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Recently, several datasets have become available for occupant monitoring algorithm development, including real and synthetic datasets. However, real data acquisition is expensive and labeling is complex, while virtual data may not accurately reflect actual human physiology. To address these issues and obtain high-fidelity data for training intelligent driving monitoring systems, we have constructed a hybrid dataset that combines real driving image data with corresponding virtual data generated from 3D driving scenarios. We have also taken into account individual anthropometric measures and driving postures. Our approach not only greatly enriches the dataset by using virtual data to augment the sample size, but it also saves the need for extensive annotation efforts. Besides, we can enhance the authenticity of the virtual data by applying ergonomics techniques based on RAMSIS, which is crucial in dataset construction.

With the extension of intelligent vehicles from individual intelligence to group intelligence, intelligent vehicle platoons on intercity highways are important for saving transportation costs, improving transportation efficiency and road utilization, ensuring traffic safety, and utilizing local traffic intelligence [1]. However, there are several problems associated with vehicle platoons including complicated vehicle driving conditions in or between platoon columns, a high degree of mutual influence, dynamic optimization of the platoon, and difficulty in the cooperative control of lane change. Aiming at the dual-column intelligent vehicle platoon control (where “dual-column” refers to the vehicle platoon driving mode formed by multiple vehicles traveling in parallel on two adjacent lanes), a multi-agent model as well as a cooperative control method for lane change based on null space behavior (NSB) for unmanned platoon vehicles are established in this paper.

The catalyzed diesel particulate filter with Pt and Pd noble metals as the main loaded active components are widely used in the field of automobile engines, but the high cost makes it face huge challenges. Rare earth element doping can improve the soot oxidation performance of the catalyzed diesel particulate filter and provide a new way to reduce its cost. In this paper, thermogravimetric tests and chemical reaction kinetic calculations were used to explore the effect of Pt-Pd catalysts doped Ce, and La rare earth elements on the oxidation properties of soot. The results shown that, among Pt-Pd-5%Ce, Pt-Pd-5%La, and Pt-Pd-5%Ce-5%La catalysts, Pt-Pd-5%La catalyst has the highest soot conversion, the highest low-temperature oxidation speed, and the activation energy is the smallest. Compared with soot, this catalyst reduced T10 and T20 by 82% and 26%, respectively, meaning the catalytic activity of Pt-Pd-5%La catalyst was the best.

The driving conditions of commercial logistics vehicles have the characteristics of combined urban and suburban roads with relatively fixed mileage and cargo load alteration, which affect the vehicular fuel economy. To this end, an adaptive equivalent consumption minimization strategy (A-ECMS) with vehicle speed and weight recognition is proposed to improve the fuel economy for a range-extender electric van for logistics in this work. The driving conditions are divided into nine representative groups with different vehicle speed and weight statuses, and the driving patterns are recognized with the use of the bagged trees algorithm through vehicle simulations. In order to generate the reference SOC near the optimal values, the optimal SOC trajectories under the typical driving cycles with different loads are solved by the shooting method and the optimal slopes for these nine patterns are obtained.

Durability performance prediction is a critical issue in fuel cell research. During the demonstration operation of fuel cell commercial vehicles in China, this issue has attracted more attention. In this article, the long short-term memory neural network (LSTMNN), which is an improved recurrent neural network (RNN), and the demonstration operation data are used to establish the prediction model to predict the durability performance of the fuel cell stack. Then, a model based on a back-propagation neural network (BPNN) is established to be a control group. The demonstration operation data is divided into training group and validation group. The former is used to train the prediction model, and the latter is used to verify the validity and accuracy of the prediction model. The outputs of the prediction model, as the durability performance evaluation indexes of the fuel cell, are the polarization curve (current-voltage curve) and the voltage decay curve (time-voltage curve).

This study investigated the plastic deformation behavior of 304 stainless steel thin-walled tubes under axial compression by means of numerical calculation and theoretical analysis. It was found that the plastic deformation length of thin-walled tube determined the formability of folds and the work done in the whole axial compression process. To reveal the relation between the range of plastic deformation length and tube geometry parameters, regression equations were established using the quadratic regression orthogonal design method. Experiments were conducted to validate the equations. The process windows for forming a single fold and tube joining at ends had been printed ultimately. The results showed that the regression equations can accurately predict the range of plastic deformation length for forming a single fold.

To overcome some drawbacks of using UHSS (Ultra High Strength Steel) in vehicle weight reduction, like spot weld HAZ (Heat Affected Zone) softening, hard machining and brittleness, a new solution of ultra-high stress strengthening was proposed and applied to the ridgelines of thin-walled structures in this paper. Firstly, stress distribution characteristics, the laws of stress variation and the compressed plate buckling process of the rectangular thin-walled beam under compressive and bending load were analyzed in elastic plastic stage by theory and Finite Element (FE) simulation. Secondly, based on elastic plastic buckling theory of the compressed plate and stress distribution similarity of the buckling process of the thin-walled box structure, three factors influencing the ultimate resistance enhancement of thin-walled hat section beam were found, and the rationality and accuracy of cross section ultimate resistance prediction formulas were also verified by FE simulation.

For the DCC (Dynamic Chassis Control) system, in addition to the requirement of ride and comfort, it is also necessary to consider the requirement of handling and stability, and these two requirements are often not met at the same time. This poses a great challenge to the design of the controller, especially in the face of complex working conditions. In order to solve this problem, this paper proposes a comprehensive DCC controller that considers road roughness class recognition. Firstly, a quarter vehicle model is established, the road surface roughness is calculated from the vertical acceleration of the wheels measured by the sensors. Then we calculate the autocorrelation function and the Fourier transform to estimate the PSD (Power Spectral Density) to get the road roughness class. Then control algorithms are designed for the vertical motion control, roll control and pitch control.

Object detection is an important visual content of the autonomous vehicle, the traditional detecting methods usually cost a lot of computational memory and elapsed time. This paper proposes to use lightweight deep convolutional neural network (MobilenetV3-SSDLite) to carry out the object detection task of autonomous vehicles. Simulation analysis based on this method is implemented, the feature layer obtained after h-swish activation function in the first Conv of the 13th bottleneck module in MobilenetV3 is taken as the first effective feature layer, and the feature layer before pooling and convolution of the antepenultimate layer in MobilenetV3 is taken as the second effective feature layer, and these two feature layers are extracted from the MobilenetV3 network.

The vehicle permanent magnet synchronous motor has the advantages of high power density, compact structure and small size, which makes it generate heat obviously in the process of energy conversion, which seriously affects the service life of the motor and the performance of permanent magnet. Predicting magnet temperature is a challenging task, in lab and various specialized applications, infrared sensors or thermocouples are used to measure the temperature, but it cost a lot. In order to predict the temperature field of the motor, the hysteresis characteristic test of the core material of the motor is carried out in this paper. The hysteresis characteristic and loss of electrical steel under different temperature, magnetic field intensity and magnetic field frequency are tested. It is found that the loss of electrical steel increases with the increase of magnetic induction intensity and magnetic field frequency.

Carbon fiber reinforced plastic (CFRP) composite materials have gained particular interests due to their high specific modulus, high strength, lightweight and perfect corrosion resistance. However, in reality, CFRP composite materials cannot be used alone in some critical places such as positions of joints with hinges, locks. Therefore, metal reinforcements are usually necessary in local positions to prevent structure damage. Besides, if uncertainties present, obtained optimal structures may experience in failures as the optimization usually pushes solutions to the boundaries of constraints and has no room for tolerance and uncertainties, so robust optimization should be considered to accommodate the uncertainties in practice. This paper proposes a mixed topology method to optimize metal and carbon fiber reinforced plastic composite materials simultaneously under nondeterministic load with random magnitude and direction.

The influence of heat flow and cooling water characteristics on motor temperature cannot be accurately reflected by the traditional motor temperature analysis method. In order to study the motor and its key components’ temperature characteristics under different temperatures and flow rates of cooling water, this paper establishes the lumped parameter transient thermal model which includes cooling water module, based on a 50kW permanent magnet synchronous motor. The transient and steady temperature is calculated through this model together with the motor loss calculation module in the electric drive system model. The influence of different temperature and flow rate of cooling water on motor and its key components’ temperature characteristics is compared. During the modeling process, the motor body is divided into 14 parts, based on the internal heat flow path of the motor. The thermal resistance of each key component and cooling water is calculated.

Because of the high frequency and serious consequences of traffic accidents in the intersection area, it is of great significance to study the vehicle conflict and collision scenarios of the intersection area. Due to few actual crash accidents occurring in naturalistic driving studies data or field operational tests data, the data of traffic accident database should be also used to analyze the intersection conflict and collision. According to the China Field Operation Test (China-FOT) database and the China in Depth Accident Study (CIDAS) database, the distribution feature of the respective intersection scenario type is obtained from the data analysis. Based on the intersection scenario type, two characters of intersection conflict and collision, the environmental character and the vehicle dynamic character, are used to analyze for the integration process of intersection conflict and collision.

Wheel Aerodynamics is an important part of vehicle aerodynamics. The wheels can notably influence the total aerodynamic drag, lift and ventilation drag of vehicles. In order to simulate the real on-road condition of driving cars, the moving ground and wheel rotation is of major importance in CFD. However, the wheel rotation condition is difficult to be represented exactly, so this is still a critical topic which needs to be worked on. In this paper, a study, which focuses on two types of cars: a fastback sedan and a notchback DrivAer, is conducted. Comparing three different wheel rotating simulation methods: steady Moving wall, MRF and unsteady Sliding Mesh, the effects of different methods for the numerical simulation of vehicle aerodynamics are revealed. Discrepancies of aerodynamic forces between the methods are discussed as well as the flow field, and the simulation results are also compared with published experimental data for validation.

For a practical pad design, a magnetic shielding layer is imperative which is made of ferrite, aluminum or some other metallic material. However, once the magnetic shielding layer is added, not only the mutual inductance but also the self-inductance of the coupling coils vary with the lateral misalignment which is inevitable for a human driver. The change of self-inductance will also result in the mistuning problem in the resonant circuit, which can significantly reduce the transmission efficiency of the whole wireless power transfer (WPT) system. This paper proposed a method of parameter identification of self-inductance based on the least square in order to solve the mistuning problem. In order to verify the proposed method, both the simulation model and the experiment set-up are built.

Fuel cell vehicle commercialization and mass production are challenged by the durability of fuel cells. In order to research the durability of fuel cell stack, it is necessary to carry out the related durability test. The performance prediction of fuel cell stack can be based on a short time durability test result to accurately predict the performance of the fuel cell stack, so it can ensure the timeliness of the test results and reduce the cost of test. In this paper, genetic algorithm-BP neural network (GA-BPNN) is proposed to modeling automotive fuel cell stack to predict the performance of it. Based on the strong global searching ability of genetic algorithm, the initial weights and threshold selection of neural networks are optimized to solve the shortcoming that the random selection of the initial weights and thresholds of BP neural network which can easily lead to the local optimal value.

Recent toxicological and epidemiologic studies have shown that diesel emissions have been a significant toxic air contaminant. Catalyzed DPF (CDPF) not only significantly reduces the PM mass emissions (>90%), but also further promotes carrier self-regeneration and oxidize more harmful gaseous pollutants by the catalyst coated on the carrier. However, some ultrafine particles and potentially harmful gaseous pollutants, such as VOCs species, originally emitted in the vapor-phase at high plume temperature, may penetrate through the CDPF filter. Furthermore, the components and content of catalyst coated on the CDPF could influence the physicochemical properties and toxicity intensity of those escaping ultrafine particles and gaseous pollutants. In this work, (1) we investigated the influence of precious metal content as a variable parameter on the physicochemical properties and catalytic activities of the small CDPF samples.

In order to research the effect of process parameters (laser power, welding speed, wire-feed speed, spot diameter) on mechanical properties of Zn-coated Steel Laser Brazing Lap Joint for vehicle, the influence of welding parameters on energy input of brazing seam cross section was theoretically analyzed, and then a great number of laser brazing experiments of 0.7mm galvanized steel was carried out. After that, the tensile strength and micro-hardness tests were also done for well-formed joints of galvanized steel formed in the laser brazing. The results show that joints with good mechanical properties and surface morphology can be formed when laser power is in the range of 2500-3200W and the other parameters keep in a specified range. Joint performance significantly reduces when the value of wire-feed speed exceeds 3.0m/min for that a wider brazing seam cross section can’t be formed.

Carbon Fiber Reinforced Plastic (CFRP) tube is an important material for the lightweight design of automotive structures. Simulation method of CFRP thin-walled tubes subjected to axial compression using MAT54 in LS-DYNA was investigated. Based on the two-layer shell model combined with MAT54, failure strategy and the parameters sensitivity of the model were discussed in detail. Then the simulation model was verified by using duplicate specimens comprised of carbon fiber/epoxy unidirectional prepreg tape. Furthermore, the modeling methods of crush trigger and different types of loading speed were analyzed. In addition, based on the method of equal energy absorption, energy absorption performance of thin-walled circular and square tubes made from four materials including mild steel, high strength steel, aluminum alloy and CFRP were also compared.

X-in-the-loop (XiL) framework is a validation concept for vehicle product development, which integrates different virtual and physical components to improve the development efficiency. In order to develop and validate an extended validation method based on XiL, Tongji University in Shanghai, China and the Karlsruhe Institute of Technology (KIT) in Karlsruhe, Germany co- performed a feasibility study about an X-in-the-distance-loop demonstration platform. The X-in-the-distance-loop demonstration platform includes a MATLAB/Simulink software platform and geographically distributed equipment (driver simulator, driving electric motor and dynamometer test stand), which are used to conduct bidirectional experiments to test communication of powertrain data between China and Germany.