CN114655206A - Following target decision method, vehicle and storage medium - Google Patents
Following target decision method, vehicle and storage medium Download PDFInfo
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- CN114655206A CN114655206A CN202210466400.6A CN202210466400A CN114655206A CN 114655206 A CN114655206 A CN 114655206A CN 202210466400 A CN202210466400 A CN 202210466400A CN 114655206 A CN114655206 A CN 114655206A
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- 238000012216 screening Methods 0.000 claims abstract description 11
- 230000004888 barrier function Effects 0.000 claims abstract description 5
- 238000005070 sampling Methods 0.000 claims description 8
- 230000001133 acceleration Effects 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 7
- 238000001914 filtration Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 abstract description 6
- 230000008859 change Effects 0.000 description 5
- 230000006870 function Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
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- 230000009286 beneficial effect Effects 0.000 description 1
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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
- B60W30/14—Adaptive cruise control
- B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
- B60W30/162—Speed limiting therefor
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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
- B60W30/14—Adaptive cruise control
- B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
- B60W30/165—Automatically following the path of a preceding lead vehicle, e.g. "electronic tow-bar"
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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
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/50—Barriers
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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
- B60W2554/00—Input parameters relating to objects
- B60W2554/80—Spatial relation or speed relative to objects
- B60W2554/802—Longitudinal distance
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Abstract
The invention relates to a vehicle following target decision method, a vehicle and a storage medium. The invention adopts global path information as prior, combines self-vehicle positioning information and state information to establish a motion model and sense barrier data information, and more accurately describes a lane model by utilizing a cubic fitting curve of a grid space compared with lane line detection under the condition of not needing additional lane line detection so as to complete the initial modeling of a lane and the environment where the self vehicle is positioned. And further screening out a car following target according to the spatial position relation. The scheme ensures the safety and stability of the vehicle following when the front vehicle is cut in and cut out in low-speed running environments such as parking and the like. By adopting the method, the influence of scene complexity and real-time performance can be avoided on the basis of considering the cost, so that the safety and riding comfort of the automatic driving vehicle are effectively improved.
Description
Technical Field
The invention belongs to the technical field of target decision, and particularly relates to a vehicle following target decision method, a vehicle and a storage medium.
Background
The following target decision is an important ring for automatic driving environment cognition, and can provide a more accurate and stable following target for longitudinal speed planning. The method has the main functions of finishing the generation of the virtual own lane of the intelligent driving vehicle by combining the sensing and measuring barrier data and the global planning path information in the driving process of the automatic driving vehicle, identifying scenes such as lane change of the own vehicle and the like by combining the own vehicle information, and further adjusting the position of the virtual own lane. And processing the sensed and measured obstacles based on the generated virtual lane, screening out the obstacle vehicles in front of the lane, selecting the obstacle vehicle closest to the vehicle as an acceleration and deceleration planning target of a longitudinal speed planning module, and realizing safe and stable following in the automatic driving process.
Forward following target decision-making has a lot of research and volume production schemes in the field of automatic driving, for example, in CN110696828A, a forward target selection method is described, which mainly utilizes a forward target selection model and real-time sensing data to determine a real-time forward target; the model is obtained through integrated learning method training. In order to solve the problems of selection omission and selection error caused by different performance performances of the sensors in the curve scene, in CN111469841A, the target vehicle is determined by combining the first motion information of the intelligent driving vehicle and the target selection section. Both methods related to the above two patents need sensing data to complete lane line target measurement, and perform forward target primary selection and determine a target selection section based on the measured lane line target data. In the application of measuring lane line targets without perception data, CN105631217A —, a system and a method for selecting front effective targets based on the self-adaptive virtual lane of the vehicle are firstly initialized to generate the self-adaptive virtual lane of the vehicle, self-adaptive adjustment is carried out on the virtual lane of the vehicle according to the motion state information of the vehicle and the perception data of the vehicle-mounted radar, the probability that the virtual lane of the vehicle is located in the lane of the vehicle is calculated according to the position of the virtual lane of the vehicle where the radar measurement target is located, and effective target selection is carried out. Although the method can select and release the cut-in and cut-out targets in time, the requirements on the accuracy of the virtual own lane and the calibration of the self-adaptive parameters are high.
Disclosure of Invention
In view of the above disadvantages of the prior art, the technical problem to be solved by the present invention is to provide a vehicle following target decision method, a vehicle and a storage medium, so as to avoid the problems that the cost of a sensor and a computing device is too high, the algorithm is too complex, and the stability and the feasibility of the vehicle driving cannot be ensured.
In order to solve the technical problems, the invention adopts the following technical scheme:
a car following target decision method comprises the following steps:
s1: calculating the distance between the lane and the global path according to the global path, the real-time positioning information and the state information of the vehicle, and judging whether the vehicle changes lanes to finish the decision of the lane where the vehicle is located;
meanwhile, calculating and generating a lane space corresponding to the global path at the current position of the vehicle according to the position of the global path;
s2: judging whether the vehicle is in a lane where the global path is located, and if so, entering barrier projection; if the vehicle is not in the lane where the global path is located, the vehicle firstly enters a self-adaptive adjustment cost generation lane and then enters an obstacle projection;
s3: if the vehicle is in a lane where the global path is located, a direct obstacle is projected in a lane space where the global path is located based on obstacle positioning data;
if the vehicle is not in the lane where the global path is located, finishing offset adjustment of the corresponding lane position according to a decision result of the lane where the vehicle is located to obtain a final lane where the vehicle is located, and then projecting a direct obstacle in a lane space where the global path is located based on obstacle positioning data;
s4: filtering out obstacles which are not in the lane where the vehicle is finally located;
s5: screening the obstacles projected to the lane where the vehicle is finally located according to the real-time longitudinal distance between the obstacles and the vehicle;
s6: and (4) screening the obstacle closest to the longitudinal distance of the vehicle according to the real-time longitudinal distance between the screened obstacle and the vehicle in the step (S5), and performing longitudinal planning to calculate the acceleration of the vehicle so as to control the acceleration and the deceleration of the vehicle.
Further perfecting the above technical solution, the step S1 of calculating the distance between the host vehicle and the global path to determine whether the host vehicle changes lanes includes:
if the distance is within a half width range of the calibrated lane, the lane where the vehicle is located in the global path is judged;
if the distance exceeds the left boundary of the half-width range of the calibration lane, judging that the vehicle carries out left lane changing self-adaptive processing to obtain the final lane where the vehicle is located;
if the distance exceeds the right boundary of the half-width range of the calibration lane, judging that the vehicle carries out right lane changing self-adaptive processing to obtain the final lane where the vehicle is located;
and finishing the decision of the lane where the vehicle is located.
Further, in the step S1:
after receiving the real-time positioning information of the vehicle, generating a sampling space in a coordinate system where the vehicle is located according to the calibrated transverse and longitudinal resolution, calculating and generating a lane space corresponding to the global path at the current position of the vehicle according to the position and angle information of the global path, wherein the lane space is used for subsequent lane modeling.
Further, when the obstacle projection is performed in step S3, the obstacle sensing positioning data is obtained in real time, the obstacle expansion boundary calibration is performed for different scenes, and the obstacle is projected into the sampling space.
The invention also relates to a vehicle, which adopts the steps of the vehicle following target decision method.
The invention also relates to a storage medium storing one or more programs which, when executed by a processor, perform the steps of a car following goal decision method as described above.
Compared with the prior art, the invention has the following beneficial effects:
according to the vehicle following target decision method, global path information is used as a priori, a motion model is established by combining self-vehicle positioning information and state information, obstacle data information is sensed, under the condition that extra lane line detection is not needed, a lane model is more accurately described by using a cubic fitting curve of a grid space compared with lane line detection, and initial modeling of a lane and the environment where the self vehicle is located is completed. And further screening out a car following target according to the spatial position relation. The scheme ensures the safety and stability of the vehicle following when the front vehicle is cut in and cut out in low-speed running environments such as parking and the like. By adopting the method, the influence of scene complexity and real-time performance can be avoided on the basis of considering the cost, so that the safety and riding comfort of the automatic driving vehicle are effectively improved.
Drawings
Fig. 1 is a flowchart of a following target decision method according to an embodiment.
Detailed Description
The following provides a more detailed description of embodiments of the present invention, with reference to the accompanying drawings.
Referring to fig. d, a following target decision method according to an embodiment includes the following steps:
s1: calculating the distance between the lane and the global path according to the global path, the real-time positioning information and the state information of the vehicle, and judging whether the vehicle changes lanes to finish the decision of the lane where the vehicle is located;
meanwhile, calculating and generating a lane space corresponding to the global path at the current position of the vehicle according to the position of the global path;
s2: judging whether the vehicle is in a lane where the global path is located, and if so, entering barrier projection; if the vehicle is not in the lane where the global path is located, the vehicle firstly enters a self-adaptive adjustment cost generation lane and then enters an obstacle projection;
s3: if the vehicle is in a lane where the global path is located, projecting a direct obstacle in a lane space where the global path is located based on the obstacle positioning data;
if the vehicle is not in the lane where the global path is located, finishing offset adjustment of the corresponding lane position according to a decision result of the lane where the vehicle is located to obtain a final lane where the vehicle is located, and then projecting a direct obstacle in a lane space where the global path is located based on obstacle positioning data;
s4: filtering out obstacles which are not in the lane where the vehicle is finally located;
s5: screening the obstacles projected to the final lane where the vehicle is located according to the real-time longitudinal distance between the obstacles and the vehicle to complete the screening of the obstacles in front of the vehicle;
s6: and (4) screening the obstacle closest to the longitudinal distance of the vehicle according to the real-time longitudinal distance between the screened obstacle and the vehicle in the step (S5), and performing longitudinal planning to calculate the acceleration of the vehicle so as to control the acceleration and the deceleration of the vehicle.
According to the vehicle following target decision method, global path information is used as a priori, a motion model is established by combining self-vehicle positioning information and state information, obstacle data information is sensed, under the condition that extra lane line detection is not needed, a lane model is more accurately described by using a cubic fitting curve of a grid space compared with lane line detection, and initial modeling of a lane and the environment where the self vehicle is located is completed. And further screening out a car following target according to the spatial position relation. The scheme ensures the safety and stability of the vehicle following when the front vehicle is cut in and cut out in low-speed running environments such as parking and the like. By adopting the method, the influence of scene complexity and real-time performance can be avoided on the basis of considering the cost, so that the safety and riding comfort of the automatic driving vehicle are effectively improved.
Wherein the step S1 of calculating the distance between the host vehicle and the global path to determine whether the host vehicle changes lanes includes:
if the distance is within a half width range of the calibrated lane, judging that the vehicle is in the lane of the global path;
if the distance exceeds the left boundary of the half-width range of the calibration lane, judging that the vehicle changes lanes to a left lane;
if the distance exceeds the right boundary of the half-width range of the calibration lane, judging that the vehicle changes lanes to the right lane;
and finishing the decision of the lane where the vehicle is located.
And the calibration lane is the lane where the global path is located.
In practice, the step S1 further includes: the method comprises the steps of obtaining real-time positioning information, speed, steering wheel turning angle and yaw velocity of a vehicle to form a vehicle motion model, and calibrating the ID of a lane where the vehicle is located by combining the spatial position relation between the real-time positioning information and a global path based on the motion model so as to judge whether the vehicle changes lanes or not;
if the lane is marked as 0, the vehicle is in the lane of the global path, and lane changing is not needed;
if the lane change criterion is 1, judging that the vehicle is in a left adjacent lane of the global path, and performing left lane change self-adaptive processing to obtain a final lane where the vehicle is located;
if the lane change is marked as 2, the vehicle is in the right adjacent lane of the global path, and the vehicle is judged to be subjected to right lane change self-adaptive processing to obtain the final lane where the vehicle is located.
Wherein, in the step S1: after receiving the real-time positioning information of the vehicle, generating a sampling space in a coordinate system where the vehicle is located according to the calibrated transverse and longitudinal resolution, calculating and generating a lane space corresponding to the global path at the current position of the vehicle according to the position and angle information of the global path, wherein the lane space is used for subsequent lane modeling.
In the implementation, because the acquisition of the vehicle positioning information is real-time, a grid space (i.e., the sampling space) of a lane where the front global path is located is continuously generated in a coordinate system where the vehicle is located according to the calibrated transverse and longitudinal resolution, and is used for expressing the lane where the global path is located.
When the step S3 is performed to project the obstacle, the obstacle sensing positioning data is obtained in real time, the obstacle expansion boundary calibration is performed for different scenes, and the obstacle is projected into the sampling space.
In practice, the different scenarios include car following, car meeting and crossing.
When the method is implemented, acquiring obstacle sensing positioning data in real time, carrying out environment modeling, projecting the obstacles into the sampling space, then judging whether the obstacles are in the final lane where the vehicle is located (namely the current lane where the vehicle is located), and obtaining the lane marker bits where the obstacles are located:
if the mark bit is 1, the obstacle is in the lane where the vehicle is finally located;
if the flag bit is 0, the obstacle is not in the lane where the vehicle is finally located, and the target is filtered.
For the obstacle with the mark position of 1 in the final lane where the vehicle is located, the relative front-back position relationship between the obstacle and the vehicle is judged by combining the real-time position information of the obstacle in the vehicle coordinate system, and the corresponding mark position is obtained:
if the flag bit is 1, the obstacle is in front of the vehicle;
if the flag bit is 0, the obstacle is behind the vehicle, and the target is filtered.
Furthermore, for the obstacle in front of the vehicle in the lane where the final vehicle is located with the flag bit of 1, the obstacle closest to the longitudinal distance of the vehicle is screened out as the final following target of the vehicle based on the real-time longitudinal distance information in the vehicle coordinate system.
The invention also provides a vehicle, and the steps of the vehicle following target decision method are adopted.
The present invention also provides a storage medium storing one or more programs which, when executed by a processor, perform the steps of a vehicle following goal decision method as described above.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.
Claims (6)
1. A car following target decision method is characterized by comprising the following steps: the method comprises the following steps:
s1: calculating the distance between the lane and the global path according to the global path, the real-time positioning information and the state information of the vehicle, and judging whether the vehicle changes lanes to finish the decision of the lane where the vehicle is located;
meanwhile, calculating and generating a lane space corresponding to the global path at the current position of the vehicle according to the position of the global path;
s2: judging whether the vehicle is in a lane where the global path is located, and if so, entering barrier projection; if the vehicle is not in the lane where the global path is located, the vehicle firstly enters a self-adaptive adjustment cost generation lane and then enters an obstacle projection;
s3: if the vehicle is in a lane where the global path is located, projecting a direct obstacle in a lane space where the global path is located based on the obstacle positioning data;
if the vehicle is not in the lane where the global path is located, finishing offset adjustment of the corresponding lane position according to a decision result of the lane where the vehicle is located to obtain a final lane where the vehicle is located, and then projecting a direct obstacle in a lane space where the global path is located based on obstacle positioning data;
s4: filtering out obstacles which are not in the lane where the vehicle is finally located;
s5: screening the obstacles projected to the lane where the vehicle is finally located according to the real-time longitudinal distance between the obstacles and the vehicle;
s6: and (4) screening the obstacle closest to the longitudinal distance of the vehicle according to the real-time longitudinal distance between the screened obstacle and the vehicle in the step (S5), and performing longitudinal planning to calculate the acceleration of the vehicle so as to control the acceleration and the deceleration of the vehicle.
2. The car-following goal decision method according to claim 1, characterized in that: the step S1 of calculating the distance between the host vehicle and the global path to determine whether the host vehicle changes lanes includes:
if the distance is within a half width range of the calibrated lane, the lane where the vehicle is located in the global path is judged;
if the distance exceeds the left boundary of the half-width range of the calibration lane, judging that the vehicle carries out left lane changing self-adaptive processing to obtain the final lane where the vehicle is located;
if the distance exceeds the right boundary of the half-width range of the calibration lane, judging that the vehicle carries out right lane changing self-adaptive processing to obtain the final lane where the vehicle is located;
and finishing the decision of the lane where the vehicle is located.
3. The car-following goal decision method according to claim 1, characterized in that: in the step S1:
after receiving the real-time positioning information of the vehicle, generating a sampling space in a coordinate system where the vehicle is located according to the calibrated transverse and longitudinal resolution, calculating and generating a lane space corresponding to the global path at the current position of the vehicle according to the position and angle information of the global path, wherein the lane space is used for subsequent lane modeling.
4. The vehicle following goal decision method according to claim 3, characterized in that: when the step S3 is performed to project the obstacle, the obstacle sensing positioning data is obtained in real time, the obstacle expansion boundary calibration is performed for different scenes, and the obstacle is projected into the sampling space.
5. A vehicle, characterized in that: the steps of a car following objective decision method according to any of claims 1-4.
6. A storage medium, characterized by: the storage medium stores one or more programs which, when executed by a processor, perform the steps of a method of following objective decision as claimed in any one of claims 1-4.
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