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In the racing world, speed is everything, and the Formula Student cars are no different. As one of the key means to improve the speed of the car, lightweight plays an important role in the racing world. The weight reduction of unsprung metal parts can not only improve the driving speed, but also effectively optimize the dynamic of the car, so the lightweight design of unsprung parts has attracted much attention. In the traditional Formula Student racing car, the hub and spoke are two independent parts, they are fixed by four hub bolts or a central locking nut, the material of these fasteners is usually steel, so it brings a lot of weight burden. In order to achieve unsprung lightweight, a new type of wheel part design of Formula Student racing car is proposed in this paper. The hub and spoke are designed as integrated aluminum alloy parts, effectively eliminating the mass of hub bolts or central locking nuts.

Gear vibration and noise evaluation approaches are commonly proposed with a variety of way in a transmission system while complicated and obscured, it is difficult to provide a feasible basis for the gear development of E-drive system.

Decision-making of lane-change for autonomous vehicles faces challenges due to the behavioral differences among human drivers in dynamic traffic environments. To enhance the performances of autonomous vehicles, this paper proposes a game theoretic decision-making method that considers the diverse Social Value Orientations (SVO) of drivers. To begin with, trajectory features are extracted from the NGSIM dataset, followed by the application of Inverse Reinforcement Learning (IRL) to determine the reward preferences exhibited by drivers with distinct Social Value Orientation (SVO) during their decision-making process. Subsequently, a reward function is formulated, considering the factors of safety, efficiency, and comfort. To tackle the challenges associated with interaction, a Stackelberg game model is employed.

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.

The multi-functional mechanical arm equipped on engineering vehicle can achieve different functions by installing different mechanism devices through the interface at the end of the mechanical arm. It can achieve functions like engineering construction and road rescue. Mechanical arm systems often work in complex environments, which requires good reliability and safety of the boom system. When the mechanical boom is working, the pressure of each luffing cylinder is large, and the contact force and acceleration of each boom are complex, which requires a certain degree of verification and optimization before it can be put into production. In this paper, a virtual prototype of a vehicle mounted hydraulic mechanical arm with four booms is established. Through ADAMS, the dynamic analysis of mechanical arm under multiple working conditions is carried out, the movement parameter changes and the pressure changes of each luffing cylinder are analyzed.

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.

Preventing skidding is essential for studying the safety of driving in curves. However, the adhesion of the vehicle during the driving process on the wet and slippery road will be significantly reduced, resulting in lateral slippage due to the low adhesion coefficient of the road surface, the speed exceeding the turning critical, and the turning radius being too small when passing through the corner. Therefore, to reduce the incidence of traffic accidents of passenger cars driving in curves on rainy and snowy days and achieve the purpose of planning safe driving speed, this paper proposes a curve active safety system based on a deep learning algorithm and vehicle dynamics model. First,we a convolutional neural network (CNN) model is constructed to extract and judge the characteristics of snow and ice adhesion on roads.

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.

At present, most of the game decision lane-changing models only consider the state data of the vehicle at the current moment. However, the driving style has a significant impact on the vehicle trajectories, which should be taken into account in the lane-changing process. Moreover, most of the game models are static and do not take into account the sequence of the vehicle lane-changing. This paper proposed a Stackelberg-game-based vehicle lane-changing model considering driving style. Firstly, the NGSIM public dataset is selected for this research and the data screen flow is processed. The K-means algorithm is applied to exchange data clustering. Based on the analysis of vehicle lane changing features under different driving style, the characteristics of the corresponding data under different style are extracted. The quantic-polynomial programming algorithm is used to generate a vehicle lane changing trajectory under different driving styles.

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.

The sweeper vehicle plays a very key role in maintaining the urban environment. If the sweeper vehicle can accurately and efficiently identify and classify the ground garbage in the working process, it can greatly improve the working efficiency of the sweeper vehicle and reduce the consumption of manpower. Although the deep learning algorithm based on DUC and PSPNet has high accuracy, the recognition speed is low. ENet is a lightweight network, which greatly improves efficiency, but significantly sacrifices accuracy. This paper presents an improved real-time detection lightweight network based on PSPNet, which takes into account the operation speed and accuracy. The network takes PSPNet as the backbone network, and increases the stride in the convolution process, to reduce the size of the feature map and reduce the amount of calculation.

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.

With the rapid development of new energy vehicles, the echelon utilization of retired power battery has become an important factor to promote the healthy development of this industry, while the Remaining Useful Life (RUL), as the key reference factor for the echelon utilization of retired power battery, has attracted the attention and research of many scholars in recent years. At present, most prediction methods are based on off-line data, which cannot process real-time data in time, so it is difficult to realize online prediction of RUL. In order to realize the real-time online monitoring and high-precision calculation of lithium-ion battery RUL, this paper proposes a lithium-ion battery RUL prediction method based on data-driven and multi-model fusion. The one-dimensional Convolutional Neural Network (1D_CNN) is used for fast online feature extraction of one-dimensional battery capacity time series data to mine potential hidden information.

The articulated steering system is widely used in engineering vehicles due to its high mobility and low steering radius. The design parameters have a vital impact on the selection of the steering system assemblies, such as the operation stroke, pressure, and force of the hydraulic cylinders during the steering process, which will affect the system weight. The system energy consumption is also relevant to the geometry parameters. According to the kinetic analysis of the steering system and dynamic analysis of the steering process, the kinetic model of an engineering vehicle steering system is built, and the length and pressure variation of the cylinder is calculated and validated by the field test. The influence of the factors is analyzed based on the established model. To lower the system weight, needed pressure, and force, the multi-objective particle swarm optimization method is initiated to optimize the geometry parameter of the articulated steering system.

According to the requirements of high transmission ratio and high load torque of high-speed electric drive axle planetary gear system, the design and analysis of fine-pitch planetary gear system with small modulus, small pressure angle and high full tooth height of are carried out. In order to improve the bearing capacity of gear and reduce gear meshing noise, the tooth profile modification parameters of gear system are optimized. In this paper, the tooth modification methods are analyzed and the gear train parameters are determined. The influence degree of different tooth modification methods on the transmission performance of the gear train is determined by orthogonal experiment method. The transmission error is reduced, the stress fluctuation is improved, and the gear meshing performance is greatly improved by adopting the appropriate modification scheme, which proves the effectiveness of the tooth modification scheme.

Lane detection is an important component in automatic pilot system and advanced driving assistance system (ADAS). The stability and precision of lane detection will directly determine precision of control and lane plan of vehicles. Traditional mechanical vision lane detection approaches in complicated environment have the deficiencies of low precision and feature semantic description disabilities. But the lane detection depending on deep learning, e.g. SCNN network, LaneNet network, ENet-SAD network have imbalance problems of splitting precision and storage usage. This paper proposes an approach of high-efficiency deep learning Segnet-LSTM semantic segmentation network. This network structure is composed with encoding network and corresponding decoding networks. First, convolution and maximum pooling. The proposal extracts texture details of five images and stores searching position of maximum pooling. Meanwhile, it will implement interpolate processing to the lost points.

Correct shifting schedule of vehicle coasting mode play a vital role in improving vehicle comfort and economy. At present, the calibration of the transmission shifting schedule ignores the impact of vehicle’s dynamic mass. This paper proposes a method for optimizing the shifting schedule of the coasting modes with gear based on the dynamic mass identification of the vehicle. This method identifies the dynamic mass of the vehicle during driving and substitute them into the process of solving the shifting schedule parameters. Then we get the optimal shifting schedule. At first, establish the Extended Kalman Filter to Pre-process the experimental data, reducing errors caused by excessive data fluctuations. Then, establishing a weighted squares estimation model based on particle swarm optimization to identify the dynamic mass of the vehicle.

Driver distraction detection is crucial to driving safety when autonomous vehicles are co-piloted. Recognizing drivers’ behaviors that are highly related with distraction from real-time video stream is widely acknowledged as an effective approach mainly due to its non-intrusiveness. In recently years, deep learning neural networks have been adopted to by-pass the procedure of designing features artificially, which used to be the major downside of computer-vision based approaches. However, the detection accuracy and generalization ability is still not satisfying since most deep learning models extracts only spatial information contained in images. This research develops a driver distraction model based on a two-stream, spatial and temporal, convolutional neural network (CNN).

Electronic Stability Control (ESC) is an important measure to proactively guarantee vehicle safety. In this paper, the method of four-wheel hub-motor torque control is compared with the traditional single-wheel hydraulic brake control in ESC system. The control strategy adopts the hierarchical structure. In upper controller, the stability of the vehicle is identified by threshold method, the additional yaw moment control uses a way to get the moment including feedforward and feedback parts based on the linear quadratic regulator (LQR). The medium controller is tire slip rate control, in order to get the optimal target slip rate from the upper additional yaw moment, a method of quadratic programming to optimize the longitudinal force is proposed for each wheel. The inputs of tire state for the magic tire model is introduced so as to calculate the target slip rate from the target longitudinal force.

In summer, the relatively low temperature water condenses in the evaporator when the vehicle air-conditioning (AC) is running. At present, the vehicle AC condensate water without well utilization is directly wasted. The condenser’s thermal transfer performance has a great influence on the AC performance, and to increase the convective heat transfer coefficient (CHTC) is the key to its design. In this paper, a method of using atomized condensate water (CW) to enhance the condenser’s thermal transfer performance is proposed, which can make the most of the CW's cold energy. It achieves the reuse of CW and increases the condenser’s CHTC. First, the CW flow calculation model in the evaporator and the calculation model of the condenser enhanced thermal transfer using atomized CW are both set up. The influence of the evaporation degree of atomized CW particles in the air on the enhancement effect is comprehensively considered.