Energy management determines the power distribution of the power system of fuel cell vehicles （FCVs） and affects the economy and durability of FCVs. As the operating conditions of vehicles are complex and variable，energy management can improve the output performance of FCV power system by integrating traffic information. In this paper，the optimization objectives of FCV energy management are summarized，and the traditional rule-based and optimization-based energy management strategies are analyzed. Then，focusing on the analysis and prediction of traffic information such as vehicle speed and traffic condition，prediction methods such as Markov method and artificial intelligence are reviewed，and the research progress of FCV energy management strategies by integrating traffic information is summarized. Finally，the research direction of development of FCV energy management by integrating traffic information is proposed.

For the dynamic multi-objective requirements of yaw maneuverability and lateral stability under different driving states，an adaptive control strategy for vehicle yaw stability is proposed. Firstly，the vehicle dynamics model is established by piecewise linear fitting technology，and the dynamic stability region boundary related to road adhesion and longitudinal velocity is obtained in phase plane by integrated application of improved two-line method and fuzzy theory. Then，the stability risk during vehicle driving is quantitatively characterized and the dynamic multi-objective mapping function is introduced to adjust the built-in parameters of the stability control strategy designed based on the model predictive theory so as to match the dynamic multi-objective demand of vehicle lateral stability，yaw maneuverability and actuator energy consumption. Finally，the simulation test results prove that the designed control strategy can help the vehicle obtain safer and better stability control effect than the traditional method under various conditions.

The front axle manufactured through forging has large dimension，and the stress on it is complex. It is difficult to lightweight due to the limitation of the I-beam structure. In this paper，the design method of hollow front axle is given，and the hollow rectangular structure with variable cross-section and wall thickness and a combination of leaf spring seats are used to achieve lightweight and improve bending and torsion resistance. The influence of wall thickness at the kingpin hole on the strength of the fist is revealed，and the appropriate wall thickness range of the fist and the height and width coefficient of the plugging hole is given，by the vertical and longitudinal working conditions of the finite element mode. The 1：1 hollow front axle sample with an axle load of 5 t is produced by using seamless steel pipes，which is 10.75% lighter than the forged front axle. The variation law of vertical displacement and axial stress changes on the lower surface of the shaft and the stress distribution at the edge of the sealing hole on the outer end face of the fist are revealed through stiffness tests under vertical and longitudinal working conditions，as well as static strength and fatigue life tests under vertical working condition. The results show that the stiffness and static strength of the variable cross-section hollow rectangular front shaft meet industry standards，and the fatigue life under vertical working condition is much higher than industry standards. It is indicated that the variable cross-section hollow front axle can improve performance and achieve lightweight.

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.