Map generation method and device and medium

Abstract

The invention relates to a map generation method and device and a medium. The map generation method comprises the following steps: acquiring road data, wherein the road data comprises first road data conforming to intersection characteristics and second road data conforming to road characteristics between intersections; target data used for generating a map are extracted from the road data, and the target data comprise the first road data and do not comprise the second road data; and generating the map according to the target data. According to the method, data redundancy is reduced, the map is light in weight and high in expansibility, and navigation errors caused by untimely map data updating can be reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of autonomous driving technology, and in particular to a map generation method, device and medium. Background Art

[0002] With the advancement of science and technology, autonomous driving technology is developing rapidly. Autonomous driving vehicles can use maps to drive autonomously. The current high-precision maps include various detailed data on the road, such as lane data and turn sign data. On the other hand, during the driving process, the vehicle can also use its installed sensing equipment to obtain the surrounding road data in real time to provide a basis for the vehicle's driving. Summary of the invention

[0003] In order to overcome the problems existing in the related art, the present disclosure provides a map generation method, device and medium.

[0004] According to a first aspect of an embodiment of the present disclosure, a map generation method is provided, comprising:

[0005] Acquire road data, the road data comprising first road data conforming to the characteristics of the intersection and second road data conforming to the characteristics of the road between intersections;

[0006] extracting target data for generating a map from the road data, the target data including the first road data and excluding the second road data;

[0007] The map is generated according to the target data.

[0008] Optionally, the first road data includes the location of an intersection and feature data within a predetermined range around the intersection;

[0009] The second road data includes feature data outside a predetermined range around the intersection.

[0010] Optionally, extracting target data for generating a map from the road data includes:

[0011] Determine the location of intersections in the road;

[0012] The first road data is extracted from the road data according to the position of the intersection.

[0013] Optionally, determining the location of an intersection on a road includes:

[0014] A position on the road where there is a stop line and the lane line is disconnected is determined as the position of the intersection.

[0015] Optionally, the first road data includes the location of a fork and feature data within a predetermined range around the fork;

[0016] The second road data includes feature data outside a predetermined range around the fork.

[0017] Optionally, the feature data includes lane line position, road edge position and road centerline position.

[0018] Optionally, extracting target data for generating a map from the road data includes:

[0019] Determine the location of forks in the road;

[0020] The first road data is extracted from the road data according to the position of the fork.

[0021] Optionally, determining the location of a fork in the road includes:

[0022] In the case where one lane of the road is divided into two lanes, the turning start position of the vehicle's driving trajectory is determined as the position of the fork.

[0023] Optionally, the first road data includes a location of an intersection and ground markings within a predetermined range around the intersection, and generating the map according to the target data includes:

[0024] Determine the connection relationship between lanes within the predetermined range around the intersection according to ground markings within the predetermined range around the intersection;

[0025] Based on the determined connection relationship, a virtual lane is generated in the map.

[0026] According to a second aspect of an embodiment of the present disclosure, a map generating device is provided, the device comprising:

[0027] An acquisition module is configured to acquire road data, wherein the road data includes first road data that conforms to the characteristics of an intersection and second road data that conforms to the characteristics of a road between intersections;

[0028] an extraction module, configured to extract target data for generating a map from the road data, wherein the target data includes the first road data and does not include the second road data;

[0029] A generating module is configured to generate the map according to the target data.

[0030] According to a third aspect of an embodiment of the present disclosure, there is provided a map generating device, including:

[0031] A second processor;

[0032] a second memory for storing instructions executable by the second processor;

[0033] Wherein, the second processor is configured as:

[0034] The steps for implementing the map generation method provided in the first aspect of the present disclosure are as follows.

[0035] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by a first processor, the steps of the map generating method provided in the first aspect of the present disclosure are implemented.

[0036] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0037] Obtain road data, the road data includes first road data that meets the characteristics of the intersection and second road data that meets the characteristics of the road between intersections; extract target data for generating a map from the road data, the target data includes the first road data and does not include the second road data; generate a map based on the target data. That is, data that meets the characteristics of the intersection is screened out from the road data to generate a map, and data that meets the characteristics of the road between intersections is not marked in the map. Compared with the high-precision map in the related art, the data that meets the characteristics of the road between intersections is removed in the map generated according to the method disclosed in the present invention. Since the road characteristics between intersections are usually more obvious, the state of the vehicle changes less when driving between intersections. Therefore, this part of the data is obtained in real time during the driving process of the vehicle through the sensing device installed on the vehicle itself, which can also meet the needs of navigation. This method not only reduces the redundancy of data, making the map lightweight and highly scalable, but also reduces navigation errors caused by untimely updates of map data.

[0038] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0040] Figure 1 The figure is a flowchart of a map generating method according to an exemplary embodiment.

[0041] Figure 2 is a schematic diagram of an intersection according to an exemplary embodiment.

[0042] Figure 3 is a schematic diagram of a bifurcation according to an exemplary embodiment.

[0043] Figure 4The diagram is a schematic diagram showing a method of constructing a virtual lane according to an exemplary embodiment.

[0044] Figure 5 The figure is a block diagram of a map generating device according to an exemplary embodiment.

[0045] Figure 6 It is a block diagram of a device for map generation according to an exemplary embodiment. DETAILED DESCRIPTION

[0046] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0047] It should be noted that all actions of acquiring signals, information or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where they are located, and with the authorization given by the owner of the corresponding device.

[0048] In the related art, "high-precision" maps include various detailed data of roads, such as lane lines, stop lines, and ground markings. During driving, the vehicle itself can also obtain road data within a predetermined range around it. In this way, during the driving of an autonomous driving vehicle, there is a certain redundancy between the road data obtained by the vehicle in real time and the road data included in the map, and sometimes there are some errors in the map data due to untimely updates. Therefore, the inventors thought of generating a map based on data that meets the characteristics of the intersection, and the data that meets the characteristics of the road between intersections is obtained by the vehicle in real time, thereby reducing data redundancy, making the map lightweight and highly scalable, and reducing errors in map data. The present disclosure provides a practical and feasible lightweight map construction method.

[0049] Figure 1 FIG. 1 is a flow chart of a map generation method according to an exemplary embodiment. Figure 1 As shown, the method includes the following steps.

[0050] In step S101, road data is acquired, where the road data includes first road data that conforms to the characteristics of an intersection and second road data that conforms to the characteristics of a road between intersections.

[0051] In step S102, target data for generating a map is extracted from the road data, the target data including the first road data and excluding the second road data.

[0052] In step S103, a map is generated according to the target data.

[0053] The vehicle can be arranged to actually travel on the road in advance, and during the travel, the perception device (e.g., image acquisition device, etc.) carried in the vehicle can be used to record the road data. For example, when the vehicle travels to an intersection, the image acquisition device can be used to record the image of the intersection passed, and the image acquired by the image acquisition device can be processed to obtain the road data.

[0054] The intersection feature is a road feature related to the intersection. The first road data that meets the intersection feature may include, for example, lane lines and stop lines near the intersection. The road feature between intersections is a road feature on a road between two adjacent intersections. The second road data that meets the road feature between intersections may include, for example, lane lines and road edges between two adjacent intersections. The road data may also include other data in addition to the first road data and the second road data, such as specific ground markings.

[0055] Since the road conditions between intersections are relatively uncomplicated, the sensing equipment carried by the vehicle has a larger sensing range. The road data acquired by the sensing equipment in real time can usually meet the needs of driving between intersections, and can assist the vehicle in performing autonomous driving operations such as lane keeping and assisted lane change. After acquiring the road data, this solution extracts the first road data from it and discards the second road data to generate a map. In this way, when the autonomous driving vehicle uses the generated map for navigation, when driving near an intersection, it can drive according to the first road data recorded in the map; when driving between intersections, it can drive according to the second road data acquired by the vehicle in real time.

[0056] Through the above technical solution, road data is obtained, the road data includes first road data that meets the characteristics of the intersection and second road data that meets the characteristics of the road between intersections, and target data for generating a map is extracted from the road data, the target data includes the first road data, and does not include the second road data, and the map is generated according to the target data. That is, the data that meets the characteristics of the intersection is screened out from the road data to generate a map, and the data that meets the characteristics of the road between intersections is not marked in the map. Compared with the high-precision map in the related art, the data that meets the characteristics of the road between intersections is removed in the map generated according to the method disclosed in the present invention. Since the road characteristics between intersections are usually more obvious, the state change of the vehicle when driving between intersections is small. Therefore, this part of the data is obtained in real time during the driving process of the vehicle through the sensing device installed on the vehicle itself, which can also meet the needs of navigation. This method not only reduces the redundancy of data, makes the map lightweight and highly scalable, but also reduces navigation errors caused by untimely updating of map data.

[0057] In yet another embodiment, the first road data includes the location of the intersection and feature data within a predetermined range around the intersection; and the second road data includes feature data outside the predetermined range around the intersection.

[0058] The road conditions around the intersection are complex, and there are many types of road data, which can be pre-stored in the map. The first road data that meets the characteristics of the intersection may include the location of the intersection and the characteristic data within a predetermined range around the intersection. The characteristic data within the predetermined range around the intersection may include lane lines within the predetermined range around the intersection. For example, if the predetermined range is 100 meters, the first road data may include lane lines within 100 meters around the intersection.

[0059] The feature data outside the predetermined range around the intersection may include the lane line between two adjacent intersections. For example, if the predetermined range is 100 meters and the distance between the two intersections is 500 meters, then the second road data may include the lane line in the middle 300 meters.

[0060] In this embodiment, the location of the intersection and feature data within a predetermined range around the intersection are set in the map, while feature data outside the predetermined range around the intersection are not set in the map, thereby ensuring driving safety while building a lightweight map.

[0061] In yet another embodiment, the step of extracting target data for generating a map from road data includes:

[0062] Determine the location of intersections in the road;

[0063] The first road data is extracted from the road data according to the position of the intersection.

[0064] Intersections are important nodes on the road. After acquiring road data, the location of the intersection on the road is first determined, and then the target data for generating the map is extracted from the road data based on the determined location of the intersection, that is, the feature data within a predetermined range around the intersection is extracted.

[0065] A variety of methods may be used to determine the location of an intersection on a road. For example, a location with a stop line on the road may be determined as the location of the intersection.

[0066] In this embodiment, target data can be extracted from road data reliably and quickly according to the location of the intersection.

[0067] In yet another embodiment, the determining the location of the intersection in the road includes:

[0068] A location where there is a stop line on the road and the lane lines are disconnected is determined as the location of the intersection.

[0069] Figure 2is a schematic diagram of an intersection according to an exemplary embodiment. Figure 2 As shown, vehicles at the intersection can choose from multiple driving directions, and different driving directions correspond to different lanes, and the lane lines at the intersection are disconnected. In addition, since vehicles in all directions of the intersection need to pass alternately, a stop line is set at the intersection. When the traffic light at the intersection ahead of the vehicle turns red, the vehicle needs to wait behind the stop line.

[0070] The stop line is located at a more obvious position on the road ahead of the vehicle, and can be reliably detected from the image acquired by the vehicle-mounted camera. Whether the lane line is disconnected can also be determined by image processing of the image acquired by the vehicle-mounted camera. When the stop line and the lane line disconnection are detected, the position of the vehicle can be determined as the position of the intersection. In this embodiment, the position where there is a stop line on the road and the lane line is disconnected is determined as the intersection position, and the method is simple and the processing speed is fast.

[0071] In another embodiment, the first road data includes the location of the fork and feature data within a predetermined range around the fork; the second road data includes feature data outside the predetermined range around the fork.

[0072] The road conditions at the fork and around the fork are also more complicated. The first road data that meets the characteristics of the intersection may include the location of the fork, and the characteristic data within a predetermined range around the fork. The characteristic data within a predetermined range around the fork may include lane lines within a predetermined range around the fork, for example, the predetermined range is within 60 meters, then the first road data includes lane lines within 60 meters around the fork.

[0073] The feature data outside the predetermined range around the fork may include lane lines between two adjacent forks that are not within the predetermined range around any fork. For example, if the predetermined range is within 60 meters and the distance between the two intersections is 400 meters, then the second road data includes the middle 280 meters of lane lines.

[0074] In this embodiment, the location of the fork and the feature data within a predetermined range around the fork are set in the map, while the feature data outside the predetermined range around the fork are not set in the map, thereby ensuring driving safety while building a lightweight map.

[0075] In yet another embodiment, the above-mentioned feature data includes lane line position, road edge position and road centerline position.

[0076] The position of lane lines is used to distinguish the lanes in which vehicles are traveling, the position of road edges is used to assist vehicles in lane changes and vehicle positioning, and the position of road center lines is used to separate opposite lanes.

[0077] In this embodiment, by acquiring the lane line position, the road edge position and the road centerline position, lanes within a predetermined range around the intersection can be reliably generated in the map, thereby generating an accurate map.

[0078] In yet another embodiment, the step of extracting target data for generating a map from road data includes:

[0079] Determine the location of forks in the road;

[0080] The first road data is extracted from the road data according to the position of the fork.

[0081] The fork is also an important node in the road. After acquiring the road data, the position of the fork in the road is determined. Secondly, the first road data for generating the map is extracted from the road data according to the determined position of the fork, that is, the feature data within a predetermined range around the fork is extracted.

[0082] There are many methods that can be used to determine the location of a fork in the road. For example, a neural network model can be obtained by training a large number of ground images obtained at the fork, and the ground images obtained when recording road data can be input into the trained neural network model to obtain the location of the fork.

[0083] In this embodiment, target data can be extracted from road data reliably and quickly according to the location of the fork.

[0084] In another embodiment, the first road data may include the location of the intersection and feature data within a predetermined range around the intersection, the location of the fork and feature data within a predetermined range around the fork; the second road data may include feature data outside the predetermined range around the intersection and feature data outside the predetermined range around the fork. After acquiring the road data, the location of the intersection and the location of the fork in the road are determined, and based on the determined location of the intersection and the location of the fork, feature data that fits within the predetermined range around the intersection and feature data that fits within the predetermined range around the fork are extracted from the road data as target data for generating a map.

[0085] In yet another embodiment, the above-mentioned determining the location of the fork in the road includes:

[0086] In the case where one lane of the road is divided into two lanes, the turning start position of the vehicle's driving trajectory is determined as the position of the fork.

[0087] Figure 3 FIG. 1 is a schematic diagram of a bifurcation according to an exemplary embodiment. Figure 3As shown, in the case where a lane on the road is divided into two lanes, since there is no clear starting position of the fork, if the vehicle travels to the corresponding lane in a turning posture during the process of acquiring road data, the turning starting position of the driving trajectory can be determined as the fork.

[0088] During the process of driving and entering data on the spot, the vehicle may pass through the same fork multiple times, passing through a different road at the fork each time.

[0089] Specifically, based on the data recording of the vehicle's driving trajectory and speed at the same fork multiple times, the situation where one lane is divided into two lanes can be determined. The situation where the vehicle changes lanes normally can be excluded by the angle between the driving directions of the vehicle after the lane change, the duration of the angle, and the speed. For example, if the angle between the driving directions of the vehicle after the lane change is greater than the angle threshold (e.g., 5°), the duration of the angle is greater than the duration threshold (e.g., 1min), and the speed is greater than the speed threshold (e.g., 40km / h), it is determined that one lane is divided into two lanes, that is, the vehicle has passed the fork.

[0090] In the process of a vehicle driving through a fork, the turning starting position of the driving track can be directly determined according to the vehicle driving track. For example, the original driving track is a straight line, which is later changed into a curve, and the tangent point of the curve and the straight line is used as the turning starting position of the vehicle driving track. Or, in the process of a vehicle driving through a fork, the turning starting position of the driving track can be determined according to the turning angle of the vehicle steering wheel. For example, the original steering angle of the steering wheel is zero, which is later changed to 5° and lasts for more than 30s when the vehicle speed is greater than 20km / h, then the position of the vehicle when the steering wheel begins to change is used as the turning starting position of the vehicle driving track. In another embodiment, there is no stop line at the determined fork position and the center line of the road is continuous. In this embodiment, when there is a lane in the road that separates to two lanes, the turning starting position of the vehicle driving track is determined as the fork, and the method is simple and reliable.

[0091] In yet another embodiment, the first road data includes the location of an intersection and ground markings within a predetermined range around the intersection.

[0092] Generating a map according to target data includes: determining a connection relationship between lanes within a predetermined range around the intersection according to ground marks within a predetermined range around the intersection; and generating a virtual lane in the map according to the determined connection relationship.

[0093] Figure 4 FIG. 1 is a schematic diagram showing a method of constructing a virtual lane according to an exemplary embodiment. Figure 4As shown, each lane in each direction within the predetermined range around the intersection has a ground mark. The ground mark is used to indicate the driving direction of the vehicle in the lane. The connection relationship between the lanes within the predetermined range around the intersection can be determined based on the ground mark within the predetermined range around the intersection. For example, if the ground mark of the first lane A1 is a straight-ahead mark, then the first lane A1 is connected to the second lane C1 corresponding to the straight-ahead; if the ground mark of the third lane A2 is a right-turn mark, then the third lane A2 is connected to the fourth lane D2 corresponding to the right-turn.

[0094] After the connection relationship of each lane is determined, a virtual lane can be generated in the map according to the determined connection relationship. For example, if the determined connection relationship is that the fifth lane D3 is connected to the sixth lane C2, a virtual lane is generated between the fifth lane D3 and the sixth lane C2 in the map; if the determined connection relationship is that the seventh lane C3 is connected to the eighth lane B3, a virtual lane is generated between the seventh lane C3 and the eighth lane B3 in the map; if the determined connection relationship is that the first lane A1 is connected to the second lane C1, a virtual lane is generated between the first lane A1 and the second lane C1 in the map; if the determined connection relationship is that the third lane A2 is connected to the fourth lane D2, a virtual lane is generated between the third lane A2 and the fourth lane D2 in the map.

[0095] In this embodiment, the connection relationship between lanes within a predetermined range around the intersection is determined based on ground markings within a predetermined range around the intersection, and virtual lanes can be reliably and accurately generated in the map based on the determined connection relationship.

[0096] Based on the same inventive concept, the present disclosure also provides a map generating device. Figure 5 FIG. 1 is a block diagram of a map generating device according to an exemplary embodiment. Figure 5 As shown, the map generating device 400 includes an acquisition module 401 , an extraction module 402 and a generation module 403 .

[0097] The acquisition module 401 is configured to acquire road data, the road data including first road data conforming to the characteristics of the intersection and second road data conforming to the characteristics of the road between intersections;

[0098] The extraction module 402 is configured to extract target data for generating a map from the road data, wherein the target data includes the first road data and does not include the second road data;

[0099] The generating module 403 is configured to generate a map according to the target data.

[0100] Optionally, the first road data includes the location of the intersection and feature data within a predetermined range around the intersection;

[0101] The second road data includes characteristic data outside a predetermined range around the intersection.

[0102] Optionally, the extraction module 402 includes a first determination submodule and a first extraction submodule.

[0103] The first determination submodule is configured to determine a location of an intersection in the road.

[0104] The first extraction submodule is configured to extract first road data from the road data according to the location of the intersection.

[0105] Optionally, the first determining submodule is further configured to:

[0106] A location on the road where there is a stop line and the lane lines are disconnected is determined as an intersection.

[0107] Optionally, the first road data includes the location of the fork and feature data within a predetermined range around the fork;

[0108] The second road data includes characteristic data outside a predetermined range around the fork.

[0109] Optionally, the feature data includes lane line positions, road edge positions and road centerline positions.

[0110] Optionally, the extraction module 402 further includes a second determination submodule and a second extraction submodule.

[0111] The second determination submodule is configured to determine a location of a fork in the road.

[0112] The second extraction submodule is configured to extract first road data from the road data according to the position of the fork.

[0113] Optionally, the second determining submodule is further configured to:

[0114] In the case where one lane of the road is divided into two lanes, the turning start position of the vehicle's driving trajectory is determined as the position of the fork.

[0115] Optionally, the first road data includes the location of the intersection and ground markings within a predetermined range around the intersection, and the generating module 403 includes a third determining submodule and a generating submodule.

[0116] The third determining submodule is configured to determine the connection relationship between lanes within a predetermined range around the intersection according to ground signs within a predetermined range around the intersection.

[0117] The generating submodule is configured to generate a virtual lane in the map according to the determined connection relationship.

[0118] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0119] The above technical solution is adopted to obtain road data, which includes first road data that meets the characteristics of the intersection and second road data that meets the characteristics of the road between intersections. Target data for generating a map is extracted from the road data, and the target data includes the first road data and does not include the second road data. The map is generated according to the target data. That is, data that meets the characteristics of the intersection is screened out from the road data to generate a map, and data that meets the characteristics of the road between intersections is not marked in the map. Compared with the high-precision map in the related art, the data that meets the characteristics of the road between intersections is removed in the map generated according to the method disclosed in the present invention. Since the road characteristics between intersections are usually more obvious, the state change of the vehicle when driving between intersections is small. Therefore, this part of the data is obtained in real time during the driving process of the vehicle through the sensing device installed on the vehicle itself, which can also meet the needs of navigation. This method not only reduces the redundancy of data, makes the map lightweight and highly scalable, but also reduces navigation errors caused by untimely updating of map data.

[0120] The present disclosure also provides a map generating device, comprising:

[0121] A second processor;

[0122] a second memory for storing second processor executable instructions;

[0123] Wherein, the second processor is configured as:

[0124] The steps for implementing the map generation method provided by the present disclosure are as follows.

[0125] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, and when the program instructions are executed by a first processor, the steps of the map generating method provided by the present disclosure are implemented.

[0126] Figure 6 800 is a block diagram of a device for map generation according to an exemplary embodiment. For example, the device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0127] Reference Figure 6 , the device 800 may include one or more of the following components: a processing component 802 , a second memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output interface 812 , a sensor component 814 , and a communication component 816 .

[0128] The processing component 802 generally controls the overall operation of the device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more second processors 820 to execute instructions to complete all or part of the steps of the above-mentioned map generation method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0129] The second memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the device 800, contact data, phone book data, messages, pictures, videos, etc. The second memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0130] The power supply component 806 provides power to the various components of the device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 800.

[0131] The multimedia component 808 includes a screen that provides an output interface between the device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0132] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the device 800 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the second memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0133] The input / output interface 812 provides an interface between the processing component 802 and the peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0134] The sensor assembly 814 includes one or more sensors for providing various aspects of the status assessment of the device 800. For example, the sensor assembly 814 can detect the open / closed state of the device 800, the relative positioning of components, such as the display and keypad of the device 800, and the sensor assembly 814 can also detect the position change of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, the orientation or acceleration / deceleration of the device 800, and the temperature change of the device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0135] The communication component 816 is configured to facilitate wired or wireless communication between the device 800 and other devices. The device 800 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0136] In an exemplary embodiment, the device 800 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above-mentioned map generation method.

[0137] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a second memory 804 including instructions, and the instructions can be executed by the second processor 820 of the device 800 to complete the above-mentioned map generation method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0138] In addition to being an independent electronic device, the above-mentioned device can also be a part of an independent electronic device. For example, in one embodiment, the device can be an integrated circuit (IC) or a chip, wherein the integrated circuit can be one IC or a collection of multiple ICs; the chip can include but is not limited to the following types: GPU (Graphics Processing Unit), CPU (Central Processing Unit), FPGA (Field Programmable Gate Array), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), SOC (System on Chip, SoC), etc. The above-mentioned integrated circuit or chip can be used to execute executable instructions (or codes) to implement the above-mentioned map generation method. The executable instructions can be stored in the integrated circuit or chip, or can be obtained from other devices or equipment, for example, the integrated circuit or chip includes a processor, a memory, and an interface for communicating with other devices. The executable instructions may be stored in the memory, and when the executable instructions are executed by the processor, the above-mentioned map generation method is implemented; alternatively, the integrated circuit or chip may receive the executable instructions through the interface and transmit them to the processor for execution, so as to implement the above-mentioned map generation method.

[0139] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the present disclosure. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0140] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A map generation method, characterized in that: include: Acquire road data, the road data comprising first road data conforming to the characteristics of the intersection and second road data conforming to the characteristics of the road between intersections; extracting target data for generating a map from the road data, the target data including the first road data and excluding the second road data; The map is generated according to the target data.

2. The method according to claim 1, characterized in that The first road data includes the location of the intersection and feature data within a predetermined range around the intersection; The second road data includes feature data outside a predetermined range around the intersection.

3. The method according to claim 2, characterized in that The step of extracting target data for generating a map from the road data comprises: Determine the location of intersections in the road; The first road data is extracted from the road data according to the position of the intersection.

4. The method according to claim 3, characterized in that Determining the location of the intersection in the road includes: A position on the road where there is a stop line and the lane line is disconnected is determined as the position of the intersection.

5. The method according to claim 1, characterized in that The first road data includes the location of the fork and feature data within a predetermined range around the fork; The second road data includes feature data outside a predetermined range around the fork.

6. The method according to claim 2 or 5, characterized in that: The feature data includes lane line position, road edge position and road center line position.

7. The method according to claim 5, characterized in that The step of extracting target data for generating a map from the road data comprises: Determine the location of forks in the road; The first road data is extracted from the road data according to the position of the fork.

8. The method according to claim 7, characterized in that Determining the location of the fork in the road includes: In the case where one lane of the road is divided into two lanes, the turning start position of the vehicle's driving trajectory is determined as the position of the fork.

9. The method according to claim 1, characterized in that: The first road data includes the location of an intersection and ground markings within a predetermined range around the intersection, and generating the map according to the target data includes: Determine the connection relationship between lanes within the predetermined range around the intersection according to ground markings within the predetermined range around the intersection; Based on the determined connection relationship, a virtual lane is generated in the map.

10. A map generating device, characterized in that: The device comprises: An acquisition module is configured to acquire road data, wherein the road data includes first road data that conforms to the characteristics of an intersection and second road data that conforms to the characteristics of a road between intersections; an extraction module, configured to extract target data for generating a map from the road data, wherein the target data includes the first road data and does not include the second road data; A generating module is configured to generate the map according to the target data.

11. A map generating device, characterized in that: include: A second processor; a second memory for storing instructions executable by the second processor; Wherein, the second processor is configured as follows: Implement the steps of the method according to any one of claims 1 to 9.

12. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the program instructions are executed by the first processor, the steps of the method described in any one of claims 1 to 9 are implemented.

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