Browse Topic: Computer software and hardware
Software-defined, highly customizable vehicle architectures drastically increase the number of hardware–software constellations that must be validated, especially under safety and timing constraints. Traditional unit and integration testing, as well as current regression and combinatorial methods, cannot practically cover this configuration space or reliably capture emergent effects arising from complex interactions, such as bandwidth contention and non-linear latency behavior. This work presents a proof-of-concept for predictive, situational validation of self-describing hardware and software components within realistic automotive E/E architectures. Proposing a novel Machine Learning- (ML) based method for early systemic feasibility prediction of automotive configurations using Graph Neural Networks (GNNs). Specifically, the subclass Graph Isomorphism Networks (GINs) is applied to predict the compatibility of a randomly composed configuration of software and hardware components
Despite advances in CFD, wind tunnel testing remains indispensable for aerodynamic validation, correlation, and homologation. Increasing configuration complexity, shortened development cycles, and stringent result robustness and documentation requirements demand a shift from isolated facilities to integrated, data-driven ecosystems within the overall development and company-wide test processes. We present a software-centric approach integrating wind tunnel operations into a strategic element of the Digital Thread. By orchestrating test planning, execution, data acquisition, and documentation within a unified framework, experimental data becomes reusable across projects and traceable for compliance and homologation. The interaction between CFD and physical testing is important. Such approach systematically improves simulation models with wind tunnel tests. And CFD results guide efficient test matrix definition. Extended measurement methodologies include automated actuation of active
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
In the two months since Microvision bought Luminar and acquired key tech and talent, the sensor company has been busy. In that time, they've merged key lidar units from each company and created a perception software stack to run it in a convincing demo of its ADAS and autonomous capabilities. The company is also pushing innovative lidar tech into the defense drone and antidrone markets, already working with a German defense supplier that works with NATO member countries.
This Surface Vehicle & Aerospace Recommended Practice offers best practices and a methodology by which IVHM functionality relating to components and subsystems should be integrated into vehicle or platform level applications. The intent of the document is to provide practitioners with a structured methodology for specifying, characterizing and exposing the inherent IVHM functionality of a component or subsystem using a common functional reference model, i.e., through the exchange of design-time data and the application of standard vehicle data communications interfaces. This document includes best practices and guidance related to the specification of the information that must be exchanged between the functional layers in the IVHM system or between lower-level components/subsystems and the higher-level control system to enable health monitoring and tracking of system degradation severity. The intent is to provide an IVHM system that can robustly report the degradation of a given
In response to the problems of urban traffic congestion and the limited expansion of infrastructure, this paper conducts two core research focusing on the intelligent chassis system of split-type flying vehicle. Firstly, an autonomous navigation strategy for the intelligent chassis module is proposed based on chassis module Navigation 2 architecture, which fuses LIDAR and IMU positioning to plan paths using the A* global planning algorithm on a global cost map, and update the local cost map in real time with sensor data. It is orchestrated by the BT Navigator using a behavior tree, with failures handled by the Recovery Server, to achieve autonomous driving across multiple waypoints. In simulation and closed-field experiments, the system can stably reach the preset target points. The positioning accuracy and trajectory tracking performance can meet the design requirements. Secondly, a mechanical slide rail-type docking structure adapted to the split flying vehicle architecture is
The aging of the population has been a key issue worldwide, with mobility and fall of the elderly an important problem to be solved. In this paper, we propose an elderly mobility assist system based on the intelligent power-assisted device consisting of an assistive cane and an intelligent companion. It has the functions of standing support after falling, daily support and on-site rest. The assistive cane adopts a two-stage expansion mechanism of crank and slider structure, which forms a stable triangular support after unfolding, so that the patient can stand safely. The intelligent companion platform is driven by drive wheels, equipped with pushrod motors and vacuum suction devices, it can automatically approach the user and form an stable support column when the cane is in the out-of reach range; the control system is designed by combining microcontroller, camera object recognition, wristband remote control, to realize automatic steering and autonomous navigation at differential
The convergence of highly capable edge AI models and advanced commercial-off-the-shelf (COTS) edge AI accelerators is reshaping how computation is deployed across defense, aerospace, and commercial platforms. Mission-critical decisions increasingly must be made at the edge, onboard vehicles, satellites, and infrastructure nodes, where latency, connectivity, and power availability are constrained.
The automotive industry is evolving from a reactive, independently self-determined approach to cybersecurity, complicated by a complex supply chain. Over time, this has resulted in a fragmented industry comprised of any number of proprietary solutions verses a standardized, regulated paradigm to facilitate a platform-oriented approach. This document, an update on collaborative work from the SAE Vehicle Electrical Hardware Security Task Force (TEVEES18B) and GlobalPlatform Automotive Task Force, outlines this transition strategy. An extensible number of additional examples of use cases of Global Platform Technologies are explored in this document.
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
The useability of development processes in the automotive sector has decreased in the past years to a level at which their application and true benefit to is being questioned. Such degradation can be attributed to new additions to the processes and introduction of FuSa and Cybersecurity standards. The processes try to keep up with the shift from the traditional ‘plan–implement–test–roll-out' methodology to more agile methods. In addition, process departments typically in charge of these processes, focus on compliance to the letter of the standard to achieve certification, often with little thought to the actual implementation and the process they will be used by their engineering teams. Process growth to meet the needs of new and more complex technologies often mandates the use of new tools, which if implemented incorrectly can lead to unnecessary bureaucracy and additional overheads. Furthermore, the language of these new processes is in a form from assessor, making it difficult for
Patching vulnerabilities in safety-critical domains such as automotive and aerospace is costly and complex. A small code modification can trigger a complete rebuild, producing a binary with widespread changes. This inflates patch size, complicates regression testing, and makes over-the-air (OTA) updates inefficient, as traditional binary patches often replace large portions of the executable. We present a binary rewriting–based experiment that shows the feasibility of a patch that updates only the affected bytes by computing the impact of a code change at the binary level. This produces minimal, localized patches rather than regenerated executables. The preliminary experiment shows that a single source change, which leads to thousands of modified bytes after recompilation, can be captured with only a few bytes using our method. For automotive and aerospace systems, this technique reduces patch size, conserves bandwidth, and minimizes disruption to certified software, offering a
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
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
Autonomous platforms such as self-driving vehicles, advanced driver-assistance systems (ADAS), and intelligent aerial drones demand real-time video perception systems capable of delivering actionable visual information at ultra-low latency. High-resolution vision pipelines are often hindered by delays introduced at multiple stages—sensor acquisition, video encoding, data transmission, decoding, and display—undermining the responsiveness required for safety-critical decision making. This study introduces a holistic system-level optimization framework that systematically reduces end-to-end video latency while maintaining image fidelity and perception accuracy. The proposed approach integrates hardware-accelerated encoding, zero-copy direct memory access (DMA), lightweight UDP-based RTP transport, and GPU-accelerated decoding into a unified pipeline. By minimizing redundant memory copies and software bottlenecks, the system achieves seamless data flow across hardware and software
The transition to software-defined vehicles (SDVs) necessitates a paradigm shift in both control strategies and vehicle architecture. The EU-funded R&D project SmartCorners addresses this challenge by developing integrated, modular, and scalable smart corner systems (SCS) that combine in-wheel motor (IWM)-based propulsion, brake blending, active suspension system, and steer-by-wire functionality in one module. These SCS can be retrofit or smoothly integrated into the highly adaptable skateboard chassis architecture of modern electric vehicles (EVs), enabling scalable deployment across diverse vehicle types. The central approach of this paper is the utilization of artificial intelligence (AI) and machine learning (ML) to implement multi-layer, data-driven control strategies, facilitating real-time actuation, fault mitigation, and user-centric EV architecture. The SmartCorners project strives to demonstrate significant enhancements, including improved real-world driving range due to
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
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