Browse Topic: Computer software and hardware

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This paper examines the technological and architectural transformations critical for advancing Software-Defined Vehicles (SDVs), emphasizing the decoupling of hardware from software. It highlights the limitations of traditional development models and proposes modern architectural approaches, including MPU-based designs and virtualization techniques, to foster flexible and scalable software ecosystems. Central to this vision is the concept of a Virtual Development Kit (VDK), which enables the design, validation, and scaling of SDVs even before physical hardware is available. The VDK integrates hardware platform emulators, operating systems, software stacks, and middleware optimized for high-performance computing (HPC) environments, providing developers with tools for early-stage testing, debugging, and integration while minimizing dependence on physical prototypes. As the automotive industry increasingly relies on software-defined features as primary drivers of innovation and
Khan, Misbah UllahGupta, Vishal
Software-defined vehicles are those whose functionalities and features are primarily governed by software, thus allowing continuous updates, upgrades, and the introduction of new capabilities throughout their lifecycle. This shift from hardware-centric to software-driven architectures is a major transformation that reshapes not only product development and operational strategies but also business models in the automotive industry. An SDV operating system provides the base platform to manage vehicle software and enable those advanced functionalities. Unlike traditional embedded or general-purpose operating systems, it is designed to meet the particular demands of modern automotive architectures. Reliability, safety, and security become crucial because even minor faults may have serious consequences. Key challenges to be handled by the SDV OS include how to handle software bugs, perform real-time processing, address functional safety and SOTIF compliance, adhere to regulations, minimize
Khan, Misbah UllahGupta, Vishal
As the automotive industry transitions toward software-defined vehicles and highly connected ecosystems, cybersecurity is becoming a foundational design requirement. A challenge arises with the advent of quantum computing, which threatens the security of widely deployed cryptographic standards such as RSA and ECC. This paper addresses the need for quantum-resilient security architectures in the automotive domain by introducing a combined approach that leverages Post-Quantum Cryptography (PQC) and crypto-agility. Unlike conventional static cryptographic systems, our approach enables seamless integration and substitution of cryptographic algorithms as standards evolve. Central to this work is the role of Hardware Security Modules (HSMs), which provide secure, tamper-resistant environments for cryptographic operations within vehicles. We present how HSMs can evolve into crypto-agile, quantum-safe platforms capable of supporting both hybrid (RSA/ECC + PQC) and fully post-quantum
Kuntegowda, Jyothi
Modern vehicles require sophisticated, secure communication systems to handle the growing complexity of automotive technology. As in-vehicle networks become more integrated with external wireless services, they face increasing cybersecurity vulnerabilities. This paper introduces a specialized Proxy based security architecture designed specifically for Internet Protocol (IP) based communication within vehicles. The framework utilizes proxy servers as security gatekeepers that mediate data exchanges between Electronic Control Units (ECUs) and outside networks. At its foundation, this architecture implements comprehensive traffic management capabilities including filtering, validation, and encryption to ensure only legitimate data traverses the vehicle's internal systems. By embedding proxies within the automotive middleware layer, the framework enables advanced protective measures such as intrusion detection systems, granular access controls, and protected over-the-air (OTA) update
M, ArvindPraneetha, Appana DurgaRemalli, Ravi Teja
As electric vehicles adoption becomes more common, power grid operators are facing new challenges in managing the unpredictable and varying energy demands in the existing electrical infrastructure. Moreover, the cost of Electric vehicle is high when compared to fuel vehicle it has limited access to charging infrastructure along with the driving range that act as a key barrier preventing the drivers from making shift to EVs. When the EV usage integrates with blockchain, it mitigates the limitation in charging station infrastructure along with the former problem discussed. The lack of trust exists between EV owners and charging station providers can be solved through secure and transparent payment processing possible by blockchain based smart contract. Building charging station on blockchain will ease the automated payment through the use of smart contract and create more efficient EV charging network. Also, the blockchain-based charging system would enable EV owners know if they are
Govindasamy, DhivyaR, Rajarajeswari
This study presents the design and implementation of an advanced IoT-enabled, cloud-integrated smart parking system, engineered to address the critical challenges of urban parking management and next-generation mobility. The proposed architecture utilizes a distributed network of ultrasonic and infrared occupancy sensors, each interfaced with a NodeMCU ESP8266 microcontroller, to enable precise, real-time monitoring of individual parking spaces. Sensor data is transmitted via secure MQTT protocol to a centralized cloud platform (AWS IoT Core), where it is aggregated, timestamped, and stored in a NoSQL database for scalable, low-latency access. A key innovation of this system is the integration of artificial intelligence (AI)-based space optimization algorithms, leveraging historical occupancy patterns and predictive analytics (using LSTM neural networks) to dynamically allocate parking spaces and forecast demand. The cloud platform exposes RESTful APIs, facilitating seamless
Deepan Kumar, SadhasivamS, BalakrishnanDhayaneethi, SivajiBoobalan, SaravananAbdul Rahim, Mohamed ArshadS, ManikandanR, JamunaL, Rishi Kannan
The paper presents the design and implementation of an AI-enabled smart timer-based power control and energy monitoring solution for household appliances. The proposed system integrates real-time sensing of electrical device parameters with cloud artificial intelligence for predictive analytics and automatic control. Continuous measurement of voltage, current and power consumption of the connected appliances are performed for analysis of the usage patterns. The appliance operation is completely automated by choosing between the best option which is the user-defined schedule or the load shifted schedule recommended by AI. The AI recommendation depends on peak demand of the day and the current load requirement thereby aiding approximate smoothening of daily load curve and improving load factor. The data collected is transmitted to the cloud for real-time and historical data collection, for prediction of consumption patterns, anomaly detection, and clustering appliances according to their
D, AnithaD, SuchitraJain, UtsavMaity, SouvikDinda, Atish
Mining operations are important to industrial growth, but they expose the mining workers to risk including hazardous gases, elevated ambient temperatures, and dynamic structural instabilities within underground environments. Safety systems in the past, typically based on fixed sensor networks or manual patrols, fall short in accurate hazard detection amidst shifting mine conditions. The proposed project Miner's Safety Bot advanced this paradigm by leveraging an ESP 32 microcontroller as a mobile platform that integrates gas sensing, thermal monitoring, visual inspection and autonomous obstacle avoidance. The system incorporates MQ7 semiconductor gas sensor to monitor real time carbon monoxide (CO), offering detection range from 5 to 2000 ppm with accuracy of 5 ppm. Temperature and humidity are monitored through DHT11 digital sensor, calibrated to ensure reliability across the harsh microclimates in mines. Navigation and autonomous movement are enabled by Ultrasonic Sensor (HC-SR04
D, SuchitraD, AnithaMuthukumaran, BalasubramaniamMohanraj, SiddharthSubash Chandra Bose, Rohan
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.
Wolfe, Matt
The advanced construction equipment packing the convention center halls and surrounding lots will understandably be the stars of the triennial CONEXPO trade show, taking place March 3-7 in Las Vegas. But the latest technologies in fluid power and motion control that help those machines operate efficiently will also command attention from showgoers. The Bosch Rexroth mobile hydraulics team will be on-site in a joint booth with partner HydraForce (Booth S80245), showcasing their current product portfolio. Rafael Cardoso, Bosch Rexroth engineering manager, mobile systems and software, expects to have conversations about advanced control and automation, “focused on the demand for smarter, software-driven control strategies that enhance precision, productivity, downtime reduction and operator assistance features.”
Gehm, Ryan
With the rapid adoption of electric vehicles (EVs), ensuring the reliability, safety, and cost-effectiveness of power electronic subsystems such as onboard chargers, DC-DC converters, and vehicle control units (VCUs) has become a critical engineering focus. These components require thorough validation using precise calibration and communication protocols. This paper presents the development and implementation of an optimized software stack for the Universal Measurement and Calibration Protocol (XCP), aimed at real-time validation of VCUs using next-generation communication methods such as CAN, CAN-FD, and Ethernet. The stack facilitates read/write access to the ECU’s internal memory in runtime, enabling efficient diagnostics, calibration, and parameter tuning without hardware modifications. It is designed to be modular, platform-independent, and compatible with microcontrollers across different EV platforms. By utilizing the ASAM-compliant protocol architecture, the proposed system
Uthaman, Sreekumar
Computer vision has evolved from a supportive driver-assistance tool into a core technology for intelligent, non-intrusive occupant health monitoring in modern vehicles. Leveraging deep learning, edge optimization, and adaptive image processing, this work presents a dual-module Driver Health and Wellness Monitoring System that simultaneously performs fatigue detection and emotional wellbeing assessment using existing in-cabin RGB cameras without requiring additional sensors or intrusive wearables. The fatigue module employs MediaPipe-based facial and skeletal landmark analysis to track Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), head posture, and gaze dynamics, detecting early drowsiness and postural deviations. Adaptive, driver-specific thresholds combined with CAN-bus data fusion minimize false positives, achieving over 92% detection accuracy even under variable lighting and demographics. The emotional wellbeing module analyzes micro-expressions and facial action units to
Iqbal, ShoaibImteyaz, Shahma
Edge Artificial Intelligence (AI) is poised to usher in a new era of innovations in automotive and mobility. In concert with the transition towards software-defined vehicle (SDV) architectures, the application of in-vehicle edge AI has the potential to extend well beyond ADAS and AV. Applications such as adaptive energy management, real-time powertrain calibration, predictive diagnostics, and tailored user experiences. By moving AI model execution right into edge, i.e. the vehicle, automakers can significantly reduce data transmission and processing costs, ensure privacy of user data, and ensure timely decision-making, even when connectivity is limited. However, achieving such use of edge AI will require essential cloud and in-vehicle infrastructure, such as automotive-specific MLOps toolchains, along with the proper SDV infrastructure. Elements such as flexible compute environments, deterministic and high-speed networks, seamless access to vehicle-wide data and control functions. This
Khatri, SanjaySah, Mohamadali
Software-Defined Vehicles (SDV) are fostered through initiatives like SOAFEE and Eclipse SDV promoting the use of cloud-native approaches, distributed workloads and service-oriented architectures (SOA). This means that in these systems each vehicle is connected to the cloud and functions are executed both inside the vehicle and in the cloud. So far, there are no established solutions for monitoring and diagnosing SDVs. In designing these solutions, the cost-sensitive nature of every component inside a vehicle must be considered since it makes it unlikely that significant resources will be provided just for diagnostics. Therefore, conventional data centre monitoring approaches that usually rely on transferring large amounts of data to dedicated servers are not directly applicable in this scenario. To illustrate the challenges in providing new solutions for diagnosing and monitoring SDVs, a SOA that has been defined and studied in research projects is introduced. In this architecture
Böhlen, BorisFischer, Diana
Refined NVH performance of a vehicle is a mark of premium quality. Achieving the desired NVH performance in different vehicle operating conditions is always a Herculean task and early stage “CAE design recommendations” play crucial role in overall vehicle design development. This becomes tougher when the program is very much cost, weight and timeline sensitive. This paper explores simulation approach for addressing a major noise issue for a vehicle running at a constant speed on a rough road. While working on any issue, the first and the most critical step is to identify the exact root cause of the issue. Hence, we propose a detailed full vehicle level “contribution analysis (CA) + transfer path analysis (TPA)” methodology (everything done through the simulation) and then go for the design recommendations to improve the performance. We used road excitation power spectral density (PSD) as the input at all the four wheels (spindle locations) calculated through MBD software. The first
Mahajani, MihirNascimento, FabioAdinarayana Reddy, KodidelaMatyal, MahanteshTenagi, IrappaSardar, Chenna
Software Defined Vehicles (SDV), Software Defined Networks (SDN), Software Defined (Power) Grids (SDG) are just a few examples of how the Software Defined Transformation is unfolding across many industries today (collectively being referred to as Software Defined X – SDX). This paper defines a maturity model for Software Defined Transformation and evaluates different industries including Automotive on their evolution so far. This cross-industry view of SDX helps in analyzing where SDV’s could be headed. A 2020 paper [1] lays out the complexity of the automotive software, with companies pursuing several directions in this transformation. The automotive industry has not yet reached a consensus on the direction it is taking on SDV. While companies like Tesla are already making software centric cars, traditional OEMs like General Motors, Toyota, Ford etc. are making huge investments and redefining their business models, tech stacks and operations to leverage the power of software. There is
Mathur, Akshay RajMisra, AmitMakam, Sandeep
Automotive Over-the-Air (OTA) software updating has become a cornerstone of the modern connected vehicle, enabling manufacturers to remotely deploy bug fixes, security patches, and new features. However, this convenience comes with significant cybersecurity challenges. This paper provides a detailed examination of automotive OTA update security and the software store (software Applications & services store) mechanisms. I discuss the current industry standards and regulations, notably ISO/SAE 21434 and the United Nations Economic Commission for Europe (UNECE) regulations UN R155 (cybersecurity) and UN R156 (software updates) and explain their relevance to secure OTA and software update management. I then explored the Uptane framework, an open and widely adopted architecture specifically designed to secure automotive OTA updates. Next, OTA-specific threat models are analyzed, detailing potential attack vectors and corresponding mitigation strategies. Real-world case studies are presented
Kurumbudel, Prashanth Ram
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