To fully utilize the energysaving and emissionreduction performance of the oilelectric hybrid system on mine truck, a dualmode energy management strategy (EMS) is proposed for a series hybrid electric mine truck. The back propagation neural network (BPNN) was used to construct the models of "optimal fuel consumption mode of engine" and "optimal efficiency mode of range extender" in this EMS. On this basis, a dualmode EMS was designed to adjust the power output of the range extender and battery pack, realizing the realtime adjustment of the energy consumption for the vehicle under complex working conditions. Finally, the proposed EMS was verified by hardware in the loop simulation with actual working condition data. The results show that compared with the rule strategy and equivalent consumption minimization strategy, the fuel saving rate of the dualmode EMS increases by 12.74% and 7.4% respectively, further improving the fuel saving performance of the realtime strategy.

Stress distribution at the tireground contact interface on soft terrain becomes increasingly complex under the influence of tire slip and sinkage, making it difficult to accurately model tire behavior. Using finite element analysis, the paper simulated tire longitudinal slip/skid under constant sinkage conditions. The variation in stress distribution at the tireground contact interface was investigated as the slip/skid degree changed. The results show three distinct stress distribution patterns corresponding to slip, small skid and large skid states, respectively. Soil characteristic parameters were obtained through simulating sinkage and shear tests, and the stress distribution model was established for the three slip/skid states. On this basis, the tiredeformable terrain interaction model for longitudinal slip was further developed, which effectively represents the inplane characteristics of tires on soft terrain.

Aiming at the lack of effective methods for testing abnormal signals during the operation of unmanned vehicles, the paper focuses on signal anomalies caused by environmental disturbances in reliability driving tests. By using the correlation of signals from multiple sensors in both the time domain and spatial domain, a crossmathematical model is established based on the multisensor data. The signals collected from sensors are assigned as the row elements and the sensors as the column elements within the signal matrix. This numerical method transforms the original multisensor signals into a parameterized signal matrix model. A method combining matrix completion and deep matrix decomposition fusion (MC+DMF) is proposed to recover certain abnormal signals resulting from environmental disturbances. According to the forward propagation characteristics of the neural network, dimensionality reduction is applied to the row vectors (data collected by individual sensors at time i ) and column vectors (sensor arrays) in the original matrix. This process reduces the computational load during feature extraction from the distorted signal matrix. Additionally, the Hadamard product is used to regularize the MC+DMF loss function after feature extraction to avoid overfitting. The proposed method is applied on the SODA10M and KITTI public datasets, and comparing with traditional approaches, such as the single matrix factorization (MF), probability matrix factorization (PMF) and BiasSVD, the experiments using root mean square error (RMSE) show that the method can effectively detect abnormal sensor signals caused by vibration interference during driving. The results show that the MC+DMF method can greatly reduce the data recovery error and time. Compared with the probability matrix decomposition method, it achieves a 1% lower error rate and approximately 20.65% less recovery time.

Aiming at acceptance criteria for automated driving, this paper reviews the best practices in relevant regulations, standards, and evaluation methods, identifying five safety concepts and their interrelationships. The paper focuses on the concept and research status of behavioral safety, centered around "reasonably foreseeable and preventable” behaviors. By combining scenario data statistics with the driver's emergency response mechanism, a quantitative research framework is proposed for reasonably foreseeable and preventable situations. Finally, combining traffic accident data and practical experience, the paper provides a method for using behavioral safety in autonomous driving evaluation, along with a closedloop certification and approval process based on this concept. The research in this paper serves as a reference for authorities, third parties, and R&D companies to establish relevant R&D, testing, and processes centered around behavioral safety.

Conducting a thorough driving risk assessment is important for the driving safety of autonomous vehicles. In this paper, the existing driving risk assessment methods are divided into three categories, namely, the single objectoriented methods, the reachability setbased methods, and the potential fieldbased methods. In order to conduct a comprehensive comparison of these methods and reveal their distinct characteristics and applicability, the paper proposes five evaluation dimensions, including realtime capability, the duration of the valid prediction horizon, application feasibility, the inclusion of various risk sources and adaptability in different scenarios. The research gaps and potential future research directions in driving risk assessment for autonomous vehicles are analyzed and prospected.

Lane line detection is a key technology in the field of autonomous driving, and it currently faces many challenges. The sparsity of the lane line supervision signal, as well as factors such as occlusion and shadows in complex scenes, can affect detection accuracy and realtime performance. Based on this, this paper proposes a lane line detection model that integrates the CBAM attention mechanism and a line anchor feature aggregation module. The proposed algorithm achieves an accuracy of 96.19% and a comprehensive F1 score of 76.24% on the Tusimple and CULane datasets, respectively. Real vehicle tests show that the algorithm detects a frame rate of 67 fps, allowing for realtime detection in complex traffic scenarios and more effectively addressing the problem of lane line occlusion.

In order to accelerate the gathering, sharing, development and utilization of ICV data resources, this paper proposes the establishment of a “public service platform for data interaction and comprehensive application of ICV” with a multicenter architecture comprising national, regional and enterprise levels. The platform is based on the standardized industry data collection and transmission protocols and extensively utilizes modern information technologies such as big data, cloud computing and blockchain. It can efficiently achieve the realtime collection, analysis and processing of data from tens of millions of vehicles, providing a basic support platform for promoting the mining and utilization of industry data. Based on the data resources collected and stored by the platform, we have explored comprehensive applications across multiple scenarios, including vehicle test and evaluation, safe operation monitoring, and data analysis and mining. These applications verified the platform's innovativeness and feasibility in actual use.

In the visual perception task of autonomous driving, it is crucial to accurately and quickly extract the cognitive and accidental uncertainties to effectively resolve the Safety of the Intended Functionality (SOTIF) issues associated with autonomous driving. In traditional methods such as Monte Carlo dropout and deep ensembles, uncertainty is estimated by sampling the prediction results of different submodels, which slows down the estimation and tends to occupy a large amount of memory in the processor during the model inference stage. A fast Monte Carlo dropout method and a technique for correcting subsequent detection results are proposed to address the issues of slow estimation of uncertainty in Monte Carlo dropout and the selection of subsequent detection results. This method uses a multihead mechanism to replace the traditional multiple sampling mechanism in Monte Carlo dropout, thereby saving time in both sampling and inference throughout the uncertainty estimation process.

The threedimensional form of the metal foam flow field is established. Through threedimensional numerical simulation, the results show that the metal foam flow field can effectively reduce the concentration polarization loss, thereby improving the performance of the fuel cell at high current densities. The increase in output power density is significant, while the corresponding pumping power loss is negligible. Furthermore, under the influence of the metal foam flow field, the oxygen concentration distribution inside the PEMFC is more uniform than that in the traditional straightchannel fuel cells. Bench experiments were conducted to observe the flow state of liquid water in the foam flow field and similar flow phenomenon was obtained through threedimensional numerical simulations. Compared with the traditional straightchannel designs,

In order to reduce the adverse effects of pressure fluctuations on the service life of fuel cells, the effectiveness of adding a bypass valve to control pressure fluctuations was investigated through simulation and comparative experiments. Based on the analysis of the fuel cell output characteristics and operating principles of each component, the mechanism and control model was established. An inverted decoupling method based on active disturbance rejection was used to achieve decoupling control of flow and pressure. Pressure fluctuation control was implemented using a fuzzy PI control. The effect of decoupling control within this system structure was verified using the Matlab/Simulink platform. The peak pressure fluctuation in the comparative experiments is 1.09 kPa with the bypass valve and 1.82 kPa without the bypass valve respectively. The addition of a bypass valve can reduce pressure fluctuations, thereby increasing the service life of the fuel cell.