Browse Topic: Embedded software
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.
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.
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.
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.
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.
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.
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).
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).
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.
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.
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.
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.
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.
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.
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 Tier 1s, which can then focus on what makes sense for them as a brand or for a specific customer, Justin Moon, vice president of Core Product Engineering at QNX, told SAE Media.
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