CN110850878B - Intelligent vehicle control method, device, equipment and medium - Google Patents
Intelligent vehicle control method, device, equipment and medium Download PDFInfo
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- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
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Abstract
The embodiment of the invention discloses an intelligent vehicle control method, device, equipment and medium. The method comprises the following steps: determining the length of a sampling road section in a completed path of the tracking path; average segmentation is carried out on the length of the sampling road section, and the average curvature of the sampling road section is determined according to the curvature of the sampling sub-road section obtained by the average segmentation; determining a pre-aiming distance in a tracking path according to the average curvature of the sampling road section and the vehicle speed; and determining control parameters of the intelligent vehicle in the tracking path according to the pre-aiming distance so as to effectively improve the running stability of the vehicle.
Description
Technical Field
The embodiment of the invention relates to an intelligent vehicle control technology, in particular to an intelligent vehicle control method, an intelligent vehicle control device, intelligent vehicle control equipment and an intelligent vehicle control medium.
Background
In recent years, with the advent of 5G communication, artificial intelligence, automobile drive-by-wire technology, chip integration, and high-performance sensors, intelligent vehicle technology has been rapidly developed.
At present, in the tracking algorithm of the intelligent vehicle, the selection of the pre-aiming distance directly relates to the tracking performance, and the problem that the vehicle is unstable in movement and even oscillates due to the fact that the pre-aiming distance is too short, and the problem that the vehicle is understeered at a large corner is caused due to the fact that the pre-aiming distance is too long.
Therefore, a method for accurately determining the pre-aiming distance is urgently needed, and the running stability of the vehicle can be effectively improved.
Disclosure of Invention
The invention provides an intelligent vehicle control method, device, equipment and medium, which are used for effectively improving the running stability of a vehicle.
In a first aspect, an embodiment of the present invention provides an intelligent vehicle control method, including:
determining the length of a sampling road section in a completed path of the tracking path;
average segmentation is carried out on the length of the sampling road section, and the average curvature of the sampling road section is determined according to the curvature of the sampling sub-road section obtained by the average segmentation;
determining a pre-aiming distance in a tracking path according to the average curvature of the sampling road section and the vehicle speed;
and determining control parameters of the intelligent vehicle in the tracking path according to the pre-aiming distance.
In a second aspect, an embodiment of the present invention further provides an intelligent vehicle control apparatus, including:
the sampling road section length determining module is used for determining the length of the sampling road section in the completed path of the tracking path;
the average curvature determining module is used for carrying out average segmentation on the length of the sampling road section and determining the average curvature of the sampling road section according to the curvature of the sampling sub-road section obtained by the average segmentation;
the pretightening distance determining module is used for determining the pretightening distance in the tracking path according to the average curvature of the sampling road section and the vehicle speed;
and the control parameter determining module is used for determining the control parameters of the intelligent vehicle in the tracking path according to the pre-aiming distance.
In a third aspect, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the program to implement the method for controlling an intelligent vehicle according to any one of the embodiments of the present invention.
In a fourth aspect, embodiments of the present invention further provide a computer-readable storage medium having stored thereon a computer program that, when executed by a processor, implements the intelligent vehicle control method according to any of the embodiments of the present invention.
The embodiment of the invention determines the length of the sampling road section in the completed path of the tracking path; average segmentation is carried out on the length of the sampling road section, and the average curvature of the sampling road section is determined according to the curvature of the sampling sub-road section obtained by the average segmentation; determining a pre-aiming distance in a tracking path according to the average curvature of the sampling road section and the vehicle speed; and determining control parameters of the intelligent vehicle in the tracking path according to the pre-aiming distance so as to effectively improve the running stability of the vehicle.
Drawings
Fig. 1 is a schematic flow chart of an intelligent vehicle control method according to an embodiment of the invention;
fig. 2a is a schematic flow chart of an intelligent vehicle control method according to a second embodiment of the present invention;
FIG. 2b is a schematic illustration of a geometry for determining intelligent vehicle control parameters according to a second embodiment of the present invention;
fig. 2c is a schematic structural diagram of a smoothing process for control parameters of an intelligent vehicle according to a second embodiment of the present invention;
FIG. 2d is a schematic diagram of a trace path result according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an intelligent vehicle control apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an apparatus according to a fourth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts steps as a sequential process, many of the steps may be implemented in parallel, concurrently, or with other steps. Furthermore, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Example 1
Fig. 1 is a flow chart of an intelligent vehicle control method according to an embodiment of the present invention, where the embodiment is applicable to a situation where an unmanned vehicle drives according to a preset track, the method may be performed by an intelligent vehicle control device, and the device may be implemented in a software and/or hardware manner and may be integrated in an electronic device, and specifically includes the following steps:
s110, determining the length of the sampling road section in the completed path of the tracking path.
In this embodiment, the tracking path refers to a track traveled by the intelligent vehicle, and the completed path refers to a track traveled by the intelligent vehicle in the tracking path. The intelligent vehicle can better run along the track by sampling the completed path.
In this technical scheme, optionally, the length of sampling highway section is according to intelligent vehicle's speed and brake time determination.
In this embodiment, the length of the sampling road section needs to consider the speed of the intelligent vehicle, and the greater the speed of the intelligent vehicle, the longer the length of the required sampling road section; the length of the sampling road section also needs to consider the braking time of the intelligent vehicle, and the longer the braking time of the intelligent vehicle is, the longer the length of the required sampling road section is.
In this technical solution, optionally, the step of determining the length of the sampling road section according to the speed and the braking time of the intelligent vehicle includes:
determining the initial length of a sampling road section according to the product of the speed of the intelligent vehicle and the braking time;
and determining the minimum length of the sampling road section according to the speed of the intelligent vehicle and the length of the body of the intelligent vehicle.
In this embodiment, by determining the initial length of the intelligent vehicle in the sampling section and further determining the minimum length of the sampling section in the initial length, the advantage of this arrangement is that the length of the sampling section can be ensured to be effective and safe even in the case of different intelligent vehicles and different speeds of the same intelligent vehicle.
Further, the initial length is determined using the following formula:
distance＝V·T S ；
wherein distance is the initial length, V is the speed of the intelligent vehicle, T S The braking time is;
the initial length is obtained by multiplying the speed of the intelligent vehicle and the braking time, and in actual operation, the speed of the intelligent vehicle and the braking time under the speed are recorded to obtain the initial length of the sampling road section.
The minimum length is determined using the following formula:
wherein distance1 is the minimum length, V thr2 >V thr1 ，D thr3 >D thr2 >D thr1 。
In this embodiment, a segment-by-segment speed sampling mode is used to obtain the minimum length of the sampling road section, where the minimum length of the sampling road section is related to the length of the body of the intelligent vehicle by the speed of the intelligent vehicle, and by way of example,
assuming that the body length of the intelligent vehicle is 1.5 meters, when V thr1 =10 km/h, at which point D thr1 Can be set to be 2 times of the length of the vehicle body, namely the minimum length of the sampling road section in the speed interval can not be less than 3 meters; when V is thr1 =20 km/h, at which point D thr2 Can be set to 6 m, D thr3 Can be set to 8 meters.
The minimum length setting of the sampling road section in each speed interval is mainly considered in combination with the minimum vehicle speed and the vehicle body length of the interval, and can be properly increased.
And S120, carrying out average segmentation on the length of the sampling road section, and determining the average curvature of the sampling road section according to the curvature of the sampling sub-road section obtained by the average segmentation.
After the length of the sampling road section is obtained, discretizing the length of the sampling road section, specifically, carrying out average segmentation on the length of the sampling road section, wherein the interval length of each segment is delta, the smaller the delta is, and the more accurate the average curvature of the sampling road section obtained by the last segmentation is.
Specifically, the number of sampling sub-segments can be obtained by the following formula:
and obtains the coordinates (x) of the ending end point of each sampling sub-section i ,y i )。
In this technical solution, optionally, determining the average curvature of the sampled road segment according to the curvature of the sampled sub-road segment obtained by the average segmentation includes:
the following formula is adopted for determination:
wherein K is r Is the average curvature of the sampled road segment, k i Is the curvature of the sampled sub-segment and n is the number of sampled sub-segments.
K r Reflecting the tortuous condition of the track in the sampling road section, K r The larger the track in the sampling road section is, the more tortuous the track is, K r Smaller means that the trajectory tends to be straight within the sampled road segment.
Further, k i The calculation process of (2) is as follows:
wherein L is di Representing the current position (x, y) of the intelligent vehicle and the coordinates (x) of the end points of the sampled sub-section i ,y i ) And the linear distance between the two is the included angle between the course angle of the intelligent vehicle and the end point of the sampling sub-road section.
S130, determining a pretightening distance in a tracking path according to the average curvature of the sampling road section and the vehicle speed.
In this embodiment, the pre-aiming distance is a pre-selected advance track of the intelligent vehicle when the intelligent vehicle runs on the sampling road section, and the more accurate the pre-aiming distance is, the higher the stability and the higher the safety of the intelligent vehicle when the intelligent vehicle runs on the sampling road section.
In this technical solution, optionally, determining the pre-aiming distance in the tracking path according to the average curvature of the sampled road section and the vehicle speed includes:
the following formula is adopted for determination:
wherein L is d And V is the vehicle speed, and is the pretightening distance.
And S140, determining control parameters of the intelligent vehicle in the tracking path according to the pre-aiming distance.
Finding a corresponding pretightening point on the tracking path according to the calculated pretightening distance, and calculating control parameters of the intelligent vehicle by using a pureburst tracking algorithm to realize tracking of the tracking path. Wherein the control parameters are the speed of the intelligent vehicle and the angular speed of the front wheels.
The embodiment of the invention determines the length of the sampling road section in the completed path of the tracking path; average segmentation is carried out on the length of the sampling road section, and the average curvature of the sampling road section is determined according to the curvature of the sampling sub-road section obtained by the average segmentation; determining a pre-aiming distance in a tracking path according to the average curvature of the sampling road section and the vehicle speed; and determining control parameters of the intelligent vehicle in the tracking path according to the pre-aiming distance so as to effectively improve the running stability of the vehicle.
Example two
Fig. 2a is a schematic flow chart of an intelligent vehicle control method according to a second embodiment of the present invention, where the present embodiment is applicable to a situation where an unmanned vehicle drives according to a preset track, and the method may be executed by an intelligent vehicle control device, and specifically includes the following steps:
s210, determining the length of the sampling road section in the completed path of the tracking path.
S220, carrying out average segmentation on the length of the sampling road section, and determining the average curvature of the sampling road section according to the curvature of the sampling sub-road section obtained by the average segmentation.
S230, determining a pretightening distance in a tracking path according to the average curvature of the sampling road section and the vehicle speed.
S240, determining control parameters of the intelligent vehicle in the tracking path according to the pre-aiming distance.
In this embodiment, a corresponding pretightening point on the tracking path is found according to the pretightening distance obtained by calculation, and the control parameter of the intelligent vehicle is calculated by using the pure public tracking algorithm, so as to realize trackingTracking of the trace path. In particular, reference may be made to a geometric schematic for determining control parameters of an intelligent vehicle as shown in fig. 2 b. Wherein (C) x ,C y ) Representing position coordinates of the intelligent vehicle, (G) x ,G y ) Representing the position coordinates of the pretightening point. L (L) d For the pretightening distance, R represents a radius of curvature of the tracking path, and specifically, the curvature of the tracking path can be calculated by the following formula:
k＝2·sinα/L d ；
k is the curvature of the tracking path.
According to the relation between the front wheel rotation angle and the curvature of the tracking path, the value of the front wheel rotation angle delta can be obtained as follows:
δ＝tan -1 (k·L)；
wherein L is the wheelbase of the vehicle.
The angular speed W corresponding to the front wheel rotation angle can be obtained according to the speed of the intelligent vehicle:
s250, performing smoothing processing on the control parameters.
In this embodiment, since the intelligent vehicle receives the lateral force during the turning movement, in order to ensure the smooth movement of the intelligent vehicle during the turning, the intelligent vehicle is prevented from turning on one's side, and the lateral acceleration during the movement of the intelligent vehicle is limited according to the parameters of the intelligent vehicle itself, so as to smooth the control parameters. For a specific process, see a schematic structural diagram of a smoothing process for the control parameters of the intelligent vehicle shown in fig. 2 c.
According to maximum speed V of intelligent vehicle max Minimum turning radius R min The maximum lateral acceleration Acc which can be born by the intelligent vehicle can be calculated max The calculation formula is as follows:
to ensure the smoothness of the intelligent vehicle turning, the maximum lateral acceleration threshold may be set to 0.5 acc max 。
According to the angular velocity W and the vehicle speed V corresponding to the front wheel steering angle, the current lateral acceleration value can be obtained, namely:
Acc＝V·W；
if the current lateral acceleration value is larger than the set threshold value, taking the current lateral acceleration value as a new threshold value, and obtaining the vehicle speed V according to the current lateral acceleration value and the angular speed W corresponding to the front wheel steering angle so as to ensure the stability and the comfort of the intelligent vehicle in the turning process.
In order to avoid the shake of the intelligent vehicle caused by abrupt change of control instructions, the control parameters are smoothed, specifically, the control parameters are filtered by adopting a low-pass filter in the system, and the calculation formula is as follows:
V com ＝α·V last +(1-α)·V cur ；
W com ＝β·W last +(1-β)·W cur ；
wherein V is com Is the vehicle speed after smoothing, V cur Is the current speed of the vehicle, V last Is the vehicle speed at the previous time. W (W) com Is the angular velocity after smoothing, W cur Is the current angular velocity, W last Is the angular velocity at the last moment.
Alpha and beta represent the proportional coefficients of the vehicle speed and the angular speed respectively, and the control parameters can be smoothed by adjusting the proportional coefficients to prevent the control command from changing too fast, and in general, the parameters of alpha and beta are 0.7-0.9. After smoothing the control parameters, a specific tracking path may be referred to as a tracking path result schematic shown in fig. 2 d.
According to the embodiment of the invention, the control parameters are smoothed, so that the intelligent vehicle shake caused by abrupt change of the control instructions can be avoided, and the running stability of the vehicle is further improved.
Example III
Fig. 3 is a schematic structural diagram of an intelligent vehicle control device according to a third embodiment of the present invention. The intelligent vehicle control device provided by the embodiment of the invention can execute the intelligent vehicle control method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. As shown in fig. 3, the apparatus includes:
a sampling segment length determining module 301, configured to determine a length of a sampling segment in a completed path of the tracking path;
the average curvature determining module 302 is configured to perform average segmentation on the length of the sampling road segment, and determine an average curvature of the sampling road segment according to the curvature of the sampling sub-road segment obtained by the average segmentation;
a pretightening distance determining module 303, configured to determine a pretightening distance in a tracking path according to an average curvature of the sampled road section and a vehicle speed;
and the control parameter determining module 304 is configured to determine a control parameter of the intelligent vehicle in the tracking path according to the pre-aiming distance.
The sampling road section length determination submodule is specifically used for: the length of the sampling road section is determined according to the speed and the braking time of the intelligent vehicle.
The sampling road section length determining submodule is specifically used for determining the initial length of the sampling road section according to the product of the speed of the intelligent vehicle and the braking time;
and determining the minimum length of the sampling road section according to the speed of the intelligent vehicle and the length of the body of the intelligent vehicle.
The sampling road section length determination submodule is specifically used for: the initial length is determined using the following formula:
distance＝V·T S ；
wherein distance is the initial length, V is the speed of the intelligent vehicle, T S The braking time is;
the minimum length is determined using the following formula:
wherein distance1 is the minimum length, V thr2 >V thr1 ，D thr3 >D thr2 >D thr1 。
The average curvature determination module 302 is configured to determine using the following formula:
wherein k is i Is the curvature of the sampled sub-segment and n is the number of sampled sub-segments.
The pretighting distance determining module 303 is configured to determine using the following formula:
wherein L is d And V is the vehicle speed, and is the pretightening distance.
The apparatus further comprises:
and the control parameter processing module is used for carrying out smoothing processing on the control parameters.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the above-described apparatus may refer to the corresponding process in the foregoing method embodiment, which is not repeated herein.
Example IV
Fig. 4 is a schematic structural diagram of an apparatus provided in a fourth embodiment of the present invention, and fig. 4 shows a schematic structural diagram of an exemplary apparatus suitable for implementing an embodiment of the present invention. The device 12 shown in fig. 4 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 4, device 12 is in the form of a general purpose computing device. Components of device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. Device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard disk drive"). Although not shown in fig. 4, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, implementing an intelligent vehicle control method provided by an embodiment of the present invention, including:
determining the length of a sampling road section in a completed path of the tracking path;
average segmentation is carried out on the length of the sampling road section, and the average curvature of the sampling road section is determined according to the curvature of the sampling sub-road section obtained by the average segmentation;
determining a pre-aiming distance in a tracking path according to the average curvature of the sampling road section and the vehicle speed;
and determining control parameters of the intelligent vehicle in the tracking path according to the pre-aiming distance.
Example five
A fifth embodiment of the present invention further provides a computer readable storage medium having stored thereon a computer program (or called computer executable instructions) that when executed by a processor implements a method for controlling an intelligent vehicle according to any of the above embodiments, including:
determining the length of a sampling road section in a completed path of the tracking path;
average segmentation is carried out on the length of the sampling road section, and the average curvature of the sampling road section is determined according to the curvature of the sampling sub-road section obtained by the average segmentation;
determining a pre-aiming distance in a tracking path according to the average curvature of the sampling road section and the vehicle speed;
and determining control parameters of the intelligent vehicle in the tracking path according to the pre-aiming distance.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (9)
1. An intelligent vehicle control method, characterized by comprising:
determining the length of a sampling road section in a completed path of the tracking path;
average segmentation is carried out on the length of the sampling road section, and the average curvature of the sampling road section is determined according to the curvature of the sampling sub-road section obtained by the average segmentation;
determining a pre-aiming distance in a tracking path according to the average curvature of the sampling road section and the vehicle speed;
determining control parameters of the intelligent vehicle in a tracking path according to the pre-aiming distance;
and determining the pretightening distance in the tracking path according to the average curvature of the sampling road section and the vehicle speed, wherein the pretightening distance is determined by adopting the following formula:
wherein L is d Is the pretightening distance, V is the vehicle speed, K r Is the average curvature of the sampled road segment.
2. The method of claim 1, wherein the length of the sampled road segments is determined based on a speed of the intelligent vehicle and a braking time.
3. The method of claim 2, wherein the step of determining the length of the sampling section according to the speed and the braking time of the intelligent vehicle comprises:
determining the initial length of a sampling road section according to the product of the speed of the intelligent vehicle and the braking time;
and determining the minimum length of the sampling road section according to the speed of the intelligent vehicle and the length of the body of the intelligent vehicle.
4. A method according to claim 3, comprising:
the initial length is determined using the following formula:
distance＝V·T S ；
wherein distance is the initial length, V is the speed of the intelligent vehicle, T S The braking time is;
the minimum length is determined using the following formula:
wherein distance1 is the minimum length, V thr2 ＞V thr1 ，D thr3 ＞D thr2 ＞D thr1 。
5. The method of claim 1, wherein determining the average curvature of the sampled road segments based on the curvature of the sampled sub-segments resulting from the average segmentation comprises:
the following formula is adopted for determination:
wherein K is r Is the average curvature of the sampled road segment, k i Is the curvature of the sampled sub-segment and n is the number of sampled sub-segments.
6. The method of claim 1, further comprising, after determining the control parameters of the intelligent vehicle in the tracking path based on the pre-sighting distance:
and smoothing the control parameters.
7. An intelligent vehicle control apparatus, comprising:
the sampling road section length determining module is used for determining the length of the sampling road section in the completed path of the tracking path;
the average curvature determining module is used for carrying out average segmentation on the length of the sampling road section and determining the average curvature of the sampling road section according to the curvature of the sampling sub-road section obtained by the average segmentation;
the pretightening distance determining module is used for determining the pretightening distance in the tracking path according to the average curvature of the sampling road section and the vehicle speed;
and determining the pretightening distance in the tracking path according to the average curvature of the sampling road section and the vehicle speed, wherein the pretightening distance is determined by adopting the following formula:
wherein L is d Is the pretightening distance, V is the vehicle speed, K r Is the average curvature of the sampled road segment;
and the control parameter determining module is used for determining the control parameters of the intelligent vehicle in the tracking path according to the pre-aiming distance.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the intelligent vehicle control method of any of claims 1-6 when the program is executed by the processor.
9. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when executed by a processor, implements the intelligent vehicle control method according to any one of claims 1-6.
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