CN106767866B - Method and device for local path planning - Google Patents
Method and device for local path planning Download PDFInfo
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- CN106767866B CN106767866B CN201611110564.6A CN201611110564A CN106767866B CN 106767866 B CN106767866 B CN 106767866B CN 201611110564 A CN201611110564 A CN 201611110564A CN 106767866 B CN106767866 B CN 106767866B
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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
- G01C21/20—Instruments for performing navigational calculations
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
- G05D1/0274—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means using mapping information stored in a memory device
Abstract
The application discloses a local path planning method and device, a path planning method and device, and a test method and device based on local path planning. One specific implementation of the test method based on the local path planning includes: acquiring a planning index for testing, which is obtained by driving the unmanned vehicle based on road network path planning data and local path planning data; acquiring an actual planning index obtained by driving the unmanned vehicle based on the detailed path planning data; comparing the planning index for testing with the actual planning index; and determining whether the detailed path planning data is reasonable or not according to the comparison result and a planning rationality determination rule. The implementation mode realizes that the detailed path planning data does not need to be tested manually, improves the efficiency of testing the detailed path planning data and improves the accuracy of the test result.
Description
Technical Field
The application relates to the technical field of navigation and testing, in particular to the technical field of unmanned vehicle navigation testing, and particularly relates to a local path planning method and device.
Background
In the existing unmanned vehicle path planning, a path planning module performs detailed path planning before an unmanned vehicle is started, and if a lane change line or an unmanned driving position is inconsistent with the detailed path planning in the early stage in the driving process, the detailed path planning is frequently triggered, so that whether the detailed path planning is accurate or not and whether the detailed path planning is an optimal path or not need to be tested.
Currently, a manual testing method is usually adopted to test whether the detailed path planning is accurate and whether the detailed path planning is an optimal path.
However, when these planned paths are verified by using a manual test method, the test takes a long time, and the content of the test cannot generally test whether the detailed path plan is accurate and whether the detailed path plan is an optimal path.
Disclosure of Invention
The application aims to provide a local path planning method and a local path planning device.
In a first aspect, the present application provides a local path planning method, including: acquiring road network path planning data; in the process that an unmanned vehicle runs on the basis of road network path planning data, map data and a lane line of a perceived actual road, in response to the fact that a current road or a lane of a forward road is located in a local planning region, constructing more than one directed connected graph pointing to the forward direction on the path of the local planning region; determining local path planning data based on one or more of the following for each directed connectivity graph: path distance, number of traffic lights passing through and amount of congestion.
In some embodiments, the method further comprises: judging whether the path track of the road network path planning data in the local planning area is coincident with the path track of the local path planning data; if the road network path planning data are overlapped, the unmanned vehicle is guided to run according to the road network path planning data; and if the local path planning data do not coincide with the local path planning data, the unmanned vehicle is guided to run according to the local path planning data.
In some embodiments, the determining the local path planning data based on the path distance, the number of traffic lights passing through, and the congestion amount of each directed connected graph includes: determining a directed connected graph with the highest grade in the more than one directed connected graphs based on the weighted grading result of the path distance, the number of passing traffic lights and the congestion amount of each directed connected graph; and determining the path planning data based on the lane lines corresponding to the directed connected graph with the highest score as the local path planning data.
In some embodiments, the local planning region comprises one or more of: obstacle areas, road change areas, intersection areas and direction change areas.
In some embodiments, the road network path planning data is road network path planning data of an unmanned vehicle from a starting point to an end point determined based on actual road data; and/or the directed connected graph is obtained by setting mark points on the lane lines of the local planning area according to a preset distance and connecting the mark points in a directed mode by adopting connecting lines.
In a second aspect, the present application provides a path planning method, including: in the process that the unmanned vehicle runs based on the road network path planning data, the map data and the perceived lane line of the actual road, the local path planning data is determined according to the local path planning method in response to the current road or the lane of the advancing road being in the local planning area.
In a third aspect, the present application provides a test method based on local path planning, where the method includes: acquiring a planning index for testing, which is obtained by driving the unmanned vehicle based on road network path planning data and local path planning data; acquiring an actual planning index obtained by driving the unmanned vehicle based on the detailed path planning data; comparing the planning index for testing with the actual planning index; and determining whether the detailed path planning data is reasonable or not according to the comparison result and a planning rationality determination rule.
In some embodiments, the obtaining of the planning index for the test obtained by the unmanned vehicle based on the road network path planning data and the local path planning data comprises one or more of the following: the road network path planning data is determined based on actual road data, and the road network path planning data is from a starting point to an end point of the unmanned vehicle; and the local path planning data is determined based on the local planning method as described above.
In some embodiments, the obtaining of the planning index for testing obtained by the unmanned vehicle based on the road network path planning data and the local path planning data comprises: acquiring a planning index for testing, which is obtained by the unmanned vehicle based on road network path planning data and local path planning data in the process of frequently changing roads; and the step of obtaining an actual planning index obtained by the unmanned vehicle based on the detailed path planning data comprises the following steps: and acquiring an actual planning index obtained by the unmanned vehicle based on the detailed path planning data in the process of frequently changing the road.
In some embodiments, the method further comprises: and determining whether the difference between the detailed path planning data and the road network path planning data adopted in the local planning region and the local planning region is reasonable or not according to a comparison result and a difference reasonableness determination rule.
In some embodiments, the planning metrics include at least one or more of: travel trajectory, path planning time, and system overhead.
In a fourth aspect, the present application provides a local path planning apparatus, including: the road network planning acquisition unit is used for acquiring road network path planning data; the system comprises a directional connected graph building unit, a road network planning unit and a road network planning unit, wherein the directional connected graph building unit is used for responding to that a current road or a lane of a road ahead is located in a local planning area in the process that an unmanned vehicle runs on the basis of road network path planning data, map data and a perceived lane line of an actual road, and building more than one directional connected graph pointing to the advancing direction on the path of the local planning area; a local planning determination unit, configured to determine local path planning data based on one or more of the following for each directed connected graph: path distance, number of traffic lights passing through and amount of congestion.
In some embodiments, the apparatus further comprises: a track coincidence determination unit, configured to determine whether a path track of the road network path planning data in the local planning region coincides with a path track of the local path planning data; a road network planning and driving unit, configured to instruct the unmanned vehicle to drive according to the road network path planning data if a path trajectory of the road network path planning data in the local planning area coincides with a path trajectory of the local path planning data; and the local planning and driving unit is used for guiding the unmanned vehicle to drive according to the local path planning data if the path track of the road network path planning data in the local planning area is not coincident with the path track of the local path planning data.
In some embodiments, the local plan determination unit comprises: the directed connected graph determining subunit determines a directed connected graph with the highest score in the more than one directed connected graphs on the basis of the weighted scoring results of the path distance, the number of passing traffic lights and the congestion amount of each directed connected graph; and the local planning determining subunit is used for determining the path planning data based on the lane lines corresponding to the directed connected graph with the highest score as the local path planning data.
In some embodiments, the road network path planning data is road network path planning data of an unmanned vehicle from a starting point to an end point determined based on actual road data; and/or the directed connected graph is obtained by setting mark points on the lane lines of the local planning area according to a preset distance and connecting the mark points in a directed mode by adopting connecting lines.
In a fifth aspect, the present application provides a path planning apparatus, comprising: the local path planning apparatus as described above.
In a sixth aspect, the present application provides a testing apparatus based on local path planning, the apparatus including: the test index acquisition unit is used for acquiring a planning index for testing, which is obtained by driving the unmanned vehicle based on the road network path planning data and the local path planning data; the actual index acquisition unit is used for acquiring an actual planning index obtained by driving the unmanned vehicle based on the detailed path planning data; the planning index comparison unit is used for comparing the planning index for testing with the actual planning index; and the planning reasonableness determining unit is used for determining whether the detailed path planning data is reasonable or not according to the comparison result and a planning reasonability determining rule.
In some embodiments, the test indicator obtaining unit is further configured to one or more of: the road network path planning data is determined based on actual road data, and the road network path planning data is from a starting point to an end point of the unmanned vehicle; and the local path planning data is determined based on the local planning device as described above.
In some embodiments, the test indicator obtaining unit is further configured to: acquiring a planning index for testing, which is obtained by the unmanned vehicle based on road network path planning data and local path planning data in the process of frequently changing roads; and the actual index acquisition unit is further configured to: and acquiring an actual planning index obtained by the unmanned vehicle based on the detailed path planning data in the process of frequently changing the road.
In some embodiments, the apparatus further comprises: and the difference rationality determining unit is used for determining whether the difference between the detail path planning data and the road network path planning data adopted in the local planning region and the local planning region is reasonable or not according to a comparison result and a difference rationality determining rule.
The local path planning method and device, the path planning method and device and the test method and device based on the local path planning firstly acquire road network path planning data, then in the process that an unmanned vehicle runs based on the road network path planning data, map data and the perceived lane line of the actual road, in response to the fact that the current road or the lane of the advancing road is located in a local planning area, more than one directed connected graph pointing to the advancing direction is constructed on the path of the local planning area, then the local path planning data is determined based on the path distance, the number of passing traffic lights and the congestion amount of each directed connected graph, then the planning index for testing obtained by the unmanned vehicle running based on the road network path planning data and the local path planning data is acquired, and then the actual planning index obtained by the unmanned vehicle running based on the detail path planning data is acquired, and finally, determining whether the detailed path planning data is reasonable or not according to the comparison result and a planning rationality determination rule. Therefore, the method and the device have the advantages that the detailed path planning data does not need to be tested manually, the efficiency of testing the detailed path planning data is improved, and the accuracy of the test result is improved.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a schematic flow chart diagram illustrating one embodiment of a local path planning method in accordance with the present application;
FIG. 2 is a schematic flow chart diagram illustrating one embodiment of a local path planning based testing method in accordance with the present application;
FIG. 3 illustrates an exemplary application scenario of one embodiment of a local path planning based testing method according to the present application;
FIG. 4 is an exemplary block diagram of one embodiment of a local path planner according to the present application;
FIG. 5 is an exemplary block diagram of one embodiment of a local path planning based test device according to the present application;
fig. 6 is a schematic structural diagram of a computer system suitable for implementing the terminal device or the server according to the embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows a flow 100 of an embodiment of a local path planning method according to the present application. The local path planning method comprises the following steps:
In this embodiment, the road network path planning data refers to planning data of an autonomous vehicle from a starting point to an ending point, which is determined based on actual road data, where the actual road refers to a road existing in the real world, and may include a road direction and a road connection condition, and therefore, the road network path planning data is coarse-grained planning data for indicating a road that an unmanned vehicle needs to pass through, and is not limited to a specific lane or a lane line.
102, in the process that the unmanned vehicle drives based on road network path planning data, map data and the perceived lane line of the actual road, responding to that the current road or the lane of the advancing road is located in a local planning area, and constructing more than one directed connected graph pointing to the advancing direction on the path of the local planning area.
In the present embodiment, the map data refers to a machine-oriented high-precision map for use in an autonomous vehicle or a map of a mesh system composed of roads at different levels restored from the high-precision map, and the absolute precision is generally on the sub-meter level, i.e., within 1 meter, for example, within 20 cm, and the lateral relative precision (e.g., the relative position precision of a lane and a lane, and a lane line) is usually higher. And the high-precision map not only has high-precision coordinates, but also has accurate road shape, and contains data of the gradient, curvature, course, elevation and heeling of each lane. The high-precision map not only depicts roads, but also depicts how many lanes are on one road, and the actual pattern of the road can be truly reflected, for example, if the actual road is widened in some places, the road data in the high-precision map is correspondingly widened, and the actual road is narrowed down in some places due to confluence, and the high-precision map is also narrowed down due to confluence. In addition, what the lane lines between each lane and the lane are, whether the lane lines are broken lines, solid lines or double yellow lines, the color of the lines, the isolation zones of the road, the material of the isolation zones, what the road teeth are, what the material is, even the content of arrows and characters on the road, and the positions of the arrows and characters are described. Also, for consideration of automatic driving, such as speed limit of each lane, the recommended speed needs to be provided together. Like crosswalks, boards along roads, isolation zones, speed-limiting signs, traffic lights, roadside telephone boxes and the like, absolute geographical coordinates, physical dimensions, characteristic features and the like of traffic participants generally can be shown in data of high-precision maps. Meanwhile, the high-precision map needs to have the function of assisting in realizing high-precision positioning, the planning capability of road level and lane level, and the guiding capability of lane level.
The local planning area refers to an area which can trigger local planning caused by detected driving environment changes, and may include one or more of the following items: obstacle areas, road change areas, intersection areas and direction change areas. The obstacle area here means an area at a first distance from an obstacle; the road change area is an area having a second distance from the road change position; the intersection region is a region with a third distance from the intersection; the direction change region is a region where the distance direction is changed by a fourth distance. The first distance, the second distance, the third distance and the fourth distance herein only represent distances between different measurement objects and do not represent limitations of the present application, and the distance lengths of the first distance, the second distance, the third distance and the fourth distance may be set according to the needs of a user, and may be the same or different.
The more than one connected graph pointing to the forward direction is obtained by setting mark points on a lane line of a local planning area according to a preset distance and connecting the mark points in a connecting direction. When the marking points are communicated in the line direction, two marking points which are respectively positioned on lane lines at two sides of a lane and the line is perpendicular to the center line of the lane can be communicated in two directions to obtain a two-way communication line, two points which are positioned on the same lane line or two points which are respectively positioned on the two lane lines and the line is not perpendicular to the center line of the lane are communicated in one direction along the front direction of the unmanned vehicle to obtain a one-way communication line, and therefore more than one directed communication graph which is constructed on the path of a local planning area and points to the forward direction is obtained.
In this embodiment, the local path planning data is path planning data based on a lane line, and is fine-grained path planning data. The local path planning data may be determined based on one or more of path distance, number of traffic lights traversed, and amount of congestion. For example, when determining the local path planning data based on the path distance, the lane line-based path planning data corresponding to the directional connected graph having the shortest path distance may be determined as the local path planning data; for another example, when determining the local path planning data based on the number of the route traffic lights, the path planning data based on the lane line corresponding to the directed connected graph with the least number of the route traffic lights may be determined as the local path planning data; for example, when the local route planning data is determined based on the amount of traffic jam, the route planning data based on the lane line corresponding to the directional connected graph having the smallest amount of traffic jam may be determined as the local route planning data.
When determining local path planning data based on two or three items of path distance, number of passing traffic lights and congestion amount, the scores of the items can be obtained according to preset scoring rules; then calculating the scores of the items and multiplying the scores by the weight values of the items respectively to obtain the score of each item; then determining the sum of the scores of each item as the total score of the directed connected graph; and finally, taking the path planning data based on the lane line corresponding to the directed connected graph with the highest total score as local path planning data. Therefore, the local path planning data is determined by considering multiple factors, and the rationality of the local path planning data is improved. That is, when the local path planning data is determined based on two or three items of the path distance, the number of traffic lights passing through and the congestion amount, the directed connected graph with the highest score in the more than one directed connected graphs is determined according to the weighted scoring results of the items; and determining the path planning data based on the lane lines corresponding to the directional connected graph with the highest score as local path planning data.
Optionally, the local path planning method may further include: step 104, step 105 and step 106.
Wherein, step 104: and judging whether the path track of the road network path planning data in the local planning area is overlapped with the path track of the local path planning data.
In this embodiment, in order to determine whether the unmanned vehicle needs to change the current driving lane according to the local path planning data, it is necessary to determine whether the path trajectory of the road network path planning data in the local planning region coincides with the path trajectory of the local path planning data.
Step 105: and if the road network path planning data are overlapped, guiding the unmanned vehicle to run according to the road network path planning data.
In this embodiment, if the path trajectory of the road network path planning data in the local planning region is overlapped with the path trajectory of the local path planning data, it is also indicated that the unmanned vehicle only needs to travel according to the road network path planning data at the current position, and the unmanned vehicle does not need to be guided to travel by cutting into the local path planning data.
Step 106: and if the local path planning data do not coincide with the local path planning data, the unmanned vehicle is guided to run according to the local path planning data.
In this embodiment, if the path trajectory of the road network path planning data in the local planning region is not coincident with the path trajectory of the local path planning data, the local path planning data needs to be cut in to guide the unmanned vehicle to travel according to the local path planning data.
When the situation that the area triggering the local planning is driven out due to the change of the driving environment is detected, the road network path planning data does not indicate a specific lane or a lane line, and at the moment, the unmanned vehicle only needs to indicate the road to drive along the road network path planning data.
The local path planning method provided by the above embodiment of the application includes the steps of firstly obtaining road network path planning data, then responding to that a lane of a current road or a road ahead is located in a local planning region in a process that an unmanned vehicle runs on the basis of the road network path planning data, map data and a lane line of a perceived actual road, building more than one directional connected graph pointing to the direction of the road ahead on the path of the local planning region, and finally determining the local path planning data on the basis of one or more of the following directional connected graphs: the route distance, the number of traffic lights passing through and the congestion amount provide a route planning method based on a local area, the accuracy of route planning in the local planning area is improved, and the capacity of planning data to adapt to various traffic conditions is guaranteed.
In the present application, there is also provided a path planning method, including: in the process that the unmanned vehicle runs based on the road network path planning data, the map data and the perceived lane line of the actual road, the local path planning data is determined according to the unmanned vehicle local path planning method in response to the current road or the lane of the advancing road being in the local planning area.
The path planning method provided by the embodiment of the application can be used for determining the local path planning data based on the road network path planning data, the map data and the perceived lane line driving of the actual road, and when the current road or the lane of the advancing road is located in the local planning area, according to the unmanned vehicle local path planning method, the accuracy of path planning is improved in the local planning area, and the capability of the planning data to adapt to various traffic conditions is ensured.
With further reference to fig. 2, fig. 2 illustrates a flow 200 of one embodiment of a local path planning based testing method according to the present application. The test method based on the local path planning comprises the following steps:
In this embodiment, the road network path planning data refers to planning data from a starting point to an ending point of an unmanned vehicle determined based on actual road data, where the actual road refers to a road existing in the real world and may include a road direction and a road connection condition, and therefore, the road network path planning data indicates a road that the unmanned vehicle needs to pass through during driving, and does not indicate a specific lane or a lane line during driving, and is coarse-grained planning data. The local path planning data is the local path planning data determined based on the local path planning method of fig. 1 above.
The planning index for testing herein refers to an index related to path planning for testing. Optionally, the planning metrics may include at least one or more of: travel trajectory, path planning time, and system overhead. The driving track for testing is a driving track for testing the rationality of the detailed path planning data and is a driving track obtained by driving the unmanned vehicle based on the road network path planning data and the local path planning data; the path planning time for testing is the path planning time for testing the rationality of detailed path planning data and is the path planning time obtained by driving the unmanned vehicle based on the road network path planning data and the local path planning data; the system overhead for testing is planning system overhead for testing the rationality of detailed path planning data, and is planning system overhead obtained by driving an unmanned vehicle based on road network path planning data and local path planning data.
And step 202, acquiring an actual planning index obtained by the unmanned vehicle based on the detailed path planning data.
In this embodiment, the detailed path planning data refers to path planning data based on a lane line. The actual planning index is an index related to route planning, which is obtained by driving according to the detailed route planning data. In some optional implementations, the planning metrics may include at least one or more of: travel trajectory, path planning time, and system overhead. The actual driving track refers to a driving track obtained by driving based on detailed path planning data; the actual path planning time refers to the path planning time obtained by driving based on the detailed path planning data; the actual system overhead refers to planning system overhead obtained by planning data driving based on the detailed path.
In this embodiment, the items included in the planning index may be compared one by one, for example, the driving track for testing and the actual driving track, the path planning time for testing and the actual path planning time, the system overhead for testing and the actual system overhead, and the like may be compared respectively. Or scoring and weighting the data in each project, and then comparing the total score obtained by the planning index for testing according to the scoring and weighting with the total score obtained by the actual planning index according to the scoring and weighting. For example, the driving track for testing, the path planning time for testing, and the weighted scoring result of the system overhead for testing are obtained, the actual driving track, the actual path planning time, and the weighted scoring result of the system overhead are obtained, and finally the two weighted scoring results are compared.
And step 204, determining whether the detailed path planning data is reasonable or not according to the comparison result and a planning rationality determination rule.
In this embodiment, the plan rationality determining rule is a rule for determining whether the detailed path plan data is rational according to the comparison result, and if the comparison result conforms to the plan rationality determining rule, it is determined that the detailed path plan data is rational, and if the comparison result does not conform to the plan rationality determining rule, it is determined that the detailed path plan data is unreasonable.
For example, comparing the items included in the planning index one by one is taken as an example, and it is described that whether the detailed path planning data is reasonable is determined according to the comparison result and the planning rationality determination rule: here, whether the detailed path planning data can provide the shortest path or not may be determined based on a result of comparing the driving trajectory for the test with the actual driving trajectory, that is, if the detailed path planning data can provide the shortest path, the detailed path planning data is reasonable, and if the detailed path planning data cannot provide the shortest path, the detailed path planning data is unreasonable. Or, whether the detailed path planning data is reasonable may also be determined based on whether the comparison result of the system overhead for testing and the actual system overhead can satisfy the specific driving requirement of the unmanned vehicle, for example, whether the detailed path planning data is reasonable may be determined according to whether the comparison result of the system overhead below can satisfy the specific driving requirement of the unmanned vehicle: the method comprises the steps that a central processing unit CPU and a MEMORY hardware overhead MEMORY which are used by a path planning software module, GPS result processing consumption time caused by frequent positioning, calculation time and response time for the path planning software module and the like are used, if a comparison result of system overhead meets specific driving requirements of the unmanned vehicle, detailed path planning data are determined to be reasonable, and if the comparison result of the system overhead does not meet the specific driving requirements of the unmanned vehicle, the detailed path planning data are determined to be unreasonable.
In some optional implementation manners, a planning index for testing obtained by the unmanned vehicle based on road network path planning data and local path planning data in the process of frequently changing the road and an actual planning index obtained by the unmanned vehicle based on detailed path planning data in the process of frequently changing the road can be obtained, and then the planning index for testing and the actual planning index are compared; and finally, determining whether the detailed path planning data is reasonable or not according to the comparison result and a planning rationality determination rule.
For example, whether the detailed path planning data is reasonable or not can be determined according to whether the detailed path planning data can quickly and effectively provide a planned route when the path is frequently changed or whether the detailed path planning data can provide a shortest path, that is, if the detailed path planning data can provide a planned route within a predetermined time and can provide a shortest path when the path is frequently changed, the detailed path planning data is reasonable, and if the detailed path planning data cannot provide a planned route within a predetermined time or the detailed path planning data cannot provide a shortest path when the path is frequently changed, the detailed path planning data is unreasonable.
In some optional implementation manners, the above test method based on local path planning may further include: and determining whether the difference between the detailed path planning data and the local path planning data adopted in the local planning region and the road network path planning data adopted in the region except the local planning region is reasonable or not according to the comparison result and a difference reasonability determination rule.
In this implementation, the difference reasonableness determination rule is a rule for determining whether the difference between the detailed path planning data and the local path planning data in the local planning region and the road network path planning data in the region other than the local planning region is reasonable according to the comparison result, and if the comparison result conforms to the difference reasonableness determination rule, it is determined that the difference between the detailed path planning data and the local path planning data in the local planning region and the road network path planning data in the region other than the local planning region is reasonable, and if the comparison result does not conform to the difference reasonableness determination rule, it is determined that the difference between the detailed path planning data and the local path planning data in the local planning region and the road network path planning data in the region other than the local planning region is unreasonable.
For example, the comparison of the system overhead for testing and the actual system overhead may be determined based on one or more of: the method comprises the steps that a central processing unit CPU and a MEMORY hardware cost MEMORY which are used by a path planning software module, GPS result processing time consumption caused by frequent positioning, calculation time and response time for the path planning software module, and the central processing unit CPU and the MEMORY hardware cost MEMORY which are used by an actual path planning software module, GPS result processing time consumption caused by frequent positioning, calculation time and response time for the path planning software module and the like are determined, and then whether the difference between detailed path planning data and local path planning data adopted in a local planning area and road network path planning data adopted in an area except the local planning area is reasonable or not is determined according to whether the comparison result can meet the specific driving requirement of an unmanned vehicle.
According to the test method based on the local path planning, provided by the embodiment of the application, the planning index for testing obtained by driving the unmanned vehicle based on the road network path planning data and the local path planning data is firstly obtained, the actual planning index obtained by driving the unmanned vehicle based on the detail path planning data is obtained, and then the actual planning index is compared with the planning index for testing, and finally the rule is determined according to the comparison result and the planning rationality to determine whether the detail path planning data is reasonable or not, so that the fact that the manual test is not needed and the detail path planning data is based on is realized, the efficiency of the test based on the detail path planning data is improved, and the accuracy of the test result is improved.
An exemplary application scenario of an embodiment of the local path planning based testing method according to the present application is described below with reference to fig. 3.
As shown in fig. 3, the unmanned vehicle path planning module may plan a path traveled by the unmanned vehicle 330 according to a start point 301 and an end point 302 of a road.
In the process of planning based on road network path planning data and local path planning data, firstly, an unmanned vehicle 330 starts from a starting point 301, and travels into a local planning area 304 according to road network path planning data 311, and at this time, an obstacle 303 in front is detected, so that local path planning is triggered: the path planning module firstly finds the current position of the unmanned vehicle 330, and finds the position points of the lane line closest to the current position according to the GPS coordinates, such as points A and B in the figure; then marking according to a preset distance along the direction of the lane line, and connecting the points by adopting a directed connected graph mode; then, based on the path distance, the number of passing traffic lights and the congestion amount of each directed connected graph, calculating an optimal path in a weighting mode to obtain a directed connected graph with the highest score, such as directed connected graphs B-D-E in the graphs; then, the path planning module can determine a local planning path 312 according to the directional communication graph B-D-E, the position of the lane where the unmanned vehicle is located and the center line of the path terminal lane; then, the unmanned vehicle 330 drives to the corresponding lane position according to the local planned path 312, and after the unmanned vehicle 330 exits the local planned area 304, the vehicle continues to move forward by referring to the unchanged road network path planning data until entering the local planned area 305 due to the fact that the vehicle needs to be merged in front, and then the unmanned vehicle planning module starts local path planning again: selecting a directed connected graph G-I-K as an optimal path from more than one directed connected graph generated by marking points F, G, H, I and K, then determining a local planned path 313 according to the directed connected graph G-I-K, the position of the lane where the directed connected graph is located and the center line of a path end point lane, then driving along the local planned path 313 until the local planned path 305 is driven out, wherein the road planned by the road network path planning data changes, and the unmanned vehicle 330 drives to the end point 302 along the path 314 planned by the road network path planning data, so that planning indexes for testing, such as a driving track, path planning time, system overhead and the like, are obtained.
In the process of planning based on the detailed path planning data, firstly, the unmanned vehicle 330 starts from the starting point 301, runs along the path 321 planned by the detailed path planning data, detects that an obstacle 303 exists in front when reaching the local planning area 304, and then the detailed path planning data replans the path 322 at the moment, wherein the path 322 corresponds to the local planning route 312; thereafter, the unmanned vehicle 330 travels forward along the paths 322, 323, 324 re-planned in response to the presence of the obstacle 303 by the detailed path planning data until reaching the end point 302, thereby acquiring actual planning indices such as a travel track, a path planning time, and a system overhead.
After the planning indexes used for testing and the actual planning indexes are obtained, the project indexes in the testing planning indexes and the project indexes in the actual planning indexes can be compared one by one, and therefore whether the detailed path planning data are reasonable or not is determined according to a comparison result and a planning rationality determination rule. Further, in the application scenario, it may be further determined whether the difference between the detailed path planning data and the local path planning data adopted in the local planning region and the road network path planning data adopted in the region other than the local planning region is reasonable according to a comparison result and a difference rationality determination rule.
According to the test method based on the local path planning, which is provided by the application scenario, the test efficiency of the detailed path planning data is improved, and the rationality of the detailed path planning data can be determined through various test results, so that the accuracy of the test of the detailed path planning data is improved.
Further referring to fig. 4, as an implementation of the above method, the present application provides an embodiment of a local path planning apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 1, and thus, the operations and features described above for the method are also applicable to the apparatus 400 and the units included therein, and are not described again here. The device can be applied to various electronic equipment.
As shown in fig. 4, the local path planning apparatus 400 of the present embodiment includes: road network plan obtaining unit 410, directed connected graph constructing unit 420 and local plan determining unit 430.
The road network planning obtaining unit 410 is configured to obtain road network path planning data.
And the directed connected graph constructing unit 420 is used for constructing more than one directed connected graph pointing to the advancing direction on the path of the local planning area in response to that the current road or the lane of the advancing road is positioned in the local planning area in the process that the unmanned vehicle drives on the basis of the road network path planning data, the map data and the perceived lane line of the actual road.
A local planning determination unit 430, configured to determine local path planning data based on one or more of the following for each directed connected graph: path distance, number of traffic lights passing through and amount of congestion.
In some optional implementations, the apparatus further comprises: the track coincidence judging unit is used for judging whether the path track of the road network path planning data in the local planning area coincides with the path track of the local path planning data; the road network planning and driving unit is used for guiding the unmanned vehicle to drive according to the road network path planning data if the path track of the road network path planning data in the local planning area is overlapped with the path track of the local path planning data; and the local planning and driving unit is used for guiding the unmanned vehicle to drive according to the local path planning data if the path track of the road network path planning data in the local planning area is not coincident with the path track of the local path planning data.
In some optional implementations, the local plan determination unit includes: the directed connected graph determining subunit determines a directed connected graph with the highest score in the more than one directed connected graphs on the basis of the weighted scoring results of the path distance, the number of passing traffic lights and the congestion amount of each directed connected graph; and the local planning determination subunit determines the path planning data based on the lane lines corresponding to the directional connected graph with the highest score as the local path planning data.
In some optional implementation manners, the road network path planning data is determined based on actual road data, and the road network path planning data is determined from a starting point to an end point of the unmanned vehicle; and/or the directed connected graph is obtained by setting mark points on the lane lines of the local planning area according to a preset distance and connecting the mark points in a connecting line direction.
The present application further provides a path planning device, which includes: the local path planning apparatus as described above.
Further referring to fig. 5, as an implementation of the above method, the present application provides an embodiment of a testing apparatus based on local path planning, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and thus, the operations and features described above for the method are also applicable to the apparatus 500 and the units included therein, and are not described again here. The device can be applied to various electronic equipment.
As shown in fig. 5, the testing apparatus 500 based on local path planning of the present embodiment includes: a test index obtaining unit 510, an actual index obtaining unit 520, a planning index comparing unit 530 and a planning reasonableness determining unit 540.
The test index obtaining unit 510 is configured to obtain a planning index for testing, which is obtained by driving the unmanned vehicle based on the road network path planning data and the local path planning data.
And an actual index obtaining unit 520, configured to obtain an actual planning index obtained by driving the unmanned vehicle based on the detailed path planning data.
A planning index comparing unit 530, configured to compare the planning index for testing with the actual planning index.
And a planning reasonableness determining unit 540, configured to determine whether the detailed path planning data is reasonable according to the comparison result and the planning reasonableness determining rule.
In some optional implementations, the test indicator obtaining unit is further configured to one or more of: the road network path planning data is determined based on actual road data, and the road network path planning data is from a starting point to an end point of the unmanned vehicle; and the local path planning data is the local path planning data determined based on the local planning device as above.
In some optional implementations, the test indicator obtaining unit is further configured to: acquiring a planning index for testing, which is obtained by the unmanned vehicle based on road network path planning data and local path planning data in the process of frequently changing roads; and the actual index obtaining unit is further configured to: and acquiring an actual planning index obtained by the unmanned vehicle based on the detailed path planning data in the process of frequently changing the road.
In some optional implementations, the apparatus further comprises: and the difference rationality determining unit is used for determining whether the difference between the detailed path planning data and the local path planning data adopted in the local planning area and the road network path planning data adopted in the area except the local planning area is reasonable or not according to the comparison result and the difference rationality determining rule.
Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use in implementing a terminal device or server of an embodiment of the present application.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 606 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the method of the present application when executed by a Central Processing Unit (CPU) 601.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a unit, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor comprises a road network planning acquisition unit, a directed connected graph construction unit and a local planning determination unit. The names of these units do not in some cases form a limitation to the unit itself, and for example, the road network plan obtaining unit may also be described as a "unit for obtaining road network path plan data".
As another aspect, the present application also provides a non-volatile computer storage medium, which may be the non-volatile computer storage medium included in the apparatus in the above-described embodiments; or it may be a non-volatile computer storage medium that exists separately and is not incorporated into the terminal. The non-transitory computer storage medium stores one or more programs that, when executed by a device, cause the device to: acquiring road network path planning data; in the process that the unmanned vehicle drives based on road network path planning data, map data and a lane line of a perceived actual road, responding to that a current road or a lane of a forward road is located in a local planning area, and constructing more than one directed connected graph pointing to a forward direction on the path of the local planning area; determining local path planning data based on one or more of the following for each directed connectivity graph: path distance, number of traffic lights passing through and amount of congestion.
As another aspect, the present application also provides a non-volatile computer storage medium, which may be the non-volatile computer storage medium included in the apparatus in the above-described embodiments; or it may be a non-volatile computer storage medium that exists separately and is not incorporated into the terminal. The non-transitory computer storage medium stores one or more programs that, when executed by a device, cause the device to: acquiring a planning index for testing, which is obtained by driving the unmanned vehicle based on road network path planning data and local path planning data; acquiring an actual planning index obtained by driving the unmanned vehicle based on the detailed path planning data; comparing the planning index for testing with the actual planning index; and determining whether the detailed path planning data is reasonable or not according to the comparison result and a planning rationality determination rule.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention as referred to in the present application is not limited to the embodiments with a specific combination of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (15)
1. A method of local path planning, the method comprising:
acquiring road network path planning data, wherein the road network path planning data is planning data of an automatic driving vehicle from a starting point to an end point, and the planning data is determined based on actual road data;
in the process that an unmanned vehicle runs on the basis of road network path planning data, map data and a lane line of a perceived actual road, in response to the fact that a current road or a lane of a forward road is located in a local planning region, constructing more than one directed connected graph pointing to the forward direction on the path of the local planning region;
determining local path planning data based on one or more of the following for each directed connectivity graph: the road network planning data and the local path planning data are used for obtaining planning indexes used for testing to determine whether detailed path planning data are reasonable or not, and the local path planning data and the detailed path planning data are path planning data based on lane lines.
2. The method of claim 1, further comprising:
judging whether the path track of the road network path planning data in the local planning area is coincident with the path track of the local path planning data;
if the road network path planning data are overlapped, the unmanned vehicle is guided to run according to the road network path planning data;
and if the local path planning data do not coincide with the local path planning data, the unmanned vehicle is guided to run according to the local path planning data.
3. The method of claim 1, wherein determining local path planning data based on path distances, number of traffic lights traversed, and amount of congestion for each directed connectivity graph comprises:
determining a directed connected graph with the highest grade in the more than one directed connected graphs based on the weighted grading result of the path distance, the number of passing traffic lights and the congestion amount of each directed connected graph;
and determining the path planning data based on the lane lines corresponding to the directed connected graph with the highest score as the local path planning data.
4. The method of claim 1, wherein the local planning region comprises one or more of: obstacle areas, road change areas, intersection areas and direction change areas.
5. The method according to any one of claims 1-4, wherein the road network path planning data is road network path planning data for an unmanned vehicle from a starting point to an end point determined based on actual road data; and/or
The directed connected graph is obtained by setting mark points on the lane lines of the local planning area according to a preset distance and connecting the mark points in a directed mode through connecting lines.
6. A method of path planning, the method comprising:
the local path planning method according to any one of claims 1 to 5, in response to a lane of a current road or a preceding road being located in a local planning region during driving of an unmanned vehicle based on road network path planning data, map data, and a lane line of a perceived actual road.
7. A local path planning apparatus, the apparatus comprising:
the road network planning acquisition unit is used for acquiring road network path planning data, wherein the road network path planning data is planning data of the automatic driving vehicle from a starting point to an end point, which is determined based on actual road data;
the system comprises a directional connected graph building unit, a road network planning unit and a road network planning unit, wherein the directional connected graph building unit is used for responding to that a current road or a lane of a road ahead is located in a local planning area in the process that an unmanned vehicle runs on the basis of road network path planning data, map data and a perceived lane line of an actual road, and building more than one directional connected graph pointing to the advancing direction on the path of the local planning area; the directed connected graph is obtained by setting mark points on a lane line of the local planning area according to a preset distance and connecting the mark points in a directed mode through connecting lines;
a local planning determination unit, configured to determine local path planning data based on one or more of the following for each directed connected graph: the road network planning data and the local path planning data are used for obtaining planning indexes used for testing to determine whether detailed path planning data are reasonable or not, and the local path planning data and the detailed path planning data are path planning data based on lane lines.
8. The apparatus of claim 7, further comprising:
a track coincidence determination unit, configured to determine whether a path track of the road network path planning data in the local planning region coincides with a path track of the local path planning data;
a road network planning and driving unit, configured to instruct the unmanned vehicle to drive according to the road network path planning data if a path trajectory of the road network path planning data in the local planning area coincides with a path trajectory of the local path planning data;
and the local planning and driving unit is used for guiding the unmanned vehicle to drive according to the local path planning data if the path track of the road network path planning data in the local planning area is not coincident with the path track of the local path planning data.
9. The apparatus of claim 7, wherein the local plan determination unit comprises:
the directed connected graph determining subunit determines a directed connected graph with the highest score in the more than one directed connected graphs on the basis of the weighted scoring results of the path distance, the number of passing traffic lights and the congestion amount of each directed connected graph;
and the local planning determining subunit is used for determining the path planning data based on the lane lines corresponding to the directed connected graph with the highest score as the local path planning data.
10. The apparatus according to any one of claims 7 to 9,
the road network path planning data is determined based on actual road data, and the road network path planning data is from a starting point to an end point of the unmanned vehicle; and/or
The directed connected graph is obtained by setting mark points on the lane lines of the local planning area according to a preset distance and connecting the mark points in a directed mode through connecting lines.
11. A path planning apparatus, the apparatus comprising:
a local path planner according to any of the claims 7-10.
12. A terminal device, comprising:
one or more processors;
storage means having one or more programs stored thereon which, when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-5.
13. A terminal device, comprising:
one or more processors;
storage means having one or more programs stored thereon which, when executed by the one or more processors, cause the one or more processors to implement the method as claimed in claim 6.
14. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-5.
15. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method as claimed in claim 6.
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Families Citing this family (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107421553A (en) * | 2017-06-23 | 2017-12-01 | 深圳市盛路物联通讯技术有限公司 | A kind of intelligent recommendation method and system of traffic path |
| CN107272687A (en) * | 2017-06-29 | 2017-10-20 | 深圳市海梁科技有限公司 | A kind of driving behavior decision system of automatic Pilot public transit vehicle |
| CN107622684B (en) * | 2017-09-14 | 2020-07-28 | 华为技术有限公司 | Information transmission method, traffic control unit and vehicle-mounted unit |
| CN109901569A (en) * | 2017-12-11 | 2019-06-18 | 菜鸟智能物流控股有限公司 | Mobile device navigation method and related equipment |
| CN108229763A (en) * | 2018-03-27 | 2018-06-29 | 四川国际招标有限责任公司 | A kind of intelligent online Tender System |
| KR20200057507A (en) * | 2018-11-16 | 2020-05-26 | 현대자동차주식회사 | Apparatus for determine driving path of vehicle, system having the same and method thereof |
| CN109784526B (en) * | 2018-12-05 | 2023-02-28 | 阿波罗智能技术(北京)有限公司 | Method, device and equipment for planning traffic path and readable storage medium |
| CN109631929B (en) | 2019-02-20 | 2021-04-30 | 百度在线网络技术（北京）有限公司 | Re-navigation method and device based on blacklist and storage medium |
| CN109813330B (en) * | 2019-02-24 | 2021-06-08 | 深圳市灿晶电子科技有限公司 | Method and device for sending navigation route and electronic equipment |
| CN110515385A (en) * | 2019-09-09 | 2019-11-29 | 金鹏电子信息机器有限公司 | A kind of path following method and device of mobile robot |
| CN112577503B (en) * | 2019-09-30 | 2024-04-09 | 北京百度网讯科技有限公司 | Path planning method, device and equipment for vehicle starting point area |
| CN110884502B (en) * | 2019-12-06 | 2021-02-26 | 北京京东乾石科技有限公司 | Automatic driving path planning quality evaluation method, device and system |
| CN111007834B (en) * | 2019-12-13 | 2021-05-18 | 北京经纬恒润科技股份有限公司 | Laboratory test system and method for ADAS path planning function |
| CN113075921B (en) * | 2019-12-17 | 2024-02-09 | 北京京东尚科信息技术有限公司 | Local path planning method and device for unmanned equipment |
| CN111536985B (en) * | 2020-04-02 | 2022-07-01 | 东软睿驰汽车技术（沈阳）有限公司 | Planned route evaluation method and device, electronic equipment and storage medium |
| US11904890B2 (en) * | 2020-06-17 | 2024-02-20 | Baidu Usa Llc | Lane change system for lanes with different speed limits |
| CN111815161A (en) * | 2020-07-07 | 2020-10-23 | 中船重工信息科技有限公司 | Traffic control rule application method of horizontal transportation scheduling system |
| CN112346451A (en) * | 2020-09-18 | 2021-02-09 | 重庆智行者信息科技有限公司 | Safety verification method and device for automatic driving planning path and automatic driving control system |
| CN113393013A (en) * | 2021-04-13 | 2021-09-14 | 哈工大机器人集团(无锡)科创基地研究院 | Trajectory planning method for unmanned vehicle test |
| CN114842660B (en) * | 2022-06-29 | 2022-10-11 | 石家庄铁道大学 | Unmanned lane track prediction method and device and electronic equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103674048A (en) * | 2013-11-29 | 2014-03-26 | 河北工业大学 | Dynamic intelligent navigation method based on 3G (3-Generation) network |
| CN104819724A (en) * | 2015-03-02 | 2015-08-05 | 北京理工大学 | Unmanned ground vehicle self-driving assisting system based on GIS |
| CN105318888A (en) * | 2015-12-07 | 2016-02-10 | 北京航空航天大学 | Unmanned perception based unmanned aerial vehicle route planning method |
| CN106114507A (en) * | 2016-06-21 | 2016-11-16 | 百度在线网络技术（北京）有限公司 | Local path planning method and device for intelligent vehicle |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102591332B (en) * | 2011-01-13 | 2014-08-13 | 同济大学 | Device and method for local path planning of pilotless automobile |
| CN102433811B (en) * | 2011-10-15 | 2013-07-31 | 天津市市政工程设计研究院 | Method for determining minimum distance of road intersections in harbor district |
| KR101703144B1 (en) * | 2012-02-09 | 2017-02-06 | 한국전자통신연구원 | Apparatus and method for autonomous driving |
| CN103256939B (en) * | 2013-04-15 | 2015-09-23 | 李德毅 | Intelligent vehicle utilizes and becomes the method that granularity right of way radar map carries out information fusion |
| CN113654561A (en) * | 2014-12-05 | 2021-11-16 | 苹果公司 | Autonomous navigation system |
| CN105588576B (en) * | 2015-12-15 | 2019-02-05 | 招商局重庆交通科研设计院有限公司 | A kind of lane grade navigation methods and systems |
| CN105675000B (en) * | 2016-01-15 | 2018-06-19 | 武汉中海庭数据技术有限公司 | A kind of track grade paths planning method and system based on high-precision map |
| CN106017491B (en) * | 2016-05-04 | 2019-08-02 | 玉环看知信息科技有限公司 | A kind of navigation path planning method, system and navigation server |
-
2016
- 2016-12-02 CN CN201611110564.6A patent/CN106767866B/en active Active
- 2016-12-02 CN CN201910408008.4A patent/CN110132279B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103674048A (en) * | 2013-11-29 | 2014-03-26 | 河北工业大学 | Dynamic intelligent navigation method based on 3G (3-Generation) network |
| CN104819724A (en) * | 2015-03-02 | 2015-08-05 | 北京理工大学 | Unmanned ground vehicle self-driving assisting system based on GIS |
| CN105318888A (en) * | 2015-12-07 | 2016-02-10 | 北京航空航天大学 | Unmanned perception based unmanned aerial vehicle route planning method |
| CN106114507A (en) * | 2016-06-21 | 2016-11-16 | 百度在线网络技术（北京）有限公司 | Local path planning method and device for intelligent vehicle |
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|---|---|
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