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In autonomous driving and advanced driver-assistance systems (ADAS), radar sensors play a crucial role in detecting and tracking objects under diverse environmental conditions [1, 2, 3]. One persistent challenge is the accurate representation of long vehicles, such as trucks and buses, whose extended radar signatures often result in a wide spatial spread of detections with similar radial velocities, making association difficult due to gating limitations. The GRID component processes raw radar measurements to generate occupancy grids and extract candidate targets, including stationary environmental structures [4, 5]. In the case of stationary long vehicles, multiple stationary objects may overlap with grid-based environment targets, allowing them to be consolidated into a single tracked object. This paper presents a novel approach for radar object merging and retention decision-making using fuzzy logic to distinguish between true moving and stationary objects in the presence of GRID targets and object association mechanisms. The association modules link radar measurements or detections, when available, with existing tracks to maintain temporal consistency, enabling the formation of radar objects represented by track points. The proposed method fuses radar objects based on their radial velocity and spatial overlap, while a fuzzy logic–based decision engine evaluates object persistence. The approach improves object continuity and reduces false positives in driving functions such as Blind Spot Detection (BSD), Emergency Brake Assist (EBA), and Adaptive Cruise Control (ACC). Simulation and real-world validation results demonstrate significant improvements in object tracking accuracy, particularly for long vehicles in urban environments.
Mohanty, Sanket, Sarkar, Sanglap
Modern vehicles increasingly rely on software-intensive functions, which continue to drive the growth and complexity of Electronic Control Units (ECUs). Conventional architectures based on single core or statically partitioned multicore microcontrollers are becoming inadequate for meeting emerging requirements such as deterministic real time performance, high communication throughput, synchronized time bases, and secure message handling. This paper introduces a software driven multicore consolidation approach that enables multiple ECU functions to be integrated on a single high-performance microcontroller while maintaining essential attributes of safety, determinism, and responsiveness. The proposed architecture emphasizes the co-location of dependent software modules to reduce cross core communication overhead and enhance runtime efficiency. By leveraging AUTOSAR compliant multicore Basic Software (BSW) stacks, including Communication, NVM, STBM, Diagnostics, and OTA, the system supports improvements in timing accuracy, data throughput, and task synchronization. In addition, core-specific communication channel assignment, interrupt-based routing, HSM supported secure frame handling, and multicore enabled NVM with a master–satellite design collectively contribute to more efficient CPU utilization and reduced latency under high load communication scenarios. The approach supports key industry relevant capabilities (e.g., runtime remote software updates, sensor fusion) and enables efficient handling of high-volume communication activity, making it adaptable for emerging multicore ECU architectures in next generation vehicles. The theoretical analysis presented in this paper describes how the proposed software driven consolidation can reduce system overheads, improve the organization of multicore workloads, and support more structured parallel execution. The study further highlights that a well-designed multicore software strategy can serve as a practical and scalable direction for future automotive electronics, supporting higher levels of feature integration and communication flexibility, along with lower CPU load compared to traditional multicore ECU implementations. Considerations for future enhancements in software component grouping and communication stack adaptability are also discussed.
Sadhu, Santosh, Toshniwal, Sachin
The introduction of Rear Wheel Steering (RWS) to the existing multi-link suspension system requires careful analysis of kinematics & elasto-kinematics characteristics in order to deliver optimal vehicle dynamics performance. Since the existing multi-link suspension system is not optimized for the use of a steerable wheel, the introduction of RWS requires considerable hard point changes & geometric modifications in order to deliver optimal steerability performance. Steering parameters like kingpin inclination (KPI), caster orientation, scrub radius, caster trail, and lateral and longitudinal offsets of the KPI axis to the wheel center and tire contact patch become deciding parameters that must be given due consideration to ensure the desired steering dynamics. This research aims to identify principal vehicle dynamics K&C parameters important to the effective application of RWS without sacrificing handling and stability of the vehicle. The challenge is to compromise rear axle articulation with prevailing suspension geometry and packaging needs, especially for Sport Utility Vehicle (SUV) platforms with severe packaging conditions. Simulation-based design process with Adams software is utilized to iteratively tune suspension parameters while maintaining fundamental vehicle dynamics attributes. The overall research proposes a step-by-step methodology for the evaluation and optimization of suspension-level performance parameters that have influence on the vehicle dynamics characteristics of the vehicle, ultimately resulting in the synthesis of an efficient, dynamically robust, and packaging-conformant multi-link suspension system with rear wheel steering unit. The conclusions are highly valuable as feedback on vehicle dynamics adaptation techniques for advanced chassis systems with rear axle steering.
Thakur, Pragyesh Singh, Prabhakara Rao, Vageesh, Mani, Arun, J., Ramkumar, Nayak, Bhargav, M., Sudhan
This paper presents a one-dimensional digital twin of a commercial vehicle heating, ventilation, and air-conditioning (HVAC) system, developed in MATLAB and Simulink with Simscape. The model integrates a belt-driven, engine-speed-dependent R134a vapor-compression cycle with a reduced-order cabin thermal network. The refrigeration loop incorporates a positive-displacement compressor, condenser, thermostatic expansion valve, evaporator, and receiver, with the heat exchangers represented using two-phase and moist-air formulations. The cabin model accounts for six surfaces with parallel glazing and opaque thermal paths, solar and long-wave radiation on exterior panels, occupant heat and moisture loads, and speed-dependent external convection, parameterized for validation drive segments. The framework is designed for traceability from component-level state variables to system-level outcomes, enabling staged validation using pressure and temperature signals across the refrigerant loop and cabin temperature response. Validation was performed against controlled climatic chamber data, including a hot-soak period with the air-conditioning off followed by cool-down tests at two steady vehicle speeds. Simulation results were resampled to match measurement timestamps, showing strong agreement with cabin temperature dynamics, with a root mean square error of ~2 °C and correlation of 0.99 in the transient-excluded window. Refrigerant-side pressures and temperatures exhibited segment-dependent accuracy, sufficient for calibration and strategy assessment. Simulation-based screening of cool-down behaviour and refrigerant-loop states provides a reusable framework before repeated vehicle testing, reducing the calibration effort for faster HVAC system evaluation. Overall, the model supports rapid iteration, ensures thermodynamic consistency through enthalpy-based checks, and provides a practical foundation for controller development and future in-the-loop applications.
Kumar, Nitesh, Duraiswamy, Vijayaraj, M. L., Sankar, Kumar, Vijay Bhooshan
The application of Advanced Driver Assistance Systems (ADAS) in commercial vehicles marks significant challenges in mechanical design and digital validation of externally mounted sensor systems. Blind Spot Assist (BSA), a radar-based ADAS feature, depends on accurate sensor alignment and stable bracket mounting to ensure reliable detection of objects within the vehicle’s blind spots. the exposure to dynamic loading environments, BSA radar brackets must be designed to withstand operational vibrations and also should comply with international automotive standards, including ISO 16750-3 [1, 7], which outlines test procedures of mechanical vibration and shock. This paper describes a digital approach for the development of BSA radar brackets, incorporating system-level design thinking, flexible-body simulation, and vibration analysis. The method is combination of physical behavior with simulation techniques to study bracket behavior for dynamic loads and identifying probable sources of structure resonance and the interaction of the system with neighboring systems or components. A multi-dimensional design process is highlighted, where multiple iterative modeling, cross-functional teams brainstorming, and virtual validation enables the efficient exploring of alternate designs. The framework supports countermeasure studies such as geometry optimization, boundary condition refining, and system decoupling, with digital development. This approach is applicable for a wide range of ADAS sensor mounts challenges faced in heavy-duty vehicles, where the packaging constraints, harsh operating conditions, and NVH (Noise, Vibration, Harshness) requirements must be considered. This paper shows a replicable model for engineering teams to improve sensor stability, reduce physical testing and simulation cycles, and ensure compliance with vibration standards from the design evolved from simulations.
Kenchaplara, Vinay Kumar, Hubballimath, Nandesh, Sadekar, Rahul
Modern automatic transmission (AT) application software validation is predominantly dependent on Hardware-in-the-Loop (HiL) systems, resulting in significant time, cost, and scalability limitations within continuous integration/continuous deployment (CI/CD) workflows. These constraints intensify with increasing software complexity and the need to support multiple transmission variants across evolving hardware configurations. Existing validation approaches lack a scalable, software-centric surrogate capable of representing both control logic and physical system behaviour under real-time operating conditions. This study proposes a digital twin-based validation framework for AT application software, integrated within CI/CD pipelines. The framework restructures application software into a platform-based architecture enabling reuse across multiple transmission programs. A coupled plant model is developed to simulate drivetrain dynamics, ensuring system-level fidelity. The architecture also permits future data-driven augmentation for transient response correction and extended scenario coverage; however, the present study focuses on the baseline digital twin, hardware abstraction, and CI/CD validation workflow. A key contribution is the introduction of a hardware-adaptive abstraction layer, enabling the digital twin to accommodate variations in sensors, actuators, and control interfaces without full HiL dependency. Across 15 scenario hardware validation combinations, the reduced-order digital twin achieved a runtime gain of 1.20×–1.68× over the HiL proxy execution, with an average gain of approximately 1.46× and an average execution-time reduction of approximately 31% per validation case. The study demonstrates that integrating application software, plant models, and hardware-aware abstraction within a digital twin enables a transition from hardware-constrained validation to software-scalable paradigms. The framework provides a pathway toward efficient and adaptable validation in software-defined vehicle ecosystems. The proposed framework is aligned with ZF’s platform-based transmission software strategy, enabling reuse across multiple automatic transmission programs and supporting scalable validation of ZF powertrain control systems under evolving hardware configurations.
Gavara, Chandrakant, Sali, Rohan, Peddibhotla, Brahmaprakash, Wadkar, Swapnil P.
Overtaking another vehicle on a two-lane road can turn dangerous within a few seconds, especially when the gap to an oncoming vehicle is short or that vehicle is moving fast. Many driver-assistance systems rely on a simple, fast rule called the Overtake Safety Margin formula to decide whether an overtaking maneuver is safe, without running a full traffic simulation. This study checks the formula's reliability using a physics-based simulator that models three vehicles: the car overtaking, the car being overtaken, and the oncoming car. One million generated overtaking scenarios were evaluated using both the OSM formula and the kinematic simulator to identify cases where the formula classified a maneuver as SAFE while the simulator predicted an UNSAFE outcome. These mismatches, called blind spots, represented 3.98% of the evaluated scenarios and were concentrated in high oncoming-speed, short-gap situations, pointing to a specific weakness rather than random error. To catch these blind spots automatically, a small and efficient machine-learning model, a micro-transformer, was trained on the simulator's results. It reached 96.3% overall accuracy and correctly flagged 86.9% of the formula's blind spots, outperforming LightGBM, which detected 73.2%. The model's confidence scores were well calibrated, so they can be trusted to trigger an extra safety check when needed. An uncertainty-based search then used these confidence scores to find risky scenarios efficiently: 187 of the 500 most uncertain cases turned out to be unsafe, about 9.4 times more effective than random selection. Finally, the 30 highest-priority cases were replayed in the CARLA driving simulator, and 27 were confirmed to reproduce unsafe outcomes. Together, these results show that pairing a fast rule-based formula with a calibrated learning-based monitor can catch dangerous situations the formula alone would miss, supporting the safety documentation required for automotive safety-of-the-intended-functionality standards.
Sekaran, Nikhil, Sehgal, Vishal
Duplicate components in large-scale Digital Mock-Up (DMU) assemblies present a significant challenge to product integrity within Digital Twin environments operating on integrated OEM Product Lifecycle Management (PLM) platforms. These duplicates, often referred to as structural redundancies or doubloons, are characterized by geometrically identical or equivalent components occupying the same spatial location without corresponding justification in the Bill of Materials (BOM). Such inconsistencies arise from increasing assembly complexity, tool integration issues, and limitations of conventional scripting-based validation approaches. Existing duplicate detection methods rely heavily on manual inspection and isolated tool functionalities, making them impractical for large-scale assemblies. While interference analysis tools can detect spatial conflicts, they cannot reliably distinguish between valid engineering overlaps and unintended duplicate conditions. Consequently, a more structured and automated validation strategy is required. This paper presents an automated computer vision-based methodology developed within an OEM Digital Twin environment to address these challenges. The proposed framework combines interference-data extraction, image-based overlap assessment, automated duplicate screening, Active Feedback Learning, and BOM-integrated validation within a unified workflow. The methodology transforms interference-derived information into structured analytical features and uses Red Percentage–based overlap evaluation to identify potential duplicate conditions. The proposed approach enables scalable duplicate detection across complex assemblies while reducing dependence on manual review. Validation on production-scale automotive DMU datasets demonstrated significant reductions in engineering effort and substantial filtering of non-actionable interference records. By integrating computer vision analytics, feedback-driven noise elimination, and product structure validation, the proposed framework improves Digital Twin data integrity and supports efficient engineering workflows in modern OEM PLM environments.
Gnanaprakasam, Hepshibha, Sridhar, Ashok
Digital issue management is a critical enabler for accelerating vehicle development and improving launch readiness in automotive programs characterized by distributed engineering, multi-site manufacturing, and increasing electrical/electronic and software complexity. Existing practices often rely on disconnected tools, spreadsheets, and informal communication that delay prioritization, ownership assignment, root-cause analysis, and maturity-gate decisions. This paper presents a standards-anchored semantic issue data model that integrates issue information from engineering, production, quality, supplier, and aftermarket systems without replacing existing tools. The model is aligned with VDA Maturity Level Assurance, IATF 16949, AIAG APQP/PPAP, ISO 26262, Automotive SPICE, VDA 6.3, and the 8D problem-solving method. It is formalized through an entity-relationship schema and supported by a reference architecture comprising a knowledge graph, data ingestion protocols, and integration middleware. A deterministic conflict-resolution mechanism reconciles inconsistent metadata from source systems and establishes an auditable single source of truth. The framework is evaluated using published automotive benchmarks and evidence from five anonymized commercial vehicle implementations across different business units and timeframes. The results demonstrate consolidation of fragmented issue repositories, reduction of system interfaces, elimination of paper-based quality workflows, improved digital-thread coverage, and faster retrieval of previous resolution knowledge. These outcomes indicate reduced cross-functional reconciliation effort, improved ownership transparency, stronger maturity-gate evidence, and measurable launch-readiness analytics. The model also supports future integration with cross-enterprise data spaces for multi-tier supplier collaboration. The proposed framework provides a replicable approach for transforming issue management from a reactive process into a strategic capability for faster and more transparent vehicle development.
Swaminathan, Bharathiraj, Choudhary, Runa, J., Anandu
Finite Element Analysis (FEA) plays a vital role in accelerating product development and reducing overall engineering costs. Accurate simulation of powertrain components requires extensive preprocessing of solid models. Detailed contact definitions and bolt representations are required to properly evaluate screw joints. Pipe joints present similar challenges, as they often incorporate various types of nuts and connectors. As a result, engineers spend a significant amount of time manually modelling these joints. Automating the modelling process is difficult due to the high variability in bolt sizes and pipe joint designs. This paper proposes AI-based models to automatically classify these components and extract their distinctive geometric features. Because bolts and pipe joints have wide feature variations, developing a generalized AI model requires a large dataset which is often hard to obtain. To address this limitation, this paper explores strategies for generating smaller, specialized AI models tailored to each unique joint type. This study describes the development of deep learning neural network models capable of learning distinguishing features of different joint configurations from limited training data. A multilayer graphical neural network architecture is employed, enabling efficient extraction and representation of these unique geometric characteristics. The proposed models can robustly generalize different joint types while maintaining high classification accuracy and reliable feature detection despite constrained training data availability. Automated workflows have been implemented in commercially available preprocessing tools to validate the proposed AI methodology and demonstrate its effectiveness in practical engineering applications. The integration of AI model automation in preprocessing tools significantly reduces engineering effort and repetitive manual preprocessing steps. This work highlights the potential of hybrid AI models to accelerate CAE workflows and provides scalable framework for future automation in FEA.
Jayachandran, Janarthanan, Agarwal, Deepak
In current and futuristic electronic designs, ensuring the accuracy and reliability of electrical circuits over all component parameter variation and operating condition, requires validation across multiple domains - mathematical modelling, simulation, and physical measurements. Before committing to physical board fabrication, simulation has become a critical step in the circuit design process. With increasing design complexity, engineers rely heavily upon simulation tools to predict circuit behavior, optimize performance, and reduce the number of design iterations. The effectiveness of simulation lies not only in its ability to provide accurate representations of real-world circuit behavior, but also capturing key electrical characteristics such as voltage levels, frequency response, accurate modeling of component tolerances and coefficients, etc. It should also excel in efficiency-allowing rapid iteration, testing under varied conditions, evaluation of multiple scenarios without excessive computational costs, and quick comparisons of design alternatives. This paper presents an advanced semiconductor derating methodology that addresses the limitations of conventional approaches through automation. The derating algorithm has been developed and implemented using Maple (Mathematics based Tool) to generate precise worst-case values of semiconductor component parameters adjusted for specific circuit operating conditions. This automated tool significantly enhances both the efficiency and accuracy of mathematical analysis results, providing engineers with more reliable performance predictions for electronic circuit design. This paper also presents a comparative study that analyzes and correlates the performance of an electrical circuit obtained through worst-case mathematical analysis using internal tool, simulations using industry-standard tools, and the result collected during internal testing of the product. The objective is to evaluate how closely theoretical predictions and simulations align with real-world behavior and to identify factors contributing to any discrepancies.
Mishra, Anshul, M. S., Suhas
To comply with increasingly stringent emission norms and simultaneously enhance engine efficiency, durability, and power output, continuous improvements in engine performance have become essential for modern automotive engineering. However, these advancements often result in unintended consequences, particularly in the form of elevated noise and vibration (NV) levels, which negatively impact overall vehicle refinement and customer comfort. Managing NV without compromising performance has therefore emerged as a critical challenge in contemporary powertrain development. The study addresses the optimization of NV characteristics while maintaining engine functionality through a hybrid approach. This method combines transfer path of noise and vibration with a detailed evaluation of engine electronic control unit (ECU) parameters. By combining these analytical techniques, critical contributors to NV were identified, enabling the formulation of a targeted countermeasure strategy. Experimental validation was carried out on the engine intake system under controlled conditions to assess the effectiveness of proposed solutions. Results indicate that modifications to the intake system significantly reduce noise levels at the engine intake side, thereby improving acoustic comfort without adversely affecting combustion efficiency or power delivery. Furthermore, systematic optimization of ECU parameters contributed to smoother engine operation and reduced vibrational excitation across multiple operating points. The findings demonstrate that accurately targeting the appropriate engine subsystem and employing an integrated analytical-experimental framework can achieve balanced performance and acoustic refinement. This work highlights the effectiveness of integrated analytical and experimental methods for achieving balanced performance and acoustic refinement in advanced engines.
Singh, Karamdeep, Uniyal, Ravinder, Yadav, Manish, Mishra, Durga Prasad
The rapid evolution of automotive electronics has pushed traditional vehicle electrical and electronic (E/E) architectures to their limits. Conventional designs, characterized by point-to-point wiring harnesses, distributed domain-specific ECUs, and communication buses such as LIN, CAN, and Ethernet, impose significant drawbacks in terms of vehicle weight, integration complexity, and long-term scalability. As the industry moves toward the Software-Defined Vehicle (SDV) paradigm, there is a compelling need to rethink the foundational principles governing in-vehicle connectivity. This paper proposes a wireless-centric SDV architecture in which non-functional-safety-relevant sensors and actuators communicate over standardized wireless protocols, eliminating the need for their associated physical wiring. By decoupling these peripheral nodes from the wired backbone, the proposed architecture reduces harness bulk, lowers vehicle mass, minimizes redundant compute hardware, and significantly reduces integration cost. Safety-critical systems including those governing powertrain, braking, and steering are explicitly retained on hardwired, deterministic communication channels in compliance with ISO 26262 functional safety requirements. This paper additionally defines explicit assumptions and fallback behavior governing that wired/wireless boundary. The architectural framework is evaluated against key SDV design objectives such as modularity, over-the-air (OTA) updateability, and feature scalability, assessed qualitatively against published engineering precedent, with a structured validation roadmap defined to quantify latency, reliability, and coexistence performance in follow-on work. The analysis demonstrates that a hybrid wired-wireless topology can substantially simplify vehicle E/E architecture while maintaining the reliability and latency guarantees demanded by automotive-grade systems. This work contributes to a structured transition roadmap for OEMs and Tier-1 suppliers seeking to adopt SDV principles without compromising vehicle safety or regulatory compliance, together with an explicit protocol-selection framework and a functional-safety argument for the wired/wireless boundary.
Hebbale Ramkumar, Ramya, Sarkar, Dibas
Automotive radar systems do not authenticate the signals they receive, allowing an external transmitter to create phantom objects that can trigger automatic emergency braking (AEB). This paper quantifies the threat using real radar measurements from the public RadarScenes dataset and evaluates a software countermeasure that requires no hardware modification. Computer-generated phantom radar returns were added to 200 motorway sequences and adjusted so the OS-CFAR detector accepted them as real objects. In the undefended system, radar detections were tracked as obstacles and braking was initiated when an object appeared within 30 m, resulting in unnecessary braking in 87.0 percent of attacked sequences. The proposed two-layer filter combines kinematic plausibility gating with camera-radar cross-validation. Layer 1 checks track history and physical movement over three consecutive frames, while Layer 2 reduces confidence when a clear-weather radar detection lacks camera confirmation. With both layers active, false braking fell to 3.5 percent, with no genuine detections missed across 200 genuine sequences. SHAP analysis of a Random Forest classifier identified kinematic implausibility as responsible for 62.3 percent of correct suppressions and produced a machine-readable evidence record to support cybersecurity documentation under ISO/SAE 21434 and safety documentation under ISO 21448. These results apply to an offline replay experiment using synthetic injections. Validation with real attack hardware, synchronized camera data, and vehicle testing remains future work.
Sehgal, Vishal, Sekaran, Nikhil
Automotive industry is currently experiencing a major transition towards Software-Defined Vehicles (SDV) to facilitate seamless new feature integration via over the air (OTA) update. SDVs integrate more than 50 + ECUs with more than 400+ software functions, leading to significant software complexity and a substantial need for comprehensive bench level testing to prevent software issues at vehicle level. To address these challenges, a robust and edge case focused test specification is essential, ensuring maximum requirement coverage across all scenarios while incorporating insights from past bugs and field issues. Currently, the automotive industry faces challenges in producing high-quality test specifications mainly focusing on functional testing (both positive and negative scenario), fault injection testing, ISO standards testing, network management testing which often exceed 2,000 + test cases for SDV features in parallel with maintaining consistency over time, ensuring maximum system-requirement coverage, and meeting project milestones. This paper introduces a new method to overcome above said problems. We provide a comparative study involving our proposed method with other methods to show the efficacy of the proposed method.
Arumugam, Jayalakshmi
Temperature management in both residential environments and commercial vehicle cabins are rapidly evolving toward intelligent, interconnected climate-control ecosystems. However, current systems operate in isolation, with no mechanism for synchronizing thermal comfort preferences between a user’s smart home and the cabin environment of a truck used for either personal mobility, last-mile delivery, or commercial logistics. This disjointed control results in inconsistent comfort experience, unnecessary HVAC energy consumption, and the inability to anticipate user needs based on transition patterns (e.g. leaving a cool home for a hot truck cabin) between home and vehicle. To address this technical challenge, this research paper provides bidirectional communication temperature-synchronization framework in which a smart-home HVAC app communicates seamlessly with the truck’s cabin climate-control system, integrating user-specific comfort profiles(such as temperature preferences, real-time cabin and home temperature, humidity levels, HVAC operating states, outside weather forecasts, trip start times, thermal-soak estimates etc.) and environmental prediction models to maintain consistent thermal comfort across both environments. Based on user’s comfort profile, our framework derives the Comfort Deviation Index (CDI), Transient Temperature Gradient (TTG) and applies predictive algorithms to precondition the cabin or home environment accordingly. The AI technique Long Short-Term Memory (LSTM) network is used for forecasting temperature transitions across home-to-vehicle. Our technique becomes even more pronounced in small-haul trucks that operate under frequent stop-and-go conditions, where rapid cabin temperature fluctuations are common. There is currently no existing research or framework that enables seamless temperature synchronization between a user’s home environment and the truck cabin. A smart-home–to-cabin synchronization approach would fill this gap by intelligently accounting for outside weather, trip duration, thermal soak, and energy-efficiency constraints. Additional challenges include ensuring secure communication over cloud and edge networks, managing latency constraints for real-time actuation, and preventing conflicts between home HVAC automation and automotive HVAC control logic
Prajapati, Saurav, Sagar, Kshitij Singh, B. S., Sanchitha
The transition toward Software-Defined Vehicles (SDVs) [1] is driving a fundamental shift in automotive electrical and electronic architectures [1], where centralized high-performance computing platforms must support mixed-critical workloads under strict requirements for determinism, performance isolation [2], and functional safety. Consolidating safety-critical and non-critical applications onto shared System-on-Chip (SoC) platforms [3] introduces significant challenges in ensuring predictable execution behavior, bounded latency, and compliance with safety standards [4, 15]. Addressing these challenges is essential for enabling scalable and reliable next-generation automotive systems [1] [5]. This paper presents a hardware–software co-design approach to achieve deterministic and safety-certified operation in SDV [1] architectures. Unlike traditional implementations that rely on software hypervisors or hardware isolation independently, the novel core contribution of this work is an integrated, dual-layer hardware-software orchestration model. It dynamically coordinates static Stage-2 translation with hardware Extended Resource Domain Controllers (XRDC) and Memory Partitioning and Monitoring (MPAM) to enforce end-to-end spatial, temporal, and interconnect-level bus isolation. The proposed solution combines hardware-assisted resource isolation mechanisms, including Hardware-enforced resource isolation and Memory Partitioning and Monitoring (MPAM) [6], with a lightweight partitioning hypervisor and Virtual Machine Monitor (VMM). XRDC [7] provides fine-grained access control for memory and peripheral resources, while MPAM [6] enables memory bandwidth allocation and monitoring to minimize resource contention across competing workloads. On the software side, the partitioning hypervisor [8] enforces strong temporal and spatial isolation between Virtual Electronic Control Units (vECUs), allowing mixed critical applications to coexist safely on a shared platform. VMM supports efficient virtualization [9, 10] and lifecycle management of virtual machines while maintaining deterministic execution characteristics. The proposed architecture aligns with the requirements of ISO 26262 [4], enabling the certification of safety critical applications alongside non-critical services. By integrating hardware-enforced isolation with software-level partitioning, the approach ensures predictable system behavior and reduces interference between workloads. The results demonstrate that a coordinated hardware–software strategy is essential to bridge the gap between high-performance computing and functional safety, thereby enabling scalable, deterministic, and safety-compliant SDV [1] platforms for future automotive applications. System-level validation conducted on an NXP S32G3 [7] [10] vehicle network processor platform demonstrates the efficacy of the proposed co-design. Under artificial memory-bus contention generated by background non-critical (QM) Linux traffic, the hardware-enforced MPAM bandwidth regulation reduced maximum worst-case execution time (WCET) jitter for an ASIL real-time control loop from 142.6 μs down to 8.4 μs—achieving a 94.1% reduction in latency variation. Paravirtualized hypercall operations achieved a 40.2% latency reduction compared to standard Trap-and-Emulate approaches.
Khan, Misbah Ullah
Modern day vehicles are equipped with multiple features to enhance user experience, increased safety and continuous over the air updates for upgradability and updatability of vehicles over lifetime. The implementation of every embedded automotive software feature consumes microcontroller resources (like RAM and ROM) which are highly critical and need careful handling. In an SDV architecture, where continuous software upgrades and updates are enabled, optimal feature handling becomes necessary. The microcontroller resources chosen during the development phase often reaches the threshold limit even before series production. In such scenarios, software feature enhancement/updates are challenging. When the memory is limited and becomes a constraint for installing newer features / upgrades, OEM has minimal provision to enhance or introduce new features. The paper presents framework to OEM to get a summary of how to manage functionality in a vehicle variant depending on specific demographic location and user preferences. The summary of the functionality usage is reported to end user in the vehicle system.
Chandrashekar, Sandhya, Hubli, Ashwini
Automotive E/E architecture is getting transition from distributed ECUs to high performing Zonal architectures. Traditional diagnostic methodologies based on Unified Diagnostic Services (UDS/ISO 14229) is being followed for providing failure analysis. It is difficult when debugging complex software and to find issues where lies exactly at modular level called “stack trace”. This could provide solutions for software bugs, failures or even container-level crashes in Software-Defined Vehicle (SDV). This paper provides a remote diagnostic framework that makes use of the ISO 17978 (SOVD) standard to visualize a “Vehicle-wide Crashlytics” as analogous to modern mobile application analytics with Firebase SDK. Also, the comparison of failure management with traditional diagnostic method to handling fault management in remote framework with both ISO 17978 (SOVD) and ISO 20077/20078 (Remote Diagnostic) is made. It also speaks how methodology can be formed for software logs capturing and traces execution triggered by function software level anomalies. Also, the constrains such as V2X bandwidth and data is monitored and controlled with Diagnostic Saliency Algorithm that helps to determine only relevant telemetry is transmitted via 5G or V2N. This framework proposal is checked with a simple application failure scenario, which leads to the reduction in “Mean Time to Repair” (MTTR) for software bugs. The reduction in MTTR is achieved since actionable root cause of software issue is shared directly from vehicle’s run-time environment. Future development of vehicle functions (SaaS) is expected to be integrated with SOVD and cloud-native observability patterns. This is much needed towards the scalable and quick development and deployment of applications into Vehicle as Digital Hub.
Kalirajan, Vignesh, Annamalai, Viswachandar, Balakrishnan, Arun Shankar
The Software-Defined Vehicle (SDV) paradigm is reshaping automotive electronic architectures by enabling continuous software rollout & artificial intelligence (AI) evolution on platforms over the total service life of the hardware. This creates a fundamental challenge for electronic control unit (ECU) design: to enable sustained deployment, adoption & maintenance of AI models over vehicle’s lifetime. This paper presents a technology independent framework for integrating AI lifecycle management into SDV ECU architectures, while decoupling AI & software innovation from hardware constraints. It further discusses the role of scalable computing platforms based on high-performance CPUs, GPUs & SoCs, in supporting increasing AI workloads over time. In addition, the evolution of AUTOSAR-based hardware abstraction mechanisms is examined as an enabler for software portability & hardware independence. The discussed framework addresses key lifecycle aspects, including AI model deployment, versioning, rollout strategies & long-term maintainability within established automotive safety, cybersecurity frameworks. The presented work contributes to future-ready SDV systems capable of sustaining continuous AI innovation over extended operational lifetime.
Ghosh, Sayantan, Anudeepika K., Durga, Nagaraju, Yamuna
The automotive industry is leaning heavily into virtual testing to speed up vehicle design and cut physical prototyping costs. However, checking if a simulation matches a real car still relies on older, rigid math formulae and manual checks. This makes virtual testing less reliable for highly dynamic driving maneuvers, required in vehicle design. To address this, following paper introduces an automated software framework built to accurately measure the differences between physical test data and virtual simulations. The methodology was validated using a lightweight off-road prototype at the National Automotive Test Tracks (NATRAX) on a custom-engineered track inducing high-fidelity roll, pitch, and yaw excitations. To guarantee accuracy, an identical digital twin of this complex topography was constructed within a high-fidelity vehicle-dynamics simulation environment. The system features an algorithmic engine that automatically reads, cleans, and synchronizes distinct physical and virtual telemetry streams. Initial validation using standard metrics like NRMSE, RMSE, and S&G proved insufficient, failing to quantify errors in data exhibiting non-linear temporal shifts caused by stochastic human driver latencies. To overcome these limitations this study applies Windowed Dynamic Time Warping (DTW) to operationalize the ISO/TS 18571:2024 standard. This approach leverages the optimal warping path to extract continuous, vector-based error profiles, isolating phase and amplitude inconsistencies across the entire event. This automated framework significantly reduces correlation time by transitioning from subjective scalar scores to high-resolution diagnostic error vectors. By quantifying dynamic fidelity through this automated, high-precision DTW computational tool, the methodology validates virtual models as robust predictive tools, allowing simulation data to yield results comparable to physical benchmarks. This capability enables faster vehicle development by allowing rapid evaluation of multiple parameters in a virtual environment. It supports performance fine-tuning with reduced physical testing and can be integrated into automated workflows for repeatable data correlation, improving validation efficiency and reducing time.
Umorya, Divyansh, Pithadia, Chirag, S. J., Srihari, Jaiswal, Manish, Hardiya, Karan, Marmat, Hemant
In welding fixture design, ensuring accessibility for manual Metal Active Gas (MAG) welding is essential for achieving required weld quality, proper welding angles, operator efficiency, and overall manufacturability. Manual welding operations depend on handheld torches, where limitations in reachability, torch orientation, and confined working spaces can significantly impact production feasibility. Automotive body structures are typically composed of numerous small and complex components within a largely monolithic assembly. For welding such smaller parts, hand jigs are used as independent tools to ensure proper positioning and stability. Accessibility and usability of these hand jigs play a crucial role, particularly in tight and restricted areas, directly affecting operator ergonomics and weld consistency. Traditionally, validation of welding torch accessibility and hand jig usability is performed during the physical try-out stage, often leading to late identification of design issues, fixture modifications, increased lead time, and higher development costs. This study proposes a Virtual Reality (VR)-based validation methodology to evaluate manual MAG welding accessibility and hand jig usability during the fixture design phase. CAD models of fixtures and components are integrated into an immersive VR environment, enabling real-time simulation of operator interaction. Critical parameters such as reachability, collision detection, welding angle, torch orientation, operator posture, and hand jig accessibility are assessed. The VR-based approach enables realistic evaluation of human interaction and ergonomic conditions, which are not effectively captured using conventional simulation tools. Early validation through VR significantly reduces design iterations, minimizes rework, and improves fixture design robustness. Overall, the proposed methodology enhances productivity, reduces costs, and accelerates project execution in welding fixture development. To provide a quantitative ergonomic assessment, the study introduces an Ergonomic Accessibility Index based on Green and Red Zone weld classification. In the Bus Middle UB fixture case study, the EAI improved from 70.8% to 95.8% after the product geometry was repositioned upward, demonstrating improved welding accessibility and operator ergonomics.
Hanumanthappa, Subramanya, Rajagopal, Vinoth, Vakkalad, Ramesh, Nagendran, Rakesh Kumar, Soni, Sanjay
Off-highway vehicles (OHV) increasingly rely on electronically controlled, safety-critical systems such as braking, steering, powertrain, and implement control electronic control unit (ECUs). The integration of connectivity, telematics, precision-agriculture features, and semi-autonomous operation has transformed these machines into complex cyber-physical systems. While these capabilities improve productivity and operational efficiency, they also expand the attack surface, creating cybersecurity risks that can directly impact vehicle control, operator safety, and field operations. Off-highway platforms often operate in remote environments, use legacy in-vehicle networks, and have long service lifecycles, further amplifying exposure to cyber threats. Recent research and real-world demonstrations highlight that many attacks on safety-critical controllers exploit fundamental architectural weaknesses rather than advanced exploits. Common scenarios include unauthenticated CAN-based communication enabling message injection, replay, or spoofing of steering and braking commands; compromised telematics or wireless interfaces pivoting into safety-critical networks; unsecure over-the-air update mechanisms permitting unauthorized firmware installation; and misuse of diagnostic or maintenance access paths to invoke privileged ECU functions. Network-level attacks such as denial-of-service and sensor-level manipulation, including GPS or perception spoofing, further threaten the integrity and availability of control functions relied upon by modern agricultural machines. This paper examines these attack scenarios in the context of off-highway safety-critical controllers and discusses how Hardware Security Modules (HSMs) provide a practical trust anchor for mitigating such risks. HSMs onboard hardware-isolated execution, tamper-resistant key storage, and cryptographic enforcement that strengthen ECU security beyond secure boot. HSM-backed mechanisms enable authenticated and integrity-protected software updates, prevent execution of unauthorized firmware, support message authentication and freshness on in-vehicle networks, and harden diagnostic access through secure challenge-response mechanisms. By grounding cybersecurity risks in realistic off-highway use cases and mapping them to deployable HSM-backed controls, the paper illustrates how hardware-anchored security enhances runtime trust, supports safety objectives, and aligns off-highway vehicle architectures with emerging vehicle cybersecurity expectations, including the EU Cyber Resilience Act (CRA). In addition, a PoC implementation based on an automotive-grade embedded controller is presented to demonstrate secure boot, authenticated CAN communication, diagnostic access control, and runtime integrity monitoring, together with a validation methodology that demonstrates their practical effectiveness.
Maheshwaram, Chetan, Dasari, Manoj Sham
Oil canning, characterized by localized buckling and surface waviness in thin metal panels, is a critical quality parameter in the automotive industry. Since outer panels serve both structural and aesthetic functions, even minor surface distortions can adversely affect perceived vehicle quality. Traditionally, evaluation of oil canning relies on extensive nonlinear Finite Element simulations. For example, assessing a single hood design variant may requires thousands of nonlinear Finite Element analyses. Considering multiple design iterations across major vehicle panels including hood, roof, doors, fenders, and trunk, the total number of simulations can exceed tens of thousands, leading to substantial computational cost and development time. Consequently, this conventional approach is both time intensive and resource demanding. To address these limitations, advanced Machine Learning techniques are employed to significantly reduce simulation effort as well as accelerate design exploration. Initially, a deep learning based one-dimensional Reduced Order Model is developed to predict load–displacement response and oil canning propensity for a given panel. Indentation points on the hood panel are selected using a space filling Latin Hypercube sampling method. The Machine Learning based one-dimensional Reduced Order Model is trained by using 300-500 indentation points and validated using approximately 40-50 unseen indentation points. The verification is performed on multiple hood geometries. The predicted results demonstrate strong correlation with Finite Element generated load–displacement curves with that of ML based 1-dimensional reduced order model. Furthermore, to enable rapid design exploration across different panel geometries, a dataset of 40 different hood designs is utilized. A geometric deep learning approach is applied to cluster panel geometries, and the resulting representations are used within a 3-dimensional deep neural network tool Ansys SimAI™ to predict oil canning propensity and quantify it using the canning ratio. Validation study is carried out on unseen panel geometries, and the Machine Learning predicted oil canning zones show good agreement with Finite Element identified critical regions. The proposed Machine Learning framework reduces prediction time from hours to minutes, offering a scalable and cost-effective solution for early-stage design evaluation and mitigation of oil canning in automotive panels.
Sarnobat, Rajvardhan Vilasrao, Kumar, Dinesh, Jawale, Akshay, Balaji, Ananya, Kulkarni, Shreya
The increasing adoption of structural adhesives in electric vehicle battery packs introduces challenges in predicting joint performance under complex loading and operating conditions. While advanced material models can capture detailed failure mechanisms, their application in early-stage design is often limited by extensive material characterization requirements and high computational cost and time. This study presents a computationally efficient finite element methodology for screening-level assessment of adhesive joint stress distribution and utilization using readily available material data from standard datasheets. The approach is specifically developed for early design stages, where rapid assessment, limited input data, and multiple design iterations are critical. The method is compared against adhesive manufacturer material datasheet strengths using strength-normalized loading to assess consistency of load transfer representation, hotspot locations, and relative stress-utilization trends. It is further extended to evaluate multiple joint configurations, including lap joints, T-joints, and hybrid joints, enabling a broader assessment of adhesive behavior across different loading conditions. A systematic design sensitivity analysis is performed to investigate the influence of key parameters such as adhesive bonding area, and geometric variations on joint stress utilization and hotspot sensitivity. The results highlight the capability of the approach to identify key design sensitivity parameters within the studied joint configurations and support informed decision-making during the concept development phase. The study demonstrates that the proposed methodology enables efficient screening of design concepts, can reduce reliance on high-fidelity material models during early-stage concept screening, and supports reduced evaluation turnaround time. This work establishes a practical right-level modeling strategy that complements advanced simulation techniques by enabling faster iterations, reduced computational effort, and compatibility with existing automotive CAE workflows.
Sugumar, Mohanraj, Bhat, Sadashiv