Browse Topic: Computer software and hardware
High-efficiency manufacturing involves the transmission of copious amounts of data, exemplified both by trends in the automotive industry and advances in technology. In the automotive industry, products have been growing increasingly complex, owing to multiple SKUs, global supply chains and the involvement of many tier 2 / Just-In Time (JIT) suppliers. On top of that, recalls and incidents in recent years have made it important for OEMs to be able to track down affected vehicles based on their components. All of this has increased the need for OEMs to be able to collect and analyze component data. The advent of Industry 4.0 and IoT has provided manufacturing with the ability to efficiently collect and store large amounts of data, lining up with the needs of manufacturing-based industries. However, while the needs to collect data have been met, corporations now find themselves facing the need to make sense of the data to provide the insights they need, and the data is often unstructured
Security flaws in automotive software have significant consequences. Modern automotive engineers must assess software not only for performance and reliability but also for safety and security. This paper presents a tool to verify software for safety and security. The tool was originally developed for the Department of Defense (DoD) to detect cybersecurity vulnerabilities in legacy safety-critical software with tight performance constraints and a small memory footprint. We show how the tool and techniques developed for verifying legacy safety-critical software can be applied to automotive and embedded software using real-world case studies. We also discuss how this tool can be extended for software comprehension.
A hierarchical control architecture is commonly employed in hybrid torque control, where the supervisor CPU oversees system-level objectives, while the slave CPU manages lower-level control tasks. Frequently, control authority must be transferred between the two to achieve optimal coordination and synchronization. When a closed-loop component is utilized, accurately determining its actual contribution to the controlled system can be challenging. This is because closed-loop components are often designed to compensate for unknown dynamics, component variations, and actuation uncertainties. This paper presents a novel approach to closed-loop component factor transfer and coordination between two CPUs operating at different hierarchical levels within a complex system. The proposed framework enables seamless control authority transition between the supervisor and slave CPUs, ensuring optimal system performance and robustness. To mitigate disturbances and uncertainties during the transition
In the automotive industry, there have been many efforts of late in using Machine Learning tools to aid crash virtual simulations and further decrease product development time and cost. As the simulation world grapples with how best to incorporate ML techniques, two main challenges are evident. There is the risk of giving flawed recommendations to the design engineer if the training data has some suspect data. In addition, the complexity of porting simulation data back and forth to a Machine Learning software can make the process cumbersome for the average CAE engineer to set up and execute a ML project. We would like to put forth a ML workflow/platform that a typical CAE engineer can use to create training data, train a PINN (Physics Informed Neural Network) ML model and use it to predict, optimize and even synthesize for any given crash problem. The key enabler is the use of an industry first data structure named mwplot that can store diverse types of training data - scalars, vectors
E-mobility is revolutionizing the automotive industry by improving energy-efficiency, lowering CO2 and non-exhaust emissions, innovating driving and propulsion technologies, redefining the hardware-software-ratio in the vehicle development, facilitating new business models, and transforming the market circumstances for electric vehicles (EVs) in passenger mobility and freight transportation. Ongoing R&D action is leading to an uptake of affordable and more energy-efficient EVs for the public at large through the development of innovative and user-centric solutions, optimized system concepts and components sizing, and increased passenger safety. Moreover, technological EV optimizations and investigations on thermal and energy management systems as well as the modularization of multiple EV functionalities result in driving range maximization, driving comfort improvement, and greater user-centricity. This paper presents the latest advancements of multiple EU-funded research projects under
Software Defined Vehicle (SDV) is gaining attraction in the automotive industry due to its wide range of benefits like remote software/feature upgrade, scalable functionality, Electronic Control Unit (ECU) commonization, remote diagnostics, increased safety, etc. To obtain all these benefits, ECUs need to be designed accordingly. ECU hardware must be designed to support a range of vehicles with a variety of loading, scalable features, power distribution, levels of processing, and networking architecture. Each domain has unique challenges to make the ECU economical and robust to operating conditions without compromising performance. This paper illustrates the critical hardware design challenges to accommodate a scalable SDV architecture. This paper focuses electrical interface design to support wide range of input/output port loads, scalable functionality, and robust diagnostics. Also, flexibility of microprocessor processing capability, ECU networking, and communication complexity are
To meet the requirements of high-precision and stable positioning for autonomous driving vehicles in complex urban environments, this paper designs and develops a multi-sensor fusion intelligent driving hardware and software system based on BDS, IMU, and LiDAR. This system aims to fill the current gap in hardware platform construction and practical verification within multi-sensor fusion technology. Although multi-sensor fusion positioning algorithms have made significant progress in recent years, their application and validation on real hardware platforms remain limited. To address this issue, the system integrates BDS dual antennas, IMU, and LiDAR sensors, enhancing signal reception stability through an optimized layout design and improving hardware structure to accommodate real-time data acquisition and processing in complex environments. The system’s software design is based on factor graph optimization algorithms, which use the global positioning data provided by BDS to constrain
Artificial intelligence (AI) and machine learning (ML) are being adopted and deployed across the global aerospace and defense industry in a wide variety of software and hardware-defined applications right now. Here are five startups developing new and novel AI and ML technologies for aerospace and defense applications. This list is not intended to be in a ranking order.
In an era where technological advancements are rapid and constant, the U.S. Army will need a more agile and efficient approach to modernizing systems on succeeding generations of Army vehicles. Legacy platforms like Abrams, Stryker, and Bradley vehicles use multiple mission computers tied to individual sensors that often required the addition of “boxes” to accommodate new capabilities, which could take years to deploy and drove sustainment costs up due to vendor lock. In addition, this antiquated approach doesn't leverage data to converge effects across the formation in a multi-domain environment. Centralized, common computing as detailed in GCIA would help solve this problem, potentially linking all major subsystems and providing higher-speed processing to assess large datasets in real time with AI and ML algorithms. By using a common, open architecture computer, the Army will be able to rapidly integrate new capabilities inside one box, versus adding multiple boxes. This pivotal
Nestled in a commercial park in Sunnyvale, California, sits the Mercedes-Benz research and development North America office. A spinning star sits in the front of the building. It is one of six locations across North America and joins research facilities in Asia and Europe. During a recent media roundtable, Mercedes-Benz CEO Ola Källenius told journalists that the original purpose for the facility 30 years ago was because it recognized that Silicon Valley was a unique place where top academia meets with venture capital and where smart people from around the world gather. “So the very first intent with the first few baby steps of coming to Silicon Valley was like, it's almost like you send out a group of people to do reconnaissance, create contact, be part of the conversation, and figure out what's going on,” Källenius said.
The advancement of the automotive industry towards automation has fostered a growing integration between this field and automation. Future projects aim for the complete automation of the act of driving, enabling the vehicle to operate independently after the driver inputs the desired destination. In this context, the use of simulation systems becomes essential for the development and testing of control systems. This work proposes the control of an autonomous vehicle through fuzzy logic. Fuzzy logic allows for the development of sophisticated control systems in simple, easily maintainable, and low-cost controllers, proving particularly useful when the mathematical model is subject to uncertainties. To achieve this goal, the PDCA method was adopted to guide the stages of defining the problem, implementation, and evaluation of the proposed model. The code implementation was done in Python and validated using different looping scenarios. Three linguistic variables were used, one with three
Recognizing the significant challenges inherent in the analysis of periodic gas flow through reciprocating engines, one can easily appreciate the value of studying the steady flow through cylinder heads, manifolds, and exhaust systems. In these studies, flow benches are the cornerstone of the experimental apparatus needed to validate theoretical results or to perform purely experimental analysis. The Metal-Mechanics Department of IFSC owns a SuperFlow model SF-110 flow bench that has suffered some in house maintenance and received electronic sensors to allow computerized data acquisition. As the essential original sensors in this flow bench were liquid column manometer (for pressure difference across the test subject) and micromanometer (for pressure difference across the orifice plate used to measure the flow), the essential new sensors are electronic differential pressure sensors (installed in parallel with the original ones). In recent decades, however, the use of a mass air flow
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