Vehicle speed planning method and device, readable storage medium and electronic equipment

Abstract

The application provides a vehicle speed planning method and device, a readable storage medium and an electronic device, and the method comprises the following steps: collecting and rolling storage of dynamic traffic information on a target path in real time; based on the dynamic traffic information, identifying the current driving scene as a following vehicle scene or a non-following vehicle scene in real time, and generating a reference vehicle speed under the non-following vehicle scene and the following vehicle scene; constructing a dynamic safety constraint condition set containing motor speed, vehicle speed, motor torque and battery power; establishing a prediction model containing a vehicle longitudinal dynamics model and a motor energy consumption model, constructing and solving a multi-objective optimization problem under a model predictive control framework to obtain an optimal motor torque control sequence and a speed planning sequence; and controlling the vehicle according to the motor torque control sequence and the speed planning sequence. The rationality of vehicle driving speed planning under actual driving conditions is improved.

Description

Technical Field

[0001] This invention relates to the automotive field, and in particular to a vehicle speed planning method, apparatus, readable storage medium, and electronic device. Background Technology

[0002] With the development of new energy vehicle technology, users are demanding higher levels of vehicle intelligence. Intelligent transportation systems have become an important way to solve many problems, and reliable mapping and positioning capabilities play a crucial role in autonomous cruise control, collision warning, and route planning. The rational planning of vehicle speed is also vital for the safety, comfort, and economy of vehicles while driving on the road. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a method for fusing vehicle speed information and road environment information, fully leveraging the advantages of information data and intelligent algorithms, further improving the rationality of vehicle speed planning under actual driving conditions, and enabling real-time speed updates based on relevant road information along the path and the behavioral characteristics of surrounding vehicles, overcoming many problems existing in vehicle speed planning applications using single data information. To achieve the above objective, this invention provides a vehicle speed planning method, apparatus, readable storage medium, and electronic device.

[0004] This invention provides a vehicle speed planning method, comprising the following steps:

[0005] Real-time collection and continuous storage of dynamic traffic information on the target path, including traffic light timing status, surrounding vehicle information, and road terrain information;

[0006] Based on the dynamic traffic information, by judging whether the relative distance and relative speed between the vehicle and the vehicle in front have entered the preset following interaction range, the current driving scenario can be identified in real time as a following scenario or a non-following scenario.

[0007] When it is not a following vehicle scenario, a reference vehicle speed is generated based on the traffic light timing status and road terrain information;

[0008] When following another vehicle, a reference speed is generated based on the surrounding vehicle information.

[0009] Based on the surrounding vehicle information, the behavior of surrounding vehicles is predicted, and combined with the characteristics of the vehicle braking system, a set of dynamic safety constraints including motor speed, vehicle speed, motor torque and battery power is constructed.

[0010] A predictive model including a vehicle longitudinal dynamics model and a motor energy consumption model is established. The generated reference vehicle speed is used as the tracking target, and the set of dynamic safety constraints is used as the boundary. Under the model predictive control framework, a multi-objective optimization problem is constructed and solved to obtain the optimal motor torque control sequence and speed planning sequence. The objective function of the optimization problem includes at least energy consumption economy index, speed tracking index, torque comfort index and terminal penalty index.

[0011] The vehicle is controlled based on the motor torque control sequence and speed planning sequence.

[0012] Furthermore, in the above vehicle speed planning method, when it is a non-following scenario, the step of generating a reference vehicle speed includes:

[0013] Based on the timing of the two traffic lights ahead, and following the rules of green wave passage, the terminal vehicle speed and distance are inferred, and an optimization problem is constructed to calculate the reference speed for green wave passage at the current moment.

[0014] Furthermore, in the above vehicle speed planning method, when it is a following vehicle scenario, the step of generating a reference vehicle speed includes:

[0015] Calculate the safe following reference speed based on the relative distance and speed between the vehicle in front and your own vehicle, as well as your own vehicle speed.

[0016] Furthermore, in the above vehicle speed planning method, the expression for the objective function J is:

[0017] ;

[0018] in,

[0019] ,

[0020] ,

[0021] in, It predicts the time domain. It is the reference speed set by the driver. It is the actual motor torque at the current moment. For reference motor torque, These are weighting coefficients. It is the time interval from time k to time k+1. As an energy consumption economic indicator, For speed tracking indicators, For torque comfort indicators, This is a punitive indicator for the end user.

[0022] The present invention also discloses a vehicle speed planning device, comprising:

[0023] The acquisition module is used to collect and continuously store dynamic traffic information on the target path in real time. The dynamic traffic information includes the timing status of traffic lights, surrounding vehicle information, and road terrain information.

[0024] The identification module is used to identify whether the current driving scenario is a following scenario or a non-following scenario in real time by judging whether the relative distance and relative speed between the vehicle and the vehicle in front have entered a preset following interaction range based on the dynamic traffic information.

[0025] The first generation module is used to generate a reference vehicle speed based on the traffic light timing status and road terrain information when it is a non-following scenario.

[0026] The second generation module is used to generate a reference vehicle speed based on the surrounding vehicle information when it is a following vehicle scenario;

[0027] The construction module is used to predict the behavior of surrounding vehicles based on the surrounding vehicle information, and, in combination with the characteristics of the vehicle braking system, construct a set of dynamic safety constraints including motor speed, vehicle speed, motor torque and battery power.

[0028] The model prediction module is used to establish a prediction model that includes a vehicle longitudinal dynamics model and a motor energy consumption model. The generated reference vehicle speed is used as the tracking target, and the set of dynamic safety constraints is used as the boundary. Under the model prediction control framework, a multi-objective optimization problem is constructed and solved to obtain the optimal motor torque control sequence and speed planning sequence. The objective function of the optimization problem includes at least energy consumption economy index, speed tracking index, torque comfort index and terminal penalty index.

[0029] The control module is used to control the vehicle according to the motor torque control sequence and the speed planning sequence.

[0030] Furthermore, in the aforementioned vehicle speed planning device, the expression for the objective function J is:

[0031] ;

[0032] in,

[0033] ,

[0034]

[0035] in, It predicts the time domain. It is the reference speed set by the driver. It is the actual motor torque at the current moment. For reference motor torque, These are weighting coefficients. It is the time interval from time k to time k+1. As an energy consumption economic indicator, For speed tracking indicators, For torque comfort indicators, This is a punitive indicator for the end user.

[0036] The present invention also discloses an electronic device, including a memory and a processor, wherein the memory stores a program that, when executed by the processor, implements any of the methods described above.

[0037] The present invention also discloses a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements any of the methods described above.

[0038] This embodiment develops a local road condition recognition and forward road condition prediction algorithm based on traffic light timing status, surrounding vehicle information, and road terrain information, and plans the motor torque in combination with the current user's driving habits, thereby improving the rationality of vehicle speed planning under actual driving conditions. Attached Figure Description

[0039] Figure 1 A flowchart of the vehicle speed planning method provided in the first embodiment of the present invention;

[0040] Figure 2 This is a structural block diagram of the vehicle speed planning device provided in the second embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. Detailed Implementation

[0042] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0043] These and other aspects of the embodiments of the invention will become clear from the following description and accompanying drawings. In these descriptions and drawings, some specific embodiments of the invention are specifically disclosed to illustrate some ways of implementing the principles of the embodiments of the invention; however, it should be understood that the scope of the embodiments of the invention is not limited thereto. Rather, the embodiments of the invention include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0044] Please see Figure 1The vehicle speed planning method in the first embodiment of the present invention includes steps S11 to S15:

[0045] Step S11: Collect and store dynamic traffic information on the target path in real time. The dynamic traffic information includes traffic light timing status, surrounding vehicle information, and road terrain information.

[0046] Through onboard sensors and vehicle-to-everything (V2X) communication, dynamic traffic information is perceived and fused in real time, which includes at least:

[0047] Traffic signal timing status, real-time dynamic timing of one or more traffic lights on the path ahead;

[0048] Surrounding vehicle information: Real-time status of vehicles around the vehicle, including location, speed, and acceleration;

[0049] Road terrain information, including high-precision map data on slope, curvature, and speed limits.

[0050] The dynamic traffic information is stored in a rolling time window manner.

[0051] Step S12: Based on the dynamic traffic information, determine whether the relative distance and relative speed between the vehicle and the vehicle in front have entered the preset following interaction range, and identify in real time whether the current driving scenario is a following scenario or a non-following scenario.

[0052] Based on the surrounding vehicle information stored in step S11, the system determines whether the relative distance and relative speed between the vehicle and the vehicle in front have entered the preset following interaction range, identifies in real time whether the current driving scenario is a following scenario or a non-following scenario, and decides to activate the corresponding speed planning mode accordingly.

[0053] Step S13: When it is a non-following scenario, a reference vehicle speed is generated based on the traffic light timing status and road terrain information.

[0054] Specifically, in non-following scenarios, the step of generating a reference vehicle speed includes:

[0055] Based on the timing of the two traffic lights ahead, and following the rules of green wave passage, the terminal vehicle speed and distance are inferred, and an optimization problem is constructed to calculate the reference speed for green wave passage at the current moment.

[0056] In non-following mode, based on the timing of the two traffic lights ahead and following the rules of green wave passage, the final vehicle speed is inferred. Distance to terminal Construct an optimization problem to calculate the reference vehicle speed v at the current moment. dTerminal distance refers to the distance a vehicle travels to the next traffic light, which can be obtained through cloud data; terminal speed is calculated based on the time and distance traveled through the green wave. The specific optimization problem is expressed as:

[0057] ,

[0058] ,

[0059] Where, k v m v m s m u Both are correlation coefficients, v i S i u i Let i be the velocity, distance traveled, and acceleration at time i, respectively. T is the time difference between time i and time i+1. , These are the maximum and minimum vehicle speeds, respectively. , These are the maximum and minimum accelerations, respectively.

[0060] Step S14: When it is a following vehicle scenario, a reference vehicle speed is generated based on the surrounding vehicle information.

[0061] When following another vehicle, a reference speed can be calculated based on the relative speed of the vehicle in front, the relative distance between the vehicles, and the speed of your own vehicle.

[0062] Step S15: Based on the surrounding vehicle information, predict the behavior of surrounding vehicles, and combine this with the characteristics of the vehicle's braking system to construct a set of dynamic safety constraints, including motor speed, vehicle speed, motor torque, and battery power. Details are as follows.

[0063] (1) Motor speed constraint:

[0064] During operation, due to the inherent physical limitations of the motor, its operating speed has a specific range. According to data provided by the motor manufacturer, the maximum speed of the motor is... The minimum speed of the motor is Therefore, the motor speed The constraints are:

[0065]

[0066] (2) Vehicle speed constraints:

[0067] Because this control method allows vehicle speed to vary within a specific range near a reference speed set by the driver, this specific speed range needs to comprehensively consider the vehicle's power level and road conditions. An excessively high speed limit may lead to speeding, while an excessively low speed limit is detrimental to fuel economy and freight efficiency. Therefore, the speed that the state variable should satisfy... The constraints are:

[0068] ,

[0069] in, It is the minimum permissible speed. These are the maximum permissible speeds. Depending on the driving scenario (mainly referring to vehicle speed and speed limits), these two values ​​will vary accordingly.

[0070] (3) Motor torque constraint:

[0071] During vehicle motor operation, due to the inherent physical limitations of the motor, the torque range that the motor can provide varies at different motor speeds. According to data provided by the motor manufacturer, at a motor speed of... Under these conditions, the motor's maximum output torque is The minimum output torque is Therefore, the motor torque The constraints are:

[0072] .

[0073] (4) Battery power constraints:

[0074] During vehicle battery operation, due to the physical limitations of the battery itself, charging and discharging cannot exceed the allowable power range. According to the requirements, the maximum and minimum torque of the motor that meets the battery power requirements at the current moment is obtained from the VCU.

[0075] Step S16: Establish a prediction model that includes a vehicle longitudinal dynamics model and a motor energy consumption model. Using the generated reference vehicle speed as the tracking target and the set of dynamic safety constraints as the boundary, construct and solve a multi-objective optimization problem under the model predictive control framework to obtain the optimal motor torque control sequence and speed planning sequence. The objective function of the optimization problem includes at least energy consumption economy index, speed tracking index, torque comfort index, and terminal penalty index.

[0076] Model predictive control framework inputs:

[0077] Actual speed of this vehicle The generated reference vehicle speed v d The current motor speed The actual motor torque at the current moment Motor torque calculated by the controller Road terrain information (slope, speed limit), current vehicle location .

[0078] Model predictive control framework output:

[0079] Motor torque at the next moment And reference speed sequence.

[0080] The control objective is to minimize energy consumption in the predicted time domain, while ensuring that the actual vehicle speed is close to the reference vehicle speed and the torque calculated by the controller is close to the actual output torque of the motor.

[0081] In this embodiment, the vehicle's longitudinal dynamics model is simplified to a single-degree-of-freedom model. The vehicle's longitudinal motion can be represented by the distance the vehicle travels. Vehicle speed and vehicle longitudinal acceleration To describe:

[0082]

[0083] A vehicle needs to overcome various resistances to move forward. According to Newton's second law of equilibrium, we can conclude that:

[0084]

[0085] in, It is the traction force that the vehicle transmits from the motor to the wheels. It's air resistance. It is rolling resistance. It is the slope resistance. It is an acceleration resistance.

[0086] Wheel traction ( The total output torque at the wheels, after passing through the vehicle's power system and transmission system, ultimately acts on the wheels to pull the vehicle forward. The discrete equation for the wheel traction force is:

[0087] ,

[0088] in, It refers to the mechanical efficiency of the transmission system. It is the transmission ratio of the main reducer. It is the motor output torque at step k. That is the wheel radius.

[0089] Air resistance ( The force exerted by the surrounding air on the vehicle during its movement is the component of the force in the direction of travel. Due to the simplification of the vehicle model, the lateral and side dynamic characteristics of the vehicle are not studied. In this embodiment, the longitudinal dynamic model only considers air resistance along the longitudinal direction of the vehicle under windless conditions. The expression for air resistance is:

[0090] ,

[0091] in, It is the air drag coefficient. It is the vehicle's frontal area. It refers to air density.

[0092] Rolling resistance ( During the rolling process of the wheel, the generated tangential force is the rolling resistance of the tire. This tangential force is opposite to the rolling direction of the tire and is a function of the vehicle's normal load and the rolling resistance coefficient between the tire and the ground. When the vehicle is traveling on a slope, the discrete equation for the rolling resistance is:

[0093] ,

[0094] in, It is the total mass of the vehicle. It is gravitational acceleration. It is the rolling resistance coefficient of the wheel. It is the road slope at step k, and the road slope value changes as the vehicle's position changes.

[0095] ramp resistance ( The slope resistance is the component of a vehicle's weight along the slope when the vehicle is traveling on an incline. On the slope, the discrete equation for the slope resistance is:

[0096] ,

[0097] Acceleration resistance ( Inertial drag and inertial torque are forces that a vehicle needs to overcome during acceleration. Inertial force is caused by the vehicle's mass moving horizontally, while inertial torque is caused by the mass of rotating parts such as wheels. For ease of calculation, a conversion factor for the vehicle's rotating mass is introduced to convert inertial torque into inertial force. Therefore, the vehicle's acceleration resistance is as follows:

[0098] ,

[0099] in, It is the conversion factor for vehicle rotational mass.

[0100] In summary, the longitudinal dynamic equations of the vehicle in traction mode are as follows:

[0101] ,

[0102] in, It is the time interval from step k to step k+1.

[0103] 5. Energy consumption model (motor power model)

[0104] Due to motor power Its torque Rotation speed Therefore, the motor power model can be constructed as the motor torque. and rotational speed The quadratic polynomial form:

[0105] ,

[0106] in, These are the fitting coefficients.

[0107] Furthermore, the relationship between motor speed and vehicle speed is expressed as follows:

[0108] .

[0109] The expression for the objective function J is:

[0110] ,

[0111] in,

[0112] ,

[0113] ,

[0114] in, It predicts the time domain. For reference speed, It is the actual motor torque at the current moment. These are weighting coefficients. It is the time interval from time k to time k+1. As an energy consumption economic indicator, For speed tracking indicators, For torque comfort indicators, T is a punitive indicator for the end user. md Let v(N) be the expected motor output torque at time k, and v(N) be the actual vehicle speed at the end of the time domain (step N).

[0115] Energy consumption economic indicators Minimize energy consumption; speed tracking performance metrics Tracking the reference speed to avoid excessive speed deviation; torque comfort index Avoid excessive torque deviation; terminal penalty index : Ensure the vehicle speed reaches near the reference speed at the final moment. By increasing Improve vehicle speed tracking accuracy; by increasing Suppress torque fluctuations; by reducing , This reduces energy loss.

[0116] Step S17: Control the vehicle according to the motor torque control sequence and speed planning sequence.

[0117] The first control input of the motor torque control sequence is output to the motor for execution, and the motor torque control sequence is replanned and updated.

[0118] Speed ​​planning sequences can be provided to other in-vehicle systems or drivers for predictive energy management, predictive shifting, or to display predictive speed guidance curves on the dashboard, improving the driving experience and energy efficiency.

[0119] In this embodiment, the first control quantity of the optimal control sequence obtained in step S16 is output to the vehicle actuator. Furthermore, when the identified driving scenario changes, the two reference vehicle speed generation algorithms in the following scenario or the non-following scenario are smoothly transitioned, and the weight coefficients of the optimization problem in step S16 are adjusted to achieve a smooth switch between different speed planning modes.

[0120] This embodiment develops a local road condition recognition and forward road condition prediction algorithm based on traffic light timing status, surrounding vehicle information, and road terrain information, and plans the motor torque in combination with the current user's driving habits, thereby improving the rationality of vehicle speed planning under actual driving conditions.

[0121] Please see Figure 2 The vehicle speed planning device in the second embodiment of the present invention includes:

[0122] The acquisition module 21 is used to collect and store dynamic traffic information on the target path in real time. The dynamic traffic information includes traffic light timing status, surrounding vehicle information, and road terrain information.

[0123] The identification module 22 is used to identify whether the current driving scenario is a following scenario or a non-following scenario in real time by judging whether the relative distance and relative speed between the vehicle and the vehicle in front have entered a preset following interaction range based on the dynamic traffic information.

[0124] The first generation module 23 is used to generate a reference vehicle speed based on the traffic light timing status and road terrain information when it is a non-following scenario.

[0125] The second generation module 24 is used to generate a reference vehicle speed based on the surrounding vehicle information when it is a following vehicle scenario.

[0126] The construction module 25 is used to predict the behavior of surrounding vehicles based on the surrounding vehicle information, and, in combination with the characteristics of the vehicle braking system, construct a set of dynamic safety constraints including motor speed, vehicle speed, motor torque and battery power.

[0127] The model prediction module 26 is used to establish a prediction model that includes a vehicle longitudinal dynamics model and a motor energy consumption model. The generated reference vehicle speed is used as the tracking target, and the set of dynamic safety constraints is used as the boundary. Under the model prediction control framework, a multi-objective optimization problem is constructed and solved to obtain the optimal motor torque control sequence and speed planning sequence. The objective function of the optimization problem includes at least energy consumption economy index, speed tracking index, torque comfort index and terminal penalty index.

[0128] The control module 27 is used to control the vehicle according to the motor torque control sequence and the speed planning sequence.

[0129] The vehicle speed planning device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0130] In another aspect, the present invention also proposes an electronic device, please refer to [link to relevant documentation]. Figure 3 The image shows an electronic device according to the fourth embodiment of the present invention, including a processor 10, a memory 20, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, it implements the vehicle speed planning method as described above.

[0131] The electronic device may be, but is not limited to, a personal computer, a mobile phone, or other computer equipment. In some embodiments, the processor 10 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, etc.

[0132] The memory 20 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of an electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 20 can include both internal and external storage units of the electronic device. The memory 20 can be used not only to store application software and various types of data installed on the electronic device, but also to temporarily store data that has been output or will be output.

[0133] Optionally, the electronic device may further include a user interface, a network interface, a communication bus, etc. The user interface may include a display, an input unit such as a keyboard, and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface. The network interface may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface), typically used to establish communication connections between the device and other electronic devices. The communication bus is used to enable communication between these components.

[0134] It should be pointed out that, Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0135] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vehicle speed planning method as described above.

[0136] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system or apparatus (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from or in conjunction with such an instruction execution system or apparatus). For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit programs for use by or in conjunction with an instruction execution system or apparatus.

[0137] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0138] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0139] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0140] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A vehicle speed planning method, characterized in that, Including the following steps: Real-time collection and continuous storage of dynamic traffic information on the target path, including traffic light timing status, surrounding vehicle information, and road terrain information; Based on the dynamic traffic information, by judging whether the relative distance and relative speed between the vehicle and the vehicle in front have entered the preset following interaction range, the current driving scenario can be identified in real time as a following scenario or a non-following scenario. When it is not a following vehicle scenario, a reference vehicle speed is generated based on the traffic light timing status and road terrain information; When following another vehicle, a reference speed is generated based on the surrounding vehicle information. Based on the surrounding vehicle information, the behavior of surrounding vehicles is predicted, and combined with the characteristics of the vehicle braking system, a set of dynamic safety constraints including motor speed, vehicle speed, motor torque and battery power is constructed. A predictive model including a vehicle longitudinal dynamics model and a motor energy consumption model is established. The generated reference vehicle speed is used as the tracking target, and the set of dynamic safety constraints is used as the boundary. Under the model predictive control framework, a multi-objective optimization problem is constructed and solved to obtain the optimal motor torque control sequence and speed planning sequence. The objective function of the optimization problem includes at least energy consumption economy index, speed tracking index, torque comfort index and terminal penalty index. The vehicle is controlled according to the motor torque control sequence and speed planning sequence; The expression for the objective function is: ； in, ， Where J is the objective function, It predicts the time domain. It is the reference speed set by the driver. It is the actual motor torque at the current moment. For reference motor torque, These are weighting coefficients. It is the time interval from time k to time k+1. As an energy consumption economic indicator, For speed tracking performance indicators, For torque comfort indicators, This is a punitive indicator for the end user.

2. The vehicle speed planning method as described in claim 1, characterized in that, When it is not a following vehicle scenario, the step of generating a reference vehicle speed includes: Based on the timing of the two traffic lights ahead, and following the rules of green wave passage, the terminal vehicle speed and distance are inferred, and an optimization problem is constructed to calculate the reference speed for green wave passage at the current moment.

3. The vehicle speed planning method as described in claim 1, characterized in that, When it is a following vehicle scenario, the step of generating a reference vehicle speed includes: Calculate the safe following reference speed based on the relative distance and speed between the vehicle in front and your own vehicle, as well as your own vehicle speed.

4. A vehicle speed planning device, characterized in that, include: The acquisition module is used to collect and continuously store dynamic traffic information on the target path in real time. The dynamic traffic information includes the timing status of traffic lights, surrounding vehicle information, and road terrain information. The identification module is used to identify whether the current driving scenario is a following scenario or a non-following scenario in real time by judging whether the relative distance and relative speed between the vehicle and the vehicle in front have entered a preset following interaction range based on the dynamic traffic information. The first generation module is used to generate a reference vehicle speed based on the traffic light timing status and road terrain information when it is a non-following scenario. The second generation module is used to generate a reference vehicle speed based on the surrounding vehicle information when it is a following vehicle scenario; The construction module is used to predict the behavior of surrounding vehicles based on the surrounding vehicle information, and, in combination with the characteristics of the vehicle braking system, construct a set of dynamic safety constraints including motor speed, vehicle speed, motor torque and battery power. The model prediction module is used to establish a prediction model that includes a vehicle longitudinal dynamics model and a motor energy consumption model. The generated reference vehicle speed is used as the tracking target, and the set of dynamic safety constraints is used as the boundary. Under the model prediction control framework, a multi-objective optimization problem is constructed and solved to obtain the optimal motor torque control sequence and speed planning sequence. The objective function of the optimization problem includes at least energy consumption economy index, speed tracking index, torque comfort index and terminal penalty index. The control module is used to control the vehicle according to the motor torque control sequence and the speed planning sequence; The expression for the objective function is: ； in, ， Where J is the objective function, It predicts the time domain. It is the reference speed set by the driver. It is the actual motor torque at the current moment. For reference motor torque, These are weighting coefficients. It is the time interval from time k to time k+1. As an energy consumption economic indicator, For speed tracking performance indicators, For torque comfort indicators, This is a punitive indicator for the end user.

5. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a program that, when executed by the processor, implements the method as described in any one of claims 1-3.

6. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-3.

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