Search Results (986)

With the increasing complexity of electrical and electronic (E/E) components in modern powertrains, functional safety requires more systematic assessment methods. This paper presents a simulation-based approach for automated Hazard Analysis and Risk Assessment (HARA) of battery electric vehicles in accordance with ISO 26262. The method combines operational-situation parameters, including vehicle speed, road surface, vehicle gap, and road inclination, to define a structured set of hazardous events. Severity, Exposure, and Controllability are evaluated using rule-based criteria, including a dedicated Controllability rule set for longitudinal motion hazards. Quantitative erroneous acceleration and deceleration thresholds associated with different ASILs are derived using a bisection search algorithm, enabling quantifiable and testable safety goals. Comparison with manual HARA reveals systematic biases: low speed does not necessarily imply improved Controllability due to shorter vehicle gaps, while Severity may be underestimated at low speeds because of high instantaneous electric-machine torque. The maximum ASIL is often identified similarly by simulation and experts, whereas lower-ASIL hazardous events may lack consistency and coverage in manual HARA. As a practical application, the approach can be integrated into existing automotive safety workflows as a HARA support tool, improving the consistency of lower-ASIL events while allowing engineers to focus on maximum ASIL cases. Full article

Traffic accidents involving pedestrians during the hours of darkness pose a serious threat to road safety due to reduced visibility and drivers’ delayed perception of the traffic situation. Accurate estimation of the detection distance for pedestrians is essential in the reconstruction of traffic accidents. This study analyzes the relationship between driver visibility, environmental conditions, and the ability to detect pedestrians in a timely manner during nighttime driving. The study examines the main factors influencing the driver’s “perception–reaction” process, including the illumination provided by the vehicle’s headlights, the illumination of the road environment, the contrast and reflective properties of the pedestrian’s clothing, as well as the driver’s level of attention. Using a graph-analytical method, the detection distances for pedestrians under nighttime conditions are estimated. A real-life accident scenario was reconstructed to determine whether the driver had sufficient time and distance to perceive the danger and take action to avoid a collision. The results show that pedestrian visibility depends on lighting conditions, which directly affect the driver’s reaction time. These findings contribute to the refinement of the methodological approach to reconstructing traffic accidents and can assist experts in conducting automotive technical examinations. Full article

Background: Traditional value-based ABC inventory classification allocates protection according to economic value, overlooking operational risk factors such as demand variability, lead time, and assembly criticality, and it couples safety-stock and replenishment-cycle decisions. Methods: We propose a Hybrid Risk-Value framework that decouples these two decisions: stock-keeping units (SKUs) are segmented by multivariate K-means clustering on operational risk variables to set safety-stock factors (Z), while ABC economic value sets replenishment cycle coverages (d). The framework is validated through stochastic discrete-event simulation on an anonymized dataset of 200 SKUs from an automotive supplier, under base, high-demand-variability, and extended-lead-time scenarios (20 replications each), and is benchmarked against both classic ABC and a coupled ABC-XYZ policy. Results: Across all scenarios, the Hybrid framework reduces average inventory investment and total logistical cost by approximately 26–28% relative to ABC ($p<0.001$) while maintaining the service level; stockout days remain statistically unchanged except under extended lead times. The coupled ABC-XYZ benchmark performs almost identically to ABC, indicating that the gains arise from decoupling rather than from variability-based segmentation alone. Conclusions: Decoupling safety-stock and replenishment decisions offers a capital-efficient, data-driven alternative to static financial segmentation for resilient industrial inventories. Full article

The wheel hub is an important component of automotive components, and its quality directly affects the stability, safety, and comfort of the vehicle during driving. In the face of increasingly stringent industry standards, traditional runout measurement methods are no longer sufficient to fully characterize the quality attributes of wheels. In response to this demand, the processing algorithm for determining wheel hub quality through harmonic analysis is proposed in this paper, and the 12-point coordinate method (12-PCM) is applied to taking 12 pairs of measurement values at equal intervals in the wheel hub measurement data, and 12 pairs of measured values are substituted into the harmonic analysis formula to obtain the Fourier factor. According to the obtained Fourier factor, the amplitude and phase of various harmonics are calculated; furthermore, the various harmonic curve graphs of the wheel hub are drawn. The runout of various harmonics obtained is compared with the given judgment threshold to determine whether the hub quality is qualified. The threshold for determining the quality of qualified wheel hubs is set at 0.06 mm in this paper, and the allowable error threshold for multiple repeated measurements is set at 0.03 mm. The reliability and repeatability of the algorithm are verified by single loading single measurement and single loading repeated measurement. The experimental results show that the algorithm proposed has high stability and repeatability in this paper. Judging wheel hub quality through harmonic analysis, the intuitiveness of wheel hub quality judgment is enhanced, and the reliable basis for judging the quality of the wheel hub is provided. Full article

Technological developments in transportation have moved quickly, often faster than the frameworks used to evaluate their broader societal and environmental implications. This study examines the extent to which Tesla’s automotive activities contribute to long-term societal well-being from a consequentialist utilitarian perspective, focusing on two related developments: (1) vehicles equipped with advanced driver assistance systems (ADAS) and (2) vehicles powered by electricity. Tesla provides a useful focal case in that it brings these two developments together in a single, highly visible setting. Using Tesla as an exploratory qualitative case, the analysis assesses both technologies within a single ethical and sustainability framework, examining how their effects combine across safety, environmental, and broader societal outcomes. Because the two technologies act on many of the same outcomes and stakeholders, they interact: they reinforce one another in some respects and offset one another in others. In the case of road safety, for example, the additional mass of an electric vehicle raises the severity of collisions even as driver assistance works to reduce their frequency. The analysis suggests an overall net positive societal impact, while recognizing the uncertainties and trade-offs that remain. This assessment rests mainly on two considerations: the likely reduction in traffic-related injuries and fatalities associated with wider adoption of ADAS-equipped vehicles, and the expectation that, in most contexts, electric vehicles provide a net environmental benefit, particularly through lower levels of harmful air pollutants relative to internal combustion engines. These benefits are not automatic, however, but depend on broader system conditions, including whether electrification and automation move transportation beyond established patterns of car dependence or reinforce them. The paper concludes by outlining the implications of these findings, while acknowledging the limits of the analysis and pointing to areas for future research. Full article

Pedestrian detection is a fundamental component of Advanced Driver Assistance Systems (ADAS) and plays a key role in collision avoidance and the safety of vulnerable road users. This paper presents a structured review of pedestrian detection methodologies developed between 2000 and 2025, spanning classical vision techniques and modern deep learning architectures. We organize the review into two phases. First, we examine classical methods, including Histogram of Oriented Gradients (HOG)+Support Vector Machine (SVM), Viola–Jones, Deformable Part Models, and Integral Channel Features, which established the conceptual foundations of the field. Then, we analyze state-of-the-art deep learning architectures, categorized by detector stage (one-stage vs. two-stage), localization strategy (anchor-based vs. anchor-free), feature extraction paradigm (Convolutional Neural Network (CNN)-based vs. transformer-based), output representation (bounding box vs. instance segmentation), and computational profile (lightweight vs. heavyweight). Several design principles introduced by classical methods remain visible in modern architectures, indicating that they were not fully superseded. The review also examines publicly available benchmark datasets and compares the strengths and limitations of camera-, Light Detection And Ranging (LiDAR)-, radar-, and multi-sensor-fusion-based systems for ADAS deployment. We close by identifying six open problems for the field: adversarial robustness, real-time inference under embedded constraints, detection under adverse weather, dataset bias and demographic fairness, the deployment of Bird’s-Eye View (BEV) and unified perception on automotive hardware, and explainability for safety-critical use. Full article

This study investigated the relationship between fixation-frequency-based Shannon entropy and dwell-time-based entropy across two different visual task domains: emotional evaluation of automotive exterior designs and safety-critical monitoring of nuclear power plant emergency scenarios. Although gaze entropy has been widely used to explain emotional responses, task performance, and situation awareness, the relationship between entropy measures derived from fixation counts and fixation durations remains insufficiently examined. Eye-tracking data were analyzed from two experiments with different attentional characteristics. In the emotional visual task, 10 participants evaluated three automotive design images. In the safety-critical task, 20 participants performed four nuclear power plant emergency monitoring scenarios. Shannon entropy and dwell-time entropy were calculated using fixation count and fixation duration distributions across Areas of Interest, respectively. Pearson correlation and simple regression analyses were conducted within each task domain. The results showed strong positive associations between Shannon entropy and dwell-time entropy in both domains. The emotional task showed a correlation of r = 0.844, while the safety-critical task showed a correlation of r = 0.890. These findings suggest that fixation-frequency-based and dwell-time-based entropy measures exhibit substantial overlap across different visual task contexts. However, the observed associations may partly reflect mathematical dependency between fixation frequency and cumulative dwell-time, and the findings should be interpreted as exploratory evidence rather than proof of metric interchangeability. The study highlights that gaze entropy metrics should be interpreted in relation to task-dependent attentional contexts. Higher entropy may be associated with exploratory visual attention in emotional evaluation, whereas lower entropy may be associated with focused monitoring in safety-critical tasks. Full article

Walking is essential for daily physical activity, yet most existing studies focus on pedestrian counts while neglecting varied on-street activities, largely owing to data shortages. Targeting this gap, the research defines walking activity quality as the occurrence likelihood of walking-related activities and explores built environment influences. It adopts deep-learning video recognition to capture fine-grained pedestrian behaviours and quantifies walkability via activity quality. Structural equation modelling (SEM) is applied to decode causal links between urban design quality, pedestrian volume and activity quality. According to the results, urban design qualities exhibit a more pronounced influence on activity quality compared to pedestrian volume. Among a broad array of 20 physical features examined, interface density, the quantity of street furniture, walkway width, and shading rate all demonstrated significant positive effects on stationary activities. Interestingly, higher interface density and shorter crossing distances facilitated the occurrence of social activities, whereas the proportion of ground-floor windows had a notable negative impact on social activities. The findings of this study can directly inform the development of sustainable and liveable streets. Full article

Autonomous driving systems rely on a sophisticated pipeline of artificial intelligence models to perceive, predict, and plan in dynamic environments. This review presents a systematic analysis of the machine learning and deep learning models underpinning vehicle autonomy, spanning classical convolutional neural networks (CNNs) for object detection and semantic segmentation to recurrent and Transformer-based architectures for trajectory prediction and motion planning. It also provides a critical examination of the autonomous vehicle sensor stack, including cameras, LiDAR, radar, ultrasonics, and GNSS/IMU as data acquisition systems, highlighting modality-specific AI challenges such as monocular depth estimation, 3D point cloud processing, and radar Doppler interpretation. The evolution of perception and decision-making pipelines is reviewed, contrasting modular architectures with end-to-end learning paradigms that directly map raw sensor data to control commands, and discussing their trade-offs in interpretability, safety assurance, and robustness to rare edge cases. We further survey specialized hardware accelerators and heterogeneous automotive SoCs designed to meet stringent real-time and power constraints. Industrial strategies are compared, including multi-modal sensor fusion and vision-centric approaches based on large-scale imitation learning. Finally, we identify open challenges related to robustness under adverse conditions, domain shift, causal ambiguity, and the need for interpretable and certifiable AI in safety-critical autonomous driving systems. Full article

Complex cyber-physical systems in the automotive and avionics domains increasingly consolidate safety-critical and non-critical functions onto shared multicore platforms. A central design challenge in these environments is ensuring strict timing guarantees while allowing graceful degradation under resource contention. In partitioned multiprocessor mixed-criticality systems, a mode switch on one processor forces all low-criticality (LC) tasks on that processor to degrade to their mandatory execution budgets, even though neighboring processors may have spare capacity. Existing approaches either focus on uniprocessor systems or address only the offline partitioning problem without considering the runtime survivability of LC tasks. This paper proposes IMC-PALM (Imprecise Mixed-Criticality via Partitioning and Adaptive Lightweight Migration), a two-phase framework for imprecise mixed-criticality (IMC) multiprocessor systems. The offline phase combines a tightened EDF-VD-IMC schedulability test with Mode-Balanced Partitioning (MBP). The tightened test identifies high-criticality tasks whose HI-mode utilization yields a tighter bound, while MBP allocates tasks based on mode-specific residual capacity. The runtime phase migrates LC tasks from a mode-switched processor to other processors that remain in LO mode, exploiting the per-processor isolation property of partitioned scheduling. Simulation results with 2, 4, and 8 processors show that MBP with the tightened test improves the acceptance ratio by 12.3 %p over existing algorithms under standard conditions. Furthermore, runtime migration reduces the degraded job ratio by 28.1 %p compared to the no-migration baseline under these standard settings, of which 4.6 %p is attributable to home-processor recovery. The benefit grows with the number of processors and remains robust under realistic migration overheads. Full article

Wheel (mis)alignment is one of the factors influencing vehicle safety, tire wear, and energy efficiency. While alignment procedures are well established in automotive workshops, recent advances in sensing, connectivity, and data-driven methods have led to renewed academic interest. This goes against existing research, which remains fragmented around vehicle types and methodologies. This study conducts a scoping review of wheel alignment monitoring and detection methods, with a focus on passenger vehicles. Guided by PRISMA-ScR, 453 studies were identified, of which 386 were excluded and 13 were duplicates and thus removed, resulting in a small number remaining for thematic analysis. Three dominant methodological approaches emerged: (i) traditional measurement methods, (ii) sensor-based vehicle dynamics analysis, and (iii) data-driven methods employing machine learning and vehicle telemetry. The findings revealed limited research on vehicle applications, especially for intelligent, integrated, and scalable alignment technologies and real-time, in-service monitoring applications. Other challenges included data quality, calibration, and cost-effectiveness. Therefore, the development of an integrated real-time wheel misalignment detection and reporting framework grounded in systems engineering and enterprise architecture principles is proposed. Full article

The integration of additive manufacturing technology and biomimetic design provides new possibilities for functional and aesthetic innovation in automotive interiors. This study explores a roof handrail design method based on a spider web biomimetic structure from the perspectives of object character and design freedom. By transforming the spider web morphology of nature into a manufacturable parametric model, the organic unity of structural performance and visual aesthetics has been achieved. The simulation results show that the spider web biomimetic structure handrail distributed along the z-axis not only meets the mechanical performance (maximum stress of 189.11 MPa under 1500 N load) but also theoretically reduces weight by 32.03% compared to traditional designs. Material testing shows that the spider web biomimetic structure handrail made of PA6-CF material through fused deposition molding not only meets safety requirements but also has a better user experience. This study achieved organic forms that are difficult to process with traditional techniques through 3D printing technology, providing a new paradigm of “form following ecology” for automotive interior design and expanding the possibilities of functional components in user experience and spatial narrative. Full article

Discrete production planning under integer constrained resource framework is a challenging issue which requires simultaneous consideration of output maximization, resource efficiency, and balanced resource. This research focuses on simulation of an integer-driven production planning model for an automotive production system. It combines the genetic algorithm-based stochastic data generator with a precise Diophantine feasibility enumeration. The genetic algorithm is used as a constraint-aware stochastic specification generator to generate feasible production parameter sets within certain operational constraints. Its main purpose is to create representative production environments for feasibility analysis and not to optimize production. A normalized multi-objective scoring function is presented to address the imbalance in the scales of economic and operational measures. A total of 64,518 feasible automotive production plans were enumerated under engine, tire, labor and budget constraints using the proposed framework. The Pareto-efficient solutions to the cost–output space that were identified, formed a discrete, piecewise Pareto frontier. The best production plan had a total of 83 units with 99% of the labor and tire resources exploited, whereas the budget and engine capacities were not binding. The optimal strategy implies full saturation of the labor capacity (>99%) due to the binding nature of labor as an objective. In practice, a safety buffer can be imposed through the introduction of an upper-bound utilization policy (e.g., 95%), which moves the optimal solution marginally inwards along the Pareto frontier. The analysis of sensitivity to changes in resources of $\pm 10\%$ showed the preservation of the Pareto structure and resilient adaptability in the output, which validated the usefulness of the suggested strategy in discrete manufacturing decision support. Full article

As a next-generation automotive display technology, Augmented Reality Head-Up Display (AR-HUD) has demonstrated immense potential in reshaping driving safety and enhancing the human–computer interaction experience. To address the challenges of barrel distortion and perspective distortion inherent in HUD systems, we propose a joint-learning-based dual-path pre-correction method. This approach employs a shared encoder to extract image features, which are then decoupled into two parallel branches: a classification branch and a distortion flow prediction branch. Building upon this architecture, a model-fitting method is introduced to estimate the distortion model parameters in the parameter space using the predicted distortion types and flows, thereby reconstructing a refined distortion flow. Finally, image rectification is achieved through a resampling method. On the ARHDD dataset, the proposed method achieves a PSNR of 24.617 dB (barrel) and 25.062 dB (perspective), an SSIM of 0.845 and 0.873, and an NRMSE of 0.163 and 0.157, respectively. On the Places 365 dataset, it achieves a PSNR of 23.914 dB (barrel) and 21.870 dB (perspective), an SSIM of 0.812 and 0.748, and an NRMSE of 0.174 and 0.211, respectively. Both quantitative and qualitative comparative experiments against other state-of-the-art methods demonstrate that the proposed approach achieves superior correction performance for both types of distortion. Finally, the simulation verification of the HUD system proved that this correction method demonstrated excellent potential, but further verification is still needed in a real or semi-real environment. Full article

The growing complexity of software-defined automotive systems generates massive heterogeneous sensor and ECU data during real and virtual validation, and conventional rule-based analysis of such multivariate time series struggles under dynamic operating conditions, noise, and diverse fault scenarios. Deep learning-based anomaly detection has shown promising performance, yet existing approaches remain limited by static thresholds, insufficient robustness, and reduced interpretability. This study proposes an adaptive framework for intelligent fault detection in test recordings of automotive software systems (ASSs), integrating deep denoising autoencoders (DAEs), adaptive Gaussian thresholding, and explainable artificial intelligence (XAI) techniques. Four DAE architectures (ANN-, RNN-, GRU-, and LSTM-DAE) are systematically evaluated under different noise levels, system versions, and fault conditions, with detection thresholds that adapt dynamically to the statistical behavior of the reconstructed signals, thereby reducing false alarms under varying operating conditions. The framework was evaluated using real-world test recordings from IAV and Hardware-in-the-Loop (HIL)-based digital test drives, where ANN-DAE achieved the most robust detection performance, with F1-scores of 93.91% and 96.39% on the real and virtual test-drive data, respectively. Furthermore, the integration of XAI improved the transparency of anomaly interpretation at the signal level. Overall, the proposed framework shows strong potential for intelligent anomaly detection and quality assurance in safety-critical automotive systems. Full article