A vehicle following control method and related equipment

Abstract

The present invention discloses a vehicle following control method and related equipment. The method comprises: obtaining roadside environmental information in a target area; obtaining driving information of a vehicle to be followed, the driving information including wind speed information and vehicle speed information; using the vehicle driving information and roadside environmental information as inputs of a wake vortex simulation model to obtain a simulated vehicle following position of the target vehicle; determining the relative position relationship between the simulated vehicle following position and the vehicle to be followed based on lane line information; and controlling the target vehicle to follow the vehicle based on the relative position relationship. The vehicle following control method proposed in the embodiment of the present application simultaneously considers the influence of vehicle speed information, roadside environmental information and wind speed information on the wake vortex, and the obtained simulated vehicle following position is closer to the position where the wake vortex negative pressure is stronger, which can save the energy consumption of the target vehicle.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control, and more specifically, to a vehicle following control method and related equipment. Background Art

[0002] Aerodynamic drag is generally characterized by the drag coefficient. For fuel vehicles, a 10% decrease in the drag coefficient reduces fuel consumption by about 3%. For electric vehicles, a 0.02 decrease in the drag coefficient increases the driving range by about 10 km. Vehicle formation significantly reduces the drag experienced by each vehicle because the total pressure in the tail vortex area is small, so when the vehicle is driving within the tail vortex area of ​​the front vehicle, 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 schemes, the tail vortex area is usually calculated based on the speed of the vehicle in front, and the following vehicle is controlled to enter the tail vortex area to follow in order to save fuel. However, the current following method cannot make accurate judgments on the tail vortex area based on 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, which will be further described in detail in the Detailed Description of the Invention. The Summary of the 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 scope of protection of the claimed technical solution.

[0004] In order to provide a more energy-saving and convenient vehicle following method, in a first aspect, the present invention proposes a vehicle following control method, the method comprising:

[0005] Obtain roadside environment information within the target area;

[0006] Acquire driving information of the vehicle to be followed, the driving information including wind speed information and vehicle speed information;

[0007] The vehicle driving information and the roadside environment information are used as inputs of a wake vortex simulation model to obtain a simulated following position of the target vehicle, wherein the wake vortex simulation model is obtained through iterative training based on a fluid mechanics simulation method and a neural network method;

[0008] Determining a relative position relationship between the simulated following vehicle position and the vehicle to be followed based on the lane line information;

[0009] Based on the relative position relationship, the target vehicle is controlled to follow the vehicle.

[0010] Optionally, the above driving information also includes appearance information.

[0011] Optionally, the above method further includes:

[0012] Construct various 3D flow field simulation models based on fluid mechanics simulation method and vehicle shape database;

[0013] A preset simulated following vehicle position is obtained by simulation based on a vehicle speed database, a wind speed database, a roadside environment database and the above three-dimensional flow field simulation model;

[0014] The above-mentioned wake vortex simulation model is obtained by iterative training based on the neural network method through the wake vortex measurement data of the following vehicle test and the above-mentioned preset simulation following vehicle position.

[0015] Optionally, the above method further includes:

[0016] Acquiring front pressure data and rear pressure data of the target vehicle at different positions in the tail vortex area corresponding to the vehicle to be followed;

[0017] The tail vortex measurement data of the following vehicle test is obtained based on the front pressure data and the rear pressure data.

[0018] Optionally, the above-mentioned obtaining of roadside environment information in the target area includes:

[0019] Obtaining the laser radar point cloud and image information of the above target area;

[0020] Based on the above laser radar point cloud, stitching is performed to obtain the above lane line information;

[0021] The above roadside environment information is obtained by performing AI recognition based on the above image and combining the above lidar point cloud information.

[0022] Optionally, the controlling the target vehicle to follow the vehicle based on the relative position relationship includes:

[0023] When the simulated following vehicle position is in the current driving lane of the vehicle to be followed and the distance between the simulated following vehicle position and the vehicle to be followed is greater than the legal distance, obtaining the road information of the current driving lane behind the vehicle to be followed, wherein the legal distance is the shortest following distance allowed under the current road conditions and the current vehicle speed;

[0024] When there is no other vehicle traveling within a first following distance behind the vehicle to be followed, the target vehicle is controlled to travel to the simulated following position for following driving, wherein the first following distance is determined based on the simulated following position and the outer dimensions of the target vehicle.

[0025] Optionally, the controlling the target vehicle to follow the vehicle based on the relative position relationship includes:

[0026] When the simulated following vehicle position is in the adjacent lane of the vehicle to be followed, obtaining the adjacent lane road information behind the vehicle to be followed;

[0027] When there is no other vehicle traveling within a second following distance of the adjacent road behind the vehicle to be followed, the target vehicle is controlled to travel to the simulated following position for following driving, wherein the second following distance is determined based on the simulated following position, the outer dimensions of the target vehicle and the lane width.

[0028] In a second aspect, the present invention further provides a vehicle following control device, comprising:

[0029] A first acquisition unit, used to acquire roadside environment information in a target area;

[0030] A second acquisition unit is used to acquire the driving information of the vehicle to be followed, wherein the driving information includes wind speed information and vehicle speed information;

[0031] A third acquisition unit is used to use the vehicle driving information and the roadside environment information as inputs of a wake vortex simulation model to obtain a simulated following position of the target vehicle, wherein the wake vortex simulation model is obtained through iterative training based on a fluid mechanics simulation method and a neural network method;

[0032] A determination unit, used to determine the relative position relationship between the simulated following vehicle position and the vehicle to be followed based on the lane line information;

[0033] The control unit is used to control the target vehicle to follow the vehicle based on the relative position relationship.

[0034] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the vehicle following control method of any one of the above-mentioned first aspect when executing the computer program stored in the memory.

[0035] In a fourth aspect, the present invention further proposes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the vehicle following control method of any one of the above items in the first aspect is implemented.

[0036] In summary, the following vehicle control method of the embodiment of the present application includes: obtaining roadside environmental information in the target area; obtaining driving information of the vehicle to be followed, the driving information including wind speed information and vehicle speed information; using the vehicle driving information and the roadside environmental information as inputs to the wake vortex simulation model to obtain the simulated following vehicle position of the target vehicle; determining the relative position relationship between the simulated following vehicle position and the vehicle to be followed based on the lane line information; and controlling the target vehicle to follow the vehicle based on the relative position relationship. The following vehicle control method proposed in the embodiment of the present application simultaneously considers the influence of vehicle speed information, wind speed information and roadside environmental information on the wake vortex, and the obtained simulated following vehicle position is closer to the position with the maximum negative pressure of the wake vortex, which can save the energy consumption of the target vehicle.

[0037] The vehicle following control method of the present invention, and other advantages, objectives and features of the present invention will be reflected in part through the following description, and in part will be understood by those skilled in the art through research and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0039] Figure 1 A flow chart of a vehicle following control method provided in an embodiment of the present application;

[0040] Figure 2 A schematic diagram of the measurement principle of vehicle-following driving related parameters provided in an embodiment of the present application;

[0041] Figure 3 A schematic diagram of the structure of a wind measurement system provided in an embodiment of the present application;

[0042] Figure 4 A schematic diagram of a sensor arrangement for a vehicle following test provided in an embodiment of the present application;

[0043] Figure 5 A schematic diagram of another arrangement of sensors for a vehicle following test provided in an embodiment of the present application;

[0044] Figure 6 A schematic diagram of a vehicle platoon driving provided in an embodiment of the present application;

[0045] Figure 7 Another schematic diagram of vehicle platoon driving provided in an embodiment of the present application;

[0046] Figure 8A schematic diagram of another vehicle platoon driving provided in an embodiment of the present application;

[0047] Fig. 9 A schematic diagram of a simulated vehicle following position provided in an embodiment of the present application;

[0048] Fig.10 A schematic diagram of the structure of a vehicle following control device provided in an embodiment of the present application;

[0049] Fig.11 A schematic diagram of the structure of a vehicle following control electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The following vehicle control method proposed in the embodiment of the present application takes into account the vehicle speed information, the influence of the vehicle speed information and the roadside environment information on the tail vortex, and the resulting simulated following vehicle position is closer to the position with the maximum tail vortex negative pressure, which can save the energy consumption of the target vehicle.

[0051] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0052] See also Figure 1 , is a flow chart of a vehicle following control method provided in an embodiment of the present application, which may specifically include:

[0053] S110, obtaining roadside environment information in the target area;

[0054] For example, there will be green belts, sound insulation devices and other equipment on both sides of highways or urban roads. These roadside environments will affect the wake of vehicles and the propagation of natural wind. Different roadside environments will have different effects on the propagation of wake and wind, which is mainly related to the type and height of the roadside environment.

[0055] S120, obtaining driving information of the vehicle to be followed, the driving information including wind speed information and vehicle speed information;

[0056] For example, the vehicle to be followed is the front vehicle, and a millimeter-wave radar is installed on the front of the target vehicle to identify the position and relative speed of the front vehicle, and the current vehicle speed is used to calculate the speed of the front vehicle. It is understandable that if the front vehicle can communicate with the rear vehicle, the speed of the front vehicle can also be measured by the ECU (Electronic Control Unit) of the front vehicle, and the measurement result is transmitted to the rear vehicle or the cloud for calculating the following strategy. In some vehicles, the following can be used. Figure 2 The wind speed can be measured in the manner shown. In some target vehicles that implement the vehicle-following strategy, a 5-hole probe can be installed on the front of the vehicle to connect to a high-precision ALPHA sensor to identify the yaw angle. The wind speed facing the front of the vehicle can be measured through a Pitot tube, and the direction and intensity of the side wind can be calculated based on the current vehicle speed. In other vehicles, a wind speed measurement system can be set up on the roadside. The measuring device can include a wind speed and direction detection device, a road surface condition detection device, and an information processing and output device. The wind speed and direction detection device is composed of a first controller and a wind speed and direction sensor connected thereto, a first regulated power supply, and a first communication module. The first microcontroller detects the wind speed and direction at the location of the tunnel entrance through the wind speed and direction sensor, and realizes wireless communication with the information processing and output device through the first communication module. The following method can be used: Figure 3 The wind speed is measured in the manner shown. A base 101 is set on an open ground, including 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. A guide device 106 is fixed on both ends of the first slider 1021 and the base 101. The first motor 1051 and the second motor 1052 are installed on the base 101. The transmission belt 107 envelops the output shafts of the first motor 1051 and the second motor 1052, and the guide device 106 installed on the first slider 1021 and the base 101, forming an "I" shape. By controlling the speed of the motor, the moving direction of the wind measuring component 104 fixedly connected to the second slider 1031 can be controlled, thereby controlling the side wind component to measure the wind speed and wind direction at a designated location.

[0057] (When v≤3m / s)

[0058] R1 is the 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 speed of the first motor or the second motor, ensuring the rapid movement of the second slider. R2 is the speed of the second motor. The smaller the measured wind speed v, the smaller the difference between R1 and R2, and the second slider is kept moving horizontally or vertically; the greater the measured wind speed v, the greater the difference between R1 and R2, and the slider moves in the tilt direction.

[0059] (When v>3m / s)

[0060] When v>3m / s, i, j are the rotation directions of the first motor and the second motor, clockwise is the positive direction, n is a constant, the speed R2 and direction of the second motor are adaptively adjusted following the speed and direction of the first motor, c is the measured wind direction angle, the horizontal right angle is 0, and the counterclockwise angle becomes larger (such as when the wind direction points to the right of the horizontal axis, c=0, ntanc=0, the requirement is that the second slider needs to move horizontally to the left or right to be parallel to the wind direction, at this time iR1+jR2=0, the physical meaning is that the first motor and the second motor have the same speed and are in opposite directions), n is the proportional coefficient, through this method, the first motor and the second motor control the second slider to slowly translate in the direction parallel to the wind direction.

[0061] S130, using the vehicle driving information and the roadside environment information as inputs of a wake vortex simulation model to obtain a simulated following position of the target vehicle;

[0062] Exemplarily, the wake vortex simulation model can be loaded on the target vehicle or in the cloud. Before the target vehicle follows the vehicle, the vehicle speed information, wind speed information and roadside environment information are identified as input, and the wake vortex simulation model is used to obtain the simulated following position. When the intensity of the crosswind is large, the wake vortex will shift to the adjacent lane, and the simulated following position will also shift to the adjacent lane accordingly to achieve close following, using the crosswind to avoid the minimum 50m following distance in the same lane of the highway stipulated by regulations.

[0063] S140, determining a relative position relationship between the simulated following vehicle position and the vehicle to be followed based on the lane line information;

[0064] Exemplarily, based on the calculated simulated following vehicle position and lane information on the driving road, there may be three situations, namely, the simulated following vehicle position and the leading vehicle are in the same lane, the simulated following vehicle position and the leading vehicle are in adjacent lanes, and the simulated following vehicle position and the leading vehicle are on the lane line of the leading vehicle's driving lane.

[0065] S150: Based on the relative position relationship, control the target vehicle to follow the vehicle.

[0066] Exemplarily, the target vehicle is controlled to follow the vehicle according to the relative position relationship. For example, when the simulated following position is on the lane line, the rear vehicle is not allowed to occupy the lane line for a long time; when the simulated following position and the front vehicle are in the same lane, the rear vehicle follows the front vehicle in the same lane and maintains a certain safe distance to meet safety requirements; when the simulated following position and the front vehicle are in an adjacent lane, observe whether the adjacent lane is available. If the adjacent lane is available and there are no other obstacle vehicles in front and behind, the rear vehicle drives in the adjacent lane to the simulated following position.

[0067] In summary, the following vehicle control method proposed in the embodiment of the present application takes into account the vehicle speed information, and the influence of the vehicle speed information and the roadside environment information on the tail vortex. The resulting simulated following vehicle position is closer to the position with the maximum tail vortex negative pressure, which can save the energy consumption of the target vehicle.

[0068] In some examples, the driving information also includes appearance information.

[0069] For example, due to the different appearances of vehicles, that is, the length, width, height or shape of the vehicle will affect the flow field formed by the air flowing through the front vehicle when the vehicle is driving, the tail vortex areas formed by vehicles with different appearances under the same driving conditions are different. Not only the speed and wind speed of the vehicle will affect the distribution of the tail vortex, but the appearance information will also affect the formation of the tail vortex.

[0070] In summary, the vehicle following control method proposed in the embodiment of the present application takes into account the influence of the shape of the leading vehicle on the position of the tail vortex, and the obtained simulated vehicle following position is more in line with the actual situation.

[0071] In some examples, the method further includes:

[0072] Construct various 3D flow field simulation models based on fluid mechanics simulation method and vehicle shape database;

[0073] A preset simulated following vehicle position is obtained by simulation based on a vehicle speed database, a wind speed database, a roadside environment database and the above three-dimensional flow field simulation model;

[0074] The above-mentioned wake vortex simulation model is obtained by iterative training based on the neural network method through the wake vortex measurement data of the following vehicle test and the above-mentioned preset simulation following vehicle position.

[0075] Exemplarily, by means of computational fluid dynamics, a wake vortex simulation model of the preceding vehicle is obtained. The simulation model includes a database of the appearance of the vehicle to be followed, a database of the vehicle speed, a database of the roadside environment, and a database of the wind speed. The input of the wake vortex simulation model includes simulation parameters of the crosswind speed and the crosswind direction, simulation parameters of the vehicle speed of the preceding vehicle, the appearance parameters of the preceding vehicle, the roadside environment parameters, and the wind speed parameters. The output of the wake vortex simulation model is the coordinate simulation parameters of the position where the negative pressure of the wake vortex of the preceding vehicle is relatively strong, that is, the simulated following position. The simulated following position output by the flow field simulation model is combined with the wake vortex measurement data obtained from the following test to construct a neural network for iterative training to obtain the trained wake vortex simulation model. The trained wake vortex simulation model can be loaded on the target vehicle and used to determine the simulated following position according to the identified information of the appearance of the preceding vehicle, the vehicle speed, the roadside environment, and the wind speed. The wake vortex simulation model trained by the neural network has a faster calculation speed, and the error between the determined following position and the actual position with the maximum wake vortex intensity is smaller, so that a faster and more accurate following strategy can be achieved.

[0076] Specifically constructing a trained wake vortex simulation model may include the following steps:

[0077] S210, constructing an initial neural network model;

[0078] With the goal of minimizing the error between the measured value of the position where the negative pressure of the front vehicle's tail vortex is stronger and the actual position where the negative pressure of the front vehicle's tail vortex is stronger output by the initial neural network model, the crosswind speed and direction simulation parameters, the vehicle speed simulation parameters of the front vehicle and the front vehicle appearance parameters are input into the initial neural network model for iterative training to obtain a target neural network model for obtaining the measured value of the position where the negative pressure of the front vehicle's tail vortex is stronger (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 are not limited here. According to the type of the initial neural network model, the step training operation can be completed. Specifically, the initial neural network model can be understood as an untrained target neural network model, which can input the crosswind speed and direction simulation parameters and the vehicle speed simulation parameters of the front vehicle, and output the measured value of the position where the negative pressure of the initial front vehicle's tail vortex is stronger through neural network calculation. Normally, the initial neural network model may include an input layer, a hidden layer and an output layer, wherein the hidden layer is responsible for the relevant calculations of the neural network. Through iterative training, the weight parameters and other relevant transfer function parameters in the hidden layer may be gradually adjusted so that the initial wheel center vertical displacement measurement signal output by the initial neural network model meets the established training objectives. At this time, the initial neural network model may be considered as the target neural network model, and the measurement value of the position where the initial leading vehicle tail vortex negative pressure is stronger output by it may be identified as the measurement value of the position where the leading vehicle tail vortex negative pressure is stronger.

[0079] S220, obtaining the initial simulation model of the tail vortex of the preceding vehicle, including: obtaining the initial simulation model of the initial tail vortex of the preceding vehicle, inputting the crosswind speed parameter, the crosswind direction parameter, the speed parameter of the vehicle to be followed, the roadside environment parameter and the shape parameter of the vehicle to be followed into the initial simulation model of the tail vortex for simulation, and obtaining the coordinate simulation parameters of the position where the negative pressure of the tail vortex of the preceding vehicle is strong output by the initial tail vortex simulation model. The tail vortex simulation model of the preceding vehicle is obtained by the method of computational fluid dynamics; wherein the preceding vehicle selects a specific vehicle model with known shape dimension parameters in the database, i.e., the target type vehicle (also referred to as the preceding vehicle), the input of the tail vortex simulation model includes the simulation parameters of the crosswind speed and direction, the speed simulation parameters of the preceding vehicle, and the output of the tail vortex simulation model is the coordinate simulation value of the position where the negative pressure of the tail vortex of the preceding vehicle is strong, i.e., the preset simulation following vehicle position.

[0080] S230, obtaining the tail vortex measurement data of the following vehicle test, based on the actual crosswind speed and direction of the target type vehicle, the vehicle speed and the roadside environment, and obtaining the test sensor signal through the whole vehicle sensor group on the vehicle behind the target type vehicle; wherein the whole vehicle sensor group includes: one or more of ALPHA sensor, pitot tube, pressure sensor, millimeter wave radar, camera. Based on the test sensor signal and the simulation output value, the initial tail vortex simulation model of the leading vehicle is optimized to the tail vortex simulation model of the leading vehicle.

[0081] S240, training a neural network model based on the initial simulation data of the tail vortex and the tail vortex measurement data of the following vehicle test to obtain a tail vortex simulation model. The goal is to minimize the error between the initial simulation data and the tail vortex measurement data of the following vehicle test, input the crosswind speed and direction simulation parameters, and the vehicle speed simulation parameters of the front vehicle into the initial neural network model for iterative training, and obtain a target neural network model for the measurement value of the position where the front vehicle tail vortex negative pressure is strong. Specifically, using the tail vortex simulation model of the front vehicle obtained in step S220, multiple groups of crosswind speed and direction simulation parameters that meet the test accuracy, the vehicle speed simulation parameters of the front vehicle, and the coordinate simulation values ​​of the position where the front vehicle tail vortex negative pressure is strong (preset simulation following vehicle position) and the tail vortex measurement data of the following vehicle test can be obtained, so as to construct a training set of the initial neural network model, complete the iterative training of the initial neural network model, and obtain the target neural network model for the measurement value of the position where the front vehicle tail vortex negative pressure is strong, that is, the tail vortex simulation model.

[0082] In summary, the following vehicle control method proposed in the embodiment of the present application uses a trained wake vortex simulation model to determine the simulated following vehicle position based on the identified leading vehicle shape information, vehicle speed information, roadside environment information and wind speed information. The calculation speed is faster, and the error between the determined following vehicle position and the actual position with the maximum wake vortex intensity is smaller. It can achieve a faster and more accurate following strategy and can effectively save the energy consumption of the following vehicle.

[0083] In some examples, the method further includes:

[0084] Acquiring front pressure data and rear pressure data of the target vehicle at different positions in the tail vortex area corresponding to the vehicle to be followed;

[0085] The tail vortex measurement data of the following vehicle test is obtained based on the front pressure data and the rear pressure data.

[0086] For example, Figure 4 and Figure 5As shown, in some vehicles, 4 patch pressure sensors are installed in the head area of ​​the vehicle and 8 patch pressure sensors are installed in the rear area of ​​the vehicle. Simulate following the vehicle and measure the absolute pressure value of the front of the vehicle and the front and rear pressure difference at the position where the negative pressure of the tail vortex of the leading vehicle is strong. During the test, multiple measurements are made within a preset range near and at the position where the negative pressure of the tail vortex of the leading vehicle is strong to test whether the area with strong negative pressure of the tail vortex of the leading vehicle is accurate, and the measurement results are summarized to form the tail vortex measurement data of the following vehicle test, which is used to train the tail vortex simulation model. It can be understood that the following vehicle test can change the shape of the leading vehicle, the speed of the leading vehicle and the wind speed to obtain more comprehensive tail vortex measurement data of the following vehicle test under different following vehicle conditions.

[0087] In summary, the following control method provided in the embodiment of the present application, by adding pressure sensors at the front and rear ends of the target vehicle, obtains the following test tail vortex measurement data through the following test, and optimizes the tail vortex simulation model based on the neural network according to the following test tail vortex measurement data, so as to obtain a more accurate tail vortex simulation model, and can provide the target vehicle with a more accurate simulated following position in the actual following of the target vehicle, so as to make fuller use of the position with stronger tail vortex negative pressure to achieve the purpose of energy-saving following.

[0088] In some examples, the above-mentioned obtaining roadside environment information in the target area includes:

[0089] Obtaining the laser radar point cloud and image information of the above target area;

[0090] Based on the above laser radar point cloud, stitching is performed to obtain the above lane line information;

[0091] The above roadside environment information is obtained by performing AI recognition based on the above image and combining the above lidar point cloud information.

[0092] Exemplarily, the roadside environmental information in the target area can be obtained by obtaining the laser radar point cloud and image information based on the laser radar sensor and camera set at the front end of the target vehicle. First, the forward laser radar and camera are calibrated so that the camera pixels correspond to the radar point cloud, and then the vehicle position and posture are measured based on GPS and inertial navigation. The vehicle can be controlled to collect a frame of image and radar point cloud every 3 to 5 meters; point cloud splicing is performed with the radar point cloud based on the vehicle's position and posture, and the road line shape in the collected road image is extracted and measured; AI recognition and feature extraction are performed on the collected road image, and the features in the road image are preliminarily located based on graphic measurement; AI recognition and classification of features are performed, and combined with radar point cloud data, the height and continuity of the median strip are quantitatively analyzed. The form and height information of the green plants or guardrails in the median strip, that is, the roadside environmental information, is obtained.

[0093] In summary, the embodiment of the present application proposes a method for comprehensively acquiring the roadside environment based on radar laser point cloud and image information, so as to fully consider the impact of the roadside environment on eddy currents, thereby determining a more accurate following vehicle position and achieving the purpose of saving energy.

[0094] In some examples, controlling the target vehicle to follow the vehicle based on the relative position relationship includes:

[0095] When the simulated following vehicle position is in the current driving lane of the vehicle to be followed and the distance between the simulated following vehicle position and the vehicle to be followed is greater than the legal distance, obtaining the road information of the current driving lane behind the vehicle to be followed, wherein the legal distance is the shortest following distance allowed under the current road conditions and the current vehicle speed;

[0096] When there is no other vehicle traveling within a first following distance behind the vehicle to be followed, the target vehicle is controlled to travel to the simulated following position for following driving, wherein the first following distance is determined based on the simulated following position and the outer dimensions of the target vehicle.

[0097] For example, after obtaining the simulated following vehicle position through the wake vortex simulation model, the lane line information is obtained through the front radar of the target vehicle or the rear radar of the vehicle to be followed, and the simulated following vehicle position and the lane line position are judged by the vehicle to be followed, the target vehicle or the cloud. If the simulated following vehicle position is in the current driving lane of the vehicle to be followed, for example Fig.10 The recommended position A in the image is obtained. At this time, the distance between the simulated following position and the vehicle to be followed is obtained, that is, the distance between the recommended position A and the vehicle to be followed. If the distance is greater than the legal distance, then it is obtained whether there are other vehicles within the first following distance behind the vehicle to be followed, that is, it is judged whether the target vehicle can interfere with the vehicle behind if it drives to the recommended position A. If there are no other vehicles within the first following distance, the target vehicle is controlled to follow the vehicle at the recommended position A. It should be noted that the legal distance is the shortest following distance that can be allowed under the premise of ensuring the safety of following the vehicle on the current road. The first distance is determined by the simulated following position and the external dimensions of the target vehicle, that is, the distance that can ensure that the target vehicle will not affect the normal driving of the vehicle behind when following the vehicle to be followed.

[0098] In summary, the embodiments of the present application provide a method for controlling a target vehicle following a target vehicle in which a simulated following position appears on the current driving road of the vehicle to be followed, which can ensure a safe distance from the leading vehicle when following the vehicle and will not affect the normal driving of the rear vehicle.

[0099] In some examples, controlling the target vehicle to follow the vehicle based on the relative position relationship includes:

[0100] When the simulated following vehicle position is in the adjacent lane of the vehicle to be followed, obtaining the adjacent lane road information behind the vehicle to be followed;

[0101] When there is no other vehicle traveling within a second following distance of the adjacent road behind the vehicle to be followed, the target vehicle is controlled to travel to the simulated following position for following driving, wherein the second following distance is determined based on the simulated following position, the outer dimensions of the target vehicle and the lane width.

[0102] For example, if the simulated following vehicle position is in the adjacent lane of the vehicle to be followed, such as Fig.10 The recommended position B in the simulation is used to obtain whether there are other vehicles within the second following distance in the adjacent lane behind the vehicle to be followed, that is, to determine whether the target vehicle can interfere with the vehicle behind it if it drives to the recommended position B. If there are no other vehicles within the second following distance, the target vehicle is controlled to follow the vehicle at the recommended position B. It should be noted that the second distance is determined by the simulated following position, the external dimensions of the target vehicle and the lane width, that is, the distance that can ensure that the target vehicle will not affect the normal driving of the vehicle behind it when following the vehicle to be followed.

[0103] In summary, the embodiments of the present application provide a method for controlling a target vehicle following a target vehicle whose simulated following position appears on a road adjacent to a vehicle to be followed, which can effectively follow the vehicle and prevent vehicles in adjacent lanes from driving normally.

[0104] In some examples, Figure 6 Provides a vehicle following strategy with isolation belts under left and right winds; Figure 7 Provides a vehicle following strategy with a left isolation zone and left-side wind; Figure 8 A vehicle platoon driving method with an isolation belt on the left and a left-side wind is provided.

[0105] See also Fig.10 , an embodiment of the vehicle following control device in the embodiment of the present application may include:

[0106] A first acquisition unit 21 is used to acquire roadside environment information in a target area;

[0107] A second acquisition unit 22, used to acquire driving information of the vehicle to be followed, wherein the driving information includes wind speed information and vehicle speed information;

[0108] A third acquisition unit 23, used for taking the vehicle driving information and the roadside environment information as inputs of a wake vortex simulation model to acquire a simulated following position of a target vehicle;

[0109] A determination unit 24, configured to determine a relative position relationship between the simulated following vehicle position and the vehicle to be followed based on lane line information;

[0110] The control unit 25 is used to control the target vehicle to follow the vehicle based on the relative position relationship.

[0111] like Fig.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, and when the processor 320 executes the computer program 311, the steps of any of the above-mentioned vehicle following control methods are implemented.

[0112] 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, the technical personnel in this field can understand the specific implementation mode 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 is not introduced in detail here. As long as the equipment used by the 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.

[0113] In the specific implementation process, when the computer program 311 is executed by the processor, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0114] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0115] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may 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 may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0116] 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 generate 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.

[0117] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0119] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 The process of following vehicle control in the corresponding embodiment.

[0120] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)), etc.

[0121] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0122] In the several embodiments provided in the present 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 only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, 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 interfaces, devices or units, which can be electrical, mechanical or other forms.

[0123] 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.

[0124] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0125] 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 to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0126] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein by equivalents. 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 vehicle following control method, characterized in that: include: Obtain roadside environment information within the target area; Acquire driving information of the vehicle to be followed, wherein the driving information includes wind speed information, wind direction information and vehicle speed information, A measuring device is provided, through which the wind speed information and the wind direction information are determined, and the measuring device includes a first motor and a second motor; The expressions of the wind speed information and the wind direction information include: ； (when v≤3m / s); (when v>3m / s); Wherein, R1 is the speed of the first motor, v is the measured wind speed, a and b are constants, R2 is the speed of the second motor, when v>3m / s, i and j are the rotation directions of the first motor and the second motor, n is a constant, and c is the measured wind direction angle; Using the driving information and the roadside environment information as inputs of a wake vortex simulation model to obtain a simulated following position of a target vehicle; Determining a relative position relationship between the simulated following vehicle position and the vehicle to be followed based on lane line information; Controlling the target vehicle to follow the vehicle based on the relative position relationship; The controlling the target vehicle to follow the vehicle based on the relative position relationship includes: When the simulated following vehicle position is in an adjacent lane of the vehicle to be followed, acquiring road information of an adjacent lane behind the vehicle to be followed; When there is no other vehicle traveling within a second following distance of an adjacent road behind the vehicle to be followed, the target vehicle is controlled to travel to the simulated following position for following driving, wherein the second following distance is determined based on the simulated following position, the outer dimensions of the target vehicle and the lane width.

2. The method according to claim 1, characterized in that The driving information also includes appearance information.

3. The method according to claim 2, characterized in that Also includes: Construct various 3D flow field simulation models based on fluid mechanics simulation method and vehicle shape database; Perform simulation based on the vehicle speed database, the wind speed database, the roadside environment database and the three-dimensional flow field simulation model to obtain a preset simulation following vehicle position; The wake vortex simulation model is obtained by iterative training based on the neural network method using the wake vortex measurement data of the vehicle following test and the preset simulated vehicle following position.

4. The method according to claim 3, characterized in that Also includes: Acquiring front pressure data and rear pressure data of the target vehicle at different positions in the tail vortex region corresponding to the vehicle to be followed; The tail vortex measurement data of the following vehicle test is obtained based on the front pressure data and the rear pressure data.

5. The method according to claim 1, characterized in that The obtaining of roadside environment information in the target area includes: Acquire laser radar point cloud and image information of the target area; Perform stitching based on the laser radar point cloud and obtain the lane line information; Perform AI recognition based on the image and obtain the roadside environment information in combination with the lidar point cloud information.

6. The method according to claim 1, wherein the controlling the target vehicle to follow the vehicle based on the relative position relationship comprises: When the simulated following vehicle position is in the current driving lane of the vehicle to be followed and the distance between the simulated following vehicle position and the vehicle to be followed is greater than the legal distance, obtaining the road information of the current driving lane behind the vehicle to be followed, wherein the legal distance is the shortest following distance allowed under the current road conditions and the current vehicle speed; When there is no other vehicle traveling within a first following distance behind the vehicle to be followed, the target vehicle is controlled to travel to the simulated following position for following driving, wherein the first following distance is determined based on the simulated following position and the outer dimensions of the target vehicle.

7. A vehicle following control device, characterized in that: include: A first acquisition unit, used to acquire roadside environment information in a target area; a second acquisition unit, configured to acquire driving information of the vehicle to be followed, wherein the driving information includes wind speed information, wind direction information, and vehicle speed information; A measuring device is provided, through which the wind speed information and the wind direction information are determined, and the measuring device includes a first motor and a second motor; The expressions of the wind speed information and the wind direction information include: ； (when v≤3m / s); (when v>3m / s); Wherein, R1 is the speed of the first motor, v is the measured wind speed, a and b are constants, R2 is the speed of the second motor, when v>3m / s, i and j are the rotation directions of the first motor and the second motor, n is a constant, and c is the measured wind direction angle; A third acquisition unit is used to use the driving information and the roadside environment information as inputs of a wake vortex simulation model to acquire a simulated following position of a target vehicle; A determination unit, configured to determine a relative position relationship between the simulated following vehicle position and the vehicle to be followed based on lane line information; A control unit, configured to control the target vehicle to follow the vehicle based on the relative position relationship; The control unit is further used for: When the simulated following vehicle position is in an adjacent lane of the vehicle to be followed, acquiring road information of an adjacent lane behind the vehicle to be followed; When there is no other vehicle traveling within a second following distance of an adjacent road behind the vehicle to be followed, the target vehicle is controlled to travel to the simulated following position for following driving, wherein the second following distance is determined based on the simulated following position, the outer dimensions of the target vehicle and the lane width.

8. An electronic device, comprising: A memory and a processor, wherein the processor is used to implement the steps of the vehicle following control method as described in any one of claims 1 to 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: When the computer program is executed by a processor, the vehicle following control method according to any one of claims 1 to 6 is implemented.

Images