CN116252811A - Vehicle control method and apparatus - Google Patents

Vehicle control method and apparatus Download PDF

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Publication number
CN116252811A
CN116252811A CN202111502420.6A CN202111502420A CN116252811A CN 116252811 A CN116252811 A CN 116252811A CN 202111502420 A CN202111502420 A CN 202111502420A CN 116252811 A CN116252811 A CN 116252811A
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lane
lane line
curvature
line
updated
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孙广楠
赵珉乾
吕文强
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Robert Bosch GmbH
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Robert Bosch GmbH
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Priority to CN202111502420.6A priority Critical patent/CN116252811A/en
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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
    • 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
    • B60W40/06—Road conditions
    • B60W40/072—Curvature of the road
    • 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
    • B60W2552/30—Road curve radius
    • 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
    • B60W2552/53—Road markings, e.g. lane marker or crosswalk

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention relates to a vehicle control method, comprising the following steps: receiving lane line information of a lane on which the vehicle runs, wherein the lane line information comprises a first lane line length and a second lane line length of the lane on which the vehicle runs; determining the curvature of the updated lane center line by comparing the first lane line length and the second lane line length with preset thresholds respectively; and planning a path of the vehicle based on the updated lane center line. The invention also relates to a vehicle control device, a computer program product, a vehicle motion controller and a vehicle.

Description

Vehicle control method and apparatus
Technical Field
The present invention relates to the field of vehicle control, and more particularly to a vehicle control method and apparatus, a computer program product, a vehicle motion controller, and a vehicle.
Background
With the intelligent development of vehicles, more and more vehicles are equipped with TJA (traffic jam assist), ICA (high-speed intelligent navigation) functions. In using these functions, various intelligent controls (including but not limited to chassis domain control) of the vehicle are required in conjunction with information obtained by the sensors.
For example, the vehicle motion controller VMC may perform trajectory planning in conjunction with lane line information provided by various sensors (e.g., a multifunction camera MPC). When a truck or other large vehicle is in front, lane line information is blocked, so that information detected by a camera is limited and inaccurate in most cases, which may lead to inaccurate and/or frequent planning of the track of the vehicle motion controller, and continuous adjustment of the steering wheel of the vehicle occurs, so that uncomfortable experience is brought to a driver.
Disclosure of Invention
According to an aspect of the present invention, there is provided a vehicle control method including: receiving lane line information of a lane on which the vehicle runs, wherein the lane line information comprises a first lane line length and a second lane line length of the lane on which the vehicle runs; determining the curvature of the updated lane center line by comparing the first lane line length and the second lane line length with preset thresholds respectively; and planning a path of the vehicle based on the updated lane center line.
Additionally or alternatively to the above, in the above method, receiving lane line information of a lane on which the host vehicle is traveling includes: signals related to left and right lane lines of a driving lane are received from a video camera sensor with a preprocessing function.
Additionally or alternatively to the above, in the above method, the updated lane center line is represented by a third-order polynomial as follows:
y predict (x)＝c0+c1*x+1/2*c2 predict *x 2 +1/6*c3*x 3 wherein c0 is the lateral offset between the host vehicle and the lane centerline, c1 is the yaw offset relative to the lane centerline, c2 predict Is the curvature of the updated lane centerline, and c3 is the curvature change.
Additionally or alternatively to the above, in the above method, determining the curvature of the updated lane centerline by comparing the first lane line length and the second lane line length with predetermined thresholds, respectively, includes: and when the first lane line length and the second lane line length are smaller than the preset threshold value, taking the curvature of the lane center line determined in the previous period as the curvature of the updated lane center line.
Additionally or alternatively to the above, in the above method, determining the curvature of the updated lane centerline by comparing the first lane line length and the second lane line length with predetermined thresholds, respectively, includes: when the first lane line length is smaller than the preset threshold value and the second lane line is larger than the preset threshold value, the curvature c2 of the updated lane center line is the same as the curvature c2 of the updated lane center line predict The calculation is performed according to the following formula:
c2 predict ＝w1*c2 1st +w2*c2 2nd ，
wherein c2 1st And c2 2nd Respectively representing curvatures of a first lane line and a second lane line, w1 represents a weight factor corresponding to the first lane line, and w2 represents a weight factor corresponding to the second lane line, wherein w1+w2=1 and w1 < w2.
Additionally or alternatively to the above, in the above method, determining the curvature of the updated lane centerline by comparing the first lane line length and the second lane line length with predetermined thresholds, respectively, includes: when the length of the first lane line and the second lane line are both greater than or equal to the preset threshold value, the curvature c2 of the updated lane center line predict The calculation is performed according to the following formula:
c2 predict ＝1/2*c2 1st +1/2*c2 2nd ，
wherein c2 1st And c2 2nd Respectively representing the curvature of the first lane line and the second lane line.
According to another aspect of the present invention, there is provided a vehicle control apparatus including: the receiving device is used for receiving lane line information of a lane where the vehicle runs, wherein the lane line information comprises a first lane line length and a second lane line length of the lane where the vehicle runs; determining means for determining the curvature of the updated lane center line by comparing the first lane line length and the second lane line length with predetermined thresholds, respectively: and the control device is used for planning the path of the vehicle based on the updated lane center line.
Additionally or alternatively to the above, in the above apparatus, the receiving means is configured to: signals related to left and right lane lines of a driving lane are received from a video camera sensor with a preprocessing function.
Additionally or alternatively to the above, in the above apparatus, the updated lane center line is represented by a third-order polynomial as follows:
y predict (x)＝c0+c1*x+1/2*c2 predict *x 2 +1/6*c3*x 3 wherein c0 is the lateral offset between the host vehicle and the lane centerline, c1 is the yaw offset relative to the lane centerline, c2 predict Is the curvature of the updated lane centerline, and c3 is the curvature change.
Additionally or alternatively to the above, in the above apparatus, the determining means is configured to: and when the first lane line length and the second lane line length are smaller than the preset threshold value, taking the curvature of the lane center line determined in the previous period as the curvature of the updated lane center line.
Additionally or alternatively to the above, in the above apparatus, the determining means is configured to: when the first lane line length is smaller than the preset threshold value and the second lane line is larger than the preset threshold value, the curvature c2 of the updated lane center line is the same as the curvature c2 of the updated lane center line predict The calculation is performed according to the following formula:
c2 predict ＝w1*c2 1st +w2*c2 2nd ，
wherein c2 1st And c2 2nd Respectively representing curvatures of a first lane line and a second lane line, w1 represents a weight factor corresponding to the first lane line, and w2 represents a weight factor corresponding to the second lane line, wherein w1+w2=1 and w1 < w2.
In addition or alternatively to the above, in the above apparatus, the determining meansIs configured to: when the length of the first lane line and the second lane line are both greater than or equal to the preset threshold value, the curvature c2 of the updated lane center line predict The calculation is performed according to the following formula:
c2 predict ＝1/2*c2 1st +1/2*c2 2nd ，
wherein c2 1st And c2 2nd Respectively representing the curvature of the first lane line and the second lane line.
According to yet another aspect of the invention, there is provided a computer storage medium comprising instructions which, when executed, perform a method as described above.
According to a further aspect of the invention there is provided a computer program product comprising a computer program which, when executed by a processor, implements a method as described above.
According to yet another aspect of the present invention, there is provided a vehicle motion controller VMC comprising the apparatus as described above.
According to yet another aspect of the present invention, there is provided a vehicle comprising the vehicle motion controller VMC as described above.
The vehicle control scheme of the embodiment of the invention compares the first lane line length and the second lane line length in the received lane line information with preset thresholds respectively, and finally determines the curvature of the updated lane center line. In this way, under the condition that the lane line information is incomplete or missing (for example, a truck or other large-sized vehicles are in front of the vehicle, the road is maintained, the lane line is not clear due to reflection of light, covered by sandy soil and other reasons, and the like), the curvature of the updated lane center line (used for subsequent track planning and control) can be correspondingly determined by comparing the obtained lane line information with a threshold value, so that the steering wheel does not need to be frequently adjusted, the vehicle posture is more stable, and the performance and the comfort of the vehicle are greatly improved.
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The above and other objects and advantages of the present invention will become more fully apparent from the following detailed description taken in conjunction with the accompanying drawings, in which identical or similar elements are designated by the same reference numerals.
FIG. 1 illustrates a flow diagram of a vehicle control method according to one embodiment of the invention;
fig. 2 shows a schematic structural view of a vehicle control apparatus according to an embodiment of the present invention; and
fig. 3 to 5 show lane line information in three different scenarios, respectively.
Detailed Description
Hereinafter, a vehicle control scheme according to exemplary embodiments of the invention will be described in detail with reference to the accompanying drawings.
Fig. 1 shows a flow diagram of a vehicle control method 1000 according to an embodiment of the invention. As shown in fig. 1, the vehicle control method 1000 includes the steps of:
in step S110, lane line information of a lane on which the host vehicle is traveling is received, the lane line information including a first lane line length and a second lane line length of the lane on which the host vehicle is traveling;
in step S120, determining a curvature of the updated lane center line by comparing the first lane line length and the second lane line length with predetermined thresholds, respectively; and
in step S130, the own vehicle is routed based on the updated lane center line.
Lane detection is a central task in modern assistance and autopilot systems, which can locate the exact shape of each lane in a traffic scene. In particular, lane detection is more critical to further downstream trajectory planning tasks to maintain proper positioning of the vehicle with the road lane as it turns in complex road scenarios. In practical applications, lane detection is very challenging considering that lanes are of varying shapes and sizes and are likely to be obscured by other traffic objects.
The term "lane line information" means lane line information related to a current driving lane of the host vehicle, including a first lane line length and a second lane line length. Here, the left and right lane lines are distinguished by expressions such as "first" and "second". In one embodiment, the first lane line length represents a detected left lane line length and the second lane line length is a detected right lane line length. In another embodiment, the first lane line length is a detected right lane line length and the second lane line length is a detected left lane line length. In one or more embodiments, the lane line information may include a curvature of the first lane line and a curvature of the second lane line in addition to the lane line length.
In one embodiment, step S110 includes: signals related to left and right lane lines of a driving lane are received from a video camera sensor with a preprocessing function. For example, the video camera sensor is MPC, wherein the MPC multifunctional camera is arranged above a vehicle rearview mirror, so that the condition of a road ahead can be clearly monitored. In particular, the MPC combines a multi-path recognition algorithm and artificial intelligence for target recognition, so that the result of sensing the surrounding environment is more accurate and reliable, and the road traffic safety is improved.
In one or more embodiments, the "preset threshold" in step S120, which is compared to the first and second lane line lengths, is adjustable, e.g., adjustable according to lane type or condition. For example, for straight roads, the threshold may be set to 50m, while for curves, the threshold is 35m. It should be noted that the above examples are given here by way of example only and do not limit the scope of protection of the present application in any way.
The term "lane centerline", as the name implies, refers to the centerline of the current lane, which is commonly used in assisted or unmanned systems for vehicle control procedures such as vehicle path planning. It should be noted that this center line does not actually exist in the actual road, and cannot be obtained directly by detection of the sensor. In one or more embodiments, the geometric center line of the lane lines on both sides of the lane (i.e., the left and right boundary lines of the lane) may be directly taken as the lane center line.
The lane centerline may be updated on a periodic basis. In one embodimentIn the own-vehicle rear-axis coordinate system in which the x-axis points in the vehicle forward direction (i.e., upper positive and lower negative) and the y-axis points in the vehicle lateral direction (left positive and right negative), the updated lane center line is expressed in terms of the following third-order polynomial: y is predict (x)＝c0+c1*x+1/2*c2 predict *x 2 +1/6*c3*x 3 Wherein c0 is the lateral offset between the host vehicle and the lane centerline, c1 is the yaw offset relative to the lane centerline, c2 predict Is the curvature of the updated lane centerline, and c3 is the curvature change.
In one embodiment, step S120 includes: and when the first lane line length and the second lane line length are smaller than the preset threshold value (namely, the lane line information is missing), taking the curvature of the lane center line determined in the previous period as the curvature of the updated lane center line.
In another embodiment, step S120 includes: when the first lane line length is smaller than the preset threshold value and the second lane line is larger than the preset threshold value, the curvature c2 of the updated lane center line is the same as the curvature c2 of the updated lane center line predict The calculation is performed according to the following formula:
c2 predict =w1*c2 1st +w2*c2 2nd ，
wherein c2 1st And c2 2nd Respectively representing curvatures of a first lane line and a second lane line, w1 represents a weight factor corresponding to the first lane line, and w2 represents a weight factor corresponding to the second lane line, wherein w1+w2=1 and w1 < w2. For example, when the first lane line is smaller than the preset threshold d and the second lane line is larger than the preset threshold d, the weight factor w1 corresponding to the first lane line is determined to be 0.2 (i.e., the reliability is low), and the weight factor w2 corresponding to the second lane line is determined to be 0.8 (i.e., the reliability is high).
In one or more embodiments, the weight factors w1 and w2 may be determined from the product of lane line length specific gravity, the deviation of the first lane line and the second lane line (e.g., left and right lane line length deviation and curvature deviation), and the rate of change of the polynomial coefficients in the lane centerline.
In yet another embodiment, step S120 may include: when the length of the first lane line and the second lane line are both greater than or equal to the preset threshold value (i.e. lane line information acquisition is normal), the curvature c2 of the updated lane center line predict The calculation is performed according to the following formula:
c2 predict =1/2*c2 1st +1/2*c2 2nd ，
wherein c2 1st And c2 2nd Respectively representing the curvature of the first lane line and the second lane line.
In one embodiment, in step S130, the updated lane centerline is used for subsequent vehicle control (e.g., chassis domain control). In one embodiment, the lane centerline may be used as a reference trajectory for vehicle path planning, which may be used for lateral torque request planning of the vehicle.
In addition, those skilled in the art will readily appreciate that the vehicle control methods provided by one or more of the above-described embodiments of the present invention may be implemented by a computer program. For example, the computer program is embodied in a computer program product that when executed by a processor implements the vehicle control method of one or more embodiments of the invention. For another example, when a computer storage medium (e.g., a USB flash disk) storing the computer program is connected to a computer, the computer program is run to perform the vehicle control method of one or more embodiments of the present invention.
Referring to fig. 2, fig. 2 shows a schematic configuration of a vehicle control apparatus 2000 according to an embodiment of the present invention. As shown in fig. 2, the vehicle control apparatus 2000 includes: receiving means 210, determining means 220 and controlling means 230. The receiving device 210 is configured to receive lane line information of a lane where the host vehicle travels, where the lane line information includes a first lane line length and a second lane line length of the lane where the host vehicle travels; the determining device 220 is configured to determine a curvature of the updated lane center line by comparing the first lane line length and the second lane line length with preset thresholds, respectively; and the control device 230 is used for planning a path of the vehicle based on the updated lane center line.
The term "lane line information" means lane line information related to a current driving lane of the host vehicle, including a first lane line length and a second lane line length. Here, the left and right lane lines are distinguished by expressions such as "first" and "second". In one embodiment, the first lane line length represents a detected left lane line length and the second lane line length is a detected right lane line length. In another embodiment, the first lane line length is a detected right lane line length and the second lane line length is a detected left lane line length. In one or more embodiments, the lane line information may include a curvature of the first lane line and a curvature of the second lane line in addition to the lane line length.
In one embodiment, the receiving device 210 is configured to receive signals related to left and right lane lines of a driven lane from a video camera sensor with preprocessing function. For example, the video camera sensor is MPC, wherein the MPC multifunctional camera is arranged above a vehicle rearview mirror, so that the condition of a road ahead can be clearly monitored. In particular, the MPC combines a multi-path recognition algorithm and artificial intelligence for target recognition, so that the result of sensing the surrounding environment is more accurate and reliable, and the road traffic safety is improved.
In one or more embodiments, the "preset threshold" is adjustable, e.g., adjustable according to lane type or condition. For example, for straight roads, the threshold may be set to 50m, while for curves, the threshold is 35m. In addition, the term "lane centerline", as the name implies, refers to the centerline of the current lane, which is commonly used in assisted or unmanned systems for vehicle control procedures such as vehicle path planning. It should be noted that this center line does not actually exist in the actual road, and cannot be obtained directly by detection of the sensor. In one or more embodiments, the geometric center line of the lane lines on both sides of the lane (i.e., the left and right boundary lines of the lane) may be directly taken as the lane center line.
Lane center line mayUpdating is performed periodically. In one embodiment, in the host vehicle rear axis coordinate system, where the x-axis points in the vehicle forward direction (i.e., up-positive-down-negative) and the y-axis points sideways (left-positive-right-negative), the updated lane centerline is represented by the following third-order polynomial: y is predict (x)＝c0+c1*x+1/2*c2 predict *x 2 +1/6*c3*x 3 Wherein c0 is the lateral offset between the host vehicle and the lane centerline, c1 is the yaw offset relative to the lane centerline, c2 predict Is the curvature of the updated lane centerline, and c3 is the curvature change.
In various scenarios, there may be a loss or occlusion of lane line information. Referring to fig. 3, there is shown a host vehicle 310 traveling on the same lane and a lead vehicle 320, wherein both the first lane line length 330 and the second lane line length 340 are sensed by sensors on the host vehicle 310 to be below a preset threshold, i.e., lane line occlusion is severe. Turning to fig. 4, another situation is shown in which the front truck 420 only partially obscures the lane lines. The first lane-line length 430, which can be detected by the sensors of the host vehicle 410, is below a preset threshold and the second lane-line length 440, which is detected, is above the preset threshold. Referring to fig. 5, it shows a situation where the lane lines are unobstructed, i.e., the first lane line length 530 and the second lane line length 540, as detected by the sensors of the host vehicle 510, are both above a preset threshold.
In one embodiment, the determining means 220 is configured to use the curvature of the lane center line determined in the previous cycle as the curvature of the updated lane center line when both the first lane line length and the second lane line length are smaller than the predetermined threshold value (e.g. as in the case shown in fig. 3).
In another embodiment, the determining means 220 is configured to update the curvature c2 of the lane center line when the first lane line length is less than the predetermined threshold and the second lane line is greater than the predetermined threshold (e.g. the situation shown in fig. 4) prcdict The calculation is performed according to the following formula:
c2 predict ＝w1*c2 1st +w2*c2 2nd ，
wherein c2 1st And c2 2nd Respectively representing curvatures of a first lane line and a second lane line, w1 represents a weight factor corresponding to the first lane line, and w2 represents a weight factor corresponding to the second lane line, wherein w1+w2=1 and w1 < w2. For example, when the first lane line is smaller than the preset threshold d and the second lane line is larger than the preset threshold d, the weight factor w1 corresponding to the first lane line is determined to be 0.2 (i.e., the reliability is low), and the weight factor w2 corresponding to the second lane line is determined to be 0.8 (i.e., the reliability is high).
In one or more embodiments, the weight factors w1 and w2 may be determined from the product of lane line length specific gravity, the deviation of the first lane line and the second lane line (e.g., left and right lane line length deviation and curvature deviation), and the rate of change of the polynomial coefficients in the lane centerline.
In a further embodiment, the determining means 220 is configured to update the curvature c2 of the lane center line when both the first lane line length and the second lane line are greater than or equal to the predetermined threshold value (e.g. the situation shown in fig. 5) predict The calculation is performed according to the following formula:
c2 predict ＝1/2*c2 1st +1/2*c2 2nd ，
wherein c2 1st And c2 2nd Respectively representing the curvature of the first lane line and the second lane line.
The updated lane centerline may be used for subsequent vehicle control (e.g., chassis domain control). In one embodiment, the control device 230 is configured to utilize the lane centerline as a reference trajectory for vehicle path planning for lateral torque request planning of the vehicle.
In summary, the vehicle control scheme of the embodiment of the invention compares the first lane line length and the second lane line length in the received lane line information with preset thresholds respectively, and finally determines the curvature of the updated lane center line. In this way, under the condition that the lane line information is incomplete or missing (for example, a truck or other large-sized vehicles are in front of the vehicle, the road is maintained, the lane line is not clear due to reflection of light, covered by sandy soil and other reasons, and the like), the curvature of the updated lane center line (used for subsequent track planning and control) can be correspondingly determined by comparing the obtained lane line information with a threshold value, so that the steering wheel does not need to be frequently adjusted, the vehicle posture is more stable, and the performance and the comfort of the vehicle are greatly improved.
While the above description describes only some of the embodiments of the present invention, those of ordinary skill in the art will appreciate that the present invention can be embodied in many other forms without departing from the spirit or scope thereof. Accordingly, the present examples and embodiments are to be considered as illustrative and not restrictive, and the invention is intended to cover various modifications and substitutions without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (15)

1. A vehicle control method, characterized in that the method comprises:
receiving lane line information of a lane on which the vehicle runs, wherein the lane line information comprises a first lane line length and a second lane line length of the lane on which the vehicle runs;
determining the curvature of the updated lane center line by comparing the first lane line length and the second lane line length with preset thresholds respectively; and
and planning a path of the vehicle based on the updated lane center line.
2. The method of claim 1, wherein receiving lane line information of a lane in which the host vehicle is traveling comprises:
signals related to left and right lane lines of a driving lane are received from a video camera sensor with a preprocessing function.
3. The method of claim 1, wherein the updated lane centerline is represented by a third order polynomial as follows:
y predict (x)＝c0+c1*x+1/2*c2 predict *x 2 +1/6*c3*x 3 wherein c0 is the lateral offset between the host vehicle and the lane centerline, c1 is the yaw offset relative to the lane centerline, c2 predict Is the curvature of the updated lane centerline, and c3 is the curvature change.
4. A method according to claim 1 or 3, wherein determining the curvature of the updated lane centre line by comparing the first lane line length and the second lane line length, respectively, with a predetermined threshold value comprises:
and when the first lane line length and the second lane line length are smaller than the preset threshold value, taking the curvature of the lane center line determined in the previous period as the curvature of the updated lane center line.
5. A method according to claim 1 or 3, wherein determining the curvature of the updated lane centre line by comparing the first lane line length and the second lane line length, respectively, with a predetermined threshold value comprises:
when the first lane line length is smaller than the preset threshold value and the second lane line is larger than the preset threshold value, the curvature c2 of the updated lane center line is the same as the curvature c2 of the updated lane center line predict The calculation is performed according to the following formula:
c2 predict ＝w1*c2 1st +w2*c2 2nd ，
wherein c2 1st And c2 2nd Respectively representing the curvatures of a first lane line and a second lane line, w1 represents the weight corresponding to the first lane line, and w2 represents the weight corresponding to the second lane line, wherein w1 < w2.
6. A method according to claim 1 or 3, wherein determining the curvature of the updated lane centre line by comparing the first lane line length and the second lane line length, respectively, with a predetermined threshold value comprises:
when the length of the first lane line and the second lane line are both greater than or equal to the preset threshold value, the curvature c2 of the updated lane center line predict The calculation is performed according to the following formula:
c2 predict ＝1/2*c2 1st +1/2*c2 2nd ，
wherein c2 1st And c2 2nd Respectively representing the curvature of the first lane line and the second lane line.
7. A vehicle control apparatus, characterized in that the apparatus includes:
the receiving device is used for receiving lane line information of a lane where the vehicle runs, wherein the lane line information comprises a first lane line length and a second lane line length of the lane where the vehicle runs:
determining means for determining a curvature of an updated lane center line by comparing the first lane line length and the second lane line length with predetermined thresholds, respectively; and
and the control device is used for planning the path of the vehicle based on the updated lane center line.
8. The apparatus of claim 7, wherein the receiving means is configured to: signals related to left and right lane lines of a driving lane are received from a video camera sensor with a preprocessing function.
9. The apparatus of claim 7, wherein the updated lane centerline is represented by a third order polynomial as follows:
y predict (x)＝c0+c1*x+1/2*c2 predict *x 2 +1/6*c3*x 3 where cO is the lateral offset between the host vehicle and the lane centerline, c1 is the yaw offset relative to the lane centerline, c2 predict Is the curvature of the updated lane centerline, and c3 is the curvature change.
10. Apparatus according to claim 7 or 9, wherein the determining means is configured to:
and when the first lane line length and the second lane line length are smaller than the preset threshold value, taking the curvature of the lane center line determined in the previous period as the curvature of the updated lane center line.
11. Apparatus according to claim 7 or 9, wherein the determining means is configured to:
when the first lane line length is smaller than the preset threshold value and the second lane line is larger than the preset threshold value, the curvature c2 of the updated lane center line is the same as the curvature c2 of the updated lane center line predict The calculation is performed according to the following formula:
c2 predict ＝w1*c2 1st +w2*c2 2nd ，
wherein c2 1st And c2 2nd Respectively, the curvatures of a first lane line and a second lane line, w1 represents a weight factor corresponding to the first lane line, and w2 represents a weight factor corresponding to the second lane line, wherein w1+w2=1 and w1 < w2.
12. Apparatus according to claim 7 or 9, wherein the determining means is configured to:
when the length of the first lane line and the second lane line are both greater than or equal to the preset threshold value, the curvature c2 of the updated lane center line predict The calculation is performed according to the following formula:
c2 predict ＝1/2*c2 1st +1/2*c2 2nd ，
wherein c2 1st And c2 2nd Respectively representing the curvature of the first lane line and the second lane line.
13. A computer program product comprising a computer program which, when executed by a processor, implements the method of any one of claims 1 to 6.
14. A vehicle motion controller VMC, characterized in that the vehicle motion controller VMC comprises the apparatus of any one of claims 7 to 12.
15. A vehicle comprising the vehicle motion controller VMC according to claim 14.
CN202111502420.6A 2021-12-09 2021-12-09 Vehicle control method and apparatus Pending CN116252811A (en)

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