Whole vehicle braking energy statistical method and device for autonomous vehicle

Abstract

The invention relates to a vehicle braking energy statistical method and device for an automatic driving vehicle, and the method comprises the steps: obtaining a plurality of state parameters of the vehicle, constructing a vehicle state equation based on the plurality of state parameters and a Kalman filtering method, and calculating the total resistance of the vehicle, the state parameters including speed and output power; calculating the braking resistance of the vehicle based on the total resistance and the output power; calculating non-following braking loss of the vehicle according to the braking resistance and the braking working condition; and according to the preset ramp speed plan, the minimum braking energy loss in the target vehicle running process is calculated. According to the method, braking loss is put forward to serve as the key of economical efficiency optimization of the driving vehicle through a Kalman filtering algorithm in combination with actual measurement test data of sliding resistance.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle assisted driving, and specifically relates to a whole-vehicle braking energy statistics method and device for an autonomous driving vehicle. Background Art

[0002] In recent years, the number of cars has increased year by year, and the energy consumption of vehicles is huge, which has attracted great attention from the country. Among them, medium and heavy commercial vehicles have large displacement and high energy consumption, and it is urgent to reduce energy consumption and improve operational economic performance. With the competition of high-speed logistics, new market requirements have been put forward. It is necessary to ensure the time limit of fast logistics, save energy consumption of vehicles and reduce operating costs.

[0003] However, at present, OEMs have conducted little research on the evaluation and optimization of driving behavior. Most manufacturers focus on optimizing the vehicle itself, such as reducing rolling resistance, improving engine fuel efficiency, and improving transmission efficiency. With the development of autonomous driving or assisted driving, OEMs have more opportunities and dominance to intervene in driving behavior. Autonomous driving has huge energy-saving potential and can bring considerable economic benefits to end customers. In the current industry context, it is necessary to conduct in-depth research on the economical driving behavior of autonomous driving vehicles to meet the market's requirements for operating efficiency and operating costs and enhance the competitive advantage of products. Among them, brake loss accounts for more than 6% or even higher in the entire driving process. Reducing brake loss is the key direction of driving behavior optimization. At present, there are various statistical methods for brake energy loss, which are suitable for different application scenarios.

[0004] The prior art records a method, system, device and medium for predicting high-temperature tire blowouts of commercial vehicles. The kinetic energy loss, potential energy loss, windage loss, rolling resistance loss, etc. during the braking process are calculated separately, and the braking energy loss is calculated by the principle of energy conservation, thereby calculating the single braking energy loss. It is used to estimate the temperature rise of the brake drum, and then estimate the risk of vehicle tire blowout. Windage loss and rolling resistance loss are affected by factors such as vehicle load and tire pressure, and the calculation is inaccurate. And a monitoring method, storage medium and vehicle for the remaining life of a vehicle brake disc. The braking force is calculated according to the brake disc pressure, and then the braking loss can be calculated by the action distance corresponding to the braking force, and the braking loss is accumulated to estimate the wear of the brake friction pad and predict the remaining life of the friction pad. For commercial vehicles, the brake pressure is unstable and there is a certain fluctuation. There is a delay in obtaining the brake pressure information, which greatly interferes with the calculation of the braking force and causes a large error in the calculation. Summary of the invention

[0005] In order to improve the accuracy and precision of vehicle braking loss, a first aspect of the present invention provides a whole-vehicle braking energy statistics method for an autonomous driving vehicle, comprising: obtaining multiple state parameters of the vehicle, constructing a vehicle state equation based on the multiple state parameters and a Kalman filter method and calculating the total resistance of the vehicle, the state parameters including speed and output power; calculating the braking resistance of the vehicle based on the total resistance and output power; calculating the non-following braking loss of the vehicle based on the braking resistance and braking conditions; and calculating the minimum braking energy loss of the target vehicle during driving according to a preset ramp speed plan.

[0006] In some embodiments of the present invention, constructing a vehicle state equation based on the multiple state parameters and the Kalman filtering method and calculating the total resistance of the vehicle includes: establishing a state equation based on multiple state parameters of the vehicle and vehicle dynamics; calculating the Jacobian matrix, observation variables and observation matrix of the state equation; determining a state update equation through the Kalman filtering method based on the Jacobian matrix, observation variables and observation matrix; and calculating the total resistance of the vehicle based on the state update equation.

[0007] In some embodiments of the present invention, the calculation of the non-following braking loss of the vehicle based on the braking resistance and braking condition includes: judging whether the vehicle is in braking according to a pedal signal, a deceleration signal and a deceleration signal; calculating the braking loss according to the start time, end time and braking resistance of braking; judging whether the vehicle is in the following mode according to the braking condition of the vehicle, and counting the non-following braking loss of the vehicle according to the judgment result.

[0008] In some embodiments of the present invention, the calculation of the minimum braking energy loss during the driving process of the target vehicle according to the preset ramp speed planning includes: judging the top and bottom of the road according to the real-time slope information; calculating the vehicle speed in the predicted time domain after reaching the top of the slope according to the vehicle state equation and the speed at the bottom of the slope; and calculating the minimum braking energy loss during the driving process of the target vehicle based on the vehicle speed in the predicted time domain after reaching the top of the slope.

[0009] Furthermore, the calculation of the minimum braking energy loss of the target vehicle during driving based on the predicted vehicle speed in the time domain after reaching the top of the slope includes: calculating the braking loss of each section of the slope based on the predicted vehicle speed in the time domain after reaching the top of the slope and the maximum vehicle speed threshold; calculating the braking loss of all sections along the entire journey, which is the minimum braking energy loss of the target vehicle during driving.

[0010] In the above embodiment, it also includes: based on the minimum braking energy loss, judging whether the driving behavior needs to be optimized, and calculating the fuel consumption optimization ratio according to the judgment result.

[0011] According to a second aspect of the present invention, a whole-vehicle braking energy statistics device for an autonomous driving vehicle is provided, comprising: an acquisition module for acquiring multiple state parameters of the vehicle, constructing a vehicle state equation based on the multiple state parameters and a Kalman filtering method and calculating the total resistance of the vehicle, wherein the state parameters include speed and output power; a first calculation module for calculating the braking resistance of the vehicle based on the total resistance and output power; calculating the non-following braking loss of the vehicle according to the braking resistance and braking conditions; and a second calculation module for calculating the minimum braking energy loss of the target vehicle during driving according to a preset ramp speed plan.

[0012] Furthermore, the second calculation module includes: a judgment unit, used to judge whether the vehicle is in braking according to the pedal signal, the deceleration signal and the deceleration signal; a calculation unit, used to calculate the braking loss according to the start time, end time and braking resistance of the braking; a statistical unit, used to judge whether the vehicle is in the following mode according to the braking condition of the vehicle, and to count the non-following braking loss of the vehicle according to the judgment result.

[0013] According to a third aspect of the present invention, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle braking energy statistics method for an autonomous driving vehicle provided in the first aspect of the present invention.

[0014] According to a fourth aspect of the present invention, a computer-readable medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the vehicle braking energy statistics method for an autonomous driving vehicle provided in the first aspect of the present invention is implemented.

[0015] The beneficial effects of the present invention are: 1. According to the Kalman filter algorithm, the total resistance of the vehicle can be accurately calculated; 2. Based on the actual vehicle test data, the sliding resistance of the current vehicle during driving is statistically obtained, effectively eliminating the influence of the driving weather on the day, such as windy, rainy and slippery roads, etc., so that the braking resistance and braking loss obtained are closer to the actual value; 3. Analyzing the inevitable total braking loss under non-following conditions, comparing it with the total braking loss of the vehicle in actual driving, and proposing the potential for fuel saving from braking loss are the key to optimizing the economic efficiency of autonomous driving vehicles, providing optimization direction and path for economical driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a basic flow chart of a whole vehicle braking energy statistics method for an autonomous driving vehicle in some embodiments of the present invention; Figure 2 A schematic diagram of a specific process of a vehicle braking energy statistics method for an autonomous driving vehicle in some embodiments of the present invention; Figure 3 It is a schematic diagram of the process of inevitable braking loss during the whole process in some embodiments of the present invention; Figure 4 A speed trend diagram for optimal speed planning for a ramp in some embodiments of the present invention; Figure 5 It is a schematic diagram of the structure of a whole vehicle braking energy statistics device for an autonomous driving vehicle in some embodiments of the present invention; Figure 6 It is a schematic diagram of the structure of an electronic device in some embodiments of the present invention. DETAILED DESCRIPTION

[0017] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0018] refer to Figure 1 and Figure 2 In a first aspect of the present invention, a whole vehicle braking energy statistics method for an autonomous driving vehicle is provided, comprising: S100. Acquiring multiple state parameters of the vehicle, constructing a vehicle state equation based on the multiple state parameters and a Kalman filter method and calculating the total resistance of the vehicle, wherein the state parameters include speed and output power; S200. Calculating the braking resistance of the vehicle based on the total resistance and output power; S300. Calculating the non-following braking loss of the vehicle based on the braking resistance and braking conditions; and calculating the minimum braking energy loss of the target vehicle during driving under a preset ramp speed plan.

[0019] In step S100 of some embodiments of the present invention, constructing a vehicle state equation based on the multiple state parameters and the Kalman filter method and calculating the total resistance of the vehicle includes: S101. Establishing a state equation according to multiple state parameters of the vehicle and vehicle dynamics; Specifically, the total vehicle resistance is calculated using Kalman filtering according to the vehicle state parameters.

[0020] State variables: , in, x k represents the state variable, V k represents the vehicle speed, F k represents the total resistance; The state equation is established according to the vehicle dynamics equation: , , in, Indicates output power; S102. Calculate the Jacobian matrix, observation variables and observation matrix of the state equation; Find the Jacobian matrix: , The observed variables are: , Z k represents the observed variable, V test Indicates the speed during the test; The observation matrix is: .

[0021] S103. Based on the Jacobian matrix, the observation variables and the observation matrix, a state update equation is determined by a Kalman filter method; and according to the state update equation, a total resistance of the vehicle is calculated.

[0022] According to the Kalman filter calculation process, the prediction equation is: , , The state update equation is: , , ; refer to Figure 3 In step S200 of some embodiments of the present invention, the calculating of the non-following braking loss of the vehicle according to the braking resistance and the braking condition includes: S201. According to the pedal signal, deceleration signal and deceleration signal, determine whether the vehicle is in braking; S202. According to the start time, end time and braking resistance of braking, calculate the braking loss; Specifically, the vehicle resistance Fa1 in the sliding state is statistically calculated. According to the current slope size of the vehicle, the total sliding resistance of the vehicle on a flat road can be calculated, that is, Fslip=Fw+Fr=Fa1-Fi. Then, through quadratic polynomial fitting, the quadratic fitting formula of the vehicle's sliding resistance with respect to the vehicle speed is obtained. This resistance eliminates the influence of the slope.

[0023] , When the vehicle is in braking state, the power balance is used to calculate the vehicle braking resistance.

[0024] ; When the brake pedal signal is greater than 5% or the brake deceleration information is less than 0 or the retarder on signal is set to 1, the vehicle is determined to be in the braking process. The brake loss during the braking process is calculated as follows: ; S203. Determine whether the vehicle is in a following mode according to the braking condition of the vehicle, and count the non-following braking loss of the vehicle according to the determination result.

[0025] The braking loss is divided into following braking loss and non-following braking loss. The judgment is based on whether the autonomous driving vehicle is in the control mode or in the following mode. If it is in the following mode, it will not be counted. Only the braking loss in the non-following mode will be counted. _normal_sum.

[0026] refer to Figure 4 In step S300 of some embodiments of the present invention, the step of calculating the minimum braking energy loss of the target vehicle during driving according to the preset ramp speed plan includes: S301. Determine the top and bottom of the road according to the real-time slope information; Specifically, the top and bottom of the road are identified. First, according to the road slope information, the top of the slope in front is found. The search logic is based on the prediction time domain. The slope value at the previous moment is greater than 0, and the slope value at the next moment is less than or equal to 0. According to the road slope information, the bottom of the slope in front of the top of the slope is found. The search logic is based on the prediction time domain. The slope value at the previous moment is less than 0, and the slope value at the next moment is greater than or equal to 0.

[0027] S302. Calculate the vehicle speed in the predicted time domain after reaching the top of the slope according to the vehicle state equation and the speed at the bottom of the slope; Specifically, the calculation is done under reasonable vehicle speed conditions, and the inevitable loss is set. The vehicle speed at the top of the slope is set as the starting speed Vstart, and the vehicle is coasted to the end of the slope with the gear engaged. The vehicle speed Vpre(k) in the prediction time domain after the top of the slope is recursively obtained according to the vehicle dynamics state equation.

[0028] Then there is the formula:

[0029]

[0030]

[0031] in To predict the vehicle speed at any prediction point in the time domain, is the acceleration of the prediction point, m is the total mass of the vehicle, g is the acceleration of gravity, f is the rolling friction coefficient, The slope of the predicted point is obtained by TBOX, is the distance between the prediction point and the top of the slope, obtained from TBOX, A is the windward area, is the air density, is the coasting resistance with gear engaged, which is fitted according to the statistical data of engine resistance, gear position and vehicle speed, where C0, C1 and C2 are fitting coefficients.

[0032] S303. Based on the vehicle speed in the predicted time domain after reaching the top of the slope, the minimum braking energy loss of the target vehicle during driving is calculated. When any predicted vehicle speed in the predicted time domain is greater than the maximum allowed speed Vmax, in order to prevent the vehicle from speeding, braking force should be provided to maintain the vehicle speed, and the acceleration is 0, then:

[0033] That is, the braking force is: , From the time the vehicle speed reaches the speeding point until Fbrk<=0, record the braking loss of the section.

[0034] ; Calculate the total unavoidable loss Qbrk_min of all sections of the entire journey, which is the sum of the braking losses of all the above sections.

[0035] In the above embodiment, it also includes: based on the minimum braking energy loss, judging whether the driving behavior needs to be optimized, and calculating the fuel consumption optimization ratio according to the judgment result.

[0036] Specifically, when conventional brake loss When _normal_sum exceeds the unavoidable loss Qbrk_min, the driving behavior is considered to be poor. The driving behavior should be optimized. The optimization potential is:

[0037] Converted to the proportion of vehicle fuel consumption optimization: , Where K is the energy conversion factor. It is the total fuel consumption during the test.

[0038] Example 2 refer to Figure 5According to a second aspect of the present invention, a whole vehicle braking energy statistics device 1 for an autonomous driving vehicle is provided, comprising: an acquisition module 11, for acquiring a plurality of state parameters of the vehicle, constructing a vehicle state equation based on the plurality of state parameters and a Kalman filter method and calculating the total resistance of the vehicle, wherein the state parameters include speed and output power; a first calculation module 12, for calculating the braking resistance of the vehicle based on the total resistance and output power; and calculating the non-following braking loss of the vehicle according to the braking resistance and braking condition; and a second calculation module 13, for calculating the minimum braking energy loss of the target vehicle during driving according to a preset ramp speed plan.

[0039] Furthermore, the second calculation module 13 includes: a judgment unit, used to judge whether the vehicle is in braking according to the pedal signal, the deceleration signal and the deceleration signal; a calculation unit, used to calculate the braking loss according to the start time, end time and braking resistance of the braking; a statistical unit, used to judge whether the vehicle is in the following mode according to the braking condition of the vehicle, and to count the non-following braking loss of the vehicle according to the judgment result.

[0040] Example 3 refer to Figure 6 According to a third aspect of the present invention, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle braking energy statistics method for an autonomous driving vehicle according to the first aspect of the present invention.

[0041] The electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0042] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 6 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0043] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, the above functions defined in the method of the embodiment of the present disclosure are executed. It should be noted that the computer-readable medium described in the embodiment of the present disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In an embodiment of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, an apparatus, or a device. In an embodiment of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, an apparatus, or a device. The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wire, optical cable, RF (radio frequency), etc., or any suitable combination of the foregoing.

[0044] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device. The computer-readable medium carries one or more computer programs. When the one or more programs are executed by the electronic device, the electronic device: Computer program code for performing the operations of embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, Python, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, as a separate software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0045] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0046] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A vehicle braking energy statistics method for an autonomous driving vehicle, characterized in that: include: Acquire multiple state parameters of the vehicle, construct a vehicle state equation based on the multiple state parameters and a Kalman filter method, and calculate the total resistance of the vehicle, wherein the state parameters include speed and output power; Calculate the vehicle's braking resistance based on the total resistance and output power; Calculating the non-following braking loss of the vehicle according to the braking resistance and the braking condition; According to the preset ramp speed plan, the minimum braking energy loss of the target vehicle during driving is calculated.

2. The vehicle braking energy statistics method for an autonomous driving vehicle according to claim 1, characterized in that: The constructing of the vehicle state equation based on the multiple state parameters and the Kalman filter method and calculating the total resistance of the vehicle comprises: Establish the state equation according to multiple state parameters of the vehicle and vehicle dynamics; calculate the Jacobian matrix, observation variables and observation matrix of the state equation; Based on the Jacobian matrix, the observation variables and the observation matrix, a state update equation is determined by a Kalman filter method; According to the state update equation, the total drag of the vehicle is calculated.

3. The whole vehicle braking energy statistics method for an autonomous driving vehicle according to claim 1, characterized in that: The calculating of the non-following braking loss of the vehicle according to the braking resistance and the braking condition comprises: Determine whether the vehicle is in braking state according to the pedal signal, deceleration signal and deceleration signal; Calculate the braking loss based on the braking start time, end time and braking resistance; It is determined whether the vehicle is in the following mode according to the braking condition of the vehicle, and the non-following braking loss of the vehicle is counted according to the determination result.

4. The vehicle braking energy statistics method for an autonomous driving vehicle according to claim 1, characterized in that: The calculation of the minimum braking energy loss of the target vehicle during driving according to the preset ramp speed planning includes: Determine the top and bottom of the road based on real-time slope information; According to the vehicle state equation and the speed at the bottom of the slope, the vehicle speed in the predicted time domain after reaching the top of the slope is calculated; Based on the predicted vehicle speed in the time domain after reaching the top of the slope, the minimum braking energy loss of the target vehicle during its driving process is calculated.

5. The vehicle braking energy statistics method for an automatic driving vehicle according to claim 4 is characterized in that: The calculation of the minimum braking energy loss during the driving process of the target vehicle based on the predicted vehicle speed in the time domain after reaching the top of the slope includes: Based on the vehicle speed in the predicted time domain after reaching the top of the slope and the maximum vehicle speed threshold, the braking loss of each section of the slope is calculated; Calculate the braking loss of all sections of the entire journey, which is the minimum braking energy loss of the target vehicle during the driving process.

6. The vehicle braking energy statistics method for an autonomous driving vehicle according to claim 1, characterized in that: Also includes: Based on the minimum braking energy loss, determine whether the driving behavior needs to be optimized, and calculate the fuel consumption optimization ratio based on the judgment result.

7. A vehicle braking energy statistics device for an autonomous driving vehicle according to claim 1, characterized in that: include: An acquisition module, used for acquiring a plurality of state parameters of the vehicle, constructing a vehicle state equation based on the plurality of state parameters and a Kalman filter method and calculating a total resistance of the vehicle, wherein the state parameters include speed and output power; A first calculation module, used for calculating the braking resistance of the vehicle based on the total resistance and the output power; Calculating the non-following braking loss of the vehicle according to the braking resistance and the braking condition; The second calculation module is used to calculate the minimum braking energy loss of the target vehicle during the driving process according to the preset ramp speed plan.

8. The whole vehicle braking energy statistics device for an automatic driving vehicle according to claim 7, characterized in that: The second computing module includes A judgment unit, used to judge whether the vehicle is in braking state according to the pedal signal, the deceleration signal and the deceleration signal; A calculation unit, used for calculating the braking loss according to the braking start time, the braking end time and the braking resistance; The statistical unit is used to determine whether the vehicle is in a following mode according to the braking condition of the vehicle, and to count the non-following braking loss of the vehicle according to the determination result.

9. An electronic device, comprising: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the vehicle braking energy statistics method for an autonomous driving vehicle as described in any one of claims 1 to 6.

10. A computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the vehicle braking energy statistics method for an autonomous driving vehicle is implemented.

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