CN110244713B - Intelligent vehicle lane change track planning system and method based on artificial potential field method - Google Patents
Intelligent vehicle lane change track planning system and method based on artificial potential field method Download PDFInfo
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- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- 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
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
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- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
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- G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- 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
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/0278—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using satellite positioning signals, e.g. GPS
Abstract
The invention discloses an intelligent vehicle lane change track planning system and method based on an artificial potential field method, wherein a computing unit calculates a repulsive field of a front obstacle vehicle and a maximum transverse safety acceleration of the intelligent vehicle, a decision unit carries out macroscopic decision to obtain an action instruction of the intelligent vehicle for straight running or lane change, the action instruction, the repulsive field and the current vehicle speed are sent to a path planning module, the path planning module carries out path planning to obtain path point coordinates and sends the path point coordinates to a speed planning module and a vehicle control module, the speed planning module carries out speed planning to obtain the maximum safety speed at each path point coordinate, and the vehicle control module controls the intelligent vehicle to run on the planned track according to the planned path point coordinates and the maximum safety speed; the invention uses the obstacle repulsive force field model in the artificial potential field method to consider the constraint of the maximum transverse safe acceleration and the road curvature, so that the planned path is safer and more reliable.
Description
Technical Field
The invention relates to an intelligent vehicle lane change track planning method, in particular to an intelligent vehicle lane change track planning system and method based on an artificial potential field method.
Background
Improper lane changing of automobiles is one of the important causes of road traffic accidents. The intelligent vehicle is a comprehensive system integrating the functions of environment sensing, planning decision, multi-level auxiliary driving and the like, and is necessary to research a safer intelligent vehicle obstacle avoidance and lane change method in order to ensure that the intelligent vehicle can normally run in an actual traffic environment, reduce traffic accidents caused by wrong driving behaviors of a driver and improve the active safety of the vehicle. Aiming at the problem, a local track planning method and a local track planning device for an intelligent vehicle are disclosed in a document with a Chinese patent publication number of CN106114507A, the method comprises the steps of firstly obtaining path planning information from a starting point to a target point, then sampling from alternative curves from the current position of the intelligent vehicle to the target point, then carrying out speed planning on each alternative curve to obtain alternative tracks, and finally selecting an optimal track as a target track according to a certain evaluation standard, wherein the method does not consider the influence of a front obstacle vehicle on the safety of the intelligent vehicle when carrying out path planning, so that the planned path has potential safety hazards. In addition, as disclosed in the document of Chinese patent publication No. CN102609765A, an autonomous lane change motion planning method for an intelligent vehicle based on a neural network is disclosed, the method firstly carries out geometric package on a lane change vehicle and an obstacle, establishes a lane change path model taking time as an independent variable, then obtains boundary conditions of the lane change vehicle by utilizing the dynamic neural network, calculates a lane change path set under a specific boundary condition by combining a polynomial method, finally defines an index function for evaluating the performance quality of the lane change path, screens out an optimal path from the generated lane change paths according to the index function and is applied to an actual lane change process of the vehicle, and the method can improve the performance index of the efficiency path for generating the lane change path of the vehicle, but does not consider the limitation of the maximum transverse safety acceleration of the intelligent vehicle and the constraint of the lane curvature on the speed in the lane change process, so that the track planned by the method has poor stability and executable performance.
The artificial potential field method (Artifical Potential Field) is a virtual force field method, and the basic idea is to virtualize the working environment of the robot into a place where a force field exists, divide the force field into a gravitational field and a repulsive force field, and the force field generated by the target point is the gravitational field, and the force is increased as the distance between the target point and the robot is reduced. The force field generated by the obstacle is a repulsive force field, the distance between the random robot and the obstacle is reduced and increased, and the whole potential field is formed by superposing vectors of the gravitational field and the repulsive force field. The motion of the robot is controlled by resultant force generated by attraction and repulsion, so that the obstacle is avoided to reach the target point to find a path.
Disclosure of Invention
The invention aims to solve the problems of stability and potential safety hazards existing in the conventional intelligent vehicle lane change path planning, and provides an intelligent vehicle track planning system based on an artificial potential field method and a track planning method thereof, wherein the intelligent vehicle track planning system can meet the requirement of vehicle stability during lane change of an intelligent vehicle and can effectively avoid obstacles.
In order to achieve the above purpose, the intelligent vehicle lane change track planning system based on the artificial potential field method adopts the following technical scheme: the intelligent vehicle navigation system comprises a map data downloading module, a GPS/INS (Global positioning System/inertial navigation System) position locating module, a radar monitoring module, a vehicle parameter reading module and a vehicle control module, wherein the map data downloading module acquires global path information I of an intelligent vehicle and sends the global path information I to a computing unit, and the GPS/INS position locating module acquires the shortest distance rho between the intelligent vehicle and a front obstacle vehicle ob Intelligent acquisitionRoll angle of vehicle
And course angle theta information, and send the information to a computing unit, a radar monitoring module acquires obstacle information Oi around the intelligent vehicle and sends the obstacle information Oi to a decision unit, a vehicle parameter reading module reads the current speed v of the vehicle and the vehicle self weight m and sends the current speed v and the vehicle self weight m to the computing unit, and the computing unit calculates a repulsive force field U of the obstacle vehicle ahead and the maximum lateral safety acceleration a of the intelligent vehicle s Transmitting the repulsive field U and the current speed v information of the intelligent vehicle to a decision unit, and transmitting the maximum transverse safe acceleration a s And the current speed v information is sent to a speed planning module, a decision unit carries out macroscopic decision to obtain an action command e of the intelligent vehicle for straight running or lane changing, the action command e, a repulsive force field U and the current speed v are sent to a path planning module, and the path planning module carries out path planning to obtain a path point coordinate (x i ,y i ) And sending the speed planning result to a speed planning module and a vehicle control module, wherein the speed planning module performs speed planning to obtain each path point (x) i ,y i ) Maximum safe speed v (x i ) max And sent to the vehicle control module.
The track planning method of the intelligent vehicle track-changing track planning system adopts the technical scheme that the track planning method comprises the following steps:
step 1) the calculation unit calculates the minimum distance ρ ob And the current velocity v to calculate the repulsive force field
Transmitting the repulsive field U and the current speed v to a decision unit, wherein eta is a repulsive field coefficient and d 0 The minimum safety distance between a rear vehicle and a front vehicle of a vehicle running on the same lane specified in the road traffic safety law is kept, and k is a speed coefficient;
step 2) the calculation unit is according to a s ＝F f Calculating the maximum lateral safety acceleration a by/m s And send it to the speed planning module together with the current speed v, F f Friction force applied to the intelligent vehicle;
step 3) the decision unit decides the vehicle speed according to the repulsive field U, the current vehicle speed v and the obstacle information O i Macroscopic decision is carried out, when the intelligent vehicle is out of the repulsive force field U range of the front obstacle vehicle, the decision result is straight, and when the intelligent vehicle is in the repulsive force field U range of the front obstacle vehicle, the decision result is lane change;
step 4) when the action command e is channel change, the path planning module firstly calculates the pretightening distance L kv Determining the coordinate P of the pre-aiming point r (x r ,y r ) The coordinate P from the current position of the intelligent vehicle to the pre-aiming point is planned r (x r ,y r ) To obtain the coordinates (x) of the path point on the trajectory curve i ,y i )；
Step 5) the speed planning module is according to
Obtaining coordinates (x i ,y i ) Maximum safe speed v (x i ) max ，κ(x i ) Curvature at each location point on the trajectory curve;
step 6) the vehicle control module calculates the coordinates (x) of the planned route points i ,y i ) And a maximum safe speed v (x i ) max And controlling the intelligent vehicle to run on the planned track.
The invention has the beneficial effects after adopting the technical scheme that:
1. when the path planning is carried out, the obstacle repulsive force field model in the artificial potential field method is used for reference, and the repulsive force field generated by the obstacle vehicles in front is used as a part of the objective function, so that the planned path is safer and more reliable.
2. When the intelligent vehicle performs speed planning, the constraint of the maximum transverse safe acceleration and the road curvature is considered, the executable of the lane change track is improved, and the intelligent vehicle is enabled to be more stable in the running process.
Drawings
FIG. 1 is a block diagram of an intelligent vehicle lane change trajectory planning system based on an artificial potential field method;
FIG. 2 is a flow chart of a track planning method of the intelligent vehicle lane change track planning system shown in FIG. 1;
FIG. 3 is a schematic diagram of the path plan of FIG. 2;
fig. 4 is a schematic diagram of the speed plan of fig. 2.
Detailed Description
As shown in FIG. 1, the intelligent vehicle lane change track planning system based on the artificial potential field method is composed of a map data downloading module, a GPS/INS (Global positioning System/inertial navigation System) position locating module, a radar monitoring module, a vehicle parameter reading module, an information processing module, a path planning module, a speed planning module and a vehicle control module, wherein the information processing module comprises a computing unit and a decision unit, and the computing unit and the decision unit are connected in series. The output ends of the map data downloading module, the GPS/INS position positioning module, the radar monitoring module and the vehicle parameter reading module are connected with the input end of the calculating unit, the output end of the calculating unit is respectively connected with the input ends of the decision unit and the speed planning module, the output end of the decision unit is connected with the input end of the path planning module, the output end of the path planning module is respectively connected with the input ends of the speed planning module and the vehicle control module, and the output end of the speed planning module is connected with the input end of the vehicle control module.
The map data downloading module is used for acquiring the current position p of the intelligent vehicle s To the target point p g And sends the information to a calculation unit in the information processing module.
The GPS/INS position locating module collects the shortest distance rho between the intelligent vehicle and the obstacle vehicle in front ob Collecting the roll angle of an intelligent vehicle
And heading angle theta and the like, and sends the information to a computing unit in the information processing module.
The radar monitoring module is mainly based on a laser radar, a millimeter wave radar and a CCD industrial camera which are arranged on the intelligent vehicle, acquires obstacle information Oi around the intelligent vehicle, and sends the information to a decision unit in the information processing module.
The vehicle parameter reading module mainly reads the current speed v of the vehicle and the dead weight m of the vehicle from the CAN bus of the intelligent vehicle and sends the information to the calculating unit in the information processing module.
The computing unit in the information processing module obtains the shortest distance rho according to the global path information I, GPS/INS position locating module received from the map downloading module ob Roll angle
The course angle theta, the speed v and the dead weight m of the intelligent vehicle obtained from the CAN bus by the vehicle parameter reading module, and the repulsive force field U of the front obstacle vehicle and the maximum transverse safety acceleration a of the intelligent vehicle are calculated s Transmitting the repulsive force field U and the current speed v information of the intelligent vehicle to a decision unit, and transmitting the maximum transverse safe acceleration a s And the current speed v information of the intelligent vehicle is sent to the speed planning module.
The decision unit is used for deciding whether the intelligent vehicle is in a collision state or not according to the repulsive force field U, the current speed v and the obstacle information O i And carrying out macroscopic decision to obtain a well-decided action instruction e, wherein the action instruction e is an instruction for controlling the intelligent vehicle to move straight or change lanes. The decision unit sends the decided action command e, the repulsive force field U and the current speed v information of the intelligent vehicle to the path planning module.
When the path planning module receives a channel changing action instruction e, the pre-aiming distance L is calculated first kv Further obtain the coordinate (x) r ,y r ) Then, path planning is performed, and a series of planned path points (x i ,y i ) And the data are sent to a speed planning module and a vehicle control module.
The speed planning module calculates a speed of the vehicle based on the series of path points (x i ,y i ) And the intelligent vehicle current speed v and the maximum lateral safety acceleration a received from the calculation unit s Speed gaugeDividing to obtain each path point (x i ,y i ) Maximum safe speed v (x i ) max And the vehicle is transmitted to a vehicle control module, and finally the vehicle control module controls the vehicle to run on the planned track.
Referring to fig. 2, the intelligent vehicle lane change track planning system based on the artificial potential field method of the present invention performs track planning, and is divided into an information sensing link, a path planning link, a speed planning link and a vehicle control link. The method comprises the following specific steps:
information perception and processing links:
step 1: the map data downloading module downloads the current position p of the intelligent vehicle t To the target point p g The global path information I of (a) is sent in real time to a calculation unit in the information processing module.
Step 2: the GPS/INS position locating module is used for locating the shortest distance rho between the intelligent vehicle and the obstacle vehicle in front ob Intelligent vehicle roll angle
And information such as the heading angle theta is sent to a calculation unit in the information processing module in real time.
Step 3: the radar monitoring module monitors obstacle information O around the intelligent vehicle i To a decision unit in the information processing module.
Step 4: the vehicle parameter reading module reads the current speed v of the vehicle and the dead weight m of the vehicle from the intelligent vehicle CAN bus and sends the current speed v and the dead weight m of the vehicle to the calculating unit in the information processing module.
Step 5: the calculation unit in the information processing module calculates the shortest distance ρ according to the received shortest distance ρ from the GPS/INS position location module ob And the velocity v to calculate the repulsive field U and send the value of the repulsive field U to the decision unit together with the current velocity v. The method for calculating the repulsive field U comprises the following steps:
wherein: η is the repulsive force field coefficient; d, d 0 The minimum safety distance between the rear vehicle and the front vehicle of the vehicle running on the same lane specified in the road traffic safety law is required to be kept; k is a speed coefficient, and the value of k is related to the road surface material, and k=0.7 when the intelligent vehicle runs on the cement road, and k=0.6 when the intelligent vehicle runs on the asphalt road.
Step 6: the calculating unit in the information processing module calculates the maximum lateral safety acceleration a according to the dead weight m of the intelligent vehicle received from the vehicle parameter reading module s And sends it to the speed planning module along with the speed v received from the vehicle parameter reading module, wherein the maximum lateral safe acceleration a s The calculation is performed according to the following formula:
a s ＝F f /m，
wherein F is f The friction force applied to the intelligent vehicle is calculated, and m is the dead weight of the vehicle.
Step 7: the decision unit in the information processing module is used for determining the current speed v of the intelligent vehicle and the obstacle information O received from the radar monitoring module according to the repulsive force field U received from the calculation unit i And carrying out macroscopic decision, and sending a decided action instruction e (straight going or lane changing), a front obstacle vehicle repulsive field U and the current speed v of the intelligent vehicle to a path planning module. The specific decision method is as follows: as shown in fig. 3, the intelligent vehicle and the obstacle vehicle both travel on the same side of the lane boundary, and the intelligent vehicle is located behind the obstacle vehicle, the obstacle vehicle generates a potential field, when the intelligent vehicle is out of the repulsive field U of the obstacle vehicle ahead, the decision result is straight, and when the intelligent vehicle is in the repulsive field U of the obstacle vehicle ahead, the decision result is lane change.
Path planning link:
step 8: as shown in fig. 3, a coordinate system is established with the current position of the intelligent vehicle as the origin O, the forward direction as the abscissa X direction, and the road width direction as the Y direction, and the current position of the intelligent vehicle (X 0 ,y 0 ). The elliptic area in front of the intelligent vehicle is a virtual repulsive force field U generated by an obstacle vehicle, and the length A direction of the major axis of the ellipse is the obstacleThe length A is the minimum safety distance d 0 Twice as many as (2); the length B of the minor axis of the ellipse is the width omega of the current driving lane of the intelligent vehicle c . When the intelligent vehicle is in lane changing and overtaking, once the intelligent vehicle enters the repulsive force field U action area of the obstacle vehicle, the intelligent vehicle can be subjected to repulsive force action generated by the virtual repulsive force field U of the obstacle vehicle, and the nearer the distance between the intelligent vehicle and the obstacle vehicle is, the greater the speed is, the greater the repulsive force field action to which the intelligent vehicle is subjected is.
When the action command e sent by the decision unit is channel change, the path planning module performs path planning according to the received repulsive force field U and the current speed v of the intelligent vehicle. The specific planning method comprises the following steps:
first, the pretightening distance L is calculated according to the following formula kv ：
Wherein v is c Minimum lane change speed for the intelligent vehicle pre-stored in the path planning module; k is a speed coefficient, the value of k is related to the road surface material, k=0.7 when the intelligent vehicle runs on the cement road, and k=0.6 when the intelligent vehicle runs on the asphalt road.
Further determining the coordinate P of the pre-aiming point r (x r ,y r ) Wherein x is r ＝ω c ，y r ＝L kv 。
A fourth order polynomial is then designed to map out the current position (x 0 ,y 0 ) To the pretightening point coordinate P r (x r ,y r ) The equation for the trajectory curve is set as:
y＝a 4 x 4 +a 3 x 3 +a 2 x 2 +a 1 x+a 0 ，
wherein a is 1 ,a 2 ,a 3 ,a 4 Coefficients for each term of the equation.
In order to ensure that the planned path can safely avoid an obstacle, and meanwhile, the curvature of a curve and the change rate of the curve are as small as possible, so that the vehicle has better stability when driving on the planned path, and an objective function for optimizing and solving is selected to plan the path:
kappa (x) is the curvature of the planned trajectory curve at the path point (x, y),
is the curvature change rate of the track curve at the path point (x, y), U (x) is the repulsive force field, omega, suffered by the intelligent vehicle 1 、ω 2 And omega 3 All are weight coefficients, and the calculation formula of the curvature of the track curve is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
refers to the first derivative of the trajectory equation y at the path point (x, y), +.>
Refers to the second derivative of the trajectory equation y at the path point (x, y).
Thereby obtaining each coefficient a in the track curve equation 1 ,a 2 ,a 3 ,a 4 The method comprises the following steps of: a, a 4 ＝J 4 ，a 3 ＝J 3 ，a 2 ＝J 2 ，a 1 ＝J 1 ,a 0 ＝J 0 The method comprises the steps of carrying out a first treatment on the surface of the Wherein J i (i=0, 1,2,3, 4) is the current objective function
And obtaining the coefficient corresponding to each term in the track curve equation when the minimum value is obtained. The trajectory equation that is solved is:
y＝J 4 x 4 +J 3 x 3 +J 2 x 2 +J 1 x+J 0 。
finally, the path planning module calculates the planned path information (i.e., a series of path point coordinates (x i ,y i ) Respectively to the speed planning module and the vehicle control module.
And (3) a speed planning step:
step 9: the speed planning module calculates the coordinates (x) of the route point for each position based on the information received from step 7 and step 8 i ,y i ) Maximum safe speed v (x i ) max And sends the actual time to the vehicle control module. The specific planning method comprises the following steps:
the speed planning module receives a series of path point coordinates (x) from the path planning module that have been planned to complete i ,y i ) And the current speed v and the maximum lateral acceleration a of the intelligent vehicle sent by the computing unit in the information processing module s The speed planning is performed and the speed of the vehicle is calculated,
wherein each waypoint (x i ,y i ) Maximum safe speed v (x i ) max Can be obtained from the following formula:
wherein v (x i ) max Is the coordinates (x) i ,y i ) Maximum safe speed at, a s For maximum lateral safety acceleration of intelligent vehicle, κ (x i ) Is the curvature at each location point.
As shown in FIG. 4, for any point D on the trajectory, the maximum safe speed for the D point is v (x D ) max I.e. said maximum safe speed v (x i ) max ＝v(x D ) max . The speed planning curve 1 corresponds to when v=v d1 And v d1 ＞v(x D ) max When the intelligent vehicle is used, the intelligent vehicle is used asFront vehicle speed v d1 Running to the current position C point, and starting to gradually decelerate to the point D at the C point so that the vehicle speed at the point D is smaller than the maximum safe speed v (x D ) max After the intelligent vehicle passes through the point D, gradually accelerating to the original speed. The speed planning curve 2 corresponds to v=v d2 And v d2 ＜v(x D ) max At the time, the vehicle is at the current speed v d2 And (5) driving at a constant speed through the point D.
Wherein, the point C in FIG. 4 corresponds to the point O of the current position of the intelligent vehicle in FIG. 3, and the point D in FIG. 4 corresponds to the point P of the pre-aiming point from the point O of the current position of the intelligent vehicle in FIG. 3 r (x r ,y r ) Any point on the trajectory curve between.
Vehicle control link:
step 10: the vehicle control module calculates the coordinates (x) of the planned waypoints i ,y i ) And a maximum safe speed v (x) i ) max And controlling the intelligent vehicle to run on the planned track in real time.
Claims (5)
1. A track planning method of an intelligent vehicle track-changing track planning system based on an artificial potential field method adopts the intelligent vehicle track-changing track planning system, wherein the intelligent vehicle track-changing track planning system comprises a map data downloading module, a GPS/INS (global positioning system/inertial navigation system) position locating module, a radar monitoring module, a vehicle parameter reading module and a vehicle control module, the map data downloading module acquires global path information I of an intelligent vehicle and sends the global path information I to a computing unit, and the GPS/INS position locating module acquires the shortest distance rho between the intelligent vehicle and a front obstacle vehicle ob Collecting the roll angle of an intelligent vehicle
And course angle theta information, and send the information to a computing unit, a radar monitoring module acquires obstacle information Oi around the intelligent vehicle and sends the obstacle information Oi to a decision unit, a vehicle parameter reading module reads the current speed v of the vehicle and the vehicle self weight m and sends the current speed v and the vehicle self weight m to the computing unit, and the computing unit calculates a repulsive force field U of the obstacle vehicle ahead and the intelligent vehicleMaximum lateral safety acceleration a of vehicle s Transmitting the repulsive field U and the current speed v information of the intelligent vehicle to a decision unit, and transmitting the maximum transverse safe acceleration a s And the current speed v information is sent to a speed planning module, a decision unit carries out macroscopic decision to obtain an action command e of the intelligent vehicle for straight running or lane changing, the action command e, a repulsive force field U and the current speed v are sent to a path planning module, and the path planning module carries out path planning to obtain a path point coordinate (x i ,y i ) And sending the coordinates to a speed planning module and a vehicle control module, wherein the speed planning module performs speed planning to obtain coordinates (x) of each path point i ,y i ) Maximum safe speed v (x i ) max And is sent to a vehicle control module, characterized by comprising the steps of:
step 1) the calculation unit calculates the minimum distance ρ ob And the current velocity v to calculate the repulsive force field
Transmitting the repulsive field U and the current speed v to a decision unit, wherein eta is a repulsive field coefficient and d 0 The minimum safety distance between a rear vehicle and a front vehicle of a vehicle running on the same lane specified in the road traffic safety law is kept, and k is a speed coefficient;
step 2) the calculation unit is according to a s ＝F f Calculating the maximum lateral safety acceleration a by/m s And send it to the speed planning module together with the current speed v, F f Friction force applied to the intelligent vehicle;
step 3) the decision unit decides the vehicle speed according to the repulsive field U, the current vehicle speed v and the obstacle information O i Macroscopic decision is carried out, when the intelligent vehicle is out of the repulsive force field U range of the front obstacle vehicle, the decision result is straight, and when the intelligent vehicle is in the repulsive force field U range of the front obstacle vehicle, the decision is lane change;
step 4) when the action command e is channel change, the path planning module firstly calculates the pretightening distance L kv Determining the coordinate P of the pre-aiming point r (x r ,y r ) Programming a current position of the slave intelligent vehicleTo the pretightening point coordinate P r (x r ,y r ) To obtain the coordinates (x) of the path point on the trajectory curve i ,y i ) The method comprises the steps of carrying out a first treatment on the surface of the The pre-aiming distance
v c The minimum lane change speed of the intelligent vehicle is obtained, and k is a speed coefficient; the coordinate P of the pre-aiming point r (x r ,y r ) X in (2) r ＝ω c 、y r ＝L kv ，ω c The width of the current driving lane of the intelligent vehicle; the equation of the track curve is: y=a 4 x 4 +a 3 x 3 +a 2 x 2 +a 1 x+a 0 ，a 0 ,a 1 ,a 2 ,a 3 ,a 4 For each coefficient;
step 5) the speed planning module is according to
Obtaining coordinates (x i ,y i ) Maximum safe speed v (x i ) max ，κ(x i ) Curvature at each location point on the trajectory curve;
step 6) the vehicle control module calculates the coordinates (x) of the planned route points i ,y i ) And a maximum safe speed v (x i ) max And controlling the intelligent vehicle to run on the planned track.
2. The track planning method of the intelligent vehicle lane change track planning system based on the artificial potential field method as claimed in claim 1, wherein the track planning method is characterized by comprising the following steps: in the step 1), the elliptical area in front of the intelligent vehicle is the repulsive force field U generated by the obstacle vehicle, the long axis length A direction of the ellipse is the advancing direction of the obstacle vehicle, and the long axis length A is the minimum safety distance d 0 Is twice as long as the width omega of the current driving lane of the intelligent vehicle c 。
3. The track planning method of the intelligent vehicle lane change track planning system based on the artificial potential field method as claimed in claim 1, wherein the track planning method is characterized by comprising the following steps: curvature of track curve
Is the first derivative of y>
Is the second derivative of y.
4. The track planning method of the intelligent vehicle lane change track planning system based on the artificial potential field method as claimed in claim 1, wherein the track planning method is characterized by comprising the following steps: each coefficient a 0 ,a 1 ,a 2 ,a 3 ,a 4 The method comprises the following steps of: a, a 4 ＝J 4 ，a 3 ＝J 3 ，a 2 ＝J 2 ，a 1 ＝J 1 ,a 0 ＝J 0 ；J i As an objective function
Coefficient corresponding to each term in the track curve equation when the minimum value is obtained, wherein the track curve equation is y=J 4 x 4 +J 3 x 3 +J 2 x 2 +J 1 x+J 0 I=0, 1,2,3,4, κ (x) is the curvature of the planned trajectory curve at the waypoint (x, y), j ∈>
Is the curvature change rate of the track curve at the path point (x, y), U (x) is the repulsive force field, omega, suffered by the intelligent vehicle 1 、ω 2 And omega 3 Are all weight coefficients, x 0 Is the start point of the track curve, x f Is the end point of the trace curve.
5. The track planning method of the intelligent vehicle lane change track planning system based on the artificial potential field method according to claim 1, wherein in the step 5), the maximum safe speed v (x) of the D point is given to any point D on the track i ) max ＝v(x D ) max When the velocity v=v d1 And v d1 >v(x D ) max At the time, the intelligent vehicle takes the speed v d1 Gradually decelerating from the current position to a point D, wherein the vehicle speed at the point D is smaller than v (x D ) max After passing through the point D, gradually accelerating to the original speed v d1 The method comprises the steps of carrying out a first treatment on the surface of the When v=v d2 And v d2 <v(x D ) max At the time, the intelligent vehicle takes the speed v d2 And (5) driving at a constant speed through the point D.
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| CN115230729A (en) * | 2021-04-25 | 2022-10-25 | 广州汽车集团股份有限公司 | Automatic driving obstacle avoidance method and system and storage medium |
| CN113673028A (en) * | 2021-08-03 | 2021-11-19 | 中汽创智科技有限公司 | Speed planning method and device, electronic equipment and storage medium |
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