is reported to end user in the vehicle system.
Chandrashekar, Sandhya, Hubli, Ashwini
Option for Introducing Vehicle Crashlytics with SOVD2026-28-021511/5/2026
Automotive E/E architecture is getting transition from distributed ECUs to high performing Zonal architectures. Traditional diagnostic methodologies based on Unified Diagnostic Services (UDS/ISO 14229) is being followed for providing failure analysis. It is difficult when debugging complex software and to find issues where lies exactly at modular level called “stack trace”. This could provide solutions for software bugs, failures or even container-level crashes in Software-Defined Vehicle (SDV). This paper provides a remote diagnostic framework that makes use of the ISO 17978 (SOVD) standard to visualize a “Vehicle-wide Crashlytics” as analogous to modern mobile application analytics with Firebase SDK. Also, the comparison of failure management with traditional diagnostic method to handling fault management in remote framework with both ISO 17978 (SOVD) and ISO 20077/20078 (Remote Diagnostic) is made. It also speaks how methodology can be formed for software logs capturing and traces execution triggered by function software level anomalies. Also, the constrains such as V2X bandwidth and data is monitored and controlled with Diagnostic Saliency Algorithm that helps to determine only relevant telemetry is transmitted via 5G or V2N. This framework proposal is checked with a simple application failure scenario, which leads to the reduction in “Mean Time to Repair” (MTTR) for software bugs. The reduction in MTTR is achieved since actionable root cause of software issue is shared directly from vehicle’s run-time environment. Future development of vehicle functions (SaaS) is expected to be integrated with SOVD and cloud-native observability patterns. This is much needed towards the scalable and quick development and deployment of applications into Vehicle as Digital Hub.
Kalirajan, Vignesh, Annamalai, Viswachandar, Balakrishnan, Arun Shankar
Automotive System Architecture Strategy for Bridging Long-Lifecycle Hardware & Fast-Evolving Software in SDV-AI Era2026-28-021311/5/2026
The Software-Defined Vehicle (SDV) paradigm is reshaping automotive electronic architectures by enabling continuous software rollout & artificial intelligence (AI) evolution on platforms over the total service life of the hardware. This creates a fundamental challenge for electronic control unit (ECU) design: to enable sustained deployment, adoption & maintenance of AI models over vehicle’s lifetime. This paper presents a technology independent framework for integrating AI lifecycle management into SDV ECU architectures, while decoupling AI & software innovation from hardware constraints. It further discusses the role of scalable computing platforms based on high-performance CPUs, GPUs & SoCs, in supporting increasing AI workloads over time. In addition, the evolution of AUTOSAR-based hardware abstraction mechanisms is examined as an enabler for software portability & hardware independence. The discussed framework addresses key lifecycle aspects, including AI model deployment, versioning, rollout strategies & long-term maintainability within established automotive safety, cybersecurity frameworks. The presented work contributes to future-ready SDV systems capable of sustaining continuous AI innovation over extended operational lifetime.
Ghosh, Sayantan, Anudeepika K., Durga, Nagaraju, Yamuna
State-Dependent Event Tree Analysis: A Dynamic Methodological Extension for Safety of the Intended Functionality in Automotive Systems2026-28-014011/5/2026
As automotive architectures evolve toward Software Defined Vehicles (SDVs) and high levels of driving automation, traditional safety analysis techniques rooted in hardware reliability require significant adaptation. The Classical Event Tree Analysis (ETA), a cornerstone of ISO 26262 functional safety compliance, relies on static, binary branching logic where each function either fails or succeeds. While highly interpretable and effective for modelling random hardware failures, classical ETA struggles to capture the dynamic, non-linear interactions, temporal degradation, and complex human-machine interface (HMI) dependencies that are characteristic of modern Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. This paper proposes State Dependent Event Tree Analysis (SD-ETA), a methodological extension designed to integrate discrete state vectors and temporal dependencies into the classical ETA framework. SD-ETA assigns probabilities to the branches for the dynamic nonlinear interactions, temporal degradation, and complex human-machine interface (HMI) dependencies instead of binary values, thereby allowing the system state (comprising vehicle physics, environmental conditions, and driver attention metrics) to dynamically update based on the outcomes of the preceding nodes. This approach retains the visual hierarchy and regulatory familiarity of standard event trees while significantly enhancing model fidelity for Safety of the Intended Functionality (SOTIF) scenarios governed by ISO 21448. To demonstrate the necessity and utility of this methodological evolution, this paper presents a comparative case study analysing a system operating under degraded environmental conditions. The quantitative application reveals that while classical ETA predicted a relatively low collision risk by treating sensor performance as a static binary state, the dynamic SD-ETA simulation exposed a critical hidden safety gap, calculating a higher collision probability due to compounded latency and degraded physical operating parameters. Ultimately, SD-ETA equips safety practitioners and automotive regulators with a modernized, time-aware framework to accurately evaluate the complex, interacting risks of autonomous systems without sacrificing the transparency required for rigorous safety certification.
Sharma, Dhiraj Mohan, Mutagi, Megha, Reddy, Kakarla Subba
Evaluation of Safety Critical Battery Management System Functions Using Hardware-in-the-Loop Testing2026-28-018411/5/2026
Rapid electrification of the automotive industry has significantly increased the functional complexity and safety expectations of Battery Management Systems (BMS) used in electric vehicles. While Model-in-the-Loop (MIL) testing remains a standard practice for early-stage verification of control algorithms, it is inherently limited in its ability to represent real time execution effects and physical hardware interactions. Critical BMS functions, including protection timing validation, sensor interface behavior, contactor actuation logic and fault detection mechanisms are strongly influenced by hardware characteristics such as signal conditioning, analog to digital conversion, execution latency, and nonlinear component behavior, which cannot be accurately captured in a software only environment. This paper presents the application of Hardware-in-the-Loop (HIL) testing as an essential validation methodology for safety critical BMS functions by interfacing the production intent BMS controller with a real time simulated battery environment. The proposed HIL setup enables realistic interaction between embedded software and physical I/O interfaces, allowing systematic evaluation of BMS behavior under normal operating conditions as well as controlled electrical, thermal, and communication fault scenarios. Key validation aspects include protection of response timing, robustness of fault handling logic, and system behavior under transient and abnormal conditions. The study demonstrates that HIL testing effectively bridges the validation gap between MIL simulations and vehicle level testing by enabling early identification of hardware–software integration issues. This approach enhances confidence in functional safety readiness, reduces development risk, and supports compliance with established automotive development workflows. The presented methodology aligns with established automotive development practices and provides a practical framework for robust BMS validation at bench level prior to vehicle level.
Patowary, Suman, Kumar, Aashish, Choudhary, Abhilash, Kumar, Abhishek, Bora, Abhijit
A CI/CD-Integrated Digital Twin Framework for Scalable Validation of Automatic Transmission Application Software Using Platform-Based Architecture2026-28-018111/5/2026
Modern automatic transmission (AT) application software validation is predominantly dependent on Hardware-in-the-Loop (HiL) systems, resulting in significant time, cost, and scalability limitations within continuous integration/continuous deployment (CI/CD) workflows. These constraints intensify with increasing software complexity and the need to support multiple transmission variants across evolving hardware configurations. Existing validation approaches lack a scalable, software-centric surrogate capable of representing both control logic and physical system behaviour under real-time operating conditions. This study proposes a digital twin-based validation framework for AT application software, integrated within CI/CD pipelines. The framework restructures application software into a platform-based architecture enabling reuse across multiple transmission programs. A coupled plant model is developed to simulate drivetrain dynamics, ensuring system-level fidelity. The architecture also permits future data-driven augmentation for transient response correction and extended scenario coverage; however, the present study focuses on the baseline digital twin, hardware abstraction, and CI/CD validation workflow. A key contribution is the introduction of a hardware-adaptive abstraction layer, enabling the digital twin to accommodate variations in sensors, actuators, and control interfaces without full HiL dependency. Across 15 scenario hardware validation combinations, the reduced-order digital twin achieved a runtime gain of 1.20×–1.68× over the HiL proxy execution, with an average gain of approximately 1.46× and an average execution-time reduction of approximately 31% per validation case. The study demonstrates that integrating application software, plant models, and hardware-aware abstraction within a digital twin enables a transition from hardware-constrained validation to software-scalable paradigms. The framework provides a pathway toward efficient and adaptable validation in software-defined vehicle ecosystems. The proposed framework is aligned with ZF’s platform-based transmission software strategy, enabling reuse across multiple automatic transmission programs and supporting scalable validation of ZF powertrain control systems under evolving hardware configurations.
Gavara, Chandrakant, Sali, Rohan, Peddibhotla, Brahmaprakash, Wadkar, Swapnil P.
Research on Testing Methods and Practices of on Board Data Handling Subsystem Based on Spacecraft2026-99-294710/19/2026
Subsystem testing, as a crucial engineering phase in the development process of the On Board Data Handling (OBDH) subsystem to ensure product quality, is increasingly valued by engineering organizations. The OBDH subsystem, apart from its characteristics of embedded software and FPGA with strong real-time performance and close hardware relations, also features multiple external interfaces, large communication data volumes, and frequent software changes. These factors highlight the significant challenges in testing the OBDH subsystem, making it essential to develop a theoretical framework and methods to guide the testing process. Following the principles of the Director’s Order No. 7 for full-cycle and multi-dimensional work, and based on the testing characteristics of the OBDH subsystem, a full-cycle testing model for the OBDH subsystem has been established. This paper presents comprehensive research and practical implementation of OBDH subsystem testing from three aspects: test environment establishment, test method implementation, and test experience summarization.
Ye, Zhiling, Zhang, Tao
PRODUCT BRIEFS26AUTP10_1510/1/2026
A Model-Based Method for MOSA-Constrained, Victory-Informed E/E Architecture Trade Studies2026-01-75269/22/2026
Early electrical/electronic (E/E) architecture decisions in ground combat vehicles (GCVs) strongly influence lifecycle cost, upgradeability, survivability, cyber resilience, and integration risk across decades-long service lives. As Army programs transition from distributed E/E architectures toward zonal and software-defined vehicle concepts, the number of viable architectural options expands while the cost of late architectural change increases. This paper presents a detailed, model-based architecture evaluation methodology structured around Requirements–Functional–Logical–Physical (RFLP) principles, using Modular Open Systems Approach (MOSA) constraints as architectural requirements and VICTORY-aligned concepts as validation targets. The methodology emphasizes multi-variant architecture definition with large-scale reuse, early synthesis of wiring and network impacts, end-to-end traceability, change impact awareness, and continuity from architecture evaluation into detailed, multi-team design. The approach enables early, evidence-based comparison of architectural alternatives and preserves a traceable rationale to support modernization, sustainment, and future system evolution.
Anderson, Tony
Leveraging Domestic Automotive Software Development Best Practices for MOSA and GCIA/Victory Implementation2026-01-75389/22/2026
ISO 26262ISO/SAE 21434ISTQB/ASPICEMOSAGCIAVICTORY
Priemer, Douglas, Sime, Karl
Business Approach to Cope with Automotive Market Uncertainty2026-24-00249/21/2026
The global automotive industry is facing an unprecedented convergence of uncertainties driven by geopolitical tensions, evolving trade policies, emissions related regulations, and increasingly volatile consumer demand. Shifting emissions legislation, including the EU’s tightened CO2 targets and long-term plans to phase out internal combustion engines, is imposing strategic and financial pressures on automakers and suppliers as they navigate divergent regional regulatory trajectories. Demand side volatility further complicates the landscape. Consumer preferences are fluctuating due to economic pressures, infrastructure constraints, and uneven EV adoption patterns. While some markets show stagnation in battery electric vehicle uptake, hybrids are rising as consumers seek cost efficient alternatives amid uncertain energy and regulatory environments. Within this unstable context, the transition toward Software Defined Vehicles (SDVs) is emerging as a critical strategic response. SDVs, characterized by centralized computing, updatable software architectures, and over the air feature deployment, offer automakers greater adaptability in addressing regulatory shifts and market dynamics. By decoupling hardware from software cycles, SDVs enable faster innovation, reduced development risk, and new digital revenue models, while virtualization and AI driven analytics enhance development efficiency and lifecycle value.
Cavanna, Filippo, Potenza, Luca
Architecting Safety-Critical Systems for AI-Enabled Software-Defined Vehicles: A Compute, Timing, and Isolation Framework with a Brake System Case Study2026-01-08119/14/2026
Software-defined vehicle (SDV) platforms are reshaping safety-critical system design by consolidating braking and other motion-control functions on centralized heterogeneous edge compute that also executes physical-AI workloads. This consolidation breaks traditional assumptions of fixed ECUs and simple timing envelopes, complicating assurance of determinism, isolation, and fail-operational behaviour for ASIL-D brake functions. Building on a decentralized brake-by- wire (BbW) architecture with dual controllers, redundant low-voltage power grids, and smart electromechanical brake corner actuators, this paper proposes a systems-level framework for architecting safety-critical functions in AI-enabled SDVs along three dimensions: compute, timing, and isolation. The framework classifies conventional and AI-based functions and maps them to heterogeneous compute classes; defines architectural patterns that combine safety islands, power-domain redundancy, and hardware partitioning to support freedom from interference; and formalizes timing domains and contracts that bound latency, jitter, and failover dynamics across sensors, centralized controllers, and decentralized actuators. The contribution is not a new AI algorithm, but a safety-oriented architectural framework that constrains how AI-enabled functions may be integrated into fail-operational by-wire systems. A BbW case study with edge-resident AI observers and anomaly detectors shows how the framework complements System Analysis Tool (SAT)– based failure modelling and clarifies trade-offs among safety isolation, latency, and AI performance while preserving braking safety guarantees under continuous software evolution.
Srinivasaraghavan, Soumyasudharsan
Real-Time Estimation of Brake Temperature using Physics-Informed Machine Learning Model2026-01-08109/14/2026
The current work presents a novel approach to estimating brake surface temperature in real-time to aid in brake wear prognostics. Brake prognostics involve estimating brake pad wear in real-time, which enables its predictive maintenance. Brakes are a safety-critical system for vehicles; therefore, they require accurate and robust pad wear estimation to ensure vehicle safety. However, it involves several challenges. The estimation of pad wear is fundamentally a two-stage process: the first stage involves the accurate prediction of brake pad surface temperature, while the second stage utilizes this thermal history to calculate cumulative material wear. A significant challenge in estimating brake pad wear without an expensive sensor is that it is sensitive to the surface temperature prediction; any error in the thermal model propagates and compounds in the wear prediction stage. To identify surface temperature, traditional physical sensors are often cost-prohibitive or prone to failure in the harsh thermal and mechanical environments of the wheel end, necessitating a robust virtual sensing solution that can capture complex, non-linear heat transfer dynamics. The current work addresses the above challenge of identifying temperature dynamics using a Physics-informed Machine Learning approach. We employ Symbolic Regression (SR), a data-driven method that discovers the underlying mathematical expression of the system dynamics by searching for the optimal functional relationship between variables. SR provides an interpretable model that can be generalized across automotive platforms, offering a transparent, computationally efficient, and analytically tractable alternative to traditional ‘black box’ models. To generate the temperature dataset, a test vehicles were equipped with thermal sensors and underwent various braking scenarios. The SR-based virtual sensing model demonstrated strong and consistent predictive fidelity across all braking conditions tested. Under mild braking scenarios, the model achieved a Mean Absolute Percentage Error (MAPE) of approximately 6.0% in predicting brake surface temperature. This performance remained highly robust under mixed and harsh, high-speed braking, the most thermally demanding scenario, yielding MAPEs of only 11.6% and 11.9%, respectively.. Across all regimes, this level of temperature estimation fidelity directly limits error propagation into the downstream brake pad wear prediction stage, enabling reliable, sensor-less, cloud-based brake health monitoring at scale.
Gannavarapu, Shivadath, Pal, Anuj, Fan, Mengdi
SAE TOMORROW TODAY - SDVs, AI, and the Next Era of Automotive Innovation135787/28/2026
What does it really mean to build a software-defined vehicle? As AI reshapes the automotive industry, SDVs may become the foundation for the future rather than the destination. Listen in as we sit down with Jeffrey Chou, Founder and CEO of Sonatus, a leading provider of intelligence-driven SDV solutions, to explore why SDVs are best understood as a platform for innovation -- one that is scalable, upgradable, and proven at scale. This conversation dives into the evolution of SDVs, from over-the-air updates and AI-powered diagnostics to intelligent infrastructure services that could one day allow vehicles to share computing power, storage, and data with the world around them. You'll also get insight on how Sonatus scaled its software, the cultural shift required to bring Silicon Valley and automotive engineering together, and why collaboration -- not competition -- will define the future of mobility. If you're interested in automotive OS, AI, SDVs, or the future of vehicle architecture, this episode offers an insider's perspective on where the industry is headed next. We'd love to hear from you! Share your comments, questions and ideas for future topics and guests podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today-a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.
Patterson, Lori
SAE TOMORROW TODAY - Why the Biggest Challenge for SDVs Isn't the Technology135777/20/2026
Software-defined vehicles (SDVs) are transforming the automotive industry ... but are OEMs focused on the right priorities? With over 35 years of award-winning automotive software expertise, Elektrobit's comprehensive SDV ecosystem empowers OEMs, Tier 1s, ODMs and Big Tech to build future-ready solutions with speed and confidence -- driving faster innovation and seamless integration across the vehicle lifecycle. Listen in as we sit down with Dr. Moritz Neukirchner, Head of Cross-Portfolio Growth and Alliances, to discuss how automotive operating systems are reshaping the future of mobility and why software is becoming the defining factor in vehicle innovation. From over-the-air updates and AI-defined vehicles to open-source software, you'll learn why many OEMs are rethinking their SDV strategies after years of overambitious goals. This conversation also explores why organizational change, not technical capability, is now the biggest hurdle to building scalable, customer-focused SDVs. If you're interested in automotive OS, AI, SDVs, or the future of vehicle architecture, this episode offers an insider's perspective on where the industry is headed next. We'd love to hear from you! Share your comments, questions or ideas for future topics with Grayson on Twitter or send them to podcast@sae.org. Follow SAE on LinkedIn, Instagram, Facebook, Twitter, and YouTube.
Patterson, Lori
UMV Peoplemover: Towards a Modular Fully Automated Vehicle Research Platform2026-01-07847/1/2026
The UMV Peoplemover 2+2 is part of a modular vehicle family (Urban Modular Vehicle) that includes derivatives for passenger and cargo transport in urban environments. The platform supports automated movers as well as conventionally controlled vehicles with a human driver, ensuring high flexibility across applications. The modular platform enables the extensive use of common parts, allowing the efficient and cost-effective realization of multiple vehicle variants. The increased share of common parts also improves sustainability by reducing derivative-specific parts, material usage, and production complexity. A drivable demonstrator of the UMV Peoplemover 2+2 has already been realized. The vehicle is designed for the automated transport of up to four occupants in a 2+2 vis-à-vis seating arrangement and is targeted at demand-oriented shuttle services. While the drivable demonstrator validated the proof of concept, it lacked the core Level 4 hardware and software stack for automated driving functions. To address this limitation, we deployed a software-defined vehicle architecture to the concept. This paper introduces the novel e/e-architecture and software stack enabling the Peoplemover 2+2 to initiate its first shuttle service at the German Aerospace Center (DLR e.V.) in Stuttgart. We further detail the deployed multi-modal sensor suite, comprising modern solid-state LiDARs and a 4D imaging radar, which were carefully selected to meet the operational design domain requirements while also serving as a versatile research platform for future advanced perception studies. Finally, we analyze the SDV-based modular software stack, which facilitates rapid application development through straightforward switching between commercial, open-source, and in-house software domains, and supports parallel execution of domain-specific functions across all three software sources.
Pohl, Eric, Schmid, Fabian, Münster, Marco, Siefkes, Tjark, Stuebler, Tillmann, Mohammed, Shawan
Generating Machine-Processable Specifications from Natural Language Using Large Language Models in Automotive Commissioning and Testing2026-01-07687/1/2026
In vehicle production, commissioning and testing processes of electric and electronic components are essential for value creation and quality assurance. The emergence of software-defined vehicles, however, leads to an increased scope and complexity of these processes as software functions depend on electric and electronic components for perception, execution, and processing tasks. In this context, this paper tackles a common challenge: Software that is deployed in vehicle production to implement commissioning and testing processes is developed upon specifications that define prerequisites, procedures, and target results in natural language. Therefore, extensive human interpretation and manual translation into executable code are needed being susceptible to errors as well as time-consuming. The large number of vehicle configurations and rapid changes in vehicle software further complicate the development of commissioning and testing software, particularly as verbose textual dependency descriptions risk impairing comprehensibility. Machine-processable specifications facilitating automated validation and code generation or direct execution could consequently ensure consistency, reduce manual effort, and accelerate the development process. For this purpose, we examine the processability of commissioning and testing specifications in natural language by proposing a pipeline designed to systematically transform these specifications into a machine-processable format. In particular, we introduce a unified schema that serves as an input format for the large language models tasked with the transformation. Subsequently, several large language models are evaluated in practical trials, based on their ability to translate commissioning and testing specifications into a machine-processable notation. In summary, this study aims to enable more efficient and data-driven software development based on textual requirements. This work offers valuable insights into the suitability and applicability of large language models within the planning of automotive commissioning and testing processes, targeting enhanced automation and efficiency.
Köhler, Katja, El Asad, Aiman, Hahn, Michael, Reuss, Hans-Christian
Multi-Agent LLM Architecture for Systems Engineering Automation2026-01-07777/1/2026
The increasing complexity of modern software-intensive systems, particularly in the automotive domain, demands new approaches to bridge the gap between high-level engineering specifications and executable, safety-compliant code. This need is amplified by the rapid transition toward software-defined vehicles, where highly dynamic, updateable software functions significantly enlarge the scope and frequency of engineering activities and require scalable, transparent, and adaptive development processes. While recent advances in Large Language Models have demonstrated strong capabilities in automating tasks such as requirements analysis, code generation, and documentation, their deployment in safety-critical engineering workflows remains challenging due to the need for transparency, traceability, and controlled decision-making. This paper presents a modular multi-agent Large Language Model (LLM) pipeline that automates key steps of the systems engineering lifecycle - from requirement structuring and compliance checking to code and test generation - using specialized LLM agents orchestrated within a unified architecture. A central contribution of this work is the integration of a Human-in-the-Loop subsystem, which introduces configurable review checkpoints at critical stages such as requirements analysis, compliance assessment, code generation, and test creation. The human-in-the-loop module enables engineers to approve, reject, or modify intermediate results, ensuring human oversight, enhancing trustworthiness, and enabling adherence to functional safety standards. The system supports heterogeneous input formats and provides end-to-end traceability through structured outputs and detailed monitoring of performance metrics including model usage, token consumption, and automation efficiency. Initial evaluations indicate that the combination of multi-agent specialization and human-in-the-loop-guided oversight can significantly reduce engineering effort while maintaining the transparency and reliability required for regulated domains. By embedding controllable human supervision into the LLM-driven pipeline, this work offers a practical and scalable architecture for integrating Artificial Intelligence (AI) automation into safety-critical systems engineering processes, with particular relevance to automotive software development.
Padubrin, Marcel, Kulzer, André Casal, Guerocak, Erol
AI scares and excites cybersecurity professionals at WCX26AUTP06_086/1/2026
It's not difficult to find warnings about the dangers of AI. The news that Anthropic's new AI tool is too dangerous for the public due to its alleged hacking capabilities should be of concern to every company making software. That includes automakers. Software has been part of the vehicles in our driveways for decades. In the past few years, even more so, with the push for software-defined vehicles (SDV). At SAE's 2026 WCX conference, a group of cybersecurity professionals from the vehicle industry discussed what AI and SDVs mean for current and future vehicles and how their jobs are about to get simultaneously easier and more difficult.
Baldwin, Roberto
THE NAVIGATOR26AUTP06_076/1/2026
Automakers and suppliers have been embedding electronic control systems and software into vehicles since the mid-1970s. But Tesla fundamentally changed the game in 2012 with the introduction of the Model S. That car was arguably the first software-defined vehicle (SDV) and it led to a fundamental shift in how the industry designed new vehicles and how financial markets thought about the industry. However, 14 years later, as the Model S is about to drive off into the sunset, the legacy auto industry is still struggling to build vehicles with modern software and find a way to make money from them. As a reminder, prior to the Model S, software and the underlying electronics, sensors and actuators were always tightly coupled together. Aside from addressing recalls for regulatory compliance, the software was not designed to be updated once the vehicle was shipped from the factory. For the most part, a car had the same functionality after 200,000 miles of use as it did when the owner drove it off the dealer lot, and there was no expectation that manufacturers would ever push out new capabilities during the car's life.
Engineering in the second quarter of the 21st Century26AUTD05_055/1/2026
Building a trusted digital twin and decision-centric simulation ecosystem The automotive industry has been experiencing significant change and transformation. Electrification, software-defined vehicles, advanced driver assistance systems, and increasing electrical system integration are fundamentally reshaping how vehicles are designed and validated. As integration complexity continues to increase, the expectations for design cycle times are being compressed. Programs that once relied on extended validation timelines are now expected to deliver the same level of confidence in a fraction of the time. Traditional engineering workflows were built around sequential design phases, iterative simulations, and heavy reliance on physical validation. Design concepts were documented, prototypes were constructed, tests were performed, and results were compiled in reports and specifications that informed the next iteration. That approach worked well when systems were less complex and product life cycles were longer. In recent years, the volume of data, the speed of development, and the interconnected nature of modern vehicle architectures demand a different approach.
Patterson, Jeremy
Enabling certified GoogleTest for safety-critical embedded software26AUTD05_135/1/2026
Software is driving major changes in automotive design. The rise of the software-defined vehicle, combined with increasing automation, is dramatically increasing software complexity. Automotive teams must deliver larger volumes of safety-critical code on tighter schedules while maintaining strict compliance with functional safety standards. In this environment, effective testing and verification are more important than ever. Development teams are increasingly adopting shift-left testing strategies, where defects are identified early at the unit level before software progresses down the development pipeline. Detecting issues earlier reduces risk, lowers remediation costs, and improves development velocity.
Camacho, Ricardo
Keeping Pace with Changes in Defense Technology: Why Embedded Systems Must Deliver Agility, Resilience, and Endurance26AERP05_045/1/2026
On a clear afternoon over a contested airspace, a drone suddenly appears on radar. Within seconds, more follow, and they're small, fast, and unpredictable. For the U.S. Army's air and missile defense operators, every moment counts. The difference between mission success and mission failure is measured in milliseconds. During that brief window, sensors must connect instantly, embedded systems must process floods of data at the edge, and command links must hold steady even under electronic interference.
PRODUCT BRIEFS26AUTD05_165/1/2026
Orchestrating Agents for Automotive Research2026-01-50314/24/2026
Automotive research landscape currently is driven by emerging technologies such as software-defined vehicles, advanced infotainment systems, and increasingly automated driving functions. This situation calls for a bigger need for efficient, comprehensive, and agile research methods. Traditional methods require significant manual effort, leading to information synthesis and dissemination bottlenecks. After doing a thorough research on how research is carried on in automotive companies, it is inferred that a lot of time is spent on gathering information and integrating it with proprietary knowledge rather than on analysis or synthesis of the information. There are tools and platforms with artificial intelligence (AI) advancement that help with deep research of a particular topic, and there are also tools and platforms that help with synthesis of proprietary information within automotive organizations. But there is a lack of a framework that dynamically integrates the aspect of deep research with the proprietary information within the organization and draws out action items and action plans for the research to be effective and efficient. The agentic AI framework introduces efficient multi-agent orchestration and seamless integration of proprietary automotive data with external research sources, incorporating principles of building effective multi-agent systems, key metrics, validation techniques, impact and also the future potential. Initial validation demonstrates a 50% reduction in research time, a 50% faster time to insight, and much more impact.
Vemuri, Pavan
TOC99-08-02-0TOC4/13/2026
TOC
Tobolski, Sue
Design and Performance Evaluation of a Dust Contamination Simulation Test Apparatus for Automotive Cameras2026-01-00594/7/2026
This study presents the development and validation of a muddy water spray apparatus designed to simulate dust contamination on vehicle sensors for sensor cleaning system testing. It is important to have a constant and quantifiable test environment for the vehicle development process. For verifying the apparatus, muddy water, prepared by mixing standardized dust powder, salt, and water to maintain constant contamination test conditions, was sprayed onto glass specimens to evaluate equipment consistency. Deposited dust weight and thickness were measured across multiple spray cycles, with statistical analyses confirming consistent and reliable deposition. Paired t-tests indicated no significant difference between sample positions, demonstrating uniform spray distribution. The apparatus was further applied to individual infrared (IR) cameras to observe performance degradation under dry and wet contamination conditions showing statistically consistent increases in contamination levels. Application of the system in low-temperature performance testing of sensor cleaning systems on a development vehicle's rearview mirror yielded significant reductions in test time and variability compared to manual methods. These results affirm the apparatus as an effective, quantitative, and reproducible method for contamination simulation, contributing to streamlined and standardized sensor cleaning system development in the automotive industry.
Jinhyeok, Gong
Development of a Markov-Chain Prediction Model for a Work-Stealing Scheduler in Dynamic Scheduling for Multicore Embedded Software Systems2026-01-00664/7/2026
Designing embedded software that achieves effective utilization of the fast-growing multicore embedded hardware should help to reduce their execution time and power consumption and improve their reliability. AI and machine learning algorithms are making their way into such rapidly enhanced multicore embedded hardware. We have developed a Markov-chain prediction model and integrated it into a work-stealing scheduler within a dynamic scheduling runtime layer (DSRL). Dynamic scheduling with a work-stealing scheduler was adapted from MIT’s Cilk framework [1]. Dynamic scheduling allows independent computations to be spawned so they can be scheduled dynamically and executed in parallel on available cores. Cilk used a random model in its work-stealing scheduler where an idle core randomly selects other cores to steal computations from them. However, Markov-chain-based scheduler allows idle cores to make informed decisions about which cores are better to steal their computation to increase parallel executions and improve load balancing and resource utilization. We have implemented the DSRL with our proposed Markov-chain-based predictive work-stealing scheduler on QNX multicore RTOS and tested it using a 4-core NXP SBC-S32V234 development board. For performance evaluation, we have implemented a Mandelbrot sample application. We spawned parallelizable functions inside two tasks running on 2 cores to be dynamically scheduled and executed in parallel on the third core, while the fourth core drives the application. Preliminary results showed that our Markov chain prediction model outperformed the traditional random model with a 1.9x speedup compared to 1.4x in one running task. This paper provides a detailed description of the Markov chain mathematical prediction model, its integration in the work-stealing scheduler of the DSRL layer, and its application to embedded systems. This work is a part of ongoing research, and this paper discusses current challenges and future work for enhancing the scalability and performance of embedded software using this prediction model.
Sadeh, Waseem, Ganesan, Subramaniam, Qu, Guangzhi, Rawashdeh, Osamah
Intersection Assist for Vehicle with Dual-Trailers using Nonlinear Model Predictive Control and Unscented Kalman Filter2026-01-00744/7/2026
This paper presents an approach utilizing Nonlinear Model Predictive Control (NMPC) and Unscented Kalman Filter (UKF) to predict system state and control the trajectory of the vehicle with dual trailers in an intersection turn scenario. The UKF estimates vehicle and trailers’ lateral traversal velocity states and the NMPC controls the vehicle acceleration and steering to maintain the vehicle’s desired heading through the turn. The vehicle’s lateral traversal velocity function is formulated using Lyapunov based method which is used as a propagation function in the UKF to improve the estimation accuracy. The lateral traversal velocity is then used as one of the constraints in the NMPC problem. The overall estimation and the control scheme are formulated and assessed in the simulation environment. The simulation results show good tracking and curb avoidance performance.
Malla, Rijan
Architecture-Driven Fixed-Point Scaling for AUTOSAR-Based ECUs: A Production-Feasible Architecture for Centralizing Scaling Semantics2026-01-00694/7/2026
Floating-point arithmetic is widely used in automotive embedded software to scale Controller Area Network signals and calibration parameters with fractional factors such as 0.1. However, floating-point operations, even on microcontrollers equipped with floating-point units, can increase execution time and CPU load. In AUTOSAR architectures, converting floating-point scaling to fixed-point is not trivial because scaling semantics must be integrated consistently across components, yet AUTOSAR platform toolchains offer only limited automation at the Application Data Type level. Although CompuMethod definitions can express scaling, integration typically remains manual and distributed across application software components, reducing consistency and reusability. This study presents an architecture-driven methodology that formalizes fixed-point scaling as a centralized architectural service, realized through a parser-driven fixed-point macro generation pipeline. Standardized CAN DBC and calibration metadata are parsed to automatically generate integer-only macros for raw-to-physical and physical-to-raw transformations. The generated macros are integrated into dedicated AUTOSAR-compliant Scaling Service software components, consolidating scaling logic and improving reliability and maintainability. The approach requires no changes to toolchains, compiler settings, or hardware, enabling direct deployment in AUTOSAR-based software. The methodology was applied to a production-grade Integrated Charging Control Unit targeting Electric Vehicle Communication Controller software. Evaluation included cycle-accurate profiling, edge-based timing, isolated CPU load calculation, and average current measurement. Results show a 98.84% reduction in floating-point operations and a 92.67% reduction in conversion-related source lines. Task execution time decreased by 16.13%, CPU load decreased by 6.88%, and average current consumption showed a repeatable 0.81% reduction. These results demonstrate that the proposed methodology improves execution efficiency and is applicable to production AUTOSAR-based ECUs.
Lee, Hoseok, Ko, Donggun
Evolutionary Paths Toward Vehicle Zonal E/E Architecture: A Unified Architectural Framework2026-01-00714/7/2026
The automotive industry is undergoing a fundamental transformation in Electrical/Electronic (E/E) architecture, evolving from traditional distributed and domain-based designs toward zonal configurations. The rapid growth of software-defined functionality, cross-domain integration, and centralized computing has exposed inherent limitations of legacy architectures in scalability, wiring complexity, and system integration. Zonal E/E architecture addresses these challenges by consolidating computing and Input/Output (I/O) resources into high-performance controllers distributed across physical zones of a vehicle. This transformation, however, cannot occur instantaneously, as contemporary vehicle designs and E/E system solutions are the result of decades of incremental development based on distributed and domain-based paradigms. Moreover, key enabling technologies for zonal E/E architecture—such as high-performance Central Compute Platform (CCP) and zonal controllers, high-speed automotive Ethernet, and standardized software architecture—are still maturing. To ensure safety, reliability, and cost-effectiveness, Original Equipment Manufacturers (OEMs) must therefore adopt carefully planned evolution strategy to progressively consolidate functions, realizing the zonal design step by step. This paper proposes a unified architectural framework that systematically maps the full spectrum of evolutionary paths toward zonal E/E architecture. The framework identifies major transition stages, key engineering activities, and alternative migration paths, including distributed and domain-based architectures, vertical and horizontal function integration, various domain fusion patterns, mixed E/E architecture, continuous function migration to CCP and zonal controllers, and ultimately, the full realization of zonal E/E architecture. By organizing and contrasting these evolutionary paths, the framework provides OEMs with architectural insight and practical guidance for planning low-risk, staged transition toward fully zonal E/E architecture capable of supporting next-generation Software-Defined Vehicles (SDVs).
Jiang, Shugang
Holistic Solution of Unified Framework for Integration of Safety Process into Automotive Software Development Process2026-01-00684/7/2026
The rapid advancement of advanced driver assistance systems (ADAS), automated driving and electrification has significantly increased the software content and complexity within modern vehicles. Consequently, ensuring both high process quality and compliance or qualification with functional safety standards becomes critically important. Automotive Software Process Improvement and Capability Determination (ASPICE 4.0) focus on Process quality and Capability Maturity, while ISO 26262:2018 emphasizes engineering guidelines for functional safety and risk mitigation. The efficient integration of the process and standard remains a key challenge due to differences in their objectives, terminologies, and assessment criteria. The misalignment between ASPICE 4.0 and ISO 26262:2018 standard often results in duplicated efforts, rework of work products, and delays in product release schedules. This paper proposes a unified framework to bridge ASPICE 4.0 process areas with ISO 26262:2018 safety standard recommendations and activities. The framework introduces a refined V-model that integrates safety lifecycle activities directly into ASPICE 4.0 process workflows, enabling a harmonized and systematic approach to software development and safety compliance. While maintaining a focus on system engineering (SYS) and software engineering (SWE) process areas, this paper also discusses how the hardware engineering (HWE) process and support process (SUP) areas in ASPICE 4.0 can be mapped to the ISO 26262:2018 standard. In addition, the proposed framework addresses the concept phase of the safety lifecycle, encompassing item definition, HARA, safety goals and functional safety concept (FSC). This technique facilitates higher process efficiency, reduces redundant activities, and enhances product quality while maintaining compliance with both standards. The harmonized approach presented in this paper provides a holistic solution to current industry challenges by enabling incorporation of safety practices within automotive software development process. This ensures that vehicle systems meet quality and safety expectations, supporting timely product delivery in an increasingly competitive and regulated automotive market.
Ravi, Reshma, Easwaramoorthy, Prasad Vignesh, Promise, Dinu
The Simple Complexity of Reverse Engineering a Vehicle CAN Bus2026-01-00704/7/2026
Automotive Original Equipment Manufacturers (OEMs) closely guard information about their products due to the significant investment in vehicle research and development. However, advancing automotive innovation often requires insights from existing systems to improve safety, efficiency, and performance. The Controller Area Network (CAN) bus remains the industry standard for communication between electronic control units (ECUs), yet CAN message specifications are typically proprietary and undocumented. This paper presents a case study involving the reverse engineering of CAN messages from a 2024 Toyota Grand Highlander powertrain. By capturing and analyzing communication between a diagnostics tester and the vehicle’s ECUs and replicating the communication, substituting A CANcase and software in place of a diagnostics tester, we were able to reverse engineer the vehicle’s CAN bus, demonstrating a practical methodology for decoding and interpreting CAN traffic without prior access to proprietary data. The approach highlights both general principles and OEM-specific variations in message structure and encoding. The goal of this work is to support researchers and engineers in developing their own reverse engineering workflows. It illustrates that while the foundational techniques are consistent, adapting to vehicle-specific implementations is essential. The paper aims to provide a replicable process and to encourage further exploration in the field of automotive CAN analysis.
Bolarinwa, Emmanuel, Peters, Diane
Efficient Embedded Control of Nonlinear Systems for Software-Defined Vehicles2026-01-00754/7/2026
Software-defined vehicles (SDVs) are reshaping automotive control architectures by shifting intelligence to embedded systems, where computational efficiency is paramount. This paper presents a systematic evaluation of control strategies (PID, LQR, MPC) for the classical control problem involving inverted pendulum on a cart under strict embedded constraints representative of software-defined vehicle ECUs. The objective is to evaluate and compare the performance of advanced control algorithms under varying control objectives when deployed on microcontrollers with constrained computational and memory resources, representative of the limitations encountered in embedded platforms used for SDVs. Furthermore, the study illustrates systematic optimization strategies that enable these algorithms to achieve real-time execution within such resource-constrained environments. Each control strategy is implemented with careful consideration of algorithmic complexity, real-time responsiveness, and resource utilization. Performance is evaluated across key metrics, enabling a comparative analysis that highlights trade-offs between control fidelity and hardware efficiency. By demonstrating how advanced control logic can be effectively deployed on constrained hardware, this work supports the broader goal of enabling intelligent, responsive vehicle behavior through software-centric design. The findings are particularly relevant for automotive and embedded engineers developing control systems for SDVs, where balancing performance and resource constraints is critical to achieving scalable, safe, and adaptive vehicle functionality.
Vupparige, Varun, Pandya, Vidit
Integration and Validation of Adaptive Cruise Control across Different Modes2026-01-00764/7/2026
This paper presents the integration and validation of Adaptive Cruise Control (ACC) algorithms on a student-team-developed vehicle as part of the U.S. Department of Energy EcoCAR EV Challenge. The competition provided each team with a 2023 Cadillac Lyriq, which was modified to an all-wheel-drive configuration and re-architected to support the development of SAE Level 3 autonomous features including Adaptive Cruise Control (ACC), Automatic Intersection Navigation (AIN), Lane Centering Control (LCC), and Automatic Parking (AP). The scope of this paper, however, is limited to the development, implementation, and validation of a Level 2 longitudinal ADAS function. Higher-level automation requirements such as Operational Design Domain (ODD) definition and Driver Monitoring System (DMS) enforcement are addressed at the vehicle architecture and competition level but are not the focus of this work. The major contribution of this work is the development of ACC with Vehicle-to-Infrastructure (V2I) integration, highlighting the end-to-end implementation of the ACC algorithm and its interaction with key actuation systems in the modified vehicle architecture. The ACC algorithm encompassed multiple applications: conventional cruise control to maintain speed, adaptive cruise control to respond to a lead vehicle, and initial deceleration handling for intersection navigation in a single straight lane. By implementing a unified algorithm, transitions between these modes were smooth and more efficient compared to developing separate algorithms for each application. Track-based testing and calibration were conducted to validate these modes under real-world scenarios, ensuring safe operation while addressing the challenges of blended actuation. Multiple track tests were used to measure stopping distances at intersections for different entry speeds, evaluate controller performance during different driving scenarios, and identify system limitations. Results demonstrated that the controller maintained steady-state speed error within +/- 1 km/hr, preserved a minimum following distance of 8 m at a complete stop, and limited acceleration within +/- 2 m/s2 to support driver comfort. The work demonstrates the progression from simulation to real-world deployment using an empirical approach to system-level validation of ACC with V2I integration. The findings provide insights into calibration methodology, mode transition, and the benefits of a unified control framework for advancing software-defined vehicle features.
Gupta, Ishika, Estrada, Tyler, Tambolkar, Pooja, Midlam-Mohler, Shawn
Collaborative Cybersecurity in a Shifting Automotive Supply Chain2026-01-00844/7/2026
The evolution toward software-defined vehicles (SDVs) is causing disruption to the traditional automotive supply chain and breaking down the common hierarchical OEM, tier 1 supplier, and tier 2 supplier relationships. With demands for faster software release cycles, more advanced software projects involving multi-party development, and considerations for end-to-end embedded and cloud integrations, new cybersecurity challenges are introduced that no single organization can address alone. Thus, this disruption creates new trust dependencies and requires new models for collaboration, transparency, and joint responsibility in cybersecurity. This paper presents a collaborative cybersecurity model, emphasizing shared responsibility during multi-party development between OEMs, tier 1 and 2 suppliers, engineering services organizations, and technology and services providers. As such, we explore collaborative approaches for each stage in the development lifecycle including design, development, testing and validation, and post-release activities. This includes joint development frameworks, standardized communication and reporting approaches, and cooperative continuous cybersecurity activities. These collaborative approaches enable the involved parties to maintain trust, mitigate cross-organization risks, and support rapid innovation while assuring cybersecurity. The current traditional siloed approaches or purely internal monitoring practices cannot adequately address new multi-party risks. Thus, as the automotive supply chain is disrupted, cybersecurity must also be considered in a collaborative manner in order to secure vehicles throughout the development lifecycle across a distributed and rapidly changing supply chain. Therefore, our paper focuses on a collaborative model that provides a practical, pre-competitive framework that allows to tackle cybersecurity cooperatively while enabling agile software delivery.
Oka, Dennis Kengo, Vinzenz, Nico
High-Speed Flashing in the Age of the Software-Defined Vehicle2026-01-01124/7/2026
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, Boris, Budak, Oguz, Wells, Michael
Collaborative Development Framework for Electric-Based Software-Defined Vehicles – The European Research Project CODE4EV2026-01-01134/7/2026
The automotive industry is subject to major transformation initiated by societal and economical pull (reducing emissions, zero fatalities, European competitiveness) and accelerated by technology push (electrification, Cooperative, Connected and Automated Mobility (CCAM), and Cooperative Intelligent Transport Systems (C-ITS)). Following this trend, the Software-Defined Vehicle (SDV) targets the integration of software (SW) development methodologies for vehicle development as well as the value delivery shift toward customers along the entire lifecycle. It promises to create benefits for the car manufacturers in terms of faster time to market, easier update – as well as for the car users (private persons, fleet operators) in terms of personalized user experience, upgradability. At the same time, SDV requires a much more integrated and continuous development framework to enable different experts to efficiently develop and validate concurrently the different parts of the vehicles, to gather information about real operation, and to support update in the field. This paper introduces the collaborative development framework introduced in the European research program Collaborative Development Framework for electric-based Software-Defined Vehicles (CODE4EV).
Armengaud, Eric, Permann, Robert, Joergler, Sabrina, Barcelona, Miguel Angel, García, Laura, Rodriguez, José Manuel, Ivanov, Valentin, Li, Zhenqian, Nguyen Quoc, Trieu, Rodrigues, Sandy, Kowalczyk, Bogdan, Avdić Čaušević, Amra
Simulink DevOps Evolution: Bridging Interactive Platforms for Accelerated Model-Based Workflows2026-01-00804/7/2026
This paper builds on last year’s paper presenting DevOps automation in the context of model-based development. Following that paper, we interviewed Simulink users in passenger automotive, motorsports, commercial vehicles, aviation, rocketry, and industrial automation. We discovered that much of the benefit of DevOps platforms to reduce product development cycle time relies on their interactive features. We prototyped new tools to bridge interactive DevOps Git-based platforms with model-based development workflows, and then gathered reactions from another round of interviews. Here we present these interactive DevOps workflows with the feedback from these interviews to contextualize how engineering teams could adopt them to accelerate their own model-based workflows.
Mathews, Jon, Ferrero, Sergio, Tamrawi, Ahmed, Sauceda, Jeremias
A Scalable and Accessible Remote Testing Framework for Automotive Development2026-01-01914/7/2026
With the rise of software-defined vehicles and the emergence of cyber threats to vehicular systems, developing teams are compelled to conduct extensive testing on both virtual and physical prototypes at an accelerated pace. This new development landscape necessitates diagnostic tools that are both precise and adaptable. However, proprietary systems dominate this field, often hindering accessibility for students and researchers due to high costs and restrictive licensing. This paper presents the design and implementation of an open-source, low-cost remote testing system tailored for automotive development and diagnostics. The proposed system utilizes Arduino and Raspberry Pi processing units, along with relay-based switching modules, to provide secure remote control of vehicle components through a web-based dashboard equipped with authentication, scheduling, and real-time synchronization capabilities. The tested prototype showcased robust scalability, secure session handling, and seamless integration with the open-source Woodpecker EV platform at the University of Detroit Mercy. The affordability and open-source nature of the framework offer a practical alternative to proprietary tools, while also enabling future adaptation to diverse automotive contexts.
Pries, Andrew, Mohammad, Utayba
Impact of User-Adjustable Vehicle-Dynamics Tuning on Vehicle Durability2026-01-01944/7/2026
Software-defined vehicles offer customers a greater degree of customization of vehicle controls and driving experience. One such feature is user-adjustable tuning of vehicle ride and handling, where customers can vary ride height, damper stiffness, front-rear torque balance, and other aspects of vehicle dynamics. While promising a great customer experience, such a feature can expose the vehicle to a wider range of structural loads than those in the nominal design condition, particularly when such tuning is extended to cover spirited “sport” mode driving, off-road driving, etc. In this paper we present a novel methodology combining Road Load Data Acquisition (RLDA) data and real-world telemetry data to estimate the impact of user-adjustable vehicle-dynamics tuning on structural durability. In doing so, the method combines the physics of damage accumulation (from RLDA data) with user behavior (from telemetry data) to present an accurate assessment of the impact on durability, moving beyond traditional durability methods that do not model a range of real-world usage behavior. The study has been conducted using one instrumented vehicle (RLDA) and de-identified telemetry data from over 20,000 Rivian customer vehicles. The study analyzes the impact of variations in ride height, damper stiffness of active dampers, and roll stiffness of the suspension on vehicle structural durability. By combining usage frequency of the different settings with the damage accrued in these settings, the methodology estimates the high-cycle fatigue pseudo-damage variation for a wide range of customers and compares real world damage risk with the damage accounted for in the baseline durability testing. Through the analysis, we recommend a way to optimize the Accelerated Duty Cycle (ADC) for Over the Road (OTR) testing to minimize real-world risk, while keeping the duty cycle simple and practical for testing, i.e., test for an optimized combination of a few dominant settings and not a wide range of settings. The approach also suggests a path to a real-time fleet monitoring system to identify high-durability-risk customers and develop mitigation strategies.
Demiri, Albion, Ramakrishnan, Sankaran, White, Dylan, Khapane, Prashant, Borton, Zackery
Leveraging Generative AI and Cloud Computing for Enhanced Autonomous Vehicle Systems a Software-Defined Architecture Approach2026-01-50233/30/2026
The rapid evolution of autonomous vehicle (AV) systems requires scalable, adaptable, and intelligent software architectures to cater for high demands in security, reliability, and real-time processing. This paper introduces a novel software-defined architecture combining generative artificial intelligence (AI) with cloud computing for extending the performance and capabilities of AVs. The proposed methodology uses generative AI models for dynamic perception, route planning, and anomaly detection and is implemented on cloud computing infrastructure to lend orders of magnitude larger computational resources for scaling on-the-fly learning among distributed AV fleets. Decoupling hardware-specific features and transitioning toward a software-defined paradigm, the processing platform allows for quick updates, continuous learning, and flexible deployment of world-leading AI models. Experimental results and simulated scenarios show better situational awareness, response time, and system adaptability when compared to those of traditional architectures. This work outlines a promising pathway for the creation of future-proof, robust, intelligent AV ecosystems, enabled by the cooperation of generative AI and cloud computing systems.
Namburi, Venkata Lakshmi
Automotive Engineering: March 202626AUTP033/12/2026
Mercedes unveils S-Class amidst celebrating 140 years Classic design with the latest tech. Faraday Future says new robot business not a pivot, but a plus At NADA, Faraday Future introduced the FF Futurist, FF Master and FX Aegis, robots that it hopes to sell with the help of, among other things, auto dealerships. How higher-quality gasoline keeps modern engines clean and efficient Gasoline direct injection (GDI) engines are the most common technology on American roadways in 2025, and soon, an industrywide gasoline quality standard will better reflect their unique operational needs. Mobility for All with Christopher Borroni-Bird How can we make vehicles more sustainable for those who can afford to buy a new car, and how can we make mobility more affordable for the remaining 90%? Editorial Politics hits engineering, but harder Supplier Eye Feast, then famine Riding Along with Mercedes and its in-city driver assistance system Microvision acquires Luminar, plans relationship restoration, multi-industry push Startup Neumo says it can detect impaired drivers by scanning brain waves Sony Honda Afeela Prototype 2026 was the easier engineering challenge Mobileye, VW gearing up for 100,000 AVs by 2033 Aumovio's remote temp sensor far more accurate for e-mobility First Drive: 2027 Mercedes-Benz CLA Hybrid Kia makes minor updates to 2026 Sportage Hybrid, wringing out five more hp First Drive: Drifting in the electric 2027 Mercedes GLC 400 Product Briefs Spotlight: Analysis tools, sensors Q&A GM's Barra: EVs are still the future
SAE TOMORROW TODAY - Building an End-to-End Autonomous Driving System135573/2/2026
Join us as we sit down with Richard Chelminski, Senior Vice President, Head of SDV Platform, at 42dot, an autonomous driving software and mobility platform development startup from Hyundai Motor Group. We discuss how the company is pushing boundaries with its end-to-end autonomous driving system, Atria AI, which is focused on Level 4 autonomy and designed for seamless integration into consumer vehicles -- no LIDAR required! You'll also learn why camera-first and radar technologies are reshaping the industry, how software is now at the heart of vehicle development, and what the future holds for Transportation as a Service (TaaS). If you're curious about the next wave of smart, software-defined vehicles and autonomous driving innovation, this episode is a must-listen. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.
Patterson, Lori
MERCEDES unveils S-Class amidst celebrating 140 years26AUTP03_013/1/2026
Classic design with the latest tech Mercedes-Benz is celebrating a landmark. It's been 140 years since Carl Benz filed a patent for the first automobile. Fast forward to Stuttgart, Germany in January 2026, where Mercedes took the wraps off the latest generation S-Class, the jewel in the automaker's crown. Each generation of the S-Class is meant to be the pinnacle of what the automaker is working on at that moment. For the latest version of the luxury sedan, Mercedes says that 50% of the vehicle has been newly developed, updated, or refined. Much of the development and updates have to do with technology, specifically, the company's SDV (software-defined vehicle) platform.
Baldwin, Roberto
First Drive: Drifting in the electric 2027 Mercedes GLC 40026AUTP03_153/1/2026
The Mercedes-Benz electric 2027 GLC received a standard unveiling at IAA in Munich, Germany for a new vehicle built on a new platform with a new operating system. Its technological features were highlighted while the luxury brand made sure the world was aware that the EV's 800-volt architecture could charge its 94-kWh battery at up to 330kW. The GLC is part of the evolution of the German automaker's EV strategy: more efficient vehicles, an SDV architecture, the new MB.OS operating system, and a design refresh. Naturally, the best way to show off all these updates was to let me drive the AWD, electric version of the GLC 400 4Matic in the Dumont Dunes two hours outside Las Vegas.
Baldwin, Roberto
PRODUCT BRIEFS26AUTP03_163/1/2026
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