To solve the problems of long-term pedestrian trajectory prediction around autonomous vehicles with insufficient performance and poor adaptability to complex scenarios by the existing methods, the novel method was proposed. The pedestrian trajectory prediction problem was modeled, and the CrossFormer-based pedestrian trajectory prediction method was developed. The dimension-segment-wise (DSW) embedding technology was introduced to explicitly learn the correlations between adjacent time frames, and the two-stage self-attention mechanism (TSA) was combined to comprehensively capture the long-term dependencies of pedestrian trajectories. The hierarchical encoder-decoder structure was employed to adaptively capture pedestrian trajectory dependencies at different time scales for enhancing the model scalability in long-term prediction. The multi-modal information fusion, the self-attention mechanisms and the scalability optimization were innovatively integrated to achieve efficient solution for pedestrian trajectory prediction tasks. The experiments were conducted on the two datasets of ETH and Jiangsu University campus pedestrian trajectory data (JDD). The time series segmentation analysis and quantitative and qualitative evaluations were performed. The results show that by the proposed method, the values of average displacement error （ADE） and final displacement error (FDE) are respective 0.627 and 1.32 on the ETH dataset, which are significantly better than those by the traditional methods of LSTM with 0.895 and 1.74 and SR-LSTM with 0.728 and 1.66. On the JDD dataset, the values of ADE and FDE are 0.281 and 0.53, respectively, which are far superior to those of GAN with 0.562 and 1.01 and STGAT with 0.673 and 1.43. The robustness and generalization ability of the proposed method in complex scenarios are verified.

To solve the adaptability problem of adaptive cruise control system for different styles of drivers, the personalized multi-mode adaptive cruise control algorithm was proposed. Based on the next generation simulation(NGSIM) data set, the car-following data were clustered and analyzed by K-means algorithm. The driver driving style was divided into radical, general and conservative types, and the corresponding car-following distance model was constructed. The gradient algorithm based on the deep deterministic policy was designed to modularize the basic performance of adaptive cruise, and the personalized car-following distance model was integrated into the basic performance of each mode. Based on the maximum entropy inverse reinforcement learning, the weights of personalization before the reward function were designed. To verify the multi-mode adaptive cruise control, the system was run in Simulink/Carsim joint simulation environment, and the results were compared with real vehicle samples of three driving styles. The results show that the tracking performance is good, and the inter distance and speed are close to those of the real samples of three types of drivers, which conforms to the driving habits of drivers and meets the personalized driving needs.

 To solve the problem of the existing underwater detection with low detection accuracy due to single variety and dense target, the improved YOLOv5 algorithm of RSC-YOLOv5 was proposed. The RepVGG Block module was used on Backbone to improve the recognition accuracy of targets of different scales and enhance the inference speed. The shuffle attention （SA）module was added to improve the feature extraction ability of the algorithm. Content-aware reassembly of features(CARAFE) upsampling was used in Neck to obtain larger receptive field. Varifocal Loss was introduced to pay more attention to the high-quality positive samples in intensive target sample training. The experimental results show that the average accuracy of the RSC-YOLOv5 fish and crab detection algorithm is 93.6%, which verifies that the proposed algorithm is suitable for the underwater fish and crab detection.

To solve the problems of discontinuity in path curvature, low parking efficiency and low path tracking accuracy in autonomous parallel parking, the characteristics of the circular arcstraight linecircular arc initial parking path were analyzed, and the path planning method based on  fifthorder polynomial curve was adopted. To achieve the balance between path length and path curvature performance, based on the constraints of maximum path curvature, required parking space and obstacle avoidance, the weighted sum of the maximum curvature and the horizontal coordinate of parking start point was used as objective function. The particle swarm optimization algorithm with nonlinear dynamic adaptive inertia weight was employed to optimize the horizontal coordinate of the parking start point, and after optimization, the path became smooth and continuous in curvature. The path tracking control method based on model predictive control was established, and the genetic algorithm was used to optimize the prediction horizon and control horizon for reducing computational load while ensuring tracking accuracy. The real vehicle validation was conducted based on the autonomous driving developer kit of Baidu Apollo. The results show that the vehicle parking can be completed safely without collisions, which verifies the feasibility of the path planning method. Under the premise of reduced computation load, the average path tracking error is decreased by 4.348% compared to that before optimization, and more high tracking accuracy can be obtained by the proposed path planning method.

 When the vehicle works normally, the internal parts of transmission are often damaged due to high temperature caused by shifting and other behaviors, which affects the normal driving of the vehicle. Therefore, it is necessary to study the transient temperature field of the transmission. According to the thermal load characteristics of transmission components, the transient thermal network model of transmission was established by thermal network method in the forms of heat source and thermal resistance. The temperature of each transient node in the transmission was calculated, and the temperature rise of high-heat parts of wet clutch and planetary wheel during the whole process of vehicle starting to driving smoothly was analyzed. The results show that the temperature rise law of wet clutch is related to the gear shift. The temperature rise law of the gear is changed with the change of shift position. Temperature rise of parts in planetary gear is basically the same and changes steadily.

To improve the anti-wear and anti-fatigue ability of raceway surface of the shield main bearing, SYSWELD software was used to carry out the numerical simulation of laser transformation hardening under different parameters for the shield main bearing raceway material of 42CrMo steel. The effects of laser power, scanning speed and spot size on the phase transformation of 42CrMo steel for the raceway material of shield main bearing and the depth of hardened layer were investigated. The results show that with the increasing of laser power, the decreasing of laser scanning speed or the decreasing of laser spot size，the depth of raceway hardened layer is increased. When the laser power is 2 250 W with scanning speed of 20 mm/s and spot length of 5 mm, the depth of the hardened layer of the raceway can reach 1.08 mm, and the hardened layer is relatively uniform. 

To improve the work efficiency and screening yield in the circular crushing process, taking tomato straw as example, the screening performance of the spiral screening device was investigated. The numerical simulation and experimental research by EDEM software were conducted to analyze the effects of spiral speeds and charging speeds on the screening performance of spiral. The results show that with the increasing of spiral speed, the screening rate is increased with latter decreasing, and the output is gradually increased with latter unchanging. With the increasing of charging speed, the screening rate  is gradually decreased, and the output is gradually increased. Increasing the spiral speed can improve the sieving flow condition of crushed straw  particles and reduce the sieving time of unit straw  particles. The high sieving rate of 37.36% and high yield of 139.89  kg/h can be obtained when the spiral speed is 180  r/min. Increasing the charging speed can obstruct the flow of raw materials in the sieving process, which makes the sieving rate decrease. The high material concentration brings high yield, while the yield growth rate is gradually decreased. The inlet mass flow rate of 0.05 kg/s can control the screening rate more than 35% with high output, and the simulation results are verified by the experiments.

To solve the problems of poor stability and driving safety of the ego-vehicle caused by the variability of the preceding vehicle′s driving conditions, taking the hub-motor-driven electric vehicles as research object, the constant speed/follow-up cruise switching strategy was proposed with considering the inter-vehicle distance and relative speed simultaneously. The optimized safe time-headway model was designed, and the longitudinal tracking control strategy for the automotive adaptive cruise control (ACC) system was developed based on hierarchical control. The upper-level controller based on model predictive control (MPC) was established to solve the desired acceleration of vehicle. Utilizing the inverse longitudinal dynamics model, the lower-level controller was constructed to solve the output torque. The torque distribution strategy for the front and rear axles was designed, and by the electric motors, the wheel drive and braking torque were precisely controlled to achieve longitudinal tracking control for ACC. The co-simulation model was established by Matlab-Simulink and Carsim software to conduct simulation tests under three driving conditions of variable speed, variable acceleration and variable deceleration. The simulation results demonstrate that the proposed adaptive cruise control system based on hierarchical control can stably follow the preceding vehicle′s movement under different driving conditions with maintaining safe inter-vehicle distance, which exhibits excellent following stability and safety.

To reduce the temperature of heating wall and improve the uniformity of temperature distribution, the flow boiling heat transfer process of HFE-7100 in the wavy microchannel was numerically simulated, and the effects of microchannel structure and densification methods on the flow boiling characteristics were discussed. The results show that when the waviness throughout the whole flow direction is densified with increasing the ratio of wave amplitude to wave pitch from 0.04 to 0.08, the equivalent diameter of bubble at the outlet of microchannel is decreased to some extent，and the segmental annular flow is also suppressed. However, the pressure loss between the outlet and inlet of microchannel is increased significantly with the maximum increasing up to 140.54%. Increasing the waviness of the upstream, midstream or downstream in the microchannel can effectively inhibit the segmental annular flow from occurring at the outlet section and reduce the heating wall temperature for improving the temperature distribution uniformity of heating wall. The comparison illuminates that increasing the downstream waviness can more significantly reduce the bubble growth rate. The heating wall temperature and distribution can be effectively controlled as well, which is equivalent to that of the whole densified microchannel. However, the pressure loss between the outlet and inlet can be reduced by 34.23%. Increasing the downstream waviness in the wavy microchannel is  preferable method for improving the flow boiling heat transfer characteristics.

To investigate the influence of security measures on resident ride-hailing decision behavior and travel preference, the extended technology acceptance model was constructed. Based on the nationwide network questionnaire survey data, the multiple indicators and multiple causes （MIMIC）model was used to test the path of the extended technology acceptance model. The comprehensive weighted average method was used to evaluate the effectiveness of rectification measures. The results show that the security risk is the critical factor to prevent passengers from using ride-hailing. On the contrary, privacy risk has slightly negative impact on the use intention. The male, the group with high frequency of using ride-hailing and the group with old age have more positive attitude towards using ride-hailing again after safety rectification. The effectiveness analysis reveals that passengers have high recognition degree for safety response, driver reviewing and identification with scores of 6.43, 6.07 and 6.02，respectively.

To provide more reliable safety recommendations for vehicles navigating curves, the lane curvature measurement method based on binocular stereo vision was investigated. The binocular camera was calibrated by the MATLAB camera calibration toolbox to obtain intrinsic and extrinsic parameters. The original left and right images from the binocular camera were input into the stereo matching network to generate disparity map. The Sobel edge detection and threshold detection were applied to the region of interest (ROI) in the original left camera image, and the results were fused. The perspective transformation was applied to the fused results within the ROI to obtain binary image. The positions and quantities of white pixels within the ROI were statistically analyzed by the histogram. The lane lines were fitted by the least squares method, and the fitting accuracy was enhanced by Kalman filter prediction algorithm. The pixel coordinates of the lane lines were determined based on the inverse affine transformation and the fitting results. The disparity values within the disparity map were obtained according to the pixel coordinates, and the three-dimensional coordinates of the lane lines relative to the main camera of the binocular system were derived by triangulation. The distance conversion scale between the measured real-world coordinates and the image pixel coordinates was calculated and applied to the lane curvature computation. The results show that by the proposed algorithm integrating Kalman filter prediction for lane line detection and binocular stereo vision for lane curvature radius measurement, the average deviation of 5.98% in the calculated curvature radius of curves is achieved, which can ensure the robustness of lane line fitting for enhancing the accuracy and reliability of the binocular stereo vision-based lane curvature measurement method.

The video anomaly detection method based on fusion of attention with convolutional autoencoders was proposed to address the insufficient utilization of contextual semantic information in video sequences. The frame prediction strategy was adopted, and the convolutional autoencoder based on the Inception module was employed to extract the multi-scale features from input video sequences. To capture the interaction between moving objects and static backgrounds, the position attention and channel attention were incorporated. The memory enhancement module was integrated into the convolutional autoencoder to constrain generalization, and the latent loss function was introduced to enlarge the reconstruction errors of anomalous events. The calculation of anomaly scores was derived, and the dataset for anomaly detection was provided. To verify the proposed method, the qualitative analyses of abnormal behaviors, the model performance comparisons, the memory item update threshold experiments and the ablation studies were conducted. The results show that the proposed method can effectively detect the anomalies in videos with high detection accuracy. The AUC values on the UCSD Ped2, CUHK Avenue and ShanghaiTech datasets are 97.7%, 88.9% and 73.8%, respectively.

To improve the driving safety, the fault diagnosis model integrating local correlation constraints and signed directed graph (SDG) was proposed to address the challenge of fault localization in automatic emergency braking (AEB) systems. Taking the AEB system based on the time-to-collision (TTC) control strategy as research object, the SDG model of  braking system was constructed by combining vehicle dynamics model, brake pressure model and sensor system layout. To verify the proposed model, the cosimulation of Carsim and Simulink was conducted. The results demonstrate that the SDG model based on graph theory offers strong interpretability and effectively reflects fault propagation paths. Through the cosimulation tests of Carsim and Simulink, the fault diagnosis and localization during the active braking process can be efficiently achieved. By the propose method, the abnormal internal connection nodes can be detected within approximately 10 diagnostic function computations with accurate fault localization and propagation judgment.

The intake flow rate and pressure in the air supply system of proton exchange membrane fuel cells(PEMFC) are two coupled control variables, and the coupling affects the stability of the fuel cell system. According to the decoupling control requirements of intake air flow and pressure, the system openloop response experiment was designed, and the system identification method was used to obtain the model parameters of the system transfer function matrix. On this basis, the double closedloop PI, feedforward decoupling and fuzzy decoupling control strategies were designed. The decoupling effects under different control strategies and the antidisturbance abilities of different control strategies to system parameter changes were compared and analyzed. The simulation results show that the coupling effect between flow rate and pressure is significant under dual closedloop PI control. By the feedforward decoupling control, the complete decoupling can be achieved, but the decoupling effect is affected by the parameters of the controlled object model. The fuzzy decoupling control has good decoupling effect and stronger adaptability to parameter changes compared to conventional feedforward decoupling control, which has better application prospect in the multivariable strong coupling system control.

 To solve the problem of traditional Ethereum phishing scam identification and classification with high computational memory cost due to overlooking the importance of inter-subgraph relationships, the hierarchical graph attention framework was proposed to process subgraph classification tasks by leveraging graph attention technology for extracting behavioral patterns of account addresses. The hierarchical graph attention pooling encoder was constructed, and the node-level encoder was used to extract intra-subgraph node importance, while the subgraph-level encoder was employed to capture inter-subgraph significance for revealing potential associations both within and between subgraphs. Combined with graph contrastive learning techniques for joint training, the contrastive learning loss was introduced as regularize to alleviate label sparsity for improving subgraph classification performance. Comparative and ablation experiments were conducted on real Ethereum datasets, and the parameter analysis was carried out with F1 score as evaluation metric. The experimental results show that by the proposed method, the maximum improvement of 1.7 percentage points in F1 score is achieved on the real dataset with outperforming classical models of GCN, GraphSage and GAT, which requires less memory than other node classification approaches.

The direct data-driven control problem of an unknown class of Lorenz-type chaotic systems was investigated. The systems were transformed into linearly dependent form to construct the non-parameterized model. The state feedback controller was designed for the reconstructed system. The input and output data of the system were sampled offline, and the collected data were directly used to design and give the expression of the state feedback controller. The stabilization problem of the system under state feedback control was verified,and the stability of the closed-loop system was proved. Through numerical example simulation verification, the results show that the proposed direct data-driven control strategy is effective.

Based on the regulations of GB 13057—2023 "Strength of Seats and Their Anchorages of Passenger Vehicles", the optimization design was conducted for the anchorages of a certain bus seat. The side length of square tube, the wall thickness of square tube, the thickness of end cap, the thickness of side reinforcement component and the thickness of thinwalled parts used for upper and lower connections were selected as design variables. The adaptive Kriging (AKR) agent model for the seat forward displacement was established. The mass of the vehicle anchorage was calculated by the Catia parametric design method. With seat forward displacement and anchorage mass as optimization objectives, the multiobjective optimization of seat anchorage dimensions was performed by the particle swarm optimization (PSO) algorithm. The optimization results show that the seat forward displacement is reduced to 443.22 mm, which is decreased by 7.85% compared to that before optimization and can significantly enhance the seat rigidity for providing greater safety for occupants. The mass of the vehicle anchorage is reduced by 11.08% for achieving the goal of lightweight design.

To quickly and accurately detect the feature information of track cone buckets in the formula unmanned competition, the detection method fusing LiDAR point cloud data and camera image data was proposed. The region of interest (ROI) was extracted from the point cloud data, and the noise was filtered.The adaptive ground segmentation algorithm was introduced for the current ground undersegmentation and oversegmentation problems, and Euclidean clustering was used to extract the spatial location information of cone buckets. The image dataset was collected and labeled, and the posttraining detection was performed by YOLOv5 to complete the extraction of cone bucket color information. The bucket spatial position information detected by LiDAR and the bucket color information detected by camera were fused.The experiments were designed for validation. The results show that the proposed fusion detection method can solve the problems caused by single sensor detection of cone buckets, and it can quickly and accurately detect the cone buckets in the track, which provides guarantee for planning and decision making.

The three-dimensional SK-type static mixer model based on the mixer without mixing elements was established to simulate the mixing behavior of gas mixtures. The operational and design parameters were investigated, and the influence patterns of mixing performance of  uniformity index and pressure drop were analyzed. The results show that when the number of mixing elements is three, the optimal balance between performance and energy consumption is achieved. The length-to-diameter ratio of the mixing elements significantly affects the mixing efficiency, while the arrangement of mixing elements has less than 5% impact on the pressure drop. For further optimization, the response surface with low interpolation error is constructed based on the Kriging surrogate model. Combined with the global optimization capability of the genetic algorithm， the optimal flow channel structure is obtained. Compared with the base model, the uniformity index at the outlet section is improved by 7.2% after optimization,and the stratification phenomenon of hydrogen and methane is completely eliminated with improved uniformity of the flow field distribution. 

 The behaviors of adjacent lane target vehicles cutting into the own lane were investigated during the commercial autonomous vehicles cruising or following in the current lane. By the lateral safety distance threshold method, the lateral entry behavior of the target vehicle was predicted and identified. Four longitudinal distance models of active fuzzy alternative safety measurement, responsibility sensitive safety model, headway and collision time were investigated and simulated. The results of simulation testing and open source data environment testing show that the active fuzzy replacement safety measurement model can fully utilize the vehicle motion performance limitations and the road environment information transmitted by the perception module, which ensures driving safety with relatively small impact on traffic flow. The algorithm with entry recognition can adopt gentle braking before the vehicle enters the self driving lane for avoiding the dangerous behavior of emergency braking in the original algorithm and improving the driving safety and riding comfort of autonomous driving.

To solve the problem of huge cost caused by the collision of agents with obstacles when the environmental information was unknown, the robust reinforcement path planning algorithm based on Bayesian optimization and quadrature was proposed. The grid map of the environment was established for setting environmental rewards. Bayesian optimization was used to establish Gaussian Process surrogate model for historical action sets, and the mean and variance of rewards were estimated by Gaussian Process. The upper confident bound (UCB) method was selected to balance exploration and exploitation, and the action sequence was selected to avoid over-exploration and over-exploitation. Bayesian quadrature method was used to actively learn the environment, and the uncertainty of environmental information was reduced by minimizing the expected posterior variance of Q value for avoiding collision and improving robustness. The Q table was updated iteratively, and the Q learning method was used to plan the path. The simulation experiment was carried out to compare the proposed algorithm with the classical Q-learning path planning algorithm. The results show that compared to the classical algorithm, the proposed algorithm has higher learning efficiency for the environment, smaller collision probability, faster convergence speed of the optimal path steps and more effective path planning.

To solve the problems of poor generalization and defective fault dataset in the existing fault diagnosis research of vehicle steering system, the fault diagnosis method of intelligent steering system based on the random forest (RF) algorithm was proposed. The physical model of the intelligent steering system was built based on the joint simulation of Simscape and CarSim. The several critical faults of steering system were simulated, and the fault datasets under various driving conditions were collected. The fault diagnosis model based on RF algorithm was established. The input data was classified to realize the fault diagnosis of intelligent steering system, and the proposed method was compared with several typical algorithms. The steering system bench was built to collect the imbalance sample data set for further validation. The experimental results show that using the RF algorithm for fault diagnosis of the simulated fault dataset, the fault diagnosis accuracy rate is approximate 87.43%. In the experimental verification, the fault diagnosis accuracy rate is 99.93% with fast diagnosis speed and good generalization.

To investigate the difference in driver gazing characteristics and vehicle lateral offset under two navigation modes of full-touch screen vehicle infotainment system and smart phone, the driving data acquisition platform was constructed under two types of real-road conditions. The relevant data of 27 drivers and vehicles were collected under the two navigation modes of full-touch screen vehicle infotainment system and smart phone. Five indicators of total task duration, single time of looking away from front, number of gazing switches, total time of looking away from front and standard deviation of lane position were selected. Mean comparison, variance and significance analysis were carried out on 1 041 sets of valid data of the five indicators at the four vehicle speeds. The results show that at the four vehicle speeds, for the full-touch screen vehicle infotainment system navigation, the average values of four indicators of total task duration, number of gazing switches, total time of looking away from front and standard deviation of lane position are all higher than those of mobile navigation. Compared with mobile navigation, the average values of total task duration, number of gazing switches and total time of looking away from front are increased by respective 78.55%，77.17% and 49.20%, while the average value of single time of looking away from front is decreased by 15.98%. With the increasing of vehicle speed, the total task duration, single time of looking away from front and total time of looking away from front under both navigation modes show a certain downward trend, while the standard deviation of lane position shows an obviously upward trend. Based on the analysis results of the above indicators, compared with the mobile navigation mode, the full-touch screen vehicle infotainment system navigation mode causes greater operational risks during the navigation setting stage.

Using salt and tap water as raw materials, the hypochlorite solution was produced by electrolysis for using as substitute disinfectant in public places, and the substitute disinfectant was integrated into local synthesis spray sterilizer. The device was composed of two parts with pneumatic electrostatic atomization unit and disinfectant local synthesis unit. The effective operating distance was 6 m with unit time spray volume of 303 mL/min. In the E. coli test, the ORP value of the test group was high, and the change range was small when the salt water with mass fraction of  2% was reacted for 3 min. Compared to the pure water control group, the sterilization rate of the selected test group was 100.00%, and sterilization effect was significant.The spray performance test was conducted to analyze the spray performance under different conditions by changing the air pressure with or without  charging.The results show that as the air pressure is changed, the amount of spray per unit time and the spraying width and distance are changed accordingly. At the air pressure of 2.5×105 Pa, the spray volume, spray distance and spray range per unit time are 303 mL/min, 6.00 m and 1.25 m, respectively. In the charged state, the spray amplitude, deposition density and deposition amount are larger than those in the same condition. At the air pressure of 2.0×105 Pa, the maximum spray amplitude is 1.21 m, and the spray amplitude is increased by 0.20 m compared with that at noncharged condition. The deposition density of droplets can be increased by charging, and the deposition amount is increased by 0.08 μL/cm2 at the upper position of 3.70 m. At the air pressure of 2.5×105 Pa, the average particle size of droplets is 47.93 μm. The experimental results show that the total number of bacteria is less than or equal to 300 CFU/（25 cm2） when the developed substitute disinfectant local synthesis spray disinfection device is used in laboratories and public places, which meets the national standard.

To explore the phenomenon of water and gas transport and electron conduction in the cathode catalyst layer (CCL), the pore network model of CCL for the proton exchange membrane water electrolyzer was established, and the change rules of the accurate transport parameters of permeability and diffusivity and the electrochemical parameters of electronic conductivity and electrochemical activity specific surface area with the microstructure of catalyst layer were investigated. The results show that with the dcreasing of water saturation, the gas relative diffusivity and relative permeability in different directions in CCL are gradually increased, while when the water saturation is 0-0.61, the better water/gas transport characteristics are achieved. The conductivity of catalytic layer is nonlinearly increased with the increasing of platinum particle radius. When the platinum particle radius is increased from 5 nm to 20 nm, the electron conductivity is increased by 55.7%. The conductivity is decreased with the increasing of pore diameter, while the change is small. The electrochemical specific surface area of CCL is nonlinearly decreased with the increasing of platinum particle radius or pore diameter, and the decreasing is comparable.

To address the inefficiency of existing Fine-Tuning methods in handling minor visual variations, the transfer reinforcement learning model based on semantic selection was proposed. Inspired by the inattentional blindness that irrelevant background stimuli were overlooked during attentional shifts, the model was designed to decouple visual task transfer from policy control tasks through unsupervised semantic segmentation and semantic weight selection for preserving only policy-relevant visual features. The Flappy Bird game variant was utilized as experimental environment, and the transfer tests were conducted under varying visual interference conditions. The multiple soft attention mechanisms were compared in terms of transfer performance. The results show that the proposed method outperforms attention-based transfer approaches in efficiency, interpretability and adaptability to complex backgrounds. The significantly enhanced robustness is demonstrated, particularly in environments with substantial visual interference.

To solve the problem of service quality testing and evaluation for low-speed automated vehicles with low accuracy of traditional automated driving function evaluation methods, the V2I-based simulation test platform for autonomous vehicles in the loop was designed, and the FES-GALMBP evaluation algorithm was proposed. The evaluation indicators for low-speed autonomous vehicle service quality were determined by the HotSpot association rule method. The indicator weights were optimized through AHP-CRITIC subjective-objective combined weighting, and the multi-level fuzzy comprehensive evaluation model with incorporating criterion layer, standard layer and indicator layer weight calculations was constructed. The fuzzy expert system (FES) was utilized to evaluate the test sample set, and the evaluation results were used as training data for the FES-GALMBP neural network model. The Prescan/MATLAB was used to complete co-simulations and real-vehicle tests in low-speed autonomous bus scenarios. The results show that by the proposed FES-GALMBP model, the accuracy rates of 94% for operational quality and 80% for operational safety are achieved, which are significantly higher than those by the traditional BP neural network model with 59% and 53%, respectively. The AUC values for all prediction categories of the proposed model are higher than those of the traditional BP model, which illuminates that the novel model has superior classification performance.

To realize the high-precision motion trajectory tracking control of pneumatic actuators, the proportional directional valve-controlled pneumatic cylinder system was taken as research object, and the robust controller with online adaptive parameters was designed based on the  control strategy. The proportional directional valve flow model, the cylinder kinematic model, the cylinder friction model and the cylinder thermodynamic model of system were established. The robust controller was designed based on the backstepping method to suppress the effects of parameter estimation errors, uncertainty nonlinearities and external disturbances of system, and the online parameter adaptive rate was designed for the controller to effectively estimate the parameter uncertainty term in the system model in real time. The real-time control system for adaptive robust tracking control of the pneumatic motion trajectory was built by the XPC-Target module in MATLAB/Simulink. The experimental results show that by the designed controller， good trajectory tracking control performance is achieved. The maximum tracking error is 0.80 mm for tracking 0.4 Hz sinusoidal trajectory, which is about 2.67% of the amplitude.

In response to the national "double carbon" plan and to explore the potential of new carbon-free fuels in micro-power systems, the experiments were conducted to systematically investigate the flame characteristics of ammonia/hydrogen/air premixed flames in planar micro-combustor. The effects of operating parameters of flow rate and equivalence ratio on flame stability and combustor working performance were assessed. The formation mechanisms and transition laws of different flame structures were discussed in detail according to the internal flow heat transfer and external heat dissipation. The results show that as the flow rate is increased, the flame patterns appear repetitive extinction and ignition, planar, anchored, U-shaped and inclined flames in sequence. The velocity ranges of various types of stable and unstable flames are also determined. The flame stability at stoichiometric equivalent ratio is optimal when the equivalent ratio is 1.0, and the blowout limit can reach 0.35 m/s. The overall output characteristics of U-shaped flames are superior compared to other flame structures, and the maximum wall temperature can reach 900.0 K with the minimum wall temperature difference.