Browse Topic: Embedded software

Items (371)
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
Vemuri, Pavan
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Tobolski, Sue
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
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
Lee, HoseokKo, Donggun
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
Demiri, AlbionRamakrishnan, SankaranWhite, DylanKhapane, PrashantBorton, Zackery
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, JonFerrero, SergioTamrawi, AhmedSauceda, Jeremias
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
Bolarinwa, EmmanuelPeters, Diane
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
Vupparige, VarunPandya, Vidit
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
Jiang, Shugang
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
Sadeh, WaseemGanesan, SubramaniamQu, GuangzhiRawashdeh, Osamah
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
Böhlen, BorisBudak, OguzWells, Michael
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
Pries, AndrewMohammad, Utayba
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
Gupta, IshikaEstrada, TylerTambolkar, PoojaMidlam-Mohler, Shawn
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
Armengaud, EricPermann, RobertJoergler, SabrinaBarcelona, Miguel AngelGarcía, LauraRodriguez, José ManuelIvanov, ValentinLi, ZhenqianNguyen Quoc, TrieuRodrigues, SandyKowalczyk, BogdanAvdić Čaušević, Amra
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
Jinhyeok, Gong
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
Ravi, ReshmaEaswaramoorthy, Prasad VigneshPromise, Dinu
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
Oka, Dennis KengoVinzenz, Nico
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
Namburi, Venkata Lakshmi
This paper examines the technological and architectural transformations critical for advancing Software-Defined Vehicles (SDVs), emphasizing the decoupling of hardware from software. It highlights the limitations of traditional development models and proposes modern architectural approaches, including MPU-based designs and virtualization techniques, to foster flexible and scalable software ecosystems. Central to this vision is the concept of a Virtual Development Kit (VDK), which enables the design, validation, and scaling of SDVs even before physical hardware is available. The VDK integrates hardware platform emulators, operating systems, software stacks, and middleware optimized for high-performance computing (HPC) environments, providing developers with tools for early-stage testing, debugging, and integration while minimizing dependence on physical prototypes. As the automotive industry increasingly relies on software-defined features as primary drivers of innovation and
Khan, Misbah UllahGupta, Vishal
Software-defined vehicles are those whose functionalities and features are primarily governed by software, thus allowing continuous updates, upgrades, and the introduction of new capabilities throughout their lifecycle. This shift from hardware-centric to software-driven architectures is a major transformation that reshapes not only product development and operational strategies but also business models in the automotive industry. An SDV operating system provides the base platform to manage vehicle software and enable those advanced functionalities. Unlike traditional embedded or general-purpose operating systems, it is designed to meet the particular demands of modern automotive architectures. Reliability, safety, and security become crucial because even minor faults may have serious consequences. Key challenges to be handled by the SDV OS include how to handle software bugs, perform real-time processing, address functional safety and SOTIF compliance, adhere to regulations, minimize
Khan, Misbah UllahGupta, Vishal
As the automotive industry transitions toward software-defined vehicles and highly connected ecosystems, cybersecurity is becoming a foundational design requirement. A challenge arises with the advent of quantum computing, which threatens the security of widely deployed cryptographic standards such as RSA and ECC. This paper addresses the need for quantum-resilient security architectures in the automotive domain by introducing a combined approach that leverages Post-Quantum Cryptography (PQC) and crypto-agility. Unlike conventional static cryptographic systems, our approach enables seamless integration and substitution of cryptographic algorithms as standards evolve. Central to this work is the role of Hardware Security Modules (HSMs), which provide secure, tamper-resistant environments for cryptographic operations within vehicles. We present how HSMs can evolve into crypto-agile, quantum-safe platforms capable of supporting both hybrid (RSA/ECC + PQC) and fully post-quantum
Kuntegowda, Jyothi
Despite a noticeable turn away from the wall-to-wall automotive tech wizardary that was so prevalent in recent years and towards robots and other forms of “physical AI,” CES 2026 remained a good place for Qualcomm Technologies Inc. to deliver updates to the media on its various mobility-related technologies. Qualcomm invited SAE Media to Las Vegas to learn about the updates and cover other CES news in person.
Blanco, Sebastian
If you ask automotive software developers - and QNX Research did - you'll hear that OEMs would benefit from an update to their software strategies. In October, QNX released its “Under the Hood: The SDV Developer Report,” a survey of 1,100 auto industry software developers in North America, Europe, and Asia and came away with three main points. First, 58% of respondents said software recalls have “significantly” changed how they develop software. Second, 91% said they expect AI to play a “major role” in future software development, estimating AI could replace 35% of current roles by 2035. Finally, and music to QNX's ears, 80% said automakers should put their focus on application-layer innovations and not on software infrastructure. That last finding describes the space where QNX, a division of BlackBerry Limited, and automotive technology supplier Vector have created an initiative to first define what foundational software means for SDVs and then deliver those components to OEMs and
Blanco, Sebastian
The automotive industry's future hinges on a new AI-native engineering workflow that accelerates iteration, strengthens system thinking, and preserves human judgment. Automotive development cycles are compressing at a pace the industry has never seen. The shift to all-electric fleets of software-defined vehicles is moving faster than traditional processes can absorb. In parallel, regulatory pressure and customer expectations keep rising, demanding greater performance, higher safety, better energy efficiency, and sharper competitiveness. In this environment, OEMs R&D competitiveness depends on three factors: How quickly teams can explore and iterate on design choices while delivering differentiated value, product performance, and cost efficiency. How early system-level interactions can be detected, before they turn into delivery friction or costly late-stage failures. How effectively a company can encode and scale its internal engineering know-how into lean development processes.
Allard, Théophile
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
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
Ahmad, AqueelHemanth, KhimavathKumar, OmKumar, RajivHaregaonkar, Rushikesh Sambhaji
With the increasing complexity and connectivity in modern vehicles, cybersecurity has become an indispensable technology. In the era of Software-Defined Vehicles (SDVs) and Ethernet-based architectures, robust authentication between Electronic Control Units (ECUs) is critical to establish a trust. Further, the cloud connected ECUs must perform authentication with backend servers. These authentication requirements often demand multiple certificates to be provisioned within a vehicle, ensuring secure communication between various combinations of ECUs. As a result, a single ECU may end up storing multiple certificates, each serving a specific purpose. This work proposes a method to limit the number of certificates required in a given ECU without compromising security. We introduce a Cross-Intermediate Certificate Authority (Cross-ICA) Trust Architecture, which enables the use of a single certificate per ECU for inter-ECU communication as well as backend server authentication. In this
Venugopal, VaisakhGoyal, YogendraRaja J, SolomonRai, AjayRath, Sowjanya
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
Oka, Dennis KengoVadamalu, Raja Sangili
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
Saini, GouravJahagirdar, ShwetaKhandekar, Dhiraj Baburao
The past decade has seen a systemic shift in the automotive landscape and the constituent parts of a vehicle. The automotive industry has shifted from a primarily hardware components industry to a software heavy industry, with software controlling majority of the vehicle functions. Coupled with the ability to fully update or evolve a vehicle’s capabilities or functionalities, post point of sale through software updates, the technical, commercial and service landscape of the automotive industry is rapidly changing. This has brought increasing focus to the concept of Software Defined Vehicle, where the vehicle is not only constantly evolving, but is also becoming more personalised by leveraging data collected through the life of the vehicle. This requires a rethink of the current development and deployment approaches for vehicles, which are software-intensive. In this paper, we introduce a novel four-step system engineering framework for the safe development and deployment of Software
El Badaoui, HalimaJame-Elizebeth, MariatKhastgir, SiddarthaJennings, Paul
In the era of Software Defined Vehicles, the complexity and requirements of automotive systems have increased knowingly. EV Thermal management systems have become more complicated while having multiple functions and control strategies within software frameworks. This shift creates new challenges like increased development efforts and long lead time in creating an efficient thermal management system for Electric Vehicles (EV’s) due to battery charging and discharging cycles. For solving these challenges in the early stages of development makes it even more challenging due to the unavailability of key components such as fully developed ECU hardware, High voltage battery pack and the motor. To address this, a novel framework has been designed that combines virtual simulation with physical emulation at the same time, enabling the testing and validation of thermal control strategies without fully matured system and the ECU hardware. The framework uses the Speedgoat QNX machine as the
Chothave, AbhijeetS, BharathanS, AnanthGangwar, AdarshKhan, ParvejGummadi, GopakishoreKumar, Dipesh
The Vehicle software is moving towards software-centric architectures and hence software-defined vehicles. With this transition, there is a need to handle various challenges posed during development and validation. Some of the challenges include unavailability of hardware limiting the evaluation of various hardware options, board bring-up and hence leading to delays in software development targeted for the hardware, eventually leading to delayed validation cycles. To overcome the above challenges, we present in this whitepaper a virtual ECU (vECU) framework integrated with a CI/CD pipeline. A Virtual ECU (Electronic Control Unit) is a software-based emulation of a physical ECU. The adoption of virtual ECUs empowers development teams to commence software development prior to the availability of physical hardware. Multiple tools are available to demonstrate virtual ECUs, for example, QEMU, Synopsys, QNX Cabin, etc. vECU setup, when paired with a CI/CD pipeline, allows continuous
Singh, JyotsanaShaikh, ArshiyaMane, RahulBurangi, Piyush
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
Mathur, Akshay RajMisra, AmitMakam, Sandeep
The rapid evolution of in-vehicle electronic systems toward zonal based architectures introduces a new layer of complexity in automotive diagnostics. Traditional architectures, built on Controller Area Network (CAN) and Local Interconnect Network (LIN) protocols, operate on a uniform Real-Time Operating System (RTOS), enabling simplified and consistent diagnostic workflows across Electronic Control Units (ECUs). However, next-generation platforms must accommodate diverse communication protocols (e.g., CAN, LIN, DoIP, SOME/IP) and heterogeneous operating systems (e.g., RTOS, Linux, QNX), resulting in fragmented and inflexible diagnostic processes. This paper presents a Diagnostic controller that addresses these challenges by enabling unified, scalable, and adaptive diagnostic capabilities across modern vehicle platforms. The proposed system consolidates protocol handling at the application level, abstracts diagnostic complexities, and allows cross-platform communication through
Mukherjee, SoumyadeepRaman, Kothanda
The automotive industry is currently undergoing a profound transformation, driven not only by the shift toward renewable propulsion systems but also by the increasing emphasis on the software-defined vehicle (SDV), which is particularly in the domain of ADAS and the qualification of vehicles towards higher levels of autonomy important. In combination with accelerating project timelines, this shift creates challenges in integrating electrical and electronic systems throughout the complete vehicle. Magna faces these challenges by intensifying the use of virtual development, a strategy that spans the entire vehicle development process and necessitates global collaboration among engineering teams. This publication presents a real-world example of how the automotive sector can transition from a traditional on-premises-environment (OPE) simulation setup to a Simulation-as-a-Service (SIMaaS) model. Our primary focus is on operational and collaborative dimensions, illustrating the significant
Wellershaus, ChristophWakharde, SagarBernsteiner, Stefan
In era of Software Defined Vehicle (SDV), the whole ecosystem of automobile will be impacted. So, it is going to through several challenges for testing activities. In electric vehicle, most critical component is traction battery, which is controlled and operated through battery management system (BMS). BMS is an electronic system, where is going to function as per software of BMS. And in SDV, software is a key element, which is continuously keep on updating on regular basis. So, it means some of BMS functionalities, features or performance may be also altered on each time on software update, which may impact battery’s operating condition, if some scenario is not evaluated during earlier testing then there are it may bring battery out of safe operating area, which may significant impact battery safety, performance or cycle-life. In this paper, we are exploring that different testing requirements for EV Batteries, which may be part of testing practices under era of SDV. Here we will
Bhateshvar, Yogesh KrishanMulay, Abhijit B
The automotive industry is undergoing a significant technological transformation, which is continually impacting the methods used to test the functionalities, delivered to end consumer. This includes the ever-growing need to embed software-based functions to support more and more end user functionality, while at the same time retaining existing and well-established functions, all within short development timelines. This presents both opportunities and challenges, with greater potential for reuse or leverage of test assets, although the actual percentage of leverage on real world projects is practically less than anticipated for a multitude of reasons. This paper collates the various factors which effect the practical leverage of test assets from one project to another, including various workflows and the interaction across components amongst applications lifecycle management systems. Alongside, it describes the current practices of basis analysis in isolation in combination with
Venkata, ParameswaranKulkarni, ApoorvaRAJARAM, SaravananGanesh, Chamarthi
Artificial Intelligence and Machine learning models have a large scope and application in Automotive embedded systems. These models are used in the automotive world for various applications like calibration, simulation, predictions, etc. These models are generally very accurate and play the role of a virtual sensor. However, the AI/ML models are resource intensive which makes them difficult to execute on largely optimized automotive embedded systems. The models also need to follow safety standards like ASIL-D. The current work involves creating a Global DoE with ETAS ASCMO to generate data from a 125cc single to create AI/ML model for the engine outputs like Torque, T3, Mid-cat temperatures etc. The created models were validated across the operating space of the engine and found to have good accuracies. With ETAS Embedded AI Coder, the torque and T3 prediction AI models were converted to embedded code which can be easily used as a virtual sensor in real time. Using these AI models
Chouhan, Vineet SinghBulandani, SaurabhKumar, AlokVarsha, AnuroopaP R, Renjith
Vehicle door-related accidents, especially in urban environments, pose a significant safety risk to pedestrians, infrastructure and vehicle occupants. Conventional rear view systems fails to detect obstacles in blind spots directly below the Outside Rear View Mirror (ORVM), leading to unintended collisions during door opening. This paper presents a novel vision-based obstacle detection system integrated into the ORVM assembly. It utilizes the monocular camera and a projection-based reference image technique. The system captures real-time images of the ground surface near the door and compares them with calibrated reference projections to detect deviations caused by obstacles such as pavements, potholes or curbs. Once such an obstacle is detected the vehicle user is alerted in the form of a chime.
Bhuyan, AnuragKhandekar, DhirajJahagirdar, Shweta
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
Pawar, GaneshInamdar, Sumer DeepakKumar, MayankDeosarkar, PankajTayade, NikhilKanse, DattatrayChopade, Vipul
The rising software complexity in Automotive industry demands reusable, hardware-agnostic development frameworks. AUTOSAR (Automotive Open System Architecture) provides a standardized, scalable ECU software architecture but cost-effective tooling and modern workflows are critical for broad adoption and competitiveness. One such area is for AUTOSAR configuration and authoring of Autosar architecture. Current solutions include commercial offerings built by vendors on top of ARTOP (ArTOP is an eclipse-based ecosystem maintained by AUTOSAR consortium) and open-source python implementations. Commercial tools are prohibitive in cost, have complicated development workflows, are difficult to automate and lack quick integration with other tools. Python-based solutions are often community driven with small developer teams and face challenges. These tools are not mature enough, have staggered development, security concerns, liability issues, lack of approvals and other similar issues. These
Daware, KartikGarg, MuditPasupuleti, Raju
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
Khatri, SanjaySah, Mohamadali
Software-Defined Vehicles (SDV) are fostered through initiatives like SOAFEE and Eclipse SDV promoting the use of cloud-native approaches, distributed workloads and service-oriented architectures (SOA). This means that in these systems each vehicle is connected to the cloud and functions are executed both inside the vehicle and in the cloud. So far, there are no established solutions for monitoring and diagnosing SDVs. In designing these solutions, the cost-sensitive nature of every component inside a vehicle must be considered since it makes it unlikely that significant resources will be provided just for diagnostics. Therefore, conventional data centre monitoring approaches that usually rely on transferring large amounts of data to dedicated servers are not directly applicable in this scenario. To illustrate the challenges in providing new solutions for diagnosing and monitoring SDVs, a SOA that has been defined and studied in research projects is introduced. In this architecture
Böhlen, BorisFischer, Diana
Automotive systems are increasingly adopting data-driven and intelligent functionality in the areas of predictive maintenance, virtual sensors and diagnostics. This has led to a need for the AI models to be directly run on vehicle ECUs. However, most of these ECUs – especially those in cost-sensitive or legacy platforms lack the computational capacity and parallel processing support required for standard AI implementations. Given the stringent real-time and reliability requirements in automotive environments, deploying such models presents a unique challenge. This paper proposes a practical methodology to optimize both the training and deployment phases of AI models for low-computation ECUs that operate without parallelism. Designing lightweight model architectures, using pruning and quantization techniques to minimize resource utilization, and putting in place a strategy appropriate for single-threaded execution are the three main objectives of the developed approach. The goal is to
Sharma, SahilMathew, Melvin John
For a company focused on selling components to make physical connections in vehicles, TE Connectivity is more than ready for future growth in software-defined vehicles (SDVs) and the corresponding rise in vehicles with zonal architectures. Ruediger Ostermann, vice president and chief technology officer for Global Automotive at TE Connectivity, said TE agrees with industry estimates that the number of cars with a zonal architecture will rise from around 2% in 2023 to between 35-40% in the mid-2030s.
Blanco, Sebastian
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