Browse Topic: Electrical, Electronics, and Avionics

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Deriving a Data-Driven Temperature Model for Thermal Operational Safety of Automotive Vehicles2026-01-5073To be published on 09/17/2026
Rising vehicle complexity and electrification increase the thermal loads on automotive components, making reliable temperature models essential for ensuring thermal operational safety over the vehicle lifetime. Existing approaches (experimental wind tunnel testing, numerical simulation, and purely data-driven methods) lack scalability to many operating conditions, do not provide physically interpretable parameters, or yield inconsistent results when applied across multiple experiments. This paper addresses the gap of fitting a single, physics-constrained temperature model simultaneously across multiple experimental measurements, enabling consistent parameter estimation and prediction of unseen operating conditions. A lumped parameter thermal network (LPTN) is parameterized using a global minimization approach that classifies each model coefficient as global, discrete-global, or local, depending on whether it is shared across all measurements, across a subset with the same design configuration, or varies individually. The method is evaluated on an electronic control unit (ECU) installed in the BMW 7 Series, using nine wind tunnel measurements covering three different cooling strategies (ventilation, heat pipe, metal insert). A single global model fitted to six measurements achieves a root-mean-square error (RMSE) of 1.09 K, while three unseen measurements are predicted with an RMSE of 1.19 K. Compared to conventional single-measurement fitting, global estimation reduces convergence time to 21.2%, while yielding physically interpretable and consistent parameters across experiments. These results demonstrate that global LPTN parameter estimation provides a fast, robust, and physically interpretable framework for automotive thermal operational safety, capable of reliable extrapolation to unseen conditions with sparse experimental data.
Kehe, Maximilian, Enke, Wolfram, Rottengruber, Hermann
Modeling Brake Torque Hysteresis for Chassis Controls Hardware in the Loop Simulation2026-01-08249/14/2026
It is hardly a new trend for on road, vehicle intensive tuning and testing of chassis control features such as Anti-Lock Brakes, Traction Control, and Electronic Stability Control to move away from vehicle testing and towards non-vehicle test platforms such as Hardware-In the Loop (HIL) simulations and even further into pure math-based simulations. However, a significant acceleration of these activities has been occurring recently in the automotive industry, reducing or eliminating calibration time on vehicles and amplifying the demand for highly representative, non-vehicle test platforms to validate and even calibrate chassis controls features. In current state of the art HIL simulation, the input (brake pressure) to output (brake torque) of each wheel brake in a vehicle’s brake system is modeled relatively simplistically, including at most pressure and brake temperature sensitivities, usually in lookup table form. Each brake corner contains over 20 different friction interfaces, which in turn can cause hysteretic behavior (a difference in the output for a given input, depending on whether the brake is applying or releasing against the hysteretic friction). This hysteresis is neglected in most state of the art HIL simulations. Past studies by General Motors have shown that the importance of brake corner hysteresis in vehicle level, customer facing performance of chassis controls features can range from inconsequential to significant. With the crescendo-ing demand for high quality non-vehicle based methods for assessing chassis controls function, the effect of hysteresis is no longer academic. The present study starts with HIL based simulations, establishing the effect of brake corner hysteresis on one of the most visible chassis controls behaviors. An inertia dynamometer-based test was developed to exercises the subject brake corners through apply and release cycles, thus enabling any hysteretic behavior to be observed and characterized. Machine Learning models were trained with these data to represent brake corner hysteretic behavior and then deployed into an HIL simulation rig. The impact of these models – representing brake corner hysteretic behavior – was characterized for straight line stopping distance on low, medium, and high coefficient road surfaces.
Antanaitis, David, Ridenour, Nick, Miller, Bryan, Karnjate, Timothy
Architecting Safety-Critical Systems for AI-Enabled Software-Defined Vehicles: A Compute, Timing, and Isolation Framework with a Brake System Case Study2026-01-08119/14/2026
Software-defined vehicle (SDV) platforms are reshaping safety-critical system design by consolidating braking and other motion-control functions on centralized heterogeneous edge compute that also executes physical-AI workloads. This consolidation breaks traditional assumptions of fixed ECUs and simple timing envelopes, complicating assurance of determinism, isolation, and fail-operational behaviour for ASIL-D brake functions. Building on a decentralized brake-by- wire (BbW) architecture with dual controllers, redundant low-voltage power grids, and smart electromechanical brake corner actuators, this paper proposes a systems-level framework for architecting safety-critical functions in AI-enabled SDVs along three dimensions: compute, timing, and isolation. The framework classifies conventional and AI-based functions and maps them to heterogeneous compute classes; defines architectural patterns that combine safety islands, power-domain redundancy, and hardware partitioning to support freedom from interference; and formalizes timing domains and contracts that bound latency, jitter, and failover dynamics across sensors, centralized controllers, and decentralized actuators. The contribution is not a new AI algorithm, but a safety-oriented architectural framework that constrains how AI-enabled functions may be integrated into fail-operational by-wire systems. A BbW case study with edge-resident AI observers and anomaly detectors shows how the framework complements System Analysis Tool (SAT)– based failure modelling and clarifies trade-offs among safety isolation, latency, and AI performance while preserving braking safety guarantees under continuous software evolution.
Srinivasaraghavan, Soumyasudharsan
Predicting Temporal Friction Torque of Drum Brake Systems: A Mixed-Effects Random Forest Approach2026-01-08099/14/2026
Drum brake systems are becoming increasingly important in electric vehicles (EV) and purpose-built vehicles due to cost competitiveness and EURO-7 particulate emission regulations. Despite this trend, drum brake friction behavior remains incompletely characterized due to its dependence on multiple coupled variables: temperature history, braking conditions, and component interactions. To address this gap, this study presents a method for developing a time-series friction torque prediction model using the Mixed-effects Random Forest (MERF) machine learning framework. Time-series data collected from sensors during drum brake dynamometer tests were analyzed to identify the key variables that govern the friction torque. Significant inputs were selected through Exploratory Data Analysis (EDA), considering test-to-test variability and potential mixed effects, and were then used to train and tune the MERF model. Model performance was evaluated by comparing predicted friction torque with measured torque, and prediction error was quantified by using Mean Absolute Error (MAE) to check whether predicted model is reliable. The proposed prediction model demonstrates a high level of agreement with experimental measurements, confirming that the MERF approach can effectively capture the non-linear and transient characteristics of drum brake friction torque from time-series sensor signals. These results indicate that friction torque estimation is feasible using only sensor signals already available from conventional test instrumentation, without additional dedicated sensors. This capability is expected to support broader applications, including brake performance prediction for vehicles equipped with drum brakes and enhanced simulation of drum brake thermal performance across operating conditions.
Yoon, Jungro, Cho, Sunghyun, Kim, Wonjoon
Predicting Wear of NAO Brake Pad Using Scaled-Down FMVSS 135 Testing and Machine Learning2026-01-08089/14/2026
Brake pad wear is a major and growing source of non-exhaust particulate emissions, projected to reach 1.3 million tons annually by 2030 and contributing up to roughly 55% by mass of non-exhaust traffic-related PM10 in urban environments, underscoring the need for improved durability and material optimization. This study investigates a three-stage eXtreme Gradient Boosting (XGBoost) ensemble paired with a residual Fully Connected Neural Network (FCNN) corrector to predict brake pad wear rate and support formulation optimization. Experiments used a simplified FMVSS 135 protocol on a Universal Mechanical Tester (UMT) simulating realistic braking across eight friction regimes. Wear rate was the sole machine-learning prediction target, while coefficient of friction (CoF) was retained as an input feature rather than a target. Despite a limited but high-quality 280-cycle dataset, regime-aware stratified splitting, sample reweighting, and hyperparameter optimization enabled robust generalization. The three-stage XGBoost ensemble with residual FCNN correction achieved a global held-out test R2 of 0.976 for wear rate prediction. A Taguchi L8 design of experiments defined the brake pad compositions, reducing experimental time and material consumption compared to conventional approaches. The framework demonstrated strong agreement between measurements and predictions for the dominant low-severity regime, while per-regime analysis identified the high-severity minority regimes as the priority for additional data collection, since within-regime R2 remains negative for every regime given current sample sizes. A sequence-aware mean absolute scaled error (MASE) analysis further shows that, despite the high global R2, none of the four pipeline stages currently outperforms a naive one-cycle persistence forecast on absolute error, a distinction reported here for transparency. The scalable architecture enables straightforward integration of additional material and process parameters, supporting iterative brake formulation development in industrial settings and, by reducing empirical testing requirements, sustainable brake material development with reduced replacement frequency and associated emissions.
Katakam, Abhishek, Eslamiat, Hossein, Kancharla, Sai Krishna, Filip, Peter
Real-Time Estimation of Brake Temperature using Physics-Informed Machine Learning Model2026-01-08109/14/2026
The current work presents a novel approach to estimating brake surface temperature in real-time to aid in brake wear prognostics. Brake prognostics involve estimating brake pad wear in real-time, which enables its predictive maintenance. Brakes are a safety-critical system for vehicles; therefore, they require accurate and robust pad wear estimation to ensure vehicle safety. However, it involves several challenges. The estimation of pad wear is fundamentally a two-stage process: the first stage involves the accurate prediction of brake pad surface temperature, while the second stage utilizes this thermal history to calculate cumulative material wear. A significant challenge in estimating brake pad wear without an expensive sensor is that it is sensitive to the surface temperature prediction; any error in the thermal model propagates and compounds in the wear prediction stage. To identify surface temperature, traditional physical sensors are often cost-prohibitive or prone to failure in the harsh thermal and mechanical environments of the wheel end, necessitating a robust virtual sensing solution that can capture complex, non-linear heat transfer dynamics. The current work addresses the above challenge of identifying temperature dynamics using a Physics-informed Machine Learning approach. We employ Symbolic Regression (SR), a data-driven method that discovers the underlying mathematical expression of the system dynamics by searching for the optimal functional relationship between variables. SR provides an interpretable model that can be generalized across automotive platforms, offering a transparent, computationally efficient, and analytically tractable alternative to traditional ‘black box’ models. To generate the temperature dataset, a test vehicles were equipped with thermal sensors and underwent various braking scenarios. The SR-based virtual sensing model demonstrated strong and consistent predictive fidelity across all braking conditions tested. Under mild braking scenarios, the model achieved a Mean Absolute Percentage Error (MAPE) of approximately 6.0% in predicting brake surface temperature. This performance remained highly robust under mixed and harsh, high-speed braking, the most thermally demanding scenario, yielding MAPEs of only 11.6% and 11.9%, respectively.. Across all regimes, this level of temperature estimation fidelity directly limits error propagation into the downstream brake pad wear prediction stage, enabling reliable, sensor-less, cloud-based brake health monitoring at scale.
Gannavarapu, Shivadath, Pal, Anuj, Fan, Mengdi
A Study on TSC (Technical Safety Concept) Design and Verification in EMB (Brake by Wire) Systems2026-01-08019/14/2026
The Electro-Mechanical Brake (EMB) system is a dry-type Brake-by-Wire technology that eliminates hydraulic components and directly controls friction braking using electrical actuators at each wheel. The EMB architecture consists of a Main Center Control Unit, a redundant Backup Center Control Unit, and four Wheel Control Units communicating via CAN FD. Due to its direct involvement in vehicle braking, compliance with ISO 26262 functional safety requirements is critical. As system complexity increases, potential risks such as hardware failures and communication faults must be systematically addressed. The proposed TSC was developed according to ISO 26262, covering the concept phase (Part 3), system-level development (Part 4), and software implementation (Part 6). Safety goals and Functional Safety Requirements derived from HARA are used to guide system architecture design and TSC development. Key design principles include modularity, redundancy, fault detection, and fail-safe operation. Verification is conducted at both system and vehicle levels using ECU-in-the-Loop Simulation (EILS), Hardware-in-the-Loop Simulation (HILS), and real-vehicle tests. Fault scenarios, including Main Center Control Unit failures and CAN communication losses, are injected using a custom LabVIEW-based fault injection tool. The study evaluates Fault Tolerant Time Interval (FTTI) settings, error handling mechanisms, and control handover strategies under fault conditions. The results show that redundancy and localized communication enable stable operation and smooth control transfer within the FTTI window without noticeable impact on braking performance or driver awareness. This study demonstrates the robustness of the proposed EMB architecture. Future work will focus on prognostics and maintenance strategies to support safe deployment in autonomous and electric vehicles. [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Kim, Dokun
A Study on the Development of Brake Moan Analysis for CTBA Vehicles Using Simulation2026-01-08149/14/2026
Moan noise is a low-frequency noise occurring in the 170–500 Hz frequency ranges. While it frequently appears in vehicles equipped with a rear Coupled Torsion Beam Axle (CTBA), the exact cause, generation mechanism and clear solutions remain unidentified. For those reasons, we have developed a moan noise analysis method capable of representing the moan noise phenomenon in vehicles with rear CTBA along with an automation tool. From these results, we can use moan analysis models to reduce real moan noise problems. Consequently, this not only enhances customer satisfaction and vehicle quality but also significantly increases the work efficiency of vehicle designers through design modification in the preliminary stages of vehicle development
Kim, Sungho, Kim, Jeongkyu, Hwang, Jaekeun, Kang, Donghoon
Predicting Brake Squeal Occurrence Using Machine Learning with Temporal Feature Analysis of Braking-Torque Test Data (Do Not Use Abbreviations or Special Characters)2026-01-08129/14/2026
The automotive industry's paradigm shift toward autonomous driving and electrification has introduced new competitors threatening market dominance through differentiated value propositions. In this highly competitive landscape, delivering irreplaceable customer value requires providing sustainable and authentic luxury experiences. Quiet driving represents a tangible value that customers genuinely appreciate. Brake squeal—high-frequency noise arising from friction-induced vibration during braking—negatively impacts customer satisfaction and must be suppressed. Despite significant advances in brake squeal prediction modeling, the irregular nature of squeal generation mechanisms has prevented the development of a generalized predictive model applicable to product development processes. Development and verification remain largely experimental. This limitation constrains early-phase design validation, as brake squeal is highly sensitive to chassis and braking system design. When squeal issues emerge during post-design evaluation, fundamental improvements to pad materials become difficult. Consequently, damping characteristic tuning is employed for mitigation, incurring substantial development costs. This study addresses this challenge through systematic feature engineering of time-series braking data—brake torque, disc rotational speed, disc temperature, and brake pressure—collected during squeal evaluation tests. Based on the hypothesis that environmental conditions and brake system characteristics influence mechanical behavior, time-series features exhibiting strong predictive association with squeal occurrence were derived, and a machine learning model was developed to predict squeal occurrence probability using these features as input variables. The model's predictive performance was validated by comparing squeal probability predictions derived from independent torque performance evaluation data against actual squeal evaluation results. This validation confirms that the model successfully predicts squeal occurrence probability from dynamometer torque performance data alone. Consequently, this approach enables the prediction of squeal occurrence probability in early development phases before formal noise assessment is conducted, streamlining the development process and significantly reducing verification costs while contributing to quieter driving experiences.
Cho, Sunghyun, Yoon, Jungro, Kim, Yoon Cheol, Kim, Jeongkyu, Kim, Sungho, Baek, SongYi, Kim, Won Joon, Choi, Kyung Rok
Finite Element-Based Wear Analysis of Brake Pads2026-01-08179/14/2026
Brake pad wear progressively changes the pad–disc contact interface and can influence braking performance, wear uniformity, and component durability. This study presents a finite element-based procedure for predicting brake pad wear under braking conditions using generalized Archard’s wear law as the base framework. The method combines contact-pressure and slip-distance calculations with iterative geometry updating in Abaqus using the UMESHMOTION and USDFLD subroutines so that accumulated wear and evolving contact conditions can be continuously reflected during the analysis. To improve robustness in repeated-cycle wear simulation, a wear-direction algorithm, an extrapolation factor, and a contact stiffness scale factor are incorporated to reduce element distortion, enhance numerical stability, and control computational cost. Because temperature-dependent friction behavior, contact conditions, and material-property variations are strongly coupled in actual braking, their combined influence is represented through an effective wear coefficient calibrated from physical data using regression analysis, instead of independently modeling them. The proposed procedure was applied to burnish and subsequent evaluation modes, and the predicted wear results were compared with test measurements. Among the regression models considered, the log-linear model provided the best overall agreement with the experimental wear data. The results show that the proposed framework can reproduce both mean wear and location-dependent wear trends with good agreement over the evaluated operating range. The proposed procedure offers a practical numerical workflow for predicting brake pad wear under temperature-dependent operating conditions while maintaining acceptable numerical stability and computational cost.
Song, Seong Il, Joo, Sang Don, Kim, Min Sock, Kerszberg, Nicolas, Lee, Heewook
Data to Decisions: An AI-Driven Test Intelligence Platform for Integrated Analysis of Raw Data, Test Reports, and Specification Compliance in Brake System Validation2026-01-08079/14/2026
Validation of brake systems is increasing in complexity due to electrification, software-defined architecture, integrated control modules, and higher functional safety requirements. Although physical testing remains the primary source of engineering evidence, interpretation of results including DVP&R/PVP&R compliance verification, anomaly detection, documentation, and milestone decision support continues to rely heavily on manual engineering analysis. This results in extended feedback cycles, inconsistent interpretation across teams, and limited traceability between raw data, reports, and governing specifications. To support engineers with more objective validation processes, there is a growing need for structured, data-driven intelligence that transforms dispersed test artifacts into actionable engineering decisions. This paper presents Data to Decisions, an AI-driven Test Intelligence Platform designed for integrated analysis of raw measurement data, test reports, validation plans, and specification requirements in brake system development. The platform has been applied to foundation brake systems (EPB and hydraulic calipers), brake control modules (IBC/EB100), and related actuation subsystems. It ingests heterogeneous inputs including DVP&R documents, customer specifications, test summaries, deviation logs, parameter files, and build configurations and converts them into structured, traceable validation datasets. A specification centered reasoning framework extracts governing limits, acceptance thresholds, instrumentation requirements, and staged validation criteria directly from source documents. Using natural language processing, rule-based logic, and pattern recognition models, the system evaluates both discrete and continuous data sets over an unlimited range of performance characterization metrics such as leakage, drag torque, piston travel, fatigue life, NVH behavior, structural durability, and actuator performance characteristics. Results are assessed against extracted specification limits to automatically identify compliance gaps, borderline conditions, parameter inconsistencies, and build-specific variations. All findings are traceable to original requirements and test evidence. The platform further enables closed-loop validation by linking physical test outcomes with virtual analysis results, supporting correlation studies and identifying opportunities for test optimization or targeted retesting. Automated generation of engineering and management level summaries reduces documentation effort while improving consistency and auditability. Pilot deployments demonstrate reduced manual data review effort, improved traceability of specification compliance decisions, enhanced anomaly detection, and faster decision making during and after DV and PV milestones. By combining rule-based validation logic with AI and Generative AI for document interpretation, pattern recognition, and automated summarization, the platform supports engineers in efficiently navigating large volumes of test data and specifications. This paper presents the system architecture, compliance evaluation methodology, and deployment results, illustrating how AI-enabled test intelligence can serve as a practical decision-support layer in modern brake system validation workflows.
Divakaruni, Saikiran, Willey, Joseph, Srivastava, Namrata, Sankar, Arya, Namala, Divya, Gowtham, Rahul Sangani
Physics-Informed Machine Learning Digital Twin of Electro-Mechanical Brakes for Clamp-Force and Friction Estimation2026-01-08069/14/2026
Recently, there has been a drastic shift in the industry towards wire architectures like steer-by-wire and brake-by-wire. For safe and accurate force control, diagnostics, and consistent performance over the operating envelope, accurate plant modeling of the Electro-Mechanical Brake (EMB) is important. Classical approaches involved linearized dynamic EMB models and the use of the characteristic stiffness curve for calibration at the operating points. These methods often perform poorly over regions where hysteresis, compliance, and friction are strongly nonlinear. Prior research on state or force estimation for EMB has focused on pad contact detection, thermal adaptation, and hysteresis-aware clamp force estimation. However, there are still accuracy gaps in practical applications during transients and under shifting friction regimes. In this work, a digital twin based on Physics-Informed Machine Learning is introduced, following the governing dynamics of the actuator-caliper assembly of EMB while learning (i) a physically significant parameter—system damping (Bsys) and (ii) a non-linear friction term constrained as a function of the actuator motion states and operating conditions. Non-linear friction is captured through gray-box friction formulation and learning unmodeled residual dynamics such as hysteresis and backlash. An EMB test stand is used to collect steps, ramps, holds/engagements, APRBS, and swept-sine excitations, with signals including time-aligned force command, motor torque/current, actuator position/velocity, and pad force measurement from a force sensor for model training. Results demonstrate a decrease in pad-force prediction error, along with non-linear and residual friction estimation. The resulting digital twin can enable sensor-less force estimation, friction compensation design, predictive analytics, and health monitoring through tracking parameter drift and friction signatures.
Rai, Prakhar, Gadhvi, Tirth
Bridging Human Expertise and Machine Intelligence in Chassis System Teardown Evaluation2026-01-08059/14/2026
Teardown evaluation of chassis system components plays a critical role in benchmarking, failure analysis, and competitive product assessment. These inspections rely heavily on experienced engineers who interpret visual defect patterns, material conditions, wear signatures, and manufacturing variations. However, expert driven evaluation processes are often subjective, difficult to standardize, and challenging to scale across global engineering teams. This paper presents a structured AI-assisted expert evaluation framework developed to enhance consistency, preserve institutional knowledge, and enable continuous improvement in chassis component teardown analysis. The proposed system integrates convolutional neural network architectures, including ResNet18 and its variants, into a human in loop inspection workflow. AI models perform initial classification of component images (e.g., OK/not OK and defect subclasses) and provide associated confidence scores. These predictions are presented as decision support, while final authority remains with the evaluating expert. Experts can confirm or override AI outputs, annotate defect regions using bounding boxes, assign subclass categories, and provide structured technical comments. All expert interactions, including AI disagreements, are systematically recorded. Correction instances are analyzed to identify model limitations, ambiguous defect conditions, and data gaps. Expert validated evaluations are incorporated into the training dataset to enable iterative model refinement. This closed loop process supports progressive improvements in model robustness and classification accuracy across varying teardown conditions and component types. A centralized cloud-based repository maintains full traceability of inspections, including timestamps, AI confidence levels, expert modifications, and annotation metadata. This structured knowledge capture converts tacit engineering judgment into a persistent digital asset, supporting auditability, cross-site alignment, and accelerated onboarding of new engineers. The framework demonstrates how AI can be effectively deployed as an assistive technology in chassis teardown evaluation, improving repeatability, enhancing data driven benchmarking, and enabling scalable knowledge preservation without displacing expert authority.
Divakaruni, Saikiran, Vaibhav, Veer, Hansen, Scott, Hood, Trevor, Agrawal, Rahul
Advanced Brake Force Distribution and Pad Wear Life Integration in Supervisory Electronic Brake Control System for Combination Vehicles2026-01-07979/14/2026
Commercial vehicle fleets frequently operate with tractors that connect to different trailers and dollies, resulting in combinations with varying brake pad wear across wheel ends. Traditional brake-force distribution strategies do not consider these pad-life differences, which can lead to uneven brake utilization, irregular maintenance intervals, and increased total cost of ownership (TCO) in mixed-trailer operations [7, 9]. While modern electronically controlled braking systems (EBS) already incorporate pad wear based braking for the tractor itself [5], these capabilities do not extend across the entire vehicle combination because trailer-side communication is typically limited to standardized CAN protocols such as ISO 11992 and J1939 [1, 2, 3]. As braking systems become more software defined and rely heavily on distributed electronic communication, ensuring the authenticity and integrity of trailer originated brake information becomes essential for both functional safety and cybersecurity [6]. In the proposed architecture, trailers and dollies communicate brake related data to the tractor over the ISO 11992 Tractor-Trailer CAN (TT-CAN) network [1, 2], allowing the tractor Brake Control ECU to securely validate the source of the information and register each towed unit for health aware braking. Once authenticated pad life data is available, the tractor constructs a combination level brake health map covering every wheel end in the configuration. During normal braking, a supervisory allocator computes wheel end specific brake pressure targets that bias braking toward wheel ends with greater remaining pad life while ensuring full compliance with stopping distance regulations and stability requirements [4, 7]. By integrating authenticated pad wear information with tractor hosted supervisory control, the system improves braking consistency across mixed combinations, harmonizes pad utilization, enhances maintenance predictability, and reduces TCO while meeting the safety and cybersecurity expectations of modern commercial vehicle fleets.
Ganesha, Vinodkumar
Safe State Definition and Verification for Distributed Multi-Controller EMB Architectures2026-01-08009/14/2026
The Electro-Mechanical Brake (EMB) system is an essential technology for safe braking in modern vehicles. However, the adoption of multi-controller architectures has introduced new challenges to conventional Safe State strategies. Traditionally, the Safe State defined in functional safety means "function shutdown," and in accordance with ISO 26262-1:2018 (Part 1: Vocabulary), aims for an "operational mode without risks exceeding reasonable levels." However, in the multi-controller architecture of EMB systems, the Fail-Operational Safe State concept is applied, where the system continues to provide limited functions even in the event of faults. It is essential to verify whether such operational modes actually satisfy the safety requirements of ISO 26262-3 and ISO 26262-4. This paper redefines the Safe State according to failure modes in EMB systems, analyzes system state transitions, and presents a coherence analysis methodology for validating the availability of resources required to provide limited functions in the Fail-Operational Safe State. Through this approach, potential design defects in multi-controller-based EMB systems can be detected early, validated across 1,149,952 fault scenarios with zero total-failure outcomes, and traceability of functional safety requirements can be established.
Kim, Kang San
Further Investigation of the Variables Affecting Brake Squeal: Dual-Piston and Single-Piston Calipers with NAO Pads2026-01-08259/14/2026
An earlier publication reported that brake squeal occurrence increases with increasing (inboard/outboard) pads wear rate difference in the case of a front dual-piston (twin-piston) caliper for a GVW vehicle of 2,510 kg fitted with Lowmet pads of straight chamfers and diamond chamfers. The current investigation was undertaken to find out if a front dual-piston caliper for a heavier vehicle (GVW 3,200 kg) fitted with NAO pads of straight chamfers, and a lighter single-piston caliper (GVW 2,100 kg) fitted with NAO pads of straight chamfers behave the same or not, using the SAE J2521 and Los Angeles City Traffic simulation procedures. In all cases, brake squeal is found to increase with increasing (inboard/outboard) pads wear rate differences (wear differentials); increasing pad radial taper is associated with increasing (I/O) pads wear differential; pad tangential taper lowers the (I/O) pads wear differential. Increasing friction coefficients do not relate to increasing squeal occurrences. To minimize brake squeal occurrence, caliper should be designed to minimize (I/O) pads wear differential.
Sriwiboon, Meechai, Rhee, Seong Kwan, Sukultanasorn, Jittrathep, Khathinhorm, Nicha, Kunthong, Jitpanu
Fault Detection and Isolation Method for BDS/GPS Hybrid Constellation Oriented to Airworthiness Retrofit2026-99-14159/11/2026
This work deals with the topic of integrating a multi-GNSS solution within existing avionics, specifically concentrating on airworthiness approval issues, thereby addressing risks associated with susceptibility in standalone GPS solutions. An older aircraft, still relying on existing GPS solutions, remains susceptible to jamming, spoofing, or single-point failures, which, in turn, pose risks associated with integrity requirements necessary for ADS-B or TAWS operation. An approach for a fault detection and isolation (FDI) scheme applicable to a BDS/GPS hybrid satellite system configuration suitable for airworthiness projects would thus be relevant. A transparent proxy architecture is proposed, which consists of a form-fit GPS/BDS dual-mode antenna replacement and an RF power splitter. This design makes it possible to provide BeiDou Navigation Satellite System (BDS) signals with neither changes in the interfaces nor structural changes being required to existing flight management systems. It includes a signal processing module that combines adaptive quality weighting on carrier-to-noise ratios, satellite elevation angles, and code-minus-carrier differences, as well as a carrier phase smoothing process via the Hatch filter. A multiple-channel approach in a parallel position system is designed, with different channels for GPS-only, BDS-only, as well as hybrid solutions, with weighted least squares estimation incorporating the Huber robust reweighting rule, along with Receiver Autonomous Integrity Monitoring (RAIM) with fault detection and exemption in every channel. A cross-constellation integrity monitoring algorithm is presented for the purpose of identifying constellation-level spoofing attacks that evade conventional single-system RAIM integrity. Simulation results, carried out for four different interference levels, show that the hybrid channel provides a 34% improvement in horizontal positioning accuracy compared with the GPS-only mode of operation. The cross-check method is able to effectively detect GPS spoofing attacks with position divergence exceeding 50m within a detection time of 25 seconds, along with a navigation availability of 99.9% under nominal operating conditions.
Ou, Chongjie, Zhang, Kezhi, Zhu, Haijie
Adaptive MPC Path Tracking Control for Autonomous Vehicles2026-99-15709/11/2026
This work introduces a Model Predictive Control (MPC) path tracking controller, which is developed to boost robustness, tracking precision, and vehicle stability when navigating high-speed and high-curvature driving scenarios. First, a 3-degree-of-freedom (3-DOF) dynamic model of the vehicle is established to serve as a reference. Next, a linear time-varying MPC control algorithm is formulated, with constraints on yaw rate, lateral velocity, and road boundary conditions taken into account; a comprehensive performance metric that balances tracking accuracy and control smoothness is also defined. Third, the time-domain parameters of the MPC framework are optimized using an improved genetic algorithm. Finally, the effectiveness and accuracy of the proposed controller are validated via co-simulation experiments conducted on the Matlab/Simulink and Carsim platforms. Simulation results demonstrate that the controller exhibits excellent robustness: the peak lateral tracking error is only 0.05 m on high-friction roads and 0.12 m on low-friction roads, with a maximum heading error of 0.15°. Additionally, the vehicle’s dynamic stability is notably enhanced: the yaw rate is reduced by 9.6% and 15.7% on high- and low-adhesion roads, respectively, while the sideslip angle is decreased by 13.2% and 18.4% under the same conditions.
Yu, Hanzhengnan, Hou, Xiaoyi, Zhang, Hao, Zhou, Weichen, Liu, Yu
Numerical Simulation Study on Borehole Wall Stability of Ultra-Large Diameter Deep Bored Piles in Thick Sand Strata2026-99-15829/11/2026
Taking the newly constructed Maanshan Yangtze River Highway-Railway Dual-Purpose Bridge — a three-tower steel truss cable-stayed bridge with two main spans of 1120 meters — as the research object, this study systematically explores the influencing factors and evolutionary characteristics of hole wall stability for large-diameter bored piles in thick sand layers. The research results reveal the following mechanisms: with the expansion of pile diameter, the hole wall generates greater deflection, the soil’s internal arch effect is gradually attenuated, soil cohesion decreases, and the plastic zone of the soil surrounding the pile shows a tendency of outward extension, collectively increasing the susceptibility to hole collapse. To maintain hole wall stability, the resultant force of the internal circular arch support and mud pressure must exceed or equal the total lateral pressure, including active earth pressure, formation water pressure, and ground surcharge-induced lateral pressure. Notably, soil shear strength and mud relative density are two dominant factors controlling hole wall stability, and a positive correlation exists between these two parameters and stability. Specifically, a mud relative density range of 1.15–1.25 is recommended for practical construction. These findings offer valuable technical references for the design and construction of similar large-diameter bored pile projects in thick sand layers.
Ye, Tao, Wang, Ruyi
Safety Assessment Model for Airborne Time-Sensitive Networking Based on Analytic Hierarchy Process2026-99-14149/11/2026
With the growing demand for high real-time performance and high reliability in airborne networks, Time-Sensitive Networking (TSN) has been widely adopted as a core technical basis for deterministic Ethernet for next-generation avionics systems. This paper proposes an AHP-based safety assessment model for airborne TSN, introducing a hybrid evaluation strategy that integrates both subjective and objective factors. By constructing a comprehensive evaluation index system, the model quantifies the weights of traffic attributes—including time sensitivity, priority level, and bandwidth guarantee requirements—and combines them with the degree centrality of network nodes to achieve a holistic assessment of TSN safety. The proposed model not only provides theoretical support for the safety-oriented design optimization of avionics systems but also offers practical guidance for the airworthiness verification of airborne networks. Feasibility and effectiveness are verified by applying the proposed method to a representative case scenario. Moreover, the model’s scalability supports its application in more complex network environments, meeting the broader assessment needs of airborne TSN safety.
Wang, Penghui, Mei, Yanan, Fu, Jinhua
Impact of Transient Driving Characteristics on Bench-to-Road Emission Consistency of Light-Duty Vehicles2026-99-15739/11/2026
The thalweg at the outlet of the Yuxikou Waterway transitions from right to left, forming a 90-degree bend. It then merges with the Xihua Waterway after passing Xiliang Mountain, creating a main-branch confluence water area. Taking a typical main-branch confluence water area in the lower reaches of the Yangtze River as the research object, this paper reflects the current navigation status and existing problems of ships in the area through the analysis of ship traffic flow. It classifies the risk levels of passing ships, proposes suggestions for route reform and optimization, and uses a model to verify the probability of collision accidents in the area after the implementation of the round-island navigation method, providing a reference for the navigation safety of passing ships.
Qiao, Jiajun, Jin, Zhenhua, Huang, Qi, Li, Guohui, Zhang, Xinguo
Site Selection Method for Solid Waste Processing Plant of Highway Reconstruction and Expansion Project2026-99-15869/11/2026
The highway reconstruction and expansion project is accompanied by the generation of a large amount of construction solid waste. The unreasonable site selection of solid waste processing plants will increase the social, environmental, and economic burden. Taking a highway reconstruction and expansion project in Guangdong Province as an example, this study uses the combination of the analytic hierarchy process and the layer superposition method to extract the influencing factors of site selection, such as geological conditions, natural conditions, hydrological conditions, traffic conditions, and resource conditions, according to relevant specifications, and uses the analytic hierarchy process to quantify each influencing factor. From the relevant research data, official public information, and other channels, we comprehensively collected the data of topography, climate, geology, land use planning, and other aspects of Guangzhou and Dongguan along the project. With the help of buffer analysis tools and overlay analysis tools of GIS software, the optimal decision results were determined. The research results show that using this method to analyze the site selection of the relying project, the factory site selection should be located in Wangniudun Town near the project line, which has comprehensive advantages. The site selection method of a solid waste processing plant for an expressway reconstruction and expansion project proposed in this paper comprehensively considers the influence of 10 sub-factors on the site selection, and has been successfully applied to the site selection decision of an expressway reconstruction and expansion project in Guangdong Province. The final site selection result is more professional and objective than the previous site selection method, which effectively solves the site selection problem of a solid waste processing plant under the influence of economic factors, social factors, municipal factors, and environmental factors.
Zhang, Yuping, Yang, Ming, Zeng, Siqing, Long, Hao, Liu, Yuanqing, Zhao, Qiu
3D Dynamic Object Detection with Multi-Modal Temporal Fusion2026-99-11179/11/2026
This research aims to address the critical challenge of accurately detecting and estimating the state of dynamic objects in autonomous driving. Traditional 3D object detection methods often struggle with motion perception, particularly in velocity estimation, due to the lack of information in single frame perception. We propose a novel framework that enhances the BEV representation with temporal modeling. The core of our method is a two-stage temporal fusion process. First, we align historical BEV features to the current coordinate frame to eliminate the interference of ego-motion. Subsequently, a dedicated temporal fusion encoder, architected with residual connections and a Feature Pyramid Network, refines the aligned multi-frame BEV features to capture complex motion patterns and improve multi-scale object representation. This approach directly tackles the problem of motion decoupling. By aligning features, we disentangle object motion from ego-motion. The temporal fusion encoder then mitigates the positional ambiguity of moving objects in the fused BEV space, a common issue in simple feature concatenation, leading to more robust detection. We built a dataset following the structure of the nuScenes dataset, using data collected from an autonomous driving simulation platform. The evaluation results on our simulation dataset demonstrate that the proposed temporal module achieves a 13.0% improvement in NDS score and a substantial 29.7% reduction in velocity error (mAVE). These results demonstrate that our temporal fusion strategy effectively enhances 3D detection accuracy in dynamic scenarios.
Shao, Mengjia, Li, Wei, Bai, Jie, Zhu, Shaoxiong, Xu, Chenjie
Structural Airworthiness and Mechanical Characteristics Analysis of Real-Time Flight Data Acquisition Equipment2026-99-14119/11/2026
The rising complexity of civil aviation and recent incidents, including Sichuan Airlines “14 May” and China Eastern Airlines “21 March,” highlight the urgent need for real-time flight data acquisition systems to support timely safety monitoring and early warning. Current methods rely mainly on post-flight data, while real-time monitoring remains constrained by limited bandwidth and insufficient device capabilities. This study presents a comprehensive structural and mechanical analysis of a real-time flight data acquisition device designed according to ARINC-600 standards and airworthiness regulations (ED112A, DO-160G). The device integrates data acquisition, storage, processing, and power modules, utilizing a Xilinx Zynq UltraScale™ platform for hardware-software co-processing, enabling high-speed data parsing, time synchronization, encryption, redundant storage, and secure transmission. Finite element modal analysis was performed to evaluate the structural airworthiness and vibration characteristics. The first ten natural frequencies range from 246.41 Hz to 1109.2 Hz, significantly exceeding typical aerospace excitation frequencies, effectively preventing resonance under operational conditions. Mode shape analysis indicates an evolution from low-order global bending and torsion to high-order local complex deformations, revealing relative stiffness weaknesses at panels and connection points, providing guidance for structural optimization. The study establishes a closed-loop framework connecting mechanical characterization, data security, and equipment reliability. Combined simulation and experimental validation ensures accurate assessment of dynamic performance, supporting operational robustness and airworthiness. The findings not only advance the development of high-performance real-time flight data acquisition systems but also enhance risk identification, early warning, and overall flight safety management in civil aviation.
Xu, Xiaodong, Gao, Jianwei, Guo, Qiang, Zhang, Yun, Kong, Xiangjun
Scheduling Table Architecture of Remote Data Conversion Unit2026-99-14129/11/2026
This paper addresses the determinacy issue of multi-task execution in the Remote Data Conversion Unit of an integrated modular avionics (IMA) system in a non - operating system environment. A three - level hierarchical static scheduling table architecture for the Remote Data Conversion Unit is proposed. In this architecture, the maximum execution cycle of functional parameters is used as the device scheduling table cycle, the minimum execution cycle is used as the scheduling block cycle, and the worst - case execution time is used as the functional execution time. Through hierarchical design, the orderly connection of functions, scheduling blocks, and devices is achieved. A periodic interrupt mechanism is adopted between scheduling blocks to ensure time alignment at the scheduling block cycle level. Inside the scheduling block, a polling mechanism is used to perform static sorting according to the worst - case execution time of functions, and a wait function is introduced to achieve time alignment and fault isolation. For fault - tolerant faults, a delayed response strategy is adopted to avoid violating atomicity. For non - fault - tolerant faults, rapid detection and restart processing are achieved relying on periodic interrupts. A Simulink model is used to conduct a comparative simulation of the Remote Data Conversion Unit using a competition mechanism and the scheduling table mechanism. The results show that under normal and fault conditions, this architecture significantly reduces data transmission jitter, improves system determinacy and fault - tolerance ability, meets the requirements of the DO - 297 standard for functional independence and safety, and provides an effective solution for improving the determinacy of the civil aviation Remote Data Conversion Unit.
Hao, Yongqi, Wu, Meng, Miao, Zhiqi, Xu, Guanglei
Intelligent Connected Road Test Brake Throttle Robot Calibration Method2026-99-11209/11/2026
With the development of intelligent connected vehicle (ICV) technology, road testing has become a key guarantee for verifying the safety and reliability of automobiles. The brake pedal robot basically eliminates human differences in complex scenes by simulating human operation. Therefore, the accuracy of the actions performed by these robots directly determines the validity of the test results. However, the current study lacks a uniform calibration standard, resulting in reduced execution accuracy. In order to meet the requirements of precision and a unified standard for the test, this research analyzes the metrological characteristics of brake pedal robot. Based on this, a systematic calibration framework was established to verify key performance parameters. Specifically, the pedal speed is dynamically calibrated using high-precision accelerometers, and pedal force is verified through a dedicated calibration device that integrates standard force sensors. And the pedal space travel is measured using a portable three coordinate articulated arm system. Experimental verification shows that the proposed method can strictly control the pedal speed error within ± 5%, pedal force error within ± 3%, and pedal stroke error within ± 2 mm, fully meeting the requirements of ICV road testing. This study provides a standardized framework and scientific basis for calibration, improving the accuracy and credibility of road test data, thereby supporting safer deployment of intelligent driving systems.
Chen, Xi, Ma, Siyao, Feng, Zhu
Research on High-Reliability Testing Systems for Intelligent and Connected Vehicle2026-99-11119/11/2026
With the large-scale application of intelligent connected vehicles, the verification of their functional safety and reliability has become a core bottleneck in the industrial development. The traditional real- vehicle road test method can no longer meet the current demand for large-scale test verification due to problems such as high cost, low efficiency, and difficulty in reproducing dangerous scenarios. This paper studies the vehicle-in-the-loop simulation test system based on a digital twin. By constructing a virtual scenario highly consistent with the real world, physical-level multi-source perception signals are simulated and mapped to the system under test to enable high- reliability verification of real vehicles. In terms of lateral and longitudinal control functions, multiple sets of test cases are selected respectively for comparison between road tests and virtual simulation tests. The results show that the accuracy of key indicators is above 90%, which provides practical reference for the subsequent test and verification system of high-level autonomous driving.
Hou, Quanshan, Tang, Ke, Gao, Tian, Chen, Tao, Zhou, Si
2025 5th International Conference on Logistics System, Traffic and Transportation (LSTT 2025)2026-99-15009/11/2026
TOC
Tobolski, Sue
Research and Verification of an Anti-Pollution Scheme Based on a Satellite Drive Mechanism2026-99-14019/11/2026
Based on the characteristics of satellite drive mechanism products, this paper expounds on the problems in the current anti-pollution scheme process from the current status of the drive mechanism’s antipollution scheme process mode. In view of these problems, the antipollution scheme based on the satellite drive mechanism is proposed and verified from the aspects of raw materials, equipment, parameter determination, process method, and test process. The scheme is reasonable and feasible, which effectively reduces the pollution index and ensures the on-orbit operation environment of the product.
Wang, Jian, Tan, Honggen, Zhou, Shan, Zhang, Tao
An Analysis Method for the Static Stability of Helicopter Rotor in Level Flight Based on Flight Test2026-99-14029/11/2026
To provide better data support for the aerodynamic design, flight control system design, and parameter optimization of helicopters, it is necessary to obtain the aerodynamic derivatives of the helicopter rotor based on flight test data and analyze the static stability of the helicopter rotor. This paper breaks through the limitations of aerodynamic testing of helicopter rotors and establishes a quantitative analysis method for the static stability of helicopter rotors in level flight based on flight test data. Based on the flight test data of the helicopter, this study establishes an aerodynamic model of the helicopter rotor and employs a genetic algorithm for parameter identification to obtain the aerodynamic derivatives of the rotor in level flight. The identified parameters are then used to quantitatively analyze the static stability of the helicopter rotor. The results show that the method developed in this study can accurately and effectively obtain the aerodynamic derivatives of the helicopter rotor from flight test data. Furthermore, this method can reliably evaluate the static stability of the helicopter rotor, demonstrating significant value for engineering applications.
Zhao, Jingchao
A Life Prediction-Driven Maintenance Decision Method for Aircraft Engines2026-99-14069/11/2026
As the operating conditions of aircraft engines become more complex, the traditional maintenance strategies based on experience or fixed cycles are difficult to balance economic efficiency and reliability. This paper tentatively proposes a maintenance decision-making method driven by life prediction for aircraft engines. In the life prediction stage, considering the complex spatio-temporal coupling relationships contained in the degradation signals of multiple sensors, a spatio-temporal feature fusion transformation network is designed to jointly model the spatial dependence and temporal dynamics. At the same time, a probabilistic deep learning model is introduced to model the mean and uncertainty of the remaining life, providing risk warnings beyond point estimation. In the maintenance decision-making stage, the predicted distribution parameters are introduced into the Markov decision process state space. Based on the deep Q-network, a reward function is designed, and cost constraints are considered, thereby achieving a balance between safety and economy. Experimental results based on the C-MAPSS dataset show that this method achieves reasonable performance in common prediction indicators and demonstrates certain advantages over traditional maintenance strategies in the comparison experiments of maintenance decisions. The effectiveness of the method has been verified.
Yu, Sijia
Ultrasonic Frequency-Domain Reverse-Time Migration Imaging Incorporating Element Directivity Correction2026-99-14199/11/2026
The finite width of ultrasonic array elements results in a non-uniform angular radiation pattern of elastic waves in solids, deviating from the ideal point-source assumption commonly adopted in reverse-time migration (RTM). This angular radiation non-uniformity produces a crack tip-dominated imaging amplitude with weak crack flank representation, manifesting as a pronounced depth-dependent amplitude imbalance along vertically oriented defects. As a result, cracks may be misinterpreted as point reflectors, which compromises the reliability of characterization in ultrasonic nondestructive testing (NDT). This study proposes an ultrasonic frequency-domain reverse-time migration (FDRTM) imaging method incorporating element directivity correction. A longitudinal-wave directivity model in solids is formulated in the frequency domain and normalized at each frequency to ensure consistent scaling during multi-frequency stacking. During backward wavefield reconstruction using full matrix capture (FMC) data, the angular-dependent energy distribution associated with the receiving direction is explicitly corrected, rebalancing the angular energy distribution in the reconstructed wavefield. Defect imaging is then performed using a frequency-domain cross-correlation imaging condition, followed by stacking over frequency and normalization. Validation experiments were conducted on an artificially manufactured vertical crack in a 7075 aluminum alloy specimen. The results indicate that, relative to conventional RTM, the proposed method reduces crack tip dominance and enhances the relative visibility and continuity of crack flanks. Compared with conventional RTM, the peak imaging amplitude increases by 22.44%, and the amplitude at a depth of 6.8 mm is enhanced by 24.39%. In addition, the proposed method outperforms the total focusing method (TFM) in crack profile continuity and the relative visibility of crack flanks. The results confirm that incorporating element directivity correction into frequency-domain RTM mitigates depth-dependent amplitude imbalance and restores crack flank visibility, thereby improving the reliability of crack defect characterization in ultrasonic NDT.
Chen, Si, Zhang, Yifeng, Ma, Tengfei, Xu, Zheng, Jiang, Jiansheng, Gao, Jiaqi
Study on Low-Velocity Impact Damage of Glass Fiber Reinforced Composite Pipeline2026-99-14169/11/2026
Through low-velocity impact testing, the effects of punch shape (conical, hemispherical, and cylindrical) and impact energy (5, 10, and 15 J) on damage characteristics in glass fiber composite pipes were investigated. Ultrasonic A-scan inspection was employed to detect internal delamination damage at the impact points within the composite pipes. Test results indicate that the contact area between the punch and the pipe is a key factor influencing the severity of pipe damage. A smaller contact area results in a higher energy absorption rate, greater punch displacement, larger area under the load-displacement curve, and longer contact time, leading to more severe damage characteristics. When the conical punch delivered 15 J of impact energy, the energy absorption rate of the glass fiber composite pipe reached 91.6%, exhibiting multiple damage characteristics, including pitting, penetration, and cross-shaped cracks. As impact energy increases, the area of internal delamination damage caused by the three punch shapes exhibits near-linear growth. The conical punch induces severe damage characteristics in the thickness direction but results in the smallest delamination area. Blunt-shaped punches (hemispherical and cylindrical) disperse impact energy over a wider region, leading to increased delamination damage area.
Wang, Xuan, Cao, Yanzhen
Design and Simulation of an Airborne Combined Liquid Cooling System Based on Amesim2026-99-14079/11/2026
This paper designs an onboard integrated liquid cooling system for a specific electronic device’s thermal management requirements. The system combines a turbo-turbo-compressor (TTC) turbine with a liquid-cooled subsystem through heat exchanger coupling. Building on previous research, the design schematic view is divided into two components. Using the Amesim simulation platform, we developed component-specific modules and established the system’s simulation module based on this schematic view. Performance simulations under various extreme operating conditions demonstrated the system’s effective applicability across the entire flight envelope.
Zhang, Sunyan, Zheng, Wenyuan, Zhan, Hongbo
Reliability Evaluation of Transmission Accuracy for RV Reducers Based on Uncertainty Theory2026-99-11019/11/2026
In reliability evaluation of RV reducers using accelerated degradation tests, practical constraints (e.g., test costs and resource shortages) often lead to insufficient test iterations or small sample volumes. This results in limited insights into the product’s overall degradation trajectory, inadequate data for reliability analysis, and notable epistemic uncertainty. Conventional accelerated degradation models—rooted in probability theory—depend on the large numbers, rendering them ill-suited to address such uncertainty in small-sample scenarios. Uncertainty theory, a modern mathematical framework developed to characterize epistemic uncertainty, has garnered broad academic interest for its strong adaptability to small-sample contexts. Accordingly, this study explores the evaluation of RV reducer reliability using accelerated degradation data and uncertainty theory, aiming to mitigate epistemic uncertainty arising from small sample sizes. Specifically, this work develops a transmission accuracy degradation model for RV reducers based on the Liu process—a core component of uncertainty theory—that incorporates both inter-sample individual variability and the epistemic uncertainty inherent in the degradation process. This study also assesses the reducer’s reliability and service life; comparative analysis against probability-theoretic models confirms the proposed model’s advantages in small-sample scenarios.
Luo, Min, Huang, Jinxing, Dai, Jinzhen, Shu, Ke
Research on Torque Allocation Control of Multi-Articulated Vehicle2026-99-11239/11/2026
The multi-articulated vehicle uses distributed drive mode. Due to its large degree of freedom of movement and the large number of driving shafts, different torque distribution methods affect the operational stability of the vehicle, how to coordinate and distribute the torque of each driving motor has become an urgent problem to be solved. To improve drive stability of the multi-articulated vehicles, propose a layered torque allocation control strategy. The upper-layer sliding mode controller determines the required additional yaw moments of each car body based on the linear reference model, the controller is characterized by swift response and a strong ability to resist interference. The lower-level allocation module comprehensively considers the torque output limitations of the electric hub motors, the prevailing road adhesion state, and the corrective yaw moment constraints given by the upper layer, and constructs an optimization objective function centered on the uniformity and stability of tire load. The optimal distribution of driving forces for each wheel is completed by solving this function dynamically. To validate the strategy's effectiveness, a vehicle dynamics model is built in the multi-body dynamics software ADAMS/View. Using a joint simulation framework integrating ADAMS/View and MATLAB®/Simulink, the effect of the layered control strategy is evaluated in comparative simulation with uncontrolled situation under U-turn and single lane change conditions. The simulation outcomes demonstrate that, compared to uncontrolled situation, the yaw rate deviation of each car body under the torque layered control are significantly reduced, and the adhesion utilization rate of tire is also effectively controlled, thereby the driving stability is improved.
An, Guanbo, Zhang, Liwei
Train Delay Reduction Approach based on a Virtually Coupled Trains Following Model with Timetable Recovery Process: A Case of High-Speed Rail2026-99-15169/11/2026
One challenge in railway operation is how to achieve high levels of punctuality and reliability. However, especially in peak hour operation, a high volume of train traffic will affect timetables, which are more sensitive to the increase in travel time. The concept of virtual coupling has been introduced for controlling train movement mainly to increase capacity. As the operation under virtual coupling requires a short separation distance between trains, it might be applied to reduce the delay and recover the train timetable. However, there is no approach proposed detailing how the coupling is applied to reduce delay. In this paper, the virtual coupling state movement approach based on a vehicle following model with the coupling conditions determined to couple a group of trains for reducing or preventing secondary delay is proposed. The train operation under the proposed approach is simulated in MATLAB software, then applied to the hypothetical case, High-speed line, Bangkok - Nakhon Ratchasima, Thailand. The delay analysis is performed, and the waiting probability is determined to prove the effectiveness of the proposed approach. The simulation results show that trains will be virtually coupled with their front train as a form of train convoy when they cannot proceed at the ideal speed. Thus, operating train movement based on the proposed approach can reduce secondary delay and bring a train to arrive on time compared to the operation under the moving block control.
Chansong, Sukanya, Ketphat, Naphat
Analysis of Capacity of Train Operation at Junction under the Virtual Coupling System with Speed Limit Difference and Train Sequence2026-99-15209/11/2026
A railway junction is a crucial point on a railway network where multiple routes intersect and many train services run through it. Its connectivity to different railway routes can increase demand and traffic in shared corridors, turning the junction into a bottleneck. Upgrading the signalling system is one of many solutions to manage the traffic utilization and increase the capacity at the junction. The Virtual Coupling System (VCS), as the latest signalling technology, has proven to improve the capacity of the railway. However, many junction-related factors remain inadequately addressed. In this paper, we introduce the constraint of differences in speed limits between the main route and the secondary route to design a merging process in which three trains from different routes can start merging or coupling into VCS mode as they approach the converging point at a junction under different train sequence scenarios. A Finite State Machine (FSM) model is developed to control the train motion under VCS. Secondly, simulations are performed to compare the travel time required for the following train to reach the last milepost under VCS with that under the traditional Moving Block (MB) system, as well as to compare overall capacity. The simulation results show that the second and third trains can arrive at the final milepost earlier under VCS mode than under the MB system. In addition, the theoretical operational capacity under VCS mode improves compared to MB mode when a proper train sequence design is selected.
Jaimonkong, Nitipat, Ketphat, Naphat
Convoy Size Effects in Virtual Coupling: Analysis of How the Number of Trains in a Convoy Influences Capacity and Delay2026-99-15219/11/2026
Rail transportation capacity is related to the number of trains that can be grouped within a convoy under a virtual coupling (VC) system. Grouping trains into larger convoys allows them to operate as a single train, reducing headway and increasing track usage compared with classic signaling systems. However, grouping trains has limitations, such as increased communication requirements between trains and stricter safety conditions. On the other hand, smaller convoys may offer less capacity increase but give more flexibility and operational resilience. Therefore, convoy size is an important parameter in balancing system performance. The research investigates the operational impacts of varying convoy sizes on route capacity and delay using the UIC 406 standard methodology. Simulation results indicated that larger convoys increase route capacity, particularly as trains transition into convoy formation faster. In contrast, there are increased delays. These conclusions demonstrate that convoy size optimization is important for increasing capacity and managing delays, revealing the practical benefits of a convoy management system.
Prompianpong, Nammont, Ketphat, Naphat
Real-time Vehicle Speed Detection Based on YOLOv11 and DeepSORT2026-99-15959/11/2026
The technology of real-time and effective vehicle speed detection is considered a key technology to improve traffic monitoring efficiency and traffic safety management grade. To address the limitations of traditional speed detection schemes—including reliance on dedicated hardware, poor environmental adaptability, and high construction and maintenance costs—this paper proposes a r10eal-time vehicle speed detection system based on YOLOv11 and the DeepSORT algorithm. The proposed system uses the YOLOv11 target detection algorithm as its primary model. DeepSORT multi-target tracking technology is integrated to enhance tracking performance. Speed measurement is implemented using a virtual detection line. This approach enables accurate vehicle detection, continuous tracking, and real-time speed measurement within video frames. Through the experiments, the result shows that the improved YOLOv11n model reaches mAP@0.5 of 0.982 and a recall rate of 0.956 in the test set, higher than the YOLOv8n and YOLOv5s models. The speed detection error can be restricted to 3 km/h, satisfying the real-time detection need. There is no need for road surface modification, and the detection system has a flexible layout, providing a dynamic basis for traffic law enforcement and traffic data support for road construction and traffic control optimization.
Jin, Guowei, Ma, Wenlong, Jiang, Dali, Li, Nan
Optimization of Order Picking in an AMR-Assisted Picker-to-Parts System2026-99-15679/11/2026
With global retail sales expanding and same-day delivery demand on the rise, efficient order picking operations in warehouses have become critical to success. To improve order picking processes, warehouse managers increasingly rely on autonomous mobile robots (AMRs), which improve the performance of traditional picker-to-parts systems. This paper investigates an AMR-assisted picker-to-parts system in which a set of customer orders must be fulfilled. The orders are first batched, and the resulting batches are assigned to individual pickers. Each picker works in a batch-by-batch manner, manually retrieving items from picking aisles and handing over the completed batch to an AMR waiting at the cross aisle. After receiving a full batch, the AMR transports it to the designated depot before returning to serve the next batch. The objective is the minimization of the total tardiness of all orders. The problem is formulated as a mixed-integer programming (MIP) model, and several effective heuristic algorithms are developed. Extensive computational experiments are conducted to evaluate the performance of the proposed algorithms and compare them with a commercial MIP solver.
Jin, Bo, Peng, Jianxin
Research on the Application of Multi-Source Data in Bus Route Network Optimization2026-99-15669/11/2026
This research aims to optimize the bus route network in Shiyan City using bus Origin-Destination (OD) data. By integrating multi-source data, including GDP, population, and bus OD big data from 2018 to 2022, short-term and long-term indicators are forecasted by time-series methods. Shiyan city is divided into 77 Traffic Analysis Zones (TAZs) considering its mountainous terrain, population-industry distribution, and urban planning. A conventional four-stage traffic demand model is applied, calibrated with bus OD data in 2021. The investigation reveals peak-hour bus passenger travel demand of 29,700 short-term and 31,800 long-term person-trips in the city center and key corridors. Bus passenger travel forms a four-vertical and five-horizontal layout in the short term, evolving to a five-vertical, five-horizontal, and two wings pattern in the long term with eastward urban expansion. Accordingly, an optimized and upgraded bus route improvement strategy is devised. In the short term, there are seventy existing bus routes that are adjusted, creating a five-layer bus route network with diverse functions. Long-term plans involve optimizing twenty bus routes and adding eight new routes to align with urban development. This research not only aids an integrated bus route network optimization framework using bus OD data in a time-consuming way, but also provides a sample of bus route network adjustment for a typical mountainous city.
Ye, Qian, Chang, Sheng, Shen, Yucan, Li, Tanfeng, Tian, He, Tian, Shimo, Cen, Jian
Trajectory Tracking Control for UUVs Based on Deep Reinforcement Learning2026-99-11329/11/2026
Unmanned Underwater Vehicles (UUVs) operate in complex and uncertain environments, which require a suitable controller. While traditional PID controllers are widely used, they often have slow response speed and inadequate disturbance rejection, particularly under complex and uncertain conditions. To overcome these shortcomings, this paper introduces the DDPG-DLPID, an adaptive motion controller, including a Deep Deterministic Policy Gradient (DDPG) reinforcement learning that can acquire the parameters of PID controllers. In this paper, we design two loops: the inner loop handles velocity regulation, and the outer loop handles position and attitude. By using DDPG, the system can efficiently adjust the PID parameters of both loops in real time, allowing it to effectively adapt to environmental changes and achieve optimized requirements. To evaluate the controller, we design the following scenarios, including straight-line and complex path-following tasks. Compared with single-loop PID and dual-loop PID controllers, the proposed DDPGDLPID approach achieves faster response and higher tracking accuracy, while substantially reducing tracking errors under interference conditions. Physical experiments under three conditions-straight-line voyage, attitude maintaining, and depth control-were further carried out to validate the strategy’s real-world applicability. Experimental data confirm that DDPG-DLPID has better performance when compared with both traditional PID and dual-loop PID controllers across all test scenarios.
Wang, Ling, Shi, Yan
Design and Research of Large-Tire Replacement Robots2026-99-11279/11/2026
In view of the large volume and weight of the tires of mining dump trucks and the difficulty in replacing them, a large tire replacement robot is proposed based on the tire parameters and the tire replacement process. The overall research scheme for the robot was developed using the functional analysis method, and the functional element solution and combination were completed. Based on the best solution obtained, a three-dimensional model of the tire changing robot was established using the SolidWorks software, followed by control system design and workflow analysis. To investigate the robot's operational kinematics, a simulation was conducted in the SolidWorks Motion module. The motion curve of the flipping platform during its operational state was obtained. A finite element simulation of the robot's front support beam was performed using ANSYS Workbench to obtain its stress and deformation contours under both no-load and heavy-load conditions. The structural parameters of the front support beam were optimized, focusing on its mechanical characteristics under heavy-load conditions, and the response surfaces of different parameters were obtained. The optimization yielded a 9.599 kg reduction in the mass of the front support beam. The maximum stress of the grasping mechanism under static simulation analysis is 50.617 MPa, with the maximum deformation of 0.4221 mm occurring at the end of the mechanical hand. Ground contact simulation for the robot's walking tires was conducted with Abaqus. Employing the Mooney-Rivlin hyperelastic model, this study investigated the mechanical response of the tire to static and dynamic loading, leading to the identification of the optimal operational load. The simulation results show that there is no interference among the various mechanisms of the large tire changing robot during operation. It can quickly complete the tire installation and removal tasks with precise control, and its strength and rigidity meet the requirements. This verifies the rationality and feasibility of the robot. The research on the large tire changing robot can provide a new approach for the maintenance of large transport vehicles such as mining dump trucks.
Tian, Liyong, Zhang, Haijian
Research on the Stability of Three-Axis Vehicles with Coordinated Steering under High-Speed Low-Adhesion Conditions2026-99-11369/11/2026
Three-axle vehicles are widely used in engineering, transportation, and other heavy-duty applications, but they are prone to lateral instability at high speeds or on low-adhesion road conditions, which severely degrades handling stability. To enhance their dynamic performance under extreme operating conditions, this paper proposes a direct yaw-moment control (DYC) strategy based on an incremental linear quadratic regulator (ILQR) for a distributed-drive three-axle vehicle equipped with active front-wheel steering (AFS) and differential drive assist steering (DDAS), thereby improving the accuracy and responsiveness of lateral stability control. Furthermore, to mitigate the mutual coupling and interference among multiple control subsystems, a coordinated steering strategy based on phase-plane analysis is proposed to achieve effective integration and dynamic coordination of AFS, DDAS, and DYC. Co-simulation studies conducted in Matlab/Simulink and TruckSim reveal that the proposed coordinated steering strategy substantially diminishes the peak yaw rate and vehicle sideslip angle across diverse driving conditions, thereby considerably enhancing the lateral stability of the three-axle vehicle during extreme maneuvers.
Hu, Jiadong, Wang, Tie
Current Situation of Ship Navigation and Research on the Route Reform Scheme in Typical Tributary-Confluence Waters of the Lower Yangtze River2026-99-15729/11/2026
The thalweg at the outlet of the Yuxikou Waterway transitions from right to left, forming a 90-degree bend. It then merges with the Xihua Waterway after passing Xiliang Mountain, creating a main-branch confluence water area. Taking a typical main-branch confluence water area in the lower reaches of the Yangtze River as the research object, this paper reflects the current navigation status and existing problems of ships in the area through the analysis of ship traffic flow. It classifies the risk levels of passing ships, proposes suggestions for route reform and optimization, and uses a model to verify the probability of collision accidents in the area after the implementation of the round-island navigation method, providing a reference for the navigation safety of passing ships.
Liu, Ka, Xu, Yerong
A Method for Resolving Two-Vehicle Conflicts at Unsignalized Intersections Based on Adaptive TTC and Traffic Rules2026-99-11409/11/2026
To address the challenges of unsignalized intersections—where the absence of traffic signals leads to high computational complexity and poor real-time performance in existing cooperative methods—this paper proposes a lightweight, real-time conflict resolution strategy for two-vehicle scenarios. First, a traffic rule matrix is constructed based on China’s Road Traffic Safety Law Implementation Regulations, digitizing right-of-way priorities to achieve millisecond-level conflict detection. Second, we introduce an adaptive TTC threshold function to dynamically adjust the warning time between vehicles based on the vehicle speed. Finally, based on the type and priority of the conflict, the speed adjustment value is obtained through the parsing method, enabling real-time calculation of the deceleration amount without the need for iterative optimization. Simulation results in SUMO demonstrate that, compared to the default uncoordinated mode, the strategy reduces the delay in converging conflicts from 4.87 s to 2.87ds and cuts the deceleration frequency by 37.5%. For crossing conflicts, high-priority vehicle deceleration events drop from 8 to 1, and speed standard deviation decreases from 1.27 m/s to 0.78dm/s. This method reduces the computational load while ensuring security, providing a practical solution for cooperative driving at unsignalized intersections in the V2X environment.
Wang, Xinxin, Zhang, Xin
An Improved Multi-Object Vehicle Detection Algorithm Based on YOLOv11s2026-99-11299/11/2026
For object detection in complex road situations, such as inadequate detection performance and difficulties caused by vehicle occlusion and cluttered environments, this paper pursues a YOLOv11s-based object detection framework. The algorithm successfully designed a novel PEConv module. This module integrates a partial convolutional network with an efficient multi-head attention mechanism. Through a Split operation, the input image is divided into locally enhanced channels and original channels. The locally enhanced channels undergo partial convolution and feature weight allocation via the efficient multi- head attention mechanism for feature extraction. Finally, these channels are fused with the original channels before undergoing convolution. This approach preserves the original features while minimising feature loss caused by the series of operations. Therefore, the PEConv module is based on a partially convolutional network and efficient multi-head attention. It improves the detection ability by precisely giving more weight to small objects and occluded parts with augmented partial channel attention and original channel fusion. This study further enhances the model’s detection precision and improves its performance in addressing small target vehicles and severe occlusion issues by refining and upgrading the original C3K2 architecture. The LSBlock is integrated into the original model’s bottleneck structure, replacing the traditional 3x3 convolution to create the C3K2 - LSBlock module. Experimental results show that on the UA - DETRAC dataset, compared with the original YOLOv11s, the optimized YOLOv11s has improved the original mAP @ 50 by 3.4%, reaching 61.3%, and improved the original mAP @ 50: 95 by 2%, which verifies the correctness of it.
Chen, Yulin, Wang, Yini, Wang, Jianwei, Zhang, Xin
Affine Formation Control of Multi-Agent Systems with Switching Topologies2026-99-11289/11/2026
Tackling the heavy computation of affine formation control under switching topologies—rooted in frequent stress matrix recalculation—this paper presents a distributed control framework fusing consistency estimation with dynamic error constraints for efficient coordination. In a leader-follower architecture, affine transformation parameters are estimated by followers using local information—global stress matrix solutions are thus avoided. A time-varying constraint function and Lyapunov stability analysis are devised to ensure tracking errors converge to specified accuracy within a predetermined time. Both theoretical analysis and simulation results show that this method greatly simplifies computation. It also supports flexible formation transformations such as translation and scaling, making it a stable and reliable solution for dynamic scenes.
Liu, Guicai, Li, Jianzhen, Zhou, Junyi, Tang, Jiye
A Spatio-Temporal Graph Network with Informer-TCN Fusion for Vehicle Trajectory Prediction on Highways2026-99-11049/11/2026
Accurate vehicle trajectory prediction is essential for the driving safety and efficiency of autonomous vehicles. However, this task remains challenging due to the complex spatial interactions among traffic participants and the wide range of temporal dependencies in motion sequences. To address these issues, this paper proposes a novel hybrid deep learning framework suitable for cloud-based control platforms, providing a foundational algorithmic solution for vehicle-infrastructure cooperative perception and decision-making. The proposed architecture employs an Adaptive Graph Convolutional Network (AGCN) to adaptively learn spatial relationships and interactions among vehicles. It also utilizes the Informer model, known for its efficient ProbSparse self-attention mechanism, to capture long-term temporal dependencies in trajectory sequences. Furthermore, a Temporal Convolutional Network (TCN) is integrated to enhance the model’s ability to learn fine-grained local temporal features. The proposed model is evaluated on the NGSIM dataset. The dataset is chronologically ordered and split into training (80%), validation (10%), and testing (10%) sets. Experimental results show that the proposed method achieves an average minADE of 2.032 m and minFDE of 2.866 m over a 5-second prediction horizon, outperforming several baseline models such as LSTM, CNN-LSTM, and Social-GAN. These results indicate the effectiveness of the AGCN-Informer-TCN combination for trajectory prediction. The study suggests potential for integration into intelligent transportation cloud control platforms.
Liang, Ziyan, Yuan, Rui, Zhou, Pengying, Yang, Shu, Li, Weidong, Zhang, Zijian
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