A vehicle following control method and related devices

Abstract

The present invention discloses a following vehicle control method and related devices. The method includes: obtaining driving information of a vehicle to be followed; using the first vehicle speed information and wind speed information as inputs of a wake vortex simulation model to obtain a first simulated following vehicle position of a target vehicle; when there is a third vehicle in the third lane whose distance from the target vehicle is less than a preset distance, obtaining driving information of the third vehicle; controlling the target vehicle and / or the third vehicle to follow the vehicle ahead according to the driving information of the third vehicle, the first simulated following vehicle position, and the wake vortex simulation model. The following vehicle control method proposed in the embodiments of the present application considers the following vehicle method when there is a third vehicle in the third lane, and can effectively control the target vehicle and / or the third vehicle to make full use of the wake vortex of the vehicle ahead, saving fuel consumption.

Description

Technical Field

[0001] This specification relates to the field of vehicle control. More specifically, the present invention relates to a following vehicle control method and related devices. Background Art

[0002] Aerodynamic drag is generally characterized by the drag coefficient. For fuel vehicles, when the drag coefficient decreases by 10%, the fuel consumption is reduced by about 3%. For electric vehicles, when the drag coefficient decreases by 0.02, the driving range is increased by about 10 km. Vehicle platooning significantly reduces the drag experienced by each vehicle because the total pressure in the wake region is small. Therefore, when a vehicle travels within the wake region of the vehicle in front, it will obtain a smaller pressure difference drag. This reduction in drag means less fuel consumption, higher fuel efficiency, and less pollution. In some current following vehicle schemes, the wake region is usually calculated based on the speed of the vehicle in front, and the following vehicle is controlled to enter the wake region to follow in order to save fuel. However, the current following vehicle method cannot accurately judge the wake region based on the wind speed and different vehicle models, and the energy-saving effect is greatly reduced. Summary of the Invention

[0003] A series of simplified concepts are introduced in the Summary of the Invention section, which will be further elaborated in detail in the Detailed Description section. The Summary of the Invention section of the present invention does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the protection scope of the claimed technical solution.

[0004] To provide a more energy-saving and convenient following vehicle method, in a first aspect, the present invention proposes a following vehicle control method, which includes:

[0005] Obtain the driving information of the vehicle to be followed, where the driving information includes first vehicle speed information and wind speed information;

[0006] Use the first vehicle speed information and the wind speed information as inputs to a wake simulation model to obtain the first simulated following position of the target vehicle;

[0007] When there is a third vehicle in the third lane whose distance from the target vehicle is less than a preset distance, obtain the driving information of the third vehicle;

[0008] Control the target vehicle and / or the third vehicle to follow based on the driving information of the third vehicle, the first simulated following position, and the wake simulation model.

[0009] Optionally, the driving information further includes vehicle shape information;

[0010] Using the wind speed information and the vehicle speed information as inputs to a wake simulation model to obtain the simulated following position of the target vehicle includes:

[0011] Determine the vehicle shape parameters of the vehicle to be followed in the vehicle shape library of the above wake vortex simulation model based on the above vehicle shape information;

[0012] Determine the crosswind speed parameter and the crosswind direction parameter in the wind speed library of the above wake vortex simulation model based on the above wind speed information;

[0013] Determine the vehicle speed parameter in the vehicle speed library of the above wake vortex simulation model based on the above vehicle speed information;

[0014] Use the above vehicle shape parameters to be followed, the above crosswind speed parameter, the above crosswind direction parameter, and the above vehicle speed parameter as the input of the wake vortex simulation model to perform simulation calculations to obtain the simulation following vehicle position.

[0015] Optionally, the above method further includes:

[0016] Construct a variety of three-dimensional flow field simulation models based on the fluid mechanics simulation method and the vehicle shape database;

[0017] Perform simulations based on the vehicle speed database, the wind speed database, and the above three-dimensional flow field simulation model to obtain the preset simulation following vehicle position;

[0018] Perform iterative training based on the wake vortex measurement data of the following vehicle test and the above preset simulation following vehicle position using the neural network method to obtain the above wake vortex simulation model.

[0019] Optionally, the above method further includes:

[0020] Obtain the head pressure data and the tail pressure data obtained by the target vehicle at different positions in the wake vortex area corresponding to the vehicle to be followed;

[0021] Obtain the above wake vortex measurement data of the following vehicle test based on the above head pressure data and tail pressure data.

[0022] Optionally, the above preset distance includes a first preset distance;

[0023] In the case that there is a third vehicle in the third lane whose distance from the target vehicle is less than the preset distance, obtain the driving information of the third vehicle;

[0024] Control the target vehicle and / or the above third vehicle to follow the vehicle based on the driving information of the above third vehicle, the first simulation following vehicle position, and the above wake vortex simulation model, including:

[0025] When the above third vehicle is driving in the third lane closer to the vehicle to be followed and the distance from the target vehicle is less than the first preset distance, use the driving information of the above third vehicle as the input of the wake vortex simulation model to obtain the third simulation following vehicle position of the target vehicle;

[0026] Control the target vehicle to follow the vehicle ahead based on the above-mentioned first simulated following position and the third simulated following position.

[0027] Optionally, the above method further includes:

[0028] When the third vehicle exceeds the vehicle to be followed, control the target vehicle to follow the vehicle ahead based on the above-mentioned first simulated following position.

[0029] Optionally, the above preset distance includes a second preset distance;

[0030] When there is a third vehicle in the third lane whose distance from the target vehicle is less than the preset distance, obtain the driving information of the third vehicle;

[0031] Control the target vehicle and / or the third vehicle to follow the vehicle ahead according to the driving information of the third vehicle, the first simulated following position, and the above-mentioned wake vortex simulation model, including:

[0032] When the third vehicle is driving in the third lane closer to the target vehicle and the distance from the target vehicle is less than the second preset distance, use the driving information of the target vehicle as the input of the wake vortex simulation model to obtain the second simulated following position of the target vehicle;

[0033] Control the third vehicle to follow the vehicle ahead based on the above-mentioned first simulated following position and the second simulated following position.

[0034] In a second aspect, the present invention further provides a vehicle following control device, including:

[0035] A first acquisition unit, configured to acquire the driving information of the vehicle to be followed, where the above-mentioned driving information includes first vehicle speed information and wind speed information;

[0036] A second acquisition unit, configured to use the above-mentioned first vehicle speed information and the above-mentioned wind speed information as the input of the wake vortex simulation model to obtain the first simulated following position of the target vehicle;

[0037] A third acquisition unit, configured to acquire the driving information of the third vehicle when there is a third vehicle in the third lane whose distance from the target vehicle is less than the preset distance;

[0038] A control unit, configured to control the target vehicle and / or the third vehicle to follow the vehicle ahead according to the driving information of the third vehicle, the first simulated following position, and the above-mentioned wake vortex simulation model.

[0039] In a third aspect, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor is configured to implement the steps of the vehicle following control method according to any one of the first aspects when executing the computer program stored in the memory.

[0040] In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the vehicle following control method according to any one of the first aspects.

[0041] In summary, the vehicle following control method of the embodiments of the present application includes: obtaining the driving information of the vehicle to be followed, where the driving information includes first vehicle speed information and wind speed information; using the first vehicle speed information and the wind speed information as inputs to a wake vortex simulation model to obtain the first simulated following position of the target vehicle; when there is a third vehicle in the third lane whose distance from the target vehicle is less than a preset distance, obtaining the driving information of the third vehicle; and controlling the target vehicle and / or the third vehicle to follow the vehicle based on the driving information of the third vehicle, the first simulated following position, and the wake vortex simulation model. The vehicle following control method proposed by the embodiments of the present application not only considers the influence of the speed of the vehicle in front on the wake vortex, but also considers the influence of the wind speed on the wake vortex during the process, and the obtained simulated following position is more accurate. The present application also considers the vehicle following method when there is a third vehicle in the third lane, and can effectively control the target vehicle and / or the third vehicle to make full use of the wake vortex of the vehicle in front, saving fuel consumption.

[0042] For the vehicle following control method of the present invention, other advantages, objectives, and features of the present invention will be partially reflected by the following description, and will also be understood by those skilled in the art through the research and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of this specification. Also, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0044] Figure 1 is a schematic flowchart of a vehicle following control method provided by an embodiment of the present application;

[0045] Figure 2 is a schematic diagram of the measurement principle of vehicle following related parameters provided by an embodiment of the present application;

[0046] Figure 3 is a schematic diagram of the structure of a wind measurement system provided by an embodiment of the present application;

[0047] Figure 4 Schematic diagram of a following vehicle test sensor arrangement provided by an embodiment of the present application;

[0048] Figure 5 Schematic diagram of another following vehicle test sensor arrangement provided by an embodiment of the present application;

[0049] Figure 6 Schematic diagram of the first vehicle formation driving provided by an embodiment of the present application;

[0050] Figure 7 Schematic diagram of the second vehicle formation driving provided by an embodiment of the present application;

[0051] Figure 8 Schematic diagram of the third vehicle formation driving provided by an embodiment of the present application;

[0052] Figure 9 Schematic diagram of the fourth vehicle formation driving provided by an embodiment of the present application;

[0053] Figure 10 Schematic diagram of the structure of a following vehicle control device provided by an embodiment of the present application;

[0054] Figure 11 Schematic diagram of the structure of a following vehicle control electronic device provided by an embodiment of the present application. Detailed implementation manners

[0055] The following vehicle control method proposed by the embodiment of the present application not only considers the influence of the speed of the leading vehicle on the wake vortex, but also considers the influence of the wind speed during the process on the wake vortex. The obtained simulation following vehicle position is more accurate, and a more rapid and accurate following vehicle strategy can be realized. The present application also considers the following vehicle method when there is a third vehicle in the third lane, and can effectively control the target vehicle and / or the third vehicle to make full use of the wake vortex of the leading vehicle, saving fuel consumption.

[0056] In the description and claims of this application and the above-mentioned drawings, the terms "first", "second", "third", "fourth", etc. (if any) are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices. The technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments.

[0057] Please refer to Figure 1 , which is a schematic flowchart of a following vehicle control method provided by an embodiment of this application, and specifically may include:

[0058] S110. Obtain the driving information of the vehicle to be followed, where the driving information includes first vehicle speed information and wind speed information;

[0059] Exemplarily, the vehicle to be followed is the leading vehicle. As Figure 2 shown, in some target vehicles implementing a following vehicle strategy, a 5-hole probe can be installed at the front of the vehicle to connect to a high-precision ALPHA sensor to identify the yaw angle, and a pitot tube is used to measure the wind speed facing the front of the vehicle. The direction and intensity of the crosswind are calculated based on the current vehicle speed. A millimeter-wave radar is installed at the front of the vehicle to identify the position and relative speed of the leading vehicle, and the vehicle speed of the leading vehicle is calculated based on the current vehicle speed. It can be understood that if the leading vehicle can communicate with the following vehicle, the vehicle speed of the leading vehicle can also be measured by the ECU (Electronic Control Unit) of the leading vehicle and the measurement result is transmitted to the following vehicle or the cloud for calculating the following vehicle strategy. The wind speed can also be obtained from a roadside wind speed measurement system, and it can be adopted such as Figure 3The wind speed is measured in the following manner. A base 101 is set up on an open space, which includes two first sliding devices 102 and a second sliding device 103. The second slide rail 1032 of the second sliding device 103 is installed on the first sliders 1021 of the two first sliding devices 102. Guide devices 106 are fixed at both ends of the first slider 1021 and on the base 101. A first motor 1051 and a second motor 1052 are installed on the base 101. A transmission belt 107 envelopes the output shafts of the first motor 1051 and the second motor 1052, and the guide devices 106 installed on the first slider 1021 and the base 101, forming an "I" shape. By controlling the rotation speed of the motor, the moving direction of the wind measurement assembly 104 fixedly connected to the second slider 1031 can be controlled, so as to control the side wind assembly to move to a specified location to measure the wind speed and direction.

[0060] (When v ≤ 3 m / s)

[0061] R1 is the rotation speed of the first motor, v is the measured wind speed, a and b are constants. The smaller the measured wind speed v, the greater the rotation speed of the first motor or the second motor, ensuring the rapid movement of the second slider. R2 is the rotation speed of the second motor. The smaller the measured wind speed v, the smaller the difference between R1 and R2, maintaining the lateral or longitudinal movement of the second slider; the greater the measured wind speed v, the greater the difference between R1 and R2, and then it moves along the inclined direction.

[0062] (When v > 3 m / s)

[0063] When v > 3 m / s, i and j are the rotation directions of the first motor and the second motor, with the clockwise direction being the positive direction, n is a constant. The rotation speed R2 and direction of the second motor adaptively adjust following the rotation speed and direction of the first motor. c is the measured wind direction angle, with the angle to the right of the horizontal axis being 0, and the counterclockwise angle increasing (for example, when the wind direction points to the right of the horizontal axis, c = 0, ntanc = 0, and the requirement is that the second slider needs to move horizontally left or right to be parallel to the wind direction. At this time, iR1 + jR2 = 0, and the physical meaning is that the first motor and the second motor have the same rotation speed and opposite directions). n is the proportionality coefficient. Through this method, the first motor and the second motor control the second slider to slowly translate along the direction parallel to the wind direction.

[0064] S120. Use the first vehicle speed information and the wind speed information as inputs to a wake vortex simulation model to obtain the first simulated following position of the target vehicle, where the wake vortex simulation model is obtained through iterative training based on the fluid dynamics simulation method and the neural network method;

[0065] Exemplarily, the wake vortex simulation model can be loaded on the target vehicle or in the cloud. Before the target vehicle follows another vehicle, the recognized vehicle speed information and wind speed information are used as inputs, and the wake vortex simulation model is employed to obtain the simulated following position. When the intensity of the crosswind is relatively large, the wake vortex will shift to the adjacent lane, and the first simulated following position will also shift to the adjacent lane accordingly to achieve close following, thus avoiding the minimum 50m following distance required by regulations for the same lane on the highway by using the crosswind.

[0066] S130. When there is a third vehicle in the third lane whose distance from the target vehicle is less than a preset distance, obtain the driving information of the third vehicle;

[0067] Exemplarily, when there is a third vehicle in the third lane, obtain the driving information of the third vehicle. The third vehicle may be in the third lane close to the vehicle to be followed or in the third lane close to the target vehicle.

[0068] S140. Control the target vehicle and / or the third vehicle to follow another vehicle according to the driving information of the third vehicle, the first simulated following position, and the wake vortex simulation model.

[0069] Exemplarily, when the third vehicle is driving in the third lane close to the vehicle to be followed, the target vehicle can also follow another vehicle by using the wake vortex of the third vehicle. At this time, the combined influence of the common wake vortices of the vehicle to be followed and the third vehicle can be comprehensively considered to determine the optimal following position of the target vehicle, making full use of the wake vortices of the vehicle to be followed and the third vehicle. When the third vehicle is driving in the third lane close to the target vehicle, the third vehicle can follow another vehicle by using the wake vortex of the second vehicle.

[0070] In summary, the following control method proposed in the embodiments of the present application not only considers the influence of the speed of the vehicle ahead on the wake vortex, but also considers the influence of the wind speed on the wake vortex during the process. The obtained simulated following position is more accurate. The present application also considers the following method when there is a third vehicle in the third lane, and can effectively control the target vehicle and / or the third vehicle to make full use of the wake vortex of the vehicle ahead, saving fuel consumption.

[0071] In some examples, the driving information further includes vehicle shape information;

[0072] Taking the wind speed information and the vehicle speed information as inputs of the wake vortex simulation model to obtain the simulated following position of the target vehicle includes:

[0073] Determine the vehicle shape parameters of the vehicle to be followed in the vehicle shape library of the wake vortex simulation model based on the vehicle shape information;

[0074] Determine the crosswind speed parameter and the crosswind direction parameter in the wind speed library of the wake vortex simulation model based on the wind speed information;

[0075] Determine a vehicle speed parameter in the vehicle speed library of the wake vortex simulation model based on the vehicle speed information;

[0076] Perform simulation calculations using the shape parameter of the vehicle to be followed, the crosswind speed parameter, the crosswind direction parameter, and the vehicle speed parameter as inputs to the wake vortex simulation model to obtain a simulated following vehicle position.

[0077] Exemplarily, since the shapes of vehicles are different, i.e., the length, width, height, or shape will affect the flow field formed by the air flowing around the leading vehicle during vehicle driving, the wake vortex regions formed by different shaped vehicles under the same driving conditions are not the same. The influence of vehicle shape on wake vortex formation can be considered during the construction of the wake vortex model and during the driving process. The wake vortex simulation model is a model trained with a large amount of data. The trained wake vortex model provides a vehicle shape library, a wind speed library, and a vehicle speed library for the vehicle. After the target vehicle obtains the shape information, vehicle speed information, and wind speed information of the vehicle to be followed, it selects the corresponding shape parameter of the vehicle to be followed, crosswind speed parameter, crosswind direction parameter, and vehicle speed parameter in the corresponding database as inputs to the wake vortex simulation model to perform simulation calculations to obtain a simulated following vehicle position.

[0078] In summary, for the vehicle following control method proposed in this application, after obtaining the vehicle shape information, wind speed information, and vehicle speed information, the corresponding shape parameter of the vehicle to be followed, crosswind speed parameter, crosswind direction parameter, and vehicle speed parameter are selected in the database corresponding to the wake vortex simulation model, and the above parameters are used as inputs to the wake vortex simulation model, which can quickly simulate and calculate an accurate simulated following vehicle position.

[0079] In some examples, the above method further includes:

[0080] Construct a variety of three-dimensional flow field simulation models based on the fluid mechanics simulation method and the vehicle shape database;

[0081] Perform simulation based on the vehicle speed database, the wind speed database, and the above three-dimensional flow field simulation model to obtain a preset simulated following vehicle position;

[0082] Perform iterative training based on the wake vortex measurement data of the vehicle following test and the above preset simulated following vehicle position using the neural network method to obtain the above wake vortex simulation model.

[0083] Exemplarily, through the method of computational fluid dynamics, a wake vortex simulation model of the leading vehicle is obtained. The simulation model includes a database of the shapes of the vehicles to be followed, a database of vehicle speeds, and a database of wind speeds. The inputs of the wake vortex simulation model include simulation parameters of the crosswind speed and direction, the vehicle speed simulation parameters of the leading vehicle, and the shape parameters of the leading vehicle. The output of the wake vortex simulation model is the coordinate simulation parameters of the position with a relatively strong negative pressure in the wake vortex of the leading vehicle, that is, the simulated following position. Combining the simulated following position output by the flow field simulation model with the measured wake vortex data obtained from the following vehicle test to construct a neural network for iterative training can obtain a trained wake vortex simulation model. The trained wake vortex simulation model can be installed on the target vehicle to determine the simulated following position according to the recognized shape information, vehicle speed information, and wind speed information of the leading vehicle. By using the wake vortex simulation model trained by the neural network, the calculation speed is faster, the error between the determined following position and the position with the maximum actual wake vortex intensity is smaller, and a faster and more accurate following strategy can be realized.

[0084] Specifically, constructing a trained wake vortex simulation model may include the following steps:

[0085] S210. Construct an initial neural network model;

[0086] With the goal of minimizing the error between the measured value of the position with a relatively strong negative pressure in the wake vortex of the leading vehicle output by the initial neural network model and the actual position of the relatively strong negative pressure in the wake vortex of the leading vehicle, input the simulation parameters of the crosswind speed and direction, the vehicle speed simulation parameters of the leading vehicle, and the shape parameters of the leading vehicle into the initial neural network model for iterative training to obtain a target neural network model for obtaining the measured value of the position with a relatively strong negative pressure in the wake vortex of the leading vehicle (simulated following position). The type of the initial neural network model can be a feedback neural network model, a deep learning neural network model, a convolutional neural network model, etc., which is not limited here. According to the type of the initial neural network model, the operation of step training can be completed. Specifically, the initial neural network model can be understood as an untrained target neural network model, which can input the simulation parameters of the crosswind speed and direction and the vehicle speed simulation parameters of the leading vehicle, and through neural network calculation, output the measured value of the initial position with a relatively strong negative pressure in the wake vortex of the leading vehicle. Generally, the initial neural network model may include an input layer, a hidden layer, and an output layer, where the hidden layer is responsible for the relevant calculations of the neural network. Through iterative training, the relevant transfer function parameters such as the weight parameters in the hidden layer can be gradually adjusted to make the measured signal of the initial vertical displacement of the wheel center output by the initial neural network model meet the established training goal. At this time, the initial neural network model can be considered as the target neural network model, and the measured value of the initial position with a relatively strong negative pressure in the wake vortex of the leading vehicle output by it can be recognized as the measured value of the position with a relatively strong negative pressure in the wake vortex of the leading vehicle.

[0087] S220. Obtain the initial simulation model of the wake vortex of the leading vehicle, including: obtaining the initial simulation model of the initial wake vortex of the leading vehicle, inputting the crosswind speed parameter, crosswind direction parameter, speed parameter of the vehicle to be followed, and the shape parameter of the vehicle to be followed into the initial wake vortex simulation model for simulation, and obtaining the coordinate simulation parameters of the position with stronger negative pressure of the wake vortex of the leading vehicle output by the initial wake vortex simulation model. By means of computational fluid dynamics, obtain the wake vortex simulation model of the leading vehicle; wherein, the specific vehicle model with known external dimension parameters in the selected database of the leading vehicle, that is, the target type vehicle (also called the leading vehicle), the input of the wake vortex simulation model includes the crosswind speed and direction simulation parameters, the speed simulation parameter of the leading vehicle, and the output of the wake vortex simulation model is the coordinate simulation value of the position with stronger negative pressure of the wake vortex of the leading vehicle, that is, the preset simulation following position.

[0088] S230. Obtain the wake vortex measurement data of the following vehicle test. Based on the crosswind speed, direction and vehicle speed of the actual operation of the target type vehicle, and obtain the test sensing signal through the vehicle sensor group on the vehicle behind the target type vehicle; wherein, the vehicle sensor group includes one or more of an ALPHA sensor, a pitot tube, a pressure sensor, a millimeter wave radar, and a camera. Based on the test sensing signal and the simulation output value, optimize the initial wake vortex simulation model of the leading vehicle into the wake vortex simulation model of the leading vehicle.

[0089] S240. Train a neural network model according to the initial wake vortex simulation data and the wake vortex measurement data of the following vehicle test to obtain the wake vortex simulation model. With the goal of minimizing the error between the initial simulation data and the wake vortex measurement data of the following vehicle test, input the crosswind speed and direction simulation parameters, and the speed simulation parameter of the leading vehicle into the initial neural network model for iterative training, and obtain the target neural network model for the measured value of the position with stronger negative pressure of the wake vortex of the leading vehicle. Specifically, using the wake vortex simulation model of the leading vehicle obtained in step S220, multiple groups of crosswind speed and direction simulation parameters, the speed simulation parameter of the leading vehicle, the coordinate simulation value of the position with stronger negative pressure of the wake vortex of the leading vehicle (preset simulation following position), and the wake vortex measurement data of the following vehicle test that meet the test accuracy can be obtained, and thus a training set of the initial neural network model is constructed to complete the iterative training of the initial neural network model, and obtain the target neural network model for the measured value of the position with stronger negative pressure of the wake vortex of the leading vehicle, that is, the wake vortex simulation model.

[0090] In summary, the following vehicle control method proposed in the embodiment of the present application uses the trained wake vortex simulation model to determine the simulation following position according to the recognized external shape information, vehicle speed information, and wind speed information of the leading vehicle, with faster calculation speed, and the error between the determined following position and the position with the maximum actual wake vortex intensity is smaller, which can realize a faster and more accurate following strategy and can effectively save the energy consumption of the following vehicle.

[0091] In some examples, the above method further includes:

[0092] Obtain the head pressure data and tail pressure data obtained at different positions in the wake region corresponding to the vehicle to be followed by the above target vehicle;

[0093] Obtain the wake measurement data of the following vehicle test based on the above head pressure data and tail pressure data.

[0094] Exemplarily, as Figure 4 and Figure 5 shown, in some vehicles, 4 patch-type pressure sensors at the vehicle head area position and 8 patch-type pressure sensors installed at the vehicle tail area position. Simulate following vehicle driving, and measure the absolute head pressure value and the front-back pressure difference at the position where the negative pressure of the wake of the preceding vehicle is strong. During the test, measure multiple times at the position where the negative pressure of the wake of the preceding vehicle is strong and within a preset range nearby to test whether the area where the negative pressure of the wake of the preceding vehicle is strong is accurate, and summarize the measurement results to form the wake measurement data of the following vehicle test for training the wake simulation model. It can be understood that the following vehicle test can change the shape of the preceding vehicle, the speed of the preceding vehicle and the wind speed to obtain more comprehensive wake measurement data of the following vehicle test under different following vehicle conditions.

[0095] In summary, for the following vehicle control method provided by the embodiments of the present application, by adding pressure sensors at the front end and the rear end of the target vehicle, obtaining the wake measurement data of the following vehicle test through the following vehicle test, and optimizing the wake simulation model based on the neural network according to the wake measurement data of the following vehicle test, a more accurate wake simulation model can be obtained, which can provide a more accurate simulated following vehicle position for the target vehicle during actual following vehicle, so as to be able to make more full use of the position where the negative pressure of the wake is strong and achieve the purpose of energy-saving following vehicle.

[0096] In some examples, the above preset distance includes a first preset distance;

[0097] When there is a third vehicle in the third lane whose distance from the target vehicle is less than the preset distance, obtain the driving information of the third vehicle;

[0098] Control the target vehicle and / or the third vehicle to follow the vehicle based on the driving information of the third vehicle, the first simulated following vehicle position and the above wake simulation model, including:

[0099] When the third vehicle is driving in the third lane closer to the vehicle to be followed and the distance from the target vehicle is less than the first preset distance, use the driving information of the third vehicle as the input of the wake simulation model to obtain the third simulated following vehicle position of the target vehicle;

[0100] Control the target vehicle to follow the vehicle based on the first simulated following vehicle position and the third simulated following vehicle position.

[0101] Exemplarily, when there is a third vehicle in the third lane relatively close to the vehicle to be followed, the target vehicle will be affected by the wake vortices of both the third vehicle and the vehicle to be followed. As Figure 6 shown, vehicle A is the third vehicle, the leading vehicle is the vehicle to be followed, and the following vehicle is the target vehicle. The leading vehicle is traveling in the middle lane, the following vehicle is traveling in the right lane, and vehicle A accelerates from the left lane to overtake the formation. The following vehicle identifies the position and speed of vehicle A through a millimeter-wave radar, observes the vehicle model on the rear label of vehicle A through a camera, looks up the external dimensions of vehicle A in the database, and obtains the three-dimensional flow field simulation models of vehicle A and the leading vehicle through computational fluid dynamics methods. The input of the three-dimensional flow field simulation model includes the external dimensions of the leading vehicle, the external dimensions of vehicle A, the simulation parameters of the crosswind speed and direction, the simulation parameter of the leading vehicle's speed, the simulation parameter of vehicle A's speed, and the relative positions of the leading vehicle and vehicle A. The output of the three-dimensional flow field simulation model is the coordinate simulation values of the positions with relatively strong negative pressure in the wake vortices of vehicle A and the leading vehicle. Since the left lane and the middle lane are already occupied, by observing through the camera at the front of the vehicle, the relative position relationship between the position with relatively strong negative pressure jointly generated by the wake vortices of vehicle A and the leading vehicle and the driving lane. Due to the existence of a crosswind, when the position with relatively strong negative pressure in the wake vortices of vehicle A and the leading vehicle is in the first or middle lane, the following vehicle in the right lane adjusts its position to follow vehicle A or the leading vehicle in the left lane or the middle lane and maintains a distance of more than 50 m to meet the regulatory requirements; when the position with relatively strong negative pressure in the wake vortices of vehicle A and the leading vehicle is in the right lane, the following vehicle in the right lane adjusts its position to drive to the position with relatively strong negative pressure in the wake vortices of vehicle A and the leading vehicle to improve the fuel economy of the following vehicle. When vehicle A overtakes the leading vehicle, the working range of the following vehicle's radar is limited, and the leading vehicle identifies the position and speed of vehicle A through a millimeter-wave radar and continues to drive according to the above method.

[0102] In some cases, there is also another following vehicle scenario, such as Figure 7As shown, car B is the third vehicle, the front car is the vehicle to be followed, and the rear car is the target vehicle. The front car travels in the second lane, and the rear car travels in the third lane. Car B slows down from the first lane until it falls behind the formation. The front car identifies the position and speed of car B through the millimeter-wave radar. The front car observes the vehicle model on the tail label of car B through the camera, searches the external dimensions of car B in the database, and obtains the three-dimensional flow field simulation model of car B and the front car through the method of computational fluid dynamics. The input of the three-dimensional flow field simulation model includes the external dimensions of the front car, the external dimensions of car B, the simulation parameters of the crosswind speed and direction, the simulation parameters of the speed of the front car, the simulation parameters of the speed of car B, and the relative position of the front car and car B. The output of the three-dimensional flow field simulation model is the coordinate simulation value of the position where the tail vortex negative pressure generated by car B and the front car is strong. Because the first and second lanes are occupied, the vehicle's front camera observes the relative position of the strongest negative pressure areas between Car B and the preceding vehicle and the lane. Due to crosswinds, when the strongest negative pressure areas between Car B and the preceding vehicle are aligned with the preceding vehicle in the first or second lane, the following vehicle in the third lane adjusts its position and follows Car A or the preceding vehicle in the first or second lane, maintaining a gap of at least 50 meters to meet regulatory requirements. When the strongest negative pressure areas between Car B and the preceding vehicle are in the third lane, the following vehicle in the third lane adjusts its position to the strongest negative pressure areas between Car B and the preceding vehicle to improve fuel economy. When Car B is parallel to the preceding vehicle, the operating range of the preceding vehicle's radar is limited. The following vehicle uses its millimeter-wave radar to identify Car B's position and speed, continuing to follow it as described above.

[0103] In summary, the following control method provided in the embodiment of the present application, when there is a third vehicle in the third lane close to the vehicle to be followed, combines the combined influence of the tail vortices of the third vehicle and the vehicle to be followed to determine the following position of the target vehicle, thereby saving energy consumption.

[0104] In some examples, the method further includes:

[0105] In a case where the third vehicle exceeds the vehicle to be followed, the target vehicle is controlled to follow the vehicle based on the first simulated following position.

[0106] For example, when a third vehicle overtakes a target vehicle, the target vehicle blocks the trailing vortex generated by the third vehicle. This trailing vortex does not affect the target vehicle's movement. To reduce computational complexity, the target vehicle's movement information is no longer considered, and the target vehicle is controlled to follow the target vehicle using the first simulated following position.

[0107] In some examples, the preset distance includes a second preset distance;

[0108] In the above case where there is a third vehicle in the third lane whose distance to the target vehicle is less than the preset distance, obtaining the driving information of the third vehicle;

[0109] Controlling the target vehicle and / or the third vehicle to follow the vehicle according to the driving information of the third vehicle, the first simulated following vehicle position, and the tail vortex simulation model includes:

[0110] When the third vehicle is traveling in a third lane closer to the target vehicle and the distance between the third vehicle and the target vehicle is less than a second preset distance, the driving information of the target is used as an input to the wake vortex simulation model to obtain a second simulated following position of the target vehicle;

[0111] The third vehicle is controlled to follow the vehicle based on the first simulated following position and the second simulated following position.

[0112] For example, if there is a third vehicle in the third lane closer to the target vehicle, the third vehicle can travel with the help of the target vehicle's tail vortex. Figure 8 As shown, the leading vehicle is the vehicle to be followed, the trailing vehicle is the target vehicle, and vehicle C is the third vehicle. The leading vehicle is traveling in the left lane, while the trailing vehicle is traveling in the middle lane. The trailing vehicle C is following the leading vehicle in formation, utilizing the negative pressure of the trailing vortex. Trailing vehicle C is traveling in the right lane and can optionally utilize the negative pressure of the trailing vortex. Trailing vehicle C uses millimeter-wave radar to identify the leading vehicle's position and speed. Trailing vehicle C uses a camera to observe the vehicle model on the trailing vehicle's tailgate, searches a database for the leading vehicle's dimensions, and uses computational fluid dynamics to obtain a three-dimensional flow field simulation model of the leading vehicle. The inputs to the 3D flow field simulation model include the leading vehicle's dimensions, crosswind speed and direction simulation parameters, and the leading vehicle's speed simulation parameters. The output of the 3D flow field simulation model are the simulated coordinates of locations with strong negative pressure from the trailing vortex of the leading vehicle. Because the left lane and the middle lane are occupied, the camera at the front of the car observes the relative position of the position where the front car's tail vortex has stronger negative pressure and the right lane. Due to the presence of crosswind, the rear car C adjusts its position and drives to the position where the front car's tail vortex has stronger negative pressure to improve the fuel economy of the rear car C.

[0113] like Figure 9As shown in the figure, the vehicle in front is the vehicle to be followed, the vehicle behind is the target vehicle, and vehicle C is the third vehicle. The vehicle in front is traveling in the left lane, and the vehicle behind is traveling in the middle lane. The vehicle behind is following the vehicle in front by making use of the negative pressure of the wake vortex. Vehicle C is traveling in the right lane, and vehicle C can choose to make use of the negative pressure jointly generated by the vehicle in front and the vehicle behind. Vehicle C identifies the position and speed of the vehicle in front through a millimeter-wave radar, and identifies the position and speed of the vehicle behind. For the vehicle in front and the vehicle behind traveling in formation, the speeds are generally the same. Vehicle C observes the vehicle model on the tail label of the vehicle in front through a camera, and observes the vehicle model on the tail label of the vehicle in front. It looks up the external dimensions of the vehicle in front and the vehicle behind in the database, and obtains the three-dimensional flow field simulation models of the vehicle in front and the vehicle behind through the method of computational fluid dynamics; the input of the three-dimensional flow field simulation model includes the external dimensions of the vehicle in front, the external dimensions of the vehicle behind, the simulation parameters of the side wind speed and direction, the simulation parameter of the vehicle speed of the vehicle in front, the simulation parameter of the vehicle speed of the vehicle behind, and the relative position of the vehicle in front and the vehicle behind. The output of the three-dimensional flow field simulation model is the coordinate simulation value of the position with relatively strong wake vortex negative pressure jointly generated by the vehicle in front and the vehicle behind. Since the left lane and the middle lane are already occupied, by observing through the camera at the front of the vehicle, the relative position relationship between the position with relatively strong wake vortex negative pressure jointly generated by the vehicle in front and the vehicle behind and the right lane is determined. Due to the existence of side wind, vehicle C adjusts its position to drive to the position with relatively strong wake vortex negative pressure jointly generated by the vehicle in front and the vehicle behind to improve the fuel economy of vehicle C.

[0114] In summary, for the vehicle-following method proposed in the embodiment of the present application, in the case where there is a third vehicle in the third lane close to the target vehicle, based on the driving information of the target vehicle as input, the following position of the third vehicle is determined based on the wake vortex simulation model. By making use of the wake vortex formed by the target vehicle, the third vehicle can achieve the purpose of energy conservation.

[0115] Please refer to Figure 10 , an embodiment of the vehicle-following control device in the embodiment of the present application may include:

[0116] The first acquisition unit 21 is configured to acquire the driving information of the vehicle to be followed, where the driving information includes the first vehicle speed information and the wind speed information;

[0117] The second acquisition unit 22 is configured to use the first vehicle speed information and the wind speed information as the input of the wake vortex simulation model to acquire the first simulation following position of the target vehicle;

[0118] The third acquisition unit 23 is configured to acquire the driving information of the third vehicle in the case where there is a third vehicle in the third lane whose distance from the target vehicle is less than a preset distance;

[0119] The control unit 24 is configured to control the target vehicle and / or the third vehicle to follow the vehicle based on the driving information of the third vehicle, the first simulation following position, and the wake vortex simulation model.

[0120] AsFigure 11 As shown, an embodiment of the present application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 320 and executable on the processor. When the processor 320 executes the computer program 311, the steps of any of the above-mentioned vehicle following control methods are implemented.

[0121] Since the electronic device introduced in this embodiment is a device used to implement a vehicle following control device in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application will not be introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application falls within the scope of protection of this application.

[0122] In the specific implementation process, the computer program 311 can be implemented when executed by the processor Figure 1 Any implementation manner in the corresponding embodiments.

[0123] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0124] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0125] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0126] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the functions specified in one or more of the processes Figure 1 or blocks. Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing steps for implementing the functions specified in one or more of the processes

[0127] or blocks. Figure 1 or blocks. Figure 1 The present application embodiment also provides a computer program product, which includes computer software instructions. When the computer software instructions run on a processing device, the processing device is caused to execute the following

[0128] vehicle following control process in the corresponding embodiment. Figure 1

[0129] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be stored by a computer or a data storage device such as a server or data center that includes one or more integrated available media. The available medium may be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)).

[0130] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0131] ​In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0134] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0135] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A following vehicle control method, characterized in that Including: Obtain the driving information of the vehicle to be followed, where the driving information includes first vehicle speed information and wind speed information; Use the first vehicle speed information and the wind speed information as inputs to a wake vortex simulation model to obtain the first simulated following position of the target vehicle; When there is a third vehicle in the third lane whose distance from the target vehicle is less than a preset distance, obtain the driving information of the third vehicle; Control the target vehicle and / or the third vehicle to follow based on the driving information of the third vehicle, the first simulated following position, and the wake vortex simulation model; The preset distance includes a second preset distance; The controlling the target vehicle and / or the third vehicle to follow based on the driving information of the third vehicle, the first simulated following position, and the wake vortex simulation model includes: When the third vehicle is driving in the third lane closer to the target vehicle and the distance from the target vehicle is less than the second preset distance, use the driving information of the target vehicle as an input to the wake vortex simulation model to obtain the second simulated following position of the target vehicle; Control the third vehicle to follow based on the first simulated following position and the second simulated following position.

2. The method according to claim 1, wherein The driving information further includes vehicle shape information; Using the wind speed information and the vehicle speed information as inputs to a wake vortex simulation model to obtain the simulated following position of the target vehicle includes: Determine the vehicle shape parameters of the vehicle to be followed in the vehicle shape library of the wake vortex simulation model based on the vehicle shape information; Determine the crosswind speed parameters and crosswind direction parameters in the wind speed library of the wake vortex simulation model based on the wind speed information; Determine the vehicle speed parameters in the vehicle speed library of the wake vortex simulation model based on the vehicle speed information; Perform simulation calculations using the vehicle shape parameters of the vehicle to be followed, the crosswind speed parameters, the crosswind direction parameters, and the vehicle speed parameters as inputs to the wake vortex simulation model to obtain the simulated following position.

3. The method according to claim 2, characterized in that, Also including: Construct a variety of three-dimensional flow field simulation models based on the fluid dynamics simulation method and the vehicle shape database; Perform simulations based on the vehicle speed database, the wind speed database, and the three-dimensional flow field simulation model to obtain the preset simulated following position; Perform iterative training based on the wake vortex measurement data from the following vehicle experiment and the preset simulated following position using the neural network method to obtain the wake vortex simulation model.

4. The method according to claim 3, characterized in that Also including: Obtain the front head pressure data and rear tail pressure data of the target vehicle obtained at different positions in the wake vortex area corresponding to the vehicle to be followed; Obtain the wake vortex measurement data from the following vehicle experiment based on the front head pressure data and the rear tail pressure data.

5. The method according to claim 1, wherein the preset distance includes a first preset distance; When there is a third vehicle in the third lane whose distance from the target vehicle is less than the preset distance, obtain the driving information of the third vehicle; Control the target vehicle and / or the third vehicle to follow based on the driving information of the third vehicle, the first simulated following position, and the wake vortex simulation model includes: When the third vehicle is traveling in the third lane closer to the vehicle to be followed and the distance from the target vehicle is less than the first preset distance, use the driving information of the third vehicle as the input of the wake vortex simulation model to obtain the third simulated following position of the target vehicle; Control the target vehicle to follow based on the first simulated following position and the third simulated following position.

6. The method according to claim 5, further comprising: When the third vehicle overtakes the vehicle to be followed, control the target vehicle to follow based on the first simulated following position.

7. A following vehicle control device, characterized in that, Comprising: A first acquisition unit for acquiring the driving information of the vehicle to be followed, where the driving information includes first vehicle speed information and wind speed information; A second acquisition unit for using the first vehicle speed information and the wind speed information as the input of the wake vortex simulation model to obtain the first simulated following position of the target vehicle; A third acquisition unit for acquiring the driving information of the third vehicle when there is a third vehicle in the third lane whose distance from the target vehicle is less than the preset distance; A control unit for controlling the target vehicle and / or the third vehicle to follow according to the driving information of the third vehicle, the first simulated following position, and the wake vortex simulation model; The preset distance includes a second preset distance; The control unit is configured to: When the third vehicle is traveling in the third lane closer to the target vehicle and the distance from the target vehicle is less than the second preset distance, use the driving information of the target vehicle as the input of the wake vortex simulation model to obtain the second simulated following position of the target vehicle; Control the third vehicle to follow based on the first simulated following position and the second simulated following position.

8. An electronic device, comprising: A memory and a processor, characterized in that the processor is configured to implement the steps of the following control method according to any one of claims 1-6 when executing the computer program stored in the memory.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program, when executed by the processor, implements the following control method according to any one of claims 1-6.

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