CN116118780A - Vehicle obstacle avoidance track planning method, system, vehicle and storage medium - Google Patents
Vehicle obstacle avoidance track planning method, system, vehicle and storage medium Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/095—Predicting travel path or likelihood of collision
- B60W30/0953—Predicting travel path or likelihood of collision the prediction being responsive to vehicle dynamic parameters
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/095—Predicting travel path or likelihood of collision
- B60W30/0956—Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
- B60W40/105—Speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
- B60W60/0015—Planning or execution of driving tasks specially adapted for safety
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The invention relates to the technical field of automatic driving of vehicles and provides a vehicle obstacle avoidance track planning method, a system, a vehicle and a storage medium, wherein the method comprises the steps of obtaining coordinate point information of a lane line and coordinate point information of the lane center line according to vehicle positioning information; taking the central line of the lane where the host vehicle is positioned as a reference line, and converting the position, heading, speed and acceleration of the host vehicle into longitudinal position, transverse offset, transverse speed and longitudinal speed in a frenet coordinate systemLateral acceleration and longitudinal acceleration; taking the obstacle information, if an obstacle exists, converting the obstacle coordinate into S in a frenet coordinate system obs ,L obs If there are multiple obstacles, each obstacle coordinate is converted into S in the frenet coordinate system obsi ,L obsi Calculating a target point through dynamic programming by scattering point sampling; converting the target point into a coordinate in a natural coordinate system, and smoothing the coordinate by a smoothing algorithm to obtain a track point set; and fitting the track point set to generate a track equation. The invention has simple calculation process and lower cost.
Description
Technical Field
The invention relates to the technical field of automatic driving of vehicles, in particular to a vehicle obstacle avoidance track planning method, a vehicle obstacle avoidance track planning system, a vehicle and a storage medium.
Background
Along with the rise of artificial intelligence technology, the problem of motion track planning by taking an automatic driving vehicle as a research object is more and more emphasized, and obstacle avoidance track planning is a key part of the automatic driving vehicle and has great significance for the research of the automatic driving vehicle. In the running process of the automatic driving automobile, the intelligent automobile is accurate, safe and capable of avoiding obstacles in real time, so that the safety of the intelligent automobile can be improved, the traveling efficiency is improved to a certain extent, and the obstacle avoidance problem of the intelligent automobile becomes a research hot spot.
When channel changing planning is carried out, the current mainstream planning methods comprise a searching method and a sampling method; the patent with the application publication number of CN114194215A discloses an intelligent vehicle obstacle avoidance track planning method and system, a feasible track cluster is generated based on an initial return point of an operation stability limit and a polynomial curve according to road environment, obstacle information and current vehicle speed, track replacement in the feasible track cluster is subjected to pre-collision detection, and tracks which do not meet the pre-collision detection condition are screened and removed to obtain a collision-free track cluster; establishing a cost function of three evaluation indexes of comfort, obstacle avoidance efficiency and sideslip on the basis of a collision-free track cluster; and determining weights of three evaluation indexes by fuzzy reasoning, and searching out an optimal obstacle avoidance lane change return point by solving a multi-objective optimization problem, namely planning a final obstacle avoidance lane change track.
The technical scheme of the patent document can maximize performance indexes of the track such as comfort, obstacle avoidance efficiency and the like while taking the safety, the instantaneity and the curvature continuity into consideration, and effectively improve the riding experience of passengers; however, the evaluation indexes focus on indexes such as comfort, obstacle avoidance efficiency, sideslip and the like, and the method for avoiding the obstacle of the vehicle is complex in calculation, high in cost and difficult to commercially apply.
Disclosure of Invention
In view of this, an object of the embodiments of the present application is to provide a vehicle obstacle avoidance trajectory planning method, system, vehicle and storage medium, which can plan the vehicle obstacle avoidance trajectory more simply, and the whole calculation process is simple, the cost is lower, and the commercialized application is easy.
In order to achieve the technical purpose, the technical scheme adopted by the application is as follows:
in a first aspect, the present application provides a vehicle obstacle avoidance trajectory planning method, comprising the steps of,
s1, acquiring vehicle positioning information, and acquiring coordinate point information of a lane line natural coordinate system and coordinate point information of a lane center line natural coordinate system in a high-precision map according to the vehicle positioning information;
s2, taking the central line of the lane where the vehicle is located as a reference line, and converting the position, the course, the speed and the acceleration of the vehicle into a frame coordinate system S, a longitudinal position S, a transverse offset l and a transverse speed in l
Longitudinal speed l', lateral acceleration
And longitudinal acceleration l ";
s3, obtaining obstacle information, and if no obstacle exists, driving the vehicle according to the reference line; if there is an obstacle, converting the obstacle coordinates into S in a frenet coordinate system obs ,L obs If there are multiple obstacles, each obstacle coordinate is converted into S in the frenet coordinate system obsi ,L obsi Calculating a target point through dynamic programming by scattering point sampling;
s4, converting the calculated target point into a coordinate in a natural coordinate system through coordinate transformation, and then smoothing through a smoothing algorithm to obtain a track point set;
and S5, fitting the track point set to generate a track equation.
Further, the taking the lane center line of the lane where the host vehicle is located as a reference line includes: determining a lane where the vehicle is located according to the coordinates and the heading of the vehicle, and smoothing the center line of the lane where the vehicle is located to be used as the reference line; coordinates (x) of reference line points in a natural coordinate system i ,y i ) Conversion to(s) in the frenet coordinate system i ,l i )。
Further, the position, heading, speed and acceleration of the vehicle are converted into longitudinal position s, transverse offset l and transverse speed in a frame coordinate system s, l
Longitudinal speed l', lateral acceleration->
And the method of longitudinal acceleration l "comprises:
s＝s r
l'＝(1-k r l)tan(θ x -θ r )
the subscript x represents the vehicle, the subscript r represents a projection point of the vehicle on a reference line, k represents a curvature, θ represents a heading angle, v represents a speed, and a represents an acceleration.
Further, the calculating the target point via dynamic programming by scatter sampling includes: the longitudinal position, the transverse deviation and the transverse and longitudinal speed of the two sampling points are used for obtaining a penta-order polynomial coefficient connecting the two sampling points through the transverse and longitudinal acceleration, and then obtaining a cost function of a penta-order polynomial curve between the two sampling points; the cost function is the sum of the distance cost, the transverse speed cost, the transverse acceleration cost, the transverse jerk cost, the distance cost, the transverse speed cost, the transverse acceleration cost, the risk cost of any one of the transverse jerk cost and the static obstacle, which deviate from the reference line.
Further, the risk cost of the static obstacle is inversely related to the distance between the vehicle and the static obstacle, and when the distance between the vehicle and the static obstacle is smaller than limit, collision is considered, and the cost function is set to be infinite; when the distance from the host vehicle to the static obstacle is larger than limit, collision is avoided, the cost function is 0, and when the distance from the host vehicle to the static obstacle is larger than limit and smaller than limit, the cost function is inversely related to the distance from the host vehicle to the static obstacle; where limit represents a lower judgment threshold value at which the risk is considered to exist, and limit represents an upper judgment threshold value at which the risk is considered to exist.
Further, the transforming the calculated target point into coordinates in a natural coordinate system through coordinate transformation, and then smoothing the target point through a smoothing algorithm to obtain a track point set includes: converting the target point calculated under the frenet coordinate system into coordinates in the natural coordinate system through coordinate transformation:
x x ＝x r -lsin(θ r )
y x ＝y r +lcos(θ r )
wherein, the subscript x represents a sampling point, the subscript r represents a projection point of the sampling point on a reference line, and θ represents a course angle;
and then smoothing by a smoothing algorithm to obtain a track point set.
Further, the fitting the set of trajectory points to generate a trajectory equation includes: and fitting the smoothed track point set by using a least square method to generate a polynomial track equation of 3 times.
In a second aspect, the invention also discloses an automatic driving system, and the automatic driving system uses the vehicle obstacle avoidance track planning method.
In a third aspect, the invention also discloses a vehicle, which comprises a vehicle body and the automatic driving system, wherein the automatic driving system is mounted on the vehicle body.
In a fourth aspect, the present invention also discloses a computer readable storage medium, in which a computer program is stored, which when run on a computer causes the computer to perform the above-mentioned method.
The invention adopting the technical scheme has the following advantages:
the invention uses the lane information and the lane central line information of the high-precision map; establishing a frenet coordinate system by taking the smoothed central line as a reference line, projecting a static obstacle into the frenet coordinate system, and calculating a proper sampling point through dynamic programming after sampling a scattering point; converting the coordinates of the sampling points selected under the frenet coordinate system into a natural coordinate system and performing smoothing treatment; and finally, fitting the smoothed sampling points by a least square method to generate a reference track equation, wherein the whole calculation process is simple, the cost is lower, and the method is easy to apply commercially.
Drawings
The present application may be further illustrated by the non-limiting examples given in the accompanying drawings. It is to be understood that the following drawings illustrate only certain embodiments of the present application and are therefore not to be considered limiting of its scope, for the person of ordinary skill in the art may derive other relevant drawings from the drawings without inventive effort.
FIG. 1 is a flow chart showing steps of a vehicle obstacle avoidance trajectory planning method according to the present invention.
Fig. 2 is a schematic view of scattering point sampling in an embodiment of the invention.
Detailed Description
The present application will be described in detail below with reference to the drawings and the specific embodiments, and it should be noted that in the drawings or the description of the specification, similar or identical parts use the same reference numerals, and implementations not shown or described in the drawings are in forms known to those of ordinary skill in the art. In the description of the present application, the terms "first," "second," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
Example 1,
The embodiment is a vehicle obstacle avoidance track planning method, as shown in fig. 1, including the steps of:
s1, acquiring vehicle positioning information, and acquiring coordinate point information of a lane line natural coordinate system and coordinate point information of a lane center line natural coordinate system in a high-precision map according to the vehicle positioning information.
S2, determining a lane where the vehicle is located according to the coordinates and the heading of the vehicle, and smoothing the center line of the lane to serve as a reference line. Coordinates (x) of reference line points in a natural coordinate system i ,y i ) Conversion to(s) in the frenet coordinate system i ,l i )。
s 1 ＝0；l i ＝0,i＝1,2,…
Since the frenet coordinate system is established based on the reference line, the lateral offset l of the reference line point i All 0.
The position, heading, speed and acceleration of the vehicle are converted into longitudinal position (S), transverse offset (L) and transverse and longitudinal speed in a frenet coordinate system (S, L)
l') transverse and longitudinal acceleration (>
l”)。
s＝s r
l'＝(1-k r l)tan(θ x -θ r )
The subscript x represents the vehicle, the subscript r represents a projection point of the vehicle on a reference line, k represents a curvature, θ represents a heading angle, v represents a speed, and a represents an acceleration.
S3, obtaining obstacle information according to the sensor data. When no obstacle exists, the vehicle runs according to the reference line; converting the obstacle coordinates into S in the frenet coordinate system when the obstacle exists obs ,L obs . If there are multiple obstacles, each obstacle coordinate is converted into S in the frenet coordinate system obsi ,L obsi . The target point is calculated via dynamic programming from the scatter sampling of the reference line, as shown in fig. 2. The curve in the figure represents a reference line, the points A are all sampling points, the star-shaped points pointed by B represent the positions of the obstacles, and finally the point A selected by the point C pointed circle is the final calculated target sampling point. During dynamic programming calculation, the longitudinal position, the transverse offset and the transverse and longitudinal speed of the two sampling points are used for obtaining the penta polynomial coefficient connecting the two sampling points, and then the cost function of the penta polynomial curve between the two sampling points can be obtained. The cost function may be expressed as a deviation from the reference lineDistance cost (l), lateral velocity cost (l '), lateral acceleration cost (l "), lateral jerk cost (l'") and risk cost sum to static obstacles. Wherein the risk cost of a static obstacle is inversely related to the distance of the static obstacle. For example:
when the distance from the obstacle is smaller than limit, the collision is considered, the cost function can be set to infinity, and in actual operation, the cost function can be set to a very large value. And when the distance from the obstacle is larger than limit, the collision is not considered, and the cost function is 0. The cost is inversely related to the distance when there is a distance between the two, where limit represents the lower threshold of judgment that a hazard is considered to exist and limit represents the upper threshold of judgment that a hazard is considered to exist.
S4, converting the target point (shown in fig. 2) calculated under the frenet coordinate system into coordinates in a natural coordinate system through coordinate transformation.
x x ＝x r -lsin(θ r )
y x ＝y r +lcos(θ r )
The subscript x in the formula represents a sampling point, and the subscript r represents a projection point of the sampling point on a reference line. And then smoothing by a smoothing algorithm to obtain a track point set, wherein in actual calculation, the smoothing algorithm can use methods such as quadratic programming, B-spline and the like.
And S5, finally fitting the smoothed track point set by using a least square method to generate a polynomial track equation of 3 times.
EXAMPLE 2,
The present embodiment is an automatic driving system, which uses the vehicle obstacle avoidance trajectory planning method of embodiment 1. The automatic driving system of the embodiment uses the lane information and the lane center line information of the high-precision map; establishing a frenet coordinate system by taking the smoothed central line as a reference line, projecting a static obstacle into the frenet coordinate system, and calculating a proper sampling point through dynamic programming after sampling a scattering point; converting the coordinates of the sampling points selected under the frenet coordinate system into a natural coordinate system and performing smoothing treatment; and finally, fitting the smoothed sampling points by a least square method to generate a reference track equation, wherein the whole calculation process is simple, the cost is lower, and the method is easy to apply commercially.
EXAMPLE 3,
The present embodiment is a vehicle including a vehicle body and the automated driving system of embodiment 2, the automated driving system being mounted on the vehicle.
EXAMPLE 4,
The present embodiment is a computer-readable storage medium having a computer program stored therein, which when run on a computer causes the computer to perform the above-described method. From the foregoing description of the embodiments, it will be apparent to those skilled in the art that the present application may be implemented in hardware, or by means of software plus a necessary general hardware platform, and based on this understanding, the technical solution of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disc, a mobile hard disk, etc.), and includes several instructions for causing a computer device (may be a personal computer, a brake device, or a network device, etc.) to perform the methods described in the various implementation scenarios of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus, system, and method may be implemented in other manners as well. The above-described apparatus, systems, and method embodiments are merely illustrative, for example, flow charts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. In addition, the functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The foregoing is merely exemplary embodiments of the present application and is not intended to limit the scope of the present application, and various modifications and variations may be suggested to one skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (10)
1. A vehicle obstacle avoidance trajectory planning method is characterized in that: comprises the steps of,
s1, acquiring vehicle positioning information, and acquiring coordinate point information of a lane line natural coordinate system and coordinate point information of a lane center line natural coordinate system in a high-precision map according to the vehicle positioning information;
s2, taking the central line of the lane where the vehicle is located as a reference line, and converting the position, the course, the speed and the acceleration of the vehicle into a frame coordinate system S, a longitudinal position S, a transverse offset l and a transverse speed in l
Longitudinal speed l', lateral acceleration->
And longitudinal acceleration l ";
s3, obtaining obstacle information, and if no obstacle exists, driving the vehicle according to the reference line; if there is an obstacle, converting the obstacle coordinates into S in the frenet coordinate system obs ,L obs If there are multiple obstacles, each obstacle coordinate is converted into S in the frenet coordinate system obsi ,L obsi Calculating a target point through dynamic programming by scattering point sampling;
s4, converting the calculated target point into a coordinate in a natural coordinate system through coordinate transformation, and then smoothing through a smoothing algorithm to obtain a track point set;
and S5, fitting the track point set to generate a track equation.
2. The vehicle obstacle avoidance trajectory planning method of claim 1, wherein: the method for using the lane center line of the lane where the vehicle is located as a reference line comprises the following steps: determining a lane where the vehicle is located according to the coordinates and the heading of the vehicle, and smoothing the center line of the lane where the vehicle is located to be used as the reference line; coordinates (x) of reference line points in a natural coordinate system i ,y i ) Conversion to(s) in the frenet coordinate system i ,l i )。
3. The vehicle obstacle avoidance trajectory planning method of claim 2, wherein: the position, heading, speed and acceleration of the vehicle are converted into a longitudinal position s, a transverse offset l and a transverse speed in a frenet coordinate system s, l
Longitudinal speed l', lateral acceleration->
And a longitudinal acceleration l ", comprising:
s＝s r
l'＝(1-k r l)tan(θ x -θ r )
the subscript x represents the vehicle, the subscript r represents a projection point of the vehicle on a reference line, k represents a curvature, θ represents a heading angle, v represents a speed, and a represents an acceleration.
4. A vehicle obstacle avoidance trajectory planning method according to claim 3, wherein: the calculating the target point through the dynamic programming by scattering point sampling comprises the following steps:
the longitudinal position, the transverse deviation and the transverse and longitudinal speed of the two sampling points are used for obtaining a penta-order polynomial coefficient connecting the two sampling points through the transverse and longitudinal acceleration, and then obtaining a cost function of a penta-order polynomial curve between the two sampling points; the cost function is the sum of the distance cost, the transverse speed cost, the transverse acceleration cost, the transverse jerk cost, the distance cost, the transverse speed cost, the transverse acceleration cost, the risk cost of any one of the transverse jerk cost and the static obstacle, which deviate from the reference line.
5. The vehicle obstacle avoidance trajectory planning method of claim 4, wherein: the risk cost of the static obstacle is inversely related to the distance between the static obstacle and the vehicle, and when the distance between the vehicle and the static obstacle is smaller than limit, collision is considered, and the cost function is set to be infinite; when the distance from the host vehicle to the static obstacle is larger than limit, collision is avoided, the cost function is 0, and when the distance from the host vehicle to the static obstacle is larger than limit and smaller than limit, the cost function is inversely related to the distance from the host vehicle to the static obstacle; where limit represents a lower judgment threshold value at which the risk is considered to exist, and limit represents an upper judgment threshold value at which the risk is considered to exist.
6. The vehicle obstacle avoidance trajectory planning method of claim 5, wherein: the step of converting the calculated target point into coordinates in a natural coordinate system through coordinate transformation and then smoothing the target point through a smoothing algorithm to obtain a track point set, which comprises the following steps:
converting the target point calculated under the frenet coordinate system into coordinates in the natural coordinate system through coordinate transformation:
x x ＝x r -lsin(θ r )
y x ＝y r +lcos(θ r )
wherein, the subscript x represents a sampling point, the subscript r represents a projection point of the sampling point on a reference line, and θ represents a course angle;
and then smoothing by a smoothing algorithm to obtain a track point set.
7. The vehicle obstacle avoidance trajectory planning method of claim 5, wherein: fitting the track point set to generate a track equation, wherein the track equation comprises the following steps:
and fitting the smoothed track point set by using a least square method to generate a polynomial track equation of 3 times.
8. An autopilot system characterized by: the automatic driving system uses a vehicle obstacle avoidance trajectory planning method according to any one of claims 1 to 7.
9. A vehicle, characterized in that: the vehicle includes a vehicle body and the autopilot system of claim 8, the autopilot system being mounted on the vehicle body.
10. A computer-readable storage medium, characterized by: the computer readable storage medium has stored therein a computer program which, when run on a computer, causes the computer to perform a vehicle obstacle avoidance trajectory planning method as claimed in any one of claims 1 to 7.
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| CN116424319A (en) * | 2023-06-12 | 2023-07-14 | 上海鉴智其迹科技有限公司 | Vehicle control method and device, electronic equipment and computer storage medium |
| CN116424319B (en) * | 2023-06-12 | 2023-08-29 | 上海鉴智其迹科技有限公司 | Vehicle control method and device, electronic equipment and computer storage medium |
| CN117341683A (en) * | 2023-12-04 | 2024-01-05 | 苏州观瑞汽车技术有限公司 | Vehicle dynamic track fitting obstacle avoidance method and system based on multi-target recognition |
| CN117341683B (en) * | 2023-12-04 | 2024-04-23 | 苏州观瑞汽车技术有限公司 | Vehicle dynamic track fitting obstacle avoidance method and system based on multi-target recognition |
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