An intelligent driving vehicle control method and device, electronic equipment and storage medium

Abstract

This invention discloses a method, device, electronic device, and storage medium for controlling intelligent driving vehicles. The method includes: acquiring historical driving data of a target vehicle and target driving data within a preset time period; determining a continuity factor for the historical driving data at different times, and dividing the historical driving data into at least two segments based on the continuity factor; comparing the target driving data with the at least two segments, and determining at least two target segments that match the target driving data from the at least two segments; determining control parameters for the target vehicle at the next time moment based on the at least two target segments, and performing intelligent control of the target vehicle based on the control parameters. By employing the technical solution of this invention, historical driving data under manual driving mode is analyzed, and based on the control parameters under manual driving mode, human-like and anthropomorphic intelligent control of the target vehicle is achieved.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, and in particular to a method, device, electronic device, and storage medium for controlling intelligent driving vehicles. Background Technology

[0002] Autonomous vehicles, also known as driverless cars, are intelligent vehicles that achieve driverless operation through intelligent systems. Relying on the collaborative efforts of artificial intelligence, computer vision, radar, and positioning systems, autonomous vehicles can operate safely and automatically 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 cars that originally lacked autonomous driving capabilities, or had only a low level of autonomous driving functionality, are hoping to upgrade their autonomous driving capabilities through aftermarket installations. However, existing technical solutions lack consideration for the characteristics of human driving, making it difficult to match the driver's inherent expectations in terms of vehicle operation, thus hindering their application in mass-produced models.

[0004] Therefore, how to intelligently control vehicles based on the driver's driving characteristics is a problem that urgently needs to be solved by those in this technical field. Summary of the Invention

[0005] This invention provides a method, device, electronic device, and storage medium for controlling intelligent driving vehicles, in order to solve 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 for the characteristics of human driving.

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

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

[0008] A continuity factor is determined for historical driving data at different times, and the historical driving data is divided into at least two segments based on the continuity factor; wherein the continuity factor is used to determine the correlation between historical driving data at different times.

[0009] The target driving data is compared with at least two data segments, and at least two target data segments that match the target driving data are identified from the at least two data segments.

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

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

[0012] The driving data acquisition module is used to acquire the historical driving data of the target vehicle and the 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] The segmentation module is used to determine the continuity factor of historical driving data at different times, 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 historical driving data at different times.

[0014] The segment search module is used 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 based on the at least two target segment data, and to perform intelligent control of the target vehicle based on the control parameters.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device 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, which enables the at least one processor to perform the intelligent driving vehicle control method according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the intelligent driving vehicle control method according to any embodiment of the present invention.

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

[0022] The technical solution of this invention involves acquiring historical driving data generated when a driver manually drives a target vehicle and target driving data generated when the driver intelligently drives the target vehicle; determining a continuity factor for the historical driving data at different times, and dividing the historical driving data into at least two segments based on the continuity factor; comparing the target driving data with the at least two segments, and determining at least two target segments that match the target driving data; determining the control parameters of the target vehicle at the next time moment based on the at least two target segments, and intelligently controlling the target vehicle based on the control parameters. This technical solution solves the problem in the prior art where the lack of consideration for the characteristics of manual driving in intelligent driving makes it difficult for the operation mode of intelligent driving to meet the driver's expectations. By analyzing historical driving data under manual driving mode and using the control parameters under manual driving mode as a basis, human-like and anthropomorphic intelligent control of the target vehicle is achieved.

[0023] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of an intelligent driving vehicle control method provided in Embodiment 1 of the present invention;

[0026] Figure 2 This is a flowchart of an intelligent driving vehicle control method provided in Embodiment 2 of the present invention;

[0027] Figure 3 This is a schematic diagram of the structure of an intelligent driving vehicle control device according to Embodiment 3 of the present invention;

[0028] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] The acquisition, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. It should be noted that the terms "first," "second," "target," and "original," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising," "etc.," and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Example 1

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

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

[0034] Historical driving data refers to driving data generated by the driver when manually driving the target vehicle. This historical driving data is used to analyze the driver's operating methods, so as to select appropriate control parameters to control the target vehicle during intelligent driving. The historical driving data includes at least two indicators, including but not limited to the distance to the vehicle in front, the speed of the vehicle in front, the speed of the target vehicle, the acceleration of the target vehicle, the travel of the accelerator pedal, and the travel of the brake pedal.

[0035] The target driving data refers to the driving data generated by the driver when intelligently driving the target vehicle. This target driving data includes, but is not limited to, the distance to the vehicle in front, the speed of the vehicle in front, the speed of the target vehicle, and the acceleration of the target vehicle. The target driving data is driving data acquired within a preset time period. This preset time period can refer to the period closest to the current time during the operation of the autonomous driving system; for example, the first six sampling points. Therefore, acquiring the target driving data within the preset time period can mean acquiring the target driving data from the six sampling points prior to the current time.

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

[0037] The continuity factor is used to determine the correlation between historical driving data at different times, dividing highly correlated historical driving data into a single data segment. For example, if historical driving data at 10 adjacent time points are highly correlated, then these 10 adjacent time points can be divided into the same data segment. The high correlation of historical driving data is determined by the continuity factor of the historical driving data.

[0038] Optionally, the continuity factor of historical driving data is determined by the expected values ​​of the indicator data included in the historical driving data. If the expected values ​​of the indicator data meet preset judgment conditions, the indicator data can be determined to have continuity. Determining the continuity of each indicator data at the same time point determines the continuity factor of the historical driving data at that corresponding time point. For example, if the expected values ​​of 5 indicator data at the first time point meet the preset judgment conditions, then the continuity factor of the historical driving data at the first time point is 5. The higher the value of the continuity factor, the stronger the correlation of the historical driving data at the current time point.

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

[0040] The comparison can refer to comparing the similarity between the target driving data and the historical driving data in the segmented data. For example, the target driving data can be compared with the corresponding number of historical driving data in the first segmented data. If the target driving data consists of 6 sampling points, then the target driving data of the 6 sampling points can be compared with the 6 adjacent historical driving data in the first segmented data.

[0041] After comparison, at least two target data segments that match the target driving data are determined from the at least two data segments. The target data segments refer to data segments whose similarity to the target driving data meets preset conditions; for example, if the historical driving data at six sampling points (time points 111-116) in the first data segment matches the target driving data, then the historical driving data at those six sampling points (time points 111-116) is taken as the target data segments that match the target driving data.

[0042] S140. Determine the control parameters of the target vehicle at the 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] The control parameters refer to the accelerator pedal travel control parameters and brake pedal travel control parameters for intelligent driving of the target vehicle. Accelerator pedal travel refers to the distance from the initial position to the fully depressed position when the driver presses the accelerator pedal; this parameter directly affects the vehicle's power output response, acceleration performance, and driving stability. Brake pedal travel refers to the distance the pedal moves from its stop position to when resistance is felt when the driver presses the brake pedal; it is a comprehensive reflection of the brake clearance and the clearance of the braking force transmission mechanism. In this embodiment of the invention, the aforementioned accelerator pedal travel control parameters and brake pedal travel control parameters are used to intelligently control the speed and acceleration of the target vehicle during intelligent driving.

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

[0045] This invention provides an intelligent driving vehicle control method. The method involves acquiring historical driving data of a target vehicle and target driving data within a preset time period. The historical driving data is generated when the driver manually drives the target vehicle, while the target driving data is generated when the driver intelligently drives the target vehicle. The method 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 this continuity factor. The continuity factor is used to determine the correlation between historical driving data at different times. The target driving data is compared with the at least two segments to determine at least two target segments that match the target driving data. Control parameters for the target vehicle at the next time moment are determined based on the at least two target segments, and intelligent control of the target vehicle is performed based on these control parameters. By analyzing historical driving data under manual driving mode and using the control parameters under manual driving mode as a basis, human-like and anthropomorphic intelligent control of the target vehicle is achieved.

[0046] Example 2

[0047] Figure 2 This is a flowchart of an intelligent driving vehicle control method provided in Embodiment 2 of the present invention. The present invention further optimizes the aforementioned embodiments based on the above embodiments, and can be combined with various optional solutions from one or more of the above embodiments. For example... Figure 2 As shown, the method includes:

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

[0049] S220. Determine the expected value of at least one indicator data contained in the historical driving data, and determine whether the expected value meets the preset judgment conditions; if it meets the preset judgment conditions, continue to superimpose the continuity factors corresponding to the historical driving data to obtain the continuity factors of the historical driving data at different times.

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

[0051] In this embodiment of the invention, the continuity of the corresponding indicator data is determined by determining whether the expected value of any indicator data in the historical driving data meets the preset judgment conditions. When the indicator data meets the preset judgment conditions, the continuity factor of the historical driving data at the corresponding time is incremented by one, and so on, to obtain the continuity factor of the historical driving data at the corresponding time.

[0052] As an optional but non-limiting implementation, the step involves determining the expected value of at least one indicator data included in the historical driving data, and determining whether the expected value meets a preset judgment condition. If the preset judgment condition is met, the continuity factors corresponding to the historical driving data are further superimposed to obtain the continuity factors of the historical driving data at different times, including but not limited to steps A1-A3:

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

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

[0055] Step A3: Determine the expected value of the indicator data contained in the historical driving data at the first moment in sequence to obtain the target continuity factor of the historical driving data at the first moment; if the expected value of the indicator data does not meet the preset judgment conditions, add zero to the continuity factor of the previous indicator data.

[0056] The acquired historical driving data is shown in Table 1. This is used to determine T. 16 Taking the continuity factor of historical driving data at a given time as an example, X is determined respectively. 16 X 26 X 36 X 46 X 56 X 66 X 76 The expected value is determined, and when the expected value meets the preset judgment conditions, the continuity factor is incremented by one to determine T. 16 The continuity factor of historical driving data at any given time.

[0057] Table 1 Historical Driving Data

[0058]

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

[0060] Step 11: Initialize parameters. Set the cumulative amount i = 1, j = 5, where i represents the index data in the i-th column and j represents the index data in the j-th row; the continuity factors P1 = 0, P2 = 0, P3 = 0, P4 = 0 represent the data in the T-th row. 11 T 12 T 13 T 14 The continuity factor for historical driving data at any given time is 0.

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

[0062] Step 13: Let the indicator data X ij Expected value X ijpre Represented as:

[0063] Judgment indicator data X ij Expected value X ijpre Does it meet the preset judgment condition |X ijpre -X ij |≤0.2×|X ij |; If satisfied, then P j =P j +1; If not satisfied, proceed to the next step.

[0064] Step 14: Determine if i ≤ 6; if yes, then i = i + 1, and repeat step 13; if no, proceed directly to the next step.

[0065] Step 15: Determine if j ≤ n-1 is satisfied. If it is, then j = j + 1, i = 1, and repeat step 12. If it is not satisfied, then end the loop.

[0066] In the above steps, if the current indicator data does not meet the preset judgment conditions, it is then determined whether the current indicator data at the next moment meets the preset judgment conditions. This determines whether the current indicator data meets the preset judgment conditions at different times. If the preset judgment conditions are met, it is then determined whether the next indicator data meets the preset judgment conditions at the current moment. This determines the continuity factor of the historical driving data corresponding to the current moment.

[0067] S230. Determine the continuity factor value of historical driving data at different times. Based on the fact that the continuity factor value meets the preset conditions and is historical driving data at adjacent times, divide the historical driving data into at least two segments.

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

[0069] Table 2 Comparison Table of Continuity Factors in Historical Driving Data

[0070]

[0071] Specifically, the continuity factor values ​​of historical driving data at different times are analyzed to segment the historical driving data. This is represented as follows:

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

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

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

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

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

[0077] The above steps enable the effective segmentation of historical driving data fragments, removing those that do not meet the Q criteria. j Data samples corresponding to ≥1. Among them, those not satisfying Q are removed. j Data samples with a value ≥1 are those that have had discontinuous data samples removed. Table 3 represents the segmentation and the historical driving data after data removal, assuming a total of N data segments.

[0078] Table 3. Segmentation of Historical Driving Data

[0079]

[0080]

[0081] In Table 3, m refers to the m rows of historical driving data remaining after the data sample is cleared, where m ≤ n.

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

[0083] Among them, target driving data is acquired during the operation of the autonomous driving system, 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 at least two data segments and determining at least two target data segments that match the target driving data from the at least two data segments includes, but is not limited to, steps B1-B2:

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

[0088] Step B2: If the similarity value meets the preset similarity value threshold, the speed value of the target vehicle is predicted, and when the predicted speed value meets the 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 the target segment data that matches the target driving data.

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

[0090] Specifically, you can search for matching historical data segments by following these steps:

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

[0092] Step 32: Determine if i ≤ m - 5. If yes, proceed to the next step; otherwise, end the loop.

[0093] Step 33: Set similarity values

[0094]

[0095] Step 34: Determine if L is satisfied. ij ≤L yu L yu This indicates a preset similarity threshold. If the threshold is met, proceed directly to the next step; otherwise, i = i + 1, and repeat step 32.

[0096] Step 35: Determine the speed trend of this vehicle, and set the predicted speed value of this vehicle. Determine if |Y is satisfied 4jpre -Y 4j |≤max{0.1×|Y 4jpre |,0.2}andQ 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 If w = w + 1, proceed to the next step if the condition is not met; where Z 1k Z 3k Z represents the travel of the accelerator pedal of the target vehicle. 2k Z 4k Indicates the travel of the target vehicle's brake pedal;

[0097] Step 36: Determine if w≥10 is satisfied. If satisfied, end the loop; otherwise, i = i + 1 and repeat step 32. Here, w≥10 can mean acquiring at least 10 sets of target segment data similar to the target driving data.

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

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

[0100] Specifically, after finding at least two target data segments that match the target driving data, the control parameters of the target vehicle at the next moment are determined based on the at least two target data segments, and the target vehicle is intelligently controlled based on the control parameters.

[0101] As an optional but non-limiting implementation, the step of 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: Determine the target control parameters included in the at least two target segment data; the target control parameters include the accelerator pedal travel and the brake pedal travel of the target vehicle.

[0103] Step C2: Determine the control parameters of the target vehicle at the next moment based on the target control parameters, and perform intelligent control on the target vehicle based on the control parameters.

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

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

[0106] Step D1: The target control parameters, including the target vehicle accelerator pedal travel, are grouped and calculated, and the minimum value obtained from the group calculation is used 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: The target vehicle brake pedal travel, which is included in the target control parameters, is grouped and calculated, and the maximum value obtained from the group calculation is used as the brake pedal travel control parameter of the target vehicle at the next moment.

[0108] Step D3: Input the obtained accelerator pedal travel control parameters and brake pedal travel control parameters into the intelligent driving actuator of the target vehicle to perform intelligent control of the target vehicle.

[0109] The target control parameters include the accelerator pedal travel and the brake pedal travel of the target vehicle; for example, the accelerator pedal travel of the target vehicle 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's brake pedal travel includes Z. 21 Z 22 ... Z 210 and Z 41 Z 42 ... Z 410 The accelerator pedal travel and brake pedal travel of the target vehicle 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 for the accelerator pedal travel of the target vehicle 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}; where MEANS{} represents the mean function, V1 represents the accelerator pedal travel control parameter of the target vehicle at the next moment, U1 represents the mean data obtained by averaging the accelerator pedal travel of the target vehicle, and U3 represents the weighted data obtained by weighting the accelerator pedal travel of the target vehicle; the value of the weighting is not specifically limited in this embodiment of the invention, and can be set according to the actual situation, and is set to 1.1 in the current embodiment.

[0112] The control parameters for the accelerator pedal travel of the target vehicle 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}; where V2 represents the brake pedal travel control parameter of the target vehicle at the next moment, U2 represents the mean data obtained by averaging the brake pedal travel of the target vehicle, and U4 represents the weighted data obtained by weighting the brake pedal travel of the target vehicle; the value of the weighting is not specifically limited in this embodiment of the invention, and can be set according to the actual situation, and is set to 0.9 in the current embodiment.

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

[0114] In this embodiment of the invention, historical driving data generated by the driver manually driving the 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 the intelligent driving model is obtained, and the target driving data is matched with the segment data to select target segment data that matches the driving state and environmental information of the target driving data; and the control parameters of the target vehicle in the intelligent driving mode are determined based on the target segment data. The control parameters are then input into the intelligent driving actuator, thereby realizing intelligent control of the longitudinal movement of the target vehicle to achieve 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 in Embodiment 3 of the present invention. Figure 3 As shown, the device includes:

[0117] The driving data acquisition module is used to acquire the historical driving data of the target vehicle and the 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] The segmentation module is used to determine the continuity factor of historical driving data at different times, 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 historical driving data at different times.

[0119] The segment search module is used 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 based on the at least two target segment data, and to perform intelligent control of the target vehicle based on the control parameters.

[0121] Optionally, the historical driving data includes at least two indicators, namely, the distance to the vehicle in front, the speed of the vehicle in front, the speed of the target vehicle, the acceleration of the target vehicle, the travel of the accelerator pedal of the target vehicle, and the travel of the brake pedal of the target vehicle; the target driving data includes the distance to the vehicle in front, the speed of the vehicle in front, the speed of the target vehicle, and the acceleration of the target vehicle.

[0122] Optional, a segmentation module, specifically used for:

[0123] Determine the expected value of at least one indicator data contained in the historical driving data, and determine whether the expected value meets the preset judgment conditions; if it meets the preset judgment conditions, the continuity factor corresponding to the historical driving data is further superimposed to obtain the continuity factor of the historical driving data at different times; wherein, the value of the continuity factor corresponding to the historical driving data at different times represents the number of indicators in the historical driving data at different times whose expected value meets the preset judgment conditions.

[0124] Determine the continuity factor value of historical driving data at different times. Based on the historical driving data whose continuity factor value meets the preset conditions and are adjacent times, divide the historical driving data into at least two segments.

[0125] Optionally, the segmentation module is also specifically used for:

[0126] Determine the first expected value of the first indicator data contained in the historical driving data at the first moment, and determine whether the first expected value meets the preset judgment conditions; if it meets the preset judgment conditions, add one to the continuity factor corresponding to the historical driving data at the first moment to obtain the first continuity factor; wherein, the original continuity factor is zero.

[0127] Determine the second expected value of the second indicator data contained in the historical driving data at the first moment, and determine whether the second expected value meets the preset judgment conditions; if it meets the preset judgment conditions, add one to the first continuous factor corresponding to the historical driving data at the first moment to obtain the second continuous factor.

[0128] The expected values ​​of the indicator data contained in the historical driving data at the first moment are determined sequentially to obtain the target continuity factor of the historical driving data at the first moment; if the expected value of the indicator data does not meet the preset judgment conditions, zero is added to the continuity factor of the previous indicator data.

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

[0130] The target driving data is compared with any one of at least two data segments to determine the similarity value;

[0131] If the similarity value meets the preset similarity value threshold, the speed value of the target vehicle is predicted, and when the predicted speed value meets the 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 the target segment data that matches the target driving data.

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

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

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

[0135] Optionally, the intelligent control module is also specifically used for:

[0136] The target control parameters, including the accelerator pedal travel of the target vehicle, are grouped and calculated, and the minimum value obtained from the group calculation is used 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;

[0137] The target vehicle brake pedal travel, which is included in the target control parameters, is calculated in groups, and the maximum value obtained from the group calculation is used as the brake pedal travel control parameter of the target vehicle at the next moment.

[0138] The obtained accelerator pedal travel control parameters and brake pedal travel control parameters are input into the intelligent driving actuator of the target vehicle 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 embodiments of the present invention, and has the corresponding functions and beneficial effects of executing the intelligent driving vehicle control method. For details, please refer to the relevant operations of the intelligent driving vehicle control method in the foregoing embodiments.

[0140] Example 4

[0141] Figure 4A schematic diagram of an electronic device 10 that can be used to implement embodiments 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 processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the 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 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

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

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

[0145] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

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

[0147] Various embodiments of the systems and techniques described above herein 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), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0148] Computer programs used to implement 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 executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

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

[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 provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0151] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0152] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0153] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0154] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for controlling an intelligent driving vehicle, characterized in that, The method includes: Acquire historical driving data of the target vehicle and target driving data within a preset time period; wherein, historical driving data is driving data generated when the driver manually drives the target vehicle, and target driving data is driving data generated when the driver intelligently drives the target vehicle. A continuity factor is determined for historical driving data at different times, and the historical driving data is divided into at least two segments based on the continuity factor; wherein the continuity factor is used to determine the correlation between historical driving data at different times. The target driving data is compared with at least two data segments, and at least two target data segments that match the target driving data are identified from the at least two data segments. Based on the at least two target segment data, the control parameters of the target vehicle at the next moment are determined, and the target vehicle is intelligently controlled based on the control parameters.

2. The method of claim 1, wherein, The historical driving data includes at least two indicators: distance to the vehicle in front, speed of the vehicle in front, speed of the target vehicle, acceleration of the target vehicle, accelerator pedal travel of the target vehicle, and brake pedal travel of the target vehicle. The target driving data includes distance to the vehicle in front, speed of the vehicle in front, speed of the target vehicle, and acceleration of the target vehicle.

3. The method of claim 1, wherein, The process of determining the continuity factor of historical driving data at different times, and dividing the historical driving data into at least two segments based on the continuity factor, includes: Determine the expected value of at least one indicator data contained in the historical driving data, and determine whether the expected value meets the preset judgment conditions; if it meets the preset judgment conditions, the continuity factor corresponding to the historical driving data is further superimposed to obtain the continuity factor of the historical driving data at different times; wherein, the value of the continuity factor corresponding to the historical driving data at different times represents the number of indicators in the historical driving data at different times whose expected value meets the preset judgment conditions. Determine the continuity factor value of historical driving data at different times. Based on the historical driving data whose continuity factor value meets the preset conditions and are adjacent times, divide the historical driving data into at least two segments.

4. The method of claim 3, wherein, The process involves determining the expected value of at least one indicator data item contained in the historical driving data, and determining whether the expected value meets a preset judgment condition. If the preset judgment condition is met, the continuity factors corresponding to the historical driving data are further superimposed to obtain the continuity factors of the historical driving data at different times, including: Determine the first expected value of the first indicator data contained in the historical driving data at the first moment, and determine whether the first expected value meets the preset judgment conditions; if it meets the preset judgment conditions, add one to the continuity factor corresponding to the historical driving data at the first moment to obtain the first continuity factor; wherein, the original continuity factor is zero. Determine the second expected value of the second indicator data contained in the historical driving data at the first moment, and determine whether the second expected value meets the preset judgment conditions; if it meets the preset judgment conditions, add one to the first continuous factor corresponding to the historical driving data at the first moment to obtain the second continuous factor. The expected values ​​of the indicator data contained in the historical driving data at the first moment are determined sequentially to obtain the target continuity factor of the historical driving data at the first moment; if the expected value of the indicator data does not meet the preset judgment conditions, zero is added to the continuity factor of the previous indicator data.

5. The method of claim 1, wherein, The step of comparing the target driving data with at least two data segments, and determining at least two target data segments that match the target driving data from the at least two data segments, includes: The target driving data is compared with any one of at least two data segments to determine the similarity value; If the similarity value meets the preset similarity value threshold, the speed value of the target vehicle is predicted, and when the predicted speed value meets the 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 the target segment data that matches the target driving data.

6. The method of claim 1, wherein, The step of 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: Determine the target control parameters included in the at least two target segment data; the target control parameters include the accelerator pedal travel and the brake pedal travel of the target vehicle. The control parameters of the target vehicle at the next moment are determined based on the target control parameters, and the target vehicle is intelligently controlled based on the control parameters.

7. The method of claim 6, wherein, The step of determining the control parameters of the target vehicle at the next moment based on the target control parameters, and performing intelligent control of the target vehicle based on the control parameters, includes: The target control parameters, including the accelerator pedal travel of the target vehicle, are grouped and calculated, and the minimum value obtained from the group calculation is used 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; The target vehicle brake pedal travel, which is included in the target control parameters, is calculated in groups, and the maximum value obtained from the group calculation is used as the brake pedal travel control parameter of the target vehicle at the next moment. The obtained accelerator pedal travel control parameters and brake pedal travel control parameters are input into the intelligent driving actuator of the target vehicle to perform intelligent control of the target vehicle.

8. An intelligent vehicle control device, characterized by comprising: The device includes: The driving data acquisition module is used to acquire the historical driving data of the target vehicle and the 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. The segmentation module is used to determine the continuity factor of historical driving data at different times, 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 historical driving data at different times. The segment search module is used 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 based on the at least two target segment data, and to perform intelligent control of the target vehicle based on the control parameters.

9. An electronic device, comprising: The electronic device includes: 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, the computer program being executed by the at least one processor to enable the at least one processor to perform the intelligent driving vehicle control method according to any one of claims 1-7.

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

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