CN109515439B - Automatic driving control method, device, system and storage medium - Google Patents
Automatic driving control method, device, system and storage medium Download PDFInfo
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- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/18—Propelling the vehicle
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
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Abstract
The invention provides an automatic driving control method, a device, a system and a storage medium, wherein the method comprises the following steps: acquiring a high-precision map, wherein the high-precision map comprises a reliability map layer; extracting element information from the reliability map layer according to the current position of the vehicle; acquiring environmental information acquired by a vehicle sensor; determining the element state of the element information according to the environment information; wherein the element states include: reliable, unreliable, suspicious; and if the element state is reliable, generating a control decision according to the element information. Therefore, the purpose of quickly generating the automatic driving control decision according to the reliability map layer in the high-precision map is achieved, and the decision accuracy and the real-time performance of the automatic driving device are improved.
Description
Technical Field
The invention relates to the technical field of vehicle driving control, in particular to an automatic driving control method, device and system and a storage medium.
Background
In recent years, the automatic driving technology has been rapidly developed, and an automatic driving system generally comprises several large modules of high-precision maps, environment perception, decision planning and motion control. The high-precision map mainly comprises a road layer, a lane layer and a positioning data layer, is used for providing basis for vehicle positioning, guiding and decision making, and is the basis for realizing automatic driving.
At present, the automatic driving technical scheme mainly focuses on more accurate expression reality by using a lane-level road network and roadside facilities. The road layer can support automatic driving for a long distance, large-range path planning and advance the driving state in front; the lane layer data can support lane level path planning, guidance and decision-making; lane information, positioning information, along with other sensors, are used to improve autopilot positioning accuracy.
However, in this way, the amount of information of the lane-level road network layer and the roadside facility layer is large, the resolution of the vehicle sensor is low, the recognition degree is not high, and the decision information needs to be extracted and retrieved from a large amount of information, which is not beneficial to the automatic driving device to make a driving decision quickly, and affects the real-time performance and accuracy of the automatic driving control instruction.
Disclosure of Invention
The invention provides an automatic driving control method, device, system and storage medium, wherein a reliability layer is built in a high-precision map, the element state of element information in the reliability layer is determined by utilizing environmental information acquired by a vehicle sensor, and if the element state is reliable, a control decision is generated according to the element information, so that the real-time performance and the accuracy of the automatic driving control decision are improved.
In a first aspect, an automatic driving control method is provided, including:
acquiring a high-precision map, wherein the high-precision map comprises a reliability map layer;
extracting element information from the reliability map layer according to the current position of the vehicle;
acquiring environmental information acquired by a vehicle sensor;
determining the element state of the element information according to the environment information; wherein the element states include: reliable, unreliable, suspicious;
and if the element state is reliable, generating a control decision according to the element information.
In a second aspect, there is provided an automatic driving control apparatus comprising:
the first acquisition module is used for acquiring a high-precision map, and the high-precision map comprises a reliability map layer;
the extraction module is used for extracting element information from the reliability map layer according to the current position of the vehicle;
the second acquisition module is used for acquiring the environmental information acquired by the vehicle sensor;
the analysis module is used for determining the element state of the element information according to the environment information; wherein the element states include: reliable, unreliable, suspicious;
and the control module is used for generating a control decision according to the element information if the element state is reliable.
In a third aspect, an automatic driving control system is provided, including: the device comprises a memory and a processor, wherein the memory stores executable instructions of the processor; wherein the processor is configured to perform the autopilot control method of any of the first aspects via execution of the executable instructions.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the automatic driving control method according to any one of the first aspect.
The invention provides an automatic driving control method, device, system and storage medium, which is characterized in that a high-precision map is obtained, wherein the high-precision map comprises a reliability map layer; extracting element information from the reliability map layer according to the current position of the vehicle; acquiring environmental information acquired by a vehicle sensor; determining the element state of the element information according to the environment information; wherein the element states include: reliable, unreliable, suspicious; and if the element state is reliable, generating a control decision according to the element information. Therefore, the purpose of quickly generating the automatic driving control decision according to the reliability map layer in the high-precision map is achieved, and the decision accuracy and the real-time performance of the automatic driving device are improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of an application scenario of the present invention;
FIG. 2 is a flowchart of an automatic driving control method according to an embodiment of the present invention;
fig. 3 is a flowchart of an automatic driving control method according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of an automatic driving control device according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of an automatic driving control device according to a fourth embodiment of the present invention;
fig. 6 is a schematic structural diagram of an automatic driving control system according to a fifth embodiment of the present invention.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 1 is a schematic diagram of an application scenario of the present invention, as shown in fig. 1, elements in a reliability map layer of a high-precision map are determined according to environmental information collected by a vehicle sensor; if the element state is reliable, a control decision is quickly generated according to the element information; and if the element state is unreliable or suspicious, feeding back corresponding element information to the server so that the server updates the element information to obtain an updated high-precision map.
By applying the method, the reliable layer which can be identified by the low-resolution sensing device of the vehicle can be constructed in the high-precision map, the information in the reliable layer is high in real-time performance, accurate and reliable, and the automatic driving control decision can be quickly generated according to the reliable layer, so that the real-time performance and the accuracy of the automatic driving control decision generation are improved.
The following describes the technical solutions of the present invention and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
Fig. 2 is a flowchart of an automatic driving control method according to an embodiment of the present invention, and as shown in fig. 2, the method in this embodiment may include:
s101, obtaining a high-precision map, wherein the high-precision map comprises a reliability map layer.
In the embodiment, the reliability map layer of the high-precision map is generated according to element information extracted from a road layer, a lane layer and a positioning data layer of an original high-precision map; wherein the element information includes: administrative region information, traffic identification information, road information, physical partition information, and the like are important information that needs to be determined in advance in an automatic driving decision.
Specifically, the reliability map layer of the high-precision map is generated according to element information extracted from the road layer, the road layer and the positioning data layer of the original high-precision map, and the element information in the reliability map layer includes information such as an attribute ID, an element state and an element position. Wherein the element information includes: administrative region information, traffic identification information, road information, physical partition information. The administrative division information is very stable information, through the administrative division, which country and which province the automatic driving vehicle runs in are clear, and through the administrative division information, a specific traffic rule of each province can be obtained to assist the automatic driving device in making decisions. For example, traffic regulations vary from country to country, with left-hand traffic in the united kingdom and right-hand traffic in china. The traffic identification information comprises lane lines, road arrows, road prompting characters, nameplates, signal lamps, street lamps, flow guide lines and the like, and the automatic driving device is assisted to make decisions. For example, if the traffic light is determined to be red, a parking instruction is generated. The road information comprises closed attributes of driving roads, such as fully closed roads and non-fully closed roads, such as country roads and urban roads, wherein the non-fully closed roads are non-fully closed roads, and the expressway is a fully closed road, and different automatic driving control strategies can be adopted according to the closed attributes of the roads. For example, if the road information is determined to be a fully enclosed type expressway, the road information further includes ramp information, and vehicles enter or exit at the ramp of the expressway. When the vehicle runs on a highway, the automatic driving vehicle is less interfered by other vehicles and traffic, so that the vehicle can run quickly and the speed of the vehicle is improved. However, when approaching a ramp, the autonomous vehicle may be prepared for risk prevention in advance, such as controlling the vehicle speed, maintaining the vehicle distance from other vehicles, and the like. The physical isolation information comprises green isolation belts, roadblocks and the like, and the dangers such as vehicle collision and the like can be effectively prevented by judging according to the physical isolation information, for example, a high-speed entrance and a toll station are complex scenes, the traffic flow in the area is large, the road conditions are complex, and the automatic driving vehicle needs to make accurate judgment according to the judgment of a white diversion area or physical isolation. The reliability layer information is easily identified by a low-resolution sensor of the vehicle, and the real-time performance and the accuracy of automatic driving control decision generation can be improved;
it should be noted that, in this embodiment, the content of the element information of the reliability layer is not limited, and those skilled in the art may increase or decrease the element information of the reliability layer according to the actual situation.
And S102, extracting element information from the reliability map layer according to the current position of the vehicle.
In the embodiment, the current coordinates of the vehicle in the high-precision map are determined according to the current position of the vehicle; and extracting element information corresponding to the current coordinate from the reliability map layer.
And S103, acquiring environmental information acquired by the vehicle sensor.
In this embodiment, the environmental information is acquired by a vehicle distance radar, an image sensor, a positioning sensor, and the like on the autonomous vehicle. The vehicle distance radar is a component of the self-adaptive cruise control system, and can adopt a laser radar to realize the measurement of the vehicle distance. Specifically, the vehicle speed adjusting device of the adaptive cruise control system measures the separation distance and the relative speed with the preceding vehicle by the vehicle distance radar. The image sensor may be a camera, a video camera, or the like. The Positioning sensor may be a Global Positioning System (GPS), a beidou Satellite Navigation System, a Global Navigation Satellite System (GNSS), or the like.
S104, determining element states of the element information according to the environment information; wherein the element states include: reliable, unreliable, suspect.
In this embodiment, whether the element information is matched with the environment information is determined according to the environment information; if the element information is matched with the element information, determining that the element state of the element information is reliable; if not, determining that the element state of the element information is unreliable; and if the matching result is not unique, determining that the element state of the element information is suspicious. If the element state is suspicious, the element information cannot be identified by the low-resolution sensor of the vehicle. Therefore, it is possible to realize the filtering of the original high-precision map information, reduce the amount of information, and retain the element information that can be recognized by the vehicle sensors.
The element information may be information that is important and needs to be determined in advance when making an automatic driving decision, such as administrative area information, traffic identification information, road information, and physical partition information; the environment information is mainly information collected by an image sensor, a positioning sensor and the like. And in the process of matching the element information with the environment information, only information among the same attributes is compared. For example, if the element information is traffic identification information, only the traffic information identification information collected from the environment information is matched.
Specifically, by taking traffic identification information as an example, in a picture which is acquired by a current image sensor and contains the traffic identification information, the speed limit is 80 km/h; the traffic identification information in the high-precision map is the speed limit of 100 km/h; it is judged that the element information and the environment information do not match. If the current image sensor acquires a picture containing traffic identification information, the speed limit is 80 km/h; the traffic identification information in the high-precision map is the speed limit of 80 km/h; it is judged that the element information and the environment information match.
In a possible situation, it is assumed that the traffic identification information cannot be clearly recognized in a picture containing the traffic identification information collected by a current image sensor, but the profile information of the traffic signboard can be obtained, after feature point extraction, the profile of the traffic signboard is matched with a plurality of signboards such as the profile of a speed-limiting signboard and the profile of a left-turn signboard in a high-precision map, and at the moment, the matching result is not unique. And S105, if the element state is reliable, generating a control decision according to the element information.
In the embodiment, the automatic driving control decision is generated by reliable element information according to the element state, so that the accuracy of generating the automatic driving control decision is improved. In addition, because the quantity of the high-reliability layer element information is much less than that of the traditional high-precision map, the information acquisition time of the automatic driving vehicle can be shortened, so that the automatic driving vehicle can make driving decisions faster.
In this embodiment, by obtaining a high-precision map, the high-precision map includes a reliability map layer; extracting element information from the reliability map layer according to the current position of the vehicle; acquiring environmental information acquired by a vehicle sensor; determining the element state of the element information according to the environment information; wherein the element states include: reliable, unreliable, suspicious; and if the element state is reliable, generating a control decision according to the element information. Therefore, the purpose of quickly generating the automatic driving control decision according to the reliability map layer in the high-precision map is achieved, and the decision accuracy and the real-time performance of the automatic driving device are improved.
Fig. 3 is a flowchart of an automatic driving control method according to a second embodiment of the present invention, and as shown in fig. 3, the method in this embodiment may include:
s201, obtaining a high-precision map, wherein the high-precision map comprises a reliability map layer.
And S202, extracting element information from the reliability map layer according to the current position of the vehicle.
And S203, acquiring environmental information acquired by a vehicle sensor.
S204, determining the element state of the element information according to the environment information; wherein the element states include: reliable, unreliable, suspect.
And S205, if the element state is reliable, generating a control decision according to the element information.
In this embodiment, please refer to the relevant description in step S101 to step S105 in the method shown in fig. 2 for the specific implementation process and technical principle of step S201 to step S205, which is not described herein again.
And S206, if the element state is unreliable or suspicious, feeding back corresponding element information to the server so that the server judges the element information, and if the judgment result meets the element updating condition, updating the element information to obtain the updated high-precision map.
In this embodiment, the server receives the element information marked as unreliable or suspicious by the car machine system, and then determines the element information, where the element update condition includes: the definition of the image corresponding to the element information meets the preset requirement, and/or more than N vehicle-mounted systems upload the same element information. Wherein N is a natural number greater than 1.
Specifically, whether the definition of the image corresponding to the element information meets a preset requirement is judged, and if the definition of the image corresponding to the element information does not meet the preset requirement, the server does not update the element information. If the definition of the image corresponding to the element information meets the preset requirement, whether more than N vehicle-mounted computer systems upload the same element information can be further judged, and if yes, the server updates the element information.
And the high-precision map is updated according to the element state, so that the real-time performance and the accuracy of the information in the reliability map layer in the high-precision map are ensured.
And S207, receiving the updated high-precision map sent by the server.
In the embodiment, the vehicle-mounted machine system receives the updated high-precision map sent by the server in real time, so that the decision accuracy and the real-time performance of the automatic driving device are improved.
In this embodiment, by obtaining a high-precision map, the high-precision map includes a reliability map layer; extracting element information from the reliability map layer according to the current position of the vehicle; acquiring environmental information acquired by a vehicle sensor; determining the element state of the element information according to the environment information; wherein the element states include: reliable, unreliable, suspicious; and if the element state is reliable, generating a control decision according to the element information. Therefore, the purpose of quickly generating the automatic driving control decision according to the reliability map layer in the high-precision map is achieved, and the decision accuracy and the real-time performance of the automatic driving device are improved.
In addition, the embodiment can feed back element information to the server, so that the high-precision map is updated, and the updated high-precision map sent by the server is received, so that the real-time performance and the accuracy of a reliability map layer in the high-precision map are improved, and the decision accuracy and the real-time performance of the automatic driving device are improved.
Fig. 4 is a schematic structural diagram of an automatic driving control device according to a third embodiment of the present invention, and as shown in fig. 5, the automatic driving control device according to the present embodiment may include:
the first obtaining module 31 is configured to obtain a high-precision map, where the high-precision map includes a reliability map layer;
the extraction module 32 is used for extracting element information from the reliability map layer according to the current position of the vehicle;
the second acquisition module 33 is used for acquiring environmental information acquired by the vehicle sensor;
an analysis module 34, configured to determine an element state of the element information according to the environment information; wherein the element states include: reliable, unreliable, suspicious;
and the control module 35 is configured to generate a control decision according to the element information if the element state is reliable.
In one possible design, a reliability map layer of the high-precision map is generated according to element information extracted from a road layer, a lane layer and a positioning data layer of an original high-precision map; wherein the element information includes: administrative region information, traffic identification information, road information, physical partition information.
In one possible design, the extraction module 32 is specifically configured to:
determining the current coordinates of the vehicle in a high-precision map according to the current position of the vehicle;
and extracting element information corresponding to the current coordinate from the reliability map layer.
In one possible design, the analysis module 34 is specifically configured to:
judging whether the element information is matched with the environment information or not according to the environment information; if the element information is matched with the element information, determining that the element state of the element information is reliable; if not, determining that the element state of the element information is unreliable; and if the matching result is not unique, determining that the element state of the element information is suspicious.
In this embodiment, by obtaining a high-precision map, the high-precision map includes a reliability map layer; extracting element information from the reliability map layer according to the current position of the vehicle; acquiring environmental information acquired by a vehicle sensor; determining the element state of the element information according to the environment information; wherein the element states include: reliable, unreliable, suspicious; and if the element state is reliable, generating a control decision according to the element information. Therefore, the purpose of quickly generating the automatic driving control decision according to the reliability map layer in the high-precision map is achieved, and the decision accuracy and the real-time performance of the automatic driving device are improved.
The automatic driving control device of this embodiment may execute the technical solution in the method shown in fig. 2, and the specific implementation process and technical principle of the automatic driving control device refer to the related description in the method shown in fig. 2, which is not described herein again.
Fig. 5 is a schematic structural diagram of an automatic driving control device according to a fourth embodiment of the present invention, and as shown in fig. 5, the automatic driving control device according to the present embodiment may further include, on the basis of the device shown in fig. 4:
a feedback module 36, configured to, after determining an element state of the element information according to the environment information, if the element state is unreliable or suspicious, feed back corresponding element information to the server so that the server determines the element information, and if a determination result meets an element update condition, update the element information to obtain an updated high-precision map;
and the receiving module 37 is configured to receive the updated high-precision map sent by the server.
In this embodiment, by obtaining a high-precision map, the high-precision map includes a reliability map layer; extracting element information from the reliability map layer according to the current position of the vehicle; acquiring environmental information acquired by a vehicle sensor; determining the element state of the element information according to the environment information; wherein the element states include: reliable, unreliable, suspicious; and if the element state is reliable, generating a control decision according to the element information. Therefore, the purpose of quickly generating the automatic driving control decision according to the reliability map layer in the high-precision map is achieved, and the decision accuracy and the real-time performance of the automatic driving device are improved.
In addition, the embodiment can feed back element information to the server, so that the high-precision map is updated, and the updated high-precision map sent by the server is received, so that the real-time performance and the accuracy of a reliability map layer in the high-precision map are improved, and the decision accuracy and the real-time performance of the automatic driving device are improved.
The automatic driving control device of this embodiment may execute the technical solutions in the methods shown in fig. 2 and fig. 3, and the specific implementation process and technical principle of the automatic driving control device refer to the related descriptions in the methods shown in fig. 2 and fig. 3, which are not described herein again.
Fig. 6 is a schematic structural diagram of an automatic driving control system according to a fifth embodiment of the present invention, and as shown in fig. 6, the automatic driving control system 40 according to this embodiment may include: a processor 41 and a memory 42.
A memory 42 for storing computer programs (such as application programs, function modules, and the like implementing the above-described automatic driving control method), computer instructions, and the like;
the computer programs, computer instructions, etc. described above may be stored in one or more memories 42 in partitions. And the above-mentioned computer program, computer instructions, data, etc. can be called by the processor 41.
A processor 41 for executing the computer program stored in the memory 42 to implement the steps of the method according to the above embodiments.
Reference may be made in particular to the description relating to the preceding method embodiment.
The processor 41 and the memory 42 may be separate structures or may be integrated structures integrated together. When the processor 41 and the memory 42 are separate structures, the memory 42 and the processor 41 may be coupled by a bus 43.
The server in this embodiment may execute the technical solutions in the methods shown in fig. 2 and fig. 3, and the specific implementation process and technical principle of the server refer to the relevant descriptions in the methods shown in fig. 2 and fig. 3, which are not described herein again.
In addition, embodiments of the present application further provide a computer-readable storage medium, in which computer-executable instructions are stored, and when at least one processor of the user equipment executes the computer-executable instructions, the user equipment performs the above-mentioned various possible methods.
Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. Additionally, the ASIC may reside in user equipment. Of course, the processor and the storage medium may reside as discrete components in a communication device.
The present application further provides a program product comprising a computer program stored in a readable storage medium, the computer program being readable from the readable storage medium by at least one processor of a server, the computer program being executable by the at least one processor to cause the server to implement the autopilot control method of any of the embodiments of the invention described above.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. An automatic driving control method characterized by comprising:
acquiring a high-precision map, wherein the high-precision map comprises a reliability map layer; the reliability map layer of the high-precision map is generated according to element information extracted from a road layer, a lane layer and a positioning data layer of the original high-precision map;
extracting element information from the reliability map layer according to the current position of the vehicle;
acquiring environmental information acquired by a vehicle sensor;
determining the element state of the element information according to the environment information; wherein the element states include: reliable, unreliable, suspicious;
and if the element state is reliable, generating a control decision according to the element information.
2. The method according to claim 1, wherein extracting element information from the reliability map layer according to a current location of a vehicle comprises:
determining the current coordinates of the vehicle in a high-precision map according to the current position of the vehicle;
and extracting element information corresponding to the current coordinate from the reliability map layer.
3. The method of claim 1, wherein determining the element state of the element information according to the environment information comprises:
judging whether the element information is matched with the environment information or not according to the environment information; if the element information is matched with the element information, determining that the element state of the element information is reliable; if not, determining that the element state of the element information is unreliable; and if the matching result is not unique, determining that the element state of the element information is suspicious.
4. The method according to any one of claims 1 to 3, wherein, after determining the element state of the element information according to the environment information, further comprising:
if the element state is unreliable or suspicious, feeding back corresponding element information to a server so that the server judges the element information, and if the judgment result meets an element updating condition, updating the element information to obtain an updated high-precision map;
and receiving the updated high-precision map sent by the server.
5. An automatic driving control apparatus, characterized by comprising:
the first acquisition module is used for acquiring a high-precision map, and the high-precision map comprises a reliability map layer; the reliability map layer of the high-precision map is generated according to element information extracted from a road layer, a lane layer and a positioning data layer of the original high-precision map;
the extraction module is used for extracting element information from the reliability map layer according to the current position of the vehicle;
the second acquisition module is used for acquiring the environmental information acquired by the vehicle sensor;
the analysis module is used for determining the element state of the element information according to the environment information; wherein the element states include: reliable, unreliable, suspicious;
and the control module is used for generating a control decision according to the element information if the element state is reliable.
6. The apparatus of claim 5, further comprising:
the feedback module is used for feeding back corresponding element information to a server if the element state is unreliable or suspicious after determining the element state of the element information according to the environment information, so that the server updates the element information to obtain an updated high-precision map;
and the receiving module is used for receiving the updated high-precision map sent by the server.
7. An automatic driving control system, characterized by comprising: the device comprises a memory and a processor, wherein the memory stores executable instructions of the processor; wherein the processor is configured to perform the autonomous driving control method of any of claims 1-4 via execution of the executable instructions.
8. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the autopilot control method according to one of claims 1 to 4.
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Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110308978B (en) * | 2019-06-26 | 2022-05-10 | 浙江吉利控股集团有限公司 | Automatic driving software deployment method, device, terminal and server |
| CN110588665A (en) * | 2019-09-27 | 2019-12-20 | 北京经纬恒润科技有限公司 | Method, device and system for checking automatic driving infrastructure |
| CN112414416A (en) * | 2020-10-26 | 2021-02-26 | 高深智图(广州)科技有限公司 | ADAS map data system based on four-level automatic driving high precision |
| CN113879334A (en) * | 2021-09-30 | 2022-01-04 | 郑州师范学院 | Machine learning anti-attack recognition system suitable for automatic vehicle driving |
| CN114610830B (en) * | 2022-03-25 | 2023-07-21 | 江苏海洋大学 | Map element change detection method based on driving behavior data |
| CN114906154B (en) * | 2022-05-26 | 2024-10-01 | 重庆长安汽车股份有限公司 | Method, system, electronic device and storage medium for judging vehicle driving road category |
| CN115223118B (en) * | 2022-06-09 | 2024-03-01 | 广东省智能网联汽车创新中心有限公司 | High-precision map confidence judging method, system and vehicle |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105674993A (en) * | 2016-01-15 | 2016-06-15 | 武汉光庭科技有限公司 | Binocular camera-based high-precision visual sense positioning map generation system and method |
| CN106525057A (en) * | 2016-10-26 | 2017-03-22 | 陈曦 | Generation system for high-precision road map |
| WO2017159509A1 (en) * | 2016-03-15 | 2017-09-21 | 本田技研工業株式会社 | Vehicle control system, vehicle control method, and vehicle control program |
| CN107229690A (en) * | 2017-05-19 | 2017-10-03 | 广州中国科学院软件应用技术研究所 | Dynamic High-accuracy map datum processing system and method based on trackside sensor |
| WO2018126215A1 (en) * | 2016-12-30 | 2018-07-05 | DeepMap Inc. | High definition map updates |
| CN108398705A (en) * | 2018-03-06 | 2018-08-14 | 广州小马智行科技有限公司 | Ground drawing generating method, device and vehicle positioning method, device |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10309790B2 (en) * | 2015-10-28 | 2019-06-04 | Honda Motor Co., Ltd. | Automatic driving system |
| DE102016224042A1 (en) * | 2016-12-02 | 2018-06-07 | Bayerische Motoren Werke Aktiengesellschaft | Method and device for checking high-precision map data for driver assistance functions of a motor vehicle |
| CN107339996A (en) * | 2017-06-30 | 2017-11-10 | 百度在线网络技术（北京）有限公司 | Vehicle method for self-locating, device, equipment and storage medium |
| CN107845160B (en) * | 2017-10-30 | 2021-01-29 | 青岛慧拓智能机器有限公司 | Automatic drive vehicle data acquisition system |
| CN107958451A (en) * | 2017-12-27 | 2018-04-24 | 深圳普思英察科技有限公司 | Vision high accuracy map production method and device |
| CN108334078A (en) * | 2018-01-16 | 2018-07-27 | 宁波吉利汽车研究开发有限公司 | A kind of automatic Pilot method and system navigated based on high-precision map |
-
2018
- 2018-11-13 CN CN201811344936.0A patent/CN109515439B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105674993A (en) * | 2016-01-15 | 2016-06-15 | 武汉光庭科技有限公司 | Binocular camera-based high-precision visual sense positioning map generation system and method |
| WO2017159509A1 (en) * | 2016-03-15 | 2017-09-21 | 本田技研工業株式会社 | Vehicle control system, vehicle control method, and vehicle control program |
| CN106525057A (en) * | 2016-10-26 | 2017-03-22 | 陈曦 | Generation system for high-precision road map |
| WO2018126215A1 (en) * | 2016-12-30 | 2018-07-05 | DeepMap Inc. | High definition map updates |
| CN107229690A (en) * | 2017-05-19 | 2017-10-03 | 广州中国科学院软件应用技术研究所 | Dynamic High-accuracy map datum processing system and method based on trackside sensor |
| CN108398705A (en) * | 2018-03-06 | 2018-08-14 | 广州小马智行科技有限公司 | Ground drawing generating method, device and vehicle positioning method, device |
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| Publication number | Publication date |
|---|---|
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