With the development of intelligent vehicles and autonomous driving technologies，zero-gravity seat，with occupant comfort as core function，has been equipped in some vehicles. Compared to upright seating，reclined occupants face a higher risk of injury in collision，making the development of crash safety solutions imminent. In this paper，a review of the current research status and development trends regarding the crash safety of reclined occupants is conducted，focusing on injury mechanism，restraint systems，and research tools. The findings are summarized as follows： （1） Injury patterns for reclined occupants differ from those for upright occupants，and the injury mechanism at typical sites such as the lumbar spine and the iliac crest have not been fully clarified. （2） Traditional restraint systems with three-point seat belts as the core，even after improvement and optimization，is still difficult to provide effective overall protection for reclined occupants. Development of new protective means that can reasonably balance submarine and spinal injuries under the integrated active-passive safety system is a key issue in crash protection research for reclined occupants. （3） Crash dummies and human body models （HBMs），as the primary research and evaluation tools，need to improve their usability and bio-fidelity for reclined conditions.

False alarm and missed alarm of automotive radar are key factors affecting the safety and reliability of autonomous driving systems，thus requiring a large amount of labeled test data for targeted research. However，the occurrence probability of false alarm and missed alarm is low，and the unstable status of radar targets makes it difficult to label them. Therefore，in this paper，firstly efficient test schemes are designed to obtain key radar data based on the generation mechanism of radar false alarm and missed alarm. Then，by constructing a correlation function to quantify the correlation between radar targets and scene targets and using genetic algorithms to optimize this function，an automatic labeling method for radar targets is established. Finally，the effectiveness of the proposed method is verified through real data acquisition. The experimental results show that the proposed method can efficiently obtain crucial false alarm and missed alarm data. The labeling method in this paper can accurately identify radar targets corresponding to scene targets and distinguish between false alarm and real targets.

In　recent years，mega-casting aluminum alloy structures have gradually been used to replace traditional stamping-welded body-in-white structures. The study on lightweight design of mega-casting aluminum alloy rear floor structure is conduced in this paper. An equivalent analysis scheme is proposed for the analysis of mega-casting vehicle body components. Regarding the processing and performance of the mega-casting structure，the processing constraints for the design are investigated，and the heterogeneity of its material properties is clarified. Based on the topology optimization method，the design domain of the structure is analyzed and the ideal topological design is obtained. Finally，a lightweight design is carried out，achieving a 7% weight reduction while ensuring performance. The study illustrates the design and optimization process of the mega-casting vehicle body components，which has certain reference significance.

To solve the problem of large fluctuation of inlet and outlet temperature on vehicle proton exchange membrane fuel cell （PEMFC） under variable loading currents，a dynamic change particle swarm optimization （PSO）—proportional integral derivative （PID） algorithm is proposed. Firstly，the overall simulation model of the PEMFC engine system with rated power of 150 kW is built. Based on existing references，the accuracy of the output power and voltage of the model is validated； and according to the validated results，the supply of reactant gas is set following demand on currents，which reflects real working conditions of the PEMFC engine system. Based on the model built and the control strategy that the mass flow rate of cooling water following output power，PID，PSO-PID and dynamic change PSO-PID are used on the mass flow rate of cooling air from radiating fans to conduct research on the control effect of them on the inlet and outlet temperature and output power of FCs under variable loading currents. The results show that compared with PID，under PSO-PID and dynamic change PSO-PID，the transient overshoots decreasing amplitudes of inlet temperature of FCs are both 13.7%，those of outlet temperature both 36.0% and the output power reaching the stable condition faster. The time when dynamic change PSO-PID reaching the optimum values only accounts for 57.1% of that under PSO-PID，which can reduce more unnecessary computation and input the PID parameters into the stack temperature controller ahead of PSO-PID. The dynamic PSO-PID algorithm can be used on actual inlet and outlet temperature control of FCs more efficiently and faster，contributing to improving the stability of the temperature and the output power of vehicle PEMFC.

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.

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 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 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.

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.

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.