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For intelligent vehicles, a robust perception system relies on training datasets with a large variety of scenes. The architecture of federated learning allows for efficient collaborative model iteration while ensuring privacy and security by leveraging data from multiple parties. However, the local data from different participants is often not independent and identically distributed, significantly affecting the training effectiveness of autonomous driving perception models in the context of federated learning. Unlike the well-studied issues of label distribution discrepancies in previous work, we focus on the challenges posed by scene heterogeneity in the context of federated learning for intelligent vehicles and the inadequacy of a single scene for training multi-task perception models. In this paper, we propose a federated learning-based perception model training system.

As a key technology of intelligent transportation system, vehicle type recognition plays an important role in ensuring traffic safety,optimizing traffic management and improving traffic efficiency, which provides strong support for the development of modern society and the intelligent construction of traffic system. Aiming at the problems of large number of parameters, low detection efficiency and poor real-time performance in existing vehicle type recognition algorithms, this paper proposes an improved vehicle type recognition algorithm based on YOLOv5. Firstly, the lightweight network model MobileNet-V3 is used to replace the backbone feature extraction network CSPDarknet53 of the YOLOv5 model. The parameter quantity and computational complexity of the model are greatly reduced by replacing the standard convolution with the depthwise separable convolution, and enabled the model to maintain higher accuracy while having faster reasoning speed.

Flow channels in proton exchange membrane fuel cells (PEMFC) play an irreplaceable role, and flow channel design in bipolar plates is one of the most active research areas at present. The flow channel on the cathode side needs to discharge liquid water out of the fuel cell in time and allow oxygen to flow to the cathode catalytic layer as much as possible to avoid the phenomenon of cathode water flooding and mass transfer loss. In order to improve the performance of PEMFC, a method of setting both side blockages in the cathode flow channel is proposed. In this paper, lateral blockage models with three shapes are proposed to study the influence of blockage on mass transfer and performance. First, a 3D PEMFC model with a middle channel was built to calculate the fuel cell power at different discharge rates.

As a key tool to maintain urban cleanliness and improve the road environment, road cleaning vehicles play an important role in improving the quality of life of residents. However, the traditional road cleaning vehicle requires the driver to monitor the situation of road garbage at all times and manually operate the cleaning process, resulting in an increase in the driver 's work intensity. To solve this problem, this paper proposes a road garbage recognition algorithm based on improved YOLOv5, which aims to reduce labor consumption and improve the efficiency of road cleaning. Firstly, the lightweight network MobileNet-V3 is used to replace the backbone feature extraction network of the YOLOv5 model. The number of parameters and computational complexity of the model are greatly reduced by replacing the standard convolution with the deep separable convolution, which enabled the model to have faster reasoning speed while maintaining higher accuracy.

The phenomenon of liquid transfer in the liquid tank of the semi-trailer vehicle for transporting dangerous cargo (SVTDC) during braking is analyzed and the relevant mathematical model is established. The braking dynamic model of the SVTDC considering the liquid sloshing in the tank is established, and the model is verified based on the co-simulation method. Based on the typical conditions, the braking deceleration and axle load calculation functions of the model are simulated and analyzed, and the application prospect of the model in the development of driving automation control strategy is discussed.

The technology of active sound generation (ASG) for automobiles is one of the most effective methods to flexibly achieve the sound design that meets the expectations of different user groups, and the active sound synthesis algorithms are crucial for the implementation of ASG. In this paper, the Kaiser window function-based the harmonic synthesis algorithm of automobile sound is proposed to achieve the extraction of the order sounds of target automobile. And, the suitable fitting functions are utilized to construct the mathematical model between the engine speed information and the amplitude of the different order sound. Then, a random phase correction algorithm is proposed to ensure the coherence of the synthesized sounds. Finally, the analysis of simulation results verifies that the established method of the extraction and synthesis of order sound can meet the requirements of target sound quality.

Because of the long driving range and good power performance, plug-in hybrid electric vehicles (PHEV) have drawn much attention. And the current fuel-saving effect of PHEV still has a lot of room for improvement. The complex powertrain structure of PHEV makes the requirements for control strategy be increasing. Therefore, it is crucial to develop an efficient control strategy to ensure that the PHEV operates at optimal performance with an improved driving range. This paper establishes a mathematical model for fuel economy control of PHEV, by treating the torque distribution problem as a multi-stage decision optimization problem, and establishes a global energy management strategy based on a dynamic programming (DP) algorithm. Based on the actual physical model, this paper creatively solves the correction range of battery SOC value according to the charge and discharge power of the motor, which greatly reduces the calculation time of the DP algorithm.

With the development of highway transportation and automobile industry technology, highway truck overload phenomenon occurs frequently, which poses a danger to road safety and personnel life safety. So it is very important to identify the overload phenomenon. Traditionally, static detection is adopted for overload identification, which has low efficiency. Aiming at this phenomenon, a dynamic overload identification method is proposed. Firstly, the coupled road excitation model of vehicle speed and speed bump is established, and then the 4-DOF vehicle model of half car is established. At the same time, considering that the double input vibration of the front and rear wheels will be coupled when vehicle passes through the speed bump, the model is decoupled. Then, the vertical trajectory of the body in the front axle position is obtained by Carsim software simulation.

Hydrogen recirculation system (HRS) is one of the main subsystems of proton exchange membrane fuel cell (PEMFC) system, and an HRS with high performance and low cost is essential to improve the fuel cell lifetime and efficiency. The ejector is becoming an effective alternative to the hydrogen circulation pump in the hydrogen recirculation system because of its small size, low cost and no parasitic power. However, the conventional ejector can only operate at the limited output power of the fuel cell. In order to improve this drawback, a hydrogen recirculation system with a separator-ejector integrated device is proposed. The hydrogen recirculation system based on the integrated device can make the space more compact, reduce the condensation effect of the piping, improve the fuel cell performance and reduce the cost. The design and integration method of the ejector and gas-water separator in this hydrogen recirculation system are introduced.

In recent years, research on car-like robots has received more attention due to the rapid development of artificial intelligence from diverse disciplines. As essential parts, path planning and lateral path tracking control are the basis for car-like robots to complete automation tasks. Based on the two-degree-of-freedom vehicle dynamic model, this study profoundly analyzes the car-like robots’ path planning and lateral path tracking control. Three objectives: path length, path smoothness, and path safety, are defined and used to construct a multi-objective path planning model. By introducing an adaptive factor, redefining the selection of reference points, and using the cubic spline interpolation for path determination, an improved NGSA-III is proposed, which is mostly adapted in solving the multi-objective path planning problem.

In the Formula Student Electric China (FSEC), the body structure is generally divided into two types, truss steel tube body and carbon fiber load-bearing body (monocoque). The monocoque is loved by Formula Student teams around the world because it has a higher stiffness and lighter weight than the truss steel tube body. With the widespread application of monocoque, it also brings more problems. Due to the use of the monocoque, the connection between each component and the body was changed from the welding of the original truss steel pipe frame to a bolted connection. However, the bolted connection will provide a large preload force to the monocoque, resulting in the monocoque easily crushed in the local, so it is necessary to pre-bury an enhanced part in the monocoque to ensure the connection strength, that is, the embedded part. At present, aluminum plug-ins after topological hollow processing are being used.

Intelligent sweeper vehicle is gradually applied to human life, in which the accuracy of garbage identification and classification can improve cleaning efficiency and save labor cost. Although Deep Learning has made significant progress in computer vision and the application of semantic network segmentation can improve waste identification rate and classification accuracy. Due to the loss of some spatial information during the convolution process, coupled with the lack of specific datasets for garbage identification, the training of the network and the improvement of recognition and classification accuracy are affected. Based on the Unet algorithm, in this paper we adjust the number of input and output channels in the convolutional layer to improve the speed during the feature extraction part. In addition, manually generated datasets are used to greatly improve the robustness of the model.

Overloading of trucks can easily cause damage to roads, bridges and other transportation facilities, and accelerate the fatigue loss of the vehicles themselves, and accidents are prone to occur under overload conditions. In recent years, various countries have formulated a series of management methods and governance measures for truck overloading. However, the detection method for overload behavior is not efficient and accurate enough. At present, the method of dynamic load identification is not perfect. No matter whether it is the dynamic weight measurement method of reconstructing the road surface or the non-contact dynamic weight measurement method, little attention is paid to the difference of different vehicles. Especially for different vehicles, there should be different load limits, and the current devices are not smart enough.

This paper addresses the brake pad particle emission during the braking process of a vehicle in motion. The frictional-constant contact between the disc brake and pads results in an increased temperature and wear of the pads. The emission of brake pad particles into the atmosphere leads to an increase in air pollution and hence becomes hazardous to the human body. In this paper, a wheel brake disc is installed in a ventilation system where the specific air flow is introduced in order to investigate the thermal performance and the emission of particles from the brake pads. A mathematical model using the fundamental parameters of the brake disc and ventilation system is established. The behavior of the heat transfer is studied using computational fluid dynamics (CFD). The particle emission rate from the pads is calculated under the assumption of uniform constant pressure distribution at the contact surface of the brake disc and pad.

When dump trucks unload dusty materials, dust particles with a diameter of 1 to 75 microns slide out of the dump body and float into the air. Dust particles naturally settle down spending a few hours, which causes air pollution. People who work in this environment daily suffer serious physical harm. To study the movement of dust particles during the unloading process, a scaled-down model is used to simulate the process of dump truck unloading gravel, and a high frame rate camera is used to record the movement trajectory of dust particles during the unloading process. In this paper, by observing the movement process of unloading dust particles by dump trucks, based on the principle of dynamics, a mathematical model describing the unloading of dust particles in the dump body and a mathematical model of the diffusion of dust particles in the air are established. Take the small gravel sampled at the construction site as an example of the experiment.

The plug-in hybrid electric vehicle (PHEV) gradually moves into the mainstream market with its excellent power and energy consumption control, and has become the research target of many researchers. The energy management strategy of plug-in hybrid vehicles is more complicated than conventional gasoline vehicles. Therefore, there are still many problems to be solved in terms of power source distribution and energy saving and emission reduction. This research proposes a new solution and realizes it through simulation optimization, which improves the energy consumption and emission problems of PHEV to a certain extent. First, on the basis that MATLAB software has completed the modeling of the key components of the vehicle, the fuzzy controller of the vehicle is established considering the principle of the joint control of the engine and the electric motor.

Radiative cooling uses the cold space source to cool the object. The radiative cooling film prepared based on the principle can reduce the fuel consumption of automobile air conditioning refrigeration. In this paper, according to the passive radiative cooling principle, taking SiO2 as the radiative cooling film of infrared radiation material, the theoretical cooling value of the passenger compartment of the automobile is calculated and analyzed based on the heat balance equation. The influence of radiative cooling film on the temperature field of passenger cabins was studied by finite element analysis. The results show that the cooling film made of SiO2 as passive radiation material has an apparent cooling effect on the passenger cabins. At the ambient temperature of 35.15°C, the theoretical cooling temperature is 6.7K. When the radiative cooling film is applied to automobiles, the cooling value of the passenger cabin body, seat, instrument panel, and other parts reaches 2.2K-5.1K.

For series hybrid electric vehicles (SHEV), rule-based strategies are realistic and powerful in real-time applications. However, the previous rule-based strategy cannot strike a balance between the best fuel economy and the best battery performance while maintaining the advantages of real-time applications. In order to obtain higher efficiency and reduce battery consumption, we have developed an adaptive hybrid thermostat strategy. On the basis of maintaining the load leveling of the thermostat strategy, the threshold-changing mechanism is added to realize the adaptive adjustment of the engine starting power under different SOC conditions, so as to achieve the goal of prolonging the battery life. In addition, the more fuel-efficient emergency handling rules designed to further reduce comprehensive fuel consumption.

In order to study the influence of low-viscosity oil on automobile pollutant emissions reduction, three different 0W20 oil samples were prepared with oil 5W30 as the base oil. Parameters such as the oil viscosity, ash, and element content were tested at different stages, speeds, and accelerations of the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). The results showed the effects of low-viscosity oil on exhaust emissions reduction were mainly concentrated in the low-speed and extra high-speed segment. At the low-speed segment, especially in the starting stage, carbon monoxide (CO), total hydrocarbon (THC), and non-methane hydrocarbon (NMHC) emissions can be reduced. The use of low ash oil can reduce nitrogen oxides (NOx) emissions; the methane (CH4) emissions can be reduced by increasing the Zinc (Zn) content in engine oil moderately.

The lightweight design of a vehicle can save manufacturing costs and reduce greenhouse gas emissions. For the off-road vehicle and truck, the chassis frame is the most important load-bearing assembly of the separate frame construction vehicle. The frame is one of the most assemblies with great potential to be lightweight optimized. However, most of the vehicle components are mounted on the frame, such as the engine, transmission, suspension, steering system, radiator, and vehicle body. Therefore, boundaries and constraints should be taken into consideration during the optimal process. The finite element (FE) model is widely used to simulate and assess the frame performance. The performance of the frame is determined by the design parameters. As one of the largest components of the vehicle, it has a lot of parameters. To improve the optimum efficiency, sensitivity analysis is used to narrow the range of the variables.