Search Results (36)

This paper presents a high-fidelity multi-physics dynamic model for electric vehicles, serving as a fundamental building block for intelligent vehicle-to-grid (V2G) integration systems. The model accurately captures complex vehicle dynamics of the powertrain, battery, and regenerative braking, enabling precise energy consumption evaluation, including in AI-driven V2G scenarios. Validated using real-world data from a Citroën Ami operating on urban routes in Naples, Italy, it achieved exceptional accuracy with a root mean square error (RMSE) of 1.28% for dynamic state of charge prediction. This robust framework provides an essential foundation for AI-driven digital twin technologies in V2G applications, significantly advancing sustainable transportation and smart grid integration through predictive simulation. Its versatility supports diverse fleet applications, from residential energy management and coordinated charging optimization to commercial car sharing operations, leveraging backup power during peak demand or grid outages, so to maximize distributed battery storage utilization. Full article

Traditional collision risk warning methods primarily focus on vehicle-to-vehicle collisions, neglecting conflicts between vehicles and vulnerable road users (VRUs) such as pedestrians, while the difficulty in predicting pedestrian trajectories further limits the accuracy of collision warnings. To address this problem, this study proposes a vehicle-to-everything-based (V2X) multi-fusion vehicle–pedestrian collision warning method, aiming to enhance the traffic safety protection for VRUs. First, Unmanned Aerial Vehicle aerial imagery combined with the YOLOv7 and DeepSort algorithms is utilized to achieve target detection and tracking at unsignalized intersections, thereby constructing a vehicle–pedestrian interaction trajectory dataset. Subsequently, key foundational modules for collision warning are developed, including the vehicle trajectory module, the pedestrian trajectory module, and the risk detection module. The vehicle trajectory module is based on a kinematic model, while the pedestrian trajectory module adopts an Attention-based Social GAN (AS-GAN) model that integrates a generative adversarial network with a soft attention mechanism, enhancing prediction accuracy through a dual-discriminator strategy involving adversarial loss and displacement loss. The risk detection module applies an elliptical buffer zone algorithm to perform dynamic spatial collision determination. Finally, a collision warning framework based on the Monte Carlo (MC) method is developed. Multiple sampled pedestrian trajectories are generated by applying Gaussian perturbations to the predicted mean trajectory and combined with vehicle trajectories and collision determination results to identify potential collision targets. Furthermore, the driver perception–braking time (TTM) is incorporated to estimate the joint collision probability and assist in warning decision-making. Simulation results show that the proposed warning method achieves an accuracy of 94.5% at unsignalized intersections, outperforming traditional Time-to-Collision (TTC) and braking distance models, and effectively reducing missed and false warnings, thereby improving pedestrian traffic safety at unsignalized intersections. Full article

In the field of active safety technology for intelligent connected vehicles (ICVs), the reliability and safety of the Automatic Emergency Braking (AEB) system is recognized as critical to driving safety. However, existing evaluation methods have been constrained by the inadequacy of static weight assessments in adapting to diverse driving conditions, as well as by the disconnect between conventional evaluation frameworks and experimental validation. To address these limitations, a comprehensive Vehicle-in-the-Loop (VIL) evaluation system based on the dynamic weight analytic hierarchy process (DWAHP) was proposed in this study. A two-tier dynamic weighting architecture was established. At the criterion level, a bivariate variable–weight function, incorporating the vehicle speed and road surface adhesion coefficient, was developed to enable the dynamic coupling modeling of road environment parameters. At the scheme level, a five-dimensional indicator system—integrating braking distance, collision speed, and other key metrics—was constructed to support an adaptive evaluation model under multi-condition scenarios. By establishing a dynamic mapping between weight functions and driving condition parameters, the DWAHP methodology effectively overcame the limitations associated with fixed-weight mechanisms in varying operating conditions. Based on this framework, a dedicated AEB system performance test platform was designed and developed. Validation was conducted using both VIL simulations and real-world road tests, with a Volvo S90L as the test vehicle. The experimental results demonstrated high consistency between VIL and real-world road evaluations across three dimensions: safety (deviation: 0.1833/9.5%), reliability (deviation: 0.2478/13.1%), and riding comfort (deviation: 0.05/2.7%), with an overall comprehensive score deviation of 0.0707 (relative deviation: 0.51%). This study not only verified the technical advantages of the dynamic weight model in adapting to complex driving environments and analyzing multi-parameter coupling effects but also established a systematic methodological framework for evaluating AEB system performance via VIL. The findings provide a robust foundation for the testing and assessment of AEB system, offer a structured approach to advancing the performance evaluation of advanced driver assistance systems (ADASs), facilitate the safe and reliable validation of ICVs’ commercial applications, and ultimately contribute to enhancing road traffic safety. Full article

Despite significant advancements in roll stability control for individual vehicle types, comparative research across on-road and off-road vehicles remains limited, hindering cross-disciplinary innovation. This study bridges this gap by systematically analyzing roll stability control in both vehicle categories, focusing on theoretical foundations, key technologies, and experimental validation methods. On-road vehicles rely on mature technologies like active suspension, braking, and steering, which enhance safety through sensor monitoring, rollover prediction, and integrated stability control. Validation is primarily performed through hardware-in-the-loop simulations and on-road testing. Off-road vehicles, operating in more complex environments with dynamic load changes and rugged terrain, emphasize adaptive leveling, direct torque control, and active steering. Their stability control strategies must also account for terrain irregularities, real-time load shifts, and extreme slopes, validated through scaled-model tests and field trials. Comparative analysis reveals that while both vehicle types face similar challenges, their control strategies differ significantly: on-road vehicles focus on handling and high-speed stability, while off-road vehicles require more robust, adaptive mechanisms to manage environmental uncertainties. Future research should explore multi-system collaborative control, such as integrating active suspension with intelligent terrain perception, to improve adaptability and robustness across both vehicle categories. Furthermore, the integration of machine learning and advanced predictive algorithms promises to enhance the intelligence and versatility of roll stability control systems. Full article

For the commercialization of connected vehicles and smart cities, extensive research is carried out on autonomous driving, Vehicle-to-Everything (V2X) communication, and platooning. However, limitations remain, such as restrictions to highway environments, and studies are conducted separately due to challenges in ensuring reliability and real-time performance under external influences. This paper proposes a cooperative autonomous driving system based on V2X network implemented in the CARLA simulator, which simulates an urban environment to optimize vehicle-embedded systems and ensure safety and real-time performance. First, the proposed Throttle–Steer–Brake (TSB) driving technique reduces the computational overhead for following vehicles by utilizing the control commands of a leading vehicle. Second, a V2X network is designed to support object perception, cluster escape, and joining. Third, an urban perception system is developed and validated for safety. Finally, pseudonymized vehicle identifiers, Advanced Encryption Standard (AES), and the Edwards-curve Digital Signature Algorithm (EdDSA) are employed for data reliability and security. The system is validated in processing time and accuracy, confirming feasibility for real-world application. TSB driving demonstrates a computation speed approximately 466 times faster than conventional waypoints-based driving. Accurate urban perception and V2X communication enable safe cluster escape and joining, establishing a foundation for cooperative autonomous driving with improved safety and real-time capabilities. Full article

To address the challenge of repairing the damage to concrete box girder bridge piers on mountainous highways caused by falling rocks, this paper proposes an active underpinning technique that integrates a “井”-shaped cap system, graded preloading of the foundation, and synchronized beam body correction. The technique utilizes lateral beam preloading (to eliminate the inelastic deformation of the new pile foundation) and longitudinal beam connections (to form overall stiffness). The method involves building temporary and permanent support systems in stages. Through the two-stage temporary support system transition, the removal and in situ reconstruction of the old piers, a smooth transition from the pier–beam consolidation system to the basin-type bearing system is achieved while simultaneously performing precise correction of beam torsion. The structural safety during the construction process was verified through finite element simulations and dynamic monitoring. Monitoring results show that the beam torsion recovery effect is significant (maximum lift of 5.2 mm/settlement of 7.9 mm), and the pier strain (−54.5~−51.3 με) remains within a controllable range. Before the bridge was opened to traffic, vehicle load and impact load tests were conducted. The actual measured strength and vertical stiffness of the main beam structure meet the design requirements, with relative residual deformation less than 20%, indicating that the structure is in good, elastic working condition. The vehicle running and braking dynamic coefficients (μ = 0.058~0.171 and 0.103~0.163) are both lower than the theoretical value of 0.305. The study shows that this technique enables the rapid and safe repair of bridge piers and provides important references for similar engineering projects. Full article

With accelerating urbanization, vehicle conflict risk identification has become a critical research focus for improving road traffic safety. To address the discrepancies between microscopic traffic simulation outputs and real-world traffic flow characteristics caused by stochastic factors, this study proposes a vehicle conflict risk identification framework based on an enhanced intelligent driver model (IDM). Through VISSIM secondary development and scenario calibration, a simulation environment was constructed to replicate real-world road test conditions. Multi-vehicle trajectory data were employed to calibrate the IDM parameters. The conventional IDM was further improved by integrating driver-state variables and braking dynamics, while the inverse time to collision (ITTC) was adopted as the primary metric for collision risk assessment. Three risk levels were defined: potential collision (PC), general collision (GC), and serious collision (SC). The experimental results demonstrated strong alignment between the enhanced model and VISSIM simulations in vehicle speed and headway calibration, achieving classification accuracy rates exceeding 92.71%. The ITTC thresholds (0.25/s and 0.48/s) effectively differentiated between risk levels. This research provides theoretical and technical foundations for dynamic vehicle conflict risk identification and offers actionable insights for safety-critical decision making in intelligent driving systems. Full article

Traffic regulations provide a solid foundation for the safety of all road users; however, the ambiguous provisions and unclear safety thresholds within these regulations pose significant challenges to compliance, particularly concerning the safe operation of autonomous vehicles. To address this issue, this paper conducts an in-depth analysis of vehicle emergency braking behavior based on the Aerial Dataset for China Congested Highway and Expressway (AD4CHE). The extraction method for the emergency braking risk scenario of natural driving data is proposed, and the correlation between safe distance, safe speed, and driving safety under the scenario of a slightly congested expressway is elaborated in detail. The safety threshold of ambiguous traffic rules obtained can be used for the digitalization of traffic rules that can support the functional development and traffic safety testing of automated driving systems. Full article

Purpose: To assess the applicability of computational fluid dynamics (CFDs) in determining the flow parameters of inter-chamber nozzle openings in the differential section of a trailer air brake valve. Methodology: Numerical calculations were performed using SolidWorks Flow Simulation (SW-FS) and Ansys Fluent (A-F) with defined boundaries and initial conditions. The results were validated experimentally using the reservoir method and the lumped method for throughput identification. Results: CFD calculations determined the functional dependence of the mass flow rate on the nozzle diameter for a range of control nozzle bore diameters. The SW-FS 2024 and A-F 2023 software showed a mean difference of 4.66% in the total characteristics. The experimental validation resulted in differences of 6.31% (SW-FS) and 5.79% (A-F) compared to the CFD results. Theoretical contribution: This study fills a research gap in applying CFDs to brake valve performance analyses, providing a foundation for developing more complex numerical models to evaluate individual valve sections. Practical implications: The findings suggest that CFDs can be used to accurately determine the flow parameters of control nozzle orifices, with an average of a 6.05% difference from experimental tests. This approach can potentially streamline the design and optimization process for pneumatic brake valves. Full article

In recent years, with the rapid innovation of science and technology, wire control technology, as a key technology, has achieved the transmission control of vehicles through the form of “electrical signals”, which has become an important foundation for realizing the high degree of intelligence of vehicles. This paper provides a comprehensive overview of the wire control technology, its application and longitudinal control strategy, and focuses on the longitudinal control technology of intelligent vehicles based on drive/brake by wire. The specific content includes five parts: first, the principles and characteristics of wire control technology and its application in intelligent vehicles are introduced; then, two commonly used longitudinal control strategies are described; then, the application of classical control technologies (such as PID, MPC, and sliding-mode control) in the longitudinal control of intelligent vehicles is discussed, including their working principles, characteristics and related research; subsequently, the AI control technology (deep reinforcement learning) is presented in the longitudinal control of intelligent vehicles, discussing its theoretical basis, the current status of algorithm research, control methods, and practical applications, etc.; finally, the paper summarizes the advantages and disadvantages of the classical control technology and AI control technology, and looks forward to the application and development prospects of these two control technologies in the control of intelligent vehicles. Full article

Truck skidding crashes on horizontal curves pose a significant road safety concern, with improper braking being the primary cause. A data- and model-integrated driven method is proposed to investigate the mechanism and recommend the maximum safe braking deceleration rates without skidding (abbreviated as MSBDRs) for trucks on horizontal curves. Firstly, a comprehensive road–vehicle interaction model was developed, considering dynamic changes in brake force distribution, vertical tire load, and longitudinal and side friction during braking. Secondly, leveraging the “HighD” data set and employing cluster analysis principles, parameter data were extracted using Python and Matlab. Finally, through parameterizing model inputs, the transient dynamic response of trucks was examined, the potential of truck skidding was predicted, and the MSBDRs were recommended. The results indicate the following. (1) There is little concern of truck skidding during car-following braking maneuvers; however, there is a high potential of truck skidding during emergency braking maneuvers. (2) The MSBDR is 4.5 m/s² on a limit-minimum-radius horizontal curve; however, when combined with steep slopes, an overspeed exceeding 20%, and extremely wet road conditions, respectively, the MSBDRs decrease to 4 m/s², 3 m/s², and 2 m/s². These results provide a theoretical foundation for braking strategies in autonomous vehicles. Full article

High-speed dual-motor electric drive tracked vehicles (DDTVs) have emerged as a research hotspot in the field of tracked vehicles in recent years due to their advantages in fuel economy and the scalability of electrical equipment. The emergency braking of a DDTV at high speed can lead to slipping or even yawing (which is caused by a large deviation of forces at each track directly), posing significant challenges to the vehicle’s stability and safety. Therefore, the accurate real-time acquisition of critical driving parameters, such as the longitudinal force and vehicle speed, is crucial for the stability control of a DDTV. This paper developed a novel estimating algorithm of critical driving parameters for DDTVs equipped with conventional sensors such as rotary transformers at PMSMs and onboard accelerometers on the basis of their dynamics models. The algorithm includes a sensor signal preprocessing module, a longitudinal force estimation method based on a nonlinear observer, and a speed estimation method based on an adaptive Kalman filter. Through hardware-in-loop experiments based on a Speedgoat real-time target machine, the proposed algorithm is proven to estimate the longitudinal force of the track and vehicle speed accurately, whether the vehicle has stability control functions or not, providing a foundation for the further development of vehicle stability control algorithms. Full article

A derailment detection algorithm for railway freight cars based on micro inertial measurement units was designed to address the complex issue of the disassembly and assembly of derailment braking devices. Firstly, a horizontal attitude measurement model for freight cars was established, and attitude measurement algorithms based on gyroscopes and accelerometers were introduced. Subsequently, a high-precision attitude measurement algorithm based on variational Bayesian Kalman filtering was proposed, which used acceleration information as the observation data to correct attitude errors. In order to improve the accuracy of derailment detection, a dual-model instantaneous attitude difference measurement technique was further proposed. In order to verify the effectiveness of the algorithm, offline data from simulation experiments and in-vehicle experiments were used to validate the proposed algorithm. The results showed that the proposed algorithm can effectively improve the measurement accuracy of horizontal attitude changes, reducing the error by 89% compared to pure inertial attitude calculation, laying a technical foundation for improving the accuracy of derailment detection. Full article

The dynamic behaviors of articulated buses during braking and steering processes are exceedingly complex due to the transmission of various forces and torques by the articulated device. The coupling of forces between the front and rear carriages often renders the bus prone to yaw instability under extreme operating conditions. In this paper, according to the characteristics of the structure and parameter matching of an electrically driven articulated bus, a dynamic model of longitudinal and transverse coupling applied on an articulated bus is established, and the influence of the articulated structure on the yaw stability of the drive vehicle is analyzed. Combined with the relationship between the driving motor, the hinge device, and the vehicle motion, a cruise simulation model of the bus is developed, enabling a comparative analysis and verification of vehicle stability under typical road conditions. The results offer a theoretical foundation for the design and control of highly reliable articulated buses. Full article

Advancements in electric vehicle technology have promoted the development trend of smart and low-carbon environmental protection. The design and optimization of electric vehicle braking systems faces multiple challenges, including the reasonable allocation and control of braking torque to improve energy economy and braking performance. In this paper, a multi-source braking force system and its control strategy are proposed with the aim of enhancing braking strength, safety, and energy economy during the braking process. Firstly, an ENMPC (explicit nonlinear model predictive control)-based braking force control strategy is proposed to replace the traditional ABS strategy in order to improve braking strength and safety while providing a foundation for the participation of the drive motor in ABS (anti-lock braking system) regulation. Secondly, a grey wolf algorithm is used to rationally allocate mechanical and electrical braking forces, with power consumption as the fitness function, to obtain the optimal allocation method and provide potential for EMB (electro–mechanical brake) optimization. Finally, simulation tests verify that the proposed method can improve braking strength, safety, and energy economy for different road conditions, and compared to other methods, it shows good performance. Full article