CN114523978B - Rear road model generation method and device - Google Patents
Rear road model generation method and device Download PDFInfo
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
- B60W40/072—Curvature of the road
Abstract
The invention provides a method and a device for generating a rear road model, wherein the method comprises the following steps: calculating historical tracks of the front N periods of the vehicle according to the motion information of the vehicle, wherein the periods are acquisition periods of the motion information; calculating the curvature of N periods before the lane center line, the course angle and the transverse intercept at the current position of the lane center line by utilizing the lane line information of the forward camera; a rear road model is generated based on the historical track of N cycles before the vehicle, the curvature of N cycles before the lane centerline, and the heading angle and the lateral intercept at the current position of the lane centerline. The invention generates a rear road model by means of the forward cameras which are required to be equipped with the self-adaptive cruise control system and the lane keeping auxiliary system, provides a basis for judging the automatic lane changing auxiliary driving system to screen rear effective target vehicles, and reduces the mass production cost of the automatic lane changing auxiliary driving system.
Description
Technical Field
The invention relates to the technical field of intelligent driving, in particular to a method and a device for generating a rear road model.
Background
Today, intelligent driving technology is becoming mature, and more automobile manufacturers want to integrate an adaptive cruise control system (Adaptive Cruise Control, ACC), a lane keeping assist system (Lane Keeping Assist, LKA), and an automatic lane changing assist driving system (Autonomous Lane Change, ALC) to form a more powerful driving assist system. However, more powerful means that more environmental information needs to be obtained.
For the automatic lane change assisting driving system, not only the target vehicles which travel at a low speed in front of the target lane and have collision risks, but also the target vehicles which approach quickly behind the target lane and have collision risks need to be screened, and lane change is forbidden when the rear has collision risks. And the premise of screening the rear effective target vehicles is that a rear road model needs to be known. Because of the wide application and popularity of adaptive cruise control systems, the road models are basically studied for the front and little for the rear, and have yet to be studied in depth.
Therefore, how to generate the rear road model becomes a problem to be solved in the present stage.
Disclosure of Invention
In view of the above, the present invention provides a method and apparatus for generating a rear road model. The technical proposal is as follows:
a method of rear road model generation, the method comprising:
calculating historical tracks of N periods in front of a vehicle according to motion information of the vehicle, wherein the periods are acquisition periods of the motion information;
calculating the curvature of N periods before a lane center line and the course angle and the transverse intercept at the current position of the lane center line by utilizing lane line information of a forward camera;
A rear road model is generated based on the historical track of the N periods before the vehicle, the curvature of the N periods before the lane centerline, and the heading angle and the transverse intercept at the current position of the lane centerline.
Preferably, the calculating the historical track of the previous N periods of the vehicle according to the motion information of the vehicle includes:
collecting the yaw rate and the longitudinal speed of the front N periods of the vehicle;
calculating heading angles of the front N periods of the vehicle relative to a current coordinate system by utilizing the yaw rates of the front N periods of the vehicle;
and calculating the abscissa and the ordinate of the front N periods of the vehicle relative to the current coordinate system according to the longitudinal vehicle speed and the course angle of the front N periods of the vehicle.
Preferably, the calculating the curvature of the front N periods of the lane center line and the course angle and the transverse intercept of the current position of the lane center line by using the lane line information of the forward camera includes:
for each of the first N periods, acquiring curvature, heading angle, transverse intercept and confidence of a lane line in the period through the forward camera, wherein the lane line comprises a left lane line and a right lane line;
judging whether the left lane line exists in the period according to the confidence coefficient of the left lane line, and judging whether the right lane line exists in the period according to the confidence coefficient of the right lane line;
If the left lane line and the right lane line both exist in the period, calculating the curvature of the lane center line in the period according to the curvature of the left lane line in the period and the curvature of the right lane line in the period;
calculating the course angle of the lane center line in the period according to the course angle of the left lane line in the period and the course angle of the right lane line in the period;
calculating the transverse intercept of the lane center line in the period according to the transverse intercept of the left lane line in the period and the transverse intercept of the right lane line in the period;
if one lane line exists in the period of the left lane line and the right lane line, taking the curvature of the existing lane line as the curvature of the lane center line in the period;
discarding the cycle if neither the left lane line nor the right lane line is present in the cycle;
and respectively taking the course angle and the transverse intercept in the current period as the course angle and the transverse intercept at the current position of the central line of the lane.
Preferably, the generating the rear road model based on the history track of the front N periods of the vehicle, the curvature of the front N periods of the lane center line, and the heading angle and the lateral intercept at the current position of the lane center line includes:
Judging whether the rear road model is divided into two sections according to the longitudinal track of the history track of the front N periods of the vehicle;
if the rear road model is not divided into two sections, taking the longitudinal track of the history track of the front N periods of the vehicle and the curvature of the front N periods of the vehicle as inputs, inputting the longitudinal track and the curvature of the front N periods of the vehicle into a clothoid curvature model, and calculating the curvature and curvature change rate of the rear road model by using a least square method;
a rear road model is generated based on the curvature and curvature change rate of the rear road model, and the heading angle and the lateral intercept at the current position of the lane centerline.
Preferably, the generating the rear road model based on the curvature and curvature change rate of the rear road model, and the heading angle and the lateral intercept at the current position of the lane center line further includes:
if the rear road model is divided into two sections, respectively intercepting a first longitudinal track of a first section of rear road model and a second longitudinal track of a second section of rear road model from longitudinal tracks of the history tracks of the front N periods of the vehicle;
intercepting a first curvature of the first section rear road model and a second curvature of the second section rear road model from curvatures of the front N periods of the vehicle respectively;
Inputting the first longitudinal track and the first curvature into a clothoid curvature model, and calculating the curvature and curvature change rate of the road model behind the first section by using a least square method;
generating a first section of rear road model based on the curvature and curvature change rate of the first section of rear road model and the course angle and the transverse intercept at the current position of the lane center line, and calculating the abscissa, the course angle and the curvature of the starting point of the second section of rear road model;
inputting the second longitudinal track and the second curvature into the clothoid curvature model, and calculating the curvature change rate of the road model behind the second section by using the least square method;
and generating the second section rear road model based on the curvature change rate of the second section rear road model and the abscissa, the course angle and the curvature of the starting point of the second section rear road model.
A rear road model generation apparatus, the apparatus comprising:
the first calculation module is used for calculating the historical track of the front N periods of the vehicle according to the motion information of the vehicle, wherein the periods are acquisition periods of the motion information;
The second calculation module is used for calculating the curvature of N periods before the lane center line and the course angle and the transverse intercept at the current position of the lane center line by utilizing the lane line information of the forward camera;
the model generation module is used for generating a rear road model based on the historical track of the front N periods of the vehicle, the curvature of the front N periods of the lane center line and the course angle and the transverse intercept of the current position of the lane center line.
Preferably, the first computing module is specifically configured to:
collecting the yaw rate and the longitudinal speed of the front N periods of the vehicle; calculating heading angles of the front N periods of the vehicle relative to a current coordinate system by utilizing the yaw rates of the front N periods of the vehicle; and calculating the abscissa and the ordinate of the front N periods of the vehicle relative to the current coordinate system according to the longitudinal vehicle speed and the course angle of the front N periods of the vehicle.
Preferably, the second computing module is specifically configured to:
for each of the first N periods, acquiring curvature, heading angle, transverse intercept and confidence of a lane line in the period through the forward camera, wherein the lane line comprises a left lane line and a right lane line; judging whether the left lane line exists in the period according to the confidence coefficient of the left lane line, and judging whether the right lane line exists in the period according to the confidence coefficient of the right lane line; if the left lane line and the right lane line both exist in the period, calculating the curvature of the lane center line in the period according to the curvature of the left lane line in the period and the curvature of the right lane line in the period; calculating the course angle of the lane center line in the period according to the course angle of the left lane line in the period and the course angle of the right lane line in the period; calculating the transverse intercept of the lane center line in the period according to the transverse intercept of the left lane line in the period and the transverse intercept of the right lane line in the period; if one lane line exists in the period of the left lane line and the right lane line, taking the curvature of the existing lane line as the curvature of the lane center line in the period; discarding the cycle if neither the left lane line nor the right lane line is present in the cycle; and respectively taking the course angle and the transverse intercept in the current period as the course angle and the transverse intercept at the current position of the central line of the lane.
Preferably, the model generating module is specifically configured to:
judging whether the rear road model is divided into two sections according to the longitudinal track of the history track of the front N periods of the vehicle; if the rear road model is not divided into two sections, taking the longitudinal track of the history track of the front N periods of the vehicle and the curvature of the front N periods of the vehicle as inputs, inputting the longitudinal track and the curvature of the front N periods of the vehicle into a clothoid curvature model, and calculating the curvature and curvature change rate of the rear road model by using a least square method; a rear road model is generated based on the curvature and curvature change rate of the rear road model, and the heading angle and the lateral intercept at the current position of the lane centerline.
Preferably, the model generating module is further configured to:
if the rear road model is divided into two sections, respectively intercepting a first longitudinal track of a first section of rear road model and a second longitudinal track of a second section of rear road model from longitudinal tracks of the history tracks of the front N periods of the vehicle; intercepting a first curvature of the first section rear road model and a second curvature of the second section rear road model from curvatures of the front N periods of the vehicle respectively; inputting the first longitudinal track and the first curvature into a clothoid curvature model, and calculating the curvature and curvature change rate of the road model behind the first section by using a least square method; generating a first section of rear road model based on the curvature and curvature change rate of the first section of rear road model and the course angle and the transverse intercept at the current position of the lane center line, and calculating the abscissa, the course angle and the curvature of the starting point of the second section of rear road model; inputting the second longitudinal track and the second curvature into the clothoid curvature model, and calculating the curvature change rate of the road model behind the second section by using the least square method; and generating the second section rear road model based on the curvature change rate of the second section rear road model and the abscissa, the course angle and the curvature of the starting point of the second section rear road model.
The method and the device for generating the rear road model provided by the invention generate the rear road model by virtue of the adaptive cruise control system and the forward camera which is required to be equipped with the lane keeping auxiliary system, provide a basis for judging the screening of the rear effective target vehicles by the automatic lane changing auxiliary driving system, and reduce the mass production cost of the automatic lane changing auxiliary driving system.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic view of the position of a vehicle mounted four corner radars, a forward facing camera, and forward facing radars;
FIG. 2 is a flowchart of a method for generating a rear road model according to an embodiment of the present invention;
FIG. 3 is a partial flow chart of a method for generating a rear road model according to an embodiment of the present invention;
FIG. 4 is a graph of the positional relationship of a vehicle coordinate system at a previous time relative to a current time;
FIG. 5 is a schematic view of a left lane line heading angle;
FIG. 6 is a flowchart of another part of the method for generating a rear road model according to the embodiment of the present invention;
FIG. 7 is a flowchart of a method for generating a rear road model according to an embodiment of the present invention;
FIG. 8 is a flowchart of a method for generating a rear road model according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of a rear road model generating device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In this application, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The embodiment of the invention belongs to the technical field of intelligent driving, and particularly relates to a rear road model generation method in an automatic lane change auxiliary driving system.
With the rapid development of the automobile industry, attention is increasingly paid to how to improve driving safety, which is also an important problem to be solved in the technical field of intelligent driving. The intelligent driving auxiliary system takes a driver as a main body, and can remarkably reduce traffic accidents caused by misjudgment or improper decision of the driver by means of the perception and decision capability of the intelligent sensor to the environment. As a classical representation of intelligent driving assistance systems, adaptive cruise control systems (Adaptive Cruise Control, ACC), automatic emergency braking systems (Autonomous Emergency Braking, AEB) and lane keeping assistance systems (Lane Keeping Assist, LKA) are capable of improving driving performance, reducing driving pressure and relieving driving fatigue, and are accepted by a wide range of drivers.
For adaptive cruise control systems, it is desirable to quickly and accurately screen for a front active target vehicle, and to actively brake when there is a risk of collision to avoid or mitigate the hazards of the collision. To accurately screen the front effective target vehicle, firstly, a front road model, namely an equation of the center line of the lane where the vehicle is located under the vehicle coordinate system, is required to be obtained, then, the lane where the target vehicle is located is positioned according to the transverse distance between the target vehicle and the center line of the vehicle, and finally, the effective target vehicle located in the vehicle lane is selected according to the longitudinal distance between the target vehicle and the vehicle. Currently, in the research of the forward road model estimation algorithm, there are different algorithms based on different sensors: the method comprises the steps that a road mark line is identified based on a camera to obtain a front road model of the vehicle; based on the information of the millimeter wave radar and the camera, carrying out fusion estimation on the information through a Kalman filtering algorithm; based on the signal returned by the laser radar, extracting characteristics to estimate a road model in front of the vehicle; GPS and high-precision map information are combined to estimate the forward road model.
Today, intelligent driving technology is becoming mature, and more automobile manufacturers want to integrate an adaptive cruise control system, a lane keeping assist system, and an automatic lane changing assist driving system (Autonomous Lane Change, ALC) to form a more powerful driving assist system. However, more powerful means that more environmental information needs to be obtained.
For the automatic lane change assisting driving system, not only the target vehicles which travel at a low speed in front of the target lane and have collision risks, but also the target vehicles which approach quickly behind the target lane and have collision risks need to be screened, and lane change is forbidden when the rear has collision risks. And the premise of screening the rear effective target vehicles is that a rear road model needs to be known. Because of the wide application and popularity of adaptive cruise control systems, the road models are basically studied for the front and little for the rear, and have yet to be studied in depth.
In general, automobile manufacturers are equipped with forward cameras and millimeter wave radars in order to meet the environmental awareness needs of adaptive cruise control systems and lane keeping aid systems. The lane-changing assistant driving system also needs to additionally sense the target vehicles in front of the side and the target vehicles behind the side, so that two (four) millimeter wave angle radars in front of and behind each need to be equipped to detect the surrounding target vehicles, as shown in fig. 1. In order to screen for a rear valid target vehicle, it is also necessary to generate a rear road model. In order to save the cost as much as possible, the invention will accurately generate the rear road model by means of the forward camera.
The road model refers to a cubic equation of a lane center line in a vehicle coordinate system, and an x-axis of the vehicle coordinate system is a longitudinal axis direction of the vehicle.
The embodiment of the invention provides a method for generating a rear road model, wherein a flow chart of the method is shown in fig. 2, and the method comprises the following steps:
s10, calculating historical tracks of N periods in front of the vehicle according to the motion information of the vehicle, wherein the periods are acquisition periods of the motion information.
In the embodiment of the invention, the acquired motion information comprises the yaw rate, the longitudinal speed and the data acquisition period of the vehicle, and the data is preprocessed to obtain a data set:
S 1 ＝{ω,v,T}
wherein ω represents yaw rate of the vehicle, v represents longitudinal speed of the vehicle, and T represents data acquisition cycle, i.e. software operation cycle. Since the software of this embodiment operates at 50HZ, the period is 0.02s.
In a specific implementation process, step S10 "calculate a history track of the previous N periods of the vehicle according to the motion information of the vehicle" may be the following steps, and a method flowchart is shown in fig. 3:
s101, collecting the yaw rate and the longitudinal vehicle speed of N periods before the vehicle.
In this embodiment, the yaw rate and the longitudinal vehicle speed of the vehicle are saved for N periods, and the historical motion information of the vehicle is obtained:
Wherein omega i 、v i The yaw rate and the longitudinal vehicle speed of i (i=1, 2,3, … N) cycles before the vehicle are shown, respectively. Since the detection distance of the rear angle radar equipped in the automatic lane changing auxiliary driving system in this embodiment is 80m, the rear road model only needs to estimate 80m, and the minimum speed of the automatic lane changing function is 60km/h, and the vehicle can travel for 80m at 60km/h, 4.8s, that is, 240 cycles are required, so that N is 240.
S102, using the yaw rate of the front N periods of the vehicle, the course angle of the front N periods of the vehicle relative to the current coordinate system is calculated.
In the present embodiment, the heading angle of the front N cycles of the vehicle with respect to the current coordinate system is calculated using the yaw rate of the vehicle history:
wherein,respectively representing heading angles of i (i=1, 2,3, … N) periods of the front of the vehicle relative to the current coordinate system, +.>A schematic of (2) is shown in figure 4.
Further, the method comprises the steps of,the method can be calculated by the following formula:
the initial value isThe heading angle of the vehicle at the current time with respect to the coordinate system of the vehicle at the current time is represented, and thus the initial value is 0.
S103, according to the longitudinal speed and the course angle of the front N periods of the vehicle, calculating the abscissa and the ordinate of the front N periods of the vehicle relative to the current coordinate system.
In the present embodiment, the abscissa and ordinate of the front N cycles of the vehicle with respect to the current coordinate system are as follows:
wherein X is i 、Y i The ordinate and abscissa of the current coordinate system of the i (i=1, 2,3, … N) cycles before the host computer are respectively shown.
Further, X i 、Y i The method can be calculated by the following formula:
s20, calculating the curvature of N periods before the lane center line and the course angle and the transverse intercept at the current position of the lane center line by using the lane line information of the forward camera.
In the embodiment of the invention, lane line information acquired by a forward camera comprises curvature change rate, curvature, course angle, transverse intercept and confidence coefficient of left and right lane lines of a lane, and data are preprocessed to obtain a data set:
S 2 ＝{C 1L ,C 0L ,α L ,d yL ,P L ,C 1R ,C 0R ,α R ,d yR ,P R }
wherein C is 1L 、C 1R The curvature change rates of the left lane line and the right lane line of the lane are respectively shown; c (C) 0L 、C 0R Respectively representing the curvatures of the left lane line and the right lane line of the lane at the position of the vehicle coordinate system X=0; alpha L 、α R Respectively representing course angles of left and right lane lines of the lane, namely the included angles between the tangential lines of the left and right lane lines at the position of X=0 and the X axis of a vehicle coordinate system, as shown in fig. 5; d, d yL 、d yR The transverse intercept of the left lane line and the right lane line of the lane respectively, namely the intercept of the left lane line and the right lane line on the Y axis of a vehicle coordinate system; p (P) L 、P R Confidence of left and right lane lines of the lane respectively.
It should be noted that, the course angle in the present embodiment is the course angle of the lane line relative to the coordinate system of the vehicle, and the course angle in step S102 is the included angle between the coordinate system X axis of the historical moment and the coordinate system X axis of the current moment.
It should be further noted that, in this embodiment, the confidence level of the lane line indicates the confidence level that the forward camera detects the lane line, and the greater the confidence level, the greater the likelihood that the lane line exists, and conversely, the lesser the confidence level, the lesser the likelihood that the lane line exists.
In the specific implementation process, step S20 "calculate the curvature of N periods before the lane center line and the course angle and the transverse intercept at the current position of the lane center line by using the lane line information of the forward camera" may adopt the following steps, and the method flowchart is shown in fig. 6:
s201, for each of the first N periods, acquiring curvature, heading angle, transverse intercept and confidence of a lane line in the period through a forward camera, wherein the lane line comprises a left lane line and a right lane line.
S202, judging whether the left lane line exists in the period according to the confidence coefficient of the left lane line, and judging whether the right lane line exists in the period according to the confidence coefficient of the right lane line.
In this embodiment, the existence of the lane line means that the confidence of the lane line acquired from the forward camera satisfies the following condition:
P L ≥P Threshold
P R ≥P Threshold
wherein P is Threshold To determine the probability threshold for whether a lane exists, a value is selected empirically, in this embodiment 0.85.
The output result is 2, which indicates that the left lane line and the right lane line are both present, and the step S203 is performed; the output result is 1, which indicates that one of the left lane line and the right lane line exists, and the step S206 is performed; the output result is 0, which indicates that neither the left nor right lane lines are present, and the process goes to step S207.
S203, if the left lane line and the right lane line exist in the period, calculating the curvature of the lane center line in the period according to the curvature of the left lane line in the period and the curvature of the right lane line in the period.
In this embodiment, the curvature of the lane center line in this period is calculated using the following formula:
wherein C is 0C Representing the curvature of the lane centerline over the period.
S204, calculating the course angle of the lane center line in the period according to the course angle of the left lane line in the period and the course angle of the right lane line in the period.
In the present embodiment, the course angle of the lane center line in this period is calculated using the following formula:
Wherein alpha is C Indicating the heading angle of the lane centerline in the period.
S205, calculating the transverse intercept of the lane center line in the period according to the transverse intercept of the left lane line in the period and the transverse intercept of the right lane line in the period.
In this embodiment, the lateral intercept of the lane centerline in this period is calculated using the following formula:
wherein d yC Representing the transverse intercept of the lane centerline in that period.
S206, if one lane line exists in the period of the left lane line and the right lane line, taking the curvature of the existing lane line as the curvature of the lane center line in the period.
In this embodiment, if there is only one lane line, a curvature of a virtual lane center line needs to be calculated, because the lane-change-assist driving system can be activated only when two lane lines are present, where the curvature parameter is calculated to store the curvature, and the requirement of generating the rear road model later is satisfied.
The curvature of the lane center line in the period takes the curvature of the existing lane line:
C 0C ＝C 0L OR C 0C ＝C 0R
s207, if neither the left lane line nor the right lane line exists in the period, the period is discarded.
In this embodiment, if neither the left nor right lane lines are present, the curvature of the lane center line in the period is not taken as a value, and the period is discarded when the curvature is stored.
According to steps S201 to S207, the curvature of the lane center line is saved for N periods to obtain the curvature of N periods:
wherein C is 0Ci The curvature of the lane center line for the first i (i=1, 2,3, … N) cycles is shown.
S208, taking the course angle and the transverse intercept in the current period as the course angle and the transverse intercept at the current position of the lane center line respectively.
In the embodiment of the present invention, the current period refers to a period in which the current time is located or a period closest to the current time after the period is discarded.
S30, generating a rear road model based on the historical track of the front N periods of the vehicle, the curvature of the front N periods of the lane center line, and the course angle and the transverse intercept of the current position of the lane center line.
In the embodiment of the invention, the historical track and curvature of N periods are used as input to be input into a clothoid curvature model, and the curvature and curvature change rate of a rear road model are calculated by applying a least square method; further, a rear road model is generated based on the curvature and curvature change rate of the rear road model, and the heading angle and the lateral intercept at the current position of the lane center line.
In the specific implementation process, in the step S30, "generating the rear road model based on the history track of the front N periods of the vehicle, the curvature of the front N periods of the lane center line, and the heading angle and the lateral intercept at the current position of the lane center line" may employ the following steps, where the method flowchart is shown in fig. 7:
S301, judging whether the rear road model is divided into two sections according to the longitudinal track of the history track of the front N periods of the vehicle.
In this embodiment, the rule whether the rear road model needs to be divided into two segments is as follows:
wherein X is Threshold A threshold value indicating whether the rear road model needs to be divided into two segments. Since the third equation is used to describe the highway, the error is 60m at maximum within an acceptable range, X Threshold Taking-60.
If X N If the condition of dividing into segments is satisfied, outputting a result of 0, and turning to step S302; if X N And if the condition of dividing the model into two sections is met, the model output result is 1, and the step S304 is performed.
S302, if the rear road model is not divided into two sections, longitudinal tracks of historical tracks of N periods before the vehicle and curvatures of N periods before the vehicle are used as inputs and are input into a clothoid curvature model, and a least square method is applied to calculate the curvature and curvature change rate of the rear road model.
In the present embodiment, the method obtained in step S103And +.>As inputs, the following clothoid curvature model is input:
K(x)＝a+bx
where a denotes a curvature at x=0, b denotes a curvature change rate, and K (x) is a curvature corresponding to x.
Specifically, the curvature C of the rear road model 0 And rate of change of curvature C 1 The following least squares fitting formula is used for calculation:
s303, generating a rear road model based on the curvature and curvature change rate of the rear road model and the course angle and the transverse intercept at the current position of the lane center line.
In the present embodiment, the rear road model is as follows:
on this basis, step S30 "generating a rear road model based on the curvature and curvature change rate of the rear road model, and the heading angle and lateral intercept at the current position of the lane center line" further includes the steps of:
s304, if the rear road model is divided into two sections, a first longitudinal track of the first section of rear road model and a second longitudinal track of the second section of rear road model are respectively intercepted from longitudinal tracks of the history tracks of the front N periods of the vehicle.
S305, a first curvature of the first section rear road model and a second curvature of the second section rear road model are respectively cut from curvatures of N periods in front of the vehicle.
In the present embodiment, the method obtained in step S103And +.>Intercepting a part of the previous elements to obtain an input +.>And->The following are provided:
wherein the index m is a vectorAll greater than X Threshold The largest index of the elements in (a).
Further, the method of step S103And +.>Intercepting a part of the latter elements to obtain an input +.>And->The following are provided:
wherein the number q of the interception is a vectorAll of which are greater than 2*X Threshold The largest index of the elements in (a).
S306, taking the first longitudinal track and the first curvature as inputs, inputting the first longitudinal track and the first curvature into a clothoid curvature model, and calculating the curvature and curvature change rate of the road model behind the first section by using a least square method.
In the present embodiment, the method obtained in step S304And step S305 to obtain->As inputs, into a clothoid curvature model as follows:
K(x)＝a+bx
where a denotes a curvature at x=0, b denotes a curvature change rate, and K (x) is a curvature corresponding to x.
Specifically, the curvature C of the first segment rear road model 0 1 and rate of curvature change C 1 And (1) calculating by adopting the following least square fitting formula:
s307, generating a first section rear road model based on the curvature and curvature change rate of the first section rear road model and the course angle and the transverse intercept at the current position of the lane center line, and calculating the abscissa, the course angle and the curvature of the starting point of the second section rear road model.
In the present embodiment, the first section rear road model is as follows:
The abscissa y of the starting point of the road model behind the second section is calculated by adopting the following formula C 2, course angle alpha C 2 and curvature C 0 _2：
C 0 _2＝C 0 _1+C 1 _1·X m
S308, taking the second longitudinal track and the second curvature as inputs, inputting the second longitudinal track and the second curvature into a clothoid curvature model, and calculating the curvature change rate of the road model behind the second section by using a least square method.
In the present embodiment, the method obtained in step S304And step S305 to obtain->As inputs, into a clothoid curvature model as follows:
K(x)＝a+bx
where a denotes a curvature at x=0, b denotes a curvature change rate, and K (x) is a curvature corresponding to x.
Specifically, the curvature change rate C of the second-section rear road model 1 2 is calculated using the following least squares fit equation:
s309, generating the second-segment rear road model based on the curvature change rate of the second-segment rear road model and the abscissa, the course angle and the curvature of the starting point of the second-segment rear road model.
In the present embodiment, the second-section rear road model is as follows:
according to the method for generating the rear road model, the rear road model is generated by means of the adaptive cruise control system and the forward camera which is required to be equipped with the lane keeping auxiliary system, a judgment basis is provided for screening rear effective target vehicles by the automatic lane changing auxiliary driving system, and the mass production cost of the automatic lane changing auxiliary driving system is reduced.
Based on the method for generating a rear road model provided in the foregoing embodiment, in an embodiment of the present invention, a device for executing the method for generating a rear road model is correspondingly provided, and a schematic structural diagram of the device is shown in fig. 9:
the first calculation module 10 is configured to calculate a history track of the previous N periods of the vehicle according to the motion information of the vehicle, where the period is a collection period of the motion information;
a second calculation module 20 for calculating the curvature of the front N cycles of the lane center line and the course angle and the transverse intercept at the current position of the lane center line by using the lane line information of the forward camera;
the model generation module 30 is configured to generate a rear road model based on the history track of the front N cycles of the vehicle, the curvature of the front N cycles of the lane center line, and the heading angle and the lateral intercept at the current position of the lane center line.
Optionally, the first computing module 10 is specifically configured to:
collecting the yaw rate and the longitudinal speed of the front N periods of the vehicle; calculating heading angles of the front N periods of the vehicle relative to a current coordinate system by utilizing yaw rates of the front N periods of the vehicle; and calculating the abscissa and the ordinate of the front N periods of the vehicle relative to the current coordinate system according to the longitudinal speed and the course angle of the front N periods of the vehicle.
Optionally, the second computing module 20 is specifically configured to:
for each of the first N periods, acquiring curvature, heading angle, transverse intercept and confidence of a lane line in the period through a forward camera, wherein the lane line comprises a left lane line and a right lane line; judging whether the left lane line exists in the period according to the confidence coefficient of the left lane line, and judging whether the right lane line exists in the period according to the confidence coefficient of the right lane line; if the left lane line and the right lane line exist in the period, calculating the curvature of the lane center line in the period according to the curvature of the left lane line in the period and the curvature of the right lane line in the period; calculating the course angle of the lane center line in the period according to the course angle of the left lane line in the period and the course angle of the right lane line in the period; calculating the transverse intercept of the lane center line in the period according to the transverse intercept of the left lane line in the period and the transverse intercept of the right lane line in the period; if one lane line exists between the left lane line and the right lane line in the period, taking the curvature of the existing lane line as the curvature of the lane center line in the period; discarding the period if neither the left lane line nor the right lane line is present in the period; and respectively taking the course angle and the transverse intercept in the current period as the course angle and the transverse intercept at the current position of the lane center line.
Optionally, the model generating module 30 is specifically configured to:
judging whether the rear road model is divided into two sections according to the longitudinal track of the history track of the front N periods of the vehicle; if the rear road model is not divided into two sections, taking the longitudinal track of the history track of the front N periods of the vehicle and the curvature of the front N periods of the vehicle as inputs, inputting the longitudinal track and the curvature of the front N periods of the vehicle into a clothoid curvature model, and calculating the curvature and curvature change rate of the rear road model by using a least square method; a rear road model is generated based on the curvature and curvature change rate of the rear road model, and the heading angle and the lateral intercept at the current position of the lane centerline.
Optionally, the model generating module 30 is further configured to:
if the rear road model is divided into two sections, respectively intercepting a first longitudinal track of the first section of rear road model and a second longitudinal track of the second section of rear road model from longitudinal tracks of the history tracks of the front N periods of the vehicle; respectively intercepting a first curvature of a first section of rear road model and a second curvature of a second section of rear road model from curvatures of N periods in front of the vehicle; inputting the first longitudinal track and the first curvature into a clothoid curvature model, and calculating the curvature and curvature change rate of the road model behind the first section by using a least square method; generating a first section of rear road model based on the curvature and curvature change rate of the first section of rear road model and the course angle and transverse intercept at the current position of the lane center line, and calculating the abscissa, the course angle and the curvature of the starting point of the second section of rear road model; taking the second longitudinal track and the second curvature as input, inputting the second longitudinal track and the second curvature into a clothoid curvature model, and calculating the curvature change rate of the road model behind the second section by using a least square method; and generating the second section rear road model based on the curvature change rate of the second section rear road model and the abscissa, the course angle and the curvature of the starting point of the second section rear road model.
The rear road model generating device provided by the embodiment of the invention generates the rear road model by means of the front cameras which are required to be equipped with the self-adaptive cruise control system and the lane keeping auxiliary system, provides a basis for judging the screening of the rear effective target vehicles by the automatic lane changing auxiliary driving system, and reduces the mass production cost of the automatic lane changing auxiliary driving system.
The above description is made in detail on a method and apparatus for generating a rear road model provided by the present invention, and specific examples are applied to illustrate the principles and embodiments of the present invention, and the above description of the examples is only for helping to understand the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
It should be noted that, in the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described as different from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
It is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include, or is intended to include, elements inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application and are intended to be comprehended within the scope of the present application.
Claims (10)
1. A method of generating a rear road model, the method comprising:
calculating historical tracks of N periods in front of a vehicle according to motion information of the vehicle, wherein the periods are acquisition periods of the motion information; the motion information comprises a yaw rate, a longitudinal vehicle speed and the acquisition period;
Calculating the curvature of N periods before a lane center line and the course angle and the transverse intercept at the current position of the lane center line by utilizing lane line information of a forward camera;
a rear road model is generated based on the historical track of the N periods before the vehicle, the curvature of the N periods before the lane centerline, and the heading angle and the transverse intercept at the current position of the lane centerline.
2. The method of claim 1, wherein calculating a historical track of the first N cycles of the vehicle from the motion information of the vehicle comprises:
collecting the yaw rate and the longitudinal speed of the front N periods of the vehicle;
calculating heading angles of the front N periods of the vehicle relative to a current coordinate system by utilizing the yaw rates of the front N periods of the vehicle;
and calculating the abscissa and the ordinate of the front N periods of the vehicle relative to the current coordinate system according to the longitudinal vehicle speed and the course angle of the front N periods of the vehicle.
3. The method of claim 1, wherein calculating the curvature of the N cycles before the lane centerline and the heading angle and the lateral intercept at the current position of the lane centerline using the lane line information of the forward facing camera comprises:
For each of the first N periods, acquiring curvature, heading angle, transverse intercept and confidence of a lane line in the period through the forward camera, wherein the lane line comprises a left lane line and a right lane line;
judging whether the left lane line exists in the period according to the confidence coefficient of the left lane line, and judging whether the right lane line exists in the period according to the confidence coefficient of the right lane line;
if the left lane line and the right lane line both exist in the period, calculating the curvature of the lane center line in the period according to the curvature of the left lane line in the period and the curvature of the right lane line in the period;
calculating the course angle of the lane center line in the period according to the course angle of the left lane line in the period and the course angle of the right lane line in the period;
calculating the transverse intercept of the lane center line in the period according to the transverse intercept of the left lane line in the period and the transverse intercept of the right lane line in the period;
if one lane line exists in the period of the left lane line and the right lane line, taking the curvature of the existing lane line as the curvature of the lane center line in the period;
Discarding the cycle if neither the left lane line nor the right lane line is present in the cycle;
and respectively taking the course angle and the transverse intercept in the current period as the course angle and the transverse intercept at the current position of the central line of the lane.
4. The method of claim 1, wherein the generating a rear road model based on the historical track for the first N cycles of the vehicle, the curvature for the first N cycles of the lane centerline, and the heading angle and lateral intercept at the current position of the lane centerline comprises:
judging whether the rear road model is divided into two sections according to the longitudinal track of the history track of the front N periods of the vehicle;
if the rear road model is not divided into two sections, taking the longitudinal track of the history track of the front N periods of the vehicle and the curvature of the front N periods of the vehicle as inputs, inputting the longitudinal track and the curvature of the front N periods of the vehicle into a clothoid curvature model, and calculating the curvature and curvature change rate of the rear road model by using a least square method;
a rear road model is generated based on the curvature and curvature change rate of the rear road model, and the heading angle and the lateral intercept at the current position of the lane centerline.
5. The method of claim 4, wherein the generating the rear road model based on the curvature and rate of change of curvature of the rear road model, and the heading angle and lateral intercept at the current location of the lane centerline, further comprises:
if the rear road model is divided into two sections, respectively intercepting a first longitudinal track of a first section of rear road model and a second longitudinal track of a second section of rear road model from longitudinal tracks of the history tracks of the front N periods of the vehicle;
intercepting a first curvature of the first section rear road model and a second curvature of the second section rear road model from curvatures of the front N periods of the vehicle respectively;
inputting the first longitudinal track and the first curvature into a clothoid curvature model, and calculating the curvature and curvature change rate of the road model behind the first section by using a least square method;
generating a first section of rear road model based on the curvature and curvature change rate of the first section of rear road model and the course angle and the transverse intercept at the current position of the lane center line, and calculating the abscissa, the course angle and the curvature of the starting point of the second section of rear road model;
Inputting the second longitudinal track and the second curvature into the clothoid curvature model, and calculating the curvature change rate of the road model behind the second section by using the least square method;
and generating the second section rear road model based on the curvature change rate of the second section rear road model and the abscissa, the course angle and the curvature of the starting point of the second section rear road model.
6. A rear road model generation apparatus, characterized in that the apparatus comprises:
the first calculation module is used for calculating the historical track of the front N periods of the vehicle according to the motion information of the vehicle, wherein the periods are acquisition periods of the motion information; the motion information comprises a yaw rate, a longitudinal vehicle speed and the acquisition period;
the second calculation module is used for calculating the curvature of N periods before the lane center line and the course angle and the transverse intercept at the current position of the lane center line by utilizing the lane line information of the forward camera;
the model generation module is used for generating a rear road model based on the historical track of the front N periods of the vehicle, the curvature of the front N periods of the lane center line and the course angle and the transverse intercept of the current position of the lane center line.
7. The apparatus of claim 6, wherein the first computing module is specifically configured to:
collecting the yaw rate and the longitudinal speed of the front N periods of the vehicle; calculating heading angles of the front N periods of the vehicle relative to a current coordinate system by utilizing the yaw rates of the front N periods of the vehicle; and calculating the abscissa and the ordinate of the front N periods of the vehicle relative to the current coordinate system according to the longitudinal vehicle speed and the course angle of the front N periods of the vehicle.
8. The apparatus of claim 6, wherein the second computing module is specifically configured to:
for each of the first N periods, acquiring curvature, heading angle, transverse intercept and confidence of a lane line in the period through the forward camera, wherein the lane line comprises a left lane line and a right lane line; judging whether the left lane line exists in the period according to the confidence coefficient of the left lane line, and judging whether the right lane line exists in the period according to the confidence coefficient of the right lane line; if the left lane line and the right lane line both exist in the period, calculating the curvature of the lane center line in the period according to the curvature of the left lane line in the period and the curvature of the right lane line in the period; calculating the course angle of the lane center line in the period according to the course angle of the left lane line in the period and the course angle of the right lane line in the period; calculating the transverse intercept of the lane center line in the period according to the transverse intercept of the left lane line in the period and the transverse intercept of the right lane line in the period; if one lane line exists in the period of the left lane line and the right lane line, taking the curvature of the existing lane line as the curvature of the lane center line in the period; discarding the cycle if neither the left lane line nor the right lane line is present in the cycle; and respectively taking the course angle and the transverse intercept in the current period as the course angle and the transverse intercept at the current position of the central line of the lane.
9. The apparatus of claim 6, wherein the model generation module is specifically configured to:
judging whether the rear road model is divided into two sections according to the longitudinal track of the history track of the front N periods of the vehicle; if the rear road model is not divided into two sections, taking the longitudinal track of the history track of the front N periods of the vehicle and the curvature of the front N periods of the vehicle as inputs, inputting the longitudinal track and the curvature of the front N periods of the vehicle into a clothoid curvature model, and calculating the curvature and curvature change rate of the rear road model by using a least square method; a rear road model is generated based on the curvature and curvature change rate of the rear road model, and the heading angle and the lateral intercept at the current position of the lane centerline.
10. The apparatus of claim 9, wherein the model generation module is further configured to:
if the rear road model is divided into two sections, respectively intercepting a first longitudinal track of a first section of rear road model and a second longitudinal track of a second section of rear road model from longitudinal tracks of the history tracks of the front N periods of the vehicle; intercepting a first curvature of the first section rear road model and a second curvature of the second section rear road model from curvatures of the front N periods of the vehicle respectively; inputting the first longitudinal track and the first curvature into a clothoid curvature model, and calculating the curvature and curvature change rate of the road model behind the first section by using a least square method; generating a first section of rear road model based on the curvature and curvature change rate of the first section of rear road model and the course angle and the transverse intercept at the current position of the lane center line, and calculating the abscissa, the course angle and the curvature of the starting point of the second section of rear road model; inputting the second longitudinal track and the second curvature into the clothoid curvature model, and calculating the curvature change rate of the road model behind the second section by using the least square method; and generating the second section rear road model based on the curvature change rate of the second section rear road model and the abscissa, the course angle and the curvature of the starting point of the second section rear road model.
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