CN110956838A - Intelligent driving method, vector map generation method, vehicle-mounted device and storage medium - Google Patents
Intelligent driving method, vector map generation method, vehicle-mounted device and storage medium Download PDFInfo
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- CN110956838A CN110956838A CN201911296848.2A CN201911296848A CN110956838A CN 110956838 A CN110956838 A CN 110956838A CN 201911296848 A CN201911296848 A CN 201911296848A CN 110956838 A CN110956838 A CN 110956838A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/0969—Systems involving transmission of navigation instructions to the vehicle having a display in the form of a map
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Abstract
The embodiment of the disclosure relates to an intelligent driving method, a vector map generation method, a vehicle-mounted device and a storage medium, wherein the intelligent driving method comprises the following steps: acquiring sensing and positioning information and map information; the map information comprises a vector map, and the vector map comprises planning data, dynamic traffic data and positioning data; generating planning information based on the sensing positioning information and the map information; and controlling the vehicle to run based on the planning information. The vector map generation method comprises the following steps: acquiring vector map data, the vector map data including: planning data, dynamic traffic data and positioning data; a vector map is generated based on the vector map data. According to at least one embodiment of the present disclosure, vector map data is generated through planning data, dynamic traffic data and positioning data, and then a vector map is generated, which is convenient for use in an intelligent driving process.
Description
Technical Field
The disclosed embodiment relates to the technical field of intelligent driving, in particular to an intelligent driving method, a vector map generation method, vehicle-mounted equipment and a non-transitory computer readable storage medium.
Background
With the development of intelligent driving technology, the intelligent level of road traffic can be improved, the transformation and upgrade of the traffic transportation industry can be promoted, and the fusion development of industries such as traffic, automobiles and communication can be driven. The vector map can not be separated in the intelligent driving process, so that the intelligent driving method and the vector map generation method are provided, and the intelligent driving requirement is met.
Disclosure of Invention
At least one embodiment of the present disclosure provides an intelligent driving method, a vector map generation method, an in-vehicle device, and a non-transitory computer-readable storage medium.
In a first aspect, an embodiment of the present disclosure provides an intelligent driving method, including:
acquiring sensing and positioning information and map information; wherein the map information comprises a vector map, the vector map comprising: planning data, dynamic traffic data and positioning data;
generating planning information based on the sensing positioning information and the map information;
and controlling the vehicle to run based on the planning information.
In a second aspect, an embodiment of the present disclosure further provides a vector map generation method, where the method includes:
acquiring vector map data;
generating a vector map based on the vector map data, wherein the vector map is generated based on a preset map format, and the preset map format comprises: planning data, dynamic traffic data, and positioning data.
In a third aspect, an embodiment of the present disclosure further provides an on-board device, including: a processor and a memory; the processor is adapted to perform the steps of the method according to the first aspect by calling a program or instructions stored by the memory.
In a fourth aspect, the disclosed embodiments also propose a non-transitory computer-readable storage medium for storing the vector map as generated in the second aspect.
Therefore, in at least one embodiment of the present disclosure, the vector map data is generated by the planning data, the dynamic traffic data, and the positioning data, and then the vector map is generated, which is convenient for use in the intelligent driving process.
Drawings
To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art according to the drawings.
FIG. 1 is an exemplary block diagram of a smart driving vehicle provided by an embodiment of the present disclosure;
FIG. 2 is an exemplary block diagram of an intelligent driving system provided by embodiments of the present disclosure;
fig. 3 is an exemplary block diagram of an electronic device provided by an embodiment of the present disclosure;
FIG. 4 is an exemplary flow chart of a smart driving method provided by embodiments of the present disclosure;
fig. 5 is an exemplary flowchart of a vector map generation method provided by an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure can be more clearly understood, the present disclosure will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. The specific embodiments described herein are merely illustrative of the disclosure and are not intended to be limiting. All other embodiments derived by one of ordinary skill in the art from the described embodiments of the disclosure are intended to be within the scope of the disclosure.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
Fig. 1 is an exemplary block diagram of an intelligent driving vehicle provided in an embodiment of the present disclosure.
As shown in fig. 1, the smart driving vehicle includes: sensor groups, smart driving system 100, vehicle floor actuation systems, and other components that may be used to propel the vehicle and control the operation of the vehicle, such as brake pedals, steering wheel, and accelerator pedals.
And the sensor group is used for acquiring data of the external environment of the vehicle and detecting position data of the vehicle. The sensor group includes, for example, but not limited to, at least one of a camera, a laser radar, a millimeter wave radar, an ultrasonic radar, a GPS (Global positioning system), and an IMU (Inertial Measurement Unit).
In some embodiments, the sensor group is further used for collecting dynamic data of the vehicle, and the sensor group further includes, for example and without limitation, at least one of a wheel speed sensor, a speed sensor, an acceleration sensor, a steering wheel angle sensor, and a front wheel angle sensor.
The intelligent driving system 100 is configured to acquire sensing data of a sensor group, where the sensing data includes, but is not limited to, an image, a video, a laser point cloud, millimeter waves, GPS information, a vehicle state, and the like. In some embodiments, the smart driving system 100 performs environmental awareness and vehicle positioning based on the sensory data, generating awareness information and vehicle pose; the intelligent driving system 100 performs planning and decision-making based on the perception information and the vehicle pose, and generates planning decision-making information; the intelligent driving system 100 generates a vehicle control instruction based on the planning decision information and controls the vehicle to run based on the vehicle control instruction.
In some embodiments, the smart driving system 100 may be a software system, a hardware system, or a combination of software and hardware. For example, the smart driving system 100 is a software system running on an operating system, and the in-vehicle hardware system is a hardware system supporting the operating system.
In some embodiments, the smart driving system 100 may interact with a cloud server. In some embodiments, the smart driving system 100 interacts with the cloud server via a wireless communication network (e.g., a wireless communication network including, but not limited to, a GPRS network, a Zigbee network, a Wifi network, a 3G network, a 4G network, a 5G network, etc.).
In some embodiments, the cloud server is used to interact with the vehicle. The cloud server can send environment information, positioning information, control information and other information required in the intelligent driving process of the vehicle to the vehicle. In some embodiments, the cloud server may receive the sensing data, the vehicle state information, the vehicle driving information and the related information of the vehicle request from the vehicle end. In some embodiments, the cloud server may remotely control the vehicle based on user settings or vehicle requests. In some embodiments, the cloud server may be a server or a server group. The server group may be centralized or distributed. In some embodiments, the cloud server may be local or remote.
And the vehicle bottom layer execution system is used for receiving the vehicle control command and controlling the vehicle to run based on the vehicle control command. In some embodiments, vehicle under-floor execution systems include, but are not limited to: a steering system, a braking system and a drive system. In some embodiments, the vehicle bottom layer execution system may analyze the vehicle control command and issue the vehicle control command to corresponding systems such as a steering system, a braking system, and a driving system, respectively.
In some embodiments, the smart-drive vehicle may also include a vehicle CAN bus, not shown in FIG. 1, that connects to the vehicle's underlying implement system. Information interaction between the intelligent driving system 100 and the vehicle bottom layer execution system is transmitted through a vehicle CAN bus.
In some embodiments, the smart driving vehicle may be a vehicle that carries different levels of smart driving systems, e.g., an unmanned driving system, a driving assistance system, etc.
Fig. 2 is a block diagram of an intelligent driving system 200 according to an embodiment of the present disclosure. In some embodiments, smart driving system 200 may be implemented as smart driving system 100 in fig. 1 or as part of smart driving system 100.
As shown in fig. 2, the smart driving system 200 may include: the perception module 201, the planning module 202, the control module 203, and other modules may be used to control the travel of the vehicle.
The sensing module 201 is used for sensing and positioning the environment. In some embodiments, the sensing module 201 acquires data such as sensor data, V2X data, high-precision map, and the like, performs environmental sensing and positioning based on at least one of the data, and generates sensing information and positioning information. Wherein the perception information may include, but is not limited to, at least one of: obstacle information, road signs/markings, pedestrian/vehicle information, drivable zones. The positioning information includes a vehicle pose.
The planning module 202 is used for path planning and decision-making. In some embodiments, planning module 202 generates planning and decision information based on the perception information and positioning information generated by perception module 201. In some embodiments, planning module 202 may also generate planning and decision information in conjunction with at least one of V2X data, map information, and the like. The map information includes, but is not limited to, a high-precision map or a vector map, which includes planning data, dynamic traffic data, and positioning data. The planning decision information includes planning paths and decision information, wherein the decision information may include, but is not limited to, at least one of: behavior (e.g., including but not limited to following, overtaking, parking, circumventing, etc.), vehicle heading, vehicle speed, desired acceleration of the vehicle, desired steering wheel angle, etc.
The control module 203 is configured to generate a control instruction of the vehicle bottom layer execution system based on the planning and decision information, and issue the control instruction, so that the vehicle bottom layer execution system controls the vehicle to run. The control instructions may include, but are not limited to: steering wheel steering, lateral control commands, longitudinal control commands, and the like.
In some embodiments, the division of each module in the intelligent driving system 200 is only one logical function division, and there may be another division manner in actual implementation, for example, at least two of the sensing module 201, the planning module 202, and the control module 203 may be implemented as one module; the perception module 201, the planning module 202, or the control module 203 may also be divided into a plurality of sub-modules. It will be appreciated that the various modules or sub-modules can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application.
Fig. 3 is an exemplary block diagram of an electronic device provided by an embodiment of the present disclosure. In some embodiments, the electronic device may be an in-vehicle device, wherein the in-vehicle device may support operation of the smart driving system. In some embodiments, the electronic device may be a map generation device for generating a vector map.
As shown in fig. 3, the electronic apparatus includes: at least one processor 301, at least one memory 302, and at least one communication interface 303. The various components in the electronic device are coupled together by a bus system 304. A communication interface 303 for information transmission with an external device. Understandably, the bus system 304 is used to enable connective communication between these components. The bus system 304 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, the various buses are labeled as bus system 304 in fig. 3.
It will be appreciated that the memory 302 in this embodiment can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory.
In some embodiments, memory 302 stores the following elements, executable units or data structures, or a subset thereof, or an expanded set thereof: an operating system and an application program.
The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, and is used for implementing various basic services and processing hardware-based tasks. The application programs, including various application programs such as a Media Player (Media Player), a Browser (Browser), etc., are used to implement various application services. A program for implementing the intelligent driving method or the vector map generation method provided by the embodiments of the present disclosure may be included in an application program.
In the embodiment of the present disclosure, the processor 301 is configured to execute the steps of the intelligent driving method or the vector map generating method provided by the embodiment of the present disclosure by calling a program or an instruction stored in the memory 302, which may be specifically a program or an instruction stored in an application program.
The intelligent driving method or the vector map generation method provided by the embodiment of the present disclosure may be applied to the processor 301, or implemented by the processor 301. The processor 301 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 301. The processor 301 may be a general-purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of the intelligent driving method or the vector map generation method provided by the embodiment of the disclosure can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software units in the decoding processor. The software elements may be located in ram, flash, rom, prom, or eprom, registers, among other storage media that are well known in the art. The storage medium is located in the memory 302, and the processor 301 reads the information in the memory 302 and performs the steps of the method in combination with its hardware.
Fig. 4 is an exemplary flowchart of an intelligent driving method provided in an embodiment of the present disclosure. The execution subject of the method is the electronic device, and in some embodiments, the execution subject of the method can also be an intelligent driving system supported by the electronic device. For convenience of description, the following embodiments describe the flow of the intelligent driving method with an electronic device as an execution subject.
As shown in fig. 4, in step 401, the electronic device acquires perceptual positioning information and map information; wherein the map information includes a vector map, the vector map including: planning data, dynamic traffic data, and positioning data.
In step 402, the electronic device generates planning decision information based on the perceptual positioning information and the map information. For example, the electronic device generates planning decision information based on the perceptual positioning information. In some embodiments, the electronic device may also generate planning decision information in conjunction with at least one of the V2X data, map information, and the like. The map information includes, but is not limited to, a high-precision map or a vector map. The planning decision information includes planning paths and decision information, wherein the decision information may include, but is not limited to, at least one of: behavior (e.g., including but not limited to following, overtaking, parking, circumventing, etc.), vehicle heading, vehicle speed, desired acceleration of the vehicle, desired steering wheel angle, etc.
In step 403, the electronic device controls the vehicle to run based on the planning decision information. For example, the electronic device generates a control instruction of the vehicle bottom layer execution system based on the planning decision information, and issues the control instruction so that the vehicle bottom layer execution system controls the vehicle to run. The control instructions may include, but are not limited to: steering wheel steering, lateral control commands, longitudinal control commands, and the like.
In some embodiments, the planning data is used for path planning and for describing information related to vehicle travel in the environment in which the vehicle is located, such as roads, lanes, longitudinal markings, transverse markings, intersections, parking spaces, road appendages, and the like. In some embodiments, the planning data includes road information, identification information, area information. The road information is used for describing information related to route planning decision on a road where the vehicle runs. In some embodiments, the road information includes road attributes, lane attributes, longitudinal marking attributes, lateral marking attributes, intersection attributes.
In some embodiments, the road attributes are used to describe information for different roads. In some embodiments, the road attributes include at least one of: road ID, road name, road traffic direction (including one-way traffic, two-way forbidden traffic), road grade (including unknown road, main road, secondary road, general road, destination road, detailed road, walking road), road type (unknown, double-line road, single-line road, auxiliary road, rotary island, traffic square, closed traffic area, special turning lane, parking lot access passage, parking lot internal road), forward lane number (forward lane is consistent with road vector direction), reverse lane number (reverse lane is opposite to road vector direction), left side edge ID (left side edge and right side edge are judged by taking road vector direction as reference), right side edge ID, preorder associated road, subsequent associated road, time-limited scope (adopting GDF time domain format), The vehicle type is limited (including all vehicle types, cars, mini-trucks/vans, big trucks/vans, trailers, minibuses, large buses, taxis, bicycles/rickshaws, motorcycles and pedestrians), the height and the width are limited.
In some embodiments, lane attributes are used to describe information for different lanes. The information of the different lanes is the information of the center lines of the different lanes. The information of the different lanes can also be information of sidelines of different lanes. In some embodiments, the lane attributes include at least one of: lane ID, lane type (including unknown, general lane, bus bay lane, bus lane, virtual lane in intersection, left turn lane, straight lane), direction of traffic (including one-way and two-way), lane direction (including unknown, straight, left turn, right turn, straight + left turn, right turn + turn, left turn + turn), subject road ID, width, speed limit, left lane ID, left sideline ID (left lane ID without left lane ID), right lane ID, right sideline ID (right lane ID without right lane ID), left lane type (non-essential attribute, left lane type includes road boundary, broken line, solid line, double-broken line, double-solid line, left-solid-right broken line, left-virtual-right line, break), right lane type (non-essential attribute, the left lane line types include: road boundary, dotted line, solid line, double dotted line, double solid line, left solid right dotted line, left virtual right solid line, break), time-limited scope (e.g., bus lane time-limited information), vehicle type limitation (including: all models of cars, minicars, mini/trucks, big/trucks, trailers, minibuses, buses, taxis, bicycles/rickshaws, motorcycles, pedestrians).
In some embodiments, a longitudinal lane (Signline _ vertical) attribute is used to describe information about a longitudinal road traffic lane. In some embodiments, the longitudinal reticle attributes include at least one of: the road sign line comprises a longitudinal sign line ID, a longitudinal sign line type (comprising a lane line, a road boundary, a self-set road boundary, a manually set traffic direction two-side boundary, others and a road surface mark), an belonged road ID, a longitudinal sign line shape (comprising a road boundary, a dotted line, a solid line, a double dotted line, a double solid line, a left solid-right dotted line, a left virtual-right dotted line and a break, wherein for the break, when the segment has no actual lane line in an actual scene, a virtual sign line added for map complete expression and automatic driving application), a longitudinal sign line color (comprising undefined color, white color, yellow color, orange color and blue color), and a longitudinal sign line Material (Material) (comprising undefined color, a sign line, a solid protrusion, an edge, a wall and a guardrail).
In some embodiments, a lateral marking (sign _ horizontal) attribute is used to describe information of a lateral road traffic marking. In some embodiments, the lateral reticle attributes include at least one of: a lateral marking ID, a lateral marking type (including: unknown, stop line, speed bump, double dashed line), a lateral marking color (including: undefined, white, yellow, orange, blue), a road ID of which it belongs.
In some embodiments, Intersection (interaction) attributes are used to describe Intersection and road connectivity information. In some embodiments, Intersection (interaction) attributes include at least one of: intersection ID, an incoming road list, an outgoing road list and an in-intersection lane list.
In some embodiments, the identification information is an identification that affects vehicle travel, such as a crosswalk, a no-parking area, a diversion line, a guide arrow, text, a stop, a toll island, and the like. In some embodiments, the identification information includes a facet identification attribute, a road attachment attribute, a keypoint attribute. The planar identification attribute is used for describing information of different traffic identifications. In some embodiments, the planar identification attribute comprises at least one of: the method comprises the following steps of planar identification ID, planar identification type (including unknown, pedestrian crosswalk, no-parking area, guide line, guide arrow, character, parking station and toll island), belonged road ID, influence lane ID list and color (including undefined, white, yellow, orange and blue).
In some embodiments, the road attachment attributes are used to describe information of different classes of road attachments. In some embodiments, the road accessory attributes include at least one of: road accessory ID, road accessory name, road accessory type (including unknown, signpost, signal lamp, street lamp, Stop Sign, rod), belonged road ID, road accessory subtype (the signpost includes forbidden parking, speed limit, height limit, line width, etc.; the rod includes parking lot entrance/exit parking rod), influence lane ID list, shape (including square/rectangle, equilateral triangle, circle, diamond, inverted equilateral triangle, strip, irregular shape).
In some embodiments, the keypoint attributes are used to describe information for different nodes in a map, the nodes comprising: the system comprises a station, a parking point, an unloading point, an approach point, an elevator entrance, a shopping mall entrance, a stair entrance and a parking lot entrance. In some embodiments, the keypoint attributes comprise at least one of: key point ID, key point name, key point geographic type (Geotype), and key point type (including stop, parking point, unloading point, approach point, elevator entrance, mall entrance, stair entrance, and parking lot entrance).
In some embodiments, the region information is used to describe different regions in the map. In some embodiments, the zone information includes a venue attribute, a parking spot attribute. In some embodiments, a Zone attribute is used to describe different types of zones. In some embodiments, the venue attribute comprises at least one of: venue ID, venue name, venue type (including: unknown, parking lot, parking area, electronic fence, dump area, other area), relative elevation. Where the relative elevation may determine a meaning, such as a floor, based on the venue type.
In some embodiments, the parking space attributes include at least one of: parking space ID, parking space name, parking lot ID, parking space type (including: unknown, standard, handicapped, mini, mother and child, charging, stereo, woman, extra, private), parking space indicia (e.g., letters + number indicating the location of the parking space in the parking lot), associated road ID, shape angle (including: vertical, lateral, diagonal), ground lock (including no ground lock, ground lock), boundary color (including: undefined, white, yellow, orange, blue), background color (including: undefined, white, yellow, orange, blue).
In some embodiments, the dynamic traffic data includes information of dynamic traffic items that affect vehicle planning positioning, wherein the dynamic traffic items include roads, lanes, parking spaces, road appendages, key points, and positioning signs. In some embodiments, the dynamic traffic data includes at least one of: dynamic Traffic ID, Type of dynamic Traffic Item (Item _ Type) (including: road, lane, parking space, road attachment, keypoint, localizer), map element ID (Item _ ID), Traffic Type (Traffic _ Type, including Not Available, Close, Slow).
In some embodiments, the positioning data comprises locator information and a SLAM map; the positioning mark (Landmark) information is a mark for assisting vehicle positioning, such as a bumper, an arrow indicator, an entrance mark, a zone mark, a National ISO mark, and a CN ISO mark. In some embodiments, the locator information comprises at least one of: logo ID, logo type (including unknown, crashproof bar, arrow point logo, entrance logo, area logo, National ISO logo, CN ISO logo), border color (including undefined, white, yellow, orange, blue), background color (including undefined, white, yellow, orange, blue), content, picture ID. In some embodiments, the SLAM map is a map obtained using a SLAM (Simultaneous Localization And Mapping) algorithm based on the visual sensing data or the laser sensing data.
Fig. 5 is an exemplary flowchart of a vector map generation method provided in an embodiment of the present disclosure. The execution subject of the method is an electronic device. In some embodiments, the subject of execution of the method may also be an intelligent driving system supported by the electronic device.
As shown in fig. 5, in step 501, the electronic device obtains vector map data.
In step 502, the electronic device generates a vector map based on the vector map data. The vector map is generated based on a preset map format, wherein the preset map format comprises: planning data, dynamic traffic data, and positioning data.
In some embodiments, the vector map is stored based on the preset map format. The preset map format includes a plurality of fields, wherein the planning data, the dynamic traffic data, and the positioning data correspond to one or more of the fields, respectively.
The field information of the planning data, the dynamic traffic data and the positioning data are described below.
In some embodiments, the planning data is used for path planning and for describing information relating to vehicle travel in the environment in which the vehicle is located. In some embodiments, the planning data includes road information, identification information, area information.
In some embodiments, the road information is used to describe information relating to path planning decisions on the road on which the vehicle is travelling. In some embodiments, the road information includes road attributes, lane attributes, longitudinal marking attributes, lateral marking attributes, intersection attributes. Wherein the road attributes are used to describe information of different roads. The information of the different lanes is the information of the center lines of the different lanes. The information of the different lanes can also be information of sidelines of different lanes. In some embodiments, the road attributes include at least one of: road ID, road name, road traffic direction, road grade, road type, forward lane number, reverse lane number, left side edge ID, right side edge ID, pre-order associated road, subsequent associated road, time-limited scope of action, and vehicle type limited. In some embodiments, lane attributes are used to describe information for different lanes. In some embodiments, the lane attributes include at least one of: lane ID, lane type, traffic direction, lane direction, belonging road ID, width, speed limit, left lane line ID, left sideline ID, right lane line ID, right sideline ID, left lane line type, right lane line type, time limit scope, vehicle type limit. In some embodiments, the longitudinal reticle attributes are used to describe information of the longitudinal road traffic reticle. In some embodiments, the longitudinal reticle attributes include at least one of: the longitudinal marked line ID, the type of the longitudinal marked line, the ID of the road, the shape of the longitudinal marked line, the color of the longitudinal marked line and the material of the longitudinal marked line. In some embodiments, the lateral marking attributes are used to describe information of the lateral road traffic markings. In some embodiments, the lateral reticle attributes include at least one of: the type of the horizontal marked line, the color of the horizontal marked line and the ID of the road to which the horizontal marked line belongs. In some embodiments, intersection attributes are used to describe intersection and road connectivity information. In some embodiments, the intersection attributes include at least one of: intersection ID, an incoming road list, an outgoing road list and an in-intersection lane list.
In some embodiments, the identification information is an identification that affects the travel of the vehicle. In some embodiments, the identification information includes a facet identification attribute, a road attachment attribute, a keypoint attribute. The planar identification attribute is used for describing information of different traffic identifications. In some embodiments, the planar identification attribute comprises at least one of: planar identification ID, planar identification type, belonged road ID, influence lane ID list and color. In some embodiments, the road attachment attributes are used to describe information of different classes of road attachments. In some embodiments, the road accessory attributes include at least one of: road attachment ID, road attachment name, road attachment type, road-to-be-attached ID, road-to-be-attached sub-type, list of affected lane IDs, shape. In some embodiments, the keypoint attributes are used to describe information for different nodes in a map, the nodes comprising: the system comprises a station, a parking point, an unloading point, an approach point, an elevator entrance, a shopping mall entrance, a stair entrance and a parking lot entrance. In some embodiments, the keypoint attributes comprise at least one of: key point ID, key point name, key point geography type, key point type.
In some embodiments, the region information is used to describe information for different regions in the map. In some embodiments, the region information is information of different types of regions. In some embodiments, the zone information includes a venue attribute, a parking spot attribute. In some embodiments, site attributes are used to describe different types of regions. In some embodiments, the venue attribute comprises at least one of: site ID, site name, site type, relative elevation. In some embodiments, the parking space attributes include at least one of: parking space ID, parking space name, parking lot ID, parking space type, parking space sign, associated road ID, shape angle, ground lock, border color, background color.
In some embodiments, the dynamic traffic data includes information of dynamic traffic items that affect vehicle planning positioning, wherein the dynamic traffic items include roads, lanes, parking spaces, road appendages, key points, and positioning signs. In some embodiments, the dynamic traffic data includes at least one of: dynamic traffic ID, type of dynamic traffic item, map element ID, traffic type.
In some embodiments, the positioning data comprises locator information and a SLAM map; the positioning mark information is a mark for assisting vehicle positioning. In some embodiments, the locator information comprises at least one of: logo ID, logo type, border color, background color, content, picture ID. In some embodiments, the SLAM map is a map obtained using a SLAM algorithm based on visual sensory data or laser sensory data.
It is noted that, for simplicity of description, the foregoing method embodiments are described as a series of acts or combination of acts, but those skilled in the art will appreciate that the disclosed embodiments are not limited by the order of acts described, as some steps may occur in other orders or concurrently with other steps in accordance with the disclosed embodiments. In addition, those skilled in the art can appreciate that the embodiments described in the specification all belong to alternative embodiments.
The disclosed embodiments also provide a non-transitory computer-readable storage medium storing a program or instructions that cause a computer to perform, for example, an intelligent driving method or a vector map generation method. In some embodiments, the non-transitory computer readable storage medium may store a vector map generated by the vector map generation method.
It should be noted that, in this document, the term "comprises/comprising" or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than others, combinations of features of different embodiments are meant to be within the scope of the disclosure and form different embodiments.
Those skilled in the art will appreciate that the description of each embodiment has a respective emphasis, and reference may be made to the related description of other embodiments for those parts of an embodiment that are not described in detail.
Although the embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations fall within the scope defined by the appended claims.
Claims (20)
1. An intelligent driving method, characterized in that the method comprises:
acquiring sensing and positioning information and map information; wherein the map information comprises a vector map, the vector map comprising: planning data, dynamic traffic data and positioning data;
generating planning decision information based on the sensing positioning information and the map information;
and controlling the vehicle to run based on the planning decision information.
2. The method of claim 1, wherein the planning data includes road information, identification information, regional information;
the dynamic traffic data includes information affecting vehicle planned positioning;
the positioning data comprises positioning mark information and a SLAM map; wherein the locating mark information is used for assisting vehicle locating.
3. The method of claim 2, wherein the road information comprises: road attribute, lane attribute, longitudinal marking attribute, transverse marking attribute and intersection attribute;
the intersection attribute comprises intersection and road connectivity information.
4. The method of claim 3, wherein the road attribute comprises at least one of: road ID, road name, road traffic direction, road grade, road type, forward lane number, reverse lane number, left side edge ID, right side edge ID, pre-order associated road, subsequent associated road, time limitation scope, vehicle type limitation, height limitation and width limitation;
the lane attributes include at least one of: lane ID, lane type, traffic direction, lane direction, belonged road ID, width, speed limit, left lane line ID, left sideline ID, right lane line ID, right sideline ID, left lane line type, right lane line type, time limit scope and vehicle type limit;
the longitudinal reticle attributes include at least one of: the method comprises the following steps of (1) identifying a longitudinal marking ID, a longitudinal marking type, an ID of a road to which the longitudinal marking belongs, a longitudinal marking shape, a longitudinal marking color and a longitudinal marking material;
the lateral reticle attributes include at least one of: the method comprises the following steps of (1) identifying a transverse marking ID, a transverse marking type, a transverse marking color and a belonging road ID;
the intersection attributes include at least one of: intersection ID, an incoming road list, an outgoing road list and an in-intersection lane list.
5. The method of claim 2, wherein the identification information comprises: the method comprises the following steps of (1) surface-shaped identification attributes, road accessory attributes and key point attributes;
the area identification attribute comprises at least one of: the method comprises the steps of (1) identifying a planar identifier ID, a planar identifier type, a belonged road ID, an influence lane ID list and colors;
the road accessory attribute comprises at least one of: road attachment ID, road attachment name, road attachment type, road ID of the attachment, road attachment subtype, list of influencing lane IDs, shape;
the keypoint attributes include at least one of: key point ID, key point name, key point geography type, key point type.
6. The method of claim 2, wherein the region information comprises: site attribute, parking space attribute.
7. The method of claim 6, wherein the venue attribute comprises at least one of: site ID, site name, site type and relative elevation;
the parking space attributes include at least one of: parking space ID, parking space name, parking lot ID, parking space type, parking space sign, associated road ID, shape angle, ground lock, border color, background color.
8. The method of claim 2, wherein the dynamic traffic data comprises at least one of: dynamic traffic ID, type of dynamic traffic item, map element ID, traffic type.
9. The method of claim 2, wherein the locator information comprises at least one of: logo ID, logo type, border color, background color, content, picture ID.
10. A vector map generation method, characterized in that the method comprises:
acquiring vector map data;
generating a vector map based on the vector map data, wherein the vector map is generated based on a preset map format, and the preset map format comprises: planning data, dynamic traffic data, and positioning data.
11. The method of claim 10, wherein the planning data includes road information, identification information, area information;
the dynamic traffic data includes information affecting vehicle planned positioning;
the positioning data comprises positioning mark information and a SLAM map; wherein the locating mark information is used for assisting vehicle locating.
12. The method of claim 11, wherein the road information comprises: road attribute, lane attribute, longitudinal marking attribute, transverse marking attribute and intersection attribute;
the intersection attribute comprises intersection and road connectivity information.
13. The method of claim 12, wherein the road attribute comprises at least one of: road ID, road name, road traffic direction, road grade, road type, forward lane number, reverse lane number, left side edge ID, right side edge ID, pre-order associated road, subsequent associated road, time limitation scope, vehicle type limitation, height limitation and width limitation;
the lane attributes include at least one of: lane ID, lane type, traffic direction, lane direction, belonged road ID, width, speed limit, left lane line ID, left sideline ID, right lane line ID, right sideline ID, left lane line type, right lane line type, time limit scope and vehicle type limit;
the longitudinal reticle attributes include at least one of: the method comprises the following steps of (1) identifying a longitudinal marking ID, a longitudinal marking type, an ID of a road to which the longitudinal marking belongs, a longitudinal marking shape, a longitudinal marking color and a longitudinal marking material;
the lateral reticle attributes include at least one of: the method comprises the following steps of (1) identifying a transverse marking ID, a transverse marking type, a transverse marking color and a belonging road ID;
the intersection attributes include at least one of: intersection ID, an incoming road list, an outgoing road list and an in-intersection lane list.
14. The method of claim 11, wherein the identification information comprises: the method comprises the following steps of (1) surface-shaped identification attributes, road accessory attributes and key point attributes;
the area identification attribute comprises at least one of: the method comprises the steps of (1) identifying a planar identifier ID, a planar identifier type, a belonged road ID, an influence lane ID list and colors;
the road accessory attribute comprises at least one of: road attachment ID, road attachment name, road attachment type, road ID of the attachment, road attachment subtype, list of influencing lane IDs, shape;
the keypoint attributes include at least one of: key point ID, key point name, key point geography type, key point type.
15. The method of claim 11, wherein the region information comprises: site attribute, parking space attribute.
16. The method of claim 15, wherein the venue attribute comprises at least one of: site ID, site name, site type and relative elevation;
the parking space attributes include at least one of: parking space ID, parking space name, parking lot ID, parking space type, parking space sign, associated road ID, shape angle, ground lock, border color, background color.
17. The method of claim 11, wherein the dynamic traffic data comprises at least one of: dynamic traffic ID, type of dynamic traffic item, map element ID, traffic type.
18. The method of claim 11, wherein the locator information comprises at least one of: logo ID, logo type, border color, background color, content, picture ID.
19. An in-vehicle apparatus, characterized by comprising: a processor and a memory;
the processor is adapted to perform the steps of the method of any one of claims 1 to 9 by calling a program or instructions stored in the memory.
20. A non-transitory computer readable storage medium storing the vector map generated according to any one of claims 10 to 18.
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