Search Results

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.

In the process of truck overload and over-limit detection, it is necessary to detect the characteristics of the vehicle's size, type, and wheel number. In addition, in some vehicle vision-based load recognition systems, the vehicle load can be calculated by detecting the vibration frequency of specific parts of the vehicle or the change in the length of the suspension during the vehicle's forward process. Therefore, it is essential to quickly and accurately identify vehicle features through the camera. This paper proposes a vehicle feature recognition method based on image semantic segmentation and Python, which can identify the length, height, number of wheels and vibration frequency at specific parts of the vehicle based on the vehicle driving video captured by the roadside camera.

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.

Heavy commercial vehicle drivers may frequently shift gears when they are running on long and downhill roads in mountainous area. In order to improve driving safety and fuel economy, it is necessary to predict the slope of the road ahead in real time and correct the driver's shift strategy in time. At present, the road slope estimation is mainly based on the real-time estimation of the road slope at the current position of the vehicle based on the vehicle driving information obtained by the sensors, but the road slope of the road section that the vehicle is about to reach has not been predicted. In this paper, based on the road slope information of the road section that the driver has driven through, combined with Geographic Information System (GIS) information and road design standards, the slope of the road section ahead is predicted.

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.

Lane changing manoeuvers is an essential rudiment in vehicle driving and has a significant impact on the characteristics of traffic flow. In the case of traditional cars, the driver operates the vehicle to complete the lane change whilst for autonomous vehicles, completing the lane change requires planning the lane change trajectory and controlling the vehicle speed during the lane change. Unreasonable lane change trajectory and vehicle speed may cause the vehicle to lose stability, threaten driving safety, increase energy consumption and waste energy. This paper considers the safety and economy of the lane changing process, and proposes a new lane changing method for vehicles.

The non-contact overload recognition method refers to the method of detecting the vibration state of the vehicle through visual recognition without touching the vehicle, and then calculating the vehicle load in combination with the vehicle dynamics model to determine whether the passing vehicle is overloaded. Due to the convenience of detection, low cost of infrastructure and informatization, this method has great advantages in the field of overload identification. However, the model used in this recognition method is the single mass vibration model at present, which will have a large error due to the interaction between the front and rear suspension, and the position of the center of mass needs to be acquired in the recognition process, which is difficult in the actual identification process. In this paper, a vehicle vibration model containing two modes of vibration is proposed, and uses Sobol algorithm to analyze the parameter sensitivity of the model.

There are a large number of curves and slopes in the mountainous areas. Unreasonable acceleration and deceleration in these areas will increase the burden of the brake system and the fuel consumption of the vehicle. The main purpose of this paper is to introduce a speed planning and promotion system for commercial vehicles in mountainous areas. The wind, slope, curve, engine brake, and rolling resistances are analyzed to establish the thermal model of the brake system. Based on the thermal model, the safe speed of the brake system is acquired. The maximum safe speed on the turning section is generated by the vehicle dynamic model. And the economic speed is calculated according to the fuel consumption model. The planning speed is provided based on these models. This system can guide the driver to handle the vehicle speed more reasonably.

At present, many electric vehicles are often equipped with only a single-stage final drive. Although the single-stage speed ratio can meet the general driving requirements of electric vehicles, if the requirements of the maximum speed and the requirements for starting acceleration or climbing are met at the same time, the power demand of the drive motor is relatively large, and the efficient area of the drive motor may be far away from the operating area corresponding to daily driving. If the two-speed automatic transmission is adopted, the vehicle can meet the requirements of maximum speed, starting acceleration and climbing at the same time, reduce the power demand of the driving motor, and improve the economy under certain power performance. This is especially important for medium and large vehicles.

In the intelligent speed planning system, real-time estimation of road slope is the key to calculate slope resistance and realize the vehicles’ active safety control. However, if the road slope is measured by the sensor while the commercial vehicle is driving, the vibration of the vehicle body will affect its measurement accuracy. Therefore, the relevant algorithm is used to estimate the real-time slope of the road when the commercial vehicle is driving. At present, many domestic and foreign scholars have analyzed and tested the estimation of road slope by the least square method or Kalman filter algorithm. Although the two methods both can achieve the estimation, the real-time performance and accuracy still need to be improved. In this paper, for traditional fuel commercial vehicle, the Kalman filter algorithm based on the kinematics and the extended Kalman filter algorithm based on the longitudinal dynamics are respectively used to estimate the road slope.

In ITS, image processing technology is applied to a wide variety of areas such as visual-based intelligent vehicle navigation, visual-based traffic monitoring and visual-based traffic management. In the recognition system of the vehicle body characteristics, most of the recognition is the license plate and the car emblem, etc. This paper proposes an image recognition application method for the vertical motion of the car while driving, mainly including vertical height detection and vertical displacement velocity acceleration recognition. The edge detection model of the image object is established by using the gray image to obtain the car motion segmentation image. At the same time, an image length and actual length coordinate conversion model is established, which can calculate an arbitrary actual length of the image object. In this paper, Yuejin Shangjun X500 van was selected as the test vehicle, and the video data was captured with a camera.

Fluid auxiliary braking devices can provide braking torque through hydraulic damping, fluid auxiliary braking devices can also convert vehicular inertia energy into transmission fluid heat energy during the braking, which can effectively alleviate the work pressure of the main brake. Traditional hydraulic auxiliary braking devices use transmission fluids to transmit torque, however, there is a certain lag effect during the braking. The magnetorheological fluid (MR fluid) can also be used to transmit torque because it has the advantages of controlling braking torque linearly and responding fast to the magnetic field changed. The temperature of MR fluid will increase when the vehicle is engaged in continuous braking. MR fluid temperature changes will cause a bad influence on the efficiency stability of auxiliary braking.

The drivers' hysteretic perception to surrounding environment will affect vehicular fuel economy, especially for the heavy-duty vehicles driving under complex conditions and long distance in mountainous areas. Unreasonable acceleration or deceleration on the slope will increase the fuel consumption. Improving the performance of the engine and the transmission system has limited energy saving potential, and most fuel-efficient driving assistant systems don't consider the road conditions. The main purpose of this research is to introduce an economic driving scheme with consideration of the prestored slope information in which the vehicle speed in mountainous slopes is reasonably planned to guide the driver's behavior for reduction of the fuel consumption. Economic driving optimization algorithm with low space dimension and fast computation speed is established to plan accurate and real-time economic driving scheme.

The hydraulic retarder is an important auxiliary braking device. With merits such as its high braking torque, smooth braking, low noise, long service life and small size, it is widely used on modern commercial vehicles. Transmission fluid of traditional hydraulic retarder is cooled by engine cooling system, which exhausts the heat directly and need additional energy consumption for the thermal management component. On account of the working characteristics of hydraulic retarder, this study designs a set of waste heat recovery system based on the Organic Rankine Cycle (ORC). Under the premise of ensuring stable performance of hydraulic retarder, waste heat energy in transmission fluid is recycled to supplement energy requirements for cooling system. First of all, a principle model, which is scaled down according to D300 retarder`s thermal power generation ration of 1:100, is established. Then through theoretical calculations, components' structural parameters of the ORC are determined.

The hydraulic retarder is an auxiliary braking device used for commercial vehicle in a long slope brake, and its transmission oil generates a lot of heat in its working process. If the heat of transmission doesn’t go through a reasonable management, it will seriously affect the braking performance of hydraulic retarder. To cool down the transmission oil, it will aggravates the load of the engine cooling system, and the long cooling path sometimes causes heat exchange not timely. When the Rankine cycle is used for cooling the hydraulic retarder transmission oil in virtue of its good heat transfer performance in phase change process, it can make the transmission oil temperature controlled more stable. In this new system, the setting parameters of the Evaporate-condensate system will affect the stability of the transmission oil temperature in the hydraulic retarder inlet and the energy recovery efficiency of the system.

With the improvement of occupants’ awareness on the driving safety, hydraulic retarder applications increase quickly. The traditional hydraulic retarder, on the one hand, exhausts the waste heat of transmission oil by the engine cooling system; on the other hand, the engine power should be consumed to drive the water pump and the engine cooling fan for maintaining the normal operation of the auxiliary braking system. In this study, the Organic Rankine Cycle (ORC) instead of the traditional hydraulic retarder water-cooling system is applied to achieve the effective temperature control of the hydraulic retarder, while the waste heat of transmission oil could be recovered for saving vehicle energy consumption. The ORC fluid selection needs comprehensive consideration for the net power of the ORC and the optimal temperature range of the retarder transmission oil at both the inlet and outlet end, which is the key issue to ensure the stability and efficiency of the ORC system performance.

The heavy duty trucks have large engine power and drive continuously in mountainous area, so the heat dissipation of engine is very important. In the traditional cooling system with fixed transmission ratio fan, the cooling capacity is insufficient and the engine is easy to be over-heated when the engine is working in low speed and heavy load conditions. Owning to the bigger size of electric motor compared to the hydraulic motor, it is not suitably applied to the heavy duty trucks. Contrasted with the electric motor, the hydraulic drive cooling system is widely applied in heavy duty trucks due to smaller size, larger power, continuous speed modulation and flexible installation location. However, the low transmission efficiency of the pump-motor system results in high power consumption of the cooling system. In this paper, the mathematical and simulation model of hydraulic-driven fan cooling system is established for the specific engine.

Vehicle exhaust waste-heat recovery with thermoelectric power generators can improve energy efficiency, as well as vehicle fuel economy. In the conventional structure, the hot-end of thermoelectric module is directly connected with the outer wall of the exhaust pipe, while the cold-end is connected with the water pipe’s outer wall of the vehicle engine cooling cycle. However, the variety of vehicle engine operating conditions leads to the instability of the hot-end temperature, which will reduce the generating efficiency of the thermoelectric modules and also shorten its service life. This research is on the basis of constructing a heat transfer oil circulation, and to study the action principles and implementation methods of it.

The hydraulic retarder is an auxiliary braking device used in heavy duty vehicle. It generates braking forceby liquid damping effect and makes inertial energy into thermal energy of the transmission medium when the vehicleis in thedownhill. The traditional thermal management system of the hydraulic retarder dissipates the heat of transmission medium out of the vehicle directly, which causes a big waste of energy, meanwhilethe thermal management system components need to consume engine power. This study applies organic Rankine cycle (ORC)cooling system to meet the high power cooling requirements of the hydraulic retarder and recover waste heat energy from the transmission medium at the same time and then supply energy to the thermal management system, which could save the parasitic power of the engine and improve the comprehensive energy utilization ratio of the vehicle.

The hydraulic retarder, an important auxiliary brake, has been widely used in heavy vehicles. Under the non-braking working condition, the air resistance torque in the working chamber, which is formed by the rotor of hydraulic retarder's stirring the air, causes pumping loss. This research designs a new type of hydraulic retarder, whose helium is charged into working chamber through closed loop gas system under non-braking working condition, can reduce the parasitic power loss of transmission system. First, under non-braking working condition, the resistance characteristics are analyzed on the base of hydraulic retarder pumping model; then, considering some parameters, such as the volume of chambers and the initial gas pressure, the working chamber gas charge model is established, and the transient gas charge characteristics are also analyzed under non-braking working condition.