With the emergence of the Transformer attention mechanism， general-purpose large models represented by GPT have achieved the "emergence" of intelligence， bringing a dawn to the advancement towards higher levels of autonomous driving. Limited by the traditional from-scratch pre-training approach， which requires large-scale， high-quality， diverse autonomous driving data and incurs high training cost， the "large model + alignment technology" paradigm has been derived. As a bridge between general-purpose large models and autonomous driving， alignment technology， through customization methods such as fine-tuning or prompt engineering， achieves efficient and professional solutions to engineering problems within the field of autonomous driving. Alignment technology has become a hot research topic in the development of large models in vertical fields， but it lacks systematic research results. Based on this， this article firstly provides an overview of the development of autonomous driving and large model technology， thereby deriving alignment technology. Then， it reviews from the perspectives of fine-tuning and prompt engineering， systematically reviewing and analyzing the structure or performance characteristics of each classification technology， while providing actual application cases. Finally， based on existing research， the research challenges and development trends of alignment technology are proposed， offering references for promoting the advancement towards higher level of autonomous driving development.

A variable following characteristic traffic vehicle modeling method for intelligent driving system testing is proposed in this paper. Firstly， by clustering and analyzing natural driving data， a highly realistic interactive personalized car following model is established， and the model output coupling is used to assign multiple weights to construct a traffic vehicle model with variable car following characteristics that can be used for intelligent driving system testing. Then， by establishing the traffic vehicle trajectory evaluation method， the rationality， diversity and authenticity of the model's output trajectory are verified. Finally， a joint simulation platform is built to test the application of the constructed traffic vehicle model to the Automatic Emergency Braking （AEB） algorithm. The results show that the traffic vehicle model constructed in this paper can output reasonable， diverse， and realistic trajectories under different car following characteristics. When the number of trajectories reaches 60 000， the average root mean square error matched with the real natural driving speed trajectory is 0.427 m/s. Moreover， the behavioral response of the tested system varies under different traffic vehicle trajectory characteristics. By changing the weight coefficients， the evolution law of the tested system response can be revealed， and targeted testing of the tested system performance can be achieved.

The interaction ability between Highly Automated Vehicles （HAV） with human-driven vehicles is critical to the operational safety and efficiency of hybrid traffic in future. In order to test the interactivity of HAV， the background vehicle in the testing scenario needs to have naturalistic interaction characteristics and reflect the heterogeneous interaction strategy of human drivers. Based on the game theory， the Game-theoretical Strategic Interaction Model （GSIM） is developed in this paper. In the individual utility function， the interactive social characterization parameters with distinguishable values are introduced to directionally regulate the interaction strategy of the background vehicle. The test results of unprotected left-turning scenarios at intersections show that GSIM preserves the interpretability of natural driving stepwise planning and mutual interactions to ensure simulation accuracy of interactive behaviors. GSIM is also able to effectively reflect the interactive strategy of human driving in high-risk scenarios， helping to provide challenging and valuable testing scenarios. Compared to traditional Intelligent Driver Models， GSIM improves average simulation accuracy by 42.8% in unprotected left turn scenarios and serious conflicts recurrence rate by 25.8%.

With the continuous development of autonomous driving technology， accurately predicting the future trajectories of pedestrians has become a critical element in ensuring system safety and reliability. However， most existing studies on pedestrian trajectory prediction rely on fixed camera perspectives， which limits the comprehensive observation of pedestrian movement and thus makes them unsuitable for direct application to pedestrian trajectory prediction under the ego-vehicle perspective in autonomous vehicles. To solve the problem， in this paper a pedestrian trajectory prediction method under the ego-vehicle perspective based on the Multi-Pedestrian Information Fusion Network （MPIFN） is proposed， which achieves accurate prediction of pedestrians' future trajectories by integrating social information， local environmental information， and temporal information of pedestrians. In this paper， a Local Environmental Information Extraction Module that combines deformable convolution with traditional convolutional and pooling operations is constructed， aiming to more effectively extract local information from complex environment. By dynamically adjusting the position of convolutional kernels， this module enhances the model’s adaptability to irregular and complex shapes. Meanwhile， the pedestrian spatiotemporal information extraction module and multimodal feature fusion module are developed to facilitate comprehensive integration of social and environmental information. The experimental results show that the proposed method achieves advanced performance on two ego-vehicle driving datasets， JAAD and PSI. Specifically， on the JAAD dataset， the Center Final Mean Squared Error （CF_MSE） is 4 063， and the Center Mean Squared Error （C_MSE） is 829. On the PSI dataset， the Average Root Mean Square Error （ARB） and Final Root Mean Square Error （FRB） also achieve outstanding performance with values of 18.08/29.21/44.98 and 25.27/54.62/93.09 for prediction horizons of 0.5 s， 1.0 s， and 1.5 s， respectively.

The safety of automated vehicle running on the real road is related to traffic factors， driver status， and vehicle status. One major challenge faced by automated driving is that the actual traffic environment is characterized by spatial-temporal randomness of road morphology， natural environment， traffic participants and events. And the difference in complexity of testing scenarios results in the irreproducibility of the automated driving testing process and the incompatibility of testing results， which means that the evaluation of automated driving lacks a unified and quantified testing environment benchmark. In this paper， a scenario complexity calculation model for real road test based on operational design condition （ODC） is proposed. Considering the impact of network connectivity， driver perception ability， and vehicle execution ability on the complexity of automated driving vehicles facing relevant scenarios on actual roads， a complexity calculation model element database for autonomous driving actual road testing scenarios based on the eight major categories of road level， traffic facilities， temporary traffic changes， traffic participants， natural environment， network information， driver status and vehicle status. A scenario complexity computational model of real road test based on operational design condition and analytic hierarchy process （AHP） is established， The effect transmission mechanism based on intelligent and connected technology is adopted to calculate the weight coefficient of scenario elements， and the feasibility and rationality of the proposed method are validated in the real road tests.

The autonomous driving perception system must perceive the movement of the target vehicle to make reasonable interactive decisions. For the time lag in behavior perception， as well as the problem that possible fluctuations and outliers in the data lead to poor perception accuracy， an online semi-supervised hybrid approach is proposed in this paper. Firstly， a data-driven online prediction algorithm for vehicle motion state is designed using autoregressive integral moving average and online gradient descent optimizer. Then， an initial model based on micro-clusters is constructed， and an ensemble learning strategy is established using K nearest neighbor as the base classifier. Error-driven representative learning and exponential decay strategies are designed to achieve iterative updates of the initial model. Finally， experimental data to verify the effectiveness of the proposed algorithm is collected based on the driving simulation platform. The results show that the proposed method has rapid adaptability to vehicle behavior fluctuations. The online prediction algorithm can accurately predict vehicle motion trends， and the behavior perception algorithm has strong adaptability to vehicle behavior at different prediction times.

In order to ensure the safety and reliability of the high-level assisted driving system decision-making， a vehicle assisted driving behavior decision-making method based on dynamic driving risk assessment is proposed. Firstly， an obstacle risk assessment model and a virtual lane risk assessment model are established based on the potential field theory， which are used to describe the driving risk caused by dynamic traffic scenarios to the driving vehicle. Secondly， lane change behavior is divided into two stages according to the vehicle lane change process， which are lane change motivation generation and target lane safety decision-making. Further， the risk assessment indicator for lane change scenarios is proposed to formulate safe lane change rules， and the public data set is used to analyze and verify the risk assessment representation capability in lane change scenarios. Then， based on real-time traffic environment information， the driving behavior decision-making method in lane is determined to achieve safe decision-making in various driving scenarios. Finally， the proposed vehicle assisted driving behavior decision-making method is verified on the PreScan/CarSim/Simulink joint simulation platform and real vehicle road test platform. The results show that the proposed risk assessment model and driving behavior decision-making method can accurately identify and evaluate driving risk， and decide the vehicle driving behavior in real time and rationally， which effectively ensures the driving safety of the high-level assisted driving system.

The detection of free space in underground mine tunnels is the key sensing technology for underground mining autonomous driving systems. However， the characteristics of low illumination and complex working environment inside the tunnels bring great challenges to this task. In view of this， in this paper an algorithm for detecting free space in underground mine tunnels is proposed. Firstly， a dual-branch feature extraction backbone network is proposed to solve the problem of difficulty in extracting image features caused by the degradation of tunnel details. Secondly， for the problem of incomplete detection of drivable areas in underground mining tunnels， an adaptive multi-scale atrous spatial pyramid pooling feature enhancement module is proposed. Finally， a dual-branch channel attention mechanism fusion module is developed to solve the problem of inaccurate boundary extraction in the underground mine tunnels. The experiments are conducted on a self-made dataset specifically designed for underground mine tunnels. The results show that the proposed algorithm surpasses other existing methods such as Deeplabv3+， UNet， DDRNet-23， and PIDNet， with an increase of 2.07， 2.39， 1.87， and 1.92 percentage points in terms of MIoU scores， and 1.78， 2.45， 1.84， and 1.86 in terms of mAcc scores， respectively. The effectiveness of the proposed algorithm has been validated through its successful application in real mine tunnel scenarios， particularly for underground mining autonomous driving vehicles.

In view of the problems of the existing laser SLAM algorithm in dynamic scenes， which has poor robustness and the positioning and mapping accuracy is easily disturbed by dynamic objects， a real-time dynamic laser SLAM algorithm called Object-SuMa that combines object-level geometric feature and semantic information is proposed. Firstly，through processes such as ground filtering， object segmentation and pose size calculation， object-level geometric features are generated and represented as texture and used to correct semantic segmentation errors within the object. Then， in the odometry stage， the IOU calculation of the oriented bounding box is decomposed， and object-level geometric weighting and semantic weighting are introduced based on the bounding box IOU and semantic segmentation results to reduce mismatching and dynamic point matching. In addition， the graphics rendering pipeline is used to build a parallel computing process， and the computational complexity and time consuming are reduced by two-step optimization of ground point registration and non-ground point registration. Finally， tests on the KITTI odometry data set show that compared with SuMa++， the Object-SuMa algorithm has improved the relative pose accuracy by 15% and reduced the average time of ICP by 17%， which improves the positioning accuracy and robustness of laser SLAM in dynamic scenarios.

With the continuous development of mobile communication technologies in intelligent autonomous driving systems， securing vehicular communication data has become pivotal for transportation safety. Faced with threats of hackers remotely manipulating vehicles through the CAN bus network， existing frameworks can detect known attacks but falter in identifying location-based attacks. A detection framework integrating evidence-based deep learning is proposed in this paper， comprising data preprocessing， analysis， and attack detection modules. The preprocessing module employs independent hot encoding to enhance data quality and adaptability. The analysis module utilizes Generative Adversarial Networks （GANs） to bolster the framework's generalization and simulate attack scenarios. The attack detection module harnesses evidence-based deep learning to enhance the framework's capability in handling uncertainties from unknown attacks.The framework is tested on an open-source car hacking dataset and a dataset constructed based on the Chery EXEED RX model. The test results show that the framework improves the overall performance by 24.5% in detecting unknown attacks compared to traditional classification probability-based networks.

Predictive cruise control （PCC） performs long-term speed planning at the planning layer with the objective of predicting energy savings and short-term tracking control for the vehicle speed at the execution layer. Integrating these layers into a single optimal control problem poses significant challenges in system design due to the different time scale step requirements between the planning layer and the execution layer. To address this challenge， a hierarchical control approach is adopted in this paper. At the planning layer， an improved twin delayed deep deterministic policy gradient （TD3） algorithm is utilized to determine the long-term planning speed over the prediction horizon. Meanwhile， at the execution layer， based on model predictive control （MPC）， taking the planned vehicle speed as the reference speed and considering engine fuel consumption characteristics and transmission shift laws， further economic optimization and tracking control of the planned speed are carried out in the short term. The hardware-in-the-loop （HIL） validation results show that combining the improved TD3 algorithm with MPC effectively resolves the time scale inconsistency between planning and execution in PCC， which can significantly reduce both fuel consumption and shift frequency during the cruising of heavy-duty commercial vehicles.

The arrangement of charging time for pure electric vehicles is a crucial part of the daily life of car owners， directly affecting the convenience and comfortable experience of their travel. However， there are still challenges such as insufficient charging station resources and the need for advanced planning for charging. To solve the problem of car owners being unable to use the vehicle immediately due to insufficient battery， a charging time prediction solution based on the Transformer model is proposed to help car owners better plan their daily itinerary. In order to better understand the degree of battery performance degradation and capacity loss， the capacity method is used to evaluate the health status of batteries， and the charging behavior of drivers is analyzed to construct the characteristics of battery charging behavior. Savitzky Golay filter is used to smooth out the features representing battery attenuation and perform cumulative transformation， so that the features can more comprehensively represent battery information. Then the Pearson correlation coefficient and LASSO （Least Absolute Shrinkage and Selection Operator） regression algorithm are coupled to obtain the optimal feature set through secondary screening. Finally， using the Transformer model's strong attention mechanism， the charging time is predicted. Through experimental data verification， this scheme can accurately and quickly predict the charging time of pure electric vehicles， with a determination coefficient of 0.999 and a running speed of 156 ms.

For the problem of performance degradation in LiFePO₄ batteries under low-temperature conditions， a lightweight， high-strength， low-voltage， safe， and energy-efficient Fiber Carbon-nanotube film Laminated heating structure for LiFePO₄ battery is designed and developed， and experimental validation is conducted. The thermocompression technology is used to achieve the integrated molding of the Carbon-nanotube film and composite laminated structure. The experiment verifies the uniformity， stability and thermal fatigue resistance of a FCL （Fiber Carbon-nanotube film Laminated composite） heater. Furthermore， heating experiments on LiFePO₄ batteries in low-temperature environments are carried out， which is compared with the traditional Positive Temperature Coefficient （PTC） heater. The results show that compared to the traditional PTC heater， the FCL heater exhibits a 59% reduction in weight， a 3.5% decrease in energy consumption， a 26% improvement in temperature rise efficiency， and a 195% increase in power-to-weight ratio.

As a core component for regulating vehicle ride comfort， the performance of the suspension system directly determines the quality of vehicle driving. For the current problem of poor ride comfort during vehicle driving on complex roads， a composite suspension structure that is different from traditional suspensions is constructed in this paper， and the overall system architecture of this suspension is established. Firstly， in order to explore the vibration mechanism of the composite suspension of the complete vehicle， a dynamic model of the composite suspension of the complete vehicle is constructed. Secondly， combined with the complex driving requirements of the driver， a control strategy for the composite suspension system based on multiple operating conditions is constructed. The optimization effect is verified by different weighted RMS values of acceleration during vehicle driving， and the anti-air spring model is used to prove that the system can reduce the wear of the air spring. Finally， in the VI-Grade compact driving simulator， experimental verification is conducted based on the constructed complex operating conditions， and the test results of body vertical acceleration， roll angle acceleration， and pitch angle acceleration with and without control are compared. The experimental results show that the proposed composite suspension system can improve performance by 32.26%， 23.77%， and 7.38% under straight， curved， and braking conditions， respectively， through vehicle performance testing under complex conditions. It can effectively improve the ride comfort performance of vehicles while driving and solve the problem of air spring wear under normal driving conditions.

The vibration characteristics of pure electric vehicles differ significantly from traditional internal combustion engine vehicles. In this paper， a research method suitable for optimizing the vibration isolation rate of mounting systems is proposed to address the insufficient vibration isolation rate of pure electric vehicle suspension. The vibration isolation rate in all directions of the engine mounting system and main factors affecting the vibration isolation rate of the engine suspension are analyzed. The vibration isolation rate of the rear suspension is defined as the optimization object， and the method of optimizing the jog stiffness of the passive side bracket installation of the suspension is proposed to improve the vibration isolation rate. Taking the optimal isolation rate and minimum mass ratio change as the optimization objectives， and using the NSGA-II multi-objective optimization algorithm， the target value of the jog stiffness of the passive side bracket installation is optimized， and the passive side bracket structure is adjusted according to the optimization results. The test results show that the optimized rear suspension Y-direction vibration isolation rate increase from 5.61 to 18.13 dB， and the driver's right side-ear noise decreases by 9.76 and 5.03 dB（A） in the 24th and 48th orders， indicating a significant improvement in driving comfort.

Considering the limitation of global fixed bandwidth of load extrapolation for kernel density estimation， a load extrapolation method based on K-Average Nearest Neighbor Density-Based Spatial Clustering of Applications with Noise （KANN-DBSCAN） kernel density estimation （KDE） is proposed. The load data is grouped and clustered using the KANN-DBSCAN clustering algorithm， and the Rule-of-thumb （ROT） method is used to obtain the optimal bandwidth between different clusters. Then the kernel density estimation is conducted， and finally extrapolation is carried out using Monte Carlo simulation. The extrapolation rationality is verified using the measured load data of a certain electric vehicle on user road as the application object. The extrapolation effect is assessed by the three indicators of statistical parameter quantity， goodness of fit， and pseudo-damage. The results show that compared with the traditional fixed bandwidth kernel density estimation extrapolation method， the extrapolation load obtained by the DBSCSN kernel density estimation extrapolation method is closer to the actual load in statistical parameters， and the error of the mean， standard deviation， and maximum value is only 1.9%， 4.3%， and 1.9%， respectively. The magnitude cumulative frequency curve fits R² are all greater than 0.99， and the pseudo-damage is close to 1. The results show the effectiveness of the clustering method in kernel density estimation load extrapolation， which is helpful for compiling the load spectrum of electric vehicles on customer service road， and can provide reference for the load extrapolation of mechanical parts with similar load distribution characteristics.

With excellent energy absorption properties of lightweight and high specific energy absorption， honeycomb material is widely used in various energy absorption protection structures. In this article， based on Voronoi diagrams and 3D printing technology， a novel gradient random honeycomb sandwich structure is designed and prepared. A finite element model of its three-point bending load is established and experimentally validated. Subsequently， based on the numerical model， crashworthiness research and multi-objective optimization design are conducted. The results show that for uniform random honeycomb sandwich structures， those with a lower degree of randomness have better energy absorption characteristics. Increasing the wall thickness increases the specific energy absorption but also leads to a larger load fluctuation coefficient due to the meso-structural deformation mode dominated by plastic hinges. When the relative density is consistent， the specific energy absorption of the random honeycomb sandwich structure with different cell size is not much different， and the decrease of cell size makes the deformation process more stable and reduces the bearing fluctuation coefficient. For cell size and cell wall thickness gradient random honeycomb sandwich structures， the introduction of a positive gradient of leads to a deformation mode dominated by both the support end and the loading end， which improves the energy absorption indicators. Based on the Non-dominated Sorting Genetic Algorthm-II （NSGA-II）， a multi-objective optimization of the positive gradient random honeycomb sandwich structures is performed. The obtained meso-structural parameters with optimal energy absorption characteristics show a 33.9% increase in specific energy absorption compared to the uniformly random honeycomb sandwich structure without optimization design.

The design of the floating valve plate effectively addresses the problem of cylinder block tilting at high speed in axial piston pumps/motors， aligning with the trend towards high-speed development of axial piston pumps/motors and gaining attention in recent years. However， there is still a lack of systematic dynamic modeling and dynamic characteristic research for the piston pump/motor system designed with floating valve plate， which limits the design of floating valve plate piston pump/motor products. A comprehensive parameterized dynamic model of the system that considers its detailed structural features is established for the piston pump motor system designed with floating valve plate. The study focuses on the laws of dynamic changes of the pressure in the high-pressure oil circuit and the auxiliary hydraulic chambers， and the model's correctness is verified through bench tests. The results show that the high-pressure oil circuit exhibits a "sharp drop and slow rise" characteristic of "sawtooth" pressure pulsations， which are intense. At a pump speed of 1 000 revolutions per minute （r/min）， the average pressure in the high-pressure oil circuit at 20 and 40 MPa pressure levels results in pulsation amplitudes as high as ±1.5 and ±3 MPa， respectively. The fluid pressure in the auxiliary hydraulic chamber exhibits a dynamic change pattern of "rapid follow-up and slow decline，" meaning that once the auxiliary hydraulic chamber is connected with the high-speed rotating piston chamber， its fluid pressure almost immediately follows the piston chamber pressure changes without attenuation or lag. After disconnection from the piston chamber， it can still maintain the chamber pressure effectively.

The microstructure evolution and deformation behavior of resistance spot-welded joints of the two layer plates of 1500HS hot-formed steel sheets are studied in this paper. Through metallographic analysis， heat input distribution map， and alloy material property map， the microstructural changes at various positions relative to the weld nugget are analyzed. As the distance from the center area of the weld nugget increases， the microstructure of the welded joint can be divided into columnar crystal martensite， coarse-grained martensite， fine-grained martensite， ferrite-martensite dual phase microstructure and tempered martensite microstructure. Combined with Vickers hardness analysis， the differences in hardness under different organizational characteristics are clarified. The results show that the hardness decreases significantly in the ferrite martensite dual phase structure and tempered martensite region， which are the weak areas of the welding joints. Based on the experimental results of fusion size， maximum failure load， fracture surface macro-morphology， initial fracture location， and Vickers hardness of different plate thickness combinations， the influence of plate thickness and plate strength on the fracture mode， initial fracture location， and maximum failure load of the spot welded joints is explained.