Vehicle pilot assisted driving method, medium and device

Abstract

The present invention discloses a vehicle pilot-assisted driving method, medium and device, wherein the method includes: obtaining navigation instructions and learning instructions, generating a navigation route according to the navigation instructions, and starting to collect and store feature information of sections of the navigation route that are not covered by high-precision maps according to the learning instructions; generating a priori data map corresponding to the sections of the navigation route that are not covered by high-precision maps, and generating a complete pilot-assisted driving route according to the priori data map and the high-precision map, and performing pilot-assisted driving of the vehicle according to the complete pilot-assisted driving route; and being able to perform complete pilot-assisted driving of the vehicle in the event that high-precision maps of some sections of the navigation route are missing, so as to improve the user's driving experience.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle assisted driving technology, and in particular to a vehicle navigation assisted driving method, a computer-readable storage medium, and a vehicle navigation assisted driving device. Background Art

[0002] Navigation assistance refers to assisting the driver's vehicle driving behavior (for example, controlling cruise speed, merging into the main road, entering ramps, and controlling the timing of lane changes, etc.) based on navigation information (for example, navigation route, speed limit information, and traffic status information, etc.).

[0003] Related technologies rely heavily on high-definition maps when performing navigation assistance. Specifically, navigation assistance can only be enabled on roads covered by high-definition maps. Consequently, when a user activates navigation assistance while driving, if the vehicle passes through a road not covered by high-definition maps (e.g., remote urban roads or some high-speed highways), the vehicle will exit the navigation assistance state, severely impacting the driver's experience with the navigation assistance function. Summary of the Invention

[0004] The present invention aims to at least partially address one of the technical problems in the related art. To this end, one object of the present invention is to provide a vehicle navigation assistance method that can provide complete navigation assistance for vehicle driving behavior in the absence of a portion of a high-precision map, thereby improving the user's driving experience.

[0005] According to an embodiment of the present invention, the vehicle navigation assisted driving method includes the following steps: obtaining navigation instructions and learning instructions, generating a navigation route according to the navigation instructions, and starting to collect and store feature information of sections of the navigation route that are not covered by high-precision maps according to the learning instructions; generating a priori data map corresponding to the sections of the navigation route that are not covered by high-precision maps, and generating a complete navigation assisted driving route according to the priori data map and the high-precision map, and performing navigation assistance on vehicle driving according to the complete navigation assisted driving route.

[0006] According to the vehicle navigation assisted driving method of an embodiment of the present invention, first, navigation instructions and learning instructions are obtained, and a navigation route is generated according to the navigation instructions, and characteristic information collection and storage of the sections in the navigation route that are not covered by high-precision maps are started according to the learning instructions; then, a priori data map corresponding to the sections in the navigation route that are not covered by high-precision maps is generated, and a complete navigation assisted driving route is generated according to the priori data map and the high-precision map, and navigation assistance is performed on the vehicle driving according to the complete navigation assisted driving route; through the above settings, when the user turns on navigation assistance on a section corresponding to a complete navigation assisted driving route, the sections with high-precision maps can use the high-precision maps for navigation assistance, while the sections without high-precision maps can use the corresponding priori data maps for navigation assistance; the dependence of navigation assistance on high-precision maps is reduced, avoiding the situation where the navigation assistance function stops due to the lack of high-precision maps of some sections during the user's use of navigation assistance, and improving the user's driving experience.

[0007] In some embodiments, generating a priori data map corresponding to the road sections in the navigation route that are not covered by the high-precision map includes: obtaining the first positioning information of the vehicle in real time, and determining whether there is a corresponding high-precision map for the first positioning information; if not, using the vehicle sensor to collect the corresponding first road feature information, and generating a priori data map based on the first road feature information.

[0008] In some embodiments, the first road feature information includes lane model information, road component information, road attribute information, and a feature layer positioned by the vehicle sensor.

[0009] In some embodiments, the vehicle sensors include high-definition cameras, high-precision inertial navigation, lidar, and millimeter-wave radar.

[0010] In some embodiments, before generating a complete navigation assisted driving route based on the prior data map and the high-precision map, it also includes: obtaining the second positioning information of the vehicle in real time, and determining whether there is a corresponding prior data map for the second positioning information; if so, using the vehicle sensor to collect the corresponding second road feature information; comparing the second road feature information with the first road feature information to obtain a match value between the second road feature information and the corresponding prior data map; determining whether the match value is greater than a preset match value threshold; if yes, it is considered that the corresponding prior data map has been verified; if not, the corresponding prior data map is optimized and covered using the second road feature information.

[0011] In some embodiments, before generating a complete navigation assisted driving route based on the prior data map and the high-precision map, it also includes: if the second positioning information has a corresponding prior data map, then the corresponding prior data map is connected to the decision system to virtually control the vehicle, and a predicted trajectory corresponding to the virtual control is generated; the predicted trajectory corresponding to the virtual control is compared with the actual trajectory generated by the driver driving the vehicle to obtain a similarity value between the predicted trajectory and the actual trajectory; it is determined whether the similarity value is greater than a preset similarity threshold; if so, it is considered that the corresponding prior data map has been verified.

[0012] In some embodiments, before generating a complete navigation-assisted driving route based on the prior data map and the high-precision map, it also includes: determining whether the prior data maps corresponding to the navigation route have all been verified; if so, it is considered that the learning of the navigation-assisted driving route corresponding to the navigation route has been completed, so that after the learning is completed, a complete navigation-assisted driving route is generated based on all verified prior data maps and high-precision maps.

[0013] In some embodiments, navigation assistance is provided for vehicle driving according to the navigation assisted driving route, including: obtaining the vehicle's third positioning information in real time, and determining whether there is a corresponding high-precision map for the third positioning information; if so, navigation assistance is provided for vehicle driving according to the high-precision map; if not, navigation assistance is provided for vehicle driving using the corresponding prior data map.

[0014] In a second aspect, a computer-readable storage medium according to an embodiment of the present invention stores a vehicle navigation assisted driving program, which, when executed by a processor, implements the vehicle navigation assisted driving method as described above.

[0015] According to the computer-readable storage medium of an embodiment of the present invention, by storing a vehicle navigation assistance driving program, when the vehicle navigation assistance driving program is executed by a processor, it can be realized that when a user turns on navigation assistance on a road section corresponding to a navigation assisted driving route, the road sections with high-precision maps can use high-precision maps for navigation assistance, while the road sections without high-precision maps can use corresponding prior data maps for navigation assistance; this reduces the dependence of navigation assistance on high-precision maps, and avoids the situation where the navigation assistance function is stopped due to the lack of high-precision maps of some road sections during the user's use of navigation assistance; and improves the user's driving experience.

[0016] In a third aspect, a vehicle navigation assisted driving device according to an embodiment of the present invention includes: a navigation module, which is used to obtain navigation instructions and learning instructions, and generate a navigation route according to the navigation instructions, and start collecting and storing feature information of sections in the navigation route that are not covered by high-precision maps according to the learning instructions; a learning module, which is used to generate a priori data map corresponding to sections in the navigation route that are not covered by high-precision maps, and generate a complete navigation assisted driving route based on the priori data map and the high-precision map; and an assisted driving module is used to provide navigation assistance to vehicle driving according to the complete navigation assisted driving route.

[0017] According to the vehicle navigation assisted driving device of an embodiment of the present invention, the navigation module is used to obtain navigation instructions and learning instructions, and generate a navigation route according to the navigation instructions, and start collecting and storing feature information of sections in the navigation route that are not covered by high-precision maps according to the learning instructions; the learning module is used to generate a priori data map corresponding to the sections in the navigation route that are not covered by high-precision maps, and generate a complete navigation assisted driving route based on the priori data map and the high-precision map; the assisted driving module is used to provide navigation assistance for the vehicle driving according to the complete navigation assisted driving route; through the above settings, when the user turns on navigation assistance on a section corresponding to a navigation assisted driving route, the sections with high-precision maps can use the high-precision maps for navigation assistance, while the sections without high-precision maps can use the corresponding priori data maps for navigation assistance; the dependence of navigation assistance on high-precision maps is reduced, avoiding the situation where the navigation assistance function stops due to the lack of high-precision maps of some sections during the user's use of navigation assistance, and improving the user's driving experience.

[0018] In some embodiments, generating a priori data map corresponding to the road sections in the navigation route that are not covered by the high-precision map includes: obtaining the first positioning information of the vehicle in real time, and determining whether there is a corresponding high-precision map for the first positioning information; if not, using the vehicle sensor to collect the corresponding first road feature information, and generating a priori data map based on the first road feature information.

[0019] In some embodiments, the first road feature information includes lane model information, road component information, road attribute information, and a feature layer positioned by the vehicle sensor.

[0020] In some embodiments, the vehicle sensors include high-definition cameras, high-precision inertial navigation, lidar, and millimeter-wave radar.

[0021] In some embodiments, before generating a complete navigation assisted driving route based on the prior data map and the high-precision map, it also includes: obtaining the second positioning information of the vehicle in real time, and determining whether there is a corresponding prior data map for the second positioning information; if so, using the vehicle sensor to collect the corresponding second road feature information; comparing the second road feature information with the first road feature information to obtain a match value between the second road feature information and the corresponding prior data map; determining whether the match value is greater than a preset match value threshold; if yes, it is considered that the corresponding prior data map has been verified; if not, the corresponding prior data map is optimized and covered using the second road feature information.

[0022] In some embodiments, before generating a complete navigation assisted driving route based on the prior data map and the high-precision map, it also includes: if the second positioning information has a corresponding prior data map, then the corresponding prior data map is connected to the decision system to virtually control the vehicle, and a predicted trajectory corresponding to the virtual control is generated; the predicted trajectory corresponding to the virtual control is compared with the actual trajectory generated by the driver driving the vehicle to obtain a similarity value between the predicted trajectory and the actual trajectory; it is determined whether the similarity value is greater than a preset similarity threshold; if so, it is considered that the corresponding prior data map has been verified.

[0023] In some embodiments, before generating a complete navigation-assisted driving route based on the prior data map and the high-precision map, it also includes: determining whether the prior data maps corresponding to the navigation route have all been verified; if so, it is considered that the learning of the navigation-assisted driving route corresponding to the navigation route has been completed, so that after the learning is completed, a complete navigation-assisted driving route is generated based on all verified prior data maps and high-precision maps.

[0024] In some embodiments, navigation assistance is provided for vehicle driving according to the navigation assisted driving route, including: obtaining the vehicle's third positioning information in real time, and determining whether there is a corresponding high-precision map for the third positioning information; if so, navigation assistance is provided for vehicle driving according to the high-precision map; if not, navigation assistance is provided for vehicle driving using the corresponding prior data map.

[0025] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flow chart of a vehicle navigation assisted driving method according to an embodiment of the present invention;

[0027] Figure 2 2. It is a schematic diagram of the process flow of the navigation-assisted driving route learning process according to an embodiment of the present invention;

[0028] Figure 3 2. It is a schematic diagram of the process flow of the navigation-assisted driving route verification process according to an embodiment of the present invention;

[0029] Figure 4 is a schematic diagram of a process flow for navigation-assisted driving route verification according to another embodiment of the present invention;

[0030] Figure 5 is a schematic diagram of a process flow for using a navigation-assisted route according to an embodiment of the present invention;

[0031] Figure 6 4 is a block diagram of a vehicle navigation assisted driving device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0033] The vehicle navigation assisted driving method according to an embodiment of the present invention will be described below with reference to the accompanying drawings.

[0034] See also Figure 1 , Figure 1 FIG. 1 is a flow chart of a vehicle navigation assisted driving method according to an embodiment of the present invention. Figure 1 As shown, the vehicle navigation assisted driving method includes the following steps:

[0035] S101, obtaining navigation instructions and learning instructions, generating a navigation route according to the navigation instructions, and starting to collect and store feature information of sections of the navigation route that are not covered by high-precision maps according to the learning instructions.

[0036] As an example, suppose that a user's frequently used route is not fully covered by high-precision maps. If the traditional navigation assistance function is used, the navigation assistance function will stop when passing through a section of road not covered by high-precision maps. According to the vehicle navigation assisted driving method of an embodiment of the present invention, first, the user inputs a navigation instruction; specifically, the navigation instruction may include navigation starting point information and navigation end point information; then, a corresponding navigation route is generated according to the navigation starting point information and the navigation end point information; then, a learning instruction input by the user is obtained, and the learning of the navigation assisted driving route corresponding to the above navigation route is started according to the learning instruction, so that after the learning is completed, navigation assistance for vehicle driving is performed according to the learned navigation assisted driving route.

[0037] S102, generating a priori data map corresponding to the road sections in the navigation route that are not covered by the high-precision map, generating a complete navigation-assisted driving route based on the priori data map and the high-precision map, and providing navigation assistance to the vehicle driving based on the complete navigation-assisted driving route.

[0038] In some embodiments, generating a priori data map corresponding to a road section in a navigation route that is not covered by a high-precision map includes: obtaining the first positioning information of the vehicle in real time, and determining whether there is a corresponding high-precision map for the first positioning information; if not, using the vehicle sensor to collect the corresponding first road feature information, and generating a priori data map based on the first road feature information.

[0039] In some embodiments, the first road feature information includes lane model information, road component information, road attribute information, and a feature layer located by the vehicle sensor.

[0040] In some embodiments, vehicle sensors include high-definition cameras, high-precision inertial navigation, lidar, and millimeter-wave radar.

[0041] As an example, during the driving process of the vehicle, the first positioning information of the vehicle is obtained in real time through the positioning module of the vehicle, and combined with the high-precision map information, it is determined whether the first positioning information has corresponding high-precision map coverage; if not, the vehicle sensor is turned on; the first road feature information corresponding to the first positioning information is collected; preferably, the first road feature information includes a lane model (for example: detailed information of the lane and the connection relationship between the lanes), road component information (for example: traffic sign information, signboard information, gantry information, road pole information, roadside and road surface object information, etc.), road attribute information (for example: road curvature, road heading, road slope and cross slope information) and a feature layer of vehicle sensor positioning. Then, the acquired first road feature information is processed by big data through the SLAM algorithm to generate a priori data map for self-positioning and planning, and the priori data map is stored in the intelligent driving domain controller.

[0042] As another example, vehicle sensors include high-definition cameras, high-precision inertial navigation, lidar, and millimeter-wave radar. The high-definition camera is used to collect forward, rear, and surround perception information around the vehicle for lane trajectory recognition and visual SLAM positioning, and to establish a visual SLAM map. The high-precision inertial navigation is mainly used for high-precision positioning of the vehicle, and combined with visual SLAM positioning to obtain vehicle fusion positioning, achieving centimeter-level positioning of the vehicle under various working conditions. The lidar is mainly used to collect laser perception information of the vehicle's surrounding environment, generate point cloud data, and establish a laser SLAM map. The millimeter-wave radar is mainly used to collect millimeter-wave perception information of the vehicle's surrounding environment and establish a millimeter-wave SLAM map. Then, the intelligent driving domain controller fuses the visual SLAM map, vehicle fusion positioning, laser SLAM map, and millimeter-wave SLAM map through a deep learning algorithm to obtain a priori data map. Specifically, the timestamps and feature points can be associated, and the associated data can be reprojected to world coordinates to generate a priori data map, which is a 4D environmental perception map and contains road information attributes.

[0043] In some embodiments, in order to ensure the accuracy of the prior data map, the generated prior data map is further verified; before generating the navigation assisted driving route based on the prior data map and the high-precision map, it also includes: obtaining the second positioning information of the vehicle in real time, and determining whether there is a corresponding prior data map for the second positioning information; if so, using the vehicle sensor to collect the corresponding second road feature information; comparing the second road feature information with the first road feature information to obtain the match value between the second road feature information and the corresponding prior data map; determining whether the match value is greater than a preset match value threshold; if yes, it is considered that the corresponding prior data map has been verified; if not, the second road feature information is used to optimize the coverage of the corresponding prior data map.

[0044] That is, at the same spatial coordinates, the second road feature information (such as lane lines, slope, curvature, etc.) is compared with the first road feature information at the same location point (for example, for lane line information, the two results are fitted to obtain a corresponding matching value based on the degree of overlap) to obtain a matching value. Preferably, the preset matching value threshold can be selected as 95%.

[0045] As an example, assume that a user starts learning a navigation route corresponding to a pilot auxiliary route in the above manner; during the user's normal driving process, the user inputs navigation instructions; after determining the user's current navigation route according to the navigation instructions, determine whether the current navigation route passes through the pilot auxiliary route being learned; if so, during the driving of the current navigation route, obtain the vehicle's second positioning information in real time, and determine whether the second positioning information has a corresponding prior data map; if so, turn on the vehicle sensor to obtain the second road feature information, if not, do not obtain the second road feature information for verification; then, compare the second road feature information with the first road feature information to obtain a match value between the second road feature information and the corresponding prior data map; and when the match value is lower than a preset match value threshold, use the second road feature information to optimize the coverage of the prior data map.

[0046] As another example, the database stores the identifiers of road sections for which a priori data map exists but the prior data map has not been verified. Assume that a user starts learning a navigation route corresponding to a pilot auxiliary route in the above manner. During normal driving, the user inputs navigation instructions. After determining the user's current navigation route according to the navigation instructions, the database is queried according to the current navigation route to determine whether the current navigation route passes through a road section for which a priori data map exists but the prior data map has not been verified. If so, an electronic fence corresponding to the road section is generated. Then, during driving along the current navigation route, the second positioning information of the vehicle is obtained in real time, and it is determined whether the second positioning information passes through the above electronic fence. If so, the vehicle sensor is turned on to collect second road feature information, and when the match value between the second road feature information and the prior data map is greater than a preset match value threshold, the prior data map is optimized and covered using the second road feature information. If the navigation route does not pass through a priori data map, or the prior data maps of the road sections passed through have all been verified, the above operation is not performed.

[0047] In some embodiments, in order to further ensure the accuracy of the prior data map, before generating the navigation assisted driving route based on the prior data map and the high-precision map, it also includes: if the second positioning information has a corresponding prior data map, then the corresponding prior data map is connected to the decision system to virtually control the vehicle, and generate a predicted trajectory corresponding to the virtual control; the predicted trajectory corresponding to the virtual control is compared with the actual trajectory generated by the driver driving the vehicle to obtain a similarity value between the predicted trajectory and the actual trajectory; it is determined whether the similarity value is greater than a preset similarity threshold; if so, it is considered that the corresponding prior data map has been verified.

[0048] It should be noted that, as described above, determining whether the prior data map has been verified can also be performed by comparing the second road feature information with the prior data map. For example, first, the second road feature information is compared with the prior data map to obtain a corresponding match value; determining whether the match value is greater than a preset match value threshold (preferably, the preset match value threshold can be selected as 95%; it should be noted that the preset match value threshold can be adjusted according to actual needs, and the value of the preset match value threshold is not specifically limited here); if so, the prior data map is considered to have been verified; or, when the match value is greater than the preset match value threshold, the current scene is considered to have been successfully verified, and the number of successful verifications is incremented by 1; then, determining whether the number of successful verifications is greater than the preset number threshold. If so, the prior data map is considered to have been verified.

[0049] In some embodiments, before generating a navigation-assisted driving route based on the prior data map and the high-precision map, it also includes: determining whether the prior data maps corresponding to the navigation route have all been verified; if so, it is considered that the learning of the navigation-assisted driving route corresponding to the navigation route has been completed, so that after the learning is completed, the navigation-assisted driving route is generated based on all verified prior data maps and high-precision maps.

[0050] In some embodiments, navigation assistance is provided for vehicle driving according to the navigation assisted driving route, including: obtaining the vehicle's third positioning information in real time, and determining whether there is a corresponding high-precision map for the third positioning information; if so, navigation assistance is provided for vehicle driving according to the high-precision map; if not, navigation assistance is provided for vehicle driving using the corresponding prior data map.

[0051] In addition, it should be noted that when a user completes learning and obtains the corresponding navigation-assisted driving route, he or she can share the navigation-assisted driving route (for example, sending the corresponding file of the navigation-assisted driving route to the recipient; or, sending the file corresponding to the navigation-assisted driving route to the cloud platform); so that other users can obtain the shared navigation-assisted driving route when needed, saving learning time for the navigation-assisted driving route.

[0052] In a specific embodiment of the present invention, a vehicle navigation-assisted driving method includes: navigation-assisted driving route learning, navigation-assisted driving route verification, and navigation-assisted driving route use.

[0053] Among them, such as Figure 2 As shown, the navigation-assisted driving route learning includes:

[0054] S201, obtaining navigation instructions and learning instructions.

[0055] S202: Acquire first vehicle positioning information.

[0056] S203: Determine whether there is a corresponding high-precision map for the first positioning information; if not, execute step S204.

[0057] S204: Acquire first road feature information through a sensor.

[0058] S205: Generate a priori data map according to the first road feature information.

[0059] like Figure 3 As shown, the navigation-assisted driving route verification includes:

[0060] S301, obtaining the second positioning information of the vehicle in real time.

[0061] S302, determine whether the second positioning information of the vehicle has a corresponding priori data map; if yes, execute step S303.

[0062] S303: Use vehicle sensors to collect corresponding second road feature information.

[0063] S304: Compare the second road characteristic information with the priori data map to obtain a coincidence value between the second road characteristic information and the corresponding priori data map.

[0064] S305, determining whether the matching value is greater than a preset matching value threshold; if yes, executing step S307; if not, executing step S306.

[0065] S306: Use the second road feature information to optimize the coverage of the corresponding prior data map.

[0066] S307, verification passed.

[0067] like Figure 4 As shown, the navigation-assisted driving route verification also includes:

[0068] S401, obtaining the second positioning information of the vehicle in real time.

[0069] S402, determine whether the second positioning information of the vehicle has a corresponding priori data map; if yes, execute step S403.

[0070] S403 : Connect the corresponding prior data map to the decision system to perform virtual control on the vehicle, and generate a predicted trajectory corresponding to the virtual control.

[0071] S404 : Compare the predicted trajectory corresponding to the virtual control with the actual trajectory generated by the driver driving the vehicle to obtain a similarity value between the predicted trajectory and the actual trajectory.

[0072] S405 , determining whether the similarity value is greater than a preset similarity threshold; if yes, executing step S406 ; if no, executing step S407 .

[0073] S406 : The corresponding priori data map is deemed to have passed the verification, and the verified priori data map is verified to stop the optimization of the priori data map.

[0074] S407, prompting the user that the verification has failed and the current data needs to be overwritten for a second verification.

[0075] S408 , determining whether all the prior data maps in the navigation-assisted driving route learning corresponding to the prior data map have been verified; if so, executing step S409 .

[0076] S409: Generate a navigation-assisted driving route based on the verified prior data map and high-precision map.

[0077] like Figure 5 As shown, the navigation auxiliary route uses include:

[0078] S501: Obtain a navigation instruction and generate a navigation route according to the navigation instruction.

[0079] S502, determine whether the navigation instruction has a corresponding navigation assisted driving route; if so, execute step S503.

[0080] S503, obtaining the third positioning information of the vehicle in real time.

[0081] S504, determine whether the third positioning information has a corresponding high-precision map; if yes, execute step S505, if not, execute step S506.

[0082] S505: Provide navigation assistance for vehicle driving based on the high-precision map.

[0083] S506: Use the corresponding prior data map to provide navigation assistance for vehicle driving.

[0084] In summary, according to the vehicle navigation assisted driving method of an embodiment of the present invention, first, navigation instructions and learning instructions are obtained, and a navigation route is generated according to the navigation instructions, and characteristic information collection and storage of the sections in the navigation route that are not covered by high-precision maps are started according to the learning instructions; then, a priori data map corresponding to the sections in the navigation route that are not covered by high-precision maps is generated, and a complete navigation assisted driving route is generated based on the priori data map and the high-precision map, and navigation assistance is performed on the vehicle driving based on the complete navigation assisted driving route; through the above settings, when the user turns on navigation assistance on a section corresponding to a navigation assisted driving route, the sections with high-precision maps can use the high-precision maps for navigation assistance, while the sections without high-precision maps can use the corresponding priori data maps for navigation assistance; the dependence of navigation assistance on high-precision maps is reduced, avoiding the situation where the navigation assistance function stops due to the lack of high-precision maps of some sections during the user's use of navigation assistance; and improving the user's driving experience.

[0085] In order to implement the above embodiment, an embodiment of the present invention proposes a computer-readable storage medium on which a vehicle navigation assisted driving program is stored. When the vehicle navigation assisted driving program is executed by a processor, the vehicle navigation assisted driving method as described above is implemented.

[0086] According to the computer-readable storage medium of an embodiment of the present invention, by storing a vehicle navigation assistance driving program, when the vehicle navigation assistance driving program is executed by a processor, it can be realized that when a user turns on navigation assistance on a road section corresponding to a navigation assisted driving route, the road sections with high-precision maps can use high-precision maps for navigation assistance, while the road sections without high-precision maps can use corresponding prior data maps for navigation assistance; this reduces the dependence of navigation assistance on high-precision maps, and avoids the situation where the navigation assistance function is stopped due to the lack of high-precision maps of some road sections during the user's use of navigation assistance; and improves the user's driving experience.

[0087] In order to implement the above embodiment, the embodiment of the present invention proposes a vehicle navigation auxiliary driving device; Figure 6 As shown, the vehicle navigation assisted driving device includes: a navigation module 601, a learning module 602 and an assisted driving module 603.

[0088] The navigation module 601 is used to obtain navigation instructions and learning instructions, generate a navigation route according to the navigation instructions, and start learning the navigation route corresponding to the pilot-assisted driving route according to the learning instructions;

[0089] The learning module 602 is used to generate a priori data map corresponding to the road sections in the navigation route that are not covered by the high-precision map, and generate a navigation-assisted driving route based on the priori data map and the high-precision map;

[0090] The assisted driving module 603 is used to provide navigation assistance to the vehicle driving according to the navigation assisted driving route.

[0091] In some embodiments, generating a priori data map corresponding to the road sections in the navigation route that are not covered by the high-precision map includes: obtaining the first positioning information of the vehicle in real time, and determining whether there is a corresponding high-precision map for the first positioning information; if not, using the vehicle sensor to collect the corresponding first road feature information, and generating a priori data map based on the first road feature information.

[0092] In some embodiments, the first road feature information includes lane model information, road component information, road attribute information, and a feature layer positioned by the vehicle sensor.

[0093] In some embodiments, the vehicle sensors include high-definition cameras, high-precision inertial navigation, lidar, and millimeter-wave radar.

[0094] In some embodiments, before generating a navigation assisted driving route based on the prior data map and the high-precision map, it also includes: obtaining the second positioning information of the vehicle in real time, and determining whether there is a corresponding prior data map for the second positioning information; if so, using the vehicle sensor to collect the corresponding second road feature information; comparing the second road feature information with the first road feature information to obtain a match value between the second road feature information and the corresponding prior data map; determining whether the match value is greater than a preset match value threshold; if not, using the second road feature information to optimize the coverage of the corresponding prior data map.

[0095] In some embodiments, before generating a navigation-assisted driving route based on the prior data map and the high-precision map, it also includes: if there is a corresponding prior data map for the second positioning information, then the corresponding prior data map is connected to the decision system to virtually control the vehicle, and a predicted trajectory corresponding to the virtual control is generated; the predicted trajectory corresponding to the virtual control is compared with the actual trajectory generated by the driver driving the vehicle to obtain a similarity value between the predicted trajectory and the actual trajectory; it is determined whether the similarity value is greater than a preset similarity threshold; if so, it is considered that the corresponding prior data map has been verified.

[0096] In some embodiments, before generating the navigation-assisted driving route based on the prior data map and the high-precision map, it also includes: determining whether the prior data maps corresponding to the navigation route have all been verified; if so, it is considered that the learning of the navigation-assisted driving route corresponding to the navigation route has been completed, so that after the learning is completed, the navigation-assisted driving route is generated based on all the verified prior data maps and high-precision maps.

[0097] In some embodiments, navigation assistance is provided for vehicle driving according to the navigation assisted driving route, including: obtaining the vehicle's third positioning information in real time, and determining whether there is a corresponding high-precision map for the third positioning information; if so, navigation assistance is provided for vehicle driving according to the high-precision map; if not, navigation assistance is provided for vehicle driving using the corresponding prior data map.

[0098] It should be noted that the above description of the vehicle navigation assisted driving method is also applicable to the vehicle navigation assisted driving device and will not be repeated here.

[0099] In summary, according to the vehicle navigation assisted driving device of an embodiment of the present invention, the navigation module is used to obtain navigation instructions and learning instructions, and generate a navigation route according to the navigation instructions, and start learning the navigation assisted driving route corresponding to the navigation route according to the learning instructions; the learning module, the learning module is used to generate a priori data map corresponding to the road section without high-precision map coverage in the navigation route, and generate a navigation assisted driving route based on the priori data map and the high-precision map; the assisted driving module is used to perform navigation assistance for the vehicle driving according to the navigation assisted driving route; through the above settings, when the user turns on navigation assistance on the road section corresponding to the navigation assisted driving route, the road section with high-precision map can use the high-precision map for navigation assistance, and the road section without high-precision map can use the corresponding priori data map for navigation assistance; the dependence of navigation assistance on high-precision map is reduced, avoiding the situation where the navigation assistance function stops due to the lack of high-precision map of some road sections during the user's use of navigation assistance; and the user's driving experience is improved.

[0100] In order to implement the above embodiment, an embodiment of the present invention provides a vehicle equipped with the vehicle navigation auxiliary driving device as described above.

[0101] To sum up, according to an embodiment of the present invention, a vehicle is equipped with a vehicle navigation assistance driving device as described above. Through the vehicle navigation assistance driving device, when a user uses navigation assistance, when a high-precision map exists on a road section, the high-precision map can be used for navigation assistance; when a high-precision map does not exist on a road section, the corresponding prior data map can be used for navigation assistance; thereby reducing the dependence of navigation assistance on high-precision maps; avoiding the stopping of the navigation assistance function due to the lack of high-precision map coverage on some road sections, and improving the user experience.

[0102] It should be noted that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion having one or more wires (electronic device), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0103] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0104] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0105] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0106] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0107] In the present invention, unless otherwise specified or limited, the terms "installed," "connected," "connect," "fixed," etc. should be understood in a broad sense. For example, they can refer to fixed connection, detachable connection, or integration; mechanical connection, electrical connection; direct connection, or indirect connection through an intermediate medium; internal communication between two components, or interaction between two components, unless otherwise specified. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0108] In the present invention, unless otherwise expressly specified or limited, when a first feature is "above" or "below" a second feature, it may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediary. Furthermore, when a first feature is "above," "above," or "above" a second feature, it may mean that the first feature is directly above or diagonally above the second feature, or simply means that the first feature is at a higher level than the second feature. When a first feature is "below," "below," or "below" a second feature, it may mean that the first feature is directly below or diagonally below the second feature, or simply means that the first feature is at a lower level than the second feature.

[0109] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A vehicle navigation assisted driving method, characterized in that: The following steps are involved: Obtaining navigation instructions and learning instructions, generating a navigation route according to the navigation instructions, and starting to collect and store feature information of sections of the navigation route that are not covered by high-precision maps according to the learning instructions; generating a priori data map corresponding to sections of the navigation route not covered by the high-precision map, generating a complete navigation-assisted driving route based on the priori data map and the high-precision map, and providing navigation assistance to the vehicle based on the complete navigation-assisted driving route; Generating a priori data maps corresponding to sections of the navigation route that are not covered by high-precision maps, including: Acquire first positioning information of the vehicle in real time, and determine whether there is a corresponding high-precision map for the first positioning information; If not, using a vehicle sensor to collect corresponding first road feature information, and generating a priori data map based on the first road feature information; Before generating a complete navigation-assisted driving route based on the prior data map and the high-precision map, the process also includes: acquiring second positioning information of the vehicle in real time, and determining whether a corresponding priori data map exists for the second positioning information; if a corresponding priori data map exists for the second positioning information, connecting the corresponding priori data map to a decision system to perform virtual control of the vehicle, and generating a predicted trajectory corresponding to the virtual control; Comparing the predicted trajectory corresponding to the virtual control with the actual trajectory generated by the driver driving the vehicle to obtain a similarity value between the predicted trajectory and the actual trajectory; Determining whether the similarity value is greater than a preset similarity threshold; If so, it is considered that the corresponding priori data map verification has passed.

2. The vehicle navigation assisted driving method according to claim 1, characterized in that: The first road feature information includes lane model information, road component information, road attribute information and a feature layer positioned by the vehicle sensor.

3. The vehicle navigation assisted driving method according to claim 1, wherein: The vehicle sensors include high-definition cameras, high-precision inertial navigation, lidar and millimeter-wave radar.

4. The vehicle navigation assisted driving method according to claim 1, wherein: Before generating a complete navigation-assisted driving route based on the prior data map and the high-precision map, the process also includes: Acquire the second positioning information of the vehicle in real time, and determine whether there is a corresponding prior data map for the second positioning information; If yes, collecting corresponding second road feature information using a vehicle sensor; Comparing the second road characteristic information with the first road characteristic information to obtain a coincidence value between the second road characteristic information and a corresponding priori data map; Determining whether the coincidence value is greater than a preset coincidence value threshold; If yes, it is considered that the corresponding priori data map verification has passed; If not, the corresponding priori data map is optimized and covered using the second road feature information.

5. The vehicle navigation assisted driving method according to claim 1, wherein: Before generating a complete navigation-assisted driving route based on the prior data map and the high-precision map, the process also includes: Determining whether the prior data maps corresponding to the navigation route have all been verified; If so, it is considered that the learning of the navigation route corresponding to the pilot-assisted driving route has been completed, so that after the learning is completed, a complete pilot-assisted driving route is generated based on all verified prior data maps and high-precision maps.

6. The vehicle navigation assisted driving method according to claim 1, wherein: Provide navigation assistance to the vehicle according to the complete navigation assistance driving route, including: Acquire the third positioning information of the vehicle in real time, and determine whether there is a corresponding high-precision map for the third positioning information; If yes, providing navigation assistance to the vehicle driving according to the high-precision map; If not, the corresponding prior data map is used to provide navigation assistance for vehicle driving.

7. A computer-readable storage medium, characterized in that A vehicle navigation assisted driving program is stored thereon, and when the vehicle navigation assisted driving program is executed by the processor, the vehicle navigation assisted driving method as described in any one of claims 1 to 6 is implemented.

8. A vehicle navigation auxiliary driving device, characterized in that: include: a navigation module configured to obtain navigation instructions and learning instructions, generate a navigation route according to the navigation instructions, and initiate the collection and storage of feature information of sections of the navigation route not covered by a high-precision map according to the learning instructions; a learning module, the learning module being configured to generate a priori data map corresponding to sections of the navigation route not covered by the high-precision map, and to generate a complete pilot-assisted driving route based on the priori data map and the high-precision map; An assisted driving module, the assisted driving module being used to provide navigation assistance to the vehicle according to the complete navigation assisted driving route; The step of generating a priori data map corresponding to the road section in the navigation route that is not covered by the high-precision map includes: Acquire first positioning information of the vehicle in real time, and determine whether there is a corresponding high-precision map for the first positioning information; If not, using a vehicle sensor to collect corresponding first road feature information, and generating a priori data map based on the first road feature information; Before generating a complete navigation-assisted driving route based on the prior data map and the high-precision map, the process also includes: acquiring second positioning information of the vehicle in real time, and determining whether a corresponding priori data map exists for the second positioning information; if a corresponding priori data map exists for the second positioning information, connecting the corresponding priori data map to a decision system to perform virtual control of the vehicle, and generating a predicted trajectory corresponding to the virtual control; Comparing the predicted trajectory corresponding to the virtual control with the actual trajectory generated by the driver driving the vehicle to obtain a similarity value between the predicted trajectory and the actual trajectory; Determining whether the similarity value is greater than a preset similarity threshold; If so, it is considered that the corresponding priori data map verification has passed.

Images