TWI678515B - Dynamic map data classification device and method - Google Patents

Abstract

The invention discloses a dynamic map data classification device and a method thereof. The dynamic map data classification device is provided in a vehicle and includes at least an automatic driving assistance system, a wireless communication interface, a memory, a satellite positioning module and a processor. The wireless communication interface is wirelessly connected to a cloud server. The cloud server stores high-resolution map (HD Map) and three-dimensional point cloud map information. The satellite positioning module obtains the position coordinates of the vehicle on an electronic map. The storage stores at least one of at least one road curvature and at least one intersection characteristic of a road environment of a predetermined travel path of the vehicle. The processor classifies the map data to be downloaded according to at least one of the curvature of the road and the characteristics of the intersection and the degree of automatic driving assistance of the automatic driving assistance system, thereby reducing the download time of map information.

Description

Dynamic map data classification device and method

The present invention relates to a classification technology, and more particularly to a dynamic image data classification device and method.

For self-driving cars, driving safety is very important. Traditional digital navigation graphics can no longer meet the needs of self-driving cars. Therefore, it is necessary to rely on high-resolution electronic maps to obtain road environment graphics and locate themselves on the road. For the location of the vehicle, if you want to ensure driving safety, 3D point cloud image data is also necessary.

The role of high-resolution electronic maps is to provide road information to self-driving cars, such as lane lines, signs, road curvature, etc., and to provide roadside characteristics of self-driving cars to estimate vehicle positions. However, the disadvantage of high-resolution maps is that you must use other positioning technologies to accurately estimate the vehicle position. For example, the 3D point cloud map data needs to be coordinated with Simultaneous Localization and Mapping (SLAM) technology to locate the current vehicle position, so as to achieve the purpose of simultaneous positioning and map construction. However, although high-resolution electronic maps and 3D point cloud map data can provide rich and accurate road environment information, the relative amount of data is huge. Limited by the current 4G network transmission speed is still limited and unstable, so it costs more Long download time, but for self-driving cars, a small time delay may cause the system to misjudge and cause a very serious vehicle accident.

Therefore, the present invention is directed to the above-mentioned problems, and proposes a dynamic image data classification device and method to solve the problems caused by the knowledge.

The main object of the present invention is to provide a dynamic map data classification device and method. Because the amount of data of high-resolution maps and 3D point cloud map information is huge, the storage space of the dynamic map data classification device installed on the vehicle side cannot cover all the map data. , So you need to put the data on a cloud server and provide self-driving downloads. Therefore, according to at least one of the curvature of the road environment and the characteristics of the intersection and the degree of automated driving assistance of the automated driving assistance system, the map information to be downloaded is classified and a download request is made to the cloud based on the location coordinates and speed information of the vehicle Server to download map information of the area containing the road environment. Accordingly, for a relatively simple road environment, the download amount of area map information can be reduced. For more complicated road environments, you can download regional map information with a higher amount of data in advance.

To achieve the above object, the present invention provides a dynamic map data classification device, which is disposed in a vehicle and includes at least an automatic driving assistance system, a wireless communication interface, a memory, a satellite positioning module, and a processor. The satellite positioning module obtains the position coordinates of the vehicle on an electronic map. The processor is electrically connected to the satellite positioning module. The processor plans a predetermined driving route on the electronic map according to the vehicle's driving destination and location coordinates. The vehicle travels on the predetermined driving route. The road environment of the predetermined driving route includes at least one road curvature. And at least one of the features of the intersection. The autonomous driving assistance system is electrically connected to the processor. The wireless communication interface is electrically connected to the processor and is wirelessly connected to a cloud server through a wireless network. The cloud server stores high-resolution map (HD Map) and three-dimensional point cloud map information. The storage is electrically connected to the processor, and the storage stores an electronic map and at least one of at least one road curvature and at least one intersection feature. Before the processor uses the satellite positioning module to find that the vehicle has reached a road environment that includes at least one of at least one road curvature and at least one intersection feature, the processor is based on at least one of the at least one road curvature and at least one intersection feature, and The degree of automated driving assistance of the automatic driving assistance system, classifies high-resolution maps and 3D point cloud map information through wireless communication interfaces, and finds regional map information corresponding to the road environment from high-resolution maps or 3D point cloud map information for download .

The present invention also provides a method for classifying dynamic map data. First, a vehicle's location coordinates and travel destination are used to plan a predetermined travel route on an electronic map. The vehicle travels on the predetermined travel route. The road environment of the predetermined travel route includes at least At least one of a road curvature and at least one intersection characteristic. Then, at least one of at least one road curvature and at least one intersection characteristic is stored. Before the vehicle reaches a road environment that includes at least one of at least one road curvature and at least one intersection feature, based on at least one of the at least one road curvature and at least one intersection feature, and at least one autonomous driving assistance provided in the vehicle The system's degree of automated driving assistance classifies the high-resolution map and 3D point cloud map information stored in the cloud server, and finds the area map information corresponding to the road environment from the high-resolution map or 3D point cloud map information for download .

In order to make the reviewers of the Guigui have a better understanding and understanding of the structural features of the present invention and the effects achieved, I would like to refer to the preferred embodiment diagram and the detailed description, as described below:

Embodiments of the present invention will be further explained by cooperating with related drawings below. Wherever possible, in the drawings and the description, the same reference numerals represent the same or similar components. In the drawings, shapes and thicknesses may be exaggerated based on simplification and convenient labeling. It can be understood that elements not specifically shown in the drawings or described in the description have the forms known to those skilled in the art in the art. Those skilled in the art can make various changes and modifications according to the content of the present invention.

Please refer to FIG. 1, FIG. 2, FIG. 3, and FIG. 4, wherein FIG. 2 is a schematic view of a straight road of the present invention, FIG. 3 is a schematic view of a curved road of the present invention, and FIG. 4 is an intersection of the present invention. schematic diagram. The following describes a dynamic image data classification device 10 of the present invention, which is provided in a vehicle. The dynamic map data classification device 10 includes at least one automatic driving assistance system 12, a wireless communication interface 14, a memory 16, a satellite positioning module 18, and a processor 20. The number of automatic driving assistance systems 12 is taken as an example. The automatic driving assistance system 12 may be, for example, an automatic lane changing system (LCS), a lane keeping system (LKS), an automatic emergency braking system (AEB), and a lane following system (Lane Following). System (LFS) or Active Cruise Control (Adaptive Cruise Control, ACC). The satellite positioning module 18 obtains the position coordinates of the vehicle on an electronic map. The processor 20 is electrically connected to the satellite positioning module 18, and the processor 20 plans a predetermined driving route on the electronic map according to the driving destination and position coordinates of the vehicle. The vehicle drives on the predetermined driving route. The road environment 22 of the predetermined driving route includes At least one of at least one road curvature and at least one intersection characteristic. The automatic driving assistance system 12 is electrically connected to the processor 20. The wireless communication interface 14 is electrically connected to the processor 20 and is wirelessly connected to a cloud server 24 through a wireless network. The cloud server 24 stores high-resolution map (HD Map) and three-dimensional point cloud map information. Due to the huge amount of data of high-resolution maps and 3D point cloud map information, the storage space of the dynamic map data classification device cannot cover all map data, so the data needs to be placed on the cloud server 24 for self-driving download. The memory 16 is electrically connected to the processor 20, and the memory 16 stores at least one of at least one road curvature and at least one intersection characteristic and an electronic map, and at least one of the road curvature and the intersection characteristic can be wirelessly used by the processor 20 The communication interface 14 and the cloud server 24 are downloaded from the high-resolution map, but the present invention is not limited thereto. The electronic map stored in the memory 16 may also include at least one of at least one road curvature and at least one intersection feature. Before the processor 20 finds that the vehicle has reached the road environment 22 including at least one of at least one road curvature and at least one intersection characteristic by using the satellite positioning module 18, the processor 20 is based on at least one of the road curvature and at least one intersection characteristic. One, and the degree of automated driving assistance of the automatic driving assistance system 12, classify the high-resolution map and three-dimensional point cloud map information through the wireless communication interface 14, and find the corresponding road environment 22 from the high-resolution map or three-dimensional point cloud map information. Area map information for download.

In addition, the processor 20 is further electrically connected to an inertial measurement unit (IMU) 26 provided in the vehicle. The processor 20 uses the wireless communication interface 14 to obtain the file size of network speed and area map information, and uses inertia The measurement unit 26 obtains the speed of the vehicle, and uses the satellite positioning module 18 to obtain the latitude and longitude of the road environment 22 to determine the download area map according to the file's position coordinates, the above latitude and longitude, the speed of the vehicle, the network speed and the file size of the area map information. Time of information. For example, if the file size of the area map information is 50M bytes (MB), the speed of the vehicle is 45 km / h (Km / hr), and the network speed is 20M bits / second (bps), the processor 20 The time point for downloading the area map information is determined according to the speed, the network speed, and the file size of the area map information. At this time point, a download request is sent to the cloud server 24 through the wireless communication interface 14 to download the area map information. According to the above conditions, it takes 2.5 seconds to download the area map information, and the vehicle travels 12.5 meters in 1 second. Therefore, the vehicle should be at least a distance from the next road environment that contains at least one of the road curvature and the intersection characteristics. The ruler will start downloading to download the required area map data completely. Therefore, for more complicated road environments 22, regional map information with a higher amount of data can be downloaded in advance. For a simpler road environment 22, the download amount of area map information can also be reduced.

The degree of automatic driving assistance of the automatic driving assistance system 12 includes low, medium and high. The American Association of Automata Engineering (SAE) classifies autonomous driving levels as No Automation Level 0, Driver Assistance Level 1, Partial Automation Level 2, Conditional Automation Level 3 High Level 4 and Full Automation Level 5. Corresponding to this classification, the low level of the present invention belongs to the level of driver assistance level 1, the middle level belongs to partial automation level 2 or conditional automation level 3, and the high level belongs to highly automated level 4 or fully automated level 5. Generally, when the level of automated driving assistance is low, lane line information and road curvature information are required. Relatively low will provide more information for the vehicle to use. When there is danger, the automatic driving assistance system 12 provides the driver with sufficient response time. High indicates that the vehicle needs to have a high-precision map information system for the automatic driving assistance system 12 to drive.

Please refer to FIG. 1, FIG. 2, FIG. 3, FIG. 4, and FIG. 5. The following describes the dynamic image data classification method of the dynamic image data classification device of the present invention. First, as shown in step S10, the processor 20 uses the vehicle's position coordinates and travel destination to plan a predetermined travel route on an electronic map, and the vehicle travels on the predetermined travel route. Next, as shown in step S12, the processor 20 uses the wireless communication interface 14 and the cloud server 24 to download at least one of at least one road curvature and at least one intersection feature from the high-resolution map to store the at least one road curvature and at least one At least one of the characteristics of an intersection is stored in the storage 16; In addition, one of the curvature and the characteristics of the intersection can be downloaded through the cloud server 24, or it can be built into the electronic map on the vehicle. Finally, as shown in step S14, the processor 20 uses the satellite positioning module 18 to find that the vehicle has reached the road environment 22 including at least one of the road curvature and at least one intersection characteristic, and the processor 20 according to the at least one road curvature and At least one of the characteristics of at least one intersection, and the degree of automated driving assistance of the automatic driving assistance system 12, classifies the high-resolution map and the three-dimensional point cloud map information through the wireless communication interface 14, and from the high-resolution map or the three-dimensional point cloud map The information finds the map information of the area corresponding to the road environment 22 for download. The area map information has different levels. The area map information of higher levels has a larger amount of data and needs to be downloaded earlier. The area map information of lower levels has a smaller amount of data and can be downloaded when it is closer to the characteristics of the intersection. However, the area map information is bound to be downloaded before the vehicle reaches the road environment 22.

After step S14, step S16 can also be selectively performed. In step S16, the processor 20 uses the wireless communication interface 14 to obtain the file size of the network speed and the area map information, uses the inertial measurement unit 26 to obtain the speed of the vehicle, and uses the satellite positioning module 18 to obtain the latitude and longitude of the road environment 22. The time point for downloading the area map information is determined according to the location coordinates of the vehicle, the above latitude and longitude, the speed of the vehicle, the network speed, and the file size of the area map information.

The classification of high-resolution map and 3D point cloud map information is described below. This classification is shown in Table 1 and Figure 6. Degree of automated driving assistance Intersection characteristics and road curvature Level of area map information Map information level content low Intersection characteristics Second-level area map information High-resolution (HD) map information (intersection features, where the intersection features traffic signs, zebra crossings, or stop lines) low No intersection features, and road curvature is less than a preset value First-level area map information Information for high-resolution (HD) maps (lane attributes, where lane attributes include road curvature, lane line, speed limit, and number of lanes) low No intersection features, and road curvature is greater than a preset value First-level area map information Information for high-resolution (HD) maps (lane attributes, where lane attributes include road curvature, lane line, speed limit, and number of lanes) in Intersection characteristics Level 4 area map information High-resolution (HD) map information (lane attributes, intersection features, and dynamic attributes, where intersection features include traffic signs, zebra crossings, and stop lines; lane attributes include road curvature, lane lines, speed limits, and number of lanes) in No intersection features, and road curvature is less than a preset value Third-level area map information High-resolution (HD) map information (lane attributes and dynamic attributes, where lane attributes include road curvature, lane line, speed limit, and number of lanes; dynamic attributes include weather, traffic speed, and accidents, etc.) in No intersection features, and road curvature is greater than a preset value Third-level area map information High-resolution (HD) map information (lane attributes and dynamic attributes, where lane attributes include road curvature, lane line, speed limit, and number of lanes; dynamic attributes include weather, traffic speed, and accidents, etc.) high Intersection characteristics Level 6 area map information High-resolution (HD) map information (lane attributes, intersection features, and dynamic attributes, where intersection features have traffic lights, zebra crossings, or stop lines; lane attributes include road curvature, lane lines, speed limits, and number of lanes) and 3D point clouds Map information) high No intersection features, and road curvature is less than a preset value Level 5 area map information High-resolution (HD) map information (lane attributes and dynamic attributes, where lane attributes include road curvature, lane lines, speed limits, and number of lanes; dynamic attributes include weather, traffic speed and accidents, etc.) and 3D point cloud map information high No intersection features, and road curvature is greater than a preset value Level 5 area map information High-resolution (HD) map information (lane attributes and dynamic attributes, where lane attributes include road curvature, lane lines, speed limits, and number of lanes; dynamic attributes include weather, traffic speed and accidents, etc.) and 3D point cloud map information Table I

When the degree of automated driving assistance is low, and before the vehicle reaches the road environment 22 containing the intersection features as shown in Figure 4, the area map information includes the second-level area map information M2 of the high-resolution map, that is, the intersection features, where the intersection features have Traffic signs, zebra crossings or stop lines. When the degree of automated driving assistance is low, and before the vehicle reaches a road environment 22 that contains a road curvature less than a preset value but does not include the characteristics of the intersection, the area map information includes the first-level area map information M1 of the high-resolution map, that is, the lane Attributes, which include road curvature, lane lines, speed limits, and number of lanes, where intersection features have traffic signs, zebra crossings, or stop lines. This road curvature is a straight road as shown in Figure 2. When the degree of automated driving assistance is low, and before the vehicle reaches a road environment 22 that contains a road curvature greater than a preset value but does not include the characteristics of the intersection, the area map information includes the first-level area map information M1 of the high-resolution map, that is, the lane attributes , Which includes road curvature, lane lines, speed limits, and number of lanes. The curvature of this road is a curved road as shown in Figure 3. The above-mentioned lane attributes may include at least one of lane lines, lane numbers, speed limits, and lane curvatures, but the present invention is not limited thereto. For example, when the automatic driving assistance system 12 is an automatic lane switching system, the lane attributes include lane lines, road curvatures, lane numbers, and speed limits. When the automatic driving assistance system 12 is a lane maintenance system, the lane attributes include lane lines, road curvature, and speed limits. When the automatic driving assistance system 12 is a lane following system, the lane attributes include lane lines, road curvature, lane numbers, and speed limits. When the automatic driving assistance system 12 is an active distance adjustment cruise system, the lane attributes include lane lines, lane curvature, and speed limits. When the automatic driving assistance system 12 is an automatic emergency braking system, the lane attributes include a speed limit. When the degree of automatic driving assistance is medium, and before the vehicle reaches the road environment 22 containing the intersection characteristics as shown in Figure 4, the area map information includes the fourth-level area map information M4 of the high-resolution map, that is, the lane attributes, intersection characteristics, and dynamics. Attributes, where the intersection features traffic signs, zebra crossings, or stop lines; lane attributes include road curvature, lane lines, speed limits, and number of lanes; dynamic attributes include weather, traffic speed, accidents, congestion, emergency rescue, road construction, and litter , Potholes and signs are abnormal. When the degree of automated driving assistance is medium, and before the vehicle reaches a road environment 22 that includes road curvature less than a preset value but does not include intersection features, the area map information includes the third-level area map information M3 of the high-resolution map, that is, the lane Attributes and dynamic attributes, where lane attributes include road curvature, lane lines, speed limits, and number of lanes; dynamic attributes include weather, traffic speed, accidents, congestion, emergency rescue, road construction, litter, potholes, and sign anomalies. This road curvature is a straight road as shown in Figure 2. When the degree of automated driving assistance is medium, and before the vehicle reaches a road environment 22 that contains a road curvature greater than a preset value but does not include the characteristics of the intersection, the area map information includes the third-level area map information M3 of the high-resolution map, that is, the lane attributes And dynamic attributes, where lane attributes include road curvature, lane lines, speed limits, and number of lanes; dynamic attributes include weather, traffic speed, accidents, congestion, emergency rescue, road construction, litter, potholes, and sign anomalies. The curvature of this road is a curved road as shown in Figure 3.

When the degree of automated driving assistance is high, and before the vehicle reaches the road environment 22 containing the intersection characteristics as shown in Figure 4, the area map information is the sixth-level area map information M6, that is, the lane attributes, intersection features, dynamic attributes and three-dimensional points Cloud map information, combined with Simultaneous localization and mapping (SLAM) technology, to locate vehicles, where the intersection features traffic signs, zebra crossings, or stop lines; lane attributes include road curvature, lane lines, speed limits, and lanes number. When the degree of automated driving assistance is high, and before the vehicle reaches a road environment 22 that contains a road curvature less than a preset value but does not include the characteristics of the intersection, the area map information includes the fifth-level area map information M5 of the high-resolution map, that is, the lane Attributes, dynamic attributes, and 3D point cloud map information, where lane attributes include road curvature, lane lines, speed limits, and number of lanes; dynamic attributes include weather, traffic speed, and accidents ...). This road curvature is a straight road as shown in Figure 2. When the degree of automated driving assistance is high, and before the vehicle reaches a road environment 22 that contains a road curvature greater than a preset value but does not include the characteristics of the intersection, the area map information includes the fifth-level area map information M5 of the high-resolution map, that is, the lane attributes , Dynamic attributes and 3D point cloud map information. As shown in Figure 3, this road curvature is a curved road. Lane attributes include road curvature, lane line, speed limit, and number of lanes. Dynamic attributes include weather, traffic speed, accidents, congestion, emergency rescue, road construction, and debris. , Potholes and signs are abnormal.

In summary, the present invention classifies the high-resolution map and the three-dimensional point cloud map information according to at least one of the road curvature and the intersection characteristics of the road environment and the degree of automatic driving assistance of the automatic driving assistance system, and corresponds to different conditions under different conditions. Level map information, thereby reducing the download time and download volume of map information.

The above is only a preferred embodiment of the present invention, and is not intended to limit the scope of implementation of the present invention. Therefore, all equivalent changes and modifications in accordance with the shape, structure, characteristics, and spirit described in the scope of the patent application for the present invention are provided. Shall be included in the scope of patent application of the present invention.

10‧‧‧ dynamic image data classification device

12‧‧‧Automatic Driving Assistance System

14‧‧‧Wireless communication interface

16‧‧‧Memory

18‧‧‧ satellite positioning module

20‧‧‧ processor

22‧‧‧Road Environment

24‧‧‧ Cloud Server

26‧‧‧Inertial measurement unit

FIG. 1 is a device block diagram of an embodiment of a dynamic image data classification device according to the present invention. Figure 2 is a schematic diagram of a straight road of the present invention. FIG. 3 is a schematic view of a curved road of the present invention. FIG. 4 is a schematic diagram of an intersection of the present invention. FIG. 5 is a flowchart of an embodiment of a dynamic map data classification method according to the present invention. FIG. 6 is a corresponding flowchart of Table 1 of the present invention.

Claims (8)

A dynamic image data classification device is provided in a vehicle. The dynamic image data classification device includes: a satellite positioning module that obtains the position coordinates of the vehicle on an electronic map; a processor that is electrically connected to the satellite Positioning module, the processor plans a predetermined driving route on the electronic map according to the driving destination of the vehicle and the position coordinates, the vehicle travels on the predetermined driving route, and the road environment of the predetermined driving route includes at least one road At least one of curvature and at least one intersection characteristic; at least one autonomous driving assistance system electrically connected to the processor; a wireless communication interface electrically connected to the processor, and wirelessly connected to a cloud server through a wireless network, The cloud server stores high-resolution map (HD Map) and three-dimensional point cloud map information; and a memory electrically connected to the processor, the memory stores the electronic map and the at least one road curvature and the at least At least one of the characteristics of an intersection, where the processor uses the satellite positioning module to find that the vehicle has arrived including the at least one Before the road environment of at least one of the curvature and the characteristics of the intersection, the processor is based on at least one of the curvature of the at least one road and characteristics of the at least one intersection, and the degree of automatic driving assistance of the automatic driving assistance system , Classify the high-resolution map and the 3D point cloud map information through the wireless communication interface, and find area map information corresponding to the road environment from the high-resolution map or the 3D point cloud map information for download. The dynamic map data classification device according to claim 1, wherein at least one of the at least one road curvature and the at least one intersection feature is used by the processor to use the wireless communication interface and the cloud server from the high-resolution map download. The dynamic map data classification device according to claim 1, wherein the electronic map stored in the storage includes at least one of the at least one road curvature and the at least one intersection feature. The dynamic image data classification device according to claim 2 or 3, wherein the processor is more electrically connected to an Inertial Measurement Unit (IMU) provided in the vehicle, and the processor obtains the wireless communication interface The network speed and the file size of the map information of the area, and the speed of the vehicle is obtained by using the inertial measurement unit, and the latitude and longitude of the road environment are obtained by using the satellite positioning module, according to the position coordinates, the latitude and longitude, and the vehicle ’s The speed, the network speed, and the file size of the area map information determine the time point for downloading the area map information. A dynamic map data classification method includes the following steps: using a vehicle's position coordinates and travel destination to plan the predetermined travel path on an electronic map, the vehicle travels on the predetermined travel path, and the predetermined travel path The road environment includes at least one of at least one road curvature and at least one intersection characteristic; storing at least one of the at least one road curvature and the at least one intersection characteristic; and when the vehicle arrives includes the at least one road curvature and the at least one Before the road environment of at least one of the characteristics of an intersection, according to at least one of the curvature of the at least one road and the characteristics of the at least one intersection, and the degree of automatic driving assistance of at least one automatic driving assistance system provided in the vehicle , Classify the high-resolution map and 3D point cloud map information stored in the cloud server, and find the area map information corresponding to the road environment from the high-resolution map or the 3D point cloud map information for download. The dynamic map data classification method according to claim 5, wherein at least one of the at least one road curvature and the at least one intersection feature is stored in the electronic map. The dynamic map data classification method as described in claim 5, wherein at least one of the at least one road curvature and the at least one intersection feature is stored after being downloaded from the cloud server. The dynamic map data classification method according to claim 6 or 7, wherein after classifying the high-resolution map and the three-dimensional point cloud map information stored in the cloud server, obtaining the speed and network speed of the vehicle , The file size of the area map information, and the latitude and longitude of the road environment, to download the area map information according to the location coordinates, the latitude and longitude, the speed of the vehicle, the network speed, and the file size of the area map information Time.