CN117593901A - Vehicle-road cooperative lane calculation method, system and storage medium - Google Patents

Abstract

The invention discloses a vehicle-road cooperative lane calculation method, a system and a storage medium, wherein the vehicle-road cooperative lane calculation method comprises the following steps: acquiring current map corpus information and position information of a vehicle; performing likelihood road segment set screening to obtain a first likelihood road segment set; screening the first likelihood road segment set according to a specified rule to obtain a second likelihood road segment set; traversing all road sections in the second likelihood road section set, traversing all lanes in each road section, calculating the distance d between the current longitude and latitude of the vehicle and the lane center line formed by the center point sequences of all lanes, selecting one lane with the minimum d value in all lanes, and meeting the requirements ofIs the current lane of the vehicle.

Description

Vehicle-road cooperative lane calculation method, system and storage medium

Technical Field

The invention relates to the field of automobiles, in particular to a method, a system and a storage medium for calculating a cooperative lane of a vehicle and a road.

Background

In the existing automatic driving technology, lane recognition often calculates the relationship between a vehicle and a lane by matching lane lines according to a vehicle vision sensor. The visual sensor has a certain limitation in lane recognition under high-speed driving due to the interference of the sensing range of the visual sensor and external environments including lane lines, weather and the like.

Chinese patent CN201811154083.4 provides a road information fusion system and method for an autonomous vehicle. The road information fusion system comprises: the information acquisition unit is used for acquiring various road information acquired by various types of road perception sensors of the vehicle; the information initial fusion unit is used for carrying out initial fusion on the plurality of road information, wherein the initial fusion comprises information preprocessing and information validity verification; and the information depth fusion unit is used for carrying out depth fusion on the plurality of road information after the initial fusion to output a road model of the vehicle, wherein the depth fusion comprises information coordinate conversion, road feature point extraction, lane line fitting, lane line calculation and information comprehensive management. According to the scheme, the lane line is obtained through the basic information of the visual sensor and the algorithm calculation, the accuracy of the lane line can be influenced by weather, vehicle speed, sensor accuracy and the like, and lane line identification deviation occurs.

The Chinese patent CN202210396801.9 provides a method, a device and electronic equipment for generating lane lines, wherein after a road surface video frame sequence acquired by a vehicle-mounted camera is acquired in real time in the running process of a vehicle, 2D lane lines contained in a target road surface video frame at the current moment are projected to a virtual ground based on calibration parameters of the vehicle-mounted camera; projecting the selected lane line at the current moment to the virtual ground again according to equal-width assumption optimization of lane widths of two adjacent parallel selected lane lines on the virtual ground to obtain a first projected lane line; compensating the camera external parameters according to the angle variation calculated by the first projection lane line to obtain the compensated camera external parameters at the current moment; and generating a lane line under the vehicle coordinate system at the current moment based on the compensation camera external parameters. According to the scheme, the lane line is obtained through the basic information of the visual sensor and the algorithm calculation, the accuracy of the lane line can be influenced by weather, vehicle speed, sensor accuracy and the like, and lane line identification deviation occurs.

Description of prior art names;

likelihood calculation, i.e. maximum likelihood estimation, is a common parameter estimation method, mainly used for estimating the overall parameters represented by one sample.

Disclosure of Invention

In the summary section, a series of simplified form concepts are introduced that are all prior art simplifications in the section, which are described in further detail in the detailed description section. The summary of the invention is not intended to define the key features and essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

The invention aims to provide a method and a system for calculating a cooperative lane of a vehicle and a road without depending on a visual sensor,

in order to solve the technical problems, the vehicle-road cooperative lane calculating method provided by the invention is characterized by comprising the following steps of:

s1, acquiring current map corpus information and position information of a vehicle;

s2, performing likelihood road segment set screening to obtain a first likelihood road segment set;

s3, screening the first likelihood road segment set according to a specified rule to obtain a second likelihood road segment set;

s4, traversing all road sections in the second likelihood road section set, traversing all lanes in each road section, calculating the distance d between the current longitude and latitude of the vehicle and the lane center line formed by the center point sequences of all lanes, selecting one lane with the minimum d value in all lanes, and meeting the requirementIs of the lane of (a)The current lane of the vehicle;

w is the width of the lane, and acturalrror is the maximum allowable error of the perpendicular distance between the connecting line of the central points of any two continuous road sections and the central line of the real road section.

Optionally, the vehicle-road cooperative lane calculating method is further improved, and the map corpus information includes: a plurality of node information each including a position of a center point of an intersection and link information of all links entering the node

The link information includes: a road section center point sequence, a road section width and all lane information of the road section;

the lane information includes at least: a lane center sequence and a lane width;

the location information includes: current location information and heading angle information.

Optionally, the vehicle-road cooperative lane calculation method is further improved, and when step S2 is implemented, the first likelihood road segment set is obtained by screening according to the real-time longitude and latitude, the heading angle or a combination thereof of the vehicle.

Optionally, further improving the vehicle-road cooperative lane calculation method, performing likelihood road segment set screening according to the real-time longitude and latitude of the vehicle includes:

the current longitude and latitude of the vehicle is point= { Point long, point lat };

for a certain road section L, when a sequence of the road section center of the road section L exists in the map corpus information, selecting longitude and latitude information of a first point and a last point of the sequence of the road section center;

if the road section L does not have the road section center point sequence, selecting a first point and a last point of any one lane center point sequence of the road section L;

PointFirst= { PointFirstlong, pointFirstlat } and respectively

PointLast＝{PointLastlong,PointLastlat}；

When the size of PointLong is between PointFirstlon and PointLastLong, or PointLat is between PointFirstly and PointLastLat, this road segment L belongs to the first set of likelihood road segments.

Optionally, further improving the vehicle-road cooperative lane calculation method, performing likelihood road segment set screening according to a real-time heading angle of the vehicle includes:

the current longitude and latitude of the vehicle is point= { Point long, point lat };

for a certain road section L, when a sequence of the road section center of the road section L exists in the map corpus information, selecting longitude and latitude information of a first point and a last point of the sequence of the road section center;

if the road section L does not have the road section center point sequence, selecting a first point and a last point of any one lane center point sequence of the road section L;

PointFirst= { PointFirstlong, pointFirstlat } and respectively

PointLast＝{PointLastlong,PointLastlat}；

The absolute value of the vector included angle formed by the heading angle of the vehicle, the point PointFirst and the point PointLast is smaller than the preset range, and the road section L belongs to a first likelihood road section set.

Optionally, the method for calculating the cooperative lane of the vehicle road is further improved, and when the step 3 is implemented, the specified rule includes:

if the number of the road sections in the first likelihood road section set is 1, the road sections are put into the second likelihood road section set;

if the number of the road sections in the first likelihood road section set is greater than 1, screening out the road sections which accord with the characteristics of the second likelihood road section and putting the road sections into the second likelihood road section set, wherein the screened road sections have any one of the following characteristics and put the road sections into the second likelihood road section set;

a) If the road section information does not contain the road section center point sequence set;

b) d is smaller than the sum of half the average width w of the road segment and the road segment actualrror.

Optionally, further improving the vehicle-road cooperative lane calculation method, and calculating d by adopting the following mode;

constructing n-1 grids for the road segment center point sequences { P1, P2, …, pn };

for the ith grid, a wide is constructed by taking line segments Pi and Pi+i as central axesDegree ofThe rectangle of (2) re-enlarges the road section actual error to four sides;

if the Point is not within the ith grid, the distance di between the Point and the ith grid is recorded as infinity;

if Point is located within the ith grid, di is the distance between Point and the line segment constructed by Pi and Pi+i;

d is the minimum value of Point and each grid, point is the current longitude and latitude of the vehicle

Point＝{Pointlong,Pointlat}。

To solve the above-mentioned problems, the present invention provides a computer readable storage medium having a computer program stored therein, wherein the computer program is executed to implement the steps of the vehicle-road cooperative lane calculation method according to any one of the above-mentioned embodiments.

In order to solve the technical problems, the present invention provides a vehicle-road cooperative lane computing system, comprising:

the receiving module is used for acquiring current map corpus information and position information of the vehicle from the vehicle-mounted communication network or the vehicle-mounted functional module;

the screening module is used for executing screening of the likelihood road segment sets to obtain a first likelihood road segment set; screening the first likelihood road segment set according to a specified rule to obtain a second likelihood road segment set;

the calculation module is used for traversing all road sections in the second likelihood road section set, traversing all lanes in each road section, calculating the distance d between the current longitude and latitude of the vehicle and the lane center line formed by the center point sequences of all lanes, selecting one lane with the minimum d value in all lanes, and meeting the requirementThe lane of the vehicle is the current lane of the vehicle;

w is the width of the lane, and acturalrror is the maximum allowable error of the perpendicular distance between the connecting line of the central points of any two continuous road sections and the central line of the real road section.

Optionally, the vehicle-mounted communication network further improves the vehicle-road cooperative lane computing system, and includes: CAN, LIN, V2X, LTE, TBOX, WIFI, 2G, 3G, 4G and/or 5G;

the system comprises a vehicle-mounted function module, a positioning module and a V2X message module.

Optionally, the vehicle-road cooperative lane computing system is further improved, and the map corpus information includes: a plurality of node information each including a position of a center point of an intersection and link information of all links entering the node

The link information includes: a road section center point sequence, a road section width and all lane information of the road section;

the lane information includes: lane center point sequence, lane width and speed limit;

the location information includes: current location information and heading angle information.

Optionally, the vehicle-road cooperative lane computing system is further improved, and the first likelihood road segment set is obtained through screening according to the real-time longitude and latitude, the course angle or the combination of the longitude and the course angle of the vehicle.

Optionally, further improving the vehicle-road cooperative lane computing system, the screening module performs likelihood road segment set screening according to the real-time longitude and latitude of the vehicle, and includes:

the current longitude and latitude of the vehicle is point= { Point long, point lat };

for a certain road section L, when a sequence of the road section center of the road section L exists in the map corpus information, selecting longitude and latitude information of a first point and a last point of the sequence of the road section center;

if the road section L does not have the road section center point sequence, selecting a first point and a last point of any one lane center point sequence of the road section L;

PointFirst= { PointFirstlong, pointFirstlat } and respectively

PointLast＝{PointLastlong,PointLastlat}；

When the size of PointLong is between PointFirstlon and PointLastLong, or PointLat is between PointFirstly and PointLastLat, this road segment L belongs to the first set of likelihood road segments.

Optionally, further improving the vehicle-road cooperative lane computing system, the screening module performs likelihood road segment set screening according to a real-time heading angle of the vehicle, and includes:

the current longitude and latitude of the vehicle is point= { Point long, point lat };

for a certain road section L, when a sequence of the road section center of the road section L exists in the map corpus information, selecting longitude and latitude information of a first point and a last point of the sequence of the road section center;

if the road section L does not have the road section center point sequence, selecting a first point and a last point of any one lane center point sequence of the road section L;

PointFirst= { PointFirstlong, pointFirstlat } and respectively

PointLast＝{PointLastlong,PointLastlat}；

The absolute value of the vector included angle formed by the heading angle of the vehicle, the point PointFirst and the point PointLast is smaller than the preset range, and the road section L belongs to a first likelihood road section set.

Optionally, the vehicle-road cooperative lane computing system is further improved, and the specified rule includes:

if the number of the road sections in the first likelihood road section set is 1, the road sections are put into the second likelihood road section set;

if the number of the road sections in the first likelihood road section set is greater than 1, screening out the road sections which accord with the characteristics of the second likelihood road section and putting the road sections into the second likelihood road section set, wherein the screened road sections have any one of the following characteristics and put the road sections into the second likelihood road section set;

a) If the road section information does not contain the road section center point sequence set;

b) d is smaller than the sum of half the average width w of the road segment and the road segment actualrror.

Optionally, further improving the vehicle-road cooperative lane computing system, and d is computed in the following manner;

constructing n-1 grids for the road segment center point sequences { P1, P2, …, pn };

for the ith grid, toLine segments Pi, pi+i as central axis to construct a width ofThe rectangle of (2) re-enlarges the road section actual error to four sides;

if the Point is not within the ith grid, the distance di between the Point and the ith grid is recorded as infinity;

if Point is located within the ith grid, di is the distance between Point and the line segment constructed by Pi and Pi+i;

d is the minimum value of Point and each grid, point is the current longitude and latitude of the vehicle

Point＝{Pointlong,Pointlat}。

With the development of communication technology, the internet of vehicles can now provide a vehicle-road cooperative environment. In a vehicle-Road cooperative environment, an RSU (Road Side Unit) periodically sends MAP (MAP) messages and other V2X (vehicle to everything, information interaction to the outside) messages to surrounding vehicles. The On board Unit (On board Unit) of the vehicle can calculate the lane where the vehicle is located according to the parameters of each sensor of the vehicle and by combining the map data sent by the RSU, and can perform automatic driving or auxiliary driving according to the lane information. For example, in the case of accurate lane calculation, the corresponding traffic light information may be matched according to the current lane of the vehicle.

The invention realizes the calculation of the lane without depending on a sensor (especially a visual sensor) based on the cooperative vehicle-road environment. In a vehicle-road cooperative environment, a vehicle generally receives map information of a plurality of different nodes sent by a plurality of RSUs at the same time, each node further comprises a plurality of road section information, each road section has a plurality of lane information, and each lane can have up to 32 road center point sequences. If the distances are simply found for each successive center sequence point, the computational performance is enormous.

The scheme provided by the invention is simple and effective, and the distance from the point to the line is calculated only when the vehicle position point is between grids through the steps of screening out the likelihood road sections, constructing the grids and the like. The calculated amount of the lane cooperatively calculated by the vehicle and the road can be remarkably reduced. Meanwhile, the invention can be accurately suitable for different types of roads, and besides the straight road, the lane information of the vehicle can be accurately matched for the curved lanes and the branched lanes.

Drawings

The accompanying drawings are intended to illustrate the general features of methods, structures and/or materials used in accordance with certain exemplary embodiments of the invention, and supplement the description in this specification. The drawings of the present invention, however, are schematic illustrations that are not to scale and, thus, may not be able to accurately reflect the precise structural or performance characteristics of any given embodiment, the present invention should not be construed as limiting or restricting the scope of the numerical values or attributes encompassed by the exemplary embodiments according to the present invention. The invention is described in further detail below with reference to the attached drawings and detailed description:

FIG. 1 is a schematic block diagram of an embodiment of the system of the present invention.

FIG. 2 is a schematic diagram of the acturalror position of the present invention.

FIG. 3 is a schematic diagram of the rasterization process of the present invention.

FIG. 4 is a schematic diagram of a first likelihood segment set screening method of the present invention

Fig. 5 is a schematic diagram of a calculated position of the vehicle position Point to the center line distance of the road section (lane).

Fig. 6 is a schematic diagram of a calculated position of a vehicle from a road segment or a lane center line distance.

Detailed Description

Other advantages and technical effects of the present invention will become more fully apparent to those skilled in the art from the following disclosure, which is a detailed description of the present invention given by way of specific examples. The invention may be practiced or carried out in different embodiments, and details in this description may be applied from different points of view, without departing from the general inventive concept. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. The following exemplary embodiments of the present invention may be embodied in many different forms and should not be construed as limited to the specific embodiments set forth herein. It should be appreciated that these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the technical solution of these exemplary embodiments to those skilled in the art. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements present. Like reference numerals refer to like elements throughout the several views. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

A first embodiment;

the invention provides a vehicle-road cooperative lane calculation method, which comprises the following steps:

s1, acquiring current map corpus information and position information of a vehicle;

the map corpus information includes: a plurality of node information each including a position of a center point of an intersection and link information of all links entering the node

The link information includes: a road section center point sequence, a road section width and all lane information of the road section;

the location information includes: current positioning information and course angle information;

s2, performing likelihood road segment set screening to obtain a first likelihood road segment set; the first likelihood road section set is obtained through screening according to the real-time longitude and latitude, course angle or combination of the longitude and latitude and the course angle of the vehicle;

s3, screening the first likelihood road segment set according to a specified rule to obtain a second likelihood road segment set; the specified rules include:

if the number of the road sections in the first likelihood road section set is 1, the road sections are put into the second likelihood road section set;

if the number of the road sections in the first likelihood road section set is greater than 1, screening out the road sections which accord with the characteristics of the second likelihood road section and putting the road sections into the second likelihood road section set, wherein the screened road sections have any one of the following characteristics and put the road sections into the second likelihood road section set;

a) If the road section information does not contain the road section center point sequence set;

b) d is smaller than the sum of half of the average width w of the road section and the road section actual error;

d is calculated in the following way;

constructing n-1 grids for the road segment center point sequences { P1, P2, …, pn };

for the ith grid, a width is constructed by taking line segments Pi and Pi+i as central axesThe rectangle of (2) re-enlarges the road section actual error to four sides;

if the Point is not within the ith grid, the distance di between the Point and the ith grid is recorded as infinity;

if Point is located within the ith grid, di is the distance between Point and the line segment constructed by Pi and Pi+i;

d is the minimum value of Point and each grid, point is the current longitude and latitude of the vehicle

Point＝{Pointlong,Pointlat}；

S4, traversing all road sections in the second likelihood road section set, traversing all lanes in each road section, calculating the distance d between the current longitude and latitude of the vehicle and the lane center line formed by the center point sequences of all lanes, selecting one lane with the minimum d value in all lanes, and meeting the requirementThe lane of the vehicle is the current lane of the vehicle;

w is the width of the lane, and acturalrror is the maximum allowable error of the perpendicular distance between the connecting line of the central points of any two continuous road sections and the central line of the real road section.

Illustratively, in the first embodiment, performing likelihood road segment set screening according to the real-time longitude and latitude of the vehicle includes:

the current longitude and latitude of the vehicle is point= { Point long, point lat };

for a certain road section L, when a sequence of the road section center of the road section L exists in the map corpus information, selecting longitude and latitude information of a first point and a last point of the sequence of the road section center;

if the road section L does not have the road section center point sequence, selecting a first point and a last point of any one lane center point sequence of the road section L;

PointFirst= { PointFirstlong, pointFirstlat } and respectively

PointLast＝{PointLastlong,PointLastlat}；

When the size of PointLong is between PointFirstlon and PointLastLong, or PointLat is between PointFirstly and PointLastLat, this road segment L belongs to the first set of likelihood road segments.

Illustratively, in the above first embodiment, performing the likelihood road segment set screening according to the real-time heading angle of the vehicle includes:

the current longitude and latitude of the vehicle is point= { Point long, point lat };

for a certain road section L, when a sequence of the road section center of the road section L exists in the map corpus information, selecting longitude and latitude information of a first point and a last point of the sequence of the road section center;

if the road section L does not have the road section center point sequence, selecting a first point and a last point of any one lane center point sequence of the road section L;

PointFirst= { PointFirstlong, pointFirstlat } and respectively

PointLast＝{PointLastlong,PointLastlat}；

The absolute value of the vector included angle formed by the heading angle of the vehicle, the point PointFirst and the point PointLast is smaller than the preset range, and the road section L belongs to a first likelihood road section set.

A second embodiment;

the present invention provides a computer-readable storage medium having stored therein a computer program which, when executed, is adapted to carry out the steps of the vehicle-road cooperative lane calculation method according to the first embodiment.

Including both non-transitory and non-transitory, removable and non-removable media, the information storage may be implemented by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer readable media, as defined herein, does not include non-transitory computer readable media (transmission media), such as modulated data signals and carrier waves.

A third embodiment;

the invention provides a vehicle-road cooperative lane computing system which can be realized based on the existing hardware and computer programming technology, and comprises the following components:

the receiving module is used for acquiring current map corpus information and position information of the vehicle from the vehicle-mounted communication network or the vehicle-mounted functional module;

the in-vehicle communication network includes: CAN, LIN, V2X, LTE, TBOX, WIFI, 2G, 3G, 4G and/or 5G;

for example, the required data CAN be obtained through a vehicle-mounted CAN network;

or, referring to fig. 1, the V2X message receiving module is configured to receive a MAP message sent by the RSU, and obtain positioning information through an on-vehicle positioning module (ublox positioning chip);

the map corpus information includes: a plurality of node information each including a position of a center point of an intersection and link information of all links entering the node

The link information includes: a road section center point sequence, a road section width and all lane information of the road section;

the location information includes: current positioning information and course angle information;

the screening module is used for executing screening of the likelihood road segment sets to obtain a first likelihood road segment set; screening the first likelihood road segment set according to a specified rule to obtain a second likelihood road segment set; the first likelihood road section set is obtained through screening according to the real-time longitude and latitude, course angle or combination of the longitude and latitude and the course angle of the vehicle;

the specified rules include:

if the number of the road sections in the first likelihood road section set is 1, the road sections are put into the second likelihood road section set;

if the number of the road sections in the first likelihood road section set is greater than 1, screening out the road sections which accord with the characteristics of the second likelihood road section and putting the road sections into the second likelihood road section set, wherein the screened road sections have any one of the following characteristics and put the road sections into the second likelihood road section set;

a) If the road section information does not contain the road section center point sequence set;

b) d is smaller than the sum of half of the average width w of the road segment and the road segment actual error, as shown with reference to fig. 2;

d is calculated in the following manner, with reference to FIG. 3;

constructing n-1 grids for the road segment center point sequences { P1, P2, …, pn };

for the ith grid, a width is constructed by taking line segments Pi and Pi+i as central axesThe rectangle of (2) re-enlarges the road section actual error to four sides;

if the Point is not within the ith grid, the distance di between the Point and the ith grid is recorded as infinity;

if Point is located within the ith grid, di is the distance between Point and the line segment constructed by Pi and Pi+i;

d is the minimum value of Point and each grid, point is the current longitude and latitude of the vehicle

Point＝{Pointlong,Pointlat}；

Further, exemplarily, referring to fig. 4, a coordinate system is established with the east-west direction as the horizontal axis and the north-north direction as the vertical axis. The horizontal axis is longitude and the vertical axis is latitude. Assume that the current memory module stores a total of 12 node information of N1 through N12. For example, a road section (N1, N2) exists from the node N1 to the node N2, and a road section (N2, N1) exists from the node N2 to the node N1;

according to the first likelihood road segment set calculation method, the vehicle position Point is screened according to longitude and latitude, and only any one of the abscissa and the ordinate of the vehicle position Point is located at the corresponding coordinate of a certain road segment directly, so that the road segment accords with the characteristics of the first likelihood road segment. A first SET of likelihood segments SET1 may be obtained. SET1 = { (N1, N4), (N4, N1), (N4, N5), (N5, N4), (N5, N8), (N8, N5), (N8, N9), (N9, N8), (N9, N12), (N12, N9), (N7, N10), (N10, N7), (N7, N8), (N8, N7), (N6, N9), (N9, N6) };

and then screening according to the course angle, and setting a screening rule that the difference value between the road section direction (the running direction from the road section starting point to the road section ending point and the north-positive included angle) and the vehicle course angle (the running direction of the vehicle and the north-positive included angle) is smaller than 30 degrees. Then the elements in the SET1 SET become smaller after the filtering again based on heading angle. After calculation, SET1 = { (N1, N4), (N5, N8), (N9, N12), (N7, N10), (N6, N9) };

since there are 5 elements in the SET1 SET, it is necessary to calculate each road segment and screen out the road segment having the second likelihood road segment characteristics. Taking the road segments (N5, N8) as an example, the road segments are provided with a road segment center point sequence, so that the distance between a vehicle and a road segment center line formed by the road segment center point sequence needs to be calculated;

the calculation module is used for traversing all road sections in the second likelihood road section set, traversing all lanes in each road section, calculating the distance d between the current longitude and latitude of the vehicle and the lane center line formed by the center point sequences of all lanes, selecting one lane with the minimum d value in all lanes, and meeting the requirementThe lane of the vehicle is the current lane of the vehicle;

w is the width of the lane, and acturalrror is the maximum allowable error of the perpendicular distance between the connecting line of the central points of any two continuous road sections and the central line of the real road section.

In an exemplary embodiment, the screening module performs likelihood road segment set screening according to the real-time longitude and latitude of the vehicle, including:

the current longitude and latitude of the vehicle is point= { Point long, point lat };

for a certain road section L, when a sequence of the road section center of the road section L exists in the map corpus information, selecting longitude and latitude information of a first point and a last point of the sequence of the road section center;

if the road section L does not have the road section center point sequence, selecting a first point and a last point of any one lane center point sequence of the road section L;

PointFirst= { PointFirstlong, pointFirstlat } and respectively

PointLast＝{PointLastlong,PointLastlat}；

When the size of PointLong is between PointFirstlon and PointLastLong, or PointLat is between PointFirstly and PointLastLat, this road segment L belongs to the first set of likelihood road segments.

Illustratively, in the above second embodiment, the filtering module performing the likelihood road segment set filtering according to the real-time heading angle of the vehicle includes:

the current longitude and latitude of the vehicle is point= { Point long, point lat };

for a certain road section L, when a sequence of the road section center of the road section L exists in the map corpus information, selecting longitude and latitude information of a first point and a last point of the sequence of the road section center;

if the road section L does not have the road section center point sequence, selecting a first point and a last point of any one lane center point sequence of the road section L;

PointFirst= { PointFirstlong, pointFirstlat } and PointLast= { PointLastlong, pointLastlat } respectively;

the absolute value of the vector included angle formed by the heading angle of the vehicle, the point PointFirst and the point PointLast is smaller than the preset range, and the road section L belongs to a first likelihood road section set.

Further, the exemplary description of the grid and the corresponding calculation process based on the above-described fig. 2-4 is as follows;

the road section center point sequence is { P1, P2, …, pi, pi+1, …, pn }, roadThe segment width is W, and the vehicle current position point= (long, lat) is shown in fig. 5. The actual error of the road section is 2 meters. Pi coordinates are (long 1, lat 1), pi+1 coordinates are (long 2, lat 2), and as shown in FIG. 5, long1 < long2, lat1 < lat2. An alternative method may be used to construct the grid ABCD. A horizontal rectangle is constructed by taking (Pi, pi+1) as a diagonal line, such as a rectangle formed by a broken line lock in FIG. 5, and four coordinates are { (long 1, lat 2), (long 2, lat 2), (long 2, lat 1), (long 1, lat 1) }, and then w/2+actual error is expanded to four sides on the basis of the rectangle { A, B, C, D }. The expansion method is as follows: according to the earth longitude and latitude model, the latitude change degree at the Point (long, lat) is 111000 m, and the longitude change degree is 111000 x cos (lat). Recording deviceWhere lat is the latitude of the Point.

Rectangular ABCD coordinates can then be found as

A＝(long1-Δlong，lat2+Δlat)

B＝(long2+Δlong，lat2+Δlat)

C＝(long2+Δlong，lat1-Δlat)

D＝(long1-Δlong，lat1-Δlat)

Thus, determining whether Point= (long, lat) is located in the grid of center Point sequence (Pi, pi+1) can be reduced to whether the following four inequalities are satisfied simultaneously

long＞(long1-Δlong)；long＜(long2+Δlong)；lat＞(lat1-Δlat)；lat＜(lat2+Δlat)

According to the above method, in combination with the position of the vehicle Point shown in fig. 5, it is possible to calculate that Point is located in the grid EFGH formed by (Pi, pi+1) and the grid ABCD formed by (pi+1, pi+2) at the same time.

For any two adjacent sequence points (Pi, pi+1), if the Point is not in the grid formed by (Pi, pi+1), the distance di between the Point and the (Pi, pi+1) is recorded as

Referring to fig. 6, the vehicle position is Point, the lane center line sequence is (Pi, pi+1), a perpendicular line (Point, 0) is first drawn from the Point to a straight line formed by (Pi, pi+1), and if the Point 0 is located between (Pi, pi+1), the length of the line segment (Point, 0) is the distance di between the vehicle and the center Point sequence (Pi, pi+1). Otherwise the minimum of the lengths of the line segments (Point, pi) and (Point, pi+1) is di. As shown in fig. 6, the left graph shows di as the length of the line segment (Point, 0), and the right graph shows di as the length of the line segment (Point, pi).

And obtaining the minimum value of all grids, namely the distance d between the vehicle and the central line. In the national standard, the actual error of a road section is defined as 2 meters, and the actual error of a lane is defined as 0.5 meters.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

The present invention has been described in detail by way of specific embodiments and examples, but these should not be construed as limiting the invention. Many variations and modifications may be made by one skilled in the art without departing from the principles of the invention, which is also considered to be within the scope of the invention.

Claims (16)

1. The vehicle-road cooperative lane calculating method is characterized by comprising the following steps of:

s1, acquiring current map corpus information and position information of a vehicle;

s2, performing likelihood road segment set screening to obtain a first likelihood road segment set;

s3, screening the first likelihood road segment set according to a specified rule to obtain a second likelihood road segment set;

s4, traversing all road sections in the second likelihood road section set, traversing all lanes in each road section, calculating the distance d between the current longitude and latitude of the vehicle and the lane center line formed by the center point sequences of all lanes, selecting one lane with the minimum d value in all lanes, and meeting the requirementThe lane of the vehicle is the current lane of the vehicle;

w is the width of the lane, and acturalrror is the maximum allowable error of the perpendicular distance between the connecting line of the central points of any two continuous road sections and the central line of the real road section.

2. The vehicle-road cooperative lane computing method according to claim 1, wherein:

the map corpus information includes: a plurality of node information each including a position of a center point of an intersection and link information of all links entering the node

The link information includes: a road section center point sequence, a road section width and all lane information of the road section;

the location information includes: current location information and heading angle information.

3. The vehicle-road cooperative lane computing method according to claim 1, wherein: when step S2 is implemented, the first likelihood road segment set is obtained by screening according to the real-time longitude and latitude, heading angle or a combination thereof of the vehicle.

4. The vehicle-road cooperative lane computing method of claim 3, wherein performing likelihood road segment set screening according to a real-time longitude and latitude of a vehicle comprises:

the current longitude and latitude of the vehicle is point= { Point long, point lat };

for a certain road section L, when a sequence of the road section center of the road section L exists in the map corpus information, selecting longitude and latitude information of a first point and a last point of the sequence of the road section center;

if the road section L does not have the road section center point sequence, selecting a first point and a last point of any one lane center point sequence of the road section L;

PointFirst= { PointFirstlong, pointFirstlat } and PointLast= { PointLastlong, pointLastlat } respectively;

when the size of PointLong is between PointFirstlon and PointLastLong, or PointLat is between PointFirstly and PointLastLat, this road segment L belongs to the first set of likelihood road segments.

5. The vehicle-road cooperative lane computing method of claim 3, wherein performing likelihood segment set screening according to a real-time heading angle of the vehicle comprises:

the current longitude and latitude of the vehicle is point= { Point long, point lat };

for a certain road section L, when a sequence of the road section center of the road section L exists in the map corpus information, selecting longitude and latitude information of a first point and a last point of the sequence of the road section center;

if the road section L does not have the road section center point sequence, selecting a first point and a last point of any one lane center point sequence of the road section L;

PointFirst= { PointFirstlong, pointFirstlat } and PointLast= { PointLastlong, pointLastlat } respectively;

the absolute value of the vector included angle formed by the heading angle of the vehicle, the point PointFirst and the point PointLast is smaller than the preset range, and the road section L belongs to a first likelihood road section set.

6. The vehicle-road cooperative lane computing method according to claim 1, wherein: when the step 3 is implemented, the specified rule includes:

if the number of the road sections in the first likelihood road section set is 1, the road sections are put into the second likelihood road section set;

if the number of the road sections in the first likelihood road section set is greater than 1, screening out the road sections which accord with the characteristics of the second likelihood road section and putting the road sections into the second likelihood road section set, wherein the screened road sections have any one of the following characteristics and put the road sections into the second likelihood road section set;

a) If the road section information does not contain the road section center point sequence set;

b) d is smaller than the sum of half the average width w of the road segment and the road segment actualrror.

7. The vehicle-road cooperative lane calculating method according to claim 1, wherein d is calculated in the following manner;

constructing n-1 grids for the road segment center point sequences { P1, P2, …, pn };

for the ith grid, a width is constructed by taking line segments Pi and Pi+i as central axesThe rectangle of (2) re-enlarges the road section actual error to four sides;

if the Point is not within the ith grid, the distance di between the Point and the ith grid is recorded as infinity;

if Point is located within the ith grid, di is the distance between Point and the line segment constructed by Pi and Pi+i;

d is the minimum value of the Point and each grid, and the Point is the current longitude and latitude point= { Point long, point last }, of the vehicle.

8. A computer-readable storage medium, characterized by: a computer program stored therein, which when executed is adapted to carry out the steps of the vehicle road co-operative lane calculation method as claimed in any one of claims 1 to 7.

9. A vehicle lane co-ordination computing system, comprising:

the receiving module is used for acquiring current map corpus information and position information of the vehicle from the vehicle-mounted communication network or the vehicle-mounted functional module;

the screening module is used for executing screening of the likelihood road segment sets to obtain a first likelihood road segment set; screening the first likelihood road segment set according to a specified rule to obtain a second likelihood road segment set;

the calculation module is used for traversing all road sections in the second likelihood road section set, traversing all lanes in each road section, calculating the distance d between the current longitude and latitude of the vehicle and the lane center line formed by the center point sequences of all lanes, selecting one lane with the minimum d value in all lanes, and meeting the requirementThe lane of the vehicle is the current lane of the vehicle;

w is the width of the lane, and acturalrror is the maximum allowable error of the perpendicular distance between the connecting line of the central points of any two continuous road sections and the central line of the real road section.

10. The vehicular lane cooperative computing system of claim 9, wherein:

the in-vehicle communication network includes: CAN, LIN, V2X, LTE, TBOX, WIFI, 2G, 3G, 4G and/or 5G;

the system comprises a vehicle-mounted function module, a positioning module and a V2X message module.

11. The vehicular lane cooperative computing system of claim 10, wherein:

the map corpus information includes: a plurality of node information each including a position of a center point of an intersection and link information of all links entering the node

The link information includes: a road section center point sequence, a road section width and all lane information of the road section;

the location information includes: current location information and heading angle information.

12. The vehicular lane cooperative computing system of claim 10, wherein:

the first likelihood road segment set is obtained by screening according to the real-time longitude and latitude, the course angle or the combination of the longitude and latitude and the course angle of the vehicle.

13. The vehicular lane cooperative computing system of claim 10, wherein: the screening module performs likelihood road segment set screening according to the real-time longitude and latitude of the vehicle, and comprises:

the current longitude and latitude of the vehicle is point= { Point long, point lat };

for a certain road section L, when a sequence of the road section center of the road section L exists in the map corpus information, selecting longitude and latitude information of a first point and a last point of the sequence of the road section center;

if the road section L does not have the road section center point sequence, selecting a first point and a last point of any one lane center point sequence of the road section L;

PointFirst= { PointFirstlong, pointFirstlat } and PointLast= { PointLastlong, pointLastlat } respectively;

when the size of PointLong is between PointFirstlon and PointLastLong, or PointLat is between PointFirstly and PointLastLat, this road segment L belongs to the first set of likelihood road segments.

14. The vehicular lane cooperative computing system of claim 10, wherein: the screening module performs likelihood road segment set screening according to the real-time course angle of the vehicle, and the method comprises the following steps:

the current longitude and latitude of the vehicle is point= { Point long, point lat };

for a certain road section L, when a sequence of the road section center of the road section L exists in the map corpus information, selecting longitude and latitude information of a first point and a last point of the sequence of the road section center;

if the road section L does not have the road section center point sequence, selecting a first point and a last point of any one lane center point sequence of the road section L;

PointFirst= { PointFirstlong, pointFirstlat } and PointLast= { PointLastlong, pointLastlat } respectively;

the absolute value of the vector included angle formed by the heading angle of the vehicle, the point PointFirst and the point PointLast is smaller than the preset range, and the road section L belongs to a first likelihood road section set.

15. The vehicular lane cooperative computing system of claim 10, wherein: the specified rules include:

if the number of the road sections in the first likelihood road section set is 1, the road sections are put into the second likelihood road section set;

if the number of the road sections in the first likelihood road section set is greater than 1, screening out the road sections which accord with the characteristics of the second likelihood road section and putting the road sections into the second likelihood road section set, wherein the screened road sections have any one of the following characteristics and put the road sections into the second likelihood road section set;

a) If the road section information does not contain the road section center point sequence set;

b) d is smaller than the sum of half the average width w of the road segment and the road segment actualrror.

16. The vehicular lane cooperative computing system of claim 10, wherein: d is calculated in the following way;

constructing n-1 grids for the road segment center point sequences { P1, P2, …, pn };

for the ith grid, a width is constructed by taking line segments Pi and Pi+i as central axesThe rectangle of (2) re-enlarges the road section actual error to four sides;

if the Point is not within the ith grid, the distance di between the Point and the ith grid is recorded as infinity;

if Point is located within the ith grid, di is the distance between Point and the line segment constructed by Pi and Pi+i;

d is the minimum value of the Point and each grid, and the Point is the current longitude and latitude point= { Point long, point last }, of the vehicle.