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.

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.

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.

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.

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.

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 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.

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.

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.

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.