To address the deterioration of emissions during transient conditions in automotive diesel engines， the CA6DM3 National Ⅵ high-pressure common rail diesel engine was investigated. A transient measurement and control platform was constructed based on “dSPACE”， and a transient emission optimization strategy utilizing segmented fuel injection timing delay was proposed. A typical transient operating condition was selected with a speed of 1 100 r/min， a load range of 10% to 90%， and a loading time of 3 seconds. Reference air-fuel ratios slightly lower than the stable loading period， specifically 27 and 28， were chosen. A segmented fuel injection timing control strategies implemented through two methods—difference delay and proportional delay， were proposed to achieve segmented delay in fuel injection timing. The test results indicate that， while balancing the dynamic performance and fuel economy during the transient loading process of the diesel engine， adopting the difference delay segmented strategy with a reference air-fuel ratio of 28，the 6 °CA delay scheme yielded the optimal emission improvement effect. The maximum reductions in NO _x and particulate matter emissions during the transient loading process were 6.5% and 27.77%， respectively， the cumulative emission reductions were 6.8% and 31.9%.

An experimental study was conducted to investigate the effects of an insulated piston on gasoline engine performance under different loads and excess air ratios. The results show that the optimal ignition time is advanced at low load， the ignition delay period and the combustion duration are prolonged；conversely， the combustion center is postponed at high load. Additionally the use of thermal insulation coating pistons has led to an increase in NO _x emissions and a decrease in CO and HC emissions； the thermal insulation coating pistons has the same effect on engine performance during both and stoichiometric air dilution combustion at high loads combustion.

To enable accurate and real-time estimation of exhaust mass flow rate in internal combustion engines， a hydrogen internal combustion engine exhaust flow estimation method based on adaptive observer was proposed. By combining the velocity density formula， the exhaust efficiency of the internal combustion engine was estimated through real-time adaptive estimation of key parameters to be identified. Firstly， an exhaust model was established based on the pressure dynamics model at the exhaust manifold and the mass flow model of exhaust gas at the exhaust outlet. To balance signal acquisition accuracy and computational resource efficiency， the established exhaust model was discretized. Then， an adaptive state observer was designed， and its parameters were adjusted to ensure performance and convergence. Finally， the exhaust efficiency of the parameter to be identified was estimated through an adaptive observer， and the exhaust gas flow rate of the cylinder was calculated based on the exhaust gas velocity density formula. The co-simulation results using GT-SUITE and Simulink demonstrate that the proposed method exhibits good performance in both steady-state and transient operating conditions.

Addressing the issue of insufficient adaptation to driving styles in the traditional adaptive cruise system of pure electric vehicles， a humanized stop-and-go control strategy based on driving behavior characteristics was proposed. Firstly， real driver stop-and-go data were analyzed and features extracted， and construction of a driving style classification model using principal component analysis and the K-Means++ algorithm. Secondly， a two-layer control architecture was designed to implement the human-like control logic for switching between driving and braking. Thirdly， a humanization factor was designed based on Bayesian linear regression and maximum entropy inverse optimization methods. Finally， the consistency between the proposed method and real drivers' control behaviors was validated through simulation tests aralysis.

In order to improve the work efficiency of automobile enterprises， provide the design framework for designers， and effectively improve the accuracy and objectivity of the design， a method of using machine learning was proposed to fit the preliminary work of vehicle design and the later personalized cover parts design. This method is mainly based on diffusion models and text inversion technology. Firstly， the user profile was classified and the model was trained. Secondly， a high-degree fitting with the help of the existing corresponding user portrait of the molding car with high market feedback was performed. By adjusting user profile data and other information， pre defined vehicle renderings， car coverage kits， etc. can be generated. Compared with existing processes， the proposed method can highly fit the pre design work and provide designers with more intuitive and objective pre definition of vehicle models. Therefore， this method has broad application prospects in fields such as automotive design， market research， styling strategy formulation， creative draft generation， and panel design.

Addresses the issue of vehicle stability control， a design method for the lateral motion control system of automobiles based on the UniTire model was proposed. Firstly， on the premise of retaining the UniTire model to describe the dynamic characteristics of the tire in a unified exponential expression， combined with the requirements of vehicle stability control， a targeted simplified modeling method is proposed. Secondly， in combination with the model predictive control theory， a vehicle lateral prediction model was established， and a vehicle yaw moment controller was designed. Finally， the effectiveness of the control system was verified and analyzed by using the joint simulation platform of Carsim and Matlab.

In order to improve the reduction of driving scene and state in dynamic simulation of high-speed planetary rover， a rover dynamic simulation method based on single-wheel experiment is proposed. This method embeds the single-wheel experiment data of the planetary rover into the vehicle simulation to optimize the fidelity. Firstly， combined with the theory of planetary rover dynamics modeling， the influence of wheel-ground contact force accuracy on vehicle dynamics simulation is analyzed. Secondly， through the force analysis of the planetary rover wheel， the experiment-simulation associated parameters such as drawbar pull and friction coefficient are defined. Thirdly， the single-wheel experiment measurement scheme is systematically designed around the associated parameters， and the vehicle dynamics simulation principle and process based on single-wheel experiment are determined.Finally， in order to verify the effectiveness of this method， a single-wheel experiment was carried out based on the high-speed planetary rover prototype to obtain the parameter data， and the vehicle simulation test was carried out. The simulation results show that this method can better restore the driving state of high-speed planetary rover such as start acceleration， body yaw and wheel force change， and can effectively improve the accuracy of rover dynamics simulation results.

Aiming at the problem of multi-degree-of-freedom composite motion compensation of ships under the action of sea waves， a ship motion measurement and compensation algorithm based on a parallel platform to simulate sea waves was proposed，which includes an attitude calculation algorithm and a heave displacement calculation algorithm. First， a ship motion compensation experimental platform composed of two six-degree-of-freedom parallel platforms in series was built. Secondly， based on the inertial measurement unit， a Kalman filter attitude calculation algorithm was used to calculate the roll and pitch attitudes of the simulated ship motion. Thirdly，a heave displacement calculation algorithm was designed， featuring high-pass filtering， phase compensation， and sea wave frequency adaptability. Finally， ship motion compensation experiments were conducted. The experiments results showed that the upper parallel platform can effectively suppress the impact of sea waves on the ship's motion， significantly improving the stability of ship operations. The experiments validated the rationality and effectiveness of the proposed algorithm in this paper， providing an effective method for real ship motion compensation research.

On the basis of considering the influence of load deformation on the contact state， a planetary roller screw rolling kinematics model considering the load deformation of thread pairs was established， and a rolling mechanism analysis method based on motion coordination conditions was proposed.Relying on a dedicated experimental platform for evaluating the motion characteristics of roller screws， the proposed analytical model of rolling-sliding motion was experimentally validated， and the coupled rolling-sliding mechanism was quantitatively revealed. The results indicate that increasing axial load significantly intensifies load-induced deformation within the thread pair， resulting in an average increase of 0.106% in the rolling velocity component and a dramatic increase of 19.012% in the sliding velocity component. This study elucidates the rolling-sliding coupling mechanism of planetary roller screws and provides both theoretical foundations and data support for structural optimization and rolling-enhancement/sliding-reduction strategies， offering valuable insights for improving transmission performance.

To enhance the operational performance of high-speed variable-gauge trains on different gauge lines，a semi-active control method based on double sliding surfaces was used to adjust the damping force of air springs and magnetorheological （MR） dampers in the secondary suspension.Based on double sliding surfaces，a quarter-vehicle fractional-order skyhook damping control reference model was established，and a double sliding surface controller was designed.The semi-active controller was applied to secondary air springs and transverse magnetorheological dampers.Finally，a joint simulation model of the whole vehicle for the variable-gauge train suspension system was constructed.The response performance to track irregularity excitation on 1 435 mm/1 520 mm gauges is analyzed to verify the effectiveness of the semi-active control method on whole vehicle.

Based on the discrete element method， the flow characteristics of granular matter through eccentric hopper outlet driven by gravity were numerically investigated. Two different eccentricities e （distance between the center of the hopper and the center of the outlet） and s （distance between the left wall of the hopper and the left end of the hopper outlet） were used to characterize the discharge process of the eccentric hopper， the influence of the eccentricities on the distribution of particle velocity and volume fraction at the orifice was systematically studied. The results show that for different outlet sizes， when the eccentricity e is used to characterize the discharge process， the granular flow exhibits different flow patterns， while the discharge flow patterns are similar when the eccentricity variable s is used. Further， according to the analysis of eccentricity s， the distribution of particle velocity and volume fraction have self-similar characteristics， their normalized contour curves can be well described by the exponential function， and the exponential parameter determines the trend of curve contour change， which has clear physical significance. Combining the profiles of particle velocity and volume fraction， a normalized formula for predicting flow rate of rectangular eccentric hopper is proposed. It is found that it has the maximum discharge rate when the outlet is located at the wall of the hopper （s=\mathrm{0}）. At the microscopic scale， understanding the dynamic characteristics and physical laws of the particle discharging is helpful to optimize the structure of the hopper and particle flow， as well as accurately predict the discharge rate.

Finite element analysis was used to study the temperature and displacement distribution of automotive interior components at different moments during the bonding process， and the effects of boundary constraints on the model temperature， thermal stress， displacement and damage to the adhesive layer was investigated. The simulation analysis results show that with the increase of time， the temperature of the model increases， the displacement becomes larger and more obvious in the position of large curvature； the simultaneous constraints on the side and bottom surfaces of the interior components during the bonding process effectively reduce the damage of the adhesive layer and improve the overall stiffness of the structure. Comparing the temperature， stress and displacement clound maps under different boundary constraints， it is determined that the optimal design of constraining the side and bottom surfaces at the same time is important for improving the quality of bonding of automotive interior components. This study optimises the bonding process for automotive interior components and provides guidance for improving product quality during the bonding process.

A method for virtual lane lines fitting based on traffic environment information was proposed to address the problem of lane lines missing caused by wear， ice and snow coverage， etc. on certain road sections during vehicle driving. Firstly， a probability model for the lateral offset of lane lines under lane-keeping conditions of traffic vehicles was established based on the highD naturalistic driving dataset to determine the lateral position of virtual lane lines. Then， a traffic vehicle intention recognition and trajectory prediction model based on bi-directional long short-term memory networks was established to realize the condition judgment and shape generation of virtual lane lines generation. Finally， based on the information of the above， the statistical characteristics of lateral lane deviation of traffic vehicles that meet the fitting conditions were transformed into the risk field of virtual lane lines in the main vehicle coordinate system to realize the virtual lane lines fitting. Simulation and real vehicle data validation results show that this method can generate safe and effective virtual lane lines with a small error compared to the true value of lane lines， providing a reference driving space for the main vehicle and ensuring driving safety.

In order to achieve flexible allocation of capacity resources， alleviate the waste of resources due to the imbalance between system supply and demand， and reduce the travel time loss due to vehicle stopping at stations and passenger transferring outside the vehicle， an adaptive scheduling method for bus based on AMV main-auxiliary function allocation was designed. An optimized scheduling model considering route coordination and station function division was established， with minimizing vehicle operating costs and passenger travel delays as the joint optimization objectives. A hierarchical adaptive heating particle swarm-simulated annealing algorithm was designed to solve the model. To verify the effectiveness of the model， an example test was carried out based on the bus operation data in Foshan City， and the results show that compared with the traditional fixed-capacity bus and station-responsive modular bus， the proposed model reduces the total cost of the system by about 35.62% and 6.54%， and the results of this research can help to improve the efficiency of bus operation and service quality.

Based on the YOLOv5 algorithm， the feature extraction process was optimized by introducing the FasterNet lightweight network structure to reduce the computational complexity of the model. Combining Convolutional Block Attention Module （CBAM） attention mechanism to improve the model's attention to defect features. Adopting the SIoU loss function to improve the accuracy of bounding box regression and enhance localization accuracy. The experimental results show that the improved model has achieved significant results on the self built bridge defect dataset， with accuracy and mAP reaching 81.2% and 71.5%， respectively， which is a significant improvement compared to the benchmark model. This study provides technical support for the intelligent detection of bridge defects， which is of great significance for promoting the digital transformation of bridge maintenance management.

To explore the optimal road network restoration decision scheme under waterlogged conditions， a urban three-lane road sections cellular automaton simulation model was constructed considering the dynamic change of waterlogged depth. By improving the vehicle lane change and position update rules， the changes of driving behavior under waterlogged conditions were described， and the impact of waterlogged on road sections traffic capacity was simulated. The changes in the distribution of traffic flow on the road network caused by water accumulation leading to restricted passage of vehicles on certain road sections was analyzed， a recovery decision optimization model aiming at maximizing the cumulative road network performance index was established， and the genetic algorithm and Frank-Wolfe algorithm were combined to solve the problem. Sioux-Falls network was selected to verify the model，the results show that the recovery strategy obtained by the provided model has better effect on road network performance restoration than the empirical recovery strategies based on output and flow， and the improvement degree is 3.84% and 2.02%， respectively. The road section restoration order should not only consider the damage degree of the road section， but also consider the traffic demand distribution of the road network. The increase of recovery resources will significantly reduce the recovery time of the road network， but the marginal utility will gradually decrease.

To identify the key mechanisms underlying behavioral transitions， a multi-path structural model was developed encompassing user expectations， perceived service quality， overall satisfaction， complaint behavior， and continued usage intention. Empirical data were used to estimate the relationships among these variables. Building on this foundation， an agent-based simulation model was constructed， dividing users into four behavioral states “non-adoption， initial contact， active use， and regular adoption”. The model simulates the evolution of user behavior throughout the service process. Simulation results indicate that the transition rate from “active use to regular adoption” is only 36.36%， while the downgrade risk for regular adopters reaches 53.81%， representing a structural bottleneck for platform operations. Under strategy intervention scenarios， improving service reliability increases the proportion of regular adopters from 28.7% to 32.4% and reduces their downgrade rate to 50.15%. In contrast， a complaint mitigation strategy mainly targets the negative feedback pathway； although it can temporarily increase the proportion of regular adopters to 31.5%， it fails to effectively curb subsequent attrition， with the downgrade rate rising to 55.73%. This suggests that its capacity to stabilize user behavior is limited and insufficient for sustaining long-term usage relationships. The findings provide strategic support for optimizing platform service processes and managing user structure.

Aiming at the freeze-thaw instability risks of high-speed railway tunnel portal slopes in cold regions and the issue of fragmented monitoring data， this paper proposes an intelligent monitoring system integrating multi-source information （including BeiDou positioning， millimeter-wave radar and InSAR） based on a "perception-transmission-analysis-warning" framework. The system constructs three categories of monitoring indicators—structural deformation， environmental disturbance， and abnormal response， and the "point-line-surface-volume" deployment strategy was adopted to achieve multi-dimensional perception of slope conditions. Furthermore， an early warning model incorporating displacement triggering， image verification， and multi-source fusion was developed， alongside a digital platform was developed to achieve risk hierarchical response. The results of engineering validation show that the system has high adaptability during freeze-thaw periods and can effectively identify landslides precursors.

To investigate the effectiveness of open roads in the ICV driving safety test， a rating algorithm for open road in ICV test according to the theory of random forest was proposed. A rating index system for open testing roads of intelligent connected vehicles was constructed from five aspects： road complexity， safety assurance， scene diversity， facility comprehensiveness， and scene typicality. Then a random forest model for the rating of open road is built and calibrated with the data of the existing open roads in ICV test. The rating algorithm is then applied in rating of open roads in Changchun City， the results of which demonstrates the feasibility and effectiveness of the model. The results indicate that the proposed intelligent connected vehicle（ICV） open test road rating algorithm can accurately perform road rating， providing model support for the selection， planning， and construction of open test roads for ICV.

In order to further improve the diversity of the sample set， the DDHNSbS algorithm was proposed， in which the Diversity Mutation Strategy（DMS）not only preserves the diversity of the initial population， but also optimizes the effect of traditional genetic operators. Meanwhile， to make up for the limitations of novelty search， diversity detection（DD）was proposed to check and flip the assigned values of features with a certain probability. So as to further diversify the number of features selected in the sample set， probability-aware diversification via the Halton sequence（Halton-PaD）was achieved， and the generated initial configurations' uniformity was developed thanks to Halton-PaD. Experiments were carried out on 39 public SPLs， and the experimental results show that when DDHNSbS algorithm is compared with PaD-NSbS algorithm， DDHNSbS performs significantly better in 42.1% of the small SPLs， and has the same performance on other SPLs.

To achieve efficient path planning for mobile robots in complex workshop environments， a hybrid path planning method integrating improved A^* algorithm and the DWA algorithm was proposed. Firstly， the global path planning A^* algorithm was optimized by improving the heuristic function， taking into account the influences of obstacle density， the number of turns， and motion inertia factors. Secondly， weight factors were introduced to dynamically adjust the heuristic function weights， the Douglas-Peucker algorithm was employed to simplify the path， and the B-Spline algorithm was applied to smooth it. Finally， the local path planning DWA algorithm was improved， the key points of the globally optimal path were used as target points in the DWA alogorithmprocess. A trajectory evaluation function was reconstructed by introducing three evaluation terms： the distance between the current point and the target point， the distance between the current point and dynamic obstacles， and the path tracking cost. Simulation results showed that， compared with the traditional A^* algorithm， the improved A^* algorithm reduced the path length， planning time， number of expanded nodes， and number of turning points by 15.80%， 81.20%， 2.30%， and 74.10%， respectively. In scenarios involving temporary static obstacles and dynamic obstacles， the runtime of the integrated improved algorithm was reduced by 22.02% and 22.32%， respectively. Experimental results demonstrated that the integrated improved algorithm enabled efficient path planning and safe obstacle avoidance.

Regarding the issue that existing semantic segmentation algorithms fail to fully exploit target edge features in autonomous driving scenarios， an edge feature-guided semantic segmentation method for intelligent vehicles was proposed. First， an edge feature enhancement module was introduced to extract edge information from the original image as a parallel input to the encoder， strengthening the neural network's edge feature representation. Second， an edge attention module was proposed to fuse original image features and edge features， adaptively balancing their contributions in different regions for semantic segmentation. Finally， an efficient channel pyramid pooling module was designed in the decoder to improve the utilization efficiency of contextual semantic features after pooling at different scales. Experimental results on the Cityscapes dataset demonstrate that the proposed method achieves 76.1% MIoU with a processing speed of 132.6 f/s， outperforming the baseline PP-LiteSeg by 2.2% MIoU while maintaining real-time performance， balancing segmentation accuracy and computational efficiency.

In order to improve the processing efficiency of the gesture recognition algorithm for real-time gestures， an adaptive real-time classification algorithm based on gesture frame sequence extraction was proposed. The algorithm extends the frame sequence of the current frame of image data extracted in real-time， completes the classification task with the sequentially extracted multi-frame sequences and performs the joint discriminant， which can recognize static and dynamic gestures at the same time， and obtains the real-time recognition results quickly. The proposed algorithm acccupies less computational resources to shortens the recognition time， solves the delay problem of gesture recognition， and improves the real-time performance. Through the gesture recognition real-time performance experimental test， the proposed algorithm controls the average recognition time of gesture within 0.4 s.

To address the issue of reduced biochemical reaction efficiency in wastewater treatment plants located in frigid zones of China during winter， caused by excessively low temperatures of raw sewage， a variable-frequency control strategy based on an adaptive weight particle swarm optimization （PSO） algorithm was proposed. Using a wastewater treatment plant in a northeastern Chinese city as a prototype， with the goal of lowest annual operating energy consumption of the system， the frequency of the sewage pump on the heat source side was optimized for different raw sewage temperatures throughout the year. Simulation runs comparing this system's variable flow rate control scheme with constant flow rate control scheme were conducted. The simulation results demonstrated the effectiveness of the proposed algorithm.

An online data-driven predictive control method was proposed， which constructs an equivalent linear model of the controlled plant at each discrete time step of the closed-loop operation， and a recursive least squares algorithm with a dynamically adjusted forgetting factor based on parameter identification residuals was developed to estimate the model's time-varying parameters online. Furthermore， a distributed predictive control strategy was proposed based on a predecessor-leader-follower communication topology. The strategy decomposes the global optimization problem of the vehicle platoon into local subproblems for each follower vehicle， enabling parallel computation and improving solution efficiency. Co-simulation results using TruckSim and Matlab/Simulink demonstrate that the proposed data-driven model accurately captures vehicle dynamics across time-varying environments， and the designed distributed controller ensures effective longitudinal tracking and lateral lane-keeping performance of the platoon.

To ensure the safe and reasonable passage of intelligent driving vehicles through uncontrolled intersections， a sequential decision-making method based on the Level-K game model was proposed. Firstly， the initial classification of vehicle reasoning levels was conducted through trajectory prediction and correlation analysis among vehicles. On this basis， the concept of sequential priority was introduced into the improved Level-K game framework to construct decision-making model for uncontrolled intersections. Secondly， to reduce unnecessary interactions among vehicles and lower the computational complexity of the decision-making model， a game start and end discrimination mechanism was proposed to dynamically screen the objects participating in the game. Meanwhile， to ensure the safety of the decision-making， a rectangular vehicle model was proposed to assess the collision risk of vehicles. Finally， the effectiveness of the proposed method was verified through experiments. The results show that the interactive decision-making method proposed can effectively avoid potential collision risks and enable intelligent driving vehicles to pass through intersections safely and reasonably.

To address the inadequate performance of traditional sound barriers， two bionic sound barrier panels were designed based on the armadillo shell and honeycomb structure as prototypes. The performance of these panels was studied and analyzed through simulation and validated by field testing. The results indicate that the sound insulation performance of the bionic sound barrier panels significantly surpasses that of traditional steel panels. Specifically， the sound insulation of the bionic armadillo shell sound barrier panel reaches 37.711 dB， an increase of 38.1%. Furthermore， in this paper a parametric design was conducted on the height of the protrusions and the overall thickness of the bionic sound barrier panels. Field tests date show that the insertion loss of the bionic sound barrier is 23.3 dB， which is an improvement of 50.3% over traditional sound barriers. This design achieves a balance between sound insulation performance， mechanical performance， and cost， providing a new design concept and research method for highway sound barrier panels.