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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.

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 active sound generation systems (ASGS) for electric vehicles (EVs) play an important role in improving sound perception and transmission in the car, and can meet the needs of different user groups for driving and riding experiences. The active sound synthesis algorithm is the core part of ASGS. This paper uses an efficient variable-range fast linear interpolation method to design a frequency-shifted and pitch-modified sound synthesis algorithm. By obtaining the operating parameters of EVs, such as vehicle speed, motor speed, pedal opening, etc., the original sound signal is interpolated to varying degrees to change the frequency of the sound signal, and then the amplitude of the sound signal is determined according to different driving states. This simulates an effect similar to the sound of a traditional car engine. Then, a dynamic superposition strategy is proposed based on the Hann window function.

Autonomous vehicles require the collaborative operation of multiple modules during their journey, and enhancing tracking performance is a key focus in the field of planning and control. To address this challenge, we propose a cooperative control strategy, which is designed based on the integration of model predictive control (MPC) and a dual proportional–integral–derivative approach, referred to as collaborative control of MPC and double PID (CMDP for short in this article).The CMDP controller accomplishes the execution of actions based on information from perception and planning modules. For lateral control, the MPC algorithm is employed, transforming the MPC’s optimal problem into a standard quadratic programming problem. Simultaneously, a fuzzy control is designed to achieve adaptive changes in the constraint values for steering angles.

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.

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.

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.

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 sweeping vehicle has made a great contribution to the cleaning of urban roads. The traditional electric sweeping vehicle uses the main and auxiliary motors to drive the driving system and the operating system respectively. However, because the sweeper is in a low-speed working condition for a long time, and the drive motor must meet the demand for high power, there exist problems of low motor utilization and high cost. Aiming at this phenomenon, a dual-motor power coupling system based on planetary gears is proposed. First, analyze the driving mode of the dual-motor coupling power system according to the actual working scheme of the sweeper, and match the parameters of the motor based on this. Second, on the premise of meeting the power requirements, analyze and divide the working range of each drive mode based on the principle of minimum energy consumption, and then obtain the best drive mode switching control and speed and torque distribution strategy.

The turbine blade is a key component in the operation of the steam turbine hence the design and manufacturing level of the blade will directly affect the performance and efficiency of the turbine. CAD/CAM has been the foundation and important part of advanced manufacturing technology. It is important to study the steam turbine blade integrated CAD/CAM system to improve the design and manufacture of the blade. In this paper, the structure of CAD/CAM system for steam turbine blade is studied and the extensible framework structure including user layer, functional layer and system layer is proposed. Based on the control points of the turbine blade profile design method, and based on the expression between the three-dimensional parametric vector modeling method in UG-based platform, the use of UG/Open development tools and Visual C # developed a turbine blade CAD system.

Road traffic accidents resulting from alcohol-impaired driving are increasing globally despite several measures, currently in place, to curb the trend. For this reason, recent research aims at integrating alcohol early-detection systems and driving simulator experiments to identify intoxicated drivers. However, driving simulator experiments on drunk driving have focused mostly on male participants than female drivers whose characteristics have scarcely been explored. Hence in this paper, vehicle dynamic control inputs on steering, braking, and acceleration performance of 75 licensed female drivers with an upshot of alcohol at four different blood alcohol concentration (BAC) levels (0%, 0.03%, 0.05%, and 0.08%) were investigated. The participants completed simulated driving in a fixed-based simulator experiment coupled with real-time ecological scenarios to extract discrete responses.

The mountain roads are curved and complicated, with undulating terrain and large distance between charging stations. Compared with traditional powered vehicles, in addition to safety issues, pure electric vehicles also need to deal with the driving range issue. At present, the relevant researches on automobile driving in mountainous areas mainly focus on the driving safety of traditional fuel oil vehicles when going uphill and downhill, while there are few researches on the driving planning of pure electric commercial vehicles on curved slope road. This paper presents a speed planning method for pure electric commercial vehicles based on vehicle-road collaboration technology. First, establish the vehicle dynamics model, analyze the vehicle dynamics characteristics when passing the downhill curve, calculate the safe speed range of the vehicle when passing the downhill curve, and establish the safe speed model of the downhill curve.

As a traffic deceleration device, speed bumps are widely used to reduce the speed of vehicles and have a good effect. However, in special occasions such as hospital entrance, the bumps caused by ordinary speed bumps are likely to aggravate the pain of patients. In view of this situation, an intelligent speed bump is designed in this paper, which can adjust the height of the speed bump according to the speed of the passing vehicles. When a low-speed vehicle passes by, the elastic link slider module works, so that the upper surface of the speed bump can be elastically lowered to improve the ride comfort of low-speed vehicles. When a high-speed vehicle passes by, the centrifugal locking module will lock the elastic link slider module, and the upper surface of the speed bump will be locked, which plays a role in speed limit. In this paper, SolidWorks and ADAMS/car are used to analyze the process of vehicle passing through the intelligent speed bump.

The acquisition of vehicle dimensions in a vehicle’s moving process has a wide application in road monitoring, transportation, vehicle model recognition and non-contact overload recognition. At present, the detection of the vehicle dimensions mostly adopts the methods of human visual inspection and tool detection, which has a low detection efficiency and difficult to replicate on a large scale. Based on the image background subtraction method, this paper proposes a vehicle dimensions detection method, which can realize real-time detection of road vehicle dimensions. This method uses an adaptive Gaussian Mixture Model (GMM) to establish a background model based on the video stream. Initially, the moving target image is obtained by the background subtraction method, and then the edge detection under the Canny operator and Hough transform circle detection are performed on the image to obtain the pixel dimension of the vehicle's outline.

With the increasingly serious problems of environmental pollution and energy shortage, electric vehicles have a promising future. As a core component of electric vehicles, the drive motor is developing towards high power density of which remains temperature rise problems, which affects the performance, efficiency and service life of the drive motor. Liquid cooling has high energy consumption and poor reliability. The heat-pipe has excellent heat conduction and temperature uniformity capabilities. Therefore, this paper proposes a heat pipe-based drive motor heat dissipation system to make the heat-pipe act on the inside of the motor to reach a specified range of driving conditions. The drive motor can better dissipate heat through the heat-pipe. Firstly, analysis of the internal heat generation mechanism of the motor, heat transfer characteristics of the heat-pipe and the heat-pipe layout plan was established.