CN107664503A - Vehicle path planning method and device - Google Patents
Vehicle path planning method and device Download PDFInfo
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- CN107664503A CN107664503A CN201610614314.XA CN201610614314A CN107664503A CN 107664503 A CN107664503 A CN 107664503A CN 201610614314 A CN201610614314 A CN 201610614314A CN 107664503 A CN107664503 A CN 107664503A
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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
- 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
- G01C21/3407—Route searching; Route guidance specially adapted for specific applications
- G01C21/343—Calculating itineraries, i.e. routes leading from a starting point to a series of categorical destinations using a global route restraint, round trips, touristic trips
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Abstract
A kind of vehicle path planning method and apparatus, the vehicle path planning method include：Starting point and the destination of vehicle are obtained, and the starting point is positioned in map, the map includes road attribute；Gather the information of vehicles of the vehicle；Global path from the starting point to the destination is cooked up according to the information of vehicles and the road attribute.For the vehicle path planning method and apparatus when intelligent vehicle travels, the optimal global path of generation improves the enforceability of vehicle path planning.
Description
Technical field
The present invention relates to Vehicular intelligent technical field of transportation, more particularly to a kind of vehicle path planning method and device.
Background technology
With the development of airmanship, the application navigated in vehicular field is more and more extensive.Vehicular guidance system can
To receive institute in gps satellite by built-in global positioning system (Global Positioning System, GPS) antenna
The data message of transmission, thus determine the position that vehicle is presently in；Inertial navigation system (Inertial can also be passed through
Navigation System, INS) carry out automobile navigation.Inertial navigation system be one kind independent of external information, also not to
External radiation energy, such as radionavigational autonomic navigation system.Inertial navigation system is led to based on Newton mechanics law
Measurement vehicle is crossed in the acceleration of inertial reference system, the time is integrated, and is transformed in navigational coordinate system, vehicle is obtained and exists
The information such as speed, yaw angle and position in navigational coordinate system.Navigation host is by the vehicle location coordinate and electronic map of determination
Data match, accurate location of the vehicle in electronic map can be determined, obtain driver and easily drive to experience.
In the prior art, vehicle user needs to pre-set starting point and destination, vehicle mounted guidance in navigation map
System plans global path according to starting point and destination；Can also be by the global path of the artificial designated vehicle traveling of user.Its
In, the navigation map used in the prior art is topological map, and in topological map, environmental information is expressed as band node and phase
The topology diagram of wiring is correlated, wherein node represents the critical positions point (turning, traffic lights, mansion, vehicle etc.) in environment,
Side represents the annexation between node, such as road.The global path one that onboard navigation system is cooked up based on topological map
As for time from starting point to destination it is most short or apart from shortest path.
But vehicle also has road to the transit time of special car, traffic flow, car in the driving procedure of reality
The limitation of height etc., in this case, the existing global path planned based on topological map can not be performed, and be reduced
Experience of the user for automobile navigation.
The content of the invention
Present invention solves the technical problem that it is how to improve the enforceability of vehicle path planning.
In order to solve the above technical problems, the embodiment of the present invention provides a kind of vehicle path planning method, the vehicle route
Planing method includes：Starting point and the destination of vehicle are obtained, and the starting point is positioned in map, the map includes
Road attribute；Gather the information of vehicles of the vehicle；Cooked up according to the information of vehicles and the road attribute from described
Global path of the point to the destination.
Optionally, the vehicle path planning method also includes：
Based on the global path, local path is generated with reference to the environmental information of the vehicle periphery collected, it is described
Local path is used to supply the vehicle tracking；When the current location of the vehicle and the distance of the global path are less than the first threshold
Value and when the length of the local path is more than Second Threshold, performs the navigation of the global path.
Optionally, the vehicle path planning method also includes：Current location and the global path when the vehicle
Distance when being more than the length of first threshold or the local path and being less than Second Threshold, using the current location of the vehicle as
Starting point, the global path is cooked up again.
Optionally, the global road from the starting point to the destination is cooked up according to the information of vehicles and road attribute
Footpath includes：Calculate length, the slope of the respective weight of mulitpath from the starting point to destination, the weight and the path
Degree and the information of vehicles associate with the matching degree of road attribute；Calculate and determine optimal path as the global path,
Wherein, the weight in the path is bigger represents that the path is more excellent, represents the path impassabitity during weight infinity.
Optionally, the calculation formula of the weight is：W=k1*1+k2*s+f(h)+f(v)；Wherein, w is the path
Weight, l be the path length, s be the path the gradient, k1For default length parameter, k2Join for the default gradient
Number, f (h) is limit for height function, and f (v) is type of vehicle restricted function.
Optionally, the information of vehicles includes one or more of：Type of vehicle, height of car and power performance
Energy.
Optionally, the road attribute includes one or more of：Maximum height limit and limitation type of vehicle.
Optionally, when the height of car is more than the maximum height limit, the limit for height function is infinity；The vehicle is high
When degree is less than the maximum height limit, the limit for height function is 0；The type of vehicle does not meet the limitation vehicle in the path
During type, the type of vehicle restricted function is infinity；The type of vehicle meets the limitation vehicle class in the path
During type, the type of vehicle restricted function is 0.
Optionally, calculated using Dijkstra's algorithm and determine the global path.
Optionally, before the starting point and the destination that obtain vehicle, the vehicle path planning method also includes：Load describedly
Figure.
Optionally, the environmental information includes：Condition of road surface, traffic.
In order to solve the above technical problems, the embodiment of the invention also discloses a kind of vehicle path planning device, vehicle route
Device for planning includes：
Positioning unit, suitable for obtaining starting point and the destination of vehicle, and the starting point is positioned in map, it is described
Map includes road attribute；
Collecting unit, suitable for gathering the information of vehicles of the vehicle；
Global path generation unit, suitable for according to the information of vehicles and the road attribute cook up from the starting point to
The global path of the destination.
Optionally, the vehicle path planning device also includes：
Local path generation unit, suitable for based on the global path, with reference to the environment of the vehicle periphery collected
Information generates local path, and the local path is used to supply the vehicle tracking；
Navigation elements, suitable for being less than first threshold and institute when the current location of the vehicle and the distance of the global path
When stating the length of local path and being more than Second Threshold, the navigation of the global path is performed.
Optionally, when the current location of the vehicle and the distance of the global path are more than first threshold or the part
When the length in path is less than Second Threshold, the global path generation unit is using the current location of the vehicle as starting point, weight
Newly cook up the global path.
Optionally, the global path generation unit includes：
Weight calculation subelement, calculates the respective weight of mulitpath from the starting point to destination, the weight with
The information of vehicles associates with the matching degree of road attribute；
Global path computation subelement, calculate and determine optimal path as the global path, wherein, the path
Weight is more big, represents that the path is more excellent.
Optionally, the weight calculation subelement calculates the weight using equation below：W=k1*l+k2*s+f(h)+f
(v)；
Wherein, w is the weight in the path, and l is the length in the path, and s is the gradient in the path, and k1 is default
Length parameter, k2 are default gradient parameter, and f (h) is limit for height function；F (v) is type of vehicle restricted function.
Optionally, the information of vehicles includes one or more of：Type of vehicle, height of car and power performance
Energy.
Optionally, the road attribute includes one or more of：Maximum height limit and limitation type of vehicle.
Optionally, the road attribute includes the path to the maximum height limit of the vehicle and limitation type of vehicle；Institute
When stating height of car and being more than the maximum height limit, the limit for height function is infinity；It is high that the height of car is less than the limitation
When spending, the limit for height function is 0；When the type of vehicle does not meet the limitation type of vehicle in the path, the vehicle
Type restricted function is infinity；When the type of vehicle meets the limitation type of vehicle in the path, the vehicle class
Type restricted function is 0.
Optionally, the global path computation subelement is calculated using Dijkstra's algorithm and determines the global path.
Optionally, the vehicle path planning device also includes：Initial cell, suitable for obtaining the starting point and purpose of vehicle
Before ground, the map is loaded.
Optionally, the environmental information includes one or more of：Condition of road surface, traffic.
Compared with prior art, the technical scheme of the embodiment of the present invention has the advantages that：
The embodiment of the present invention gathers the information of vehicles of the vehicle by using the map comprising road attribute, and according to
The information of vehicles and the road attribute cook up the global path from the starting point to the destination.In intelligent vehicle row
When sailing, the optimal global path of generation improves the enforceability of vehicle path planning.
Further, based on the global path, local road is generated with reference to the environmental information of the vehicle periphery collected
Footpath, the local path are used for the vehicle tracking；When the current location of the vehicle and the distance of the global path are more than
When first threshold or the length of the local path are less than Second Threshold, using the current location of the vehicle as starting point, again
Cook up the global path.It is inconsistent with performing to solve the caused planning when intelligent vehicle deviates global path farther out
The problem of.
Brief description of the drawings
Fig. 1 is a kind of vehicle path planning method flow diagram of the embodiment of the present invention；
Fig. 2 is another kind vehicle path planning method flow diagram of the embodiment of the present invention；
Fig. 3 is a kind of global path schematic diagram of the embodiment of the present invention；
Fig. 4 is a kind of vehicle path planning apparatus structure schematic diagram of the embodiment of the present invention；
Fig. 5 is a kind of global path generation unit structural representation of the embodiment of the present invention.
Embodiment
As described in the background art, vehicle reality driving procedure in, also have road to special car it is current when
Between, traffic flow, the limitation of height of car etc., in this case, the existing global path planned based on topological map is not
It can be performed, reduce experience of the user for automobile navigation.
It is understandable to enable the above objects, features and advantages of the present invention to become apparent, below in conjunction with the accompanying drawings to the present invention
Specific embodiment be described in detail.
The embodiment of the present invention gathers the information of vehicles of the vehicle by using the map comprising road attribute, and according to
The information of vehicles and the road attribute cook up the global path from the starting point to the destination.In intelligent vehicle row
When sailing, the optimal global path of generation improves the enforceability of vehicle path planning.
Fig. 1 is a kind of vehicle path planning method flow diagram of the embodiment of the present invention.
Fig. 1 is refer to, vehicle path planning method can include step S101, step S102 and step S103.
Step S101, starting point and the destination of vehicle are obtained, and the starting point is positioned in map.
In the present embodiment, before vehicle sets out, map is loaded, starting point and the destination of vehicle are set by vehicle user, and
Vehicle starting point is positioned in map.Because vehicle is in the driving procedure of reality, also has road and special car is passed through
The limitation of time, height of car etc., so road attribute information is included in the map used in vehicle path planning method, it is described
Road attribute includes maximum height limit and limitation type of vehicle data, when planning vehicle running path, is carried out with reference to above-mentioned factor
Planning.
In specific implementation, the map used in vehicle path planning method is high-precision electronic map.Vehicle is travelled in car
When in road, the road attribute information closely located can be according to the data acquisition of in-vehicle camera return；In-vehicle camera can not obtain
To the track remotely of data, it is necessary to the data of onboard sensor collection and the data progress in high-precision electronic map
Match somebody with somebody, obtain road attribute information, high-precision electronic map includes maximum height limit and limitation type of vehicle data.
In specific implementation, vehicle path planning method can also obtain the direction of the course information of vehicle, i.e. headstock.Vehicle
Start position when being in one-way road or being inconvenient to the road turned to, with reference to the course of vehicle, contribute to planning distance shorter
Enforceable vehicle running path.
Step S102, gather the information of vehicles of the vehicle.
In the present embodiment, vehicle path planning method also needs to collection vehicle information, and information of vehicles includes type of vehicle, car
Height and vehicles dynamic performance.
Step S103, cooked up according to the information of vehicles and the road attribute from the starting point to the destination
Global path.
In the present embodiment, maximum height limit and limitation type of vehicle data and the vehicle class of information of vehicles based on road attribute
Type, height of car and vehicles dynamic performance data carry out path planning, obtain vehicle from the starting point to the destination most
Shortest path, as from the starting point to the global path of the destination.
In the present embodiment, the global path includes mulitpath, and the beginning and end of the mulitpath can represent
For the different crossings in map.Calculated using Dijkstra's algorithm and determine the global path.The planning process of global path
Including：The respective weight of mulitpath from the starting point to destination is calculated first；Then calculate and determine that optimal path is made
For the global path.Wherein, weight and the weight and the length, the gradient and the information of vehicles and road in the path
The matching degree association of attribute；The weight in the path is more big, represents that the path is more excellent, represents institute during the weight infinity
State path impassabitity.The matching degree of the information of vehicles and road attribute represents whether information of vehicles meets road attribute
It is required that for example, whether type of vehicle meets whether the limitation type of vehicle of road and height of car are more than or equal to the limit of road
System height, the ratio of the total item of the item number occupied road attribute of the road attribute of satisfaction is higher, then matching degree is higher.
In specific implementation, the calculation formula of the weight in Dijkstra's algorithm is：W=k1*1+k2*s+f(h)+f
(v)；Wherein, w be the path weight, l be the path length, s be the path the gradient, k1For default length
Parameter, k2For default gradient parameter, f (h) is limit for height function, and f (v) is type of vehicle restricted function.
In specific implementation, when height of car is more than the maximum height limit, the limit for height function return value is infinity, described
Weight is infinity, represents the path impassabitity.When height of car is less than the maximum height limit, the limit for height function returns
It is worth for 0, the size of the weighted value is not had an impact, i.e., does not influence the meter to weighted value according to path length and the path gradient
Calculate.When type of vehicle does not meet the limitation type of vehicle in the path, the type of vehicle restricted function return value is nothing
It is poor big, represent the path impassabitity.When the type of vehicle meets the limitation type of vehicle in the path, the car
Type restricted function return value is 0, and the size of the weighted value is not had an impact, not influenceed according to path length and path
Calculating of the gradient to weighted value.After the respective weight calculation of mulitpath from the starting point to destination comes out, power is filtered out
Weight values are infinitely great path, and the maximum path of weight selection value combines to form the overall situation as optimal path, a plurality of optimal path
Path.
In specific implementation, default length parameter k1With default gradient parameter k2Belong to set [0,1], and k1+k2=
1.In the selection of vehicle route, the link length in the path is more important than road grade, so default length parameter k1's
Value is more than default gradient parameter k2, such as, k1=0.9, k2=0.1.
It is understood that in the application of reality, default length parameter k1With default gradient parameter k2Meeting k1
+k2Under conditions of=1, the adjustment of adaptability can be done according to the application environment of reality by user.
Starting point to destination global path planning well after, vehicle mounted guidance according to the global path perform to vehicle
Navigated.In the vehicle path planning method of the present embodiment, the navigation mode for vehicle, can be inertial navigation system,
GPS navigation system or inertial navigation system and the navigation of the joint of GPS navigation system.
Fig. 2 is another kind vehicle path planning method flow diagram of the embodiment of the present invention.
Fig. 2 is refer to, in the lump reference picture 1, vehicle path planning method can include step S201 to step S208.
Step S201, starting point and the destination of vehicle are obtained, and the starting point is positioned in map.
In the present embodiment, before vehicle sets out, map is loaded, starting point and the destination of vehicle are set by vehicle user, and
Vehicle starting point is positioned in map.Road attribute information, the road are included in the map used in vehicle path planning method
Road attribute includes maximum height limit and limitation type of vehicle data, when planning vehicle running path, enters professional etiquette with reference to above-mentioned factor
Draw.Wherein, starting point can include positional information and course information, or can also only include positional information.
Step S202, the information of vehicles of collection vehicle.
In the present embodiment, vehicle path planning method also needs to collection vehicle information, and information of vehicles includes type of vehicle, car
Height and vehicles dynamic performance.
Step S203, the global road from the starting point to the destination is cooked up according to information of vehicles and road attribute
Footpath.
In the present embodiment, maximum height limit and limitation type of vehicle data and the vehicle class of information of vehicles based on road attribute
Type, height of car and vehicles dynamic performance data carry out path planning, obtain vehicle from the starting point to the destination most
Shortest path, as from the starting point to the global path of the destination.
The embodiment of step S201 to the step S203 refer to foregoing corresponding embodiment, no longer superfluous herein
State.
Step S204, based on global path, local path is generated with reference to the environmental information of the vehicle periphery collected.
In the present embodiment, the local path is used to supply the vehicle tracking.After global path is generated, with global path
To be oriented to, with reference to the local path that can track of real time environment information generation vehicle of vehicle periphery.Local path is worked as vehicle
The path of preceding state traveling, the vehicle local path of different conditions form the global path of vehicle.Wherein, the environmental information bag
Include condition of road surface and traffic.During the traveling of vehicle, the situation of present road has situations such as unimpeded, hot work in progress,
When road is in unimpeded situation, local path can be generated；When road is in hot work in progress situation, local path can not give birth to
Into or path length it is too short, vehicle can not travel.Similarly, when the traffic of present road is in unimpeded situation, Ke Yisheng
Into local path；When traffic is in congestion, local path can not generate or path length is too short, and vehicle can not go
Sail.
In specific implementation, after the local path generation of vehicle, while the controlled quentity controlled variable of vehicle is generated.The controlled quentity controlled variable is car
Speed and direction controlling parameter.By being controlled to the amount of turning of steering wheel for vehicle, change the travel direction of vehicle；Pass through
The opening degree of the gas pedal of vehicle is controlled, changes the travel speed of vehicle.
Step S205, judges whether the current location of vehicle and the distance of global path are less than first threshold, if it is,
Into step S206, otherwise into step S208.
Step S206, the length of local path are more than Second Threshold, if it is, into step S207, otherwise enter step
Rapid S208.
In the present embodiment, after the generation of the local path of vehicle, calculate vehicle current location and global path it is most short
The length of distance and local path.
In specific implementation, first threshold can be 100m, and Second Threshold can be length of wagon.First threshold and the second threshold
Value can also be arranged to any enforceable numerical value according to actual application environment.
Step S207, perform the navigation of the global path.
In the present embodiment, it is less than or equal to 100m, and local road in the beeline of the current location of vehicle and global path
When the length in footpath is more than length of wagon, the navigation of the global path is performed according to the controlled quentity controlled variable of vehicle, makes vehicle along the overall situation
Route is to destination.
Step S208, using the current location of vehicle as starting point, global path is cooked up again.
In the present embodiment, when the beeline of the current location of vehicle and global path is more than 100m, vehicle, which deviates, works as
Preceding global path, current global path can not continue as automobile navigation, it is necessary to using the current location of vehicle as starting point, advise again
Draw new global path；The length that hot work in progress situation or the congested in traffic local path for causing to generate are in road is less than car
During body length, vehicle can not continue to travel in current local path, and current global path can not continue as automobile navigation, it is necessary to will
Global path is cooked up again as starting point in the current location of vehicle.
The vehicle path planning method of the present embodiment solves global path generation problem during intelligent vehicle traveling.In car
Traveling when, generate an executable, optimal global path；Meanwhile deviate current global path in current vehicle position
When farther out, by planning that it is consistent with what is performed that global path realizes planning again.
Fig. 3 is a kind of global path schematic diagram of the embodiment of the present invention.
Fig. 3 is refer to, node P1, P2, P3, P4, P5, P6, P7, P8, P9 and P10 represent the node in map, the section
Point is the critical positions point in environment, can be as the starting point per paths.Wherein, node P0 is starting point, and node P10 is mesh
Ground.
In the present embodiment, after the weights per paths are calculated according to Dijkstra's algorithm, mulitpath P0-P2, P2-
P5, P5-P8 and P8-P10 weighted value highest, path is optimal, and as shown in lines 1, node P0, P2, P5, P8 and P10 form P0
For starting point, from starting point P0 to destination P10 global path.
In the present embodiment, vehicle from starting point P0 along global path P0-P2, P2-P5 travel to node P5 when, vehicle deviate work as
Preceding global path P5-P8 sections, and distance is more than 100m, determines vehicle yaw, global path is planned again.Now vehicle is closer
Node P7, therefore can plan and to form new global path P0, P2, P5, P7 and P10, vehicle according to new global path P5-P7 and
P7-P10 navigates again, until arriving at P10.
Fig. 4 is a kind of vehicle path planning apparatus structure schematic diagram of the embodiment of the present invention.
Fig. 4 is refer to, vehicle path planning device can include：Positioning unit 401, collecting unit 402, global path life
Into unit 403, local path generation unit 404 and navigation elements 405.
Wherein, positioning unit 401, suitable for obtaining starting point and the destination of vehicle, and the starting point is carried out in map
Positioning, the map include road attribute；
Collecting unit 402, suitable for gathering the information of vehicles of the vehicle；
Global path generation unit 403, suitable for being cooked up according to the information of vehicles and the road attribute from described
Global path of the point to the destination；
Local path generation unit 404, suitable for based on the global path, with reference to the ring of the vehicle periphery collected
Environment information generates local path, and the local path is used to supply the vehicle tracking.The environmental information include it is following a kind of or
It is a variety of：Condition of road surface, traffic；
Navigation elements 405, suitable for being less than first threshold when the current location of the vehicle and the distance of the global path
And the length of the local path performs the navigation of the global path when being more than Second Threshold.
When the distance of current location and the global path of the vehicle is more than first threshold or the local path
When length is less than Second Threshold, the global path generation unit is planned again using the current location of the vehicle as starting point
Go out the global path.
It refer to Fig. 5, Fig. 5 is a kind of global path generation unit structural representation of the embodiment of the present invention, the global road
Footpath generation unit 403 can include：
Weight calculation subelement 501, calculate the respective weight of mulitpath from the starting point to destination, the weight
Is associated with the matching degree of the information of vehicles and road attribute
Global path computation subelement 502, calculate and determine optimal path as the global path, wherein, the road
The weight in footpath is more big, represents that the path is more excellent.
In the present embodiment, the weight calculation subelement can use equation below to calculate the weight：W=k1*1+k2*s
+f(h)+f(v)；Wherein, w is the weight in the path, and l is the length in the path, and s is the gradient in the path, and k1 is pre-
If length parameter, k2 is default gradient parameter, and f (h) is limit for height function；F (v) is type of vehicle restricted function.
In the present embodiment, the information of vehicles includes can be with one or more of：Type of vehicle, height of car and vehicle
Power performance.The road attribute can include one or more of：Maximum height limit and limitation type of vehicle.The road category
Property can include the path to the maximum height limit of the vehicle and limitation type of vehicle；The height of car is more than the limitation
During height, the limit for height function is infinity；When the height of car is less than the maximum height limit, the limit for height function is 0；Institute
When stating type of vehicle and not meeting the limitation type of vehicle in the path, the type of vehicle restricted function is infinity；Institute
When stating type of vehicle and meeting the limitation type of vehicle in the path, the type of vehicle restricted function is 0.
In the present embodiment, it is described complete that the global path computation subelement can use Dijkstra's algorithm to calculate determination
Office path.
With continued reference to Fig. 4, vehicle path planning device can also include：Initial cell, suitable for obtaining the starting point of vehicle
Before destination, the map is loaded.
Embodiment refers to foregoing corresponding embodiment, and here is omitted.
It should be noted that the vehicle path planning method and apparatus of the embodiment of the present invention are smaller in global map, such as
When the planning of global path is carried out in the area of one, Shanghai City, number of nodes is few, can also using some heuristic search algorithms
Reach global path planning purpose, can be A* algorithms or AD* algorithms.When global map is larger, such as whole Shanghai
When the planning of global path is carried out in city or the whole of China or worldwide, number of nodes is huge, using some random searches
Algorithm can reach global path planning purpose, such as：Genetic algorithm, ant group algorithm, particle cluster algorithm etc., the embodiment of the present invention pair
This is not limited.
One of ordinary skill in the art will appreciate that all or part of step in the various methods of above-described embodiment is can
To instruct the hardware of correlation to complete by program, the program can be stored in computer-readable recording medium, to store
Medium can include：ROM, RAM, disk or CD etc..
Although present disclosure is as above, the present invention is not limited to this.Any those skilled in the art, this is not being departed from
In the spirit and scope of invention, it can make various changes or modifications, therefore protection scope of the present invention should be with claim institute
The scope of restriction is defined.
Claims (22)
- A kind of 1. vehicle path planning method, it is characterised in that including：Starting point and the destination of vehicle are obtained, and the starting point is positioned in map, the map includes road attribute；Gather the information of vehicles of the vehicle；Global path from the starting point to the destination is cooked up according to the information of vehicles and the road attribute.
- 2. vehicle path planning method according to claim 1, it is characterised in that also include：Based on the global path, local path, the part are generated with reference to the environmental information of the vehicle periphery collected Path is used for the vehicle tracking；When the current location of the vehicle and the distance of the global path are less than first threshold and the length of the local path During more than Second Threshold, the navigation of the global path is performed.
- 3. vehicle path planning method according to claim 2, it is characterised in that also include：It is current when the vehicle The length that the distance of position and the global path is more than the first threshold or the local path is less than the Second Threshold When, using the current location of the vehicle as starting point, the global path is cooked up again.
- 4. vehicle path planning method according to any one of claim 1 to 3, it is characterised in that according to the vehicle Information and road attribute are cooked up to be included from the starting point to the global path of the destination：Calculate the respective weight of mulitpath from the starting point to destination, the weight and length, the gradient in the path And the information of vehicles associates with the matching degree of road attribute；Optimal path is calculated and determined as the global path, wherein, the weight in the path is bigger to represent the path It is more excellent, the path impassabitity is represented during the weight infinity.
- 5. vehicle path planning method according to claim 4, it is characterised in that the calculation formula of the weight is：W= k1*l+k2*s+f(h)+f(v)；Wherein, w be the path weight, l be the path length, s be the path the gradient, k1For default length Parameter, k2For default gradient parameter, f (h) is limit for height function, and f (v) is type of vehicle restricted function.
- 6. vehicle path planning method according to claim 5, it is characterised in that the information of vehicles includes following one kind It is or a variety of：Type of vehicle, height of car and vehicles dynamic performance.
- 7. vehicle path planning method according to claim 6, it is characterised in that the road attribute includes following one kind It is or a variety of：Maximum height limit and limitation type of vehicle.
- 8. vehicle path planning method according to claim 7, it is characterised in that the height of car is more than the limitation During height, the limit for height function is infinity；When the height of car is less than the maximum height limit, the limit for height function is 0；Institute When stating type of vehicle and not meeting the limitation type of vehicle in the path, the type of vehicle restricted function is infinity；Institute When stating type of vehicle and meeting the limitation type of vehicle in the path, the type of vehicle restricted function is 0.
- 9. vehicle path planning method according to claim 4, it is characterised in that calculated using Dijkstra's algorithm true The fixed global path.
- 10. vehicle path planning method according to any one of claim 1 to 3, it is characterised in that obtain rising for vehicle Before point and destination, in addition to：Load the map.
- 11. the vehicle path planning method according to Claims 2 or 3, it is characterised in that the environmental information includes：Road Road situation or traffic.
- A kind of 12. vehicle path planning device, it is characterised in that including：Positioning unit, suitable for obtaining starting point and the destination of vehicle, and the starting point is positioned in map, the map Include road attribute；Collecting unit, suitable for gathering the information of vehicles of the vehicle；Global path generation unit, suitable for being cooked up according to the information of vehicles and the road attribute from the starting point to described The global path of destination.
- 13. vehicle path planning device according to claim 12, it is characterised in that also include：Local path generation unit, suitable for based on the global path, with reference to the environmental information of the vehicle periphery collected Local path is generated, the local path is used for the vehicle tracking；Navigation elements, suitable for being less than first threshold and the office when the current location of the vehicle and the distance of the global path When the length in portion path is more than Second Threshold, the navigation of the global path is performed.
- 14. vehicle path planning device according to claim 13, it is characterised in that when the vehicle current location with It is described when the distance of the global path is less than the Second Threshold more than the length of the first threshold or the local path Global path generation unit cooks up the global path again using the current location of the vehicle as starting point.
- 15. the vehicle path planning device according to any one of claim 12 to 14, it is characterised in thatThe global path generation unit includes：Weight calculation subelement, calculates the respective weight of mulitpath from the starting point to destination, the weight with it is described Information of vehicles associates with the matching degree of road attribute；Global path computation subelement, optimal path is calculated and determined as the global path, wherein,The weight in the path is more big, represents that the path is more excellent.
- 16. vehicle path planning device according to claim 15, it is characterised in that the weight calculation subelement uses Equation below calculates the weight：W=k1*l+k2*s+f(h)+f(v)；Wherein, w is the weight in the path, and l is the length in the path, and s is the gradient in the path, and k1 is default length Parameter, k2 are default gradient parameter, and f (h) is limit for height function, and f (v) is type of vehicle restricted function.
- 17. vehicle path planning device according to claim 16, it is characterised in that the information of vehicles is included with next Kind is a variety of：Type of vehicle, height of car and vehicles dynamic performance.
- 18. vehicle path planning device according to claim 17, it is characterised in that the road attribute is included with next Kind is a variety of：Maximum height limit and limitation type of vehicle.
- 19. vehicle path planning device according to claim 18, it is characterised in that the road attribute includes the road Maximum height limit and limitation type of vehicle of the footpath to the vehicle；When the height of car is more than the maximum height limit, the limit for height Function is infinity；When the height of car is less than the maximum height limit, the limit for height function is 0；The type of vehicle is not inconsistent When closing the limitation type of vehicle in the path, the type of vehicle restricted function is infinity；The type of vehicle meets During the limitation type of vehicle in the path, the type of vehicle restricted function is 0.
- 20. vehicle path planning device according to claim 16, it is characterised in that the global path computation subelement Calculated using Dijkstra's algorithm and determine the global path.
- 21. the vehicle path planning device according to any one of claim 12 to 14, it is characterised in that also include：Just Beginning unit, suitable for before the starting point of vehicle and destination is obtained, loading the map.
- 22. the vehicle path planning device according to claim 13 or 14, it is characterised in that the environmental information include with Lower one or more：Condition of road surface or traffic.
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