Browse Topic: Computer software and hardware
Autonomous driving systems are among the most complex software systems deployed in the commercial vehicle industry today. Modern autonomous driving platforms combine large-scale AI models, machine learning infrastructure, middle-ware, safety mechanisms and vehicle integration software into a tightly integrated software stack. This complexity introduces new cybersecurity challenges. Many of the threat modeling approaches used throughout the industry were developed around architectures with well-defined boundaries, limited software complexity and clearly documented interfaces. Autonomous driving systems operate differently.
This paper details the successful scaling demonstration of a comprehensive supply chain screening process for commercial off-the-shelf (COTS) motherboard subassemblies used in tactical servers for naval applications. Our approach leverages Power Fingerprinting (PFP) technology, which uses unintended analog emissions and machine learning to provide independent, non-destructive, and scalable integrity assessment of microelectronics. The primary goal of the effort was to demonstrate the effectiveness and scalability of the PFP screening process without disrupting or delaying the manufacturing workflow. The screening successfully detected hardware and firmware modifications and identified two cases of abnormal behavior: unusual BIOS power reset and elevated CPU sensor readings on two motherboard subassemblies. Following our quality control forensic analysis, we determined the root cause of these anomalies and their potential impact on the host platform.
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This research is meant to enhance the analytic capabilities of OneSAF for usage as a Monte Carlo-style data generator for use in large problem space trade studies. Using novel ground vehicle data such as: RHA armor values, weapon penetration prediction models, and sensor values, new models can be developed in OneSAF for use in data generation and analysis. This process and companion software developed for this purpose enables the rapid construction and evaluation of differing vehicle variants in a fraction of the time of the baseline process, improving the efficiency of using OneSAF as a data analysis tool. This approach facilitates a more comprehensive virtual experimentation approach that can use manufactured data as a part of the process.
ISO 26262ISO/SAE 21434ISTQB/ASPICEMOSAGCIAVICTORY
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.
The Electro-Mechanical Brake (EMB) system is a dry-type Brake-by-Wire technology that eliminates hydraulic components and directly controls friction braking using electrical actuators at each wheel. The EMB architecture consists of a Main Center Control Unit, a redundant Backup Center Control Unit, and four Wheel Control Units communicating via CAN FD. Due to its direct involvement in vehicle braking, compliance with ISO 26262 functional safety requirements is critical. As system complexity increases, potential risks such as hardware failures and communication faults must be systematically addressed. The proposed TSC was developed according to ISO 26262, covering the concept phase (Part 3), system-level development (Part 4), and software implementation (Part 6). Safety goals and Functional Safety Requirements derived from HARA are used to guide system architecture design and TSC development. Key design principles include modularity, redundancy, fault detection, and fail-safe operation. Verification is conducted at both system and vehicle levels using ECU-in-the-Loop Simulation (EILS), Hardware-in-the-Loop Simulation (HILS), and real-vehicle tests. Fault scenarios, including Main Center Control Unit failures and CAN communication losses, are injected using a custom LabVIEW-based fault injection tool. The study evaluates Fault Tolerant Time Interval (FTTI) settings, error handling mechanisms, and control handover strategies under fault conditions. The results show that redundancy and localized communication enable stable operation and smooth control transfer within the FTTI window without noticeable impact on braking performance or driver awareness. This study demonstrates the robustness of the proposed EMB architecture. Future work will focus on prognostics and maintenance strategies to support safe deployment in autonomous and electric vehicles. [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
The economic logic behind factory software upgrades is breaking down. For the past four decades, automating a factory has meant one thing: buying into a large industrial software platform. Manufacturers turned to established suppliers such as Rockwell Automation and Siemens for the control systems, execution layers, and quality software that run their plants, millions of lines of rigorously engineered code, developed under strict standards and wrapped in guardrails. That rigor was never optional. When software commands a robot arm, a stamping press, or a paint line, a defect is not a bug ticket; it is a safety incident. The engineering discipline behind these platforms reflects that reality, and has served the industry well. But discipline at that scale comes at a price. Platform deployments are measured in millions of dollars and years of integration work, which means they only make economic sense for the largest, most repeatable problems a plant faces. Everything else, the hundreds of smaller, plant-specific pain points that erode margins every day, has largely gone unaddressed. Running a modern factory became expensive by design.
The form factor decision comes early, and it often sticks. Once you officially spec a 3U SOSA aligned chassis for a program, you're shaping every plug-in-module (PIM) selection, cooling approach, and upgrade path that follows. Get it right, and the system integrates cleanly. Get it wrong, and you're retrofitting workarounds six months before CDR, when changing direction costs real money. Neither form factor wins outright. They're tools, and the right one depends on program requirements. Here's what to know before you commit.
In the past two decades, the global automotive industry has been flipped on its head. Even before I started writing professionally about this business, Chinese-made cars were something of a joke and European and North American automakers were dominating the still-nascent market for consumer vehicles in China. China is now the largest automotive market in the world by a wide margin, Chinese-brand vehicles are generally highly respected, and American politicians are lying about why consumers here can't get access to them. In the late 2010s, we began to see a shift in the Chinese market that has come to fruition in the past five years. When I attended the 2007 North American International Auto Show in Detroit as a member of the media for the first time, the first Chinese vehicles were being displayed on the show floor. Let's just say no one was impressed. Much of what was coming from Chinese brands at the time were either weird, outdated machines or the automotive equivalent of the Rolex knock-offs you could find on Canal St. in New York.
As a society, we have become tracked, categorized, exploited, and monetized. Tech companies have determined that our data, when sold to ad networks, is a profitable business. Automakers, eager to find additional streams of revenue, jumped aboard. Now, nearly every app, device, and vehicle harvests and shares valuable information. Most of it is (hopefully) anonymized, but not all of it. Automotive startup Slate doesn't see data as a business plan. Yes, its app will collect information, but a Slate spokesperson, when asked if collected data will be sold for additional revenue, told SAE Media a flat “No.”
Sonatus and Omdia surveyed over 500 auto executives for their thoughts on SDVs, and the responses revealed that - once again - the future is unwritten. Data monetization as a top priority dropped from 51% to 44% (YoY) as automakers focus more on using data internally, with ADAS and product improvements rising six points to 41%. We spoke with Sonatus CMO John Heinlein about the survey for a recent episode of the SAE Automotive Engineering Podcast. You can listen via the links at the bottom of this Q&A, which is an edited version of our full discussion.
With the increasing demand for material microimaging analysis, there is a growing need for advanced precision grinding and polishing equipment, especially for metals, ceramics, and composites. Existing automated systems struggle with handling complex material challenges. This paper presents a fully automated adaptive grinding and polishing machine based on an STM32 microcontroller that handles multi-material samples. The system includes modules for sample access, cleaning, pad replacement, human-computer interaction, and equipment communication. The STM32 microcontroller executes grinding and polishing tasks based on instructions from the host computer while dynamically adjusting PID control parameters using an improved weighted average optimization algorithm. This approach enhances control accuracy, stability, and overall surface treatment quality compared to traditional PID control methods.
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