Intelligent driving vehicle control method and device, electronic equipment and storage medium

Abstract

The invention discloses an intelligent driving vehicle control method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring historical driving data of a target vehicle and target driving data in a preset time period; determining continuity factors of the historical driving data at different moments, and dividing the historical driving data into at least two pieces of fragment data according to the continuity factors; comparing the target driving data with the at least two pieces of fragment data, and determining at least two pieces of target fragment data matched with the target driving data from the at least two pieces of fragment data; and control parameters of the target vehicle at the next moment are determined according to the at least two pieces of target fragment data, and the target vehicle is intelligently controlled according to the control parameters. By adopting the technical scheme of the embodiment of the invention, the historical driving data in the manual driving mode is analyzed, and the humanization and personification intelligent control of the target vehicle is realized on the basis of the control parameters in the manual driving mode.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a method, device, electronic device and storage medium for controlling an intelligent driving vehicle. Background Art

[0002] Self-driving cars, also known as driverless cars, are intelligent vehicles that operate without human intervention through intelligent systems. Relying on artificial intelligence, visual computing, radar, and positioning systems, self-driving cars can operate motor vehicles automatically and safely without any active human intervention.

[0003] In recent years, with the advancement of automotive intelligence, the number of vehicles equipped with autonomous driving features has gradually increased. Many vehicles that originally lack autonomous driving capabilities, or have low-level autonomous driving capabilities, are looking to upgrade to these features through aftermarket installations. However, existing technical solutions lack consideration for the characteristics of human drivers, resulting in vehicle operation methods that fail to meet drivers' inherent expectations, making them difficult to implement in mass-produced vehicles.

[0004] Therefore, how to intelligently control the vehicle based on the driver's driving characteristics is an urgent problem to be solved by people in this technical field. Summary of the Invention

[0005] The present invention provides an intelligent driving vehicle control method, device, electronic device and storage medium to solve the problem in the prior art that the intelligent driving operation mode is difficult to meet the driver's expectations due to the lack of consideration of human driving characteristics in the intelligent driving.

[0006] According to one aspect of the present invention, a method for controlling an intelligent driving vehicle is provided, the method comprising:

[0007] Obtaining historical driving data of the target vehicle and target driving data within a preset time period; wherein the historical driving data is driving data generated when the driver manually drives the target vehicle, and the target driving data is driving data generated when the driver intelligently drives the target vehicle;

[0008] Determining a continuity factor of the historical driving data at different moments, and dividing the historical driving data into at least two segments based on the continuity factor; wherein the continuity factor is used to determine the correlation between the historical driving data at different moments;

[0009] comparing the target driving data with at least two segment data, and determining at least two target segment data matching the target driving data from the at least two segment data;

[0010] The control parameters of the target vehicle at the next moment are determined according to the at least two target segment data, and the target vehicle is intelligently controlled according to the control parameters.

[0011] According to another aspect of the present invention, there is provided an intelligent driving vehicle control device, the device comprising:

[0012] A driving data acquisition module is used to acquire historical driving data of the target vehicle and target driving data within a preset time period; wherein the historical driving data is the driving data generated when the driver manually drives the target vehicle, and the target driving data is the driving data generated when the driver intelligently drives the target vehicle;

[0013] a segment division module, configured to determine a continuity factor of the historical driving data at different moments, and to divide the historical driving data into at least two segments based on the continuity factor; wherein the continuity factor is used to determine the correlation between the historical driving data at different moments;

[0014] a segment search module, configured to compare the target driving data with at least two segment data, and determine at least two target segment data that match the target driving data from the at least two segment data;

[0015] The intelligent control module is used to determine the control parameters of the target vehicle at the next moment according to the at least two target segment data, and to perform intelligent control on the target vehicle according to the control parameters.

[0016] According to another aspect of the present invention, an electronic device is provided, comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the intelligent driving vehicle control method described in any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the intelligent driving vehicle control method described in any embodiment of the present invention when executed.

[0021] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the intelligent driving vehicle control method according to any embodiment of the present invention.

[0022] The technical solution of the embodiment of the present invention obtains historical driving data generated when the driver manually drives the target vehicle and target driving data generated when the driver intelligently drives the target vehicle; determines the continuity factor of the historical driving data at different times, and divides the historical driving data into at least two segments of data based on the continuity factor; compares the target driving data with the at least two segments of data, and determines at least two target segments of data that match the target driving data from the at least two segments of data; determines the control parameters of the target vehicle at the next moment based on the at least two target segments of data, and intelligently controls the target vehicle based on the control parameters. The technical solution of the embodiment of the present invention solves the problem in the prior art that the operation mode of intelligent driving is difficult to meet the driver's expectations due to the lack of consideration of manual driving characteristics in intelligent driving. By analyzing the historical driving data in the manual driving mode and based on the control parameters in the manual driving mode, human-like and anthropomorphic intelligent control of the target vehicle is achieved.

[0023] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 This is a flow chart of an intelligent driving vehicle control method provided according to the first embodiment of the present invention;

[0026] Figure 2 This is a flow chart of an intelligent driving vehicle control method provided according to the second embodiment of the present invention;

[0027] Figure 3 This is a schematic structural diagram of an intelligent driving vehicle control device provided according to a third embodiment of the present invention;

[0028] Figure 4 It is a structural diagram of an electronic device provided by the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0030] Among them, the acquisition, storage, use and processing of data in the technical solution of this application are in compliance with the relevant provisions of laws and regulations. It should be noted that the terms "first", "second", "target", "original", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including", "etc." and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] Example 1

[0032] Figure 1 A flowchart of an intelligent driving vehicle control method is provided for the first embodiment of the present invention. This embodiment is applicable to the case where the control parameters of the target vehicle at the next intelligent driving moment are determined based on historical manual driving data. The method can be executed by an intelligent driving vehicle control device, which can be implemented in the form of hardware and / or software. The intelligent driving vehicle control device can be configured in any electronic device with network communication function. Figure 1 As shown, the method includes:

[0033] S110 : Acquire historical driving data of a target vehicle and target driving data within a preset time period.

[0034] The historical driving data is generated by the driver while manually driving the target vehicle. This historical driving data is used to analyze the driver's operating methods to facilitate the selection of appropriate control parameters for controlling the target vehicle during intelligent driving. The historical driving data includes at least two indicators, including but not limited to the distance to the preceding vehicle, the speed of the preceding vehicle, the speed of the target vehicle, the acceleration of the target vehicle, the accelerator pedal travel of the target vehicle, and the brake pedal travel of the target vehicle.

[0035] The target driving data is driving data generated by the driver while intelligently driving the target vehicle. The target driving data includes, but is not limited to, the distance to the preceding vehicle, the preceding vehicle's speed, the target vehicle's speed, and the target vehicle's acceleration. The target driving data is driving data acquired within a preset time period. The preset time period may be the time period closest to the current moment during the operation of the autonomous driving system; for example, the first six sampling points. Acquiring the target driving data within the preset time period may refer to acquiring the target driving data six sampling points prior to the current moment.

[0036] S120: Determine continuity factors of historical driving data at different moments, and divide the historical driving data into at least two data segments based on the continuity factors.

[0037] The continuity factor is used to determine the correlation between historical driving data at different times, classifying historical driving data with high correlation into a single data segment. For example, if historical driving data at ten adjacent time points are highly correlated, the historical driving data at those ten adjacent time points can be classified into the same data segment. The high correlation of the historical driving data is determined by the continuity factor of the historical driving data.

[0038] Optionally, the continuity factor of the historical driving data is determined by the expected amount of indicator data included in the historical driving data. If the expected amount of the indicator data meets a preset judgment condition, the indicator data is considered to be continuous. Determining the continuity of each indicator data at the same moment can determine the continuity factor of the historical driving data at that moment. For example, if the expected amount of five indicator data at the first moment meets the preset judgment condition, the continuity factor of the historical driving data at the first moment is 5. A higher value of the continuity factor indicates a stronger correlation between the historical driving data at the current moment.

[0039] S130: Compare the target driving data with at least two segment data, and determine at least two target segment data that match the target driving data from the at least two segment data.

[0040] The comparison may refer to performing a similarity comparison between the target driving data and the historical driving data in the divided segment data; for example, performing a similarity comparison between the target driving data and the corresponding number of historical driving data in the first divided segment data; e.g., if the target driving data is data of 6 sampling points, performing a similarity comparison between the target driving data of the 6 sampling points and the adjacent 6 historical driving data in the first segment data.

[0041] After the comparison, at least two target segments of data that match the target driving data are determined from the at least two segments of data. The target segments of data refer to segments of data whose similarity to the target driving data satisfies a preset condition. For example, if the historical driving data at the six sampling points at time points 111-116 in the first segment of data matches the target driving data, the historical driving data at the six sampling points at time points 111-116 are used as the target segments of data that match the target driving data.

[0042] S140: Determine control parameters of the target vehicle at a next moment based on the at least two target segment data, and perform intelligent control on the target vehicle based on the control parameters.

[0043] Among them, the control parameters may refer to the accelerator pedal stroke control parameters and brake pedal stroke control parameters when the target vehicle is intelligently driven. The accelerator pedal stroke refers to the distance from the initial position to the position where the driver steps on the accelerator pedal. This parameter directly affects the vehicle's power output response, acceleration performance, and driving stability. The brake pedal stroke refers to the distance the driver moves from the pedal's stop position to the point where resistance is felt when the brake pedal is stepped on. It is a comprehensive reflection of the brake clearance and the clearance of the braking force transmission mechanism. In the embodiment of the present invention, the accelerator pedal stroke control parameters and brake pedal stroke control parameters are used to intelligently control the speed and acceleration of the target vehicle during intelligent driving.

[0044] Among them, after determining at least two target segment data, the target vehicle accelerator pedal stroke and the target vehicle brake pedal stroke included in the at least two target segment data are obtained; the control parameters of the target vehicle at the next moment are determined based on the target vehicle accelerator pedal stroke and the target vehicle brake pedal stroke included in the at least two target segment data, and the target vehicle is intelligently controlled based on the control parameters.

[0045] An embodiment of the present invention provides an intelligent driving vehicle control method, which obtains historical driving data of a target vehicle and target driving data within a preset time period; wherein the historical driving data is driving data generated when the driver manually drives the target vehicle, and the target driving data is driving data generated when the driver intelligently drives the target vehicle; determines a continuity factor for the historical driving data at different times, and divides the historical driving data into at least two segments based on the continuity factor; wherein the continuity factor is used to determine the correlation between the historical driving data at different times; compares the target driving data with the at least two segments, and determines at least two target segments that match the target driving data from the at least two segments; determines control parameters for the target vehicle at the next moment based on the at least two target segments, and intelligently controls the target vehicle based on the control parameters. Using the technical solutions of the embodiments of the present invention, historical driving data under manual driving mode is analyzed, and the control parameters under manual driving mode are used as a basis to achieve human-like and anthropomorphic intelligent control of the target vehicle.

[0046] Example 2

[0047] Figure 2 This is a flow chart of a method for controlling an intelligent driving vehicle provided by the second embodiment of the present invention. The embodiment of the present invention further optimizes the above embodiment on the basis of the above embodiment. The embodiment of the present invention can be combined with various optional solutions in one or more of the above embodiments. Figure 2 As shown, the method includes:

[0048] S210: Acquire historical driving data of the target vehicle and target driving data within a preset time period.

[0049] S220. Determine an expected amount of at least one indicator data item included in the historical driving data, and determine whether the expected amount satisfies a preset judgment condition. If so, further superimpose the continuity factors corresponding to the historical driving data to obtain continuity factors for the historical driving data at different times.

[0050] Among them, the value of the continuity factor corresponding to the historical driving data at different moments represents the number of indicators whose expected amounts of indicator data in the historical driving data at different moments meet the preset judgment conditions; for example, at the first moment, if there are 5 expected amounts of indicator data that meet the preset judgment conditions, then the value of the continuity factor of the historical driving data at the first moment is 5.

[0051] In the embodiment of the present invention, whether the expected amount of any indicator data in the historical driving data meets the preset judgment conditions is determined to determine whether the corresponding indicator data has continuity. When it is determined that the indicator data meets the preset judgment conditions, the continuity factor of the historical driving data at the corresponding moment is increased by one, and so on and so forth to obtain the continuity factor of the historical driving data at the corresponding moment.

[0052] As an optional but non-limiting implementation, determining an expected amount of at least one indicator data item included in the historical driving data and determining whether the expected amount meets a preset judgment condition; if the preset judgment condition is met, then further superimposing the continuity factors corresponding to the historical driving data to obtain the continuity factors of the historical driving data at different times includes but is not limited to steps A1-A3:

[0053] Step A1: Determine a first expected amount of first indicator data contained in the historical driving data at a first moment, and determine whether the first expected amount meets a preset judgment condition; if the preset judgment condition is met, add one to the original continuity factor of the continuity factor corresponding to the historical driving data at the first moment to obtain a first continuity factor; wherein the original continuity factor is zero.

[0054] Step A2: Determine a second expected amount of the second indicator data contained in the historical driving data at the first moment, and determine whether the second expected amount meets a preset judgment condition; if it meets the preset judgment condition, add one to the first continuity factor corresponding to the historical driving data at the first moment to obtain a second continuity factor.

[0055] Step A3: sequentially determining the expected amount of index data included in the historical driving data at the first moment, and obtaining a target continuity factor for the historical driving data at the first moment; wherein, if the expected amount of index data does not meet the preset judgment condition, then adding zero to the continuity factor of the previous index data.

[0056] The historical driving data obtained are shown in Table 1. 16 Taking the continuity factor of the historical driving data at the moment as an example, determine X 16 、X 26 、X 36 、X 46 、X 56 、X 66 、X 76 When the expected value meets the preset judgment condition, the continuity factor is increased by one to determine T 16 The continuity factor of the historical driving data at time t.

[0057] Table 1 Historical driving data

[0058]

[0059] Optionally, the process of determining the continuity factor of historical driving data in the embodiment of the present invention can be expressed as follows:

[0060] Step 11: Initialize the parameters, set the cumulative amount i = 1, j = 5, i represents the index data of the i-th column, j represents the index data of the j-th row; the continuity factor P1 = 0, P2 = 0, P3 = 0, P4 = 0 represents the T-th 11 、T 12 、T 13 、T 14 The continuity factor of the historical driving data at time t is 0.

[0061] Step 12: Set the continuity factor P j =0;P j represents the continuity factor of the j-th row of historical driving data.

[0062] Step 13: Set the indicator data X ij The expected amount X ijpre Expressed as:

[0063] Judgment indicator data X ij The expected amount X ijpre Whether the preset judgment conditions are met|X ijpre -X ij |≤0.2×|X ij |; If satisfied, then P j =P j +1; if not satisfied, proceed to the next step directly;

[0064] Step 14: Determine whether i≤6 is satisfied; if so, i=i+1 and re-execute step 13; if not, directly execute the next step;

[0065] Step 15: Determine whether j≤n-1 is satisfied. If so, j=j+1, i=1, and re-execute step 12; if not, end the loop.

[0066] In the above steps, if the current indicator data does not meet the preset judgment condition, then the current indicator data at the next moment is determined to meet the preset judgment condition, thereby determining whether the current indicator data meets the preset judgment condition at different moments. If the preset judgment condition is met, then the next indicator data is determined to meet the preset judgment condition at the current moment, thereby determining the continuity factor of the historical driving data corresponding to the current moment.

[0067] S230: Determine continuity factor values ​​of historical driving data at different moments, and divide the historical driving data into at least two segments based on the continuity factor values ​​satisfying a preset condition and being historical driving data at adjacent moments.

[0068] Among them, the continuity factor values ​​of historical driving data at different times are determined, and a continuity factor comparison table of historical driving data is constructed, as shown in Table 2.

[0069] Table 2 Comparison table of continuity factors of historical driving data

[0070]

[0071] The continuity factor values ​​of historical driving data at different times are analyzed to divide the historical driving data into segments. Specifically, it is expressed as:

[0072] Step 21: Initialize parameters, set the accumulated amount i = 1, j = 2, K = 1, and the intermediate storage value j s =1;

[0073] Step 22: Determine whether P is satisfied j ≥5, if satisfied, proceed to the next step; if not satisfied, Q j =0, go to step 25; where Q j Indicates the segment number into which the historical driving data is divided;

[0074] Step 23: Determine whether P is satisfied j-1 <5, if satisfied, j s =j; if not satisfied, proceed to the next step;

[0075] Step 24: Determine whether it is satisfied And P j+1 <5 and If satisfied, the fragment number ..., Q j =K, K=K+1; if not satisfied, proceed directly to the next step;

[0076] Step 25: Determine whether j≤n-1 is satisfied. If so, j=j+1 and re-execute step 22; if not, end the loop.

[0077] Through the above steps, it is possible to effectively divide the historical driving data segments and remove those that do not meet the Q j ≥1 corresponding data samples. Among them, clear the data samples that do not meet Q j ≥1, that is, the discontinuous data samples are cleared. The historical driving data after segment division and data clearing is shown in Table 3. Assume that there are N segments of segment data in total.

[0078] Table 3 Division of historical driving data segments

[0079]

[0080]

[0081] Among them, m in Table 3 refers to the m rows of historical driving data remaining after the historical driving data are cleared of data samples, where m≤n.

[0082] S240: Compare the target driving data with at least two segment data, and determine at least two target segment data that match the target driving data from the at least two segment data.

[0083] The target driving data during the operation of the automatic driving system is obtained, and the target driving data is shown in Table 4.

[0084] Table 4 Target driving data

[0085]

[0086] As an optional but non-limiting implementation, comparing the target driving data with the at least two segment data and determining at least two target segment data that match the target driving data from the at least two segment data include but are not limited to steps B1-B2:

[0087] Step B1: Compare the target driving data with any one of the at least two segment data to determine a similarity value.

[0088] Step B2: If the similarity value satisfies a preset similarity value threshold, the speed value of the target vehicle is predicted, and when the speed prediction value satisfies a preset speed condition and the segment data compared with the target driving data belongs to the same segment time, the segment data is determined to be target segment data that matches the target driving data.

[0089] The target driving data is compared with any one of the at least two segment data to determine a similarity value; if the similarity value satisfies a preset similarity value threshold, a speed value of the target vehicle is predicted, and when the speed prediction value satisfies a preset speed condition and the segment data compared with the target driving data belongs to the same segment time, the segment data is determined to be target segment data that matches the target driving data.

[0090] Specifically, you can search for matching historical fragment data by following the steps below:

[0091] Step 31: Initialize parameters, set the accumulated amount i=1, j=i+5, w=0;

[0092] Step 32: Determine whether i≤m-5 is satisfied. If so, proceed to the next step. If not, end the loop.

[0093] Step 33: Set similarity value

[0094]

[0095] Step 34: Determine whether L is satisfied ij ≤L yu , L yu Represents the preset similarity value threshold. If it is satisfied, the next step is executed directly; otherwise, i=i+1 and step 32 is executed again;

[0096] Step 35: Determine the speed trend of the vehicle and set the predicted speed value of the vehicle Determine whether |Y is satisfied 4jpre -Y 4j |≤max{0.1×|Y 4jpre |, 0.2} and Q i =Q i+1 =Q i+2 =Q i+3 =Q i+4 =Q i+5 =Q i+6 =Q i+7 , if satisfied, then Z 1k =X 6i+6 , Z 2k =X 7i+6 , Z 3k =X 6i+7 , Z 4k =X 7i+7 , w=w+1, if not satisfied, proceed to the next step directly; where Z 1k , Z 3k represents the target vehicle's accelerator pedal travel, Z 2k , Z 4k Indicates the target vehicle's brake pedal travel;

[0097] Step 36: Determine whether w≥10 is satisfied. If so, end the loop. If not, set i=i+1 and re-execute step 32. Wherein, w≥10 may mean obtaining at least 10 sets of target segment data similar to the target driving data.

[0098] In an embodiment of the present invention, during the operation of the autonomous driving system, the target driving data of the six most recent sampling points in history are recorded, and at least two fragments of the historical driving data are searched to match the vehicle historical driving data that is most similar to the target driving data of the intelligent driving vehicle.

[0099] 250. Determine control parameters of the target vehicle at a next moment based on the at least two target segment data, and perform intelligent control on the target vehicle based on the control parameters.

[0100] After searching for at least two target segment data that match the target driving data, control parameters of the target vehicle at the next moment are determined based on the at least two target segment data, and the target vehicle is intelligently controlled based on the control parameters.

[0101] As an optional but non-limiting implementation, determining the control parameters of the target vehicle at the next moment based on the at least two target segment data, and performing intelligent control of the target vehicle based on the control parameters, includes but is not limited to steps C1-C2:

[0102] Step C1: determining target control parameters included in the at least two target segment data; the target control parameters include a target vehicle accelerator pedal travel and a target vehicle brake pedal travel.

[0103] Step C2: determining the control parameters of the target vehicle at the next moment according to the target control parameters, and performing intelligent control on the target vehicle according to the control parameters.

[0104] Among them, the target vehicle accelerator pedal stroke and the target vehicle brake pedal stroke included in the at least two target segment data are determined; and the control parameters of the target vehicle at the next moment in the intelligent driving mode are determined based on the target vehicle accelerator pedal stroke and the target vehicle brake pedal stroke, and the target vehicle is intelligently controlled based on the control parameters.

[0105] As an optional but non-limiting implementation, determining the control parameters of the target vehicle at the next moment based on the target control parameters and performing intelligent control on the target vehicle based on the control parameters include but are not limited to steps D1-D3:

[0106] Step D1: performing group calculation on the target vehicle accelerator pedal travel included in the target control parameters, and taking the minimum value obtained by the group calculation as the accelerator pedal travel control parameter of the target vehicle at the next moment; wherein the group calculation includes mean calculation and weighted processing.

[0107] Step D2: performing group calculation on the target vehicle brake pedal stroke included in the target control parameters, and taking the maximum value obtained from the group calculation as the brake pedal stroke control parameter of the target vehicle at the next moment.

[0108] Step D3: Input the obtained accelerator pedal stroke control parameter and brake pedal stroke control parameter into the target vehicle intelligent driving actuator to perform intelligent control on the target vehicle.

[0109] The target control parameters include the target vehicle accelerator pedal stroke and the target vehicle brake pedal stroke; for example, the target vehicle accelerator pedal stroke obtained from at least two target segment data includes Z 11 , Z 12 、......、Z 110 and Z 31 , Z 32 、......、Z 310 , the target vehicle brake pedal travel includes Z 21 , Z 22 、......、Z 210 and Z 41 , Z 42 、......、Z 410 The target vehicle accelerator pedal stroke and the target vehicle brake pedal stroke are grouped and calculated to determine the control parameters of the target vehicle at the next moment.

[0110] Specifically, it can be expressed as:

[0111] The control parameters of the target vehicle's accelerator pedal travel can be expressed as: V1 = min{U1, U3}, U1 = MEANS{Z 11 , Z 12 、……、Z 110}, U3=1.1×min{Z 31 , Z 32 、......、Z 310}; wherein, MEANS{} represents the mean function, V1 represents the accelerator pedal stroke control parameter of the target vehicle at the next moment, U1 represents the mean data obtained by performing mean calculation on the accelerator pedal stroke of the target vehicle; U3 represents the weighted data obtained by performing weighted processing on the accelerator pedal stroke of the target vehicle; the weighted processing value is not specifically limited in the embodiment of the present invention, and can be set according to actual conditions. In the current embodiment, it is set to 1.1.

[0112] The control parameters of the target vehicle's accelerator pedal travel can be expressed as: V2 = max{U2, U4}, U2 = MEANS{Z 21 , Z 22 、......、Z 210},U4=0.9×max{Z 41 , Z 42 、......、Z 410}; wherein, V2 represents the brake pedal stroke control parameter of the target vehicle at the next moment, U2 represents the mean data obtained by performing mean calculation on the brake pedal stroke of the target vehicle; U4 represents the weighted data obtained by performing weighted processing on the brake pedal stroke of the target vehicle; the weighted processing value is not specifically limited in the embodiment of the present invention, and can be set according to actual conditions. In the current embodiment, it is set to 0.9.

[0113] Optionally, after determining the accelerator pedal stroke control parameters and brake pedal stroke control parameters of the target vehicle in the intelligent driving mode, the accelerator pedal stroke control parameters and brake pedal stroke control parameters are input into the intelligent driving actuator to perform intelligent control on the target vehicle at the next moment.

[0114] In an embodiment of the present invention, historical driving data generated by a driver when manually driving a target vehicle is analyzed, and the historical driving data is segmented according to the continuity factor of the historical driving data at different times; target driving data under an intelligent driving model is obtained, and the target driving data is matched with the segment data, and target segment data that matches the driving state and environmental information of the target driving data is selected; and control parameters of the target vehicle under the intelligent driving mode are determined based on the target segment data, and the control parameters are input into an intelligent driving actuator, so that intelligent control of the longitudinal movement of the target vehicle can be achieved, thereby realizing human-like and anthropomorphic intelligent driving.

[0115] Example 3

[0116] Figure 3 This is a schematic diagram of the structure of an intelligent driving vehicle control device provided by the third embodiment of the present invention. Figure 3 As shown, the device includes:

[0117] A driving data acquisition module is used to acquire historical driving data of the target vehicle and target driving data within a preset time period; wherein the historical driving data is the driving data generated when the driver manually drives the target vehicle, and the target driving data is the driving data generated when the driver intelligently drives the target vehicle;

[0118] a segment division module, configured to determine a continuity factor of the historical driving data at different moments, and to divide the historical driving data into at least two segments based on the continuity factor; wherein the continuity factor is used to determine the correlation between the historical driving data at different moments;

[0119] a segment search module, configured to compare the target driving data with at least two segment data, and determine at least two target segment data that match the target driving data from the at least two segment data;

[0120] The intelligent control module is used to determine the control parameters of the target vehicle at the next moment according to the at least two target segment data, and to perform intelligent control on the target vehicle according to the control parameters.

[0121] Optionally, the historical driving data includes at least two index data, and the at least two index data include the distance to the preceding vehicle, the speed of the preceding vehicle, the target vehicle speed, the target vehicle acceleration, the target vehicle accelerator pedal travel and the target vehicle brake pedal travel; the target driving data includes the distance to the preceding vehicle, the speed of the preceding vehicle, the target vehicle speed and the target vehicle acceleration.

[0122] Optional, fragment partitioning module, specifically used to:

[0123] determining an expected amount of at least one indicator data item included in the historical driving data, and determining whether the expected amount satisfies a preset judgment condition; if the preset judgment condition is satisfied, further superimposing continuity factors corresponding to the historical driving data to obtain continuity factors for the historical driving data at different moments; wherein the values ​​of the continuity factors corresponding to the historical driving data at different moments represent the number of indicators for which the expected amount of the indicator data in the historical driving data at different moments satisfies the preset judgment condition;

[0124] Determine continuity factor values ​​of historical driving data at different times, and divide the historical driving data into at least two segments based on the continuity factor values ​​satisfying a preset condition and being historical driving data at adjacent times.

[0125] Optionally, the fragment partitioning module is further specifically used to:

[0126] determining a first expected amount of first indicator data included in the historical driving data at a first moment, and determining whether the first expected amount meets a preset judgment condition; if the preset judgment condition is met, adding one to an original continuity factor corresponding to the historical driving data at the first moment to obtain a first continuity factor; wherein the original continuity factor is zero;

[0127] determining a second expected amount of second indicator data included in the historical driving data at the first moment, and determining whether the second expected amount meets a preset judgment condition; if the preset judgment condition is met, adding one to the first continuity factor corresponding to the historical driving data at the first moment to obtain a second continuity factor;

[0128] The expected amount of the index data included in the historical driving data at the first moment is determined in sequence to obtain a target continuity factor of the historical driving data at the first moment; wherein, if the expected amount of the index data does not meet the preset judgment condition, zero is added to the continuity factor of the previous index data.

[0129] Optional, fragment search module, specifically used for:

[0130] Comparing the target driving data with any one of the at least two fragment data to determine a similarity value;

[0131] If the similarity value satisfies a preset similarity value threshold, the speed value of the target vehicle is predicted, and when the speed prediction value satisfies a preset speed condition and the segment data compared with the target driving data belongs to the same segment time, the segment data is determined to be target segment data that matches the target driving data.

[0132] Optional intelligent control module, specifically used for:

[0133] Determining target control parameters included in the at least two target segment data; the target control parameters include a target vehicle accelerator pedal travel and a target vehicle brake pedal travel;

[0134] The control parameters of the target vehicle at the next moment are determined according to the target control parameters, and the target vehicle is intelligently controlled according to the control parameters.

[0135] Optional intelligent control module is also used for:

[0136] performing group calculation on the target vehicle accelerator pedal travel included in the target control parameters, and using the minimum value obtained from the group calculation as the accelerator pedal travel control parameter of the target vehicle at the next moment; wherein the group calculation includes mean value calculation and weighted processing;

[0137] Calculating the target vehicle's brake pedal stroke included in the target control parameters in groups, and using the maximum value obtained from the group calculation as the target vehicle's brake pedal stroke control parameter at the next moment;

[0138] The obtained accelerator pedal travel control parameters and brake pedal travel control parameters are input into the target vehicle intelligent driving actuator to perform intelligent control of the target vehicle.

[0139] The intelligent driving vehicle control device provided in the embodiments of the present invention can execute the intelligent driving vehicle control method provided in any of the above embodiments of the present invention, and has the corresponding functions and beneficial effects of executing the intelligent driving vehicle control method. For detailed processes, please refer to the relevant operations of the intelligent driving vehicle control method in the above embodiments.

[0140] Example 4

[0141] Figure 4A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0142] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0143] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0144] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the intelligent driving vehicle control method.

[0145] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product that includes a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication unit 19, or installed from the storage unit 18, or installed from the ROM 12. When the computer program is executed by the processor 11, the above-mentioned functions defined in the method of the embodiment of the present invention are performed.

[0146] In some embodiments, the intelligent driving vehicle control method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the intelligent driving vehicle control method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the intelligent driving vehicle control method in any other appropriate manner (for example, by means of firmware).

[0147] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0148] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0149] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0150] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0151] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0152] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0153] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0154] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for controlling an intelligent driving vehicle, characterized in that: The method comprises: Obtaining historical driving data of the target vehicle and target driving data within a preset time period; wherein the historical driving data is driving data generated when the driver manually drives the target vehicle, and the target driving data is driving data generated when the driver intelligently drives the target vehicle; Determining a continuity factor of the historical driving data at different moments, and dividing the historical driving data into at least two segments based on the continuity factor; wherein the continuity factor is used to determine the correlation between the historical driving data at different moments; comparing the target driving data with at least two segment data, and determining at least two target segment data matching the target driving data from the at least two segment data; The control parameters of the target vehicle at the next moment are determined according to the at least two target segment data, and the target vehicle is intelligently controlled according to the control parameters.

2. The method according to claim 1, characterized in that The historical driving data includes at least two index data, and the at least two index data include the distance to the preceding vehicle, the speed of the preceding vehicle, the target vehicle speed, the target vehicle acceleration, the target vehicle accelerator pedal stroke and the target vehicle brake pedal stroke; the target driving data includes the distance to the preceding vehicle, the speed of the preceding vehicle, the target vehicle speed and the target vehicle acceleration.

3. The method according to claim 1, characterized in that The determining of the continuity factors of the historical driving data at different moments and dividing the historical driving data into at least two segments according to the continuity factors includes: determining an expected amount of at least one indicator data item included in the historical driving data, and determining whether the expected amount satisfies a preset judgment condition; if the preset judgment condition is satisfied, further superimposing continuity factors corresponding to the historical driving data to obtain continuity factors for the historical driving data at different moments; wherein the values ​​of the continuity factors corresponding to the historical driving data at different moments represent the number of indicators for which the expected amount of the indicator data in the historical driving data at different moments satisfies the preset judgment condition; Determine continuity factor values ​​of historical driving data at different times, and divide the historical driving data into at least two segments based on the continuity factor values ​​satisfying a preset condition and being historical driving data at adjacent times.

4. The method according to claim 3, characterized in that The method further comprises determining an expected amount of at least one indicator data item included in the historical driving data, and determining whether the expected amount satisfies a preset judgment condition; if the preset judgment condition is satisfied, then further superimposing the continuity factors corresponding to the historical driving data to obtain the continuity factors of the historical driving data at different times, including: determining a first expected amount of first indicator data included in the historical driving data at a first moment, and determining whether the first expected amount meets a preset judgment condition; if the preset judgment condition is met, adding one to an original continuity factor corresponding to the historical driving data at the first moment to obtain a first continuity factor; wherein the original continuity factor is zero; determining a second expected amount of second indicator data included in the historical driving data at the first moment, and determining whether the second expected amount meets a preset judgment condition; if the preset judgment condition is met, adding one to the first continuity factor corresponding to the historical driving data at the first moment to obtain a second continuity factor; The expected amount of the index data included in the historical driving data at the first moment is determined in sequence to obtain a target continuity factor of the historical driving data at the first moment; wherein, if the expected amount of the index data does not meet the preset judgment condition, zero is added to the continuity factor of the previous index data.

5. The method according to claim 1, wherein The comparing the target driving data with the at least two segment data and determining at least two target segment data matching the target driving data from the at least two segment data includes: Comparing the target driving data with any one of the at least two fragment data to determine a similarity value; If the similarity value satisfies a preset similarity value threshold, the speed value of the target vehicle is predicted, and when the speed prediction value satisfies a preset speed condition and the segment data compared with the target driving data belongs to the same segment time, the segment data is determined to be target segment data that matches the target driving data.

6. The method according to claim 1, characterized in that Determining the control parameters of the target vehicle at the next moment based on the at least two target segment data, and intelligently controlling the target vehicle based on the control parameters, includes: Determining target control parameters included in the at least two target segment data; the target control parameters include a target vehicle accelerator pedal travel and a target vehicle brake pedal travel; The control parameters of the target vehicle at the next moment are determined according to the target control parameters, and the target vehicle is intelligently controlled according to the control parameters.

7. The method according to claim 6, characterized in that Determining the control parameters of the target vehicle at the next moment based on the target control parameters, and performing intelligent control on the target vehicle based on the control parameters, includes: performing group calculation on the target vehicle accelerator pedal travel included in the target control parameters, and using the minimum value obtained from the group calculation as the accelerator pedal travel control parameter of the target vehicle at the next moment; wherein the group calculation includes mean value calculation and weighted processing; Calculating the target vehicle's brake pedal stroke included in the target control parameters in groups, and using the maximum value obtained from the group calculation as the target vehicle's brake pedal stroke control parameter at the next moment; The obtained accelerator pedal travel control parameters and brake pedal travel control parameters are input into the target vehicle intelligent driving actuator to perform intelligent control of the target vehicle.

8. An intelligent driving vehicle control device, characterized in that: The device comprises: A driving data acquisition module is used to acquire historical driving data of the target vehicle and target driving data within a preset time period; wherein the historical driving data is the driving data generated when the driver manually drives the target vehicle, and the target driving data is the driving data generated when the driver intelligently drives the target vehicle; a segment division module, configured to determine a continuity factor of the historical driving data at different moments, and to divide the historical driving data into at least two segments based on the continuity factor; wherein the continuity factor is used to determine the correlation between the historical driving data at different moments; a segment search module, configured to compare the target driving data with at least two segment data, and determine at least two target segment data that match the target driving data from the at least two segment data; The intelligent control module is used to determine the control parameters of the target vehicle at the next moment according to the at least two target segment data, and to perform intelligent control on the target vehicle according to the control parameters.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the intelligent driving vehicle control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the intelligent driving vehicle control method according to any one of claims 1 to 7 when executed.

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