A method for memorizing driving and an intelligent navigation and control system

Abstract

The application relates to a memory driving method and an intelligent navigation and control system, and relates to the technical field of intelligent driving. The method comprises the following steps: after entering a route learning mode, a smart driving controller constructs a memory driving map according to road information; after ending the route learning mode, the smart driving controller stores and generates a mapping quality level of each road section according to the road information of each road section in the memory driving map; the smart driving controller synchronizes the route identification and the mapping quality level of the memory driving map to a navigation system; after entering a memory driving mode, the navigation system plays back a target memory driving map corresponding to a target route identification, and displays the mapping quality level of each road section in real time; after ending the memory driving mode, the smart driving controller updates the road information and the mapping quality level of the traveled road section, and synchronizes the route identification and the mapping quality level to the navigation system. The application can improve the information transparency in the human-machine co-driving process and improve the mapping quality.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to a driving memory method and an intelligent navigation and control system. Background Art

[0002] With the advancement of vehicle technology, intelligent vehicle services are becoming increasingly important, and memory driving is a crucial component of these services. The map constructed through memory driving route learning and mapping is based on the recognition of environmental information by on-board sensors. This recognition process is essentially a process of real-time positioning, mapping, and storage.

[0003] Memory Driving relies heavily on the route learning map construction process. If the route learning map is not accurate enough, the driver will not be able to effectively reposition the learned route and control the vehicle when using the Memory Driving function. For a fixed memory driving route, repeated route learning is required to continuously optimize sections with poor mapping quality to achieve a relatively good driving experience. The more times the experience is, the better. This means that drivers may experience a poor experience in the early stages of a memory driving route. Summary of the Invention

[0004] Based on this, it is necessary to provide a memory driving method and an intelligent navigation and control system to address the above technical problems.

[0005] In a first aspect, a method for memory driving is provided, the method being applied to an intelligent navigation and control system, the system including an intelligent driving controller, a vehicle-side sensor, and a navigation system, the method comprising:

[0006] After entering the route learning mode, the intelligent driving controller builds a memory driving map based on the road information collected by the vehicle-side sensor;

[0007] After the route learning mode ends, the intelligent driving controller stores the memory driving map and generates a mapping quality level corresponding to each road section based on the road information of each road section in the memory driving map;

[0008] The intelligent driving controller synchronizes the route identification corresponding to the memory driving map and the mapping quality level of each road section to the navigation system;

[0009] After entering the memory driving mode, the navigation system plays back the target memory driving map corresponding to the target route identifier selected by the user, and displays the mapping quality level of each road section in the target memory driving map in real time;

[0010] After ending the memory driving mode, the intelligent driving controller updates the road information of the traveled section in the target memory driving map and the mapping quality level of the traveled section, and synchronizes the route identification corresponding to the target memory driving map and the mapping quality level of the traveled section to the navigation system.

[0011] As an optional implementation manner, generating a mapping quality level corresponding to each road section according to the road information of each road section in the memorized driving map includes:

[0012] For each road section in the memory driving map, if the road information of the road section contains various types of feature points, the mapping quality level of the road section is a good quality level;

[0013] If the road information of the road section contains feature points of various key types, but lacks some feature points of common types, the mapping quality level of the road section is general quality level;

[0014] If some key types of feature points are missing from the road information of the road section, the mapping quality level of the road section is poor.

[0015] As an optional implementation manner, the key types of feature points include road grade, intersection type, lane line type, lane guide arrows, speed limit signs, zebra crossings and traffic lights.

[0016] As an optional implementation, the method further includes:

[0017] After entering the route learning mode, the intelligent driving controller sends a route drawing start request to the navigation system;

[0018] The navigation system draws the traveled route track in real time and displays the route track to the user.

[0019] As an optional implementation, the method further includes:

[0020] After the route learning mode ends, the intelligent driving controller sends a route drawing end request to the navigation system;

[0021] The navigation system ends drawing the route trajectory.

[0022] As an optional implementation, after the intelligent driving controller synchronizes the route identifier corresponding to the memorized driving map and the mapping quality level of each road segment to the navigation system, the method further includes:

[0023] The navigation system stores the route identifier, starting point, end point, route trajectory and mapping quality level corresponding to the memory driving map.

[0024] As an optional implementation manner, the method for entering the memory driving mode includes:

[0025] The intelligent driving controller receives a target route identifier selected by the user, determines a target memory driving map corresponding to the target route identifier, and prompts the user to drive to the route of the target memory driving map;

[0026] When the vehicle is on the route of the target memory driving map, the intelligent driving controller controls the vehicle to enter the memory driving mode.

[0027] As an optional implementation, after the memory driving mode ends, the intelligent driving controller updates the road information of the traveled section in the target memory driving map and the mapping quality level of the traveled section, including:

[0028] If the road information of the traveled road segment contains a new feature point, the mapping quality level of the traveled road segment is updated according to the road information of the traveled road segment.

[0029] In a second aspect, an intelligent navigation and control system is provided, which includes an intelligent driving controller, a vehicle-side sensor and a navigation system to implement the method described in any one of the first aspects.

[0030] The present application provides a method for memory driving and an intelligent navigation and control system. The technical solution provided by the embodiments of the present application brings at least the following beneficial effects: after entering the route learning mode, the intelligent driving controller constructs a memory driving map based on the road information collected by the vehicle-side sensor; after ending the route learning mode, the intelligent driving controller stores the memory driving map and generates a mapping quality level corresponding to each road section based on the road information of each road section in the memory driving map; the intelligent driving controller synchronizes the route identifier corresponding to the memory driving map and the mapping quality level of each road section to the navigation system; after entering the memory driving mode, the navigation system replays the target memory driving map corresponding to the target route identifier according to the target route identifier selected by the user, and displays the mapping quality level of each road section in the target memory driving map in real time; after ending the memory driving mode, the intelligent driving controller updates the road information of the traveled road section in the target memory driving map and the mapping quality level of the traveled road section, and synchronizes the route identifier corresponding to the target memory driving map and the mapping quality level of the traveled road section to the navigation system. This application uses a fusion navigation method for learning and replaying memory driving routes. Users can see the current operating status of memory driving in real time, improving information transparency during the human-machine co-driving process. While replaying the route using memory driving navigation, users can view the mapping quality level of each road section, increasing user trust in memory driving and improving the driving experience. As users repeatedly use memory driving, the mapping quality is continuously optimized and improved.

[0031] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0033] Figure 1 A schematic diagram of the structure of an intelligent navigation and control system provided in an embodiment of the present application;

[0034] Figure 2 A flowchart of a method for memorizing driving provided in an embodiment of the present application;

[0035] Figure 3 A flowchart of another method for memorizing driving provided in an embodiment of the present application;

[0036] Figure 4 A flowchart of another method for memorizing driving provided in an embodiment of the present application;

[0037] Figure 5 A flowchart of another method for memorizing driving provided in an embodiment of the present application;

[0038] Figure 6 A flowchart of another method for memorizing driving provided in an embodiment of the present application;

[0039] Figure 7 A flowchart of an example of a method for memorizing driving provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0041] The memory driving method provided in the embodiment of the present application can be applied to intelligent navigation and control systems. Figure 1 As shown, the intelligent navigation and control system includes an intelligent driving controller 110, a vehicle-side sensor 120 and a navigation system 130.

[0042] The following will describe in detail a method for memory driving provided by an embodiment of the present application in conjunction with specific implementation methods. Figure 2 A flowchart of a method for memorizing driving provided in an embodiment of the present application is shown as follows: Figure 2 The specific steps are as follows:

[0043] Step 201: After entering the route learning mode, the intelligent driving controller constructs a memory driving map based on the road information collected by the vehicle-side sensors.

[0044] In implementation, after entering the route learning mode, the intelligent driving controller can use vehicle-side sensors (such as cameras and lidars, etc.) to collect road information. Through the data from these sensors, the intelligent driving controller can create a detailed road model, which is a "memory driving map." The memory driving map not only records the physical characteristics of the road (such as road width, curves, slopes, etc.), but also contains dynamic information such as traffic signs, traffic light locations, road speed limit information, etc. In addition, this map can also record environmental changes, such as weather conditions, lighting conditions, etc. In actual applications, when the vehicle enters a new route, the intelligent driving controller activates the route learning mode. While the user drives the vehicle along the road, the intelligent driving controller records every detail of the road in real time. For example, when driving to an intersection, the system will pay special attention to recording the location of road signs and traffic lights.

[0045] In step 202 , after the route learning mode ends, the intelligent driving controller stores the memorized driving map and generates a mapping quality level corresponding to each road section based on the road information of each road section in the memorized driving map.

[0046] In implementation, when the route learning mode ends, the intelligent driving controller can store the constructed memory driving map. Based on the completeness and accuracy of the collected road information, the mapping quality level of each road section is evaluated. The mapping quality level reflects the accuracy and reliability of the map data. These levels can help users understand the data quality of a certain road section, so as to decide whether further learning or data updates are needed. The quality level may be affected by many factors, such as the accuracy of the sensor, the environmental conditions for data collection (such as insufficient light), etc. In addition, during the memory driving process, the user understands the mapping quality level, and can stay focused on the vehicle driving when the mapping quality is poor, take over in time, and avoid errors or accidents. For example, the data collected on a certain section of road at night or in the rain may not be clear enough due to light or weather reasons. The intelligent driving controller will mark it as low quality when generating the mapping quality level. On the contrary, if the data is collected on a sunny day with sufficient light, the quality level will be higher.

[0047] As an optional implementation, Figure 3 A flowchart of another method for memorizing driving provided in an embodiment of the present application is shown as follows: Figure 3 As shown, the specific steps of generating the mapping quality level corresponding to each road section according to the road information of each road section in the driving map in step 202 are as follows:

[0048] In step 301 , for each road section in the memorized driving map, if the road information of the road section contains various types of feature points, the mapping quality level of the road section is a good quality level.

[0049] In practice, when building a memory driving map, the intelligent driving controller can evaluate the detailed information of each road section, including various types of feature points. These feature points may include traffic signs, lane lines, road signs, and traffic light locations. If all the feature points of a road section are fully recorded and accurately identified, the mapping quality level of the road section will be rated as good quality. A good quality level means that the system can accurately perceive and identify all feature points of the road, which is critical to the accuracy and safety of the autonomous driving system. High-quality map data can help vehicles make better navigation decisions, especially in complex urban environments. For example, on a main urban road, the on-board sensors fully record all lane markings, traffic lights, speed limit signs and other important road feature points. All the information on this road section is accurate and complete, and the intelligent driving controller marks it as a good quality level. This means that the navigation system can rely on this data to provide accurate guidance to the vehicle and ensure driving safety.

[0050] In step 302 , if the road information of the road section contains feature points of various key types but lacks some feature points of common types, the mapping quality level of the road section is a general quality level.

[0051] In implementation, if the road information of a certain section of road contains key types of feature points (such as traffic lights, important traffic signs, etc.), but lacks some common types of feature points (such as lane lines, road surface markings, etc.), then the mapping quality level of the section will be rated as general quality level. The general quality level indicates that the information of the section is still useful for navigation and driving, but some details are not complete, which affects certain driving scenarios, such as automatic parking or handling of complex intersections. For example, on a certain rural road, the system records the main road signs and speed limit signs, but due to poor road conditions or insufficient light, the lane line information is incomplete. In this case, although the key navigation information exists, the missing details may affect the accuracy of the autonomous driving system, so the section is rated as general quality level.

[0052] In step 303 , if some key types of feature points are missing from the road information of the road section, the mapping quality level of the road section is a poor quality level.

[0053] In practice, when the road information of a certain section is missing some key types of feature points, the mapping quality level of the section will be rated as poor. The missing key feature points may include traffic lights, important traffic signs, etc. These missing points seriously affect the safety and decision-making capabilities of autonomous driving. The poor quality level means that the map data of the section is unreliable, which may cause the autonomous driving system to not work properly in some cases. For example, at a complex intersection, if the system does not accurately record the location and status of the traffic lights, or misses key traffic signs, this will cause the system to be unable to correctly judge driving priority or speed limit information. Due to the lack of these key feature points, the mapping quality level of the section is rated as poor. The system may warn the user that the navigation information of this section is incomplete and the mapping quality level of the section must be continuously improved during subsequent memory driving.

[0054] As an optional implementation, key types of feature points include road grade, intersection type, lane line type, lane guide arrows, speed limit signs, zebra crossings, and traffic lights.

[0055] In step 203 , the intelligent driving controller synchronizes the route identification corresponding to the memorized driving map and the mapping quality level of each road section to the navigation system.

[0056] During implementation, the intelligent driving controller can synchronize the mapping quality level and route markings of each road section in the memorized driving map to the navigation system. The navigation system can provide users with more accurate navigation suggestions based on this information. The synchronization process ensures that the navigation system has the latest map data and quality information, making it more valuable for reference when planning routes and making driving suggestions. Optionally, when selecting a route, the user can decide whether to use a certain road section based on the mapping quality level. For example, a road section with a low quality level may affect the performance of the autonomous driving system, so the user can choose to avoid these sections. In actual use, when the user sets the destination, the navigation system can call the memorized driving map and recommend the best route to the user based on the current traffic conditions and mapping quality level. If the mapping quality level of a certain road section is low, the system will prompt the user that there may be uncertainties and recommend other alternative routes.

[0057] As an optional implementation, after step 203, the following method is further included:

[0058] The navigation system stores and memorizes the route identification, starting point, end point, route trajectory and mapping quality level corresponding to the driving map.

[0059] During implementation, after the intelligent driving controller synchronizes the information in the memorized driving map to the navigation system, the navigation system will save this data, including route markers, starting and ending points, route trajectories, and the mapping quality level of each road section. Route markers are used to distinguish different routes, the starting and ending points mark the scope of the journey, and the route trajectory records the vehicle's travel path in detail. This data can also help users understand and select sections of road with higher driving quality and avoid areas with lower quality. In addition, saving information on mapping quality levels can enable the system to provide safer and more reliable options in subsequent route recommendations and autonomous driving. For example, after a user completes a new route, the navigation system can record the detailed information of this route. If the user plans to travel this route again, the navigation system can use the previously saved route trajectory and mapping quality level to provide the user with relatively safe and high-quality navigation suggestions. In addition, users can also view the mapping quality level of each section of the journey to decide whether the data of certain sections needs to be relearned or updated.

[0060] Step 204 , after entering the memory driving mode, the navigation system plays back the target memory driving map corresponding to the target route identifier selected by the user, and displays the mapping quality level of each road section in the target memory driving map in real time.

[0061] In practice, after entering the memory driving mode, the navigation system can replay the memory driving map corresponding to the target route selected by the user based on the target route identifier. The system also displays the map quality level of each road section in real time. The playback function allows users to preview their entire trip before departure and understand the conditions of each road section. The display of map quality level can help users identify potential driving risk areas. This process is particularly useful for planning long trips or unfamiliar routes.

[0062] Alternatively, if a user plans to travel from city A to city B, the navigation system can first display an overview of the entire trip and mark sections of road that may have problems (such as sections with low map quality). Users can then review the road conditions before or during the trip.

[0063] In step 205, after the memory driving mode ends, the intelligent driving controller updates the road information and mapping quality level of the traveled road segments in the target memory driving map, and synchronizes the route identification and mapping quality level of the traveled road segments corresponding to the target memory driving map to the navigation system.

[0064] In implementation, after the memory driving mode ends, the intelligent driving controller can update the road information and mapping quality level of the traveled section in the target memory driving map. These updated data will be synchronized to the navigation system again to ensure the real-time and accuracy of its data. The process of updating data can include recording new changes to the road, such as new traffic signs, changes in road conditions, etc. The synchronized navigation system can provide more reliable information for future driving, which is especially important for frequently changing urban roads. Suppose that when a vehicle passes through a certain section of road, the speed limit sign was not collected due to environmental reasons in the previous route learning phase, or a new speed limit sign was added to the section. The intelligent driving controller can record this change and update the road information. Subsequently, the navigation system will obtain the latest data, so that the next time it passes through the section, it can provide the latest speed limit information, improve the mapping quality level, and help users avoid violations.

[0065] As an optional implementation, after the memory driving mode ends in step 205, the intelligent driving controller updates the road information of the traveled section and the mapping quality level of the traveled section in the target memory driving map in the following specific method: if the road information of the traveled section contains new feature points, the mapping quality level of the traveled section is updated according to the road information of the traveled section.

[0066] In practice, after ending the memory driving mode, the intelligent driving controller can analyze the latest road information for the traveled road segments. If it determines that these road segments contain new feature points (such as previously undetected feature points, newly added traffic signs, new road markings, and changes in road structure), the intelligent driving controller can update the mapping quality level of these road segments accordingly. The information from these new feature points can improve the map accuracy and quality level of these road segments, ensuring safer and more accurate autonomous driving and navigation in the future. Timely updates to this information help keep the map up to date, thereby supporting more accurate navigation and autonomous driving decisions. The updated mapping quality level also reflects the completeness and accuracy of the data, allowing the system to consider the latest road conditions when planning routes. In practice, suppose a vehicle is traveling on a recorded route and the intelligent driving controller detects a new speed limit sign or a new pedestrian crossing. These are new feature points. The intelligent driving controller will record these new feature points and add them to the memory driving map. The intelligent driving controller can compare the road segment information before and after the update. If the new information significantly improves the data completeness or accuracy, the intelligent driving controller will upgrade the mapping quality level of this road segment. For example, after the originally unmarked speed limit information is added, all the features of the road section are complete and the quality level can be improved from "fair" to "good".

[0067] As an optional implementation, Figure 4 A flowchart of another method for memorizing driving provided in an embodiment of the present application is shown as follows: Figure 4The specific steps are as follows:

[0068] Step 401: After entering the route learning mode, the intelligent driving controller sends a route drawing start request to the navigation system.

[0069] In implementation, after entering the route learning mode, the intelligent driving controller can also record the vehicle's driving route. To this end, the intelligent driving controller can send a request to the navigation system to start the route drawing function so that the navigation system is ready to start recording and drawing the vehicle's real-time driving trajectory. This process is the starting point of the entire route learning, and its purpose is to provide basic data for memorizing driving maps. The navigation system not only records the vehicle's driving route, but also simultaneously collects detailed information related to the road, such as geographic coordinates, speed, road conditions, etc. These data will be used for subsequent map construction and analysis. For example, when the user drives the vehicle into a new route, the intelligent driving controller detects the new driving path and sends a "start drawing route" request to the navigation system through the on-board communication system. After receiving the request, the navigation system immediately begins to record every movement of the vehicle to ensure that detailed data of the entire route is captured.

[0070] In step 402 , the navigation system draws the traveled route in real time and displays the route to the user.

[0071] In practice, after receiving a request from the intelligent driving controller, the navigation system begins to draw the vehicle's driving trajectory in real time. This includes the vehicle's current route, the places it has passed through, and the surrounding environment. The navigation system will display this data graphically on the screen, allowing users to see the vehicle's driving path in real time. Users can intuitively see the driving route on the screen, which facilitates real-time judgment and planning of road conditions. At the same time, the real-time route trajectory can also be used as a driving record for future review or analysis. In actual application, while the vehicle is driving, the navigation system displays a dynamically updated route trajectory on the screen. This trajectory shows the vehicle's current position, the route it has traveled, and the destination. Users can clearly see the roads they have traveled on the navigation system's screen, providing users with a visual reference.

[0072] As an optional implementation, Figure 5 A flowchart of another method for memorizing driving provided in an embodiment of the present application is shown as follows: Figure 5 The specific steps are as follows:

[0073] Step 501: After the route learning mode ends, the intelligent driving controller sends a route drawing end request to the navigation system.

[0074] In implementation, when the route learning mode ends, the intelligent driving controller sends a "route end drawing request" to the navigation system and instructs the navigation system to stop recording and drawing the current route trajectory. The end of the route learning mode may be due to the user arriving at the destination, the preset learning of the driving route is completed, or the user manually ends the learning mode. In actual use, assuming that the user has completed the learning of a specific route (such as a new route from home to the company), when the user arrives at the destination or chooses to end the route learning mode, the intelligent driving controller will send a request to end the drawing to the navigation system to stop the current data recording and save the collected information.

[0075] Step 502: The navigation system ends drawing the route track.

[0076] In practice, upon receiving a "route drawing end request" from the intelligent driving controller, the navigation system stops drawing the current route. This process may include stopping data recording, performing a final review, and saving the collected route information. Users can later review the saved route or use the data for further analysis and optimization.

[0077] As an optional implementation, Figure 6 A flowchart of another method for memorizing driving provided in an embodiment of the present application is shown as follows: Figure 6 As shown in the figure, the specific steps to enter the memory driving mode are as follows:

[0078] In step 601, the intelligent driving controller receives a target route identifier selected by the user, determines a target memory driving map corresponding to the target route identifier, and prompts the user to drive to the route of the target memory driving map.

[0079] In implementation, after receiving the target route identifier selected by the user, the intelligent driving controller can determine the target memory driving map corresponding to the target route identifier. This map contains detailed information about the specific route the user plans to travel. The intelligent driving controller then provides instructions to the user to guide the vehicle to the starting point of the target route in order to start entering the memory driving mode. This step ensures that both the user and the vehicle are on a known and well-recorded route, allowing the system to provide navigation and driving assistance more effectively. Prompting the user to go to the starting point of a specific route also helps the system confirm that the vehicle is about to enter the correct navigation area, avoiding data confusion or navigation errors caused by entering other routes by mistake. For example, the user wants to retake a previously recorded route. The user selects the identifier of this route in the navigation system. After receiving the selection, the intelligent driving controller displays the starting point of this route and guides the user to drive to the starting point through navigation. When the user approaches the starting point, the intelligent driving controller can remind the user to prepare to enter the memory driving mode through voice or screen prompts.

[0080] Step 602: When the vehicle is on the route of the target memory driving map, the intelligent driving controller controls the vehicle to enter the memory driving mode.

[0081] In implementation, when the vehicle reaches the starting point of the target memory driving map or any part of the route, the intelligent driving controller can detect that the vehicle's position has entered the target route range. The intelligent driving controller automatically or prompts the user to enter the memory driving mode. In this mode, the system will enable relevant automatic driving or driving assistance functions to assist the user in driving based on the data in the memory driving map. For example, when the user's vehicle arrives at the starting point of the target route, the intelligent driving controller confirms that the current position matches the target memory driving map and prompts the user to "enter memory driving mode." The intelligent driving controller automatically takes over some driving tasks, such as staying in a specific lane, controlling vehicle speed, identifying traffic signals, etc. The user can choose to accept or cancel the start of this mode. If accepted, the system will automatically drive or provide highly assisted driving based on the previously recorded map data to ensure safe and smooth driving on the target route.

[0082] As an optional implementation, Figure 7 This is a flowchart of an example of a method for memorizing driving provided in an embodiment of the present application, such as Figure 7 The specific steps are as follows:

[0083] In step 701, the user manually drives the vehicle to begin learning the route. Upon receiving this instruction, the intelligent driving controller enters route learning mode and uses on-board sensors to identify road environment information and begin mapping. The intelligent driving controller sends a route drawing start request to the navigation system. Upon receiving this request, the navigation system begins drawing the route track from the starting point on the current map interface and displays it to the user.

[0084] In step 702, the user manually drives the vehicle to the destination, completing route learning. Upon receiving this command, the intelligent driving controller enters map storage mode and saves the created map. The intelligent driving controller issues a request to complete route drawing and simultaneously sends the route identifier and mapping quality level information to the navigation system. Upon receiving this information, the navigation system completes route drawing and saves the route identifier, start and end points, GPS track information, and mapping quality level information for the current route.

[0085] Step 703: After the user manually drives the vehicle and completes route learning for the first time, the intelligent driving controller constructs map data based on the route surrounding environment information collected by the vehicle-side sensors. Combined with the GPS trajectory information, it determines whether the road feature points required for different sections of the learned route have been learned and collected completely. Finally, it generates map quality level information for different sections, such as road grade, intersection type, lane line type, lane guide arrows, speed limit signs, zebra crossings, traffic lights, etc. A good quality level indicates that all the required feature points for a certain section have been learned and collected completely. A fair quality level indicates that key feature points for a certain section have been learned and collected, but general feature points are missing. A poor quality level indicates that key feature points are missing for a certain section.

[0086] In step 704, the user selects a learned route and drives the vehicle onto the route, or drives the vehicle onto a learned route. After the route is successfully located, the memory driving mode is activated and the intelligent driving controller enters the memory driving mode. The intelligent driving controller issues a route playback request and simultaneously sends the successfully located route identifier to the navigation system. After receiving this information, the navigation system extracts the relevant route information based on the route identifier, plays the route back to the current map interface, and simultaneously displays the route's mapping quality level information to the user.

[0087] In step 705, after the vehicle reaches the route's destination and exits the memory driving mode, the intelligent driving controller updates the map information for the route. If missing feature points are collected during learning for sections with average or poor quality, the quality of the section is improved. A request to update the route's mapping quality level is then sent to the navigation system. Upon receiving this request, the navigation system updates the mapping quality level for the route.

[0088] An embodiment of the present application provides a method for memory driving, and the technical solution provided by the embodiment of the present application brings at least the following beneficial effects: after entering the route learning mode, the intelligent driving controller constructs a memory driving map based on the road information collected by the vehicle-side sensor; after ending the route learning mode, the intelligent driving controller stores the memory driving map, and generates a mapping quality level corresponding to each road section based on the road information of each road section in the memory driving map; the intelligent driving controller synchronizes the route identification corresponding to the memory driving map and the mapping quality level of each road section to the navigation system; after entering the memory driving mode, the navigation system replays the target memory driving map corresponding to the target route identification according to the target route identification selected by the user, and displays the mapping quality level of each road section in the target memory driving map in real time; after ending the memory driving mode, the intelligent driving controller updates the road information and mapping quality level of the traveled road section in the target memory driving map, and synchronizes the route identification and mapping quality level of the traveled road section corresponding to the target memory driving map to the navigation system. This application uses a fusion navigation method for learning and replaying memory driving routes. Users can see the current operating status of memory driving in real time, improving information transparency during the human-machine co-driving process. While replaying the route using memory driving navigation, users can view the mapping quality level of each road section, increasing user trust in memory driving and improving the driving experience. As users repeatedly use memory driving, the mapping quality is continuously optimized and improved.

[0089] It should be understood that although Figures 2 to 7 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figures 2 to 7 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The order of execution of these steps or stages is not necessarily one by one, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0090] It can be understood that the same / similar parts between the various embodiments of the above method in this specification can be referred to each other, and each embodiment focuses on the differences from other embodiments. For related parts, please refer to the description of other method embodiments.

[0091] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0092] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0093] It should also be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data for analysis, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0094] Each embodiment in this specification is described in a related manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiment is generally similar to the method embodiment, so the description is relatively simple. For related parts, refer to the description of the method embodiment.

[0095] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0096] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for memorizing driving, characterized in that: The method is applied to an intelligent navigation and control system, which includes an intelligent driving controller, a vehicle-side sensor, and a navigation system. The method includes: After entering the route learning mode, the intelligent driving controller builds a memory driving map based on the road information collected by the vehicle-side sensor; After the route learning mode ends, the intelligent driving controller stores the memory driving map and generates a mapping quality level corresponding to each road section based on the road information of each road section in the memory driving map; The intelligent driving controller synchronizes the route identification corresponding to the memory driving map and the mapping quality level of each road section to the navigation system; After entering the memory driving mode, the navigation system plays back the target memory driving map corresponding to the target route identifier selected by the user, and displays the mapping quality level of each road section in the target memory driving map in real time; After ending the memory driving mode, the intelligent driving controller updates the road information of the traveled section in the target memory driving map and the mapping quality level of the traveled section, and synchronizes the route identification corresponding to the target memory driving map and the mapping quality level of the traveled section to the navigation system.

2. The method according to claim 1, characterized in that Generating a mapping quality level corresponding to each road section according to the road information of each road section in the memorized driving map includes: For each road section in the memory driving map, if the road information of the road section contains various types of feature points, the mapping quality level of the road section is a good quality level; If the road information of the road section contains feature points of various key types, but lacks some feature points of common types, the mapping quality level of the road section is general quality level; If some key types of feature points are missing from the road information of the road section, the mapping quality level of the road section is poor.

3. The method according to claim 2, characterized in that The key types of feature points include road grade, intersection type, lane line type, lane guide arrows, speed limit signs, zebra crossings and traffic lights.

4. The method according to claim 1, wherein The method further comprises: After entering the route learning mode, the intelligent driving controller sends a route drawing start request to the navigation system; The navigation system draws the traveled route track in real time and displays the route track to the user.

5. The method according to claim 4, characterized in that The method further comprises: After the route learning mode ends, the intelligent driving controller sends a route drawing end request to the navigation system; The navigation system ends drawing the route trajectory.

6. The method according to claim 5, characterized in that After the intelligent driving controller synchronizes the route identifier corresponding to the memory driving map and the mapping quality level of each road segment to the navigation system, the method further includes: The navigation system stores the route identifier, starting point, end point, route trajectory and mapping quality level corresponding to the memory driving map.

7. The method according to claim 1, characterized in that The method for entering the memory driving mode includes: The intelligent driving controller receives a target route identifier selected by the user, determines a target memory driving map corresponding to the target route identifier, and prompts the user to drive to the route of the target memory driving map; When the vehicle is on the route of the target memory driving map, the intelligent driving controller controls the vehicle to enter the memory driving mode.

8. The method according to claim 6, characterized in that After the memory driving mode is ended, the intelligent driving controller updates the road information of the traveled road section in the target memory driving map and the mapping quality level of the traveled road section, including: If the road information of the traveled road segment contains a new feature point, the mapping quality level of the traveled road segment is updated according to the road information of the traveled road segment.

9. An intelligent navigation and control system, characterized in that: The system includes an intelligent driving controller, a vehicle-side sensor and a navigation system, and implements the method described in any one of claims 1 to 8.

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