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 the road surface friction coefficient of the road surface to be passed within the target area; obtaining the driving information of the vehicle to be followed, where the driving information includes wind speed information and vehicle speed information; using the vehicle driving information as the input of 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 and the road surface friction coefficient. The following vehicle control method proposed in the embodiments of the present application uses the wind speed information and the vehicle speed information as the input of the wake vortex simulation model, and the obtained simulated following vehicle position is more accurate. At the same time, the present application adjusts the following vehicle strategy for the road surface that is prone to wet and slippery, which can fully ensure the safety of vehicle driving.

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 equipment. 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 cruising range increases by about 10 km. Vehicle platooning significantly reduces the drag experienced by each vehicle because the total pressure in the wake region is relatively 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 described 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] In order 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 road surface friction coefficient of the road surface to be passed within the target area, where the target area includes bridges and tunnels;

[0006] Obtain the driving information of the vehicle to be followed, where the driving information includes wind speed information and vehicle speed information;

[0007] Use the vehicle driving information as the input of the wake simulation model to obtain the simulated following vehicle position of the target vehicle;

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

[0009] Control the target vehicle to follow based on the relative position relationship, the wind speed information, and the road surface friction coefficient.

[0010] Optionally, the driving information further includes shape information.

[0011] Optionally, the method further includes:

[0012] Construct a variety of three-dimensional flow field simulation models based on the hydrodynamic simulation method and the vehicle shape database;

[0013] Based on the vehicle speed database, the wind speed database and the above three-dimensional flow field simulation model, perform simulations to obtain the preset simulation following position;

[0014] Through the wake measurement data of the following vehicle test and the above preset simulation following position, perform iterative training based on the neural network method to obtain the above wake simulation model.

[0015] Optionally, the above method further includes:

[0016] Obtain the front head pressure data and the rear tail pressure data obtained by the target vehicle at different positions in the wake area corresponding to the following vehicle;

[0017] Based on the above front head pressure data and the rear tail pressure data, obtain the wake measurement data of the following vehicle test.

[0018] Optionally, the above controlling the target vehicle to follow the vehicle based on the relative position relationship, the wind speed information and the road surface friction coefficient includes:

[0019] When the wind speed information is less than or equal to the preset wind speed and the road surface friction coefficient is greater than or equal to the preset friction coefficient, determine the first safety distance based on the road surface friction coefficient;

[0020] When the simulation following position is in the current driving lane of the following vehicle and the distance between the simulation following position and the following vehicle is greater than the first safety distance, obtain the road information of the current driving lane behind the following vehicle;

[0021] When there are no other vehicles driving within the first following distance behind the following vehicle, control the target vehicle to drive to the simulation following position to perform following driving, where the first following distance is determined based on the simulation following position, the external dimensions of the target vehicle and the first safety distance.

[0022] Optionally, the above controlling the target vehicle to follow the vehicle based on the relative position relationship, the wind speed information and the road surface friction coefficient includes:

[0023] When the simulation following position is in the adjacent lane of the following vehicle, obtain the road information of the adjacent lane behind the following vehicle;

[0024] When there are no other vehicles driving within the second following distance on the adjacent road behind the vehicle to be followed, control the target vehicle to drive to the simulation following position for following driving, where the second following distance is determined based on the simulation following position, the external dimensions of the target vehicle, the lane width, and the first safety distance.

[0025] Optionally, the controlling the target vehicle to perform following driving based on the relative position relationship, the wind speed information, and the road surface friction coefficient includes:

[0026] When the wind speed information is greater than a preset wind speed and / or the road surface friction coefficient is less than a preset friction coefficient, determine a second safety distance based on the road surface friction coefficient;

[0027] Control the target vehicle to follow the vehicle in the current driving lane of the vehicle to be followed at the second safety distance.

[0028] In a second aspect, the present invention also provides a following control device, including:

[0029] A first acquisition unit, configured to acquire the road surface friction coefficient of the road surface to be passed within a target area, where the target area includes bridges and tunnels;

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

[0031] A third acquisition unit, configured to use the vehicle driving information as an input to a wake vortex simulation model to obtain the simulation 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;

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

[0033] A control unit, configured to control the target vehicle to perform following driving based on the relative position relationship, the wind speed information, and the road surface friction coefficient.

[0034] 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 following control method according to any one of the first aspects when executing the computer program stored in the memory.

[0035] In a fourth aspect, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the following control method according to any one of the first aspects.

[0036] In summary, the vehicle following control method of the embodiments of the present application includes: obtaining the road surface friction coefficient of the road surface to be passed within the target area, where the target area includes bridges and tunnels; obtaining the driving information of the vehicle to be followed, and the driving information includes wind speed information and vehicle speed information; using the vehicle driving information as the input of the wake vortex simulation model to obtain the simulated vehicle 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; determining the relative position relationship between the simulated vehicle following position and the vehicle to be followed based on the lane line information; and controlling the target vehicle to follow based on the relative position relationship, the above wind speed information, and the road surface friction coefficient. In the vehicle following control method proposed by the embodiments of the present application, the wind speed and vehicle speed are used as the input of the wake vortex simulation model, and the obtained simulated vehicle following position is more accurate. The present application adjusts the vehicle following strategy for road surfaces prone to wet sliding and areas prone to rollover, which can fully ensure the safety of vehicle driving.

[0037] 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

[0038] By reading the following detailed description of the preferred embodiments, 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 limit this specification. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0039] Figure 1 It is a schematic flowchart of a vehicle following control method provided by an embodiment of the present application;

[0040] Figure 2 It is a schematic diagram of the measurement principle of vehicle following related parameters provided by an embodiment of the present application;

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

[0042] Figure 4 It is a schematic diagram of the sensor arrangement method for vehicle following tests provided by an embodiment of the present application;

[0043] Figure 5 It is another schematic diagram of the sensor arrangement method for vehicle following tests provided by an embodiment of the present application;

[0044] Figure 6 It is a schematic diagram of the simulated vehicle following position provided by an embodiment of the present application;

[0045] Figure 7 A schematic diagram of platooning driving provided by an embodiment of the present application;

[0046] Figure 8 Another schematic diagram of platooning driving provided by an embodiment of the present application

[0047] Figure 9 A schematic structural diagram of a car-following control device provided by an embodiment of the present application;

[0048] Figure 10 A schematic structural diagram of a car-following control electronic device provided by an embodiment of the present application. Detailed implementation manners

[0049] The car-following control method proposed by the embodiment of the present application uses the wind speed and vehicle speed as the inputs of the wake vortex simulation model, and the obtained simulated car-following position is more accurate. The present application adjusts the car-following strategy for roads that are prone to slipperiness and areas that are prone to rollover, which can fully ensure the safety of vehicle driving.

[0050] The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and drawings of the present application are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order different from that shown 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 have to be limited 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 the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments.

[0051] Please refer to Figure 1 , which is a schematic flowchart of a car-following control method provided by an embodiment of the present application, and specifically may include:

[0052] S110. Obtain the road surface friction coefficient of the road surface to be passed in the target area, where the above target area includes bridges and tunnels;

[0053] Exemplarily, the lane lines of a tunnel or a bridge are generally solid lines. For safety reasons, lane changes of vehicles are not allowed after entering the tunnel or the bridge. Tunnels generally pass through mountains, and bridges are generally built over rivers. In mountainous areas or near rivers, temperature and humidity vary greatly, making it easy to form road waterlogging or icing. It is necessary to detect road condition information to obtain the friction coefficient of the road and transmit this data to the following vehicles for changing the following strategy. The friction coefficient of the road can be measured by a road condition detection device, which can be composed of a second microcontroller and an icing, temperature and humidity sensor, a second voltage stabilizer power supply and a second communication module connected thereto. The second microcontroller collects the icing condition of the bridge road surface and the temperature and humidity of the surrounding environment through the icing, temperature and humidity sensor, and realizes wireless communication with the information processing and output device through the second communication module; when the target vehicle approaches a certain distance (for example, 500 meters) from the tunnel entrance or the bridge, the vehicle receives the signal of the wireless communication of the information processing and output device. The friction coefficient of the current road can be calculated based on the received temperature information, humidity information and icing information, so as to determine the safe driving distance.

[0054] S120. Obtain the driving information of the vehicle to be followed, where the driving information includes wind speed information and vehicle speed information;

[0055] Exemplarily, the vehicle to be followed is the vehicle in front, as Figure 2 shown, a millimeter-wave radar is installed at the front of the vehicle to identify the position and relative speed of the vehicle in front, and the vehicle speed of the vehicle in front is calculated based on the current vehicle speed. It can be understood that if the vehicle in front can communicate with the following vehicle, the vehicle speed of the vehicle in front can also be measured by the ECU (Electronic Control Unit) of the vehicle in front and the measurement result can be transmitted to the following vehicle or the cloud for calculating the following strategy.

[0056] When entering a bridge or a tunnel, the crosswind level will suddenly change to a relatively large value, which poses a great safety hazard to the following or formation driving of vehicles. During high-speed driving, due to the influence of air flow, it is very easy to occur phenomena such as rollover. Therefore, it is necessary to measure the crosswind magnitude at the tunnel entrance and the bridge and give a warning to the vehicle, so that the vehicle can disband the formation or adjust the following strategy in time before entering the tunnel or the bridge. In some vehicles, such as Figure 2The wind speed can be measured in the manner shown. In some target vehicles that implement the following vehicle 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 at the entrance of a tunnel or bridge. The measuring device can include a wind speed and direction detection device, a road 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 voltage-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]

[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]

[0060] 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 rotational speed R2 and direction of the second motor adaptively adjust following the rotational speed and direction of the first motor, c is the measured wind direction angle, with the horizontal right direction being the 0 point 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 rotational 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.

[0061] S130. Use the vehicle driving information as the input of the wake simulation model to obtain the simulated following position of the target vehicle, where the wake simulation model is obtained through iterative training based on the fluid dynamics simulation method and the neural network method;

[0062] Exemplarily, the wake simulation model can be installed on the target vehicle or in the cloud. Before the target vehicle follows another vehicle, by using the recognized wind speed information and vehicle speed information as the input, the wake simulation model is used to calculate the simulated following position, and the error between the determined following position and the position with the maximum actual wake intensity is smaller, enabling a faster and more accurate following strategy.

[0063] S140. Determine the relative position relationship between the above-mentioned simulated following position and the vehicle to be followed based on the lane line information;

[0064] Exemplarily, according to the calculated simulated following position and the lane information on the driving road, there may be three situations, namely, the simulated following position is in the same lane as the vehicle in front, the simulated following position is in an adjacent lane to the vehicle in front, and the simulated following position is on the lane line of the lane in which the vehicle in front is driving.

[0065] S150. Control the target vehicle to follow the vehicle in front based on the above-mentioned relative position relationship, the above-mentioned wind speed information, and the above-mentioned road surface friction coefficient.

[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 following vehicle is not allowed to occupy the lane line for a long time; when the simulated following position and the preceding vehicle are in the same lane, the following vehicle follows the preceding vehicle in the same lane and maintains a certain safety distance to meet the safety requirements; when the simulated following position and the preceding vehicle are in adjacent lanes, it is observed whether the adjacent lane is available. If the adjacent lane can be driven and there are no other obstacle vehicles in the front and rear, the following vehicle drives in the adjacent lane to the simulated following position. However, during the following process, the influence of the road surface friction coefficient and wind speed on following needs to be considered. If the road surface friction is too small, it is easy to skid, and if the wind speed is too large, it is easy to cause rollover and other phenomena. At this time, the following strategy should be adjusted in time, with safe driving as the main purpose, taking into account the influence of the wake on following.

[0067] In summary, for the following control method proposed in the embodiment of the present application, the wind speed and vehicle speed are used as the inputs of the wake simulation model, and the obtained simulated following position is more accurate. The present application adjusts the following strategy for roads prone to wet sliding and areas prone to rollover, which can fully ensure the safety of vehicle driving.

[0068] In some examples, the above driving information further includes the shape information.

[0069] Exemplarily, not only the vehicle speed and wind speed of the vehicle will affect the distribution of the wake, but also due to the different shapes of the vehicles, that is, the length, width, height or the shape of the vehicle will affect the flow field formed by the air flowing through the preceding vehicle during vehicle driving, and the wake regions formed by vehicles with different shapes under the same driving conditions are not the same.

[0070] In summary, for the following control method proposed in the embodiment of the present application, taking the shape information of the preceding vehicle as the input, the obtained simulated following position is closer to the position with the strongest wake negative pressure, which can save more energy consumption of the following vehicle.

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

[0072] Constructing a variety of three-dimensional flow field simulation models based on the fluid mechanics simulation method and the vehicle shape database;

[0073] Performing 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 position;

[0074] Performing iterative training based on the following test wake measurement data and the above preset simulated following position by the neural network method to obtain the above wake simulation model.

[0075] Exemplarily, by means 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 the simulation parameters of the crosswind speed and direction, the simulation parameters of the speed 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.

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

[0077] S210. Construct an initial neural network model;

[0078] 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 with a relatively strong negative pressure in the wake vortex of the leading vehicle, input the simulation parameters of the crosswind speed and direction, the simulation parameters of the speed 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 operations 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 simulation parameters of the speed of the leading vehicle, and through neural network calculation, output the measured value of the position with a relatively strong negative pressure in the initial 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. By 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 position with a relatively strong negative pressure in the initial 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.

[0079] S220. Obtain the initial simulation model of the wake vortex of the leading vehicle, including: obtaining the initial simulation model of the 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 simulation model of the wake vortex 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 simulation model of the wake vortex. Through the method of computational fluid dynamics, obtain the simulation model of the wake vortex of the leading vehicle; wherein, the specific vehicle model with known external dimension parameters in the selected database of the leading vehicle 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.

[0080] S230. Obtain the wake vortex measurement data of the following vehicle test. Based on the crosswind speed, direction and speed of the target type vehicle during actual operation, and obtain the test sensing signal through the vehicle-mounted sensor group on the vehicle behind the target type vehicle; wherein, the vehicle-mounted 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.

[0081] S240. Train a neural network model according to the initial simulation data of the wake vortex 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, speed simulation parameters of the leading vehicle, coordinate simulation values of the position with stronger negative pressure of the wake vortex of the leading vehicle (preset simulation following position), and 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.

[0082] 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, speed information and wind speed information of the leading vehicle, calculates faster, and the error between the determined following position and the position with the maximum actual wake vortex intensity is smaller, 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 above method further includes:

[0084] 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 target vehicle;

[0085] Obtain the wake measurement data of the following vehicle test based on the above head pressure data and tail pressure data.

[0086] Exemplarily, as Figure 4 and Figure 5 shown, in some vehicles, 4 patch-type pressure sensors at the vehicle head region position and 8 patch-type pressure sensors installed at the vehicle tail region 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 front vehicle is relatively strong. During the test, measure multiple times at the position where the negative pressure of the wake of the front vehicle is relatively strong and within a preset range nearby, test whether the region where the negative pressure of the wake of the front vehicle is relatively 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 front vehicle, the vehicle speed of the front vehicle and the wind speed to obtain more comprehensive wake measurement data of the following vehicle test under different following vehicle conditions.

[0087] 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, obtain the wake measurement data of the following vehicle test through the following vehicle test, and optimize the wake simulation model based on this wake measurement data of the following vehicle test by means of a neural network, so as to obtain a more accurate wake simulation model, which can provide a more accurate simulated following vehicle position for the target vehicle during actual following vehicle, and thus can make more full use of the position where the negative pressure of the wake is relatively strong to achieve the purpose of energy-saving following vehicle.

[0088] In some examples, the above controlling the target vehicle to follow the vehicle based on the above relative position relationship, the above wind speed information and the above road surface friction coefficient includes:

[0089] When the above wind speed information is less than or equal to the preset wind speed and the above road surface friction coefficient is greater than or equal to the preset friction coefficient, determine a first safe distance based on the above road surface friction coefficient;

[0090] When the above 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 above first safe distance, obtain the current driving lane road information behind the vehicle to be followed;

[0091] When there is no other vehicle driving within the first following vehicle distance behind the vehicle to be followed, control the target vehicle to drive to the above simulated following vehicle position to perform following vehicle driving, where the above first following vehicle distance is determined based on the above simulated following vehicle position and the external dimension of the target vehicle.

[0092] Exemplarily, the preset wind speed is the maximum wind speed that will not cause the risk of vehicle rollover, and the preset friction coefficient is the minimum friction coefficient that will not cause the risk of skidding at the current vehicle speed. When the wind speed is less than or equal to the preset wind speed and the road surface friction coefficient is less than or equal to the preset friction coefficient, the first safe distance that can be braked in time under such road conditions is calculated at this time. After obtaining the simulated following vehicle position through the wake vortex simulation model, lane line information is obtained through the front radar of the target vehicle or the radar behind the vehicle to be followed. A judgment is made on the position of the simulated following vehicle position and the lane line 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 Figure 6 the recommended position A in

[0093] At this time, the distance between the simulated following vehicle position and the vehicle to be followed is obtained, that is, the vehicle distance between the recommended position A and the vehicle to be followed. If the vehicle distance is greater than the first safe distance, it is necessary to obtain whether there are other vehicles within the first following distance behind the vehicle to be followed, that is, to judge whether the target vehicle will interfere with the vehicles behind if it travels 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 first distance is determined by the simulated following vehicle 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 vehicles behind during the process of following the vehicle to be followed. It can be understood that if the distance of the vehicle to be followed is less than the first safe distance, that is, the recommended position A does not meet the shortest braking distance under the current road conditions, the target vehicle should be controlled to be at least the first safe distance from the vehicle to be followed to ensure driving safety.

[0094] In some examples, the above-mentioned control of the target vehicle to follow the vehicle based on the above-mentioned relative position relationship, the above-mentioned wind speed information and the above-mentioned road surface friction coefficient includes:

[0095] When the simulated following vehicle position is in the adjacent lane of the vehicle to be followed, obtain the adjacent lane road information behind the vehicle to be followed;

[0096] When there are no other vehicles driving within the second following distance of the adjacent road behind the vehicle to be followed, control the target vehicle to travel to the simulated following vehicle position to follow the vehicle, where the second following distance is determined based on the simulated following vehicle position, the external dimensions of the target vehicle and the lane width.

[0097] Exemplarily, if the simulated following position is in the adjacent lane of the vehicle to be followed, that is, there is no need to consider the safety distance from the vehicle in front when braking. For example, Figure 6 in the recommended position B in

[0098] it is necessary 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 judge whether the target vehicle will interfere with the vehicles behind if it travels to the recommended position B. If there are no other vehicles within the second following distance, control the target vehicle 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, it can ensure that the target vehicle will not affect the normal driving of the vehicles behind during the process of following the vehicle to be followed.

[0099] In summary, the embodiment of the present application provides a method for controlling the following of a target vehicle when the simulated following position appears on the adjacent road of the vehicle to be followed, which can effectively follow the vehicle and will not affect the normal driving of the vehicles in the adjacent lane.

[0100] When the above wind speed information is greater than the preset wind speed and / or the above road surface friction coefficient is less than the preset friction coefficient, determine a second safety distance based on the above road surface friction coefficient;

[0101] Control the above target vehicle to follow the vehicle in the current driving lane of the above vehicle to be followed at the above second safety distance.

[0102] Exemplarily, when the wind speed information is greater than the preset wind speed and / or the road surface friction coefficient is less than the preset friction coefficient, there is a risk of rollover or skidding on the current road surface. Determine the second safety distance in this case according to the road surface friction coefficient, and control the target vehicle to drive in the same lane as the vehicle to be followed, leaving the adjacent lane to avoid accidents and improve the safety of following driving.

[0103] In summary, the following control method provided by the embodiment of the present application calculates the second safety distance according to the friction coefficient of the road under the condition of too high wind speed or slippery road, and avoids accidents by controlling the target vehicle and the vehicle to be followed to drive in the same lane at the second safety distance.

[0104] In some examples, such as Figure 7 and Figure 8 shown, multiple vehicles can drive in formation. The vehicles within the formation can stagger to make full use of the negative pressure of the wake of the vehicle in front to save fuel or energy consumption when the following vehicle is driving. Two or more vehicles can stagger and follow each other in the same or adjacent lanes and temporarily occupy the lane line to drive.

[0105] Please refer to Figure 9 , an embodiment of the vehicle-following control device in the embodiments of the present application may include:

[0106] A first acquisition unit 21, configured to acquire the road surface friction coefficient of the road surface to be passed within a target area, where the target area includes bridges and tunnels;

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

[0108] A third acquisition unit 23, configured to use the vehicle driving information as an input to a wake vortex simulation model to obtain the simulated vehicle-following position of the target vehicle, where the wake vortex simulation model is obtained through iterative training based on the fluid mechanics simulation method and the neural network method;

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

[0110] A control unit 25, configured to control the target vehicle to perform vehicle-following driving based on the relative position relationship, the wind speed information, and the road surface friction coefficient.

[0111] As Figure 10 shown, the embodiments of the present application further provide an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored on the memory 320 and executable on the processor. When the processor 320 executes the computer program 311, the steps of any of the above vehicle-following control methods are implemented.

[0112] Since the electronic device introduced in this embodiment is the device used to implement a vehicle-following control device in the embodiments of the present application, based on the method introduced in the embodiments of the present application, those skilled in the art can understand the specific implementation manners and various variations of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device implements the method in the embodiments of the present application will not be described in detail here. As long as the device used by those skilled in the art to implement the method in the embodiments of the present application belongs to the scope protected by the present application.

[0113] In the specific implementation process, when the computer program 311 is executed by the processor, it can implement Figure 1 any of the implementation manners in the corresponding embodiments.

[0114] It should be noted that in the above embodiments, the descriptions of each embodiment have their own focuses. For the parts 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 should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0116] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the processes Figure 1 or blocks.

[0117] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the processes Figure 1 or blocks.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the processes Figure 1 or blocks.

[0119] The embodiments of the present application also provide 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 Figure 1 vehicle-following control process in the corresponding embodiment.

[0120] A computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. 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 computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (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, data center, etc. 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)), etc.

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

[0122] In 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 merely illustrative. For example, the division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other may be indirect couplings or communication connections through some interfaces, devices, or units, and may be in electrical, mechanical, or other forms.

[0123] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to 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, in each embodiment of the present application, each functional unit can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[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, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0126] The above, the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A vehicle following control method, characterized in that, Including: Obtain the road surface friction coefficient of the road surface to be passed within the target area, where the target area includes bridges and tunnels; Obtain the driving information of the vehicle to be followed, where the driving information includes wind speed information and vehicle speed information; Use the vehicle driving information as the input of the wake simulation model to obtain the simulated following position of the target vehicle; Obtain lane line information through the front radar of the target vehicle or the radar behind the vehicle to be followed, and determine the relative position relationship between the simulated following position and the vehicle to be followed based on the lane line information; Control the target vehicle to follow the vehicle based on the relative position relationship, the wind speed information, and the road surface friction coefficient, including: In the case where the wind speed information is less than or equal to the preset wind speed and the road surface friction coefficient is greater than or equal to the preset friction coefficient, determine the first safety distance based on the road surface friction coefficient; in the case where the simulated following position is in the current driving lane of the vehicle to be followed and the distance between the simulated following position and the vehicle to be followed is greater than the first safety distance, obtain the road information of the current driving lane behind the vehicle to be followed; in the case where there is no other vehicle driving within the first following distance behind the vehicle to be followed, control the target vehicle to drive to the simulated following position to follow the vehicle, where the first following distance is determined based on the simulated following position and the external dimensions of the target vehicle; In the case where the simulated following position is in the adjacent lane of the vehicle to be followed, obtain the road information of the adjacent lane behind the vehicle to be followed; in the case where there is no other vehicle driving within the second following distance of the adjacent road behind the vehicle to be followed, control the target vehicle to drive to the simulated following position to follow the vehicle, where the second following distance is determined based on the simulated following position, the external dimensions of the target vehicle, and the lane width; In the case where the wind speed information is greater than the preset wind speed and / or the road surface friction coefficient is less than the preset friction coefficient, determine the second safety distance based on the road surface friction coefficient; control the target vehicle to follow the vehicle in the current driving lane of the vehicle to be followed at the second safety distance; 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 measurement data of the following vehicle test and the preset simulated following position using the neural network method to obtain the wake simulation model; Obtain the crosswind magnitude at the tunnel entrance and the bridge; Based on the crosswind magnitude at the tunnel entrance and the bridge, give a warning to disband the formation or adjust the following strategy before entering the tunnel or the bridge.

2. The method according to claim 1, characterized in that, The driving information further includes shape information.

3. The method according to claim 1, characterized in that, Also including: Obtain the head pressure data and the tail pressure data obtained by the target vehicle at different positions in the wake area corresponding to the vehicle to be followed; Obtain the wake measurement data of the following vehicle test based on the head pressure data and the tail pressure data.

4. A vehicle following control device, characterized in that, Including: The first acquisition unit is configured to acquire the road surface friction coefficient of the road surface to be passed within the target area, where the target area includes bridges and tunnels; The second acquisition unit is configured to acquire the driving information of the vehicle to be followed, where the driving information includes wind speed information and vehicle speed information; The third acquisition unit is configured to use the vehicle driving information as the input of the wake simulation model to obtain the simulated following position of the target vehicle; The determination unit is configured to obtain lane line information through the front radar of the target vehicle or the radar behind the vehicle to be followed, and determine the relative position relationship between the simulated following position and the vehicle to be followed based on the lane line information; The control unit is configured to control the target vehicle to follow the vehicle based on the relative position relationship, the wind speed information, and the road surface friction coefficient, including: When the wind speed information is less than or equal to the preset wind speed and the road surface friction coefficient is greater than or equal to the preset friction coefficient, determine a first safety distance based on the road surface friction coefficient; when the simulated following position is in the current driving lane of the vehicle to be followed and the distance between the simulated following position and the vehicle to be followed is greater than the first safety distance, obtain the road information of the current driving lane behind the vehicle to be followed; when there is no other vehicle driving within the first following distance behind the vehicle to be followed, control the target vehicle to drive to the simulated following position to follow the vehicle, where the first following distance is determined based on the simulated following position and the external dimensions of the target vehicle; When the simulated following position is in the adjacent lane of the vehicle to be followed, obtain the road information of the adjacent lane behind the vehicle to be followed; when there is no other vehicle driving within the second following distance of the adjacent road behind the vehicle to be followed, control the target vehicle to drive to the simulated following position to follow the vehicle, where the second following distance is determined based on the simulated following position, the external dimensions of the target vehicle, and the lane width; When the wind speed information is greater than the preset wind speed and / or the road surface friction coefficient is less than the preset friction coefficient, determine a second safety distance based on the road surface friction coefficient; control the target vehicle to follow the vehicle in the current driving lane of the vehicle to be followed at the second safety distance; 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 measurement data of the following vehicle test and the preset simulated following position using the neural network method to obtain the wake simulation model; Obtain the crosswind magnitude at the tunnel entrance and the bridge; Based on the crosswind magnitude at the tunnel entrance and the bridge, give an early warning to disband the formation or adjust the following strategy before entering the tunnel or the bridge.

5. An electronic device, comprising: A memory and a processor, characterized in that when the processor executes the computer program stored in the memory, it implements the steps of the following control method according to any one of claims 1-3.

6. A computer-readable storage medium, on which a computer program is stored, characterized in that: When the computer program is executed by a processor, it implements the following vehicle-following control method according to any one of claims 1-3.

Images