CN109785667A - Deviation recognition methods, device, equipment and storage medium - Google Patents
Deviation recognition methods, device, equipment and storage medium Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- 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
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
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- 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
- G01C21/3407—Route searching; Route guidance specially adapted for specific applications
- G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
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- 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
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection
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Abstract
The embodiment of the invention discloses a kind of deviation recognition methods, device, equipment and storage mediums, this method comprises: data, target navigation data and positioning data of vehicles according to the map, determines the target travel line of vehicle；Prediction lane line is determined, wherein the incidence relation surveys lane line by history and history driving line is fitted determination according to the incidence relation between driving line and lane line based on the target travel line；The deviation information of the vehicle is determined according to current vehicle location and the prediction lane line.The technical solution of the embodiment of the present invention determines deviation information by map datum, target navigation data and positioning data of vehicles, reduces dependence of the deviation information generating process to image recognition technology, can reduce the security risk of vehicle driving.
Description
Technical field
The present embodiments relate to intelligent driving technical field more particularly to a kind of deviation recognition methods, device, set
Standby and storage medium.
Background technique
With the arrival in 5G epoch, automatic Pilot technology necessarily rapidly develops therewith, as the important function in automatic Pilot field
The deviation of energy, which differentiates, becomes the hot spot studied in the industry.
In the prior art, to realize that deviation differentiates, it is often used location technology identification vehicle location, then know based on image
Other technology judges practical lane locating for vehicle, to determine whether vehicle deviates current lane.Herein on basis, benefit
Vehicle shift amount is predicted with the data such as speed and steering wheel angle, and then early warning is carried out to automotive run-off-road.
But above-mentioned solution needs to identify lane line when realizing that deviation differentiates, due to weather or
The reason of person's road conditions, when lane line can not be recognized, it is difficult to generate deviation information, it is inclined lane cannot to be issued in time
From early warning, leading to vehicle driving, there are security risks.
Summary of the invention
The embodiment of the present invention provides a kind of deviation recognition methods, device, equipment and storage medium, inclined to reduce lane
From information generating process to the dependence of lane line image recognition, the safety of intelligent driving technology is improved.
In a first aspect, the embodiment of the invention provides a kind of deviation recognition methods, comprising:
Data, target navigation data and positioning data of vehicles according to the map determine the target travel line of vehicle；
Prediction lane line is determined according to the incidence relation between driving line and lane line based on the target travel line,
In, the incidence relation surveys lane line by history and history driving line is fitted determination；
The deviation information of the vehicle is determined according to current vehicle location and the prediction lane line.
Second aspect, the embodiment of the invention also provides a kind of deviation identification devices, comprising:
Driving line determining module determines vehicle for data, target navigation data and positioning data of vehicles according to the map
Target travel line；
Prediction module, according to the incidence relation between driving line and lane line, is determined for being based on the target travel line
Predict lane line, wherein the incidence relation surveys lane line by history and history driving line is fitted determination；
Warning module, for determining that the deviation of the vehicle is believed according to current vehicle location and the prediction lane line
Breath.
The third aspect, the embodiment of the invention also provides a kind of equipment, the equipment includes:
One or more processors；
Memory, for storing one or more programs；
When one or more of programs are executed by one or more of processors, so that one or more of processing
Device realizes the deviation recognition methods as described in any in the embodiment of the present invention.
Fourth aspect, the embodiment of the invention also provides a kind of computer readable storage mediums, are stored thereon with computer
Program realizes deviation recognition methods described in any embodiment of that present invention when the program is executed by processor.
The technical solution of the embodiment of the present invention determines vehicle by using map datum, target navigation data and vehicle data
Target travel line, by target travel line using between driving line and lane line incidence relation determine prediction lane line, lead to
Cross current vehicle location and predict that lane line determines the deviation information of vehicle, the generating process of deviation information without pair
Lane line image recognition prevents from causing deviation information failed regeneration because identifying less than lane line, enhances vehicle driving
Safety.
Detailed description of the invention
Fig. 1 is a kind of flow chart for deviation recognition methods that the embodiment of the present invention one provides；
Fig. 2 is a kind of flow chart of deviation recognition methods provided by Embodiment 2 of the present invention；
Fig. 3 is a kind of offset distance schematic diagram that the embodiment of the present invention two improves；
Fig. 4 is a kind of structural schematic diagram for deviation identification device that the embodiment of the present invention three provides；
Fig. 5 is a kind of functional block diagram for deviation identification device that the embodiment of the present invention three provides；
Fig. 6 is a kind of structural schematic diagram for equipment that the embodiment of the present invention four provides.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is used only for explaining the present invention rather than limiting the invention.It also should be noted that in order to just
Only the parts related to the present invention are shown in description, attached drawing rather than entire infrastructure, in addition, in the absence of conflict, this
The feature in embodiment and embodiment in invention can be combined with each other.
Embodiment one
Fig. 1 is a kind of flow chart for deviation recognition methods that the embodiment of the present invention one provides, and the present embodiment is applicable
In identified using the vehicle of intelligent driving technology to deviation the case where, this method can be by deviation identification device
It executes, which can be realized by the way of hardware and/or software, and generally can integrate in intelligent driving vehicle
In control system, referring to Fig. 1, the method for the embodiment of the present invention includes:
Step 101, according to the map data, target navigation data and positioning data of vehicles determine the target travel line of vehicle.
Wherein, map datum can be the data for being used to characterize geographical location relationship stored in digital form, can wrap
Traffic map data and geographical map data etc. are included, the concrete form of map datum can be map data base, can store
Cloud server or vehicle are local, can be high-precision map, are also possible to general map；Target navigation data, which can be, to be used for
The relevant information for characterizing vehicle destination, may include the information such as longitude and latitude and the number of destination or other can be anti-
Reflect the data that vehicle finally travels destination or next stage traveling destination；Positioning data of vehicles, which can be, passes through location technology
Determining vehicle position data may include the current location data of vehicle and the historical position data of vehicle, vehicle location number
According to the set for the positioning data of vehicles that can be in a period of time, vehicle location can be got by existing vehicle positioning technology
Data, for example, laser radar positioning, satellite positioning and mobile base station positioning etc.；Target travel line is based on automobile navigation data
It calculates and determines vehicle program travel route, target travel line can be as accurate as the traveling lane order of magnitude of vehicle, compared with lane
The width of target travel line can be ignored, it is believed that be the line being made of coordinate points specific in traveling lane.
Specifically, existing location technology, which can be used, gets positioning data of vehicles, it can be in conjunction with map datum with vehicle
Location data is starting point, target navigation data are terminal, randomly chooses location point, the mesh of vehicle can be determined based on location point
Mark driving line.The mode of selection location point can be chosen from map datum.The position precision of different map datums is different,
The mode for characterizing the position interest points such as road, building is also different.For example, the linear road in map vector, generally to fall into reality
Multiple latitude and longitude coordinates points characterize the road along within the scope of the road of border.Coordinate points can be road as location point
Center line is also possible to edge line, is distributed generally according to preset interval.For specific positions such as crossing, detours, usually by multiple
Maneuver point characterizes the road, for example, central point, curb are intersected lighting, put along detour etc..Location point can be linear road
In all or part of coordinate points, can also be with maneuver point some or all of in the special road such as detour.Then by location point meter
It calculates and determines target travel line.It is understood that positioning data of vehicles can be with vehicle driving real-time change, target travel line
It can also change with the transformation of vehicle driving line.
Step 102 is based on the target travel line, according to the incidence relation between driving line and lane line, determines prediction
Lane line, wherein the incidence relation surveys lane line by history and history driving line is fitted determination.
Wherein, driving line can be the estimation route of vehicle driving, can be determined by positioning data of vehicles；Lane line can be with
With dotted line or depicted as solid lines in pavement of road for guiding the road traffic marking of vehicle driving, can by camera or
Person's sensor measurement obtains, and incidence relation can be the relationship between vehicle actual travel route and lane line, may include away from
From relationships such as relationship and positional relationships, such as；Parallel relation and overlapping relation etc., incidence relation embody driving line and lane line it
Between constant position relationship.When lane line can not be obtained by image recognition mode, then can predict to obtain lane line.Prediction
Lane line can be the lane line according to incidence relation prediction；History actual measurement lane line can be to be passed through in vehicle travel process
The Road that image recognition technology determines will be known for example, it may be vehicle can recognize in the history driving process of lane line
The lane line being clipped to is stored as history actual measurement lane line；History driving line, which then can be, calculates determining navigation using foregoing manner
The trajectory line that the location point of route is constituted.
Specifically, the incidence relation between driving line and lane line can be indicated with functional relation, it is based on target travel
The data of line and known incidence relation instead release the position of prediction lane line.
Step 103, the deviation information that the vehicle is determined according to current vehicle location and the prediction lane line.
Wherein, deviation information can be for characterize vehicle whether the information of run-off-road, may include vehicle away from
The distance and vehicle of lane line reach the information such as the time of lane line；Current vehicle location can be vehicle and carry out deviation knowledge
Vehicle location when other can be obtained by location technology.
Specifically, can be calculated based on the location of current vehicle and prediction lane line, current vehicle institute is determined
Locate position and predicts the relationship of lane line, deviation information of the relationship as the vehicle after determining, such as locating for current vehicle
Position is outside prediction lane line, then deviation information can have deviated from lane.Further, in above-described embodiment
On the basis of, determining deviation information can also be prompted to by way of voice or figure vehicle driver or
The safety of vehicle is further enhanced according to deviation information auxiliary operation vehicle to increase the safety guarantee of vehicle driving.
The technical solution of the embodiment of the present invention determines vehicle by map datum, target navigation data and vehicle data
Target travel line is based on target travel line, prediction lane line is determined according to the incidence relation of driving line and lane line, by current
The relationship of vehicle location and prediction lane line determines the deviation information of vehicle；It is realized using location technology to deviation
Identification reduces the dependence to the identification of lane line graph, prevents from leading to deviation recognition failures less than lane line because identifying, can
Reduce the security risk of vehicle driving.
Optionally, on the basis of the above embodiments, it is based on the target travel line, according between driving line and lane line
Incidence relation, determine prediction lane line before, further includes:
Obtain current vehicle location, history actual measurement lane line and history driving line；It determines the current vehicle location and goes through
The second relationship between the first relationship and the current vehicle location and history driving line between historical facts measuring car diatom；It is based on
First relationship and the second relationship are fitted, to generate the incidence relation between driving line and lane line.
Wherein, the first relationship can be the relationship for characterizing current vehicle location and history actual measurement lane line position, can
To be distance value or direction vector etc., the second relationship, which can be, travels the pass of line position for characterizing current vehicle location and history
System, can be distance value or direction vector etc..
Specifically, front truck can be being worked as by location technology acquisition in driving process of the image recognition to lane line
Position obtains actual measurement lane line and current driving line, can calculate current vehicle location and survey the first of lane line and close
Second relationship of system and current vehicle location and driving line, wherein calculating may include real to history by current vehicle location
Measuring car diatom and history driving line do tangent line respectively, using corresponding length of tangent line as the first relationship and the second relationship, may be used also
It is closed using calculating current vehicle location to the nearest distance of history actual measurement lane line and history driving line as the first relationship and second
System, can use the first relationship being calculated and the second relationship generates being associated with for driving line and lane line by way of fitting
Relationship, it is to be understood that in such a way that the first relationship and the second relationship generate driving line and lane line incidence relation not only
It is limited to be fitted, the mode that gradient can also be used to decline, for example, the corresponding functional relation of incidence relation, by the first relationship and the
Two relationships gradually correct the corresponding function of incidence relation until getting satisfactory association pass as the parameter that gradient declines
It is corresponding functional relation, that is, satisfactory incidence relation.
Determine the process of incidence relation, it can be can be completed during image recognition lane line, continuous progress function
It updates.When occurring the case where being unable to image recognition lane line in driving process, then turn by speculating lane line using incidence relation
The mode of position.Both of which can be alternately.
Embodiment two
Fig. 2 is a kind of flow chart of deviation recognition methods provided by Embodiment 2 of the present invention, and the present embodiment is upper
The materialization on the basis of embodiment is stated, correspondingly, referring to fig. 2, the method for the embodiment of the present invention includes:
Step 201 generates standard navigation road using positioning data of vehicles and target navigation data as beginning and end
Line.
Wherein, standard navigation route, which can be, determines navigation routine based on existing map datum precision level, for example, working as
Preceding map datum can be as accurate as road, then standard navigation route can be the navigation circuit using road as accuracy.
Specifically, the existing method for generating navigation routine can be used with positioning data of vehicles and target navigation data point
Not Wei beginning and end generate navigation routine, be denoted as standard navigation route.
Step 202, along the standard navigation route in the map datum chosen position point.
Wherein, location point can be for locative coordinate data in map datum, and location point can be map number
According to the coordinate data inside middle road, central point and marginal point including road etc..
Specifically, can be that foundation chooses the location point belonged to inside road in map datum with standard navigation route.
Step 203 carries out interpolation fitting to each location point, to determine the target travel line of vehicle.
Specifically, location point be generally spaced it is larger, then can by the location point chosen carry out interpolation fitting, will fitting life
At target travel line of the function as vehicle, improve the precision of target travel line and the density of continuity point.
Step 204 obtains current vehicle location, actual measurement lane line and driving line.
Step 205, according to the actual measurement lane line computation lane center.
Wherein, lane center can be intended to indicate that the line of lane line position, can be according to actual measurement lane line center
Point determines.
Specifically, can be clicked through to each center according to the corresponding central point of actual measurement lane line computation actual measurement lane line
Row the Fitting Calculation obtains lane center.
Lateral distance between step 206, the calculating positioning data of vehicles and lane center, is denoted as the first relationship.
Fig. 3 is a kind of offset distance schematic diagram that the embodiment of the present invention two improves, referring to Fig. 3, wherein lane center 22
Can be lane line 21 midpoint formed line, lateral distance 23 can be between vehicle and lane center 22 laterally away from
From.
Specifically, can determine the position that vehicle is presently according to positioning data of vehicles, calculate in the position and lane
Lateral distance between heart line 22, can be using the lateral distance being calculated as the first relationship.
Lateral distance between step 207, the calculating positioning data of vehicles and driving line, is denoted as the second relationship.
Specifically, can determine the position that vehicle is presently according to positioning data of vehicles, the position and driving line are calculated
Between lateral distance, can be using the lateral distance being calculated as the second relationship.
Step 208 is fitted based on first relationship and the second relationship, to generate between driving line and lane line
Incidence relation.
Optionally, on the basis of the above embodiments, incidence relation can be linear function or quadratic function, certainly,
Incidence relation can also determine higher order functionality according to the relationship between actual track.
Specifically, incidence relation can be set to y=x+b, in order to correct the error of prediction lane line, can further by
Correlation function is set as y=ax+b, and incidence relation can also be quadratic function, for example, y=ax2+ bx+c can make in fitting
It is the incidence relation of quadratic function with quadratic function fitting formula generation form.
Step 209 determines current second relationship according to the current vehicle location and target travel line.
Wherein, current second relationship can be the pass that vehicle carries out current vehicle and target travel line when deviation identification
System, may include distance relation and positional relationship etc..
Specifically, can according to current vehicle the location of when carrying out deviation identification with target travel line computation
Lateral distance, can be using lateral distance as current second relationship.
The current second relationship substitution incidence relation is determined current first relationship by step 210.
Specifically, the lateral distance of the current vehicle location being calculated and target travel line can be substituted into incidence relation
The lateral distance of the prediction lane line and current vehicle location as the first relationship is calculated.
Step 211 determines prediction lane line according to the current vehicle location and current first relationship.
Specifically, can predict lane line with the lateral distance of current vehicle location on the basis of in conjunction with current vehicle position
The position for being back-calculated to obtain prediction lane line is set, realizes and determines prediction lane line.
Step 212, the threshold value of warning for obtaining the vehicle.
Wherein, threshold value of warning can be used for characterizing the minimum value of automotive run-off-road, and threshold value of warning can be wide with lane
The value spending relevant value or being set in advance.
Specifically, the average value of lane line computation lane width can be surveyed according to history, lane width can be averaged
The half of value is as threshold value of warning, so that threshold value of warning with vehicle driving real-time update, is convenient for the standard in vehicle travel process
Really identification deviation.
Step 213, the offset distance for calculating the current vehicle location and the prediction lane line.
Wherein, offset distance can be current vehicle location and predict the lateral distance of lane line.
Specifically, the current vehicle present position when vehicle carries out deviation identification can be calculated and predict lane line
Lateral distance, using lateral distance as the offset distance of current vehicle location and prediction lane line.
If step 214, the offset distance are greater than the threshold value of warning, it is determined that the automotive run-off-road.
Specifically, offset distance and threshold value of warning can be compared, if offset distance is greater than threshold value of warning,
It can determine automotive run-off-road；If offset distance is less than or equal to threshold value of warning, it can determine that vehicle also travels in lane.
Optionally, on the basis of the above embodiments, institute is being determined according to current vehicle location and the prediction lane line
After the deviation information for stating vehicle, further includes:
The Vehicle Speed and steering wheel angle of the vehicle are obtained, and according to the Vehicle Speed, steering wheel
Angle and the deviation information carry out lane departure warning.
Specifically, the Vehicle Speed and steering wheel angle of available vehicle, are calculated using above-described embodiment
Deviation information to deviation carry out early warning, for example, can according to current vehicle location and prediction lane line offset
Distance calculates the time that vehicle is driven out to lane, Ke Yigen under current vehicle travel speed and the precondition of steering wheel angle
Deviation early warning is carried out to vehicle according to the time for being driven out to lane.
The technical solution of the embodiment of the present invention, by the way that positioning data of vehicles and target navigation data are determined standard navigation road
Line is based on map datum and standard navigation route chosen position point, clicks through the mesh that row interpolation fitting generates higher precision to position
Mark driving line, using positioning data of vehicles, history actual measurement lane line and history driving line determine positioning data of vehicles respectively with go through
Lateral distance between historical facts measuring car diatom and history driving line determines incidence relation by lateral distance, according to current vehicle
Position and target travel line computation obtain current second relationship, determine prediction lane using current second relationship and incidence relation
Line calculates current vehicle location and predicts that the offset distance of lane line determines that vehicle is inclined if offset distance is greater than threshold value of warning
From lane；High-precision target travel line is determined using existing map datum, reduces the cost of deviation identification, is used
The lateral distance of current vehicle location, history actual measurement lane line and history driving line determines incidence relation, and computation complexity is low, can
The delay for reducing deviation identification, further improves the safety of vehicle driving.
Optionally, on the basis of the above embodiments, position is chosen in the map datum along the standard navigation route
It sets a little, comprising: the corresponding road of the standard navigation route is divided into navigation section by data according to the map；According to the map
Data determine the road condition in the navigation section；If the road condition is linear road, on the corresponding navigation road
The location point of straight-line threshold quantity is chosen in section, and the machine of non-rectilinear number of thresholds is otherwise chosen in the corresponding navigation section
Dynamic point is simultaneously denoted as location point.
Wherein, navigation section, can be divided into the corresponding road of standard navigation route in section；Road condition can be
The road condition that data determine according to the map, may include linear road, bend and crossing etc.；Straight-line threshold quantity can be
The amount threshold of data decimation location point according to the map in linear road, it be not linear road that non-rectilinear number of thresholds, which can be,
In data decimation location point according to the map amount threshold.
Specifically, can according to the map data by the corresponding road of standard navigation route be divided into navigation section, can sentence
Whether break in each navigation section is linear road, if it is linear road, then can be obtained in corresponding navigation section compared with
The location point of small number, if not linear road, then greater number of location point can be obtained in corresponding navigation section,
It and can will not be the corresponding location point to navigate in section of linear road labeled as maneuver point.
Optionally, in the data according to the map, target navigation data and positioning data of vehicles, the target line of vehicle is determined
Before sailing line, further includes: determine the road condition of road in the vehicle front pre-determined distance according to the map datum；If institute
Stating road condition is non-rectilinear road, then stops carrying out deviation identification.
Specifically, data whether can determine vehicle front according to the map before carrying out deviation identification to vehicle
It is not linear road, if not linear road, is optionally working as then deviation identification no longer can be carried out to vehicle
When vehicle in front position is less than set distance at a distance from the maneuver point in target travel line, no longer deviation is identified,
For example, when vehicle distances crossing is less than 30 meters, vehicle no longer carries out deviation identification, so that vehicle is in non-rectilinear road
It is identified without deviation, avoids causing to identify mistake to deviation because of non-rectilinear terrain vehicle diatom inaccuracy.
Embodiment three
Fig. 4 is a kind of structural schematic diagram for deviation identification device that the embodiment of the present invention three provides, can straight line this hair
The deviation recognition methods that bright any embodiment provides, has the corresponding functional module of execution method and beneficial effect.The dress
Setting can be specifically included by software and or hardware realization: driving line determining module 301, prediction module 302 and warning module
303。
Wherein, driving line determining module 301, for data, target navigation data and positioning data of vehicles according to the map, really
Determine the target travel line of vehicle；
Prediction module 302, for being based on the target travel line, according to the incidence relation between driving line and lane line,
Determine prediction lane line, wherein the incidence relation surveys lane line by history and history driving line is fitted determination；
Warning module 303, for determining that the lane of the vehicle is inclined according to current vehicle location and the prediction lane line
From information.
The technical solution of the embodiment of the present invention, by driving line determining module using map datum, target navigation data and
Vehicle data determines the object form line of vehicle, and prediction module uses the pass between driving line and lane line based on target travel line
Connection relationship determines that prediction lane line, warning module determine that the deviation of vehicle is believed according to current vehicle location and prediction lane line
Breath prevents lane caused by can not identifying because of lane line so that deviation information is generated without carrying out image recognition to lane line
Runout information generation error improves the safety of vehicle driving, reduces the security risk of vehicle.
On the basis of the above embodiments, driving line determining module 301 specifically includes:
Standard navigation unit, for raw using the positioning data of vehicles and target navigation data as beginning and end
At standard navigation route.
Location point selection unit, for along the standard navigation route in the map datum chosen position point.
Fitting unit, for carrying out interpolation fitting to each location point, to determine the target travel line of vehicle.
On the basis of the above embodiments, location point selection unit specifically includes:
Subelement is divided, the corresponding road of the standard navigation route is divided into navigation road for data according to the map
Section.
State determines subelement, for determining the road condition in the navigation section according to the map datum.
Subelement is chosen, if being linear road for the road condition, is chosen in the corresponding navigation section
Otherwise the location point of straight-line threshold quantity chooses the maneuver point and note of non-rectilinear number of thresholds in the corresponding navigation section
For location point.
On the basis of the above embodiments, deviation identification device, further includes:
Parameter acquisition module, for obtaining current vehicle location, history actual measurement lane line and history driving line.
Relationship determination module, for determine the current vehicle location and history actual measurement lane line between the first relationship with
And the second relationship between the current vehicle location and history driving line.
Incidence relation module, for being fitted based on first relationship and the second relationship, to generate driving line and vehicle
Incidence relation between diatom.
On the basis of the above embodiments, the incidence relation is linear function or quadratic function.
On the basis of the above embodiments, relationship determination module specifically includes:
Centerline cell, for surveying lane line computation lane center according to the history.
First relation unit is denoted as calculating the lateral distance between the positioning data of vehicles and lane center
One relationship.
Second relation unit is denoted as calculating the lateral distance between the positioning data of vehicles and history driving line
Two relationships.
On the basis of the above embodiments, prediction module 302 specifically includes:
Relationship computing unit, for determining current second relationship according to the current vehicle location and target travel line.
Relation determination unit determines current first relationship for current second relationship to be substituted into the incidence relation.
Lane line unit is predicted, for determining prediction lane line according to the current vehicle location and current first relationship.
On the basis of the above embodiments, warning module 303 specifically includes:
Threshold value of warning unit, for obtaining the threshold value of warning of the vehicle.
Offset distance unit, for calculating the offset distance of the current vehicle location and the prediction lane line.
Prewarning unit, if being greater than the threshold value of warning for the offset distance, it is determined that the automotive run-off-road.
On the basis of the above embodiments, deviation identification device, further includes:
State determining module, for determining the road of road in the vehicle front pre-determined distance according to the map datum
State.
Stopping modular stops carrying out deviation identification if being non-rectilinear road for the road condition.
On the basis of the above embodiments, deviation identification device, further includes:
Second warning module, for obtaining the Vehicle Speed and steering wheel angle of the vehicle, and according to the vehicle
Travel speed, steering wheel angle and the deviation information carry out lane departure warning.
Illustratively, Fig. 5 is a kind of functional block diagram for deviation identification device that the embodiment of the present invention three provides；Ginseng
See Fig. 5, it may include that lane line is known that deviation identification device provided in an embodiment of the present invention, which divide according to functional module,
Other 501, fusion positioning 502, lateral distance 503, maneuver point information 504, target travel line 505, voice prompting 506 and vision mention
Show 507, the lane line that camera will acquire is sent to Lane detection 501, steering wheel angle sensor, vehicle speed sensor and
The number of acquisition is sent to fusion positioning 502 by inertial navigation, and fusion positioning 502 will after carrying out fusion positioning according to the data of acquisition
Current vehicle location is sent to 203 lateral distances, and recognition result is sent to lateral distance 503, maneuver point by Lane detection 501
Target travel line is sent to lateral distance 503 by information 504 and target travel line 505, and lateral distance 503 is current based on the received
Vehicle location, target travel line and history actual measurement lane line calculate lateral distance, and deviation information is sent to voice and is mentioned
Show 506 and visual cues 507.
Method provided by any embodiment of the invention can be performed in above-mentioned apparatus, has the corresponding functional module of execution method
And beneficial effect.The not technical detail of detailed description in the present embodiment, reference can be made to the method that any embodiment of that present invention provides.
Example IV
Fig. 6 is a kind of structural schematic diagram for equipment that the embodiment of the present invention four provides, as shown in fig. 6, the equipment includes place
Manage device 60, memory 61, input unit 62 and output device 63；The quantity of processor 60 can be one or more in equipment,
In Fig. 6 by taking a processor 60 as an example；Processor 60, memory 61, input unit 62 and output device 63 in equipment can be with
It is connected by bus or other modes, in Fig. 6 for being connected by bus.Equipment provided by the present embodiment preferably configures
Controller or control system in intelligent driving vehicle.
Memory 61 is used as a kind of computer readable storage medium, can be used for storing software program, journey can be performed in computer
Sequence and module, if the corresponding program module of deviation recognition methods in the embodiment of the present invention is (for example, deviation identifies
Driving line determining module 301, prediction module 302 and warning module 303 in device).Processor 60 is stored in by operation
Software program, instruction and module in reservoir 61 are realized thereby executing the various function application and data processing of equipment
Above-mentioned deviation recognition methods.
Memory 61 can mainly include storing program area and storage data area, wherein storing program area can store operation system
Application program needed for system, at least one function；Storage data area, which can be stored, uses created data etc. according to terminal.This
Outside, memory 61 may include high-speed random access memory, can also include nonvolatile memory, for example, at least a magnetic
Disk storage device, flush memory device or other non-volatile solid state memory parts.In some instances, memory 61 can be further
Including the memory remotely located relative to processor 60, these remote memories can pass through network connection to equipment.It is above-mentioned
The example of network includes but is not limited to internet, intranet, local area network, mobile radio communication and combinations thereof.
Input unit 62 can be used for receiving the number or character information of input, and generate with the user setting of equipment and
The related key signals input of function control.Output device 63 may include that display screen etc. shows equipment.
Embodiment five
The embodiment of the present invention five also provides a kind of storage medium comprising computer executable instructions, and the computer can be held
Row instruction is used to execute a kind of deviation recognition methods when being executed by computer processor, this method comprises:
Data, target navigation data and positioning data of vehicles according to the map determine the target travel line of vehicle；
Prediction lane line is determined according to the incidence relation between driving line and lane line based on the target travel line,
In, the incidence relation surveys lane line by history and history driving line is fitted determination；
The deviation information of the vehicle is determined according to current vehicle location and the prediction lane line.
Certainly, a kind of storage medium comprising computer executable instructions, computer provided by the embodiment of the present invention
The method operation that executable instruction is not limited to the described above, can also be performed deviation provided by any embodiment of the invention
Relevant operation in recognition methods.
By the description above with respect to embodiment, it is apparent to those skilled in the art that, the present invention
It can be realized by software and required common hardware, naturally it is also possible to which by hardware realization, but in many cases, the former is more
Good embodiment.Based on this understanding, technical solution of the present invention substantially in other words contributes to the prior art
Part can be embodied in the form of software products, which can store in computer readable storage medium
In, floppy disk, read-only memory (Read-Only Memory, ROM), random access memory (Random such as computer
Access Memory, RAM), flash memory (FLASH), hard disk or CD etc., including some instructions are with so that a computer is set
Standby (can be personal computer, server or the network equipment etc.) executes method described in each embodiment of the present invention.
It is worth noting that, included each unit and module are only in the embodiment of above-mentioned deviation identification device
It is to be divided according to the functional logic, but be not limited to the above division, as long as corresponding functions can be realized；Separately
Outside, the specific name of each functional unit is also only for convenience of distinguishing each other, the protection scope being not intended to restrict the invention.
Note that the above is only a better embodiment of the present invention and the applied technical principle.It will be appreciated by those skilled in the art that
The invention is not limited to the specific embodiments described herein, be able to carry out for a person skilled in the art it is various it is apparent variation,
It readjusts and substitutes without departing from protection scope of the present invention.Therefore, although being carried out by above embodiments to the present invention
It is described in further detail, but the present invention is not limited to the above embodiments only, without departing from the inventive concept, also
It may include more other equivalent embodiments, and the scope of the invention is determined by the scope of the appended claims.
Claims (13)
1. a kind of deviation recognition methods characterized by comprising
Data, target navigation data and positioning data of vehicles according to the map determine the target travel line of vehicle；
Prediction lane line is determined according to the incidence relation between driving line and lane line based on the target travel line, wherein
The incidence relation surveys lane line by history and history driving line is fitted determination；
The deviation information of the vehicle is determined according to current vehicle location and the prediction lane line.
2. the method according to claim 1, wherein the data according to the map, target navigation data and vehicle
Location data determines the target travel line of vehicle, comprising:
Standard navigation route is generated using the positioning data of vehicles and target navigation data as beginning and end；
Along the standard navigation route in the map datum chosen position point；
Interpolation fitting is carried out to each location point, to determine the target travel line of vehicle.
3. according to the method described in claim 2, it is characterized in that, it is described along the standard navigation route in the map datum
Middle chosen position point, comprising:
The corresponding road of the standard navigation route is divided into navigation section by data according to the map；
The road condition in the navigation section is determined according to the map datum；
If the road condition is linear road, the position of straight-line threshold quantity is chosen in the corresponding navigation section
Otherwise point chooses the maneuver point of non-rectilinear number of thresholds in the corresponding navigation section and is denoted as location point.
4. the method according to claim 1, wherein the target travel line is based on, according to driving line and lane
Incidence relation between line determines before predicting lane line, further includes:
Obtain current vehicle location, history actual measurement lane line and history driving line；
Determine the current vehicle location and history actual measurement lane line between the first relationship and the current vehicle location and
The second relationship between history driving line；
It is fitted based on first relationship and the second relationship, to generate the incidence relation between driving line and lane line.
5. according to the method described in claim 4, it is characterized in that, the determination current vehicle location and history survey vehicle
The second relationship between the first relationship and the current vehicle location and history driving line between diatom, comprising:
Lane line computation lane center is surveyed according to the history；
The lateral distance between the positioning data of vehicles and lane center is calculated, the first relationship is denoted as；
The lateral distance between the positioning data of vehicles and history driving line is calculated, the second relationship is denoted as.
6. according to the method described in claim 5, it is characterized in that, the incidence relation is linear function or quadratic function.
7. according to the method described in claim 5, it is characterized in that, it is described be based on the target travel line, according to driving line with
Incidence relation between lane line determines prediction lane line, comprising:
Current second relationship is determined according to the current vehicle location and target travel line；
Current second relationship is substituted into the incidence relation and determines current first relationship；
Prediction lane line is determined according to the current vehicle location and current first relationship.
8. the method according to claim 1, wherein described according to current vehicle location and the prediction lane line
Determine the deviation information of the vehicle, comprising:
Obtain the threshold value of warning of the vehicle；
Calculate the offset distance of the current vehicle location and the prediction lane line；
If the offset distance is greater than the threshold value of warning, it is determined that the automotive run-off-road.
9. the method according to claim 1, wherein in the data according to the map, target navigation data and vehicle
Location data, before the target travel line for determining vehicle, further includes:
The road condition of road in the vehicle front pre-determined distance is determined according to the map datum；
If the road condition is non-rectilinear road, stop carrying out deviation identification.
10. any method in -9 according to claim 1, which is characterized in that described according to current vehicle location and institute
It states after prediction lane line determines the deviation information of the vehicle, further includes:
The Vehicle Speed and steering wheel angle of the vehicle are obtained, and according to the Vehicle Speed, steering wheel angle
And the deviation information carries out lane departure warning.
11. a kind of deviation identification device characterized by comprising
Driving line determining module determines the target of vehicle for data, target navigation data and positioning data of vehicles according to the map
Driving line；
Prediction module, according to the incidence relation between driving line and lane line, determines prediction for being based on the target travel line
Lane line, wherein the incidence relation surveys lane line by history and history driving line is fitted determination；
Warning module, for determining the deviation information of the vehicle according to current vehicle location and the prediction lane line.
12. a kind of equipment, which is characterized in that the equipment includes:
One or more processors；
Memory, for storing one or more programs；
When one or more of programs are executed by one or more of processors, so that one or more of processors are real
The now deviation recognition methods as described in any in claim 1-10.
13. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is by processor
The deviation recognition methods as described in any in claim 1-10 is realized when execution.
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| CN202110970102.6A CN113538919B (en) | 2019-03-11 | 2019-03-11 | Lane departure recognition method, device, equipment and storage medium |
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Also Published As
| Publication number | Publication date |
|---|---|
| CN113538919A (en) | 2021-10-22 |
| CN113538919B (en) | 2022-10-28 |
| CN109785667B (en) | 2021-08-03 |
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Effective date of registration: 20211021 Address after: 100176 101, floor 1, building 1, yard 7, Ruihe West 2nd Road, Beijing Economic and Technological Development Zone, Daxing District, Beijing Patentee after: Apollo Zhilian (Beijing) Technology Co.,Ltd. Address before: 100085 Baidu Building, 10 Shangdi Tenth Street, Haidian District, Beijing Patentee before: BAIDU ONLINE NETWORK TECHNOLOGY (BEIJING) Co.,Ltd. |