CN107328423B

CN107328423B - Curve identification method and system based on map data

Info

Publication number: CN107328423B
Application number: CN201610277789.4A
Authority: CN
Inventors: 周贺杰; 季刚; 崔跃
Original assignee: Xiamen Yaxon Networks Co Ltd
Current assignee: Xiamen Yaxun Zhilian Technology Co ltd
Priority date: 2016-04-28
Filing date: 2016-04-28
Publication date: 2020-10-16
Anticipated expiration: 2036-04-28
Also published as: CN107328423A

Abstract

The invention discloses a curve identification method and a system based on map data, wherein the method comprises the following steps: matching the position of the vehicle in the map to obtain the road where the vehicle is located; acquiring road nodes in a first distance preset behind a vehicle position on a road and road nodes in a second distance preset in front of the vehicle position on the road to obtain a node chain table; calculating the direction angle of a line segment taking each road node as a starting point and the next road node as an end point in the node linked list; calculating the direction angle difference between the line segment and the previous line segment; recording the direction angle difference value as the direction angle difference value of the road node corresponding to the starting point of the line segment; and obtaining a curve node according to the direction angle difference value to obtain a curve linked list. The invention combines with map data to judge, even in rainy days with low visibility, at night with insufficient light or when the road surface has no any characteristic, the invention can identify the bending degree of the curve ahead of the vehicle in real time, so that the curve identification can be carried out in all weather without being influenced by the road surface.

Description

Curve identification method and system based on map data

Technical Field

The invention relates to the field of driving safety, in particular to a curve identification method and a curve identification system based on map data.

Background

The driver of a vehicle is not attentive or familiar with the road ahead, and the driver often causes a serious traffic accident when the driver turns a curve ahead and does not take necessary measures. Therefore, rapidly and effectively identifying a curve existing on the road ahead and estimating the degree of curve of the curve have become one of the important technologies of the current intelligent safety assistant driving system for automobiles.

The traditional curve identification technology is mainly characterized in that a CCD image sensor is arranged in front of a vehicle to obtain visual information, a series of digital images are obtained, curves formed by road lane lines are identified through methods such as mathematical modeling and image processing, curve characteristics are detected, and follow-up early warning processing is carried out according to the curve characteristics. However, this technique is limited by the image sensor field of view and the road surface. When the visibility is low in rainy days or at night with insufficient light or the road surface has no features, the road lane line is difficult to be effectively identified by adopting the technology, so that the identification is failed.

In the chinese patent publication No. CN1796940, a curve safety warning method for a navigation system is proposed, in which a current coordinate point of a vehicle is known by using a GPS, a vehicle traveling direction is known by using an inertia measurement device, a distance from the current coordinate point of the vehicle is taken forward as an estimated distance, a comparison coordinate point is taken out, a comparison is performed according to a current vehicle speed and a safety vehicle speed value of the comparison coordinate point prestored in an electronic map, and a warning is given if the vehicle speed exceeds the safety vehicle speed value. The curvature of all road nodes in the electronic map is estimated in advance, the safe vehicle speed value is calculated, and then the safe vehicle speed value is written into a map file.

Disclosure of Invention

The technical problem to be solved by the invention is as follows: a curve recognition method and system based on map data are provided to recognize a curve ahead of a vehicle in real time and improve the accuracy of curve curvature calculation.

In order to solve the technical problems, the invention adopts the technical scheme that: a method of curve identification based on map data, comprising:

matching the position of the vehicle in the map to obtain the road where the vehicle is located;

acquiring road nodes in a first distance preset behind the vehicle position on the road and road nodes in a second distance preset in front of the vehicle position on the road to obtain a node chain table;

calculating the direction angle of a line segment which takes each road node as a starting point and the next road node as an end point in the node linked list;

calculating the direction angle difference value of the line segment and the previous line segment;

recording the direction angle difference as the direction angle difference of the road node corresponding to the starting point of the line segment;

and obtaining a curve node according to the direction angle difference value to obtain a curve linked list.

The invention also relates to a curve identification system based on map data, comprising:

the matching module is used for matching the position of the vehicle in the map to obtain the road where the vehicle is located;

the first obtaining module is used for obtaining road nodes in a first distance preset behind the vehicle position on the road and road nodes in a second distance preset in front of the vehicle position on the road to obtain a node chain table;

the first calculation module is used for calculating the direction angle of a line segment which takes each road node as a starting point and the next road node as an end point in the node linked list;

the second calculation module is used for calculating the direction angle difference value of the line segment and the previous line segment;

the marking module is used for recording the direction angle difference as the direction angle difference of the road node corresponding to the starting point of the line segment;

and the second obtaining module is used for obtaining a curve node according to the direction angle difference value to obtain a curve linked list.

The invention has the beneficial effects that: extracting road node linked lists in a certain range in front of and behind the current vehicle position from map data, distinguishing a curve or a straight road according to a direction angle deviation value of the road node trend, extracting curve nodes and obtaining curve linked lists; the invention combines with map data to judge, even in rainy days with low visibility, at night with insufficient light or when the road surface has no any characteristic, the bending degree of the curve ahead of the vehicle can be identified in real time, so that the curve identification can be carried out in all weather without being influenced by the road surface; meanwhile, only road node data within a certain range in front of and behind the vehicle position are obtained to identify the curve, and the efficiency is guaranteed while the curve is identified in real time.

Drawings

FIG. 1 is a flow chart of a method for curve identification based on map data in accordance with the present invention;

FIG. 2 is a flowchart of a method according to a first embodiment of the present invention;

FIG. 3 is a flowchart of a method according to a second embodiment of the present invention;

FIG. 4 is a flowchart of a method according to a third embodiment of the present invention;

FIG. 5 is a flowchart of a method according to a fourth embodiment of the present invention;

fig. 6 is a schematic road node diagram of a road on which a vehicle according to a fifth embodiment of the present invention is located;

FIG. 7 is a schematic diagram of a curve identification system based on map data according to the present invention;

fig. 8 is a schematic structural diagram of a system according to a sixth embodiment of the present invention.

Description of reference numerals:

1. a matching module; 2. a first obtaining module; 3. a first calculation module; 4. a second calculation module; 5. a marking module; 6. a second obtaining module; 7. a third calculation module; 8. a deletion module; 9. a first update module; 10. a second update module; 11. an acquisition module; 12. a fourth calculation module;

101. a first acquisition unit; 102. a second acquisition unit; 103. a first judgment unit; 104. a rejection unit; 105. a first calculation unit; 106. selecting a unit;

601. a first division unit; 602. a first adding unit; 603. a first deletion unit; 604. a first obtaining unit; 605. a second adding unit; 606. a traversing unit; 607. a second calculation unit; 608. a second dividing unit; 609. a third calculation unit; 610. a second deletion unit;

1201. a projection unit; 1202. a rotation unit; 1203. a translation unit; 1204. a fourth calculation unit; 1205. a fitting unit; 1206. and a fifth calculation unit.

Detailed Description

In order to explain technical contents, objects and effects of the present invention in detail, the following detailed description is given with reference to the accompanying drawings in conjunction with the embodiments.

The most key concept of the invention is as follows: and identifying a curve in front of the vehicle position according to the direction angle deviation value of the road node trend.

Referring to fig. 1, a method for identifying a curve based on map data includes:

From the above description, the beneficial effects of the present invention are: through the GPS and the map data, the bending degree of the curve in front of the vehicle can be identified in real time even in rainy days with low visibility, nights with insufficient light or when the road surface has no any characteristics, so that the curve identification can be carried out all the day without being influenced by the road surface.

Further, the step of matching the vehicle position in the map to obtain the road where the vehicle is located specifically includes:

acquiring current GPS data of a vehicle;

acquiring candidate roads in a preset range around the vehicle according to the coordinate information in the GPS data to obtain a first candidate road set;

judging the connectivity between the road matched with the vehicle last time and the candidate road;

removing disconnected candidate roads in the first candidate road set to obtain a second candidate road set;

calculating the similarity between each candidate road in the second candidate road set and the vector position information in the GPS data;

and selecting the candidate road with the minimum similarity as the road where the vehicle is located.

According to the description, the road with the minimum similarity is selected from the candidate road communicated with the road matched last time according to the GPS data, the road where the vehicle is located can be matched correctly, and the road matching accuracy is improved.

Further, after the obtaining the node linked list, the method further includes:

calculating the distance between two adjacent nodes in the node linked list;

and if the distance is smaller than a preset third distance, deleting any node in the two nodes.

As can be seen from the above description, it is possible to prevent a large error from occurring in calculating the direction angle due to two nodes that are too close in the map data, and to improve the accuracy of curve recognition.

Further, after the "difference of direction angles between the line segment and the previous line segment", the method further includes:

if the direction angle difference S 'between one line segment and the previous line segment is greater than 180 degrees, updating the direction angle difference to be | S' | -360;

and if the direction angle difference S 'between one line segment and the previous line segment is less than-180 degrees, updating the direction angle difference to be 360- | S' |.

It can be known from the above description that, because the direction angle of two adjacent line segments does not change greatly in the map data, when the absolute value of the direction angle difference is greater than 180 °, it is considered that the current line segment direction angle and the previous line segment direction angle are located on both sides of the 0 ° axis, and by updating the direction angle difference, the misjudgment is prevented, and the accuracy of curve identification is ensured.

Further, the step of obtaining a curve node according to the direction angle difference to obtain a curve linked list specifically includes:

according to the condition that the direction angle difference value is a positive value, a negative value or zero, sequentially dividing the road nodes except the first road node in the node chain table to obtain sequentially arranged node groups;

adding a first road node of the node group to a previous node group;

deleting node groups of which the direction angle difference values of the road nodes except the last road node are zero;

and obtaining a curve linked list formed by the rest node groups, wherein one node group corresponds to one curve.

As can be seen from the above description, the north direction is set to be the 0 ° axis, and the clockwise direction is the positive direction; when the direction angle difference value of a road node is a negative value, the direction angle of a line segment taking the road node as a starting point is smaller than that of the previous line segment, and the road turns left; when the difference value of the direction angle of a road node is a positive value, the direction angle of a line segment taking the road node as a starting point is increased compared with that of the previous line segment, and the road turns right; when the direction angle difference of the road node is 0, it indicates that the road has no direction change.

Further, after the obtaining of the curve linked list formed by the remaining node groups, and one of the node groups corresponding to a curve, the method further includes:

and if the node group only comprises two road nodes, adding the previous road node adjacent to the two road nodes into the node group.

As can be seen from the above description, since a segment of arc line is composed of at least three points, if a node group is determined that a curve only includes two road nodes, it is necessary to add neighboring points before the two points into the curve to ensure that the number of curve nodes is more than three.

Further, after obtaining the curve linked list formed by the remaining node groups, and one node group corresponding to a curve, the method further includes:

traversing a curve node of each curve in the curve linked list;

if the direction angle difference value corresponding to one curve node is smaller than a preset first difference value, calculating the distance between the curve node and the next curve node;

if the distance is greater than a preset fourth distance, dividing the curve corresponding to the curve node into two curves according to the curve node;

respectively calculating the absolute value of the difference value of the entrance angle and the exit angle of each curve;

and if the absolute value of the difference value of the entrance angle and the exit angle of a bend is smaller than a preset second difference value, deleting the bend from a bend chain table.

According to the description, the curve in the curve linked list is corrected, so that the judgment error caused by the error of the map data is reduced, and the accuracy of curve identification is further improved.

Further, after the "obtaining a curve node according to the direction angle difference value to obtain a curve linked list", the method further includes:

if the vehicle is located at a corresponding curve in the curve linked list after the preset time, acquiring a curve node set corresponding to the curve;

and performing curve fitting according to the curve node set, and calculating the curvature radius of the curve.

Further, the "performing curve fitting according to the curve node set, and calculating the curvature radius of the curve" specifically includes:

projecting the curve node of the curve into a rectangular coordinate system;

rotating the curve node to be parallel to the X-axis direction of the rectangular coordinate system according to the connecting line direction of the head node and the tail node of the curve;

translating the curve node to the origin of the rectangular coordinate system according to the curve first node;

calculating the relative coordinates of each curve node relative to the curve head node;

performing quadratic polynomial least square fitting on the curve node according to the relative coordinates to obtain a fitting coefficient of a curve;

and calculating the curvature radius of the curve according to the fitting coefficient.

According to the description, curve fitting is carried out according to the curve node set in the curve linked list, the curvature radius of the curve is calculated, and the curve node set can be used for carrying out safety early warning on the speed of the curve and improving driving safety.

Referring to fig. 7, the present invention further relates to a curve identification system based on map data, including:

Example one

Referring to fig. 2, a first embodiment of the present invention is: a curve identification method based on map data can be used for curve curvature estimation and curve vehicle speed safety early warning, and specifically comprises the following steps:

s1: matching the position of the vehicle in the map to obtain the road where the vehicle is located; the vehicle is driven on a road, and therefore the position of the vehicle can be matched without exception to a certain road in the map.

S2: acquiring road nodes in a first distance preset behind the vehicle position on the road and road nodes in a second distance preset in front of the vehicle position on the road to obtain a node chain table; the roads are formed in a node form on the map, and the roads in the road network have a certain topological relation; generally speaking, vehicles generally have coherence when running on a road at a higher level, that is, the current position is the same as the front and rear road levels and the road names are the same, so as to search out road nodes in a certain distance, such as 2000m forward and 500m backward, in front of and behind the current position, and form a road node linked list.

S3: calculating the direction angle of a line segment which takes each road node as a starting point and the next road node as an end point in the node linked list; the north direction is set as 0 degree axis, and the clockwise direction is positive direction.

S4: calculating the direction angle difference value of the line segment and the previous line segment; because the direction angles of two adjacent line segments do not change greatly in the map data, when the absolute value of the direction angle difference is greater than 180 degrees, the direction angle of the current line segment and the direction angle of the previous line segment are considered to be positioned on two sides of the 0-degree axis, and therefore, if the direction angle difference S 'of one line segment and the previous line segment is greater than 180 degrees, the direction angle difference is updated to be | S' | -360; if the direction angle difference S 'between one line segment and the previous line segment is smaller than minus 180 degrees, updating the direction angle difference to be 360- | S' |; by updating the direction angle difference value, the judgment error is prevented, and the accuracy of curve identification is ensured.

S5: recording the direction angle difference as the direction angle difference of the road node corresponding to the starting point of the line segment; in the node linked list, except the first node and the last node, all the other road nodes correspond to a direction angle difference value, and the direction angle difference value is the difference between a line segment direction angle taking the road node as a starting point and a line segment direction angle taking the road node as an end point. The road nodes in the node chain table are numbered in sequence according to the advancing direction of the road, when the direction angle difference value of one road node is a negative value, the direction angle of a line segment taking the road node as a starting point is smaller than that of the previous line segment, and the road turns left; when the difference value of the direction angle of a road node is a positive value, the direction angle of a line segment taking the road node as a starting point is increased compared with that of the previous line segment, and the road turns right; when the direction angle difference of the road node is 0, it indicates that the road has no direction change.

S6: and according to the condition that the direction angle difference value is a positive value, a negative value or zero, sequentially dividing the road nodes except the first road node in the node chain table to obtain sequentially arranged node groups. If the direction angle difference values corresponding to the road nodes in a node chain table are respectively 0, 8, 15, 6, 0, -5, -10, -7, 4, 9 and 0, the road nodes are divided into five node groups of (8, 15, 6), (0, 0), (-5, -10, -7), (4, 9) and (0) in sequence.

S7: adding a first road node of the node group to a previous node group; the first road node in the node group is both the starting point of the link corresponding to the node group and the ending point of the previous link.

S8: and judging whether the direction angle difference values of the road nodes except the last road node in the node grouping are zero, if so, executing the step S9.

S9: and deleting the node groups to obtain a curve linked list formed by the rest node groups, wherein one node group corresponds to one curve.

S10: and judging whether the remaining node groups only comprise two road nodes, if so, executing step S11, and if not, executing step S12. Because one section of arc line is composed of at least three points, if a curve only contains two road nodes, the adjacent points before the two points are added into the curve to ensure that the number of the road nodes of the curve is more than three.

S11: adding a previous road node adjacent to the two road nodes to the node group; step S12 is executed.

S12: correcting the curve in the curve linked list; due to errors in the map data, there are two cases:

a. mistakenly extracting two sections of curved roads into one section of curved road; although the difference of the external direction angles of the nodes except the end road node is both positive or negative, road nodes with the direction angle difference value being too close to 0 may exist in the middle of the extracted node group, the nodes reflect straight roads appearing in a curve on a map, and due to errors of map data, the difference of the direction angles of the road nodes at the position cannot be calculated into 0, and the road nodes at the position cannot be disconnected.

b. Mistakenly extracting a straight road into a curved road; although the difference values of the external direction angles of the nodes except the end road node in the extracted node group are both positive or negative, the difference values of the direction angles of all road nodes are probably too close to 0, the curve is reflected on a map and is a straight road, and the difference value of the direction angle cannot be calculated to be 0 due to the error of map data.

Therefore, the curves in the curve linked list need to be corrected, so that the judgment error caused by the error of the map data can be reduced, and the accuracy of curve identification is improved.

Preferably, between step S2 and step S3, the distance between two adjacent nodes in the node chain table may be calculated first; and if the distance is smaller than a preset third distance, such as 30m, deleting any node in the two nodes. Because there is an error in the map data, points with too close distance mean that a large error is generated when the direction angle is calculated, and the error is reduced and the accuracy of curve identification is improved by deleting too close road nodes.

According to the method, the road node linked lists in a certain range in front of and behind the current vehicle position are extracted from the map data, and the curve or the straight road is distinguished according to the direction angle deviation value of the road node trend, so that the bending degree of the curve in front of the vehicle can be identified in real time even in rainy days with low visibility, at night with insufficient light or when the road surface has no any characteristics, and the curve identification can be carried out in all weather without being influenced by the road surface; meanwhile, only road node data within a certain range in front of and behind the vehicle position are obtained to identify the curve, and the efficiency is guaranteed while the curve is identified in real time.

Example two

Referring to fig. 3, the present embodiment is a further development of the first embodiment, and a curve curvature calculation is performed according to further analysis of the curve linked list obtained in the first embodiment, so as to perform a curve vehicle speed safety warning; the same points are not described in a repeated way, and the difference is that the method also comprises the following steps:

s13: calculating the running distance of the vehicle within preset time and the instantaneous speed after the preset time according to the current speed and the acceleration of the vehicle; the motion model of the vehicle can be simplified into a uniform acceleration (deceleration) motion model according to the current speed v of the vehicle₀And an acceleration a obtained by an acceleration sensor of the vehicle, and calculating a distance s of the vehicle after t seconds as v₀t+1/2at²The instantaneous velocity v after t seconds is equal to v₀+ at, where t is a preset time; when the acceleration is 0, the vehicle moves at a constant speed.

S14: and determining the position of the vehicle after the preset time in the node linked list according to the distance.

S15: and judging whether the vehicle is in a corresponding curve in the curve linked list after the preset time, if so, executing the step S16.

S16: and obtaining a curve node set corresponding to the curve.

S17: and projecting the curve node of the curve into a rectangular coordinate system.

S18: and rotating the curve nodes to be parallel to the X-axis direction of the rectangular coordinate system according to the connecting line direction of the curve head and tail nodes.

S19: and translating the curve node to the origin of the rectangular coordinate system according to the curve first node.

S20: and calculating the relative coordinates of each curve node relative to the curve head node.

S21: performing quadratic polynomial least square fitting on the curve node according to the relative coordinates to obtain a fitting coefficient of a curve; for the curve model, a quadratic parabolic model is used, for a quadratic polynomial y (x) ═ b₀+b₁X+b₂X²Knowing n discrete points (X)_i,Y_i) I is 1,2 … n, and can be found:

wherein:

s22: calculating the curvature radius of the curve according to the fitting coefficient; the curvature radius value of the curve is different at each point on the curve; for judging the curve, the point where the curvature radius is the smallest, namely the curve most bending point, most represents the bending degree of the curve, the curvature radius at the point is estimated, namely 100 points are equally divided in the horizontal axis direction at the initial point and the final point of the curve, the curvature radius of the 100 points is calculated, and the minimum value is calculated. The formula for calculating the radius of curvature is:

s23: calculating the maximum speed of the vehicle which can safely pass through the curve according to the curvature radius; for a highway, the estimation model is as follows: car V-14.741R^_.03194Large truck V-4.941R^_.04345(ii) a For the second-level road, the estimation model is as follows: car V-14.391R^_.02757Large truck V-8.503R^_.03168。

S24: carrying out safety early warning according to the instantaneous speed after the preset time and the maximum speed; and if the instantaneous speed after t seconds calculated in the step S13 is greater than the maximum speed calculated in the step S23, warning and reminding are carried out on the driver.

And the curvature radius of the identified curve is estimated, and the curve is used for safety early warning of the vehicle curve, so that the driving safety can be improved.

EXAMPLE III

Referring to fig. 4, the present embodiment is a specific implementation manner of the step S1, and includes the following steps:

SS 11: acquiring current GPS data of a vehicle; the GPS data includes coordinate information and vector position information.

SS 12: and acquiring candidate roads in a preset range around the vehicle according to the coordinate information in the GPS data to obtain a first candidate road set.

SS 13: judging the connectivity between the road matched with the vehicle last time and the candidate road; the last matched road end point is a starting search point, the search times are preset, for example, 6 times, a child node pointed by the starting point, a child node of the child node are searched in the search times in an iterative mode, and the like, and if the node searched in the preset search times is a node forming a road in a candidate road set, the candidate road is communicated with the starting road; otherwise, it is not connected.

SS 14: and removing disconnected candidate roads in the first candidate road set to obtain a second candidate road set.

SS 15: calculating the similarity between each candidate road in the second candidate road set and the vector position information in the GPS data; vector position information is directional position information, and the similarity can be defined according to the formula S ═ w_aa+w_dd is obtained by calculation, wherein a and d are respectively the angle difference and the distance between the current vector position information and the candidate road, and w_aAnd w_dThe weight values of the angle difference and the distance are used.

SS 16: and selecting the candidate road with the minimum similarity as the road where the vehicle is located.

Example four

Referring to fig. 5, the present embodiment is a specific implementation manner of the step S12, and includes the following steps:

and the SS 121: and traversing the curve nodes of each curve in the curve linked list.

And the SS 122: judging whether the direction angle difference value corresponding to the curve node is smaller than a preset first difference value or not, if so, executing a step SS 123; preferably, the first difference is 5 °.

And (4) SS 123: and calculating the distance between the curve node and the next curve node.

And SS 124: judging whether the distance is greater than a preset fourth distance, if so, executing a step SS 125; preferably, the fourth distance is 250 m.

And (4) SS 125: dividing the curve corresponding to the curve node into two curves according to the curve node; namely, the curve corresponding to the curve node is disconnected at the curve node, and the curve is divided into two curves.

And the SS 126: respectively calculating the absolute value of the difference value of the entrance angle and the exit angle of each curve; the difference of the entrance angle and the exit angle is the difference of the direction angles of a first line segment (a line segment which takes a first curve node of the curve as a starting point and takes a second curve node of the curve as an end point) forming the curve and a last line segment (a line segment which takes a penultimate curve node of the curve as a starting point and takes an end curve node of the curve as an end point) forming the curve.

And SS 127: judging whether the absolute value of the difference value of the entrance angle and the exit angle of the curve is smaller than a preset second difference value, if so, executing a step SS 128; preferably, the second difference is 5 °.

And SS 128: and deleting the curve from a curve linked list.

According to the method and the device, the curves in the curve linked list are corrected, so that the judgment error caused by the error of the map data is reduced, and the accuracy of curve identification is further improved.

EXAMPLE five

This embodiment is a specific implementation of the above embodiment.

As shown in fig. 6, the vehicle position is matched in the map, the road in the map is matched, 19 road nodes in the front and back of the vehicle position are obtained, the numbers are 0-18 respectively, and a node linked list consisting of the 19 road nodes is obtained; calculating the distance between every two road nodes, setting the third distance to be 30m, calculating that the distance between the road nodes of No. 10 and No. 11 is less than 30m, and deleting the road node of No. 11 in the node chain table; the direction angle difference values corresponding to the remaining road nodes are calculated as shown in table 1.

Road node numbering	Coordinates of the object	Difference of direction angle
			0	118.117732,24.433752	0
1	118.119032,24.433921	3
			2	118.120414,24.434047	0
3	118.124017,24.434338	7
			4	118.125200,24.434321	17
5	118.126510,24.433952	7
			6	118.129280,24.432789	-8
7	118.130106,24.432559	-9
			8	118.131882,24.432346	-10
9	118.132719,24.432372	-10
			10	118.134160,24.432637	-19
12	118.135618,24.433418	-19
			13	118.136415,24.434256	-12
14	118.136968,24.435178	-1
			15	118.143383,24.446467	12
16	118.144329,24.447511	10
			17	118.144898,24.447958	8
18	118.145577,24.448349	0

TABLE 1

According to the condition that the direction angle difference value is a positive value, a negative value or zero, the road nodes except the first and last road nodes in the node chain table are sequentially divided, and sequentially arranged node groups are obtained, as shown in table 2.

Node packet numbering	Road node numbering
		0	1
1	2
		2	3、4、5
3	6、7、8、9、10、12、13、14
		4	15、16、17
5	18

TABLE 2

The first road node of the node group is added to the previous node group as shown in table 3.

Node packet numbering	Road node numbering
		0	1、2
1	2、3
		2	3、4、5、6
3	6、7、8、9、10、12、13、14、15
		4	15、16、17、18
5	18

TABLE 3

Deleting the node groups of which the direction angle difference values of the road nodes except the last road node are zero, namely deleting the node groups of No. 1 and No. 5; if the node group only containing two road nodes exists in the rest node groups, adding the previous road node adjacent to the two road nodes into the node group, namely adding the road node of 0 into the node group of 0 to obtain a curve linked list, wherein one node group corresponds to one curve, and calculating the absolute value of the difference value of the entrance angle and the exit angle of each curve, as shown in table 4.

TABLE 4

Correcting the bend in the bend chain table, and deleting the 0 th bend with the absolute value of the difference value of the entrance angle and the exit angle smaller than 5 degrees; with reference to table 1, since the absolute value of the direction angle difference between the road node No. 14 and the road node No. 15 is less than 5 °, the distance between the absolute value and the road node No. 14 needs to be calculated, the calculated distance value is 1415m and is greater than the fourth distance 250m of the set threshold, and therefore the curve No. 2 is disconnected from the point No. 14.

Assuming that the preset time is 10 seconds, the position of the vehicle after 10 seconds is calculated to be the No. 2 curve, so that a quadratic curve of the curve is fitted, and the fitted curve analytical formula is as follows: y (X) 198-0.8531X +0.0009214X²The radius of curvature of the curve is estimated as: 543m, 543 m. And estimating the safe vehicle speed at the No. 2 bend according to the second-level highway truck estimation model as follows: 82 km/h. And estimating the vehicle speed after 10 seconds according to the current running speed and acceleration of the vehicle, and performing early warning if the vehicle speed is more than 82km/h, or not performing early warning.

On the basis of GPS real-time positioning information and map data, the direction angle difference value is calculated by utilizing longitude and latitude coordinates of front and rear road nodes extracted within a certain range, a curve or a straight road is identified according to the direction angle difference value, the solution of a curve equation is realized by utilizing a curve fitting technology according to the identified curve, the curvature radius of the curve is estimated, the safe speed of a vehicle passing through the curve is estimated, and early warning is carried out on the basis of the curve. On the premise of ensuring the early warning accuracy, all-weather curve recognition and curve early warning are realized, the stability of curve early warning is effectively improved, and the driving safety is ensured.

EXAMPLE six

Referring to fig. 8, the present embodiment is a curve identification system based on map data corresponding to the above embodiments, including:

the matching module 1 is used for matching the position of a vehicle in a map to obtain the road where the vehicle is located;

the first obtaining module 2 is used for obtaining road nodes in a first distance preset behind the vehicle position on the road and road nodes in a second distance preset in front of the vehicle position on the road to obtain a node chain table;

the first calculation module 3 is used for calculating the direction angle of a line segment which takes each road node as a starting point and takes the next road node as an end point in the node linked list;

the second calculating module 4 is used for calculating the direction angle difference value of the line segment and the previous line segment;

the marking module 5 is used for recording the direction angle difference as the direction angle difference of the road node corresponding to the starting point of the line segment;

and the second obtaining module 6 is used for obtaining a curve node according to the direction angle difference value to obtain a curve linked list.

The matching module 1 comprises:

a first acquisition unit 101, configured to acquire current GPS data of a vehicle;

the second obtaining unit 102 is configured to obtain candidate roads in a preset range around the vehicle according to the coordinate information in the GPS data to obtain a first candidate road set;

a first judging unit 103, configured to judge connectivity between a road that is last matched by the vehicle and the candidate road;

a removing unit 104, configured to remove disconnected candidate roads from the first candidate road set to obtain a second candidate road set;

a first calculating unit 105, configured to calculate similarity between each candidate road in the second candidate road set and vector position information in the GPS data;

and the selecting unit 106 is configured to select the candidate road with the minimum similarity as the road where the vehicle is located.

The system further comprises:

a third calculating module 7, configured to calculate a distance between two adjacent nodes in the node linked list;

and the deleting module 8 is configured to delete any node of the two nodes if the distance is smaller than a preset third distance.

The system further comprises:

a first updating module 9, configured to update the direction angle difference to | S '| -360 if the direction angle difference S' between one line segment and the previous line segment is greater than 180 degrees;

the second updating module 10 is configured to update the direction angle difference to 360- | S '| if the direction angle difference S' between one line segment and the previous line segment is smaller than-180 degrees.

The second obtaining module 6 includes:

the first dividing unit 601 is configured to sequentially divide road nodes in the node chain table except for a first road node according to a condition that the direction angle difference is a positive value, a negative value, or zero, and obtain sequentially arranged node groups;

a first adding unit 602, configured to add a first road node of the node group to a previous node group;

a first deleting unit 603 configured to delete a node group in which direction angle difference values of road nodes other than the end road node are zero;

a first obtaining unit 604, configured to obtain a curve linked list formed by remaining node groups, where one node group corresponds to a curve;

a second adding unit 605, configured to add, if the node group only includes two road nodes, a previous road node adjacent to the two road nodes into the node group;

a traversing unit 606, configured to traverse a curve node of each curve in the curve linked list;

a second calculating unit 607, configured to calculate a distance between a curve node and a next curve node if a direction angle difference value corresponding to the curve node is smaller than a preset first difference value;

a second dividing unit 608, configured to divide, according to the curve node, a curve corresponding to the curve node into two curves if the distance is greater than a preset fourth distance;

a third calculating unit 609, configured to calculate absolute values of the difference between the entrance angle and the exit angle of each curve;

the second deleting unit 610 is configured to delete the curve from the curve linked list if the absolute value of the difference between the entrance angle and the exit angle of the curve is smaller than a preset second difference.

The system further comprises:

the obtaining module 11 is configured to obtain a curve node set corresponding to a curve if the vehicle is located in the corresponding curve in the curve linked list after a preset time;

and a fourth calculating module 12, configured to perform curve fitting according to the curve node set, and calculate a curvature radius of the curve.

The fourth calculation module 12 includes:

a projection unit 1201, configured to project a curve node of the curve into a rectangular coordinate system;

a rotation unit 1202, configured to rotate the curve node to an X-axis direction parallel to the rectangular coordinate system according to a connection line direction of the curve first and last nodes;

a translation unit 1203, configured to translate the curve node to an origin of the rectangular coordinate system according to a curve head node;

a fourth calculating unit 1204, configured to calculate a relative coordinate of each curve node with respect to a curve head node;

a fitting unit 1205, configured to perform quadratic polynomial least square fitting on the curve node according to the relative coordinate to obtain a fitting coefficient of a curve;

a fifth calculating unit 1206, configured to calculate a curvature radius of the curve according to the fitting coefficient.

In summary, the curve identification method and system based on map data provided by the invention are combined with map data for judgment, so that the curve degree of the curve in front of the vehicle can be identified in real time even in rainy days with low visibility and insufficient light or at night when the road surface has no any features, and the curve identification can be carried out in all weather without being influenced by the road surface; meanwhile, only road node data within a certain range in front of and behind the vehicle position is obtained to identify the curve, so that the efficiency is ensured while the curve is identified in real time; and curve fitting is carried out according to a curve node set in a curve linked list, the curvature radius of the curve is calculated, and the curve node set can be used for carrying out safety early warning on the speed of the curve and improving the driving safety.

The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to the related technical fields, are included in the scope of the present invention.

Claims

1. A method for curve recognition based on map data, comprising:

according to the direction angle difference value, obtaining a curve node to obtain a curve linked list;

the step of obtaining a curve node according to the direction angle difference value to obtain a curve linked list specifically comprises the following steps:

adding a first road node of the node group to a previous node group;

2. A curve identification method based on map data as claimed in claim 1, wherein the "matching vehicle position in map to obtain road on which vehicle is located" is specifically:

acquiring current GPS data of a vehicle;

3. A curve identifying method based on map data as claimed in claim 1, wherein after obtaining the node chain table, further comprising:

calculating the distance between two adjacent nodes in the node linked list;

4. The map data-based curve recognition method according to claim 1, wherein after the "difference in direction angle between the line segment and its preceding line segment", further comprising:

5. A curve identifying method based on map data as claimed in claim 1, wherein after obtaining a curve linked list composed of the remaining node groups, one of the node groups corresponding to a curve, further comprising:

6. A curve identifying method based on map data as claimed in claim 1, wherein after obtaining a curve linked list composed of the remaining node groups, one of the node groups corresponding to a curve, further comprising:

traversing a curve node of each curve in the curve linked list;

7. A curve identification method based on map data as claimed in claim 1, wherein after obtaining the curve node and obtaining the curve linked list according to the direction angle difference, further comprising:

8. A curve identification method based on map data as claimed in claim 7, wherein said "performing curve fitting according to the curve node set, and calculating the curvature radius of the curve" specifically comprises:

projecting the curve node of the curve into a rectangular coordinate system;

9. A map data-based curve identification system, comprising:

the second obtaining module is used for obtaining a curve node according to the direction angle difference value to obtain a curve linked list;

the second obtaining module includes:

the first dividing unit is used for sequentially dividing the road nodes except the first road node in the node chain table according to the condition that the direction angle difference value is a positive value, a negative value or zero, and acquiring sequentially arranged node groups;

a first adding unit, configured to add a first road node of the node group to a previous node group;

the first deleting unit is used for deleting node groups of which the direction angle difference values of the road nodes except the last road node are zero;

the first obtaining unit is used for obtaining a curve linked list formed by the rest node groups, and one node group corresponds to one curve.