The field of vision-based autonomous driving perception is currently facing challenges such as low efficiency in mapping images to three-dimensional space， insufficient modeling of long-range dependencies， and poor prediction accuracy for small objects in three-dimensional semantic occupancy prediction. This paper proposes the Cross-Dimensional Branch Occupancy Network （XDB-Occ）， which significantly improves feature mapping efficiency and semantic understanding capabilities in complex scenes through an efficient image feature conversion mechanism and a dual-path Transformer architecture. The network adopts a multi-branch architecture design， leveraging its efficient image feature extraction capabilities to significantly enhance the effectiveness of feature mapping from images to high-dimensional space， thereby enabling more precise semantic occupancy prediction. Additionally， for the high computational cost of voxel feature computation in traditional BEV encoders， particularly the technical bottleneck where new modules struggle to effectively capture long-range dependencies due to computational resource constraints， this paper introduces the Efficient Pyramid Branch Fusion Module， which resolves the computational bottleneck of BEV encoders， significantly enhancing the model's ability to model long-range dependencies and complex scenes while reducing resource consumption. This paper achieves performance that significantly surpasses existing state-of-the-art methods on the SemanticKITTI and NuScenes datasets. XDB-Occ achieves a 1.16% improvement in mIoU on the SemanticKITTI dataset and a 1.6% improvement in mIoU on the NuScenes dataset.

With the advancement of intelligent connected vehicles， in-vehicle network communication is transitioning towards an architecture primarily based on in-vehicle Ethernet， supplemented by CAN and other bus systems. However， existing intrusion detection systems relying on machine learning and deep learning struggle to detect unknown attacks within in-vehicle Ethernet， while also suffering from insufficient real-time performance and high computational resource consumption. This paper proposes a novel intrusion detection model consisting of a data preprocessing module， a feature extraction module， and an evidence classification module. The data preprocessing module employs grey-scale image encoding for data processing. The feature extraction module combines spatio-temporal features extracted via time convolutional networks and multi-branch convolutional neural networks， while the evidence classification module quantifies the overall uncertainty of the classification. The model is tested on publicly available in-vehicle Ethernet datasets. The results demonstrate detection accuracy exceeding 99.70% for known attacks and surpassing 99.10% for unknown attacks.

This paper firstly constructs the boundary of the vehicle's lateral stability domain using the phase plane method， quantifies the influence of vehicle speed and adhesion coefficient on the lateral stability domain， and establishes the expression of the highest safe vehicle speed under different longitudinal lane-changing distance and adhesion coefficient， providing precise lateral stability domain constraints for trajectory planning. Secondly， it analyzes the transient characteristics of the vehicle's roll dynamics during the lane-changing process， establishes the rollover stability evaluation index based on the lateral load transfer rate， and forms a three-dimensional safety boundary by combining the lateral stability domain constraints and rollover prevention constraints. Finally， to reduce computational cost， a differentiated trajectory planning strategy for different working conditions is proposed， converting the trajectory planning problem into a nonlinear programming problem. A multi-performance collaborative optimization objective function is constructed， taking into consideration of comfort， safety， economy， and traffic efficiency， which fully utilizes the vehicle's performance limit while ensuring the safety and feasibility of the trajectory. The results show that the proposed method can effectively complete the lane-changing collision avoidance task under the premise of ensuring vehicle stability， which is of great significance for improving the safety of autonomous driving vehicles.

In confined environment， limited drivable space for multi-vehicle systems poses significant challenges in solving the multi-vehicle motion planning problem. Addressing the aforementioned issue， this paper proposes a spatiotemporal decomposition planning approach. Firstly， a numerical optimization problem is established to generate smooth motion primitives， which are used to search for a path that satisfies the vehicle’s incomplete constraints and collision-free from environmental obstacles for each autonomous vehicle. Then on this basis， for the conflicts areas between different paths， the vehicle crossing order planning is incorporated in the speed planning and the speed-planning scheme is established with logical constraints. The logical constraints are approximated to obtain a nonlinear speed optimization problem considering the crossing order for conflict areas. Finally， smooth speed curves over the fixed paths are obtained by solving the nonlinear speed optimization problem， achieving spatiotemporal decomposition motion planning for multi-vehicle systems. The simulation experiment results show that the proposed multi-vehicle spatiotemporal decoupling motion planning approach has a higher computational speed compared to the spatiotemporal enhanced conflict-based search using motion primitives （MP-ECBS） and priority-based search using motion primitives （MP-PBS） method， and the computational time is reduced by more than 37.1% in the test scenario. The proposed speed coordination planning method has a more stable performance compared to the first-in-first-out （FIFO） speed planning approach. The real vehicle experiment results show that the proposed method can effectively achieve motion coordination for multiple autonomous vehicles in confined environment.

Scene-based testing is an important means to evaluate the safety performance of autonomous vehicles， among which vehicle cut-in scenarios are common in real collision accidents. When using the traditional Analytic Hierarchy Process （AHP） method to assess the risk of such scenarios， the pairwise comparison matrix usually relies on manual assignment， which is highly subjective， and the risk boundaries of key scenarios are ambiguous， limiting the accuracy of risk assessment. To address these issues， this paper proposes an improved AHP method considering risk boundary constraints. This method takes the minimum relative distance between the host vehicle and surrounding vehicles as the performance index， and uses the Particle Swarm Optimization algorithm to optimize the correlation coefficient between this index and the risk index so as to realize automatic generation of the pairwise comparison matrix and reduce the interference of subjective factors. At the same time， boundary constraints for key scenarios are established to narrow the risk area range of key scenarios. The results show that the correlation coefficient between the performance index and the risk index obtained by the improved AHP method increases to -0.947， which is better than -0.926 of the traditional AHP while the risk boundary of key scenarios is reduced from 0.017 00 to 0.001 46， effectively compressing the risk area range of key scenarios and improving the accuracy of risk assessment for test scenarios. This study provides an objective and efficient theoretical method for risk quantification and boundary optimization of autonomous vehicle cut-in scenarios.

For the limited path-tracking accuracy of conventional model predictive control （MPC） under complex driving conditions， this paper proposes an adaptive MPC method based on reinforcement learning. Firstly， a steering-increment MPC controller is developed using the vehicle dynamics model. The cost function introduces in a dynamically adjustable global weight coefficient and astep-varying weighting coefficients are set to realize differentiated weight allocation in the prediction time domain， enhancing the flexibility of the controller’s weight design. Subsequently， a reinforcement learning model based on the Twin Delayed Deep Deterministic Policy Gradient （TD3） algorithm is constructed， where the training objective jointly considers tracking accuracy and driving stability， which enables the weighting parameters to adaptively and collaboratively adjust according to vehicle state and road conditions. Finally， TruckSim/Simulink co-simulation and real-vehicle experiments are conducted. The experimental results show that the proposed method achieves lateral error variance， heading error variance， and steering angle variance of 0.041 m， 0.106 rad， and 234.226°， respectively， all superior to the baseline algorithm， thereby verifying the proposed method’s advantages in control performance and adaptive adjustment capability.

To alleviate increasingly severe urban road traffic congestion， low-altitude economic policies for urban three-dimensional transportation have been successively introduced， promoting the research and development of various new types of transport vehicles. The split flying car， with its dual capabilities of ground autonomous driving and low-altitude autonomous flight， has emerged as a highly promising solution. However， when the flight module of the split flying car switches between land and air modes at ultra-low altitude， the environment is filled with numerous dense and lightweight obstacles， which can easily be drawn into the rotor vortex， potentially causing safety incidents. To address this issue， this paper proposes a flight trajectory planning method for split flying cars based on vortex risk perception. Firstly， the vortex influence range of the open rotor under the platform landing condition is modeled using the vortex theory， and high， medium， and low-risk areas are delineated based on the evolution mechanism of the vortex ring state. Subsequently， an obstacle expansion map considering vortex risk is constructed. Then， based on the obtained expansion map， B-spline curves are used to represent the flight path， and the local A* algorithm is combined to plan the obstacle avoidance path in real time. Trajectory optimization is carried out under constraints such as safety， smoothness， and dynamic reachability. The experimental results show that the flight trajectory planning method proposed in this paper can effectively avoid lightweight obstacles such as lines and trees.

3D object detection is a key technology for autonomous driving environment perception. However， high-performance detection models typically require substantial computational resources， making them difficult to deploy in real-world application. Therefore， this paper proposes a Geometry-Significance-Based Adaptive Distillation （GSAD） algorithm for point cloud 3D object detection. Firstly， a geometric significance-filtering scheme is designed that computes geometric significance information from point clouds and generates heat maps to identify salient points containing critical information， thereby reducing noise interference. Secondly， the saliency heat map is used to perform adaptive weighted knowledge distillation. By using a lightweight weight generation network， distillation weight is dynamically allocated based on spatial locations， guiding the student model to focus on learning key geometric features from the teacher model， thus improving both distillation efficiency and model performance. The extensive experiments conducted on the KITTI dataset demonstrate that the proposed algorithm achieves a computational cost of 13.46 GFLOPS， only 11.66% of the base model's consumption. The mean average precision reaches 77.71%， an improvement of 2.97 percentage points over the base model. Overall， the proposed method outperforms the baseline model， achieving an optimal balance between accuracy and efficiency.

Low-temperature environment significantly degrades the prediction accuracy of the remaining driving range （RDR） of battery electric vehicles （BEVs）， thereby exacerbating drivers’ range anxiety and reducing the efficiency of energy management. To address this issue， this study proposes a driving-range prediction method that integrates driving-condition clustering with a low-temperature grouping strategy， to improve prediction accuracy under cold conditions. Using real-world winter operational data from BEVs in Northeast China， combined with high-resolution meteorological data， the proposed method firstly applies a fixed-step segmentation approach to divide discharge data into driving-condition segments and employs K-Means clustering to identify four representative driving patterns. Subsequently， a low-temperature grouping strategy covering eight temperature intervals is constructed， and a buffer zone with sample-size constraints is introduced to enhance the adaptability of the model to temperature fluctuations. Finally， vehicle operating features， driving-behavior parameters， driving-condition categories， and ambient temperature are used as input to construct XGBoost-based short-segment prediction models for each temperature group. During prediction， the corresponding temperature-specific model is selected， or a neighboring-temperature compensation mechanism is applied， to estimate the total range for complete discharge segments. The experimental results show that the proposed method achieves MAPE and SMAPE values of 4.04% and 3.90%， respectively. The prediction accuracy is significantly higher than that of the benchmark models， with particularly superior performance under low-temperature conditions. The findings provide effective technical support for energy management and route planning of BEVs in cold regions.

For the problem of power loss caused by sluggish dynamic response during load transient in fuel cell systems， this study proposes a Forecast Proposed Sliding Mode Control （FP-SMC） controller with a dual-layer optimization architecture integrating observation and prediction. Firstly， a fuel cell system model is developed by combining mechanistic analysis with experimental data， resulting in a 12th-order nonlinear state-space equation tailored for control design. Subsequently， a multi-objective sliding mode controller （Proposed Sliding Mode Control， P-SMC） based on a High-order Sliding Mode Observer （HSMO） is designed. The simulation results demonstrate that the observation error of the HSMO is reduced by approximately 50% compared to a conventional Sliding Mode Observer （SMO）. Under dynamic operating conditions， the P-SMC controller effectively restricts cathode/anode pressure differential fluctuations to within 0.03 bar and maintains stack temperature variation within a minimal range around the target value （353.15 K）. Further， the future system power demand and its trend over a prediction horizon are forecasted using a Least Squares Support Vector Machine （LSSVM）， which forms the basis for feedforward optimization and adaptive adjustment of the oxygen excess ratio control and hydrogen supply pressure. The simulation results under dynamic load conditions show that， compared to PID and P-SMC controllers， the FP-SMC controller reduces response time by 72.0% and 77.8%， respectively， and decreases average settling time by 70.2% and 54.8%. These findings show that the proposed FP-SMC method significantly enhances the dynamic response speed and stability of fuel cell systems during load variation.

For the challenge of accurately measuring the specific heat capacity of power lithium-ion batteries， this study proposes an in-situ measurement theoretical model and a liquid-immersion method based on the entire process of battery heating， temperature equalization， and cooling， taking into full account heat loss （thermal loss） factors. The influence of thermal loss on measurement results is analyzed， and calibration of the measurement is performed using standard samples. The results show that when thermal loss is considered， the measured specific heat capacity of the battery is 1 023.41 J/（kg·℃）， whereas neglecting thermal loss yields a value of 1 622.78 J/（kg·℃）， indicating a significant difference between the two. Using an aluminum alloy block as the calibration standard， the measured value considering thermal loss is 859.32 J/（kg·℃）， which closely matches the actual value of 896.00 J/（kg·℃）， achieving an accuracy of 95.9%. In contrast， the measurement ignoring thermal loss is 1 371.40 J/（kg·℃）， deviating considerably from the true value. By applying the error in aluminum block specific heat capacity measurement as the calibration coefficient to correct the battery's specific heat capacity， the calibrated result becomes 1 065.37 J/（kg·℃）. This method requires no specialized equipment and enables rapid determination of battery specific heat capacity within three hours， offering advantages of high precision and safety， which provides valuable reference for researchers to accurately determine battery specific heat capacity parameters.

Turbocharging technology has been widely applied in internal combustion engines. However， due to the low exhaust temperature and limited expansion energy of vehicular fuel cell， conventional turbines struggle to efficiently recover exhaust energy. Therefore， this paper proposes a high-efficiency and high-safety PEMFC air supply system based on a variable geometry turbine （VGT） and an air bypass circuit， along with its corresponding optimization and control strategy. Firstly， experimental tests are conducted to characterize the fuel cell stack and the compressor/turbine at different VGT vane angles. Based on this， a multilayer perceptron （MLP）-based surrogate model is developed to accurately predict the isentropic power recovery of the VGT. The accelerated particle swarm optimization （APSO） algorithm is then employed for global optimization of the vane angle. The test results show that the VGT significantly reduces the power consumption of the air supply system， leading to an approximate 5.4% increase in net power of the fuel cell system under full load. Furthermore， for the problem of high tailpipe hydrogen concentration and residual moisture during practical vehicle tests， a bypass-assisted purging start-stop strategy is designed and validated， which effectively reduces both the peak hydrogen concentration in tailpipe and the time to establish open-circuit voltage during startup by about 50%， while also significantly improving system insulation performance after shutdown. The proposed technology has been successfully applied in China’s first 800?km hydrogen-powered heavy-duty truck green transportation route between Zhengzhou and Qingdao Port.

Plug-in hybrid technology represents an important technical route for recreational vehicle （RV） powertrains， and the development of energy management strategies （EMS） that align with real-world operating characteristics is essential for improving overall vehicle efficiency. For the limited adaptability of existing rule-based EMS to variable driving conditions， this study proposes a traffic-information-integrated State-of-Charge （SOC） trajectory-planning module that serves as the core optimization layer of the EMS. By leveraging preview information on trip distance and expected average vehicle speed， the module generates a reference SOC trajectory that provides anticipatory decision-making cues for energy allocation. Model-in-the-loop simulation under three distinct driving cycles are conducted. The results show that compared with a baseline rule-based strategy， the proposed SOC-trajectory planner consistently delivers fuel-saving benefits exceeding 2% on both the CWTVC and CHTC-LT standard cycles under various battery-charge scenarios， and yields 0.5%~2% economy improvement on a real-road RV cycle. Subsequent analysis reveals a significant reduction in engine-on duration within all three cycles， accompanied by a measurable shift of engine operating points toward higher-efficiency regions. On this basis， the feasibility of in-vehicle deployment is verified through chassis-dynamometer tests that compare the CHTC-LT and the real-road cycle on the same RV.

To investigate the electromagnetic noise characteristics of high-speed permanent magnet drive motors for vehicles under variable operating conditions， this paper takes an 8-pole 48 slot permanent magnet synchronous drive motor with a rated power of 40 kW as the research object， and conducts a systematic study on the spatiotemporal distribution characteristics of radial electromagnetic force in the air gap of the motor in a wide speed range under variable operating conditions at a maximum speed of 20 000 r/min， and the changes in noise response caused by it. Firstly， based on the electromagnetic force analysis model， the core electromagnetic force order that causes significant electromagnetic noise is derived. Secondly， through multi-physics coupling simulation， the variation of the 24th and 48th order noise sound pressure level of the motor in the full speed domain with operating conditions is analyzed. Finally， relevant operating condition tests are conducted through bench tests to verify the correctness of theoretical analysis and simulation results， as well as the effectiveness of theoretical and simulation models. The research shows that the 24th order noise sound pressure level is higher in the low-speed range of low load and medium high load conditions， while the 48th order noise sound pressure level is prominent in the medium high speed range of medium high load conditions. The noise of each order shows attenuation as the load decreases. When the load condition changes from wide open throttle to no-load， the average sound pressure level of the 48th order noise decreases by 28.9% in simulation and 20.9% in experiments， which is the most sensitive to the change of operating conditions.

To improve pedestrian protection and sustainability of automotive front bumpers， three types of sandwich-like bumper systems of beverage cans （RSVBS）， straw structures （SSVBS） and hybrid structures （HSVBS） are proposed in this study using recyclable empty beverage cans （REBCs） and straw structures （SSs） as energy absorbers， combined with a newly designed energy absorbing fixing plate for fixing REBS and SS. Based on a validated finite element （FE） model of REBCs from quasi-static compression tests， a vehicle-pedestrian lower limb collision model is established. Then， the differences of pedestrian lower limb injuries between the proposed three bumper systems and the conventional foam bumper system （OFVBS） are compared by simulation experiments. The results show that all three systems achieve better pedestrian protection， with the REBC-based system （RSVBS） performing best， with femur and tibia stresses decreased by 26.3% and 18.0%， and medial collateral ligament （MCL） and cruciate ligament （CL） strains by 19.1% and 44.2%， respectively. The V-shaped collapse mode of REBCs and SSs converts the “<”-shaped asynchronous intrusion of the foam structure into a coordinated synchronous intrusion， leading to smoother energy absorption. In addition， the independent energy absorbing unit is also easy to replace and recycle， realizing better pedestrian lower limb protection and carbon reduction potential simultaneously.

Random porous materials are frequently employed in automotive energy-absorbing components due to their excellent energy absorption properties， for enhancing lightweight design and crashworthiness. However， the complex and disordered meso-structure of these materials makes it challenging to predict the highly nonlinear relationship between structural parameters and mechanical response， while also hindering the inverse design according to specific requirements. To address these issues， random honeycomb materials are designed and fabricated in this study. Quasi-static compression tests are conducted to validate the numerical model. Based on the finite element method， the crashworthiness of the random honeycomb materials is investigated. Furthermore， a deep learning model named V-FINN， composed of a Variational Autoencoder （VAE） and an Artificial Neural Network （ANN）， is developed to achieve data-driven prediction of mechanical properties and inverse design of the random honeycomb materials， which realizes bidirectional high-precision mapping of microstructure characteristics and macroscopic mechanical properties of random honeycomb materials. The results indicate that compared with regular honeycombs， the random honeycomb materials exhibit more complex deformation modes and more stable mechanical response. Increase in the number of cells and wall thickness raises the relative density of the material， thereby significantly improving energy absorption performance. The data-driven forward prediction model achieves a coefficient of determination （R²） of 0.915 on the test dataset. The error between the predicted curves from the inverse design model and the simulation curves is within 10% for major energy absorption evaluation indicators. These findings demonstrate that the proposed deep learning model has good performance in both forward prediction and reverse design.

With the popularization of Autonomous Emergency Braking （AEB）， when the AEB of the front vehicle is activated， the occupants will be out of position（OOP） .If a collision caused by the rear vehicle during the AEB operation， it will lead to an increase in head and neck injuries of the occupants. This paper firstly constructs a general whiplash pulse under the action of AEB based on the emergency braking acceleration of nine vehicles， and verifies the applicability of the BioRID II dummy through the OOP test. Six seat variants are then compared in both normal and OOP postures， followed by ten additional sled runs on three selected seats to quantify the injury-mitigation potential of different active restraint concepts. The results indicate that all injury criteria rise sharply in the OOP condition； notably， NIC and upper-neck F_x increase by 262% and 1 831%， respectively. Combining an active head restraint with an active seat-belt offers the greatest reduction in whiplash-induced occupant injury under AEB， providing clear guidance for the development of next-generation active restraint systems.

In the context of autonomous driving， multi axle heavy-duty vehicles are widely used for cargo transportation. However， due to such inherent characteristics as large cargo capacity and high center of mass， it is prone to instability accidents under extreme conditions such as high-speed driving and large angle steering， which seriously restricts its transportation safety. The multi-objective coordinated motion controller with good path tracking capability and improved lateral stability performance has become the key technical support for solving the above problems. Therefore， a multi axle heavy-duty vehicle motion control strategy based on Variable Priority Level Model Predictive Control （VPL-MPC） is proposed. The designed vehicle motion control system adopts a hierarchical design architecture. In the dynamic coordination layer， based on the traditional \beta -\dot{\beta } phase plane， the stability boundary is further divided considering the lateral angular velocity limit and handling performance constraints， providing weight factors for the controller to prioritize coordinated control. In the optimization control layer， a variable priority motion control method based on MPC is designed to smoothly coordinate stability and tracking accuracy， and a zero moment point roll instability judgment model is introduced to ensure rollover safety. The simulation results show that the controller reduces the root mean square error of the yaw rate by about 55%， the root mean square error of the sideslip angle by about 51%， and the maximum Load Transfer Rate by about 51.4% while meeting the tracking accuracy requirement， effectively ensuring the stability of the vehicle.

For challenges of reliability and durability in the Electronic Mechanical Brake （EMB） system， this paper proposes a method for analyzing the operational characteristics and assessing potential damage based on the actual operation data of users. Firstly， five typical braking conditions are identified through UMAP dimensionality reduction and K-means clustering， and the time and mileage proportion of each condition is statistically analyzed. Secondly， the EMB braking model is constructed through the joint simulation of CarSim and MATLAB/Simulink， which is verified by real vehicle tests. The simulation results have an error of less than 5% compared with the actual measurement， ensuring the reliability of the model. Finally， based on this model， the potential damage of the brake under different conditions is analyzed in combination with the Miner rule. The results show that the emergency braking condition caused the most severe damage to the brake， with the damage to the front wheels significantly higher than that to the rear wheels. This study reveals the damage distribution patterns under different conditions， providing theoretical support for the formulation of reliability test standards for EMB systems and the establishment of an evaluation system that meets the needs of domestic users.

Vehicle ride comfort is a core indicator of the driving and riding experience. As a key regulating system， semi-active suspension （SAS） often suffers from limited control performance under complex working conditions， nonlinear dynamics， and unknown disturbances. To address the lack of robustness in model-driven methods and the “black-box” limitation of purely data-driven deep reinforcement learning （DRL）， this paper proposes a comfort optimization control architecture named DGRL-SAS， which integrates differential geometry （DG） and DRL. The approach utilizes exact feedback linearization from differential geometry to decouple the kinetic coupling and extract key comfort-related state variables， constructing a feature space with clear physical interpretability for DRL. Then， the deterministic experience replay mechanism is incorporated to design the DRL optimization module for intelligent generation of damping control instructions. The simulation results show that DGRL-SAS significantly suppresses vehicle vibration， reducing the root mean square of vertical acceleration by 43.2% compared to the passive suspension， and improving ride comfort by 16.5% and 10.1% over TD3 （Twin Delayed Deep Deterministic Policy Gradient） and LQR （Linear Quadratic Regulator）， respectively. Moreover， it maintains a comfort optimization rate exceeding 35% under untrained conditions and parameter perturbations， demonstrating excellent robustness and generalization capability. By combining mechanism-driven analysis with data-driven optimization in a closed-loop design， the proposed framework offers a new pathway for SAS comfort control that embodies both theoretical depth and practical applicability.

To enhance the body posture of vehicles under steering conditions， this paper proposes a roll control strategy based on a fully active suspension system. Firstly， the influence of suspension linkage design on vehicle response is emphasized， and an eight-degree-of-freedom handling-roll vehicle dynamics model is established. The accuracy of the model is validated through vehicle tests. Secondly， a feedforward-feedback control method is adopted to design and optimize the active suspension roll control strategy. Finally， vehicle tests are conducted based on a car equipped with a fully active suspension system to evaluate the performance of the control algorithm. The results show that the proposed roll control strategy effectively reduces the vehicle roll angle under the steering test conditions of steady circular test， slalom test， and double lane-change， with the root mean square （RMS） value of the roll angle reduced by more than 60%， and its peak value decreased by over 50%.

As global emission standards for motor vehicles become increasingly stringent， exhaust emission of vehicles has been effectively controlled. Consequently， non-exhaust emission now accounts for a growing proportion of total vehicle emission， which underscores the urgent need to establish effective control measures for these non-exhaust sources. Tire wear particle is an important non-exhaust pollutant. In this paper， the research status of tire wear particles is reviewed from three aspects of the collection and test methods， the emission characteristics of particle mass （PM） and particle number （PN）， and the morphology and composition of particles. The research shows that the consistency and reliability of TWP emission results under different test conditions need to be further verified， and the TWP generated in the actual driving process can better represent the authenticity of tire wear. The emission characteristics show that the PM₁₀ ranges from 410~1 680 μg/km， and the PN ranges from 2.1 × 10⁴ to 1.7 × 10⁷ #/cm³. The morphologies of the particles are mainly tubular particles and spherical particles， and the key elements are zinc （Zn）， cadmium （Cd） and barium （Ba）. This paper provides research support for further controlling tire emission and standardizing the measurement method of tire wear particles.

This paper proposes a multi-source perception information fusion method for pavement type identification based on an intelligent tire embedded sensor array. Firstly， a sensor array for intelligent tires is constructed， consisting of one acceleration sensor （ACC） and three polyvinylidene fluoride （PVDF） piezoelectric film sensors， to collect real vehicle test data of multi-source sensors under different pavement and various speed conditions. Secondly， time-frequency domain analysis is conducted on the sensor data of different pavement， and the results show that the Z-axis signal of the acceleration sensor and the signal of the PVDF1 sensor at the right shoulder of the tire interior have significant differences in impact response under different pavement. Thirdly， the contact area between the tire and the pavement is identified based on energy features in the signals of the two types of sensors， and the contact area data is decomposed into intrinsic mode functions through ensemble empirical mode decomposition as the input of the pavement identification model. Finally， a CNN-LSTM-Attention pavement identification model is constructed. By comparing the pavement classification accuracy of single sensors and three multi-sensor combination schemes， the results show that the identification accuracy of fusing the information of the acceleration sensor and the PVDF1 sensor is the highest， reaching 99.78%. The effectiveness of the CNN-LSTM-Attention model is verified through ablation experiments. Compared with other baseline models， the method proposed in this paper can effectively improve the accuracy of pavement type identification.

Vehicle inertial parameters are essential for dynamic response and control performance， but they can vary significantly with loading conditions. Existing methods often rely on fixed values or online estimation based solely on onboard sensors， which limits the accuracy and applicability. This paper proposes a real-time estimation approach that integrates in-tire and onboard sensing. Firstly， tire vertical and lateral forces are estimated online by combining in-tire acceleration measurement with Gaussian process regression. Then， based on load transfer and yaw dynamics models， an extended Kalman filter （EKF） is designed to estimate the vehicle mass， longitudinal center of gravity position， and center of gravity height. The estimated parameters are subsequently provided to a Kalman filter （KF） for further identification of the yaw moment of inertia. The proposed method converges rapidly and achieves high accuracy， while remaining unaffected by the coupling of vehicle state， which can significantly enhance the reliability and applicability of inertial parameter estimation. The real-vehicle validation results show that the relative estimation error of vehicle mass， longitudinal center-of-gravity position， center-of-gravity height， and yaw moment of inertia is 0.85%， 0.65%， 4.62%， and 2.61%， respectively.

Air conditioning calibration is an important part of vehicle development， mainly involving tuning and optimizing various control parameters， the results of which directly affect the comfort and energy efficiency of the vehicle. Road calibration is an important means of air conditioning calibration， but uncontrollable environment， and poor repeatability causes low efficiency and poor accuracy. On the contrary， automotive climatic wind tunnels can achieve repeatable and precise control of environmental conditions. Therefore， this paper proposes using the climatic wind tunnel for air conditioning calibration， which is then compared with the road calibration. The results show that the climatic wind tunnel can reproduce the dynamic road environment， with a root-mean-square error （RMSE） of 0.6 ℃ for temperature and 2% for humidity， respectively. The maximum RMSE of the average head temperature is 1 ℃， and the RMSE of the compressor current is 0.9 A， with consistent dynamic trends. The study indicates that there is high consistency between the climatic wind tunnel tests and road calibration tests. Therefore， the road spectrum can be converted into a climatic wind tunnel spectrum to establish the climatic wind tunnel calibration test standard. This standard can replace road calibration to achieve accurate off-season calibration， thereby shortening the R&D cycle and improving calibration accuracy.