CN106969779B - DSRC-based intelligent vehicle map fusion system and method - Google Patents
DSRC-based intelligent vehicle map fusion system and method Download PDFInfo
- Publication number
- CN106969779B CN106969779B CN201710161021.5A CN201710161021A CN106969779B CN 106969779 B CN106969779 B CN 106969779B CN 201710161021 A CN201710161021 A CN 201710161021A CN 106969779 B CN106969779 B CN 106969779B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- map
- target
- target vehicle
- local
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
- H04W4/026—Services making use of location information using location based information parameters using orientation information, e.g. compass
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Automation & Control Theory (AREA)
- Data Mining & Analysis (AREA)
- Signal Processing (AREA)
- General Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention requests to protect an intelligent vehicle map fusion system and method based on DSRC. The vehicle positioning module is used for acquiring the position and the posture of the main vehicle; the local map generation module generates a local map corresponding to the current position of the main vehicle according to the data of the vehicle-mounted sensor; the DSRC communication module is used for receiving position, posture and local map information issued by a target vehicle in a communication range and simultaneously transmitting the position, posture and local map of the main vehicle; the ADAS map interface module provides a protocol and an interface for accessing the ADAS map; the target vehicle screening module screens a target vehicle which is positioned on the same road with the main vehicle; the map fusion module fuses the main vehicle and the local map of the screened target vehicle to generate an expanded local map of the main vehicle. The invention realizes the sharing of the perception capability of multiple vehicles to expand the perception range of the environment, and is beneficial to improving the auxiliary driving/automatic driving performance of intelligent vehicles.
Description
Technical Field
The invention belongs to the technical field of automation, communication and computers, and particularly relates to a DSRC (dedicated short Range Communications) based intelligent vehicle map fusion system and method.
Background
Environmental perception and map generation are one of key technologies of intelligent vehicles, in order to improve the performance of intelligent vehicle auxiliary driving safety early warning and automatic driving path planning, the intelligent vehicle needs to have an environmental perception range as large as possible, but in the traditional method, the intelligent vehicle generally only depends on a vehicle-mounted sensor of the vehicle, so that the perception capability of the vehicle is extremely limited.
The Chinese patent application: a vehicle navigation system and a vehicle navigation method (application number: CN201010110837.3) disclose a vehicle navigation system and a vehicle navigation method, wherein the system plans an optimal global driving path from a starting point to a terminal point of a vehicle according to collected real-time road conditions and map information before the vehicle starts, so that the vehicle can avoid congested roads and non-drivable roads. The method is a macroscopic method and does not have real-time safety early warning and local path planning capability. The Chinese patent application: a multi-laser radar grid map fusion system (application number: CN201410252993.1) based on an unmanned automobile discloses a multi-laser radar grid map fusion system based on an unmanned automobile, which generates a grid map of the surrounding environment of an automobile by fusing a plurality of laser radar data installed at different positions of the automobile, and solves the problem that an intelligent automobile collides with an obstacle. However, the method is only the fusion of multiple sensors on the same vehicle, and essentially depends on the vehicle-mounted sensors of the vehicle, so that the sensing capability is also limited. The Chinese patent application: an ADASIS extended information output device and method based on a safe driving map (application number: CN201510496628.X) discloses an ADASIS extended information output device and method based on a safe driving map. The Chinese patent application: an ADAS-based driving assistance system (application number: CN201511010294.7) discloses an ADAS-based driving assistance system, which realizes sensor information interaction among different vehicles through a workshop communication technology and solves the problem that the safety early warning range of the vehicles is limited by the detection distance of a vehicle-mounted sensor. The system does not effectively screen the interactive vehicles, and the utility of the interactive information is influenced. The Chinese patent application: the DSRC-based vehicle-mounted mobile terminal and the communication method thereof (application number: CN201410397940.9) realize information interaction between a vehicle, an external vehicle and a base station through the DSRC communication technology, mainly comprise vehicle position, running state and road condition information, and enhance the understanding of a vehicle driver on the road condition information. The system also does not effectively screen the interactive vehicles to distinguish the validity of the interactive information.
The invention provides a DSRC-based intelligent vehicle map fusion system and method, aiming at the problems that the existing intelligent vehicle is limited in environment perception capability and the validity of interactive information is not distinguished during information interaction between vehicles. In the system, the main vehicle realizes the local map information sharing with a plurality of target vehicles in a communication range through DSRC, effective target vehicles are screened according to the positions of the target vehicles and ADAS map information, the local maps of the effective target vehicles are fused with the local map of the main vehicle, and an expanded local map is generated, so that the environmental perception range of the main vehicle is expanded, and the auxiliary driving/automatic driving performance of intelligent vehicles is improved.
Disclosure of Invention
Aiming at the defects of the prior invention, the invention provides the DSRC-based intelligent vehicle map fusion system and the DSRC-based intelligent vehicle map fusion method to effectively expand the environment perception capability and the auxiliary driving/automatic driving performance of the intelligent vehicle in order to solve the problems that the environment perception capability of the prior intelligent vehicle is limited by a vehicle-mounted sensor and interactive vehicles cannot be effectively screened during information interaction between the vehicles.
The technical scheme of the invention is as follows:
the utility model provides an intelligent vehicle map fusion system based on DSRC, its includes vehicle orientation module, local map generation module, DSRC communication module, ADAS map interface module, target vehicle screening module and map fusion module, wherein:
the vehicle positioning module is used for acquiring the position and the posture information of the main vehicle through satellite positioning equipment; the local map generation module is used for detecting the surrounding environment of the main vehicle through a vehicle-mounted sensor and generating a local map corresponding to the current position of the main vehicle according to the measurement data of the sensor; the DSRC communication module is used for issuing the current time position, the posture and the local map information of the main vehicle and receiving the position, the posture and the local map information issued by the target vehicle in the communication range; the ADAS map interface module is used for providing a protocol and an interface for accessing the ADAS map; the target vehicle screening module is used for acquiring the current road Id number of the main vehicle through an ADAS map interface according to the current position of the main vehicle, and screening out a target vehicle on the same road as the main vehicle according to the road Id number; and the map fusion module is used for carrying out map fusion according to the position, the posture and the local map information of the main vehicle and the screened target vehicle to generate an expanded local map of the main vehicle.
Furthermore, the vehicle positioning module collects and calculates the position L of the main vehicle under the geographic coordinate system through satellite positioning equipmentk h＝(xk h,yk h,zk h) And attitude Pk hWherein the attitude is defined as an included angle between the driving direction of the vehicle and the true north direction of the geographic coordinate system;
further, the local maps of the host vehicle and the target vehicle generated by the local map generation module are in a grid map format. The local map is represented as a rectangular area with the current position of the vehicle as the origin, the direction of the vehicle head as the positive direction of the y axis, the length of the rectangular area being L and the width of the rectangular area being W. Selecting a unit grid with the specification of R to discretize the local map into n m (n is L/R, m is W/R) grids, wherein each grid is represented as s by a vectorj h＝(xj h,yj h,fj h)TThe local map is represented as a grid vector sj hMatrix Grid of elementsh＝[sj h]1,n*mWherein x isj h、yj hAs the grid center coordinate, fj hIn a grid state, fj h1 is occupied state, indicating that there is an obstacle at the grid, fj h0 is in an unoccupied state.
Further, in the local map generation module, the vehicle-mounted sensor detects and acquires the position and size information of obstacles (vehicles, pedestrians and other obstacles) in the surrounding environment of the main vehicle on a grid basisCalculating the grid area covered by each obstacle in the graph coordinate system, calculating the grid state of the covered grid area as covering one grid when not covering one grid, and setting the grid state of the covered grid area as the occupying state sh i,j1. The generated local grid Map is Mapk h＝{L，W，Gridh}。
Further, the DSRC communication module has the functions of transmitting and receiving, and transmitting the current time position L of the main vehiclek hPosture Pk hAnd a local grid Mapk hSimultaneously receiving the self position, the attitude and the local grid map information issued by the target vehicle in the communication range, including the position l of the target vehiclek o(i)＝(xk o(i),yk o(i),zk o(i) Pose p) — attitude pk o(i) And a local grid mapk o(i)＝{L，W，Grido(i)}。
Further, the target vehicle screening module obtains Id numbers of the current roads of the host vehicle and the target vehicle through the ADAS map interface module according to the positions of the host vehicle and the target vehicle, and screens out the target vehicle on the same road as the host vehicle, wherein the steps include:
(1) current road Id query: according to the current time position L of the host vehiclek hObtaining the Id number Id of the current road of the main vehicle through an ADAS map interface modulek h；
(2) Screening target vehicles: for all target vehicles, according to the current time position l of the target vehicle ik o(i) Acquiring the current Id number Id of the road where the target vehicle i is located through an ADAS map interface modulek o(i) If id is satisfiedk o(i)＝Idk hAnd then, the information of the vehicle i is kept, and finally, the target vehicle on the same road as the host vehicle is screened out.
Further, the step of fusing the screened target vehicle local map and the main vehicle local map by the map fusion module to generate an expanded local map comprises:
(1) respectively calculating coordinate transformation parameters between a coordinate system of the target vehicle and a coordinate system of the main vehicle according to the position and the posture of each target vehicle and the position and the posture of the main vehicle, wherein the coordinate transformation parameters comprise rotation parameters and translation parameters, and transforming a local grid map of the target vehicle into the coordinate system of the main vehicle;
(2) on the basis of the local map of the current moment of the main vehicle, respectively fusing the local grid map of the target vehicle after transformation to obtain an expanded local grid map of the main vehicle, wherein the fusion rule is as follows: and carrying out grid fusion processing on the map overlapping area according to OR, and carrying out splicing processing on the non-overlapping area.
An intelligent vehicle map fusion method based on the system comprises the following steps:
(1) detecting the position and the posture of the main vehicle: at the current moment k, the main vehicle positioning module collects and calculates the position L of the main vehicle under the geographic coordinate system at the moment k through the satellite positioning equipmentk h＝(xk h,yk h,zk h) And attitude Pk hWherein the attitude is defined as an included angle between the driving direction of the vehicle and the true north direction of the geographic coordinate system;
(2) and local map generation: the vehicle-mounted sensor collects the observation data of the surrounding environment information of the main vehicle to generate a local grid Map of the main vehicle at the time of Kk h＝{L，W，Gridh}；
(3) DSRC module information transmission and reception: DSRC communication module transmits the current time position L of the host vehiclek hPosture Pk hAnd a local grid Mapk hSimultaneously receiving the self position, the attitude and the local grid map information issued by the target vehicle in the communication range, and setting the ith target vehicle position lk o(i)＝(xk o(i),yk o(i),zk o(i) In a posture of p)k o(i) Map of local grid mapk o(i)＝{L，W，Grido(i)}，i∈[0,nk]，nkThe number of target vehicles in the communication range of the main vehicle at the current moment is obtained;
(4) screening target vehicles: the target vehicle screening module firstly screens the current time position L of the main vehiclek hObtaining the Id number Id of the current road of the main vehicle through an ADAS map interface modulek hAccording to the position l of each target vehicle ik o(i) Obtaining the current Id number Id of the road where the target vehicle is located through an ADAS map interface modulek o(i) If id isk o(i)＝Idk hThe information of the target vehicle i is retained, otherwise, the information is deleted until the N is screened outkA target vehicle on the same road as the host vehicle, wherein Nk≤nk；
(5) Local grid map fusion: map fusion module uses local grid Map of main vehicle at current momentk hBased on the screened NkLocal grid map of individual target vehiclek o(i) Transforming to the host coordinate system and matching Mapk hFusing and generating an expanded local grid Map of the main vehiclek h_extern。
The invention has the following advantages and beneficial effects:
the invention provides a DSRC-based intelligent vehicle map fusion system and method. On one hand, the method distributes the position, the posture and the local map information of the main vehicle through the DSRC communication device, receives the position, the posture and the local map information of the target vehicle and fuses the position, the posture and the local map information to generate an expanded local map, so that the perception capability is shared among a plurality of vehicles, and the environment perception range of the intelligent vehicle is expanded; on the other hand, effective target vehicles for fusion are screened according to the positions of the target vehicles and ADAS map information, and the map fusion efficiency is improved. Through innovation and improvement of the two aspects, the invention realizes the sharing of the perception capability of multiple vehicles, effectively expands the perception range of the environment of the intelligent vehicle and has important significance and use value for improving the auxiliary driving/automatic driving performance of the intelligent vehicle.
Drawings
FIG. 1 is a DSRC-based intelligent vehicle map fusion system overall framework provided by the present invention;
FIG. 2 illustrates the format and coordinate system definition of a local map of an intelligent vehicle according to the present invention;
FIG. 3 is a flow chart of a DSRC-based intelligent vehicle map fusion method of the present invention;
FIG. 4 is a flow chart of a method of screening target vehicles in accordance with the present invention;
FIG. 5 is a flow chart of a map fusion method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
the invention provides a DSRC-based intelligent vehicle map fusion system and method, which realize effective sharing of a local map between a main vehicle and a target vehicle through a DSRC communication technology, expand the environment perception range of an intelligent vehicle, screen effective target vehicles for fusion through ADAS map information and improve the map fusion efficiency.
The following description of the embodiments of the present invention refers to the accompanying drawings and specific examples.
1. Fig. 1 shows the general framework of a DSRC-based intelligent vehicle map fusion system according to the present invention. The system comprises a vehicle positioning module, a local map generation module, a DSRC communication module, an ADAS map interface module, a target vehicle screening module and a map fusion module, wherein:
the vehicle positioning module is used for acquiring the position and the posture information of the main vehicle under a geographic coordinate system through satellite positioning equipment; the local map generation module is used for detecting the surrounding environment of the main vehicle through a vehicle-mounted sensor and generating a local map corresponding to the current position of the main vehicle according to the measurement data of the sensor; the DSRC communication module is used for issuing the current time position, the posture and the local map information of the main vehicle and receiving the position, the posture and the local map information issued by the target vehicle in the communication range; the ADAS map interface module is used for providing a protocol and an interface for accessing the ADAS map; the target vehicle screening module is used for acquiring the current road Id number of the main vehicle through an ADAS map interface according to the current position of the main vehicle, and screening out a target vehicle on the same road as the main vehicle according to the road Id number; and the map fusion module is used for carrying out map fusion according to the position, the posture and the local map information of the main vehicle and the screened target vehicle to generate an expanded local map of the main vehicle.
2. Fig. 2 shows the format and coordinate system definition of the local map of the intelligent vehicle adopted by the invention. The local maps of the host vehicle and the target vehicle are both in the format of a grid map. The local map is represented as a rectangular area with the current position of the vehicle as the origin, the direction of the vehicle head as the positive direction of the y axis, the length of the rectangular area being L and the width of the rectangular area being W. Selecting a unit grid with the specification of R to discretize the local map into n m (n is L/R, m is W/R) grids, wherein each grid is represented as s by a vectorj h＝(xj h,yj h,fj h)TThe local map is represented as a grid vector sj hMatrix Grid of elementsh＝[sj h]1,n*mWherein x isj h、yj hIs the coordinate of the center of the grid, taking an integer, fj hIn a grid state, fj h1 is occupied state, indicating that there is an obstacle at the grid, fj h0 is in an unoccupied state.
3. Fig. 3 shows a flow of a DSRC-based intelligent vehicle map fusion method provided by the present invention, which includes the following steps:
(1) detecting the position and the posture of the main vehicle: at the current moment k, the main vehicle positioning module collects and calculates the position L of the main vehicle under the geographic coordinate system at the moment k through the satellite positioning equipmentk h＝(xk h,yk h,zk h) And attitude Pk hWherein the attitude is defined as an included angle between the driving direction of the vehicle and the true north direction of the geographic coordinate system;
(2) and local map generation: the vehicle-mounted sensor collects the observation data of the surrounding environment information of the main vehicle to generate a main vehicle kLocal grid Map of time of dayk h＝{L，W，GridhWhere L is the local map length, W is the local map width, Gridh＝[sj h]1,n*mRepresenting a grid state;
(3) DSRC module information transmission and reception: DSRC communication module transmits the current time position L of the host vehiclek hPosture Pk hAnd a local grid Mapk hSimultaneously receiving the self position, the attitude and the local grid map information issued by the target vehicle in the communication range, and setting the ith target vehicle position lk o(i)＝(xk o(i),yk o(i),zk o(i) In a posture of p)k o(i) Map of local grid mapk o(i)＝{L，W，Grido(i)}，i∈[0,nk]，nkThe number of target vehicles, Grid, in the communication range of the host vehicle at the current momento(i)＝[sj o(i)]1,n*m，sj o(i)＝(xj o(i),yj o(i),fj o(i))TWherein x isj o(i)、yj o(i) As the grid center coordinate, fj o(i) In a grid state, fj o(i) 1 is occupied state, indicating that there is an obstacle at the grid, fj o(i) 0 is in an unoccupied state.
(4) Screening target vehicles: the target vehicle screening module firstly screens the current time position L of the main vehiclek hObtaining the Id number Id of the current road of the main vehicle through an ADAS map interface modulek hAccording to the position l of each target vehicle ik o(i) Obtaining the current Id number Id of the road where the target vehicle is located through an ADAS map interface modulek o(i) If id isk o(i)＝Idk hThe information of the target vehicle i is retained, otherwise, the information is deleted until the N is screened outkA target vehicle on the same road as the host vehicle, wherein Nk≤nk；
(5) Local grid map fusion: map fusion module uses local grid Map of main vehicle at current momentk hBased on the screened NkLocal grid map of individual target vehiclek o(i) Transforming to the host coordinate system and matching Mapk hFusing and generating an expanded local grid Map of the main vehiclek h_extern。
4. As shown in fig. 4, a flow of the target vehicle screening method of the present invention is shown, wherein Id numbers of roads where the host vehicle and the target vehicle are currently located are obtained through an ADAS map interface module according to the locations of the host vehicle and the target vehicle, and a target vehicle on the same road as the host vehicle is screened, and the steps include:
(1) acquiring the Id number of the road where the main vehicle is located: according to the current time position L of the host vehiclek hObtaining the Id number Id of the current road of the main vehicle through an ADAS map interface modulek h；
(2) Acquiring the Id number of the current road of the target vehicle i: according to the current time position l of the target vehicle ik o(i) Acquiring the current Id number Id of the road where the target vehicle i is located through an ADAS map interface modulek o(i)；
(3) Screening target vehicles: for the target vehicle i, if id is satisfiedk o(i)＝Idk hIf yes, the information of the target vehicle i is reserved, otherwise, the information is deleted;
(4) repeating the steps (2) to (3) until n is pairedkAll target vehicles are screened to obtain NkA target vehicle on the same road as the host vehicle, wherein Nk≤nk。
5. As shown in fig. 5, the map fusion method of the present invention converts the screened local map of the target vehicle into the coordinate system of the main vehicle for map fusion to generate the expanded local map of the main vehicle, and includes the following steps:
(1) calculating coordinate transformation parameters of the target vehicle and the main vehicle: for the target vehicle i, according to its position lk o(i)＝(xk o(i),yk o(i),zk o(i) In the posture p), posture pk o(i) And the position L of the main vehiclek h＝(xk h,yk h,zk h) Posture Pk hCalculating coordinate transformation parameters between the coordinate system of i and the coordinate system of the host vehicle, including rotation parameters theta (i) and translation parameters deltax, regardless of the z-axisk(i)、Δy(i)：
(2) Transforming the local grid map of the target vehicle to a host coordinate system: according to the rotation parameter theta (i) and the translation parameter delta xk(i) Δ y (i), map the local grid map of the target vehicle ik o(i) Transforming into the main coordinate system to obtain mapk o-h(i)＝{L，W，Grido-h(i) Where Grid is present }o-h(i)＝[sj o-h(i)]1,n*m，sj o-h(i)＝(xj o-h(i),yj o-h(i),fj o-h(i))T，xj o-h(i)、yj o-h(i) Is a grid center coordinate, takes an integer fj o(i) For the grid state, the transformation relationship is:
(3) local map fusion of the main vehicle and the target vehicle: local Map of the current time of the host vehiclek hOn the basis of mapk o-h(i) And fusion, wherein the fusion rule is as follows: and carrying out grid fusion processing on the map overlapping area according to OR, and carrying out splicing processing on the non-overlapping area. The fusion process is as follows: for mapk o-h(i) Middle Grido-h(i) Each component s ofj o-h(i)，
a. Judging an overlapping area: if Map existsk hMiddle GridhComponent s ofl hSatisfies the condition xj o-h(i)＝xl hAnd yj o-h(i)＝yl hThen sj o-h(i) The represented grid belongs to an overlapping region, otherwise, belongs to a non-overlapping region;
b. and (3) fusion of overlapping regions: for overlapping area sj o-h(i) Performing a raster fusion process according to the OR to update the Mapk hUpper corresponding grid state:
fl h＝fl hfj o-h(i) (3)
c. splicing non-overlapping areas: for non-overlapping region sj o-h(i) Supplement it to Mapk hGrid ofhThe method comprises the following steps:
Gridh＝[Gridh,sj o-h(i)](3)
(4) repeating the steps (1) - (3) until N is reachedkAll the target vehicles are fused to obtain an expanded local grid Map of the main vehiclek h_extern。
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (4)
1. The utility model provides an intelligent vehicle map fusion system based on DSRC which characterized in that, includes vehicle orientation module, local map generation module, DSRC special short range wireless communication module, ADAS advanced driver assistance system map interface module, target vehicle screening module and map fusion module, wherein: the vehicle positioning module is used for acquiring the position and posture information of the main vehicle under a geographic coordinate system through satellite positioning equipment; the local map generation module is used for detecting the surrounding environment of the main vehicle through a vehicle-mounted sensor and generating a local map corresponding to the current position of the main vehicle according to the measurement data of the sensor; the DSRC special short-range wireless communication module is used for issuing the current time position, the gesture and the local map information of the main vehicle and receiving the position, the gesture and the local map information issued by the target vehicle in a communication range; the ADAS advanced driver assistance system map interface module is used for providing a protocol and an interface for accessing an ADAS map; the target vehicle screening module is used for acquiring the current road Id number of the main vehicle through an ADAS map interface according to the current position of the main vehicle, and screening out a target vehicle on the same road as the main vehicle according to the road Id number; the map fusion module is used for carrying out map fusion according to the position, the posture and the local map information of the main vehicle and the screened target vehicle to generate an expanded local map of the main vehicle;
the target vehicle screening module acquires the Id numbers of the roads where the host vehicle and the target vehicle are located through the ADAS map interface module according to the positions of the host vehicle and the target vehicle, screens out the target vehicle which is located on the same road as the host vehicle, and comprises the following steps:
(1) acquiring the Id number of the road where the main vehicle is located: according to the current time position L of the host vehiclek hObtaining the Id number Id of the current road of the main vehicle through an ADAS map interface modulek h；
(2) Acquiring the Id number of the current road of the target vehicle i: according to the current time position l of the target vehicle ik o(i) Acquiring the current Id number Id of the road where the target vehicle i is located through an ADAS map interface modulek o(i)；
(3) Screening target vehicles: for the target vehicle i, if id is satisfiedk o(i)＝Idk hIf yes, the information of the target vehicle i is reserved, otherwise, the information is deleted;
(4) repeating the steps (2) to (3) until n is pairedkAll target vehicles are screened to obtain NkA target vehicle on the same road as the host vehicle, wherein Nk≤nk。
2. The DSRC-based smart vehicle map fusion system of claim 1, wherein the local map generation module generates maps of the primary and target vehiclesThe format of the local map is a grid map, the local map is represented as a rectangular area which takes the current position of the vehicle as an origin, the direction of the vehicle head is the positive direction of the y axis, the length is L, and the width is W; selecting a unit grid with the specification of R to discretize the local map into n M grids, wherein n is L/R, m is W/R, and each grid is represented as s by a vectorj h＝(xj h,yj h,fj h)TThe local map is represented as a grid vector sj hMatrix Grid of elementsh＝[sj h]1,n*mWherein x isj h、yj hAs the grid center coordinate, fj hIn a grid state, fj h1 is occupied state, indicating that there is an obstacle at the grid, fj h0 is in an unoccupied state.
3. An intelligent vehicle map fusion method based on DSRC is characterized by comprising the following steps:
(1) detecting the position and the posture of the main vehicle: at the current moment k, the main vehicle positioning module collects and calculates the position L of the main vehicle under the geographic coordinate system at the moment k through the satellite positioning equipmentk h＝(xk h,yk h,zk h) And attitude Pk h，xk h、yk h、zk hRespectively representing coordinates of the main vehicle under an x axis, a y axis and a z axis under a geographic coordinate system at the moment k, wherein the posture is defined as an included angle between the vehicle running direction and the true north direction of the geographic coordinate system;
(2) and local map generation: the vehicle-mounted sensor collects the observation data of the surrounding environment information of the main vehicle to generate a local grid Map of the main vehicle at the time of Kk h＝{L，W，Gridh}；
(3) DSRC module information transmission and reception: DSRC communication module transmits the current time position L of the host vehiclek hPosture Pk hAnd a local grid Mapk hWhile receiving the target vehicles within the communication rangeSetting the position l of the ith target vehiclek o(i)＝(xk o(i),yk o(i),zk o(i) In a posture of p)k o(i) Map of local grid mapk o(i)＝{L，W，Grido(i)}，i∈[0,nk]，xk o(i)、yk o(i)、zk o(i) Respectively representing the coordinates of the target vehicle under the x axis, the y axis and the z axis of the geographic coordinate system at the moment k, o representing the target vehicle and Grido(i) Matrix representing the ith target vehicle, nkThe number of target vehicles in the communication range of the main vehicle at the current moment is obtained;
(4) screening target vehicles: the target vehicle screening module firstly screens the current time position L of the main vehiclek hObtaining the Id number Id of the current road of the main vehicle through an ADAS map interface modulek hAccording to the position l of each target vehicle ik o(i) Obtaining the current Id number Id of the road where the target vehicle is located through an ADAS map interface modulek o(i) If id isk o(i)＝Idk hThe information of the target vehicle i is retained, otherwise, the information is deleted until the N is screened outkA target vehicle on the same road as the host vehicle, wherein Nk≤nk；
(5) Local grid map fusion: map fusion module uses local grid Map of main vehicle at current momentk hBased on the screened NkLocal grid map of individual target vehiclek o(i) Transforming to the host coordinate system and matching Mapk hFusing and generating an expanded local grid Map of the main vehiclek h_extern；
The target vehicle screening module acquires the Id numbers of the roads where the host vehicle and the target vehicle are located through the ADAS map interface module according to the positions of the host vehicle and the target vehicle, screens out the target vehicle which is located on the same road as the host vehicle, and comprises the following steps:
(1) acquiring the Id number of the road where the main vehicle is located: root of herbaceous plantAccording to the current time position L of the host vehiclek hObtaining the Id number Id of the current road of the main vehicle through an ADAS map interface modulek h；
(2) Acquiring the Id number of the current road of the target vehicle i: according to the current time position l of the target vehicle ik o(i) Acquiring the current Id number Id of the road where the target vehicle i is located through an ADAS map interface modulek o(i)；
(3) Screening target vehicles: for the target vehicle i, if id is satisfiedk o(i)＝Idk hIf yes, the information of the target vehicle i is reserved, otherwise, the information is deleted;
(4) repeating the steps (2) to (3) until n is pairedkAll target vehicles are screened to obtain NkA target vehicle on the same road as the host vehicle, wherein Nk≤nk。
4. The DSRC-based intelligent vehicle map fusion method of claim 3, wherein the map fusion module transforms the screened target vehicle local map into a main vehicle coordinate system, performs map fusion, and generates a main vehicle expansion local map, and the steps comprise:
(1) calculating coordinate transformation parameters of the target vehicle and the main vehicle: for the target vehicle i, according to its position lk o(i)＝(xk o(i),yk o(i),zk o(i) In the posture p), posture pk o(i) And the position L of the main vehiclek h＝(xk h,yk h,zk h) Posture Pk hCalculating coordinate transformation parameters between the coordinate system of i and the coordinate system of the host vehicle, including rotation parameters theta (i) and translation parameters deltax, regardless of the z-axisk(i)、Δy(i)；
(2) Transforming the local grid map of the target vehicle to a host coordinate system: according to the rotation parameter theta (i) and the translation parameter delta xk(i) Δ y (i), map the local grid map of the target vehicle ik o(i) Transforming into the main coordinate system to obtain mapk o-h(i)；
(3) Local map fusion of the main vehicle and the target vehicle: local Map of the current time of the host vehiclek hOn the basis of mapk o-h(i) And fusion, wherein the fusion rule is as follows: carrying out grid fusion processing on the map overlapping area according to OR, and carrying out splicing processing on the non-overlapping area;
(4) repeating the steps (1) - (3) until N is reachedkAll the target vehicles are fused to obtain an expanded local grid Map of the main vehiclek h_extern。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710161021.5A CN106969779B (en) | 2017-03-17 | 2017-03-17 | DSRC-based intelligent vehicle map fusion system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710161021.5A CN106969779B (en) | 2017-03-17 | 2017-03-17 | DSRC-based intelligent vehicle map fusion system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106969779A CN106969779A (en) | 2017-07-21 |
| CN106969779B true CN106969779B (en) | 2020-05-12 |
Family
ID=59329491
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710161021.5A Active CN106969779B (en) | 2017-03-17 | 2017-03-17 | DSRC-based intelligent vehicle map fusion system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106969779B (en) |
Families Citing this family (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109900490B (en) * | 2017-12-11 | 2020-11-03 | 上海交通大学 | Vehicle motion state detection method and system based on autonomous and cooperative sensors |
| CN109993813B (en) * | 2017-12-29 | 2023-06-27 | 长城汽车股份有限公司 | Method, device, vehicle and readable storage medium for creating map |
| CN108284838A (en) * | 2018-03-27 | 2018-07-17 | 杭州欧镭激光技术有限公司 | A kind of detecting system and detection method for detecting outside vehicle environmental information |
| CN108680176A (en) * | 2018-05-16 | 2018-10-19 | 电子科技大学 | A kind of generation method of blind man navigation avoidance map |
| CN108646752B (en) * | 2018-06-22 | 2021-12-28 | 奇瑞汽车股份有限公司 | Control method and device of automatic driving system |
| CN110736474B (en) * | 2018-07-18 | 2021-09-24 | 郑州宇通客车股份有限公司 | Map information acquisition method and device for vehicle |
| CN110851545B (en) * | 2018-07-27 | 2023-11-14 | 比亚迪股份有限公司 | Map drawing method, device and equipment |
| CN109278752B (en) * | 2018-09-26 | 2020-06-05 | 郑州轻工业学院 | Energy optimization control method for plug-in hybrid electric vehicle based on cooperative sensing |
| US11448518B2 (en) | 2018-09-27 | 2022-09-20 | Phiar Technologies, Inc. | Augmented reality navigational overlay |
| US10495476B1 (en) * | 2018-09-27 | 2019-12-03 | Phiar Technologies, Inc. | Augmented reality navigation systems and methods |
| DE102018217194A1 (en) * | 2018-10-09 | 2020-04-09 | Robert Bosch Gmbh | Method for locating a vehicle |
| CN110263607B (en) * | 2018-12-07 | 2022-05-20 | 电子科技大学 | Road-level global environment map generation method for unmanned driving |
| CN111486853B (en) * | 2019-01-28 | 2024-03-22 | 阿里巴巴集团控股有限公司 | Electronic horizon generation method, device and related system |
| CN109781129A (en) * | 2019-01-28 | 2019-05-21 | 重庆邮电大学 | A kind of road surface safety detection system and method based on inter-vehicular communication |
| CN110796852B (en) * | 2019-11-07 | 2020-09-11 | 中南大学 | Vehicle queue map building method and self-adaptive following distance calculation method thereof |
| CN110827541B (en) * | 2019-11-08 | 2022-04-26 | 腾讯科技（深圳）有限公司 | Information acquisition method, device, equipment and storage medium |
| CN111160420B (en) * | 2019-12-13 | 2023-10-10 | 北京三快在线科技有限公司 | Map-based fault diagnosis method, map-based fault diagnosis device, electronic equipment and storage medium |
| WO2021168841A1 (en) * | 2020-02-28 | 2021-09-02 | 华为技术有限公司 | Positioning method and apparatus |
| CN112414416A (en) * | 2020-10-26 | 2021-02-26 | 高深智图(广州)科技有限公司 | ADAS map data system based on four-level automatic driving high precision |
| CN112344956B (en) * | 2020-11-05 | 2022-02-25 | 腾讯科技（深圳）有限公司 | Map display method and device |
| CN113947937B (en) * | 2021-10-29 | 2022-08-05 | 沈阳世纪高通科技有限公司 | Road danger prompting method based on vehicle sensor and dynamic traffic fusion |
| CN114018240A (en) * | 2021-10-29 | 2022-02-08 | 广州小鹏自动驾驶科技有限公司 | Map data processing method and device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102354449A (en) * | 2011-10-09 | 2012-02-15 | 昆山市工业技术研究院有限责任公司 | Internet of vehicles-based method for realizing image information sharing and device and system thereof |
| CN105701479A (en) * | 2016-02-26 | 2016-06-22 | 重庆邮电大学 | Intelligent vehicle multi-laser radar fusion recognition method based on target features |
| CN105741546A (en) * | 2016-03-18 | 2016-07-06 | 重庆邮电大学 | Intelligent vehicle target tracking system through integration of road side equipment and vehicle sensor and method thereof |
| CN106485949A (en) * | 2015-07-20 | 2017-03-08 | 德韧营运有限责任公司 | The sensor fusion of the video camera for vehicle and V2V data |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8660316B2 (en) * | 2010-03-04 | 2014-02-25 | Navteq B.V. | Navigating on images |
-
2017
- 2017-03-17 CN CN201710161021.5A patent/CN106969779B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102354449A (en) * | 2011-10-09 | 2012-02-15 | 昆山市工业技术研究院有限责任公司 | Internet of vehicles-based method for realizing image information sharing and device and system thereof |
| CN106485949A (en) * | 2015-07-20 | 2017-03-08 | 德韧营运有限责任公司 | The sensor fusion of the video camera for vehicle and V2V data |
| CN105701479A (en) * | 2016-02-26 | 2016-06-22 | 重庆邮电大学 | Intelligent vehicle multi-laser radar fusion recognition method based on target features |
| CN105741546A (en) * | 2016-03-18 | 2016-07-06 | 重庆邮电大学 | Intelligent vehicle target tracking system through integration of road side equipment and vehicle sensor and method thereof |
Non-Patent Citations (1)
| Title |
|---|
| 基于图像配准的栅格地图拼接方法;祝继华 等;《自动化学报》;20150228;第41卷(第2期);正文第285-289页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106969779A (en) | 2017-07-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106969779B (en) | DSRC-based intelligent vehicle map fusion system and method | |
| CN107063275B (en) | Intelligent vehicle map fusion system and method based on road side equipment | |
| CN108445503B (en) | Unmanned path planning algorithm based on fusion of laser radar and high-precision map | |
| KR102622571B1 (en) | Directions for autonomous driving | |
| US11125566B2 (en) | Method and apparatus for determining a vehicle ego-position | |
| CN111949943A (en) | Vehicle fusion positioning method for V2X and laser point cloud registration for advanced automatic driving | |
| CN108305475A (en) | A kind of traffic lights recognition methods and device | |
| WO2019183609A1 (en) | Traffic boundary mapping | |
| US20220373353A1 (en) | Map Updating Method and Apparatus, and Device | |
| CN110648548A (en) | Road side equipment-based road surface safety detection system and method | |
| US20230077837A1 (en) | Collaborative perception for autonomous vehicles | |
| JP2023126642A (en) | Information processing device, information processing method, and information processing system | |
| US10857892B2 (en) | Solar vehicle charging system and method | |
| CN111508276B (en) | High-precision map-based V2X reverse overtaking early warning method, system and medium | |
| US11812197B2 (en) | Information processing device, information processing method, and moving body | |
| JPWO2018142560A1 (en) | Vehicle control system, vehicle control method, and vehicle control program | |
| CN112469970A (en) | Method for estimating a localization quality in terms of a self-localization of a vehicle, device for carrying out the method steps of the method, and computer program | |
| US20220090939A1 (en) | Ehorizon upgrader module, moving objects as ehorizon extension, sensor detected map data as ehorizon extension, and occupancy grid as ehorizon extension | |
| US11754415B2 (en) | Sensor localization from external source data | |
| WO2023065342A1 (en) | Vehicle, vehicle positioning method and apparatus, device, and computer-readable storage medium | |
| CN110244719A (en) | Automatic Pilot cognitive method, vehicle, system, equipment and storage medium | |
| JPWO2018142561A1 (en) | Vehicle control system, vehicle control method, and vehicle control program | |
| WO2023213155A1 (en) | Vehicle navigation method and apparatus, computer device and storage medium | |
| CN109906657B (en) | Resource multiplexing method, terminal and related equipment | |
| CN114255275A (en) | Map construction method and computing device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |