Browse Topic: Computer software and hardware
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.
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
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
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
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
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
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
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
The term Software-Defined Vehicle (SDV) describes the vision of software-driven automotive development, where new features, such as improved autonomous driving, are added through software updates. Groups like SOAFEE advocate cloud-native approaches – i.e., service-oriented architectures and distributed workloads – in vehicles. However, monitoring and diagnosing such vehicle architectures remain largely unaddressed. ASAM’s SOVD API (ISO 17978) fills this gap by providing a foundation for diagnosing vehicles with service-oriented architectures and connected vehicles based on high-performance computing units (HPCs). For service-oriented architectures, aspects like the execution environment, service orchestration, functionalities, dependencies, and execution times must be diagnosable. Since SDVs depend on cloud services, diagnostic functionality must extend beyond the vehicle to include the cloud for identifying the root cause of a malfunction. Due to SDVs’ dynamic nature, vehicle systems
The fusion of virtualized base software with simulation technologies has transformed the methods used for development and system testing. This paper examines the architecture, implementation, and advantages of employing virtualization to improve simulation environments. Virtualized base software enables the creation of isolated, scalable, and replicable settings, essential for executing complex simulations that replicate real-world situations. Utilizing virtualization enhances simulations by making them more efficient, flexible, and cost-effective. The study covers the essential elements of virtualized simulation platforms, such as containerization, network abstraction and virtual drivers. It also analyzes how these components collaborate to create a strong framework for simulating diverse applications, ranging from software testing to hardware emulation. This approach offers several benefits, including better resource utilization, quicker deployment times, and the flexibility to
With the trend of increasing technological complexity, software content and mechatronic implementation, there are increasing risks from systematic failures and random hardware failures, which is to be considered within the scope of functional safety. ISO 26262 series of standards provides guidance to mitigate these risks by providing appropriate requirements and processes. To develop a safe product with respect to above mentioned complexities, it is very critical to develop a safe system and hence a thorough and robust “Technical Safety Concept” is very important to ensure absence of unreasonable risk due to hazards caused by malfunctions of E/E systems. ISO26262-Part 4 provides guidelines for “Product development at the system level”, to design safety-related systems that include one or more electrical and/or electronic (E/E) systems and that are installed in series production road vehicles. Defining requirements at system level for each individual technology and systematically
In the rapidly evolving field of automotive engineering, the drive for innovation is relentless. One critical component of modern vehicles is the automotive ECU. Ensuring the reliability and performance of ECU is paramount, and this has led to the integration of advanced testing methodologies such as Hardware-in-the-Loop (HIL) testing. In conjunction with HIL, the adoption of Continuous Integration (CI) and Continuous Testing (CT) processes has revolutionized how automotive ECU are developed and validated. This paper explores the integration of CI and CT in HIL testing for automotive ECU, highlighting the benefits, challenges, and best practices. Continuous Integration and Continuous Test (CI/CT) are essential practices in software development. Continuous Integration process involves regularly integrating code changes into the main branch, ensuring that it does not interfere with the work of other developers. The CI/CT server automatically build and test code whenever a new commit is
Emergence of Software Defined Vehicles (SDVs) presents a paradigm shift in the automotive domain. In this paper, we explore the application of Model-Based Systems Engineering (MBSE) for comprehensive system simulation within the SDV architecture. The key challenge for developing a system model for SDV using traditional methods is the document centric approach combined with the complexity of SDV. This MBSE approach can help to reduce the complexity involved in Software-Defined Vehicle Architecture making it more flexible, consistent, and scalable. The proposed approach facilitates the definition and analysis of functional, logical, and physical architecture enabling efficient feature and resource allocation and verification of system behaviour. It also enables iterative component analysis based on performance parameters and component interaction analysis (using sequence diagrams).
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