Search Results (1,021)

Validation of Advanced Driver Assistance Systems (ADAS) relies on standardized pedestrian soft targets to ensure comparable safety assessment. However, real-world pedestrians differ from standardized targets in clothing and carried objects, potentially altering radar-based sensing. This exploratory study investigates how such variations affect the sensor-reported apparent radar cross section (RCS) of a pedestrian soft target. Eight configurations were evaluated on a closed proving ground. For each configuration, one 30 s stationary measurement at approximately 5 m and three vehicle-approach runs ranging from 30 to 5 m were performed using a vehicle-mounted automotive radar. Dynamic runs were evaluated in three distance bands using run-level median apparent RCS values, differences from the reference, and run-to-run standard deviation. The backpack and notebook bag produced the largest observed positive deviations, reaching +9.17 and +7.25 dB at 20–30 m. The visibility vest showed a moderate, consistent positive shift, whereas the winter coat and jacket remained closer to the reference in most bands. The umbrella configuration exhibited the greatest run-to-run variability. Among the investigated configurations, the largest observed differences were associated with the backpack and notebook bag, both of which substantially altered the external shape of the pedestrian surrogate. Because only three dynamic repetitions, one radar sensor, one pedestrian surrogate, and one side-profile orientation were investigated, the findings should be interpreted as an exploratory, sensor-specific sensitivity analysis rather than as a definitive characterization of pedestrian RCS or ADAS performance. Full article

Sudden medical illnesses (SMI), including myocardial infarctions and occult arrhythmias, represent a primary etiology of catastrophic motor vehicle collisions. Although consumer wearable technologies provide a non-invasive modality for continuous driver monitoring, their measurement accuracy within dynamic automotive environments requires systematic evaluation. This investigation establishes an empirical validation paradigm for assessing the pulse-rate (PR) accuracy of consumer-grade wrist and finger photoplethysmography (PPG) devices against a reference heart rate (HR) value derived from an electrocardiographic (ECG) signal. To secure an uncompromised physiological reference, an analytical signal-processing pipeline was implemented, incorporating an Adaptive Layer-Dependent Thresholding Function (ALDTF) coupled with Maximal Overlap Discrete Wavelet Transform (MODWT) decomposition to suppress high-frequency motion artifacts and baseline wander (BW) in the ECG signal without inducing morphological distortion. Nineteen real-world driving sessions, totaling over 18 h of continuous physiological telemetry, were evaluated via Lin’s Concordance Correlation Coefficient and Bland–Altman limit-of-agreement statistics. The findings demonstrate a distinct performance bifurcation governed by device form factor and internal data filtering. The evaluated smartwatch exhibited reliable tracking (MAE = 2.73 BPM) and preserved underlying autonomic variability, attaining distributional alignment with the ECG reference in 47% of trials. Conversely, ring-based form factors demonstrated substantial perfusion decoupling induced by isometric manual gripping forces, alongside aggressive proprietary smoothing filters that suppressed micro-fluctuations in heart rate variability (0% distributional alignment). These outcomes indicate that while wrist-worn form factors possess sufficient fidelity for detecting subclinical autonomic shifts, their implementation in automotive safety systems requires access to raw data to circumvent proprietary algorithmic filtering. Full article

The braking system is among the most critical active-safety technologies in road vehicles, and its continuous development has been driven by increasing requirements for vehicle safety, performance, and reliability. The analysis and optimization of these systems require the monitoring of numerous operating parameters, making the Digital Twin concept a promising solution for creating a virtual representation of the physical system. This paper analyzes and synthesizes the main research directions reported in the literature regarding the application of the Digital Twin concept to automotive braking systems. Current approaches include condition monitoring, fault diagnosis, and predictive maintenance. In addition, the paper reviews existing Digital Twin architectures and presents a conceptual architecture for automotive braking systems. Comparative analyses of representative Digital Twin implementations and Digital Twin maturity frameworks are presented to assess the current maturity of Digital Twin solutions for braking systems. The review also discusses the main advantages of this technology, including maintaining braking-system performance, supporting component-life optimization through condition monitoring and predictive maintenance, and enhancing data-driven decision making, together with current limitations such as modeling complexity, the need for high-quality data, and integration challenges. Finally, the review identifies research gaps and outlines future research directions for the development and application of Digital Twin technology in automotive braking systems, positioning Digital Twin-enabled braking as a key enabler of next-generation active vehicle safety systems. Full article

Automotive wiring harnesses are safety-critical subassemblies whose complexity grows with driver-assistance systems, electrification, and connectivity, while pre-assembly and final assembly remain predominantly manual. This structured narrative review examines how collaborative robots (cobots), AI-based perception and planning, and Digital Twins (DTs) support harness production, with deformable-linear-object (DLO) perception and manipulation treated as a cross-cutting harness-specific domain. A database search (2015–early 2026) yielded 64 studies; citation tracking and a targeted update to 30 August 2026 added 19, giving an evidence base of 83 studies. Reported outcomes were separated into algorithm, task, operator, and system levels, and maturity was graded with explicit, review-specific Technology Readiness Level (TRL) criteria. Most evidence is at the algorithm, task, or operator level. Harness-specific cobot workstations reach industrially relevant prototype validation for single operations—for example, cobot-assisted taping with an approximately 50% reduction in ergonomic risk scores and a 12% shorter cycle—whereas harness-specific perception and DLO manipulation remain mainly at laboratory level. System-level evidence on throughput, changeover, and cost is scarce, and no continuously operating cell integrating the three core domains was identified. Key gaps concern branched DLO modelling, ergonomics-aware task planning, Human Digital Twins, connector design for automation, DT-supported safety verification, digital continuity, and reconfigurable hardware. Full article

The research demonstrates a national project combining solar photovoltaics with second-life automotive lithium batteries for local-scale agricultural irrigation and greenhouse climate control. The auto-opening greenhouse system can adapt to various climate conditions in resource-limited areas. Applying in-house battery management system (BMS) based on adaptive control algorithm, it allows real-time monitoring and prediction of critical parameters such as effective capacity and safety, ensuring safe operation and longer battery life. Results show that, with optimized management, the lifespan of second-life batteries can be extended by an additional 7.8–9.3 years, achieving a maximum discharge power of 3.3 kW while keeping thermal control deviations within 0.3%. Both grid-connected and stand-alone microgrid setups have been successfully tested in the field, improving local energy independence and reducing reliance on external power sources. In addition, to avoid the harm of chemical fertilizers to the environment, air nitrogen fixation technology is employed to extract nitrogen fertilizer from the air to irrigate crops such as tomatoes and cucumbers. The control system uses solar power to store electricity in batteries daily to provide nitrogen fixation and basic greenhouse needs, and can maintain greenhouse operations without relying on power grid. This work offers a practical, scalable model for low-carbon agricultural energy solutions, reducing carbon emissions by about 1.28 metric tons of CO₂ per system each year. It provides a framework to guide policies, promote low-carbon transitions, and support global sustainable development goals in underserved areas. Full article

Background: Automotive safety-component suppliers often must add capacity at short notice, and automation is common. This study quantifies the capacity, equipment-effectiveness, process-capability and nonconformity effects of integrating an automated screw-fastening machine (OP8) into an assembly line. Methods: An Action-Research DMAIC study combined 2024 monthly capacity, OEE and nonconformity records with ordered 100-part series of fastening torque and nut height from the original equipment (OP6/OP7) and OP8 for two product variants, analyzed with I-MR charts, resolution-aware Anderson-Darling tests, normal and Weibull capability indices with confidence intervals, and Brown-Forsythe, Hodges-Lehmann, Cliff’s delta and Welch tests. Results: Rated capacity rose from 117 to 153 parts/h (+30.8%), of which 8 parts/h (22% of the gain) is attributable to OP8; OEE remained at 88.0–91.7%. Torque standard deviation on OP8 was 2.3–3.0 times that on OP6 (Brown-Forsythe p < 0.001), whereas nut-height spread was 11–23% lower. Poor screw-fastening reached 0.66% of OP8 output in June, July and September, about 21 times the OP6 rate, although no logged reading was out of specification. Conclusions: Capacity increased, but capability indices computed from machine-logged, apparently screened data neither explained nor predicted the attribute defect rate, and the defect reduction cannot yet be credited to the improvement actions. Full article

Automotive functional safety increasingly depends on interactions among safety requirements, architecture allocation, runtime supervision, communication behavior, and deployment constraints. This paper proposes CLESA, a traceability-driven runtime safety assurance framework that formalizes a hazard-to-evidence closure chain linking safety requirements, architecture elements, runtime monitors, supervisory actions, fallback behavior, and reviewable evidence. CLESA is instantiated on a brake-by-wire system with nonlinear actuator and hydraulic dynamics, multi-sensor feedback, communication delay, actuator saturation, and multiple injected faults; it distinguishes physical safety violation from unrecovered hazardous exposure and uses a runtime risk score linked to HARA/ASIL assumptions. One hundred Monte Carlo runs compare CLESA with an ISO-style offline assurance reference and three online supervisory baselines: rule-based, finite-state-machine, and behavior-tree-only supervisors. CLESA reduces mean unsafe exposure from 1.72 s to 0.62 s. Desktop MATLAB profiling over 10,000 control cycles yields a mean execution time of 0.011349 ms and a maximum of 0.578300 ms under a 20 ms sampling period. Host-based SIL verification of five monitored runtime outputs shows a maximum absolute difference of zero between normal simulation and SIL executions. These results provide simulation- and host-platform-level evidence of CLESA’s methodological effectiveness and traceability under repeatable fault-injection conditions. Full article

Symbolic execution is a key program analysis technique for ensuring the quality, security and safety of embedded, cyber–physical and electronic systems, but it is limited by path explosion and costly constraint solving. This study systematically reviews how artificial intelligence (AI) is used to guide symbolic execution. Following the PRISMA 2020 guidelines, five bibliographic sources were searched, yielding 679 records; after removing 170 duplicates and screening 509 titles and abstracts, 54 full texts were assessed and 37 studies met the inclusion and quality criteria. We propose a taxonomy that classifies these studies by the stage of symbolic execution they support, mainly path selection, constraint solving, state management and environment modeling, and by how AI is integrated, covering reinforcement learning, graph neural networks, neural constraint solving and large language models. The evidence is strongest for learned path selection and learned solver strategies, whereas learned state management, neural constraint approximation and learned function summarization remain largely unevaluated, and only four studies were evaluated on embedded, IoT or safety-critical software. We discuss applications in firmware and IoT security, automotive and aerospace verification and smart contract auditing, and derive a research agenda covering explainable AI guidance, cross-domain generalization and real-time adaptability for dependable embedded systems. Full article

The increasing computing power required by autonomous vehicles imposes stringent requirements on automotive microprocessor power supplies in terms of efficiency, scalability, and fault tolerance. Conventional phase-shedding strategies for multiphase converters rely on centralized control, which introduces a single point of failure (SPOF). This paper presents the experimental validation of a distributed phase-shedding method in which each phase is managed by a local controller that determines its activation or deactivation based on local inductor current measurements and communicates with neighboring modules. The proposed strategy is first evaluated through PLECS simulations and then experimentally validated using a hardware-in-the-loop implementation with a six-phase 12 V/1.2 V, 100 A converter. The simulation and experimental results demonstrate stable sequential phase activation and deactivation under slow load variations and rapid activation of all phases under fast load transients, while maintaining balanced phase currents. Experimental measurements further confirm that the distributed phase-shedding strategy maintains the converter within the targeted efficiency range over a wide range of load conditions. Additional fault injection simulations show that the converter can maintain operation after the removal of individual Follower modules. Overall, these results demonstrate the feasibility of distributed phase-shedding as a scalable architecture for energy efficient automotive microprocessor power supplies. Full article

The transition of highly automated vehicle (HAV) steering to a by-wire architecture (SbW) eliminates the backup mechanical link between the steering wheel and the road wheels and places a failure of the actuation subsystem in the highest safety integrity category, automotive safety integrity level D (ASIL D) under ISO 26262. Most known solutions achieve the required integrity level through duplication of steering angle sensors and electromechanical actuators, which increases cost and complicates certification. The aim of this work is the development and experimental validation of an independent algorithm for indirect kinematic verification of the steering actuation system that requires no additional sensors and relies exclusively on the standard wheel speed sensor signals of the anti-lock braking system/electronic stability program (ABS/ESP system). The computed steering angle of the road wheels is determined from the two-track Ackermann kinematic model based on the difference between the linear speeds of the non-steered axle wheels. An empirical speed-dependent correction is introduced to compensate for lateral tyre slip. The dynamic tolerance threshold for the angular disagreement is derived from the geometric condition that the vehicle must not depart from its lane within the reaction time. Risk accumulation is implemented by a hybrid algorithm combining cumulative sums with a weighted integrator whose growth aggressiveness depends on vehicle speed. The algorithm was implemented in MATLAB/Simulink (The MathWorks, Inc., Natick, MA, USA) and verified both on synthesized data and on telemetry from a real vehicle recorded via the Unified Diagnostic Services (UDS) diagnostic protocol. No false positives occurred throughout the entire experimental run. An artificially injected fault was reliably detected by the algorithm, with a subsequent correct decay of the risk indicator after the fault was removed. The proposed approach establishes a diagnostic channel that is physically independent of the steering actuation system and is based on already certified standard sensors, which reduces the implementation cost of the safety function without loss of sensitivity to the characteristic failure modes of SbW. The algorithm was validated on a production vehicle equipped with conventional electric power steering (EPS) using standard ABS/ESP telemetry; it is therefore presented as a candidate diagnostic channel, and extension to a dedicated SbW platform with architecture-specific fault injection—including a hardware-in-the-loop study—is identified as future work. Full article

The hot stamping of boron-alloyed steel integrates forming with in-die quenching and is a core route to 1500 MPa class automotive safety components, yet its parameter design still lacks a unified theoretical basis. This article critically reviews the theoretical foundations of the process and establishes a framework covering four pillars: analytical parameter-design models (heating time, forming force, springback, and die cooling-channel sizing); hot-plastic-deformation mechanisms interpreted through a molecular dynamics (MD)–discrete dislocation dynamics (DDD)–crystal plasticity finite-element (CPFE) multi-scale hierarchy; phase transformation kinetics extended by a Ginzburg–Landau phase-field description of variant selection; and heat transfer with thermo-mechanical coupling. B1500HS is adopted as the primary grade, with 22MnB5 and BR1500HS serving as benchmarks for cross-validation. The calibrated hyperbolic-sine Arrhenius model yields a deformation activation energy of 245.4 kJ·mol⁻¹, consistent with dislocation-climb-controlled deformation of austenite, and a worked cooling-channel example (380 kW heat load) confirms engineering applicability. The framework provides quantitative theoretical boundary conditions for process optimization, die design, and quality evaluation of hot-stamped components. Full article

The automotive industry prioritises passive safety, emphasising the development of safer cars through sustainable designs rather than performance alone. The objective of this study is to investigate the development and implementation of waste-filled portable crash boxes to improve the crashworthiness performance of road service vehicles using sustainable materials such as beverage cans. Experimental studies were conducted to determine material properties; consequently, quasi-static and impact tests were performed to evaluate the crashworthiness performance of sustainable absorbers. Various finite element models were developed in LS-DYNA to assess the crashworthiness of structures, enabling the identification of potential improvements to crash components for road safety vehicles under real-world conditions. The outcomes of this investigation aim to demonstrate the feasibility of repurposing recycled aluminium beverage cans as sustainable, low-cost, and efficient energy absorbers to enhance road safety and vehicle crash protection. Finally, the crashworthiness parameters, such as specific energy absorption (SEA) and mean crush force (Fm), are compared between experimental and numerical results with errors of less than 10%. Full article

Rapid pressure unloading is essential for the back pressure assembly of injector test systems. In this study, a high-speed unloading valve was developed for the back pressure assembly of an injector test system, and its pressure unloading characteristics were investigated through coupled numerical simulation and experimental testing. An electromagnetic–mechanical–fluid coupled dynamic model was established to describe the interactions among electromagnetic actuation, valve core motion, and hydraulic forces. Three-dimensional CFD simulations were performed to investigate the steady flow and hydraulic-force characteristics, followed by a two-dimensional transient model for analyzing the hydraulic force during valve closure. A coupled Simulink–AMESim model was then established to evaluate the system-level pressure unloading response and compared with experimental measurements under several initial pressure conditions. The CFD results reproduced the variation in flow rate with pressure differential, while sensitivity and uncertainty analyses showed that the quantified factors could not fully account for the discrepancy in absolute flow rate. Three-dimensional CFD revealed both axial and radial hydraulic forces, while 2D–3D comparison supported the use of the two-dimensional model for transient axial-force analysis. The system-level simulation and experimental results exhibited comparable pressure unloading characteristics within the investigated operating range. Full article

Accurate, real-time temperature monitoring of traction motors, battery packs, and power electronic modules is a critical requirement for the safety, efficiency, and long-term reliability of electric vehicles (EVs), particularly on the curved, space-constrained, and vibration-prone surfaces found on motor end-windings, battery-module casings, and busbar assemblies. To meet the needs of such automotive curved-surface applications, as well as wearable devices and flexible electronic skin, this paper presents complementary metal-oxide-semiconductor (CMOS) temperature sensor. Its core includes a CMOS sensor chip with an integrated bandgap reference circuit and dual-path electrostatic discharge (ESD) protection, combined with flexible printed circuit board (FPCB/FPC) for conformal mounting on curved surfaces. Circuit-level simulation predicts a typical reference-voltage temperature coefficient of 20 ppm/°C over −40 to 125 °C, while experimental temperature characterization yields a temperature output (TEMP) sensitivity of approximately 5.0 mV/°C over the reported temperature range. Absolute temperature error and linearity are not claimed as independently verified performance metrics in the present revision because the currently available experimental documentation does not preserve the reference-temperature calibration traceability, repeated-measurement information, measurement-uncertainty analysis, or calculation definitions required to substantiate the previously reported ±1 °C and 0.99% values. After flexible integration and 100 bending cycles at a 10 mm radius, the reference-voltage variation remains below 0.1%, while the reported temperature-equivalent TEMP-output shift remains within ±0.5 °C under the tested laboratory conditions. These results demonstrate short-term laboratory bending stability and temperature-sensing performance under the tested conditions rather than long-term fatigue or automotive vibration qualification. The proposed sensor therefore demonstrates potential for curved and space-constrained thermal-monitoring applications, including permanent magnet synchronous motor (PMSM) stator windings and battery-module surfaces, while validation on actual EV components and formal automotive qualification remain necessary for production deployment. Full article

This paper demonstrates that the application of innovation methodologies and business models, such as Design Thinking (DT) and the Business Model Canvas (BMC), enables virtual reality (VR)-based training to address a training need that is difficult to cover through conventional hands-on approaches alone. The transition towards vehicle electrification is increasing both the complexity of repair operations and the importance of safety procedures, making effective training solutions an increasingly strategic requirement for the automotive aftersales sector. Despite the growing demand, the application of VR in non-formal training environments, as well as its evolution, remains comparatively underexplored. The study describes how these tools support the progressive design of a VR-based training environment focused on electric vehicle (EV) safety procedures. Furthermore, the evolution of the project and the insights gained from its initial development in 2016 to the present are presented, during which it has been maintained as a practical component integrated into a broader in-person training course. Based on the experience accumulated throughout the project, a transferable methodology for VR-based training solutions is formalized. Following the implementation of successive redesigns driven by technological obsolescence and the continuous evolution of EV technologies, the results obtained are analyzed in terms of learning outcomes and user experience (UX) across different learner profiles. To this end, a Net Promoter Score (NPS) question is incorporated to assess participants’ recommendation intention. The analysis not only shows high levels of acceptance and effective learning associated with this practice-based approach but also reveals a rapid adaptation to VR technology among younger professionals without prior experience. This finding is particularly relevant, as it suggests the potential for large-scale implementation of VR in the autonomous training of EV repair technicians. Full article