Browse Topic: Electrical, Electronics, and Avionics

Items (59,941)

Predicting Temporal Friction Torque of Drum Brake Systems: A Mixed-Effects Random Forest Approach

2026-01-0809

To be published on 09/14/2026

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 Brake Squeal Occurrence Using Machine Learning with Temporal Feature Analysis of Braking-Torque Test Data (Do Not Use Abbreviations or Special Characters)

2026-01-0812

To be published on 09/14/2026

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

Modeling Brake Torque Hysteresis for Chassis Controls Hardware in the Loop Simulation

2026-01-0824

To be published on 09/14/2026

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 Study

2026-01-0811

To be published on 09/14/2026

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

A Study on the Development of Brake Moan Analysis for CTBA Vehicles Using Simulation

2026-01-0814

To be published on 09/14/2026

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 Wear of NAO Brake Pad Using Scaled-Down FMVSS 135 Testing and Machine Learning

2026-01-0808

To be published on 09/14/2026

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

Finite Element-Based Wear Analysis of Brake Pads

2026-01-0817

To be published on 09/14/2026

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

Real-Time Estimation of Brake Temperature using Physics-Informed Machine Learning Model

2026-01-0810

To be published on 09/14/2026

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) Systems

2026-01-0801

To be published on 09/14/2026

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

Research on the Synergetic Coupling Evaluation between the Yangtze River Delta Airport Cluster and the Regional Economy

2026-99-1542

To be published on 09/11/2026

To explore the coordinated development status between the Yangtze River Delta (YRD) airport cluster and the regional economy, this study takes the period from 2015 to 2023 as the research timeframe. It constructs an evaluation index system covering two dimensions: regional economy (including scale, structure, and benefit) and airport cluster development (including transportation scale, operation efficiency, among others). The Gini coefficient method and Pearson correlation coefficient method are used to screen indicators, while the entropy weight-standard deviation combined weighting method is adopted to calculate weights. Additionally, the coupling coordination model and geographical detector are integrated for in-depth analysis. The results show that the coupling coordination degree of the Yangtze River Delta region as a whole and its internal provinces and cities has rapidly recovered from the severe imbalance during the COVID-19 pandemic, featuring an inherent characteristic of “gradient catch-up and coordinated upgrading”. Factors such as the growth rate of passenger throughput and local fiscal general budget revenue have been identified as core influencing factors, and the interaction among these factors presents trends of two-factor enhancement and nonlinear enhancement. This study provides a theoretical basis and practical reference for promoting the integrated and coordinated development of the Yangtze River Delta airport cluster and the regional economy.

You, Zihao, Li, Yanwei

Design and Simulation of an Airborne Combined Liquid Cooling System Based on Amesim

2026-99-1407

To be published on 09/11/2026

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

A Life Prediction-Driven Maintenance Decision Method for Aircraft Engines

2026-99-1406

To be published on 09/11/2026

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

Study on Low-Velocity Impact Damage of Glass Fiber Reinforced Composite Pipeline

2026-99-1416

To be published on 09/11/2026

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

Safety Assessment Model for Airborne Time-Sensitive Networking Based on Analytic Hierarchy Process

2026-99-1414

To be published on 09/11/2026

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

Site Selection Method for Solid Waste Processing Plant of Highway Reconstruction and Expansion Project

2026-99-1586

To be published on 09/11/2026

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

Research and Verification of an Anti-Pollution Scheme Based on a Satellite Drive Mechanism

2026-99-1401

To be published on 09/11/2026

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 Test

2026-99-1402

To be published on 09/11/2026

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

Research on the Stability of Three-Axis Vehicles with Coordinated Steering under High-Speed Low-Adhesion Conditions

2026-99-1136

To be published on 09/11/2026

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

Research on a Brake Energy Recovery Strategy Based on Improved ANFIS

2026-99-1134

To be published on 09/11/2026

This paper addresses the issue of regenerative braking energy recovery in new energy vehicles and designs and optimizes a braking force distribution strategy. The strategy uses an ANFIS controller to dynamically optimize the proportion of front-axle regenerative braking force. The introduction of a pruning algorithm reduces computational complexity, thereby enabling a significant increase in mileage while maintaining stable driving performance. Co- simulations integrating Simulink and AVL Cruise, alongside Hardware-in-the-Loop (HiL) tests, the proof is that this strategy can still maintain excellent stability under different braking intensities. Moreover, it exhibits significantly higher energy recovery efficiency compared to benchmark strategies, while its effectiveness and real- time performance are successfully validated.

Lin, Hui, Zhao, Xuezhan, Tian, Jiahao

A Method for Resolving Two-Vehicle Conflicts at Unsignalized Intersections Based on Adaptive TTC and Traffic Rules

2026-99-1140

To be published on 09/11/2026

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

Study on Longitudinal Vehicle Control Strategy Considering the Speed Reference Trajectory

2026-99-1135

To be published on 09/11/2026

To ensure the dynamic characteristics in the vehicle’s longitudinal control process, a longitudinal control strategy considering the speed reference trajectory is designed. Based on a hierarchical control method, the speed input in the upper-level control algorithm is designed using a reference trajectory, and the model predictive control (MPC) algorithm is applied to solve for the vehicle’s desired acceleration. In the lower-level control, a feedforward and feedback control structure is used to track the target acceleration, while an inverse longitudinal model is established to calculate the vehicle actuator outputs. Finally, simulation verification is carried out for host vehicle speed change and cut-in, cut-out situations ahead of the vehicle. The results indicate that the method achieves a smoother acceleration response, ensuring driving comfort.

Song, Jia, Li, Wenjie, Ma, Wenyu

Structural Airworthiness and Mechanical Characteristics Analysis of Real-Time Flight Data Acquisition Equipment

2026-99-1411

To be published on 09/11/2026

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 Unit

2026-99-1412

To be published on 09/11/2026

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

Ultrasonic Frequency-Domain Reverse-Time Migration Imaging Incorporating Element Directivity Correction

2026-99-1419

To be published on 09/11/2026

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

Trajectory Tracking Control for UUVs Based on Deep Reinforcement Learning

2026-99-1132

To be published on 09/11/2026

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

Reliability Evaluation of Transmission Accuracy for RV Reducers Based on Uncertainty Theory

2026-99-1101

To be published on 09/11/2026

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

Design and Research of Large-Tire Replacement Robots

2026-99-1127

To be published on 09/11/2026

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

An Improved Multi-Object Vehicle Detection Algorithm Based on YOLOv11s

2026-99-1129

To be published on 09/11/2026

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 Topologies

2026-99-1128

To be published on 09/11/2026

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 Highways

2026-99-1104

To be published on 09/11/2026

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

3D Dynamic Object Detection with Multi-Modal Temporal Fusion

2026-99-1117

To be published on 09/11/2026

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

Intelligent Connected Road Test Brake Throttle Robot Calibration Method

2026-99-1120

To be published on 09/11/2026

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 Vehicle

2026-99-1111

To be published on 09/11/2026

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

Fault Detection and Isolation Method for BDS/GPS Hybrid Constellation Oriented to Airworthiness Retrofit

2026-99-1415

To be published on 09/11/2026

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

Assessment Model for Fitness for Work of Construction Personnel in Expressway Bridge Engineering Projects

2026-99-1509

To be published on 09/11/2026

To mitigate safety risks inherent in highway bridge construction, this research establishes a practical framework for assessing workers’ fitness for work. Using grounded theory, we analyzed interview records and documented accident cases through systematic coding, identifying critical indicators spanning physiological states, safety training effectiveness, and atypical behavioral markers. Rather than relying on single-method approaches, we combined Delphi expert consultation with entropy weighting to capture both professional judgment and data-driven variance, thereby reducing bias while preserving information richness. The resulting assessment protocol enables quantifiable classification of workers into distinct risk tiers. Implementation at the Zhangjinggao Yangtze River Bridge demonstrated the system's discriminatory power through field data collection and direct behavioral monitoring, successfully segmenting the workforce into low-, medium-, and high-risk categories. Results suggest the tool functions effectively as a pre-employment screening mechanism, allowing project managers to intercept potentially unfit workers before they enter hazardous work zones, consequently lowering the incidence of human-factor accidents.

Wu, Zhongguang, Dai, Junping, Ruan, Jing, Shi, Yonglong, Yuan, Zhenzhong, Hao, Jiatian

2025 5th International Conference on Logistics System, Traffic and Transportation (LSTT 2025)

2026-99-1500

To be published on 09/11/2026

TOC

Tobolski, Sue

Train Delay Reduction Approach based on a Virtually Coupled Trains Following Model with Timetable Recovery Process: A Case of High-Speed Rail

2026-99-1516

To be published on 09/11/2026

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

Dimensional Adaptability and Traffic Organization Analysis of Ships in Group Lockage

2026-99-1510

To be published on 09/11/2026

Aiming at the technology of electric intelligent working boat towing 4 ships in group lockage during the construction period of the Gezhouba Shipping Capacity Expansion Project, this paper, starting from ship dimensions, conducts an adaptability analysis on electric intelligent working boat towing ships in group lockage for all ships passing through Gezhouba No. 1 and No. 2 Ship Locks throughout 2024, based on the two-dimensional packing model, ship lock chamber scheduling model and algorithms, navigation scheduling rules and safety management regulations, the results show that ships unsuitable for being towed in group lockage through Gezhouba No. 1 Ship Lock account for approximately 26% of the total number of ships passing through it, while those unsuitable for being towed in group lockage through Gezhouba No. 2 Ship Lock account for approximately 57% of its total passing ships. Meanwhile, ships passing through the three Gezhouba ship locks on a specific day are selected, and an adaptability analysis on the dimensional suitability of electric intelligent working boat towing these ships in group lockage is carried out under the scenario where these ships only pass through Gezhouba No. 1 and No. 2 Ship Locks. The results show that the number of ships with suitable dimensions for group lockage accounts for the majority of the ships passing through the locks on a selected specific day. On this basis, combined with the operation modes of Gezhouba No. 1 and No. 2 Ship Locks, an analysis is carried out on the traffic organization, potential risks, corresponding countermeasures, and the required quantity of electric intelligent working boats for towing ships in group lockage, targeting the three main collaborative operation modes. The results can provide a basis for the future practical application of electric intelligent working boat towing ships in group lockage during the construction and operation periods of the Gezhouba Shipping Capacity Expansion Project.

Huang, Shaowen, Yang, Xi, Wang, Jian

Effect Evaluation of Emergency Barrier-free Blocking Methods in Highway Tunnels Based on Visual/Driving Behavior

2026-99-1531

To be published on 09/11/2026

To address the lack of safe and effective on-site vehicle blocking and control methods in the event of fires or other emergencies in extra-long tunnels — which can significantly reduce traffic safety risks and prevent secondary accidents — this study proposes a novel barrier-free light–smoke curtain interception method. The method integrates conventional traffic safety warning facilities (gantry-mounted variable message signs and audio–visual alarms) with two light–smoke curtain interception images to form a composite early-warning and interception system. Driving simulation experiments were conducted to comprehensively evaluate its warning effectiveness, interception performance, and operational safety in comparison with methods employing only traditional warning facilities or light curtain images. Furthermore, field drills were performed to validate its real-world applicability and interception effectiveness under both daytime and nighttime conditions. The main findings are as follows: 1) The fixation ratio and interception success rate associated with the proposed method were significantly higher than those of the other two methods, demonstrating enhanced visual attention and superior warning and interception performance. 2) The maximum deceleration observed with the proposed method was lower than that of the light curtain–only method and did not trigger emergency braking, thereby indicating high operational stability and driver comfort. 3) In field drills, after activation of the interception equipment, only one and two vehicles entered the tunnel under daytime and nighttime conditions, respectively, and full control of on-site vehicles was achieved within two minutes without any traffic accidents, verifying the system’s rapid response and effective safety assurance.

Shi, Mingjun, Li, Shicao, Wang, Haohuan, He, Qifei, Che, Zhengzhang, Li, Yanbo

Nc5-PCS Based Heterogeneous Point Cloud Matching for Roads

2026-99-1533

To be published on 09/11/2026

Point cloud registration represents a fundamental task in geospatial informatics and 3D computer vision, aiming to align heterogeneous point clouds through rigid transformation estimation. While Super-4PCS serves as an efficient coarse registration method, it exhibits limitations when handling large-scale datasets, planar-distributed point clouds, and scenarios with unknown scale differences. To overcome these challenges, this paper proposes the Nc-5PCS (Neighborhood-constrained 5-Point Congruent Sets) algorithm. Nc-5PCS first performs approximate scale estimation through concavity-convexity similarity analysis within coarse overlap regions, addressing the inherent scale limitation in 4PCS-based approaches. Subsequently, the algorithm employs 3D Harris feature point extraction to significantly reduce data volume while preserving critical geometric characteristics. The core innovation lies in designing a non-coplanar 5-point basis with a corresponding hash-based retrieval mechanism, effectively resolving the feature degradation problem caused by coplanar 4-point bases. Furthermore, normal vector angular constraints are incorporated to enhance consensus evaluation during correspondence selection, substantially improving registration accuracy. Experimental validation demonstrates that Nc-5PCS achieves a point-to-point RMS error of ≤ 0.227 m, outperforming Super-4PCS to provide superior initial alignment for subsequent ICP refinement.

Liu, Lei, Yu, Keguang, Li, Xinyi, Sun, Guangde, Zhao, Xinyuan, Zhu, Dongni, Fan, Yabo, Guo, Shihao

A Maritime Occluded Ship Detection and Tracking Method Based on Improved YOLOv8 Fused with AIS Trajectory

2026-99-1549

To be published on 09/11/2026

Typical maritime monitoring scenarios are usually constrained by factors such as multi-scale ship density, frequent motion overlap, and limited viewing angle of shore-based cameras. These challenges often lead to trajectory interruptions and identity mismatches in target detection and multi-target tracking tasks. In order to solve these problems, this study proposes a ship occlusion detection and tracking method based on the improved YOLOv8 model and further integrates an automatic identification system (AIS) trajectory reasoning. The method builds a unified perception framework with enhanced detection architecture, multi-source data fusion, and behavioral reasoning capabilities. First, in the target detection module, the improved SEConv structure is introduced into the YOLOv8 trunk network to address challenges caused by small-scale variations and severe occlusion in maritime scenes. The ReLU activation function in SEConv is replaced by the Swish activation function to enhance the nonlinear feature representation. In addition, the optimized SEConv is embedded in the C2f structure, and the convolutional block attention module (CBAM) attention mechanism is introduced to enhance the sensitivity of the model to the occlusion area. Next, for multi-target tracking, ByteTrack is used as the basic tracking framework. AIS trajectory data is introduced as auxiliary input to compensate for trajectory losses caused by occlusion. Finally, experimental results on the SeaShips public dataset and the self-built occlusion reference dataset show that the improved YOLOv8 detector achieves stable mAP gains in mild, moderate, and severe occlusion scenarios. The AIS enhanced tracking system improves the multi-target tracking accuracy (MOTA) and identification F1 score (IDF1) by about 6.3% and 8.1%, respectively, and the average occlusion reconstruction error is controlled within 1.4 seconds. The proposed method effectively enhances the perception ability of ships in complex occlusion environments and verifies the feasibility and superiority of the strategy of combining visual detection with AIS data assistance.

Guan, Keping, Chen, Miao, Zhou, Yue

Design and Analysis on a Traction-type Power-assisted Follow-up Vehicle for Emergency Rescue

2026-99-1547

To be published on 09/11/2026

To address problems in China’s emergency rescue scenarios—such as limited functionality, insufficient mobility, poor adaptability to complex terrain, the labor-intensive nature of manual carrying, and the lack of flexibility of fully automatic carts—a traction-type emergency rescue power-assisted follow-up vehicle was designed and developed. With the core design goals of “lightweight, high mobility, and human-machine collaboration”, this power-assisted follow-up vehicle has multiple advantages. At the structural level, it supports rapid folding and unfolding, enabling convenient operation and adaptation to transportation needs in various emergency rescue scenarios. In terms of material selection, it balances strength and lightweight properties, and its key components possess anti-cutting and flame-retardant capabilities, allowing adaptation to the harsh environment of emergency rescue. The power system adopts modular replaceable batteries and is equipped with a high-performance control unit, motor, and shock-absorbing suspension design. This enables normal operation in a variety of complex terrains. The control system is centered on human-machine collaboration. It features simple operation and automatic adjustment of operating status, effectively reducing the operational burden and physical exertion of rescuers. Meanwhile, it supports the master-slave expansion function, allowing flexible switching from a two-wheel structure to a four-wheel structure to meet diverse rescue needs such as material transportation and casualty transfer. This power-assisted follow-up vehicle can effectively solve the material transportation problem in the “last few kilometers” of emergency rescue.

Xu, Jiang, Hou, Yumeng, Yang, Han

Research on Passenger Flow Prediction of Urban Rail Transit Based on Grey Improvement Model

2026-99-1564

To be published on 09/11/2026

In recent years, China's urban rail transit sector has undergone rapid expansion, with passenger demand consistently increasing. Accurate passenger flow forecasting is essential for ensuring efficient and safe metro operations. This paper takes Nantong Metro Line 1 as a case study and applies an optimized forecasting approach that integrates a grey metabolism model with the Holt double-parameter exponential smoothing method. Based on an analysis of Automated Fare Collection (AFC) data from March 2023 to February 2024, passenger flow on this line demonstrates a clear linear growth trend, which aligns well with the assumptions of the grey metabolism model. The results indicate that the optimized grey metabolism model not only significantly enhances prediction accuracy but also greatly reduces the variance ratio, demonstrating high reliability in forecasting outcomes. This improved methodology provides a more robust tool for metro operators in planning services, managing capacity, and optimizing resource allocation.

Fan, Fan, Zhao, Zeheng, Zhang, Jin, Ma, Junhao, Qian, Beiyue

Analysis of Traffic Capacity at the Intersection of Xiyou Road and Qianshan Road in Hefei City

2026-99-1551

To be published on 09/11/2026

With the advancement of urbanization and the popularization of automobiles, the traffic load on urban roads is becoming increasingly heavy, resulting in many traffic problems. Road intersections serve as crucial linchpins in the urban transportation grid, wielding considerable influence over the overall traffic capacity of a city’s road network. Enhancing intersection efficiency and cutting down on delays stand at the heart of tackling urban congestion challenges. This study zeroes in on the crossroads where Xiyou Road intersects with Qianshan Road in Hefei City. Employing hands-on observation and photographic documentation, the research examines traffic flow and signal configurations during the peak demand period (7:30-8:30). The analysis evaluates traffic capacity and utilization rates for through, left-turn, and right-turn lanes at this intersection. Findings reveal that the right-turn lane at the southern entrance and the left-turn lanes at both northern and eastern entries show relatively low saturation levels, while the saturation of other lanes is greater than or close to 1. Therefore, this intersection does not have sufficient capacity. The actual traffic operation at the intersection, particularly during peak traffic times, is analyzed to identify the reasons for congestion Finally, improvement plans for optimizing traffic organization at intersections are proposed, such as optimizing signal timing schemes and transforming traffic channelization. Simulation analysis using VISSIM shows a 9.34% reduction in total intersection parking time, a 34.26% decrease in average queue length, and an 8.12% reduction in average vehicle delay. These results provide a reference for future optimization work, including intersection signal timing and channelization.

Wang, Yanmei, Wang, Chen, Fu, Ziyue, Meng, Xianglong

Safety Analysis of Collision Avoidance for Semi-Autonomous Navigation Ship Based on STPA-CREAM

2026-99-1554

To be published on 09/11/2026

With the continuous improvement of ship intelligence, more intelligent onboard navigation equipment and intelligent navigation systems are used to assist in improving navigation efficiency. This study takes semi-autonomous navigation ships as research objects and adopts System-Theoretic Process Analysis (STPA) to model the complex interaction relationships of semi-autonomous navigation encounter scenarios and identify and analyze potential risks. To address the deficiency of STPA in human factors analysis capability, the Cognitive Reliability and Error Analysis Method (CREAM) is introduced to analyze human factors in semi-autonomous navigation. Finally, based on the results of the STPA-CREAM analysis, recommendations are provided to improve the safety of semi-autonomous navigation.

Zhang, Xiaojie

Adaptive MPC Path Tracking Control for Autonomous Vehicles

2026-99-1570

To be published on 09/11/2026

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

Real-time Vehicle Speed Detection Based on YOLOv11 and DeepSORT

2026-99-1595

To be published on 09/11/2026

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

Numerical Simulation Study on Borehole Wall Stability of Ultra-Large Diameter Deep Bored Piles in Thick Sand Strata

2026-99-1582

To be published on 09/11/2026

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

Analysis of Traffic Characteristics and Research on Variable Speed Limit Control Method in the Confluence Area of Expressway under Ice and Snow Conditions

2026-99-1535

To be published on 09/11/2026

This study investigates the traffic characteristics and delays within expressway merging areas during ice and snow conditions. Using VISSIM-based simulations, the effects of such conditions on merging zones are thoroughly examined. Regression models are developed to describe the relationships between ramp delay, mainline travel time delay, mainline traffic flow, average ramp delay, and mainline traffic volume. Findings reveal that across all environmental scenarios, ramp vehicle delay increases with rising mainline traffic, with this effect being markedly more pronounced under ice and snow. Specifically, when mainline traffic remains below 2800 veh/h, ramp delay increases gradually, but beyond this threshold, the delay escalates rapidly. Building on these results, a variable speed limit control method leveraging the Q-learning algorithm is proposed. The outcomes of this research offer valuable insights for expressway design and traffic management strategies.

Liu, Yan, Luo, Ruiqi

Spatial Sight Distance Value for Curved Tunnel of Mountainous Highway Based on Driving Behaviour

2026-99-1553

To be published on 09/11/2026

In order to conduct more in-depth research on the driving sight distance of curved tunnels in mountainous highways, a systematic theoretical calculation model of spatial sight distance of curved tunnels based on three-dimensional characteristics is established, and the spatial sight distance value of curved tunnels in mountainous highways is recommended in combination with the changes of driving behaviour under different spatial sight distances. Firstly, the concept of spatial sight distance of curved tunnels is proposed, the theoretical calculation model of spatial sight distance of curved tunnels is established, and the model is verified by a multi-scale neural network; Secondly, five UC-win/road simulation models of curved tunnel with different spatial sight distances are established, and the simulation experiments are carried out in combination with mp160 multi-channel physiological recorder and SMI etgtm eye tracker; Finally, the mathematical statistics method and SPSS software are used to analyse the operation behaviour, psychological behaviour and eye movement behaviour of drivers in curved tunnels with different spatial sight distances, and to verify the different effects of the critical value of spatial sight distance on driving behaviour in the theoretical calculation. Furthermore, by taking the spatial sight distance as the independent variable, the regression model is established with the average speed, trajectory offset, heart rate change rate, and pupil diameter change rate as the dependent variables. Based on the driver’s behaviour threshold, the recommended spatial sight distance of a curved tunnel is proposed. The results show that the recommended range of spatial sight distance of the curved tunnel of mountainous highway with a design speed of 80 km/h is 125 m to 140 m, the limit value is 110 m, and the appropriate value is 155 m. There is a critical value between two-dimensional sight distance and spatial sight distance, which has a significant impact on the change of driving behaviour in a curved tunnel.

Tang, Xie, Zheng, LiWen, Lin, GuoJin, Gao, YanYang, Lan, FuAn

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