High-precision map-based navigation method and navigation system using crowdsourcing technology

Abstract

The invention provides a navigation method based on a high-precision map. The navigation method comprises the following steps: checking whether a high-precision map which can be used for a destination exists in a vehicle or not; in response to the absence of the high-precision map available for the destination in the vehicle, requesting the high-precision map from the server; and generating, by the vehicle, a high-precision map in response to the absence of the high-precision map in the server. In addition, the invention also provides a navigation system based on the high-precision map. According to the method and the device, the cost of constructing a high-precision map can be reduced by using a crowdsourcing technology, rich real data can be provided for extreme scenes, and the user experience is improved by adopting wide-coverage map data.

Description

Technical Field

[0001] The present invention relates to a vehicle, and more particularly, to a navigation method and a navigation system based on a high-precision map using crowdsourcing technology. Background Art

[0002] High Definition Map refers to a map with high precision and detailed definition. Its accuracy needs to reach the decimeter level to distinguish each lane. Nowadays, with the development of positioning technology, high-precision positioning has become possible. Unlike traditional electronic maps, the main service objects of high-precision maps are driverless cars, or machine drivers. Unlike human drivers, machine drivers lack innate visual recognition and logical analysis capabilities. For example, people can easily and accurately use images and GPS to locate themselves and identify obstacles, people, traffic lights, etc., but this is a very difficult task for current robots. Therefore, high-precision maps are an indispensable part of current driverless car technology. High-precision maps contain a large amount of driving assistance information, among which the most important is the accurate three-dimensional representation of the road network (centimeter-level accuracy). For example, the geometric structure of the road surface, the location of road markings, and the point cloud model of the surrounding road environment. With these high-precision three-dimensional representations, the on-board robot can accurately confirm its current position by comparing the on-board GPS, LiDAR or camera data.

[0003] However, high-precision maps are expensive to produce. Some map vendors only provide usable high-precision maps for certain areas or certain scenarios (for example, certain parking lots in certain cities). In areas that are not covered by high-precision maps, driverless vehicles will have difficulty moving.

[0004] Accordingly, there is a need in the art for technology that enables driverless vehicles to continue driving in areas that are not covered by high-precision maps provided by vendors. Summary of the invention

[0005] This Summary is provided to introduce some concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0006] In view of the deficiencies in the prior art, an object of the present invention is to provide an improved navigation method and navigation system based on a high-precision map.

[0007] According to one aspect of the present invention, a navigation method based on a high-precision map is provided, comprising: checking whether there is a high-precision map that can be used for the destination in the vehicle; in response to the absence of a high-precision map that can be used for the destination in the vehicle, requesting the high-precision map from a server; and in response to the absence of the high-precision map in the server, generating the high-precision map by the vehicle.

[0008] In one embodiment, requesting a high-precision map from a server may include: requesting a high-precision map from a private cloud associated with a user of the vehicle, wherein the private cloud hosts community vehicle data associated with the user.

[0009] In one embodiment, requesting a high-precision map from a server may include: requesting a high-precision map from a public cloud associated with the vehicle, wherein the public cloud hosts customer vehicle data associated with a manufacturer of the vehicle.

[0010] In one embodiment, generating a high-precision map by a vehicle may include: using sensors of the vehicle to collect data; and generating a high-precision map based on the collected data.

[0011] In one embodiment, the sensor may include one or more of: a camera, a lidar, a radar, a global navigation satellite system (GNSS) receiver, or an ultrasonic sensor.

[0012] In one embodiment, the method may further include: after generating the high-precision map, uploading the high-precision map to a server via an air interface.

[0013] In one embodiment, the method may further include: before uploading the high-precision map, preprocessing the high-precision map to comply with data protection regulations.

[0014] In one embodiment, the high-precision map may be a high-precision map for a parking lot.

[0015] According to another aspect of the present invention, a navigation system based on a high-precision map is provided, which may include: a memory; and a processor coupled to the memory, the processor being configured to: check whether there is a high-precision map available for the destination in the vehicle; in response to the absence of a high-precision map available for the destination in the vehicle, request the high-precision map from a server; and in response to the absence of the high-precision map in the server, generate the high-precision map.

[0016] According to yet another aspect of the present invention, a vehicle is provided. The vehicle includes a navigation system based on a high-precision map according to the present invention.

[0017] By adopting the navigation method and navigation system of the present invention, rich real data can be provided for extreme scenarios, wide coverage map data can be used to improve user experience, and crowdsourcing technology can be optionally used to reduce the cost of building high-precision maps.

[0018] These and other features and advantages will become apparent by reading the following detailed description and by reference to the associated drawings.It is to be understood that the foregoing general description and the following detailed description are illustrative only and are not restrictive of the aspects of what is claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to understand the manner in which the above features of the present invention are used in detail, the above briefly summarized contents can be described in more detail with reference to various embodiments, some of which are shown in the accompanying drawings. However, it should be noted that the accompanying drawings only show some typical aspects of the present invention and should not be considered to limit its scope, because the description may allow for other equally effective aspects.

[0020] Figure 1 A block diagram illustrating various components and / or systems implemented in an exemplary vehicle according to one embodiment of the present invention.

[0021] Figure 2 Illustrated is a diagram of an exemplary vehicle configured with various sensor and communication components and / or systems in accordance with one embodiment of the present invention.

[0022] Figure 3 A flow chart according to one embodiment of the present invention is illustrated.

[0023] Figure 4 A block diagram of an exemplary computing device is illustrated in accordance with one embodiment of the invention. DETAILED DESCRIPTION

[0024] In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the described exemplary embodiments. However, it will be apparent to one skilled in the art that the described embodiments may be practiced without some or all of these specific details. In other exemplary embodiments, well-known structures or processing steps are not described in detail to avoid unnecessarily obscuring the concepts of the present disclosure.

[0025] As mentioned above, high-precision maps are an indispensable component of current driverless car technology. However, the production cost of high-precision maps is expensive. Some map suppliers only provide available high-precision maps for certain areas or certain scenes (for example, certain parking lots in certain cities). In areas where high-precision maps are not covered, driverless vehicles will be unable to move. To this end, the present invention proposes that in the case where the supplier does not provide high-precision maps for specific scenes, crowdsourcing technology can be used to provide such high-precision maps. For example, a user can first request an available high-precision map from a private cloud closely related to the user, wherein the private cloud includes community vehicle data from, for example, family members, close friends, etc. If a family member or close friend has already uploaded the relevant high-precision map to the private cloud, the user can download the high-precision map from the private cloud for use. Additionally or alternatively, the user can also request an available high-precision map from a public cloud associated with the manufacturer of his vehicle, wherein the public cloud hosts customer vehicle data associated with the manufacturer of the vehicle. If other users have already uploaded the relevant high-precision map to the public cloud, the high-precision map can be downloaded from the public cloud for use. On the other hand, if no relevant high-precision map is available in either the private cloud or the public cloud, the vehicle's own sensors can be used to collect data and generate a high-precision map based on the collected data. The generated high-precision map can then be further uploaded to the private cloud and / or public cloud for use by others.

[0026] Figure 1 A block diagram illustrating various components and / or systems implemented in an exemplary vehicle 100 according to one embodiment of the present invention.

[0027] The vehicle 100 may include one or more cameras 135. The camera may include a camera sensor and a mounting assembly. Different mounting assemblies may be used for different cameras on the vehicle 100. For example, a front camera may be mounted in a front bumper, in a rod of a rearview mirror assembly, or in other front areas of the vehicle 100. A rear camera may be mounted in a rear bumper / fender, on a rear windshield, in a trunk, or on other rear areas of the vehicle. Side mirrors may be mounted on the sides of the vehicle 100, such as integrated into a mirror assembly or a door assembly. The camera may provide object detection and distance estimation (especially for objects of known size and / or shape), and may also provide information about rotational motion relative to the vehicle's axis (such as during a turn). When used in conjunction with other sensors, the camera may be calibrated to verify travel distance and angular orientation by using other systems (such as by using LIDAR, distance sensors, etc.). The camera may similarly be used to verify and calibrate other systems to verify that distance measurements are correct, and is also used to verify that object detection is accurately performed so that objects are mapped to the correct position relative to the vehicle by LIDAR and other systems accordingly. Similarly, when combined with, for example, an accelerometer, the time to impact with a road hazard can be estimated (eg, the time elapsed before hitting a pothole).

[0028] In one embodiment, the accelerometer, gyroscope and magnetometer 140 can be used to provide and / or verify motion and direction information. The accelerometer and gyroscope can be used to monitor wheel and driveline performance. In one embodiment, the accelerometer can also be used to verify the actual collision time of road hazard obstacles (such as potholes) relative to the predicted time based on existing braking and acceleration models and steering models. In one embodiment, the gyroscope and magnetometer can be used to measure the rotation state of the vehicle and the orientation relative to magnetic north, respectively, and to measure and calibrate the estimation and / or model of the turning radius at the current speed and / or the maneuverability metric at the current speed (especially when used in conjunction with measurements from other external and internal sensors (such as other sensors 145, such as speed sensors, and / or odometer measurements)).

[0029] The Light Detection and Ranging (LIDAR, also known as laser radar) 150 subsystem uses pulsed lasers to measure the distance to an object. While cameras can be used for object detection, LIDAR 150 provides a means to more confidently detect the distance (and orientation) of an object, especially with respect to objects of unknown size and shape. LIDAR 150 measurements can also be used to estimate travel rate, vector direction, relative positioning, and braking distance by providing accurate distance measurements and incremental distance measurements.

[0030] The memory 160 may be used with the processor 110 and / or the digital signal processor (DSP) 120, and the memory may include flash memory, RAM, ROM, disk drive, or flash memory card or other memory device, or various combinations thereof. In one embodiment, the memory 160 may include instructions for implementing the various methods described throughout this specification. In one embodiment, the memory may include instructions for the following operations: operating and calibrating sensors, and receiving maps, weather, vehicles (both vehicle 100 and surrounding vehicles) and other data, and determining driving parameters (such as relative positioning, absolute positioning, braking distance, acceleration and turning radius at current speed and / or maneuverability at current speed, vehicle distance, turn initiation / timing and execution, and initiation / timing of driving operations) using various internal and external sensor measurements and received data and measurements.

[0031] In one embodiment, the power and drive systems (generator, battery, transmission, engine) and related systems 175 and systems 155 (brakes, actuators, throttle control, steering and electrical) may be controlled by processor(s) and / or hardware or software or by an operator of the vehicle or by some combination thereof. The systems 155 (brakes, actuators, throttle control, steering and electrical, etc.) and power and drive or other systems 175 may be utilized in conjunction with performance parameters and operating parameters to enable autonomous (and manual, with respect to alarms and emergency overrides / brakes / stopping) safe and accurate driving and operation of the vehicle 100, such as to merge into traffic, park, accelerate and otherwise operate the vehicle 100 safely, effectively and efficiently. In one embodiment, inputs from various sensor systems (such as cameras 135, accelerometers, gyroscopes and magnetometers 140, LIDAR 150, GNSS receivers 170, radar 153, inputs, messaging and / or measurements from wireless transceiver(s) 130 and / or other sensors 145, or various combinations thereof) may be used by the processor 110 and / or DSP 120 or other processing system to control the power and drive system 175 as well as the systems (brakes, actuators, throttle control, steering and electrical) 155.

[0032] Global Navigation Satellite System (GNSS) receivers can be used to determine positioning relative to the ground (absolute positioning) and, when used with other information (such as measurements and / or mapping data from other objects), can be used to determine positioning relative to other objects (such as relative to other vehicles and / or relative to the road).

[0033] In one embodiment, the GNSS receiver 170 may support one or more GNSS constellations and other satellite-based navigation systems. For example, in one embodiment, the GNSS receiver 170 may support global navigation satellite systems such as the Global Positioning System (GPS), the Russian Global Navigation Satellite System (GLONASS), Galileo, and / or BeiDou, or any combination thereof. In one embodiment, the GNSS receiver 170 may support regional navigation satellite systems (such as NAVIC or QZSS or a combination thereof) and various augmentation systems (e.g., satellite-based augmentation systems (SBAS) or ground-based augmentation systems (GBAS)), such as Doppler orbit diagrams and radio positioning integrated by satellite (DORIS) or wide area augmentation system (WAAS) or European Geostationary Navigation Overlay Service (EGNOS) or Multi-Function Satellite Augmentation System (MSAS) or Local Area Augmentation System (LAAS). In one embodiment, the GNSS receiver(s) 130 and the antenna(s) 132 may support multiple frequency bands and sub-bands, such as GPS L1, L2, and L5 bands, Galileo E1, E5, and E6 bands, Compass (Beidou) B1, B3, and B2 bands, GLONASS G1, G2, and G3 bands, and QZSS L1C, L2C, and L5-Q bands.

[0034] The GNSS receiver 170 may be used to determine position and relative position that may be used for positioning, navigation, and for calibrating other sensors when appropriate, such as for determining the distance between two points in time under clear sky conditions and using the distance data to calibrate other sensors (such as odometers and / or LIDAR). In one embodiment, GNSS-based relative position based on, for example, Doppler and / or pseudorange measurements shared between vehicles may be used to determine a highly accurate distance between two vehicles, and when combined with vehicle information (such as shape and model information and GNSS antenna position) may be used to calibrate, verify, and / or influence the confidence level associated with information from LIDAR, cameras, radar, sonar, and other distance estimation techniques. GNSS Doppler measurements may also be used to determine the linear and rotational motion of a vehicle or vehicles relative to another vehicle, which may be used in conjunction with gyroscopes and / or magnetometers and other sensor systems to maintain calibration of those systems based on measured position data. Relative GNSS positioning data may also be combined with high confidence absolute position from roadside equipment (also referred to as roadside units or RSUs) to determine a high confidence absolute position of a vehicle. Additionally, during inclement weather that may obscure LIDAR and / or camera-based data sources, relative GNSS positioning data may be used to avoid other vehicles and remain within lanes or other assigned road areas. For example, using an RSU equipped with a GNSS receiver and V2X capabilities, GNSS measurement data may be provided to the vehicle, which, when provided along with the absolute position of the RSU, may be used to navigate the vehicle relative to a map, thereby keeping the vehicle in a lane and / or road despite lack of visibility.

[0035] Radio detection and ranging (radar 153) uses transmitted radio waves reflected from an object. The reflected radio waves are analyzed based on the time required for the reflection to arrive and other signal characteristics of the reflected waves to determine the location of nearby objects. Radar 153 can be used to detect the location of nearby cars, roadside objects (signs, other vehicles, pedestrians, etc.), and can usually detect objects even in the presence of ambiguous weather (such as snow, rain or hail). Thus, radar 153 can be used to supplement LIDAR 150 system and camera 135 when providing ranging information to other objects by providing ranging and distance measurements and information when vision-based systems usually fail. In addition, radar 153 can be used to calibrate and / or sanity check other systems (such as LIDAR 150 and camera 135). Ranging measurements from radar 153 can be used to determine / measure braking distance at current speed, acceleration, maneuverability at current speed, and / or turning radius at current speed and / or maneuverability metrics at current speed. In some systems, ground penetrating radar can also be used to track the road surface via, for example, radar reflection marks on the road surface or terrain features such as ditches.

[0036] Figure 2 Illustrated is a diagram of an exemplary vehicle 200 configured with various sensor and communication components and / or systems in accordance with one embodiment of the present invention.

[0037] like Figure 2 As shown in , in one embodiment, the vehicle 200 may have camera(s), such as a camera 260 mounted on a rearview mirror, a camera mounted on a front fender (not shown), a camera mounted on a side mirror (not shown), and a rear camera (not shown, but typically on a trunk, hatch, or rear bumper). The vehicle 200 may also have a LIDAR subsystem 204 for detecting objects and measuring distances to those objects; the LIDAR subsystem 204 is typically mounted on the roof, however, if there are multiple LIDAR units 204, they may be oriented around the front, rear, and sides of the vehicle. The vehicle 200 may have various other location-related systems, such as a GNSS receiver 170 (typically located in a shark fin unit at the rear of the roof), various wireless transceivers 202 (such as WAN, WLAN, V2X; typically but not necessarily located in a shark fin)), a radar system 208 (typically in the front bumper), and a sonar 210 (typically located on both sides of the vehicle, if present). There may also be various wheel 212 and driveline sensors, such as tire pressure sensors, accelerometers, gyroscopes, and wheel rotation detection and / or counters. In one embodiment, distance measurements and relative positions determined via various sensors (such as LIDAR, radar, camera, GNSS, and sonar) may be combined with vehicle size and shape information and information about sensor locations to determine distances and relative positions between surfaces of different vehicles so that the distance or vector from a sensor to another vehicle or between two different sensors (such as two GNSS receivers) is incrementally increased to account for the location of the sensors on each vehicle. Therefore, the accurate GNSS distance and vector between two GNSS receivers will need to be modified based on the relative position of each vehicle surface to the GNSS receiver. For example, when determining the distance between the front bumper of a rear vehicle and the rear bumper of a front vehicle, the distance will need to be adjusted based on the distance between the GNSS receiver on the rear vehicle and the front bumper and the distance between the GNSS receiver of the front vehicle and the rear bumper of the front vehicle. As an example, the distance between the rear bumper of the leading vehicle and the front bumper of the trailing vehicle is the relative distance between the two GNSS receivers minus the distance from the trailing vehicle's GNSS receiver to the front bumper, minus the distance from the leading vehicle's GNSS receiver to the rear bumper. It is recognized that this list is not intended to be limiting, and Figure 2 The present invention is intended to provide exemplary locations of various sensors in an embodiment of the vehicle 200. Additionally, further details regarding specific sensors are provided with respect to Figure 1 to describe.

[0038] Figure 3A flowchart of a navigation method 300 based on a high-precision map according to an embodiment of the present invention is illustrated. In one embodiment, the method 300 may be performed by referring to Figure 4 In another embodiment, the method 300 may be implemented by any other suitable device or system.

[0039] At block 310, method 300 may include checking whether a high-precision map is available for the destination in the vehicle. For example, when approaching a destination (e.g., a parking lot, a park, or other area), the vehicle may check its onboard memory to find whether a high-precision map is available for the destination. If so, the high-precision map may be directly used to navigate to the corresponding destination.

[0040] At block 320 , method 300 may include requesting a high-precision map from a server in response to a high-precision map not being available in the vehicle for the destination.

[0041] In one embodiment, the server may be a private cloud associated with a vehicle user. Therefore, requesting a high-precision map from a server may include requesting a high-precision map that can be used for a destination from a private cloud associated with the vehicle user, wherein the private cloud hosts community vehicle data associated with the user. In this article, "community" may refer to a group consisting of other users with whom a user has a close relationship. In one example, the private cloud may include data uploaded by other users with whom the user has a close relationship (such as family members, close friends, etc.) (e.g., high-precision maps for specific destinations, etc.). If there is a high-precision map that can be used for the destination in the private cloud, the vehicle can download the high-precision map from the private cloud for navigation.

[0042] In one embodiment, the server may be a public cloud associated with the manufacturer of the vehicle. Thus, requesting a high-precision map from the server may include requesting a high-precision map that can be used for the purpose from a public cloud that hosts customer vehicle data associated with the manufacturer of the vehicle. In one example, the public cloud may include user-uploaded data associated with a vehicle of a particular manufacturer (e.g., a high-precision map for a particular destination, etc.). If other users have already uploaded relevant high-precision maps to the public cloud, the high-precision maps may be downloaded from the public cloud for use.

[0043] In one embodiment, a high-precision map available for the destination may be requested from a private cloud first. If the relevant high-precision map does not exist in the private cloud, the high-precision map may be requested from a public cloud; and vice versa.

[0044] In box 330, method 300 may include generating the high-precision map by the vehicle in response to the absence of a high-precision map available for the destination in the server. In one embodiment, if there is no high-precision map available for the destination in both the private cloud and the public cloud, the vehicle's sensors can collect environmental data and generate relevant high-precision maps based on the collected environmental data. Examples of sensors include, but are not limited to: cameras, lidars, radars, global navigation satellite system (GNSS) receivers, or ultrasonic sensors. Conventional methods for generating high-precision maps using data collected by various sensors are known to those of ordinary skill in the art and are therefore not described in detail herein.

[0045] In one embodiment, after generating a high-precision map, the vehicle may upload the generated high-precision map to a corresponding private cloud and / or public cloud via an air interface (e.g., an OTA interface) for use by other users. In another embodiment, before uploading the generated high-precision map to a corresponding private cloud and / or public cloud, the generated high-precision map may be preprocessed to comply with corresponding data protection regulations (e.g., removing information related to personal privacy, etc.).

[0046] Figure 4 Illustrated is a block diagram of an exemplary computing device 400 according to one embodiment of the present invention, which is one example of a hardware device applicable to aspects of the present invention.

[0047] refer to Figure 4 Now, a computing device 400 will be described, which is an example of a hardware device (e.g., user equipment, cloud server, vehicle control unit, etc.) that can be applied to various aspects of the present invention. The computing device 400 can be any machine that can be configured to perform processing and / or calculations, and can be but is not limited to a workstation, server, desktop computer, laptop computer, tablet computer, personal digital assistant, smart phone, vehicle computer, or any combination thereof. The various methods / devices / servers / user equipment described above can be implemented in whole or in part by the computing device 400 or similar devices or systems.

[0048] The computing device 400 may include components that may be connected or communicated via one or more interfaces and a bus 402. For example, the computing device 400 may include a bus 402, one or more processors 404, one or more input devices 406, and one or more output devices 408. The one or more processors 404 may be any type of processor and may include, but are not limited to, one or more general purpose processors and / or one or more special purpose processors (e.g., a dedicated processing chip). The input device 406 may be any type of device capable of inputting information to the computing device and may include, but are not limited to, a mouse, a keyboard, a touch screen, a microphone, and / or a remote controller. The output device 408 may be any type of device capable of presenting information and may include, but are not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The computing device 400 may also include or be connected to a non-transient storage device 410, which may be any storage device that is non-transient and capable of data storage, and may include, but is not limited to, a disk drive, an optical storage device, a solid-state memory, a floppy disk, a floppy disk, a hard disk, a tape or any other magnetic medium, an optical disk or any other optical medium, a ROM (read-only memory), a RAM (random access memory), a cache memory, and / or any memory chip or cassette, and / or any other medium from which a computer can read data, instructions, and / or code. The non-transient storage device 410 may be detachable from the interface. The non-transient storage device 410 may have data / instructions / code for implementing the above methods and steps. The computing device 400 may also include a communication device 412. The communication device 412 can be any type of device or system that can communicate with internal devices and / or with a network and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication device and / or a chipset, such as a Bluetooth device, an IEEE 1302.11 device, a WiFi device, a WiMax device, a cellular communication device and / or the like.

[0049] When the computing device 400 is used as an in-vehicle device, it can also be connected to external devices (e.g., a GPS receiver, sensors for sensing different environmental data (such as an accelerometer, a wheel speed sensor, a gyroscope, etc.)). In this way, the computing device 400 can, for example, receive positioning data and sensor data indicating the driving conditions of the vehicle. When the computing device 400 is used as an in-vehicle device, it can also be connected to other devices for controlling the driving and operation of the vehicle (e.g., an engine system, a wiper, an anti-lock brake system, a light control system, etc.).

[0050] In addition, the non-transitory storage device 410 may have map information and software components so that the processor 404 can implement route guidance processing. In addition, the output device 406 may include a display for displaying a map, displaying a positioning mark of the vehicle, and displaying an image indicating the driving status of the vehicle. The output device 406 may also include a speaker or a headphone interface for audio guidance.

[0051] The bus 402 may include, but is not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus. In particular, for in-vehicle devices, the bus 402 may also include a Controller Area Network (CAN) bus or other structures designed for automotive applications.

[0052] The computing device 400 may also include a working memory 414 , which may be any type of working memory capable of storing instructions and / or data that facilitate the operation of the processor 404 and may include, but is not limited to, a random access memory and / or a read-only storage device.

[0053] Software components may be located in the working memory 414, including but not limited to an operating system 416, one or more application programs 418, drivers and / or other data and codes. Instructions for implementing the above methods and steps may be included in the one or more application programs 418, and the modules / units / components of the aforementioned various devices / servers / user equipment may be implemented by the processor 404 reading and executing the instructions of the one or more application programs 418.

[0054] It should also be recognized that changes may be made according to specific needs. For example, custom hardware may also be used, and / or specific components may be implemented in hardware, software, firmware, middleware, microcode, hardware description language, or any combination thereof. In addition, connections to other computing devices, such as network input / output devices, etc. may be employed. For example, programming hardware (e.g., programmable logic circuits including field programmable gate arrays (FPGAs) and / or programmable logic arrays (PLAs)) with assembly language or hardware programming languages ​​(e.g., VERILOG, VHDL, C++) may be used to implement part or all of the disclosed methods and devices using logic and algorithms according to the present invention.

[0055] Although various aspects of the present invention have been described so far with reference to the accompanying drawings, the above-described methods, systems and devices are merely examples, and the scope of the present invention is not limited to these aspects, but is limited only by the appended claims and their equivalents. Various components may be omitted or replaced by equivalent components. In addition, the steps may be implemented in an order different from the order described in the present invention. In addition, various components may be combined in various ways. It is also important that, as technology develops, many of the components described may be replaced by equivalent components that appear later.

[0056] List of reference numerals:

[0057] 100 Vehicles

[0058] 110 processors

[0059] 120DSP

[0060] 130(s) wireless transceiver

[0061] 135 Camera

[0062] 140Accelerometer, gyroscope and magnetometer

[0063] 145 Other sensors (speed sensor / speedometer, distance / odometer, weather, light, rain sensor, tire pressure sensor, tire traction sensor (anti-lock and / or traction control), etc.)

[0064] 150LIDAR

[0065] 153 Radar

[0066] 155 systems (brakes, actuators, throttle control, steering and electrical, etc.)

[0067] 160 Memory

[0068] 170GNSS transceiver

[0069] 175 Power and drive systems (generators, batteries, transmissions, engines) and related systems

[0070] 200 vehicles

[0071] 202GNSS wireless receiver, wireless communication transceiver

[0072] 204LIDAR

[0073] 206 Camera

[0074] 208 Radar

[0075] 210 Sonar Sensor

[0076] 212 Wheel Sensor

[0077] 300 Methods

[0078] 310 Check if there is a high-precision map available for the destination in the vehicle

[0079] 320 In response to the vehicle not having a high-precision map available for the destination, requesting the server for the high-precision map

[0080] 330 In response to the high-precision map not existing in the server, the vehicle generates the high-precision map

[0081] 400 computing devices

[0082] 402 Bus

[0083] 404 Handler

[0084] 406 Input Device

[0085] 408 Output Device

[0086] 410 Storage Devices

[0087] 412 Communication equipment

[0088] 414 Working memory

[0089] 416 Operating System

[0090] 418 Applications

Claims

1. A navigation method based on a high-precision map, comprising: Check if there is a high-definition map available for the destination in the vehicle; In response to the vehicle not having a high-precision map available for the destination, requesting the high-precision map from a server; as well as In response to the high-precision map not existing in the server, the high-precision map is generated by the vehicle.

2. The method of claim 1, wherein requesting the high-precision map from the server comprises: The high-precision map is requested from a private cloud associated with a user of the vehicle, wherein the private cloud hosts community vehicle data associated with the user.

3. The method of claim 1, wherein requesting the high-precision map from the server comprises: The high-precision map is requested from a public cloud associated with the vehicle, wherein the public cloud hosts customer vehicle data associated with a manufacturer of the vehicle.

4. The method of claim 1, wherein generating the high-precision map by the vehicle comprises: using sensors of the vehicle to collect data; as well as The high-precision map is generated based on the collected data.

5. The method of claim 4, wherein the sensor comprises one or more of: a camera, a lidar, a radar, a global navigation satellite system (GNSS) receiver, or an ultrasonic sensor.

6. The method of claim 1, further comprising: After the high-precision map is generated, the high-precision map is uploaded to the server via an air interface.

7. The method of claim 6, further comprising: Before uploading the high-precision map, the high-precision map is preprocessed to comply with data protection regulations.

8. The method as claimed in claim 1, wherein the high-precision map is a high-precision map for a parking lot.

9. A navigation system based on a high-precision map, comprising: Memory; as well as a processor coupled to the memory, the processor being configured to: Check if there is a high-definition map available for the destination in the vehicle; In response to the vehicle not having a high-precision map available for the destination, requesting the high-precision map from a server; as well as In response to the high-precision map not existing in the server, the high-precision map is generated.

10. The navigation system of claim 9, wherein requesting the high-precision map from the server comprises: The high-precision map is requested from a private cloud associated with a user of the vehicle, wherein the private cloud hosts community vehicle data associated with the user.

11. The navigation system of claim 9, wherein requesting the high-precision map from the server comprises: The high-precision map is requested from a public cloud associated with the vehicle, wherein the public cloud hosts customer vehicle data associated with a manufacturer of the vehicle.

12. The navigation system of claim 9, wherein the processor is further configured to: using sensors of the vehicle to collect data; and The high-precision map is generated based on the collected data.

13. The navigation system of claim 9, wherein the processor is further configured to: After the high-precision map is generated, the high-precision map is uploaded to the server via an air interface.

14. The navigation system of claim 13, wherein the processor is further configured to: Before uploading the high-precision map, the high-precision map is preprocessed to comply with data protection regulations.

15. A vehicle comprising the navigation system according to any one of claims 9 to 14.

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