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 R ² 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.