Route planning method, navigation method and system for driving practice scene and vehicle

Abstract

The invention discloses a route planning method, a navigation method, a system and a vehicle for a driving practice scene. The route planning method 100 comprises a road difficulty classification step 102: based on static and dynamic attributes related to roads, classifying and marking the roads by using different levels of driving practice difficulties; and a route planning setting step 104: setting a planned route for the driving practice scene based on the driving preference of the user. The navigation method 200 comprises a navigation service step 202: providing route guidance for a user through audio broadcasting and / or video display based on a route planned by the method 100; and a driving feedback step 204: providing feedback for the user after the travel is finished. According to the method and the system, manual pre-research on road conditions and areas for finding a proper navigation route is not needed, so that the time for route preparation or selection of a user is saved, and the special requirements of the user on route planning and navigation service in a driving practice scene are met.

Description

Technical Field

[0001] The present invention relates to the technical field of road traffic, and in particular to a route planning method, a navigation method, a system and a vehicle for a driving practice scenario. Background Art

[0002] With the continuous development of navigation systems and autonomous driving technologies, how to perform optimal route planning is an important and popular topic. The current focus of research on route planning for navigation systems or autonomous driving vehicles is how to use GPS information, map information, and perception information from in-vehicle sensors to plan the best route.

[0003] In the prior art, a relatively conventional and well-known route planning method is to provide 2-3 alternative routes for users to select during navigation. Different route planning is usually based on factors such as the overall distance of the route, whether it passes through highway sections, the number of toll stations on the route, the number of traffic lights on the route, and the congestion status on the route.

[0004] The selectivity of different driving routes provided by existing navigation systems or vehicles is very limited and almost the same for all users, which cannot meet the needs of users for special route customization and selection. At the same time, the interaction of users after selecting the corresponding route is also very limited or almost non-existent. Moreover, in the navigation systems of the prior art, although users can select their own preferences, such as highway priority, distance priority, time priority, etc. for route planning and navigation. However, this selection is still very rough and ordinary. Therefore, when people use existing navigation systems or vehicles equipped with navigation systems, they must rely on other information to achieve special route planning and selection for specific scenarios (such as in the driving practice scenarios of novice drivers or drivers with insufficient experience).

[0005] In the prior art, a conventional and well-known route planning method requires users to set a "starting point" and an "ending point" or "waypoints", with the fundamental requirement of "reaching" the ending point or waypoints, which cannot meet the special route requirements of novice drivers or drivers with insufficient experience for driving practice scenarios: not taking "reaching" as the fundamental purpose, without setting an "ending point", but taking "driving duration", "driving practice difficulty", "driving scenario", etc. as the fundamental core requirements. Summary of the Invention

[0006] In order to solve the above problems existing in the prior art, the present invention provides a route planning method, a navigation method, a system and a vehicle, which provide intelligent route planning and navigation services for users in driving practice scenarios, without the need to conduct prior research on road conditions and road areas to find a suitable navigation route.

[0007] According to a first aspect of the present invention, there is provided a route planning method for a driving practice scenario, comprising the following steps: a road difficulty classification step: classifying and marking the road with different levels of driving practice difficulty based on static and dynamic attributes related to the road; and a route planning setting step: setting a planned route for the driving practice scenario based on the user's driving practice preferences.

[0008] According to a second aspect of the present invention, there is provided a navigation method for a driving practice scenario, comprising a navigation service step: based on the route planned by the above-mentioned route planning method, adopting the guiding mode of a virtual driving instructor, and providing guidance on the planned route to the user through audio broadcast and / or video display.

[0009] According to a third aspect of the present invention, there is provided a route planning system for a driving practice scenario, comprising a road difficulty classification unit for classifying and marking the road with different levels of driving practice difficulty based on static and dynamic attributes related to the road; and a route planning setting unit for setting a planned route for the driving practice scenario based on the user's driving practice preferences.

[0010] According to a fourth aspect of the present invention, there is provided a navigation system for a driving practice scenario, comprising a navigation service unit for providing guidance on the planned route to the user through audio broadcast and / or video display by adopting the guiding mode of a virtual driving instructor based on the route planned by the above-mentioned route planning system.

[0011] According to a fifth aspect of the present invention, there is provided a vehicle comprising the above-mentioned route planning system for a driving practice scenario and the above-mentioned navigation system for a driving practice scenario.

[0012] The route planning method, navigation method, system and vehicle according to the present invention achieve the following beneficial technical effects:

[0013] 1. Provide intelligent route planning and navigation services for specific driving practice scenarios, without any manual pre-research on road conditions and road areas to find any suitable navigation routes, saving the user's time for route preparation or selection.

[0014] 2. Provide customized route planning and navigation services for the special demands of users for specific driving practice scenarios that do not take "reaching the end point" as the core purpose, so as to focus on solving specific navigation demands: such as driving duration, driving practice difficulty, driving scenario, etc.

[0015] 3. For specific driving practice scenarios, all roads are classified and marked with different levels of driving practice difficulty based on static and dynamic attributes related to the road from map data. At the same time, by planning routes based on the driving practice preferences of users, the special requirements for route planning and navigation services in driving practice scenarios for novice drivers and drivers with less experience are met.

[0016] 4. For specific driving practice scenarios, a more perfect and fun navigation service and driving feedback service are provided through immersive and interactive guidance during the driving practice journey and similar interactive game-like feedback after the driving practice journey.

[0017] 5. Various classification methods can be flexibly adopted to pre-classify and mark road and lane data to meet the requirements of users for route planning and selection in other specific scenarios or needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of a route planning method for a driving practice scenario according to the first embodiment of the present invention.

[0019] Figure 2 is Figure 1 a flowchart of the first road difficulty classification step in the route planning method shown in

[0020] Figure 3 is Figure 1 a flowchart of the second road difficulty classification step in the route planning method shown in

[0021] Figure 4 is Figure 1 a flowchart of the route planning setting step in the route planning method shown in

[0022] Figure 5 It is a flowchart of a navigation method for a driving practice scenario according to the second embodiment of the present invention.

[0023] Figure 6 is Figure 5 a flowchart of the driving feedback step in the navigation method shown in

[0024] Figure 7 It is a structural block diagram of a navigation system for a driving practice scenario according to the third embodiment of the present invention.

[0025] Figure 8 is Figure 7 a structural block diagram of the first road difficulty classification unit in the navigation system shown in

[0026] Figure 9 is Figure 7The structural block diagram of the second road difficulty classification unit in the navigation system shown in

[0027] Figure 10 is Figure 7 The structural block diagram of the route planning setting unit in the navigation system shown in

[0028] Figure 11 The structural block diagram of the navigation system for the driving practice scenario according to the fourth embodiment of the present invention.

[0029] Figure 12 is Figure 11 The structural block diagram of the driving feedback unit in the navigation system shown in

[0030] Figures 13A - 13F The display schematic diagram of the static and dynamic attributes related to the road used in each embodiment of the present invention.

[0031] Figure 14 The display schematic diagram of classifying and marking roads according to the driving practice difficulty in each embodiment of the present invention.

[0032] Figures 15A - 15D The input display schematic diagram of the user's driving practice preference information in each embodiment of the present invention.

[0033] Figure 16 The display schematic diagram of various driving feedbacks in each embodiment of the present invention. Detailed implementation manners

[0034] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments disclosed in this application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present invention.

[0035] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the accompanying drawings are intended to cover non-exclusive inclusion. For example, a process, system, product or device including a series of steps or units is not necessarily limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, systems, products or devices.

[0036] In the present invention, unless otherwise specified, the quantifiers "a" and "one" do not exclude the scenario of multiple elements.

[0037] It should also be noted here that, in the embodiments of the present invention, for the sake of clarity and simplicity, only a part of the components or assemblies may be shown. However, those of ordinary skill in the art can understand that, under the teaching of the present invention, the required components or assemblies can be added according to the specific scenario requirements. Additionally, unless otherwise stated, the features in different embodiments of the present invention can be combined with each other.

[0038] In addition, the numbering of the steps of each method of the present invention does not limit the execution order of the method steps. Unless otherwise specified, the method steps can be executed in different orders.

[0039] The following further elaborates in detail on the route planning method, navigation method, system, and vehicle provided by the present invention for driving practice scenarios in conjunction with the accompanying drawings and specific embodiments. According to the following description, the advantages and features of the present invention will be clearer. It should be noted that the accompanying drawings are all in a very simplified form and use non-precise scales, only for conveniently and clearly assisting in explaining the purpose of the embodiments of the present invention.

[0040] First, in conjunction with Figure 1 、 Figures 13A - 13F and Figures 15A - 15D as shown. Figure 1 Figure 17 shows a route planning method 100 for driving practice scenarios according to the first embodiment of the present invention. The route planning method 100 is implemented through the in-vehicle navigation system of the vehicle or through the in-vehicle navigation system of the vehicle in combination with the cloud server connected thereto, and includes the following steps:

[0041] Road difficulty classification step 102: Classify and label roads with different levels of driving practice difficulty based on static and dynamic attributes related to the roads from map data. The classification and labeling of the driving practice difficulty of the roads will be used as the basic input data for route planning and navigation route calculation. Among them, the static and dynamic attributes related to the roads include, but are not limited to, at least one of the following: the number of intersections in the road, the topological complexity of the road (for example: how many roads a road is connected to), whether there is a road median (isolating oncoming vehicles, isolating unprotected pedestrians, cyclists or motorcyclists, tricycles, etc. in the same direction), dynamic traffic information (traffic flow, traffic congestion situation), road speed limit, etc. For specific examples of the static and dynamic attributes related to the roads, please refer to Figures 13A - 13F as Figures 13A - 13F shown, the static and dynamic attributes related to the roads include, but are not limited to, the number of intersections I in the road, the topological complexity C of the road, whether there is a road median Z, dynamic traffic information D, road speed limit including urban road speed limit CL or highway road speed limit HL, etc.; and

[0042] Route planning setting step 104: Set the planned route for the driving practice scenario based on the user's driving practice preferences, where the user's driving practice preferences include, but are not limited to, at least one of the following categories: driving duration, departure location of the route, passing locations, preferred driving practice difficulty level and corresponding percentage, preferred driving scenario percentage, etc. For an example of the input display of the user's driving practice preference information, please refer to Figures 15A - 15D , Figure 15A which exemplarily shows the driving duration input by the user. Figure 15B which exemplarily shows the departure location, destination, and passing locations of the route input by the user (in other cases, the user can only input the departure location and passing locations of the route and then set the destination during the journey). Figure 15C which exemplarily shows the preferred driving practice difficulty level and corresponding percentage input by the user (for example, the preferred driving practice difficulty levels are easy E, moderate M, and difficult H, and the corresponding percentages are 30% for easy E, 50% for moderate M, and 20% for difficult H). Figure 15D which exemplarily shows the preferred driving scenario percentage input by the user (for example, urban roads account for 70% and highways account for 30%).

[0043] Combined with Figure 2 and Figure 14 as shown. Figure 2 shows Figure 1 the first road difficulty classification step 102A in the route planning method 100 shown. The first road difficulty classification step 102A includes the following steps:

[0044] Predetermined difficulty coefficient setting step 1022: Set corresponding multiple predetermined difficulty coefficients for different static or dynamic attributes of the road. For example, set the predetermined difficulty coefficient of a straight road or a road or lane with extremely low traffic flow to 1; set the predetermined difficulty coefficient of a narrow road or a bend with a curvature value greater than a first predetermined value (for example: 0.004 m^-1) or a road with a change in the number of lanes greater than a second predetermined value (for example: changing from 2 lanes to 1) to 3; set the predetermined difficulty coefficient of a road with unprotected oncoming vehicle meeting or a multi-exit roundabout or a road with a large number of electric vehicles / bicycles or a road without pedestrian protection facilities to 5. Of course, those skilled in the art can understand that according to different specific application situations, the multiple predetermined difficulty coefficients can be formulated using different setting criteria;

[0045] Difficulty total score generation step 1024: Generate a difficulty total score for each section of the road based on the multiple predetermined difficulty coefficients. For example, the difficulty total score of a road with narrow large curvature and unprotected passing = 3 (predetermined difficulty coefficient of narrow road) + 3 (predetermined difficulty coefficient of a bend with a curvature value greater than the first predetermined value) + 5 (predetermined difficulty coefficient of an unprotected passing road) = 11); and

[0046] Difficulty level determination step 1026: Determine the level of the driving practice difficulty of each section of the road according to the interval where the difficulty total score of each section of the road is located. For example, the level of the driving practice difficulty corresponding to the interval 0 - 3 of the difficulty total score is easy E; the level of the driving practice difficulty corresponding to the interval 3 - 9 of the difficulty total score is moderate M; the level of the driving practice difficulty corresponding to the interval > 9 of the difficulty total score is difficult H). Of course, those skilled in the art can understand that according to different specific application scenarios, the levels of the driving practice difficulty can be formulated using different setting criteria; and

[0047] Classification and marking step 1028: Classify and mark each section of the road with the level of the driving practice difficulty of the corresponding section of the road. For example, mark each section of the road with different colors corresponding to different levels of the driving practice difficulty in the navigation map. For specific examples of classifying and marking roads with different levels of driving practice difficulty, please refer to Figure 14 , as Figure 14 shown, the different levels of the driving practice difficulty include three levels: easy E, moderate M, and difficult H, and are marked with corresponding different colors in the navigation map. For example, the easy level E is marked in green in the figure, the moderate level M is marked in yellow in the figure, and the difficult level H is marked in red in the figure.

[0048] Figure 3 shows Figure 1 a flowchart of the second road difficulty classification step 102B in the route planning method 100 shown in. The difference between the second road difficulty classification step 102B and the first road difficulty classification step 102A is only that: the second road difficulty classification step 102B further includes a user - defined step 1030 between the predetermined difficulty coefficient setting step 1022 and the difficulty total score generation step 1024: the user can customize one or more of the multiple predetermined difficulty coefficients to overwrite the corresponding predetermined difficulty coefficients set by the predetermined difficulty coefficient setting step 1022. For example: User A believes that the dynamic attribute of the road "unprotected passing oncoming" is a relatively simple scenario, and can customize the predetermined difficulty coefficient 5 of this dynamic attribute of the road set by the predetermined difficulty coefficient setting step 1022 to 3 to overwrite the predetermined difficulty coefficient set by the predetermined difficulty coefficient setting step 1022.

[0049] Figure 4 To show Figure 1 A flowchart of the route planning setting step 104 in the shown route planning method 100. The route planning setting step 104 specifically includes the following steps:

[0050] Driving practice preference priority presetting step 1042: The user pre-sets the priority of one or more types of driving practice preferences through the in-vehicle navigation system and stores it in the processor of the in-vehicle navigation system or the cloud server connected to the in-vehicle navigation system;

[0051] Driving practice requirement input step 1044: For the driving practice scenario, the user inputs specific values of one or more items among the one or more types of driving practice preferences, and the specific values represent the user's driving practice requirements for the driving practice scenario;

[0052] Planned route calculation step 1046: Based on the priority of the one or more types of driving practice preferences preset by the user and the specific values of one or more items among the one or more types of driving practice preferences input, the in-vehicle navigation system or the cloud server connected to it calculates the corresponding route and provides the calculation result (in this embodiment, the calculation result is displayed on the display interface of the navigation system, and of course, it can also be replaced or combined with other methods such as voice playback). The calculation result shows the proportion of the calculated corresponding route that meets the user's driving practice requirements (for example: 100% meets or 80% meets, etc.) and / or whether each specific value in the driving practice requirements is met (for example: the driving duration is met, the departure place of the route is met, the passing place is not met, the preferred driving difficulty and the corresponding percentage are met, the percentage of the preferred driving scenario is not met, etc.); and

[0053] Planned route determination step 1048: Based on the calculation result, the user determines whether to accept the calculated corresponding route as the planned route for the driving practice scenario. If the user accepts, the in-vehicle navigation system starts navigation; if the user does not accept, it returns to the driving practice requirement input step 1042, and the user re-enters the modified specific values of one or more items among the one or more types of driving practice preferences for the driving practice scenario. The subsequent steps 1044-1048 are continuously executed.

[0054] Combined with Figures 5 - 6 and Figure 16 as shown Figure 5Shows a navigation method 200 for a driving practice scenario according to the second embodiment of the present invention. The navigation method 200 is implemented by the in-vehicle navigation system of the vehicle or by the in-vehicle navigation system of the vehicle in combination with a cloud server connected thereto, and includes the following steps:

[0055] Navigation service step 202: Based on the route planned by the route planning method 100, the navigation system provides guidance on the planned route to the user in the guiding manner of a virtual driving instructor, through audio broadcast and / or video display. The guiding manner of the virtual driving instructor includes, but is not limited to, the following audio broadcast and / or video display content, for example:

[0056] 1. Guidance for the front scene or correct driving behavior:

[0057] "Attention - You will face a difficult road. Please get ready for this challenge";

[0058] "Please note that the number of lanes will change after passing the current traffic light. Please observe the target lane in advance";

[0059] "Please note that you will enter a roundabout 500m ahead. Pay attention to the traffic light directly in front of the vehicle head during driving in the roundabout";

[0060] "Please slow down and pay attention - The entrance and exit of the industrial park is 200m ahead. There may be vehicles or pedestrians entering and exiting";

[0061] "Please slow down and pay attention to avoid - It is the evening rush hour now. There are many vehicles and pedestrians at the front intersection";

[0062] "Please note to change lanes in advance - The number of lanes ahead is decreasing".

[0063] 2. Guidance for encouraging correct behavior or correcting incorrect behavior (simulating a driving practice coach) for the traveled scene:

[0064] "At the left-turn intersection just passed, you slowed down and stopped in time. Well done!";

[0065] "At the left-turn intersection just passed, you didn't look at the vehicles on the right. Please pay attention next time";

[0066] "Come on! Half of today's driving practice journey has passed. Please concentrate on driving the vehicle and keep up the good work";

[0067] "The driving practice time today has reached 1 hour. If you feel tired, a nearby parking lot can be found for you. Please don't drive tired"; and

[0068] Driving feedback step 204: After the driving journey on the route is completed, various driving feedbacks similar to interactive games are provided to the user through the in-vehicle navigation system. Among them, the driving feedback includes but is not limited to: celebration of the end of the practice journey (including but not limited to seat vibration, sound effects, ambient light effects to render the celebration atmosphere), total mileage statistics of the journey, total duration statistics of the journey, suggestions for subsequent driving practice routes, etc.

[0069] Figure 6 The flowchart of the driving feedback step 204 is shown. The driving feedback step 204 includes:

[0070] Completion score calculation step 2042: Calculate and display the completion score R through the following formula: R = (A * X) - (B * Y), where A is the total driving practice difficulty score of the road, X is the mileage correctly driven during the driving journey, B is the error / hazardous driving coefficient (which can be preset by the user or the cloud server, for example: the hazard coefficient for frequently fine-tuning the steering to control the vehicle is 1, the hazard coefficient for driving over the line for too long is 1, the hazard coefficient for not starting in time at a green light is 1, the hazard coefficient for not decelerating in time at a red light is 2, the hazard coefficient for not braking in time resulting in too close a following distance is 2, the hazard coefficient for not turning on the turn signal when turning is 2, the hazard coefficient for not observing the traffic flow in time at an intersection resulting in sudden braking is 3, the hazard coefficient for encountering a scrape is 5, etc.), and Y is the mileage of incorrect driving during the driving journey;

[0071] Hazardous behavior analysis step 2044: Record the data of hazardous behaviors during the driving journey (such as videos of hazardous behaviors captured by in-vehicle cameras or vehicle-end data of hazardous behaviors collected by other types of sensors, etc.), analyze the types and severity of the hazardous behaviors (the analysis criteria can be preset by the user or the cloud server) and provide the analysis results (for example, it can be displayed on the display interface of the in-vehicle navigation system); and

[0072] Driving feedback providing step 2046: Based on the completion score and the analysis results, generate suggestions for subsequent driving practice routes (such as suggesting the user to increase or decrease the difficulty coefficient of a certain section of the route, increase the proportion of sections with hazardous behavior occurrences in the subsequent driving practice route, etc.) and provide driving feedback including the suggestions for subsequent driving practice routes to the user through the in-vehicle navigation system.

[0073] Among them, the recorded data of hazardous behaviors can be stored in the cloud server and pushed to other users with driving practice needs for reference.

[0074] Figure 16 The specific example of the driving feedback is shown. As Figure 16As shown, the driving feedback includes the total mileage of the trip (e.g., 88 km as shown), the total duration of the trip (e.g., 3.3 h as shown), and suggestions for subsequent driving practice routes (e.g., "The speed control at the intersection scene is not smooth enough, and the acceleration start and deceleration stop are not timely enough. The subsequent navigation route will plan more urban intersection scenes for you! Keep up the good work!").

[0075] In one or more embodiments, the route planning method 100 and the navigation method 200 can be combined to obtain a route planning and navigation method for the driving practice scenario.

[0076] Combined Figure 7 、 Figures 13A - 13F and Figures 15A - 15D as shown. Figure 7 Fig. shows a route planning system 300 for a driving practice scenario according to the third embodiment of the present invention. The route planning system 300 is implemented by the in-vehicle navigation system of the vehicle or by combining the in-vehicle navigation system of the vehicle with a cloud server connected thereto, and includes:

[0077] A road difficulty classification unit 302, configured to classify and label all roads with different levels of driving practice difficulty based on static and dynamic attributes related to the roads from map data, and the classification and labeling of the driving practice difficulty of the roads will be used as the basic input data for intelligent automatic route planning and navigation route calculation. Among them, the static and dynamic attributes related to the roads include, but are not limited to, the number of intersections in the route, the topological complexity of the road (e.g., how many roads are connected by one road), whether there is a road isolation belt (isolating oncoming vehicles, isolating unprotected pedestrians, cyclists or motorcyclists, tricycles, etc. in the same direction), dynamic traffic information (traffic flow, traffic congestion), route speed limit, etc. For specific examples of the static and dynamic attributes related to the roads, please refer to Figures 13A - 13F as Figures 13A - 13F shown, the static and dynamic attributes related to the roads include, but are not limited to, the number of intersections I in the route, the topological complexity C of the road, whether there is a road isolation belt Z, dynamic traffic information D, route speed limit L (e.g., urban road speed limit CL or highway road speed limit HL), etc.; and

[0078] A route planning setting unit 304, configured to set a planned route for the driving practice scenario based on the user's driving practice preferences, where the user's driving practice preferences include, but are not limited to, the duration of driving, the departure place of the route, the passing places, the level and corresponding percentage of the preferred driving practice difficulty, the percentage of the preferred driving scenario, etc. For an example of the input display of the user's driving practice preference information, please refer to Figures 15A - 15D , Figure 15A Exemplarily shows the user inputting the duration of driving. Figure 15BExemplarily, the departure location, destination, and waypoints of the user-input route are shown (in other cases, the user may only input the departure location and waypoints of the route and then set the destination during the journey). Figure 15C Exemplarily, the levels of the preferred driving practice difficulty input by the user and the corresponding percentages are shown (for example, the levels of the preferred driving practice difficulty are easy E, moderate M, and difficult H, and the corresponding percentages are 30% for easy E, 50% for moderate M, and 20% for difficult H). Figure 15D Exemplarily, the percentages of the preferred driving scenarios input by the user are shown (for example, urban roads account for 70% and highways account for 30%).

[0079] Combined Figure 8 and Figure 14 as shown Figure 8 shows Figure 7 the first road difficulty classification unit 302A in the route planning system 300 shown in

[0080] A predetermined difficulty coefficient setting unit 3022, which is used to set a corresponding plurality of predetermined difficulty coefficients for different static or dynamic attributes of the road. For example, the predetermined difficulty coefficient of a straight road or a road / lane with extremely low traffic flow is set to 1; the predetermined difficulty coefficient of a narrow road or a bend with a curvature value greater than a first predetermined value (for example: 0.004 m^-1) or a road / lane with a change in the number of lanes greater than a second predetermined value (for example: changing from 2 lanes to 1 lane) is set to 3; the predetermined difficulty coefficient of a road with unprotected oncoming vehicle meeting or a multi-exit roundabout or a road with a large number of electric vehicles / bicycles or a road without pedestrian protection facilities is set to 5. Of course, those skilled in the art can understand that according to different specific application situations, the plurality of predetermined difficulty coefficients can be formulated using different setting criteria;

[0081] A difficulty total score generation unit 3024, which is used to generate a difficulty total score for each section of the road based on the plurality of predetermined difficulty coefficients. For example: the difficulty total score of a narrow road with a large curvature and unprotected oncoming vehicle meeting = 3 (the predetermined difficulty coefficient of a narrow road) + 3 (the predetermined difficulty coefficient of a bend with a curvature value greater than the first predetermined value) + 5 (the predetermined difficulty coefficient of a road with unprotected oncoming vehicle meeting) = 11); and

[0082] A difficulty level determination unit 3026 is configured to determine the driving practice difficulty level of each section of road according to the interval where the total difficulty score of each section of road lies. For example, the driving practice difficulty level corresponding to the interval of the total difficulty score of 0 - 3 is easy (E); the driving practice difficulty level corresponding to the interval of the total difficulty score of 3 - 9 is moderate (M); the driving practice difficulty level corresponding to the interval of the total difficulty score > 9 is difficult (H). Of course, those skilled in the art can understand that according to different specific application scenarios, the driving practice difficulty level can be formulated using different setting criteria; and

[0083] A classification and marking unit 3028 is used to classify and mark each section of road with the driving practice difficulty level of each section of road. For example, in the navigation map, each section of road is marked with different colors corresponding to different levels of driving practice difficulty. For specific examples of classifying and marking roads with different levels of driving practice difficulty, please refer to Figure 14 , such as Figure 14 shown, the different levels of driving practice difficulty include three levels: easy (E), moderate (M), and difficult (H), and are marked with corresponding different colors in the navigation map. For example, the easy level (E) is marked with green in the map, the moderate level (M) is marked with yellow in the map, and the difficult level (H) is marked with red in the map.

[0084] Figure 9 shows Figure 7 the second road difficulty classification unit 302B in the route planning system 300 shown in

[0085] Of course, based on different user needs, variants of the above embodiments can be implemented. For example, in the road difficulty classification step 102 or the road difficulty classification unit 302 (including 302A and 302B), in addition to the driving practice difficulty, all the roads are classified and marked based on one or more of the following: driving style on the road (comfortable, sporty, energy-saving, etc.); road environment (three or more lanes, two lanes, single lane, etc.); road landscape layout (more roadside trees, more flower configurations, etc.); other special road conditions (more left-turn driving requirements or more right-turn driving requirements).

[0086] Figure 10 For Figure 7 FIG. is a block diagram of the structure of the route planning setting unit 304 in the route planning navigation system 300 shown in. The route planning setting unit 304 includes:

[0087] A driving practice preference priority preset unit 3042 for enabling the user to preset the priority of one or more types of driving practice preferences through the in-vehicle navigation system and store them in the cloud server;

[0088] A driving practice requirement input unit 3044 for enabling the user to input specific values of one or more of the one or more types of driving practice preferences for the driving practice scenario, where the specific values represent the driving practice requirements of the user for the driving practice scenario;

[0089] A planned route calculation unit 3046 for calculating a corresponding route based on the priority of the one or more types of driving practice preferences preset by the user and the specific values of one or more of the one or more types of driving practice preferences input, and providing a calculation result (in this embodiment, the calculation result is displayed on the display interface of the navigation system. Of course, other methods such as voice playback can also be used for replacement or combination). The calculation result shows the proportion of the calculated corresponding route that meets the driving practice requirements of the user (for example: 100% meeting or 80% meeting, etc.) and / or whether the specific values of each item in the driving practice requirements are met (for example: the driving duration is met, the departure place of the route is met, the passing place is not met, the preferred driving difficulty and the corresponding percentage are met, the percentage of the preferred driving scenario is not met, etc.); and

[0090] A planned route determination unit 3048 is configured to enable a user to determine, based on the calculation result, whether to accept the corresponding route calculated as the planned route for the driving practice scenario set. If the user accepts, the in-vehicle navigation service unit 306 starts the navigation service; if the user does not accept, the user re-enters, through the driving practice requirement input unit 3044, modified specific values of one or more of the one or more types of driving practice preferences for the driving practice scenario.

[0091] Combined Figures 11 - 12 with Figure 16 as shown. Figure 11 Fig. 400 shows a navigation system 400 for a driving practice scenario according to a fourth embodiment of the present invention. The navigation system 400 includes a navigation service unit 402, configured to provide guidance on the planned route to the user by means of audio broadcast and / or video display in a guiding manner of a virtual driving instructor based on the route planned by the route planning system 300. The guiding manner of the virtual driving instructor includes, but is not limited to, the following content of audio broadcast and / or video display, for example:

[0092] 1. Guidance on the front scenario or correct driving behavior:

[0093] "Attention - You will face a difficult road. Please get ready for this challenge";

[0094] "Please note that the number of lanes will change after passing the current traffic light. Please observe the target lane in advance";

[0095] "Please note that you will enter a roundabout 500m ahead. Pay attention to the traffic light directly in front of the vehicle head during the roundabout driving";

[0096] "Please slow down and pay attention - 200m ahead is the entrance / exit of an industrial park. There may be vehicles or pedestrians entering or leaving";

[0097] "Please slow down and pay attention to avoid - It is the evening rush hour now. There are many vehicles and pedestrians at the intersection ahead";

[0098] "Please note to change lanes in advance - The number of lanes ahead is decreasing";

[0099] 2. Guidance on encouraging correct behaviors or correcting wrong behaviors (simulating a driving practice coach) for the traveled scenario

[0100] "For the left-turn intersection just passed, you slowed down and stopped in time. Well done!";

[0101] "For the left-turn intersection just passed, you didn't look at the vehicles on the right. Please pay attention next time";

[0102] "Come on! Half of today's driving practice has passed. Please concentrate on driving and keep up the good work.";

[0103] "The driving time today has reached 1 hour. If you feel tired, we can find the nearest parking lot for you. Please do not drive tired."

[0104] The driving feedback unit 404 is used to provide various game-like interactive feedback to the user through the navigation system after the driving trip. Among them, the driving feedback includes but is not limited to: celebration at the end of the practice trip (including but not limited to seat vibration, sound effects, ambient light effects to render the celebration atmosphere), total mileage statistics of the trip, total driving time statistics, suggestions for subsequent driving practice routes, etc.

[0105] Figure 12 The structural block diagram of the driving feedback unit 404 is shown. The driving feedback unit 404 includes:

[0106] The completion score calculation unit 4042 is used to calculate and display the completion score R through the following formula: R = (A * X) - (B * Y), where A is the total score of the driving practice difficulty of the road, X is the mileage correctly traveled during the driving trip, B is the error / hazardous driving coefficient (which can be preset by the user or the cloud server, for example: the hazard coefficient for frequently fine-tuning the steering to control the vehicle is 1, the hazard coefficient for driving over the line for too long is 1, the hazard coefficient for not starting in time at a green light is 1, the hazard coefficient for not decelerating in time at a red light is, the hazard coefficient for not braking in time resulting in too close a following distance is 2, the hazard coefficient for not turning on the turn signal when turning is 2, the hazard coefficient for not observing the traffic flow in time at an intersection resulting in sudden braking is 3, the hazard coefficient for encountering a scratch is 5, etc.), and Y is the mileage of incorrect driving during the driving trip;

[0107] The dangerous behavior analysis unit 4044 is used to record the data of dangerous behaviors during the driving trip (such as videos of dangerous behaviors captured by in-vehicle cameras or vehicle-end data of dangerous behaviors collected by other types of sensors, etc.), analyze the types and severity of the dangerous behaviors (the analysis criteria can be preset by the user or the cloud server) and provide the analysis results (such as being displayed on the display interface of the vehicle-end navigation system); and

[0108] The driving feedback providing unit 4046 is used to generate suggestions for subsequent driving practice routes (such as suggesting that the user increase or decrease the difficulty coefficient of a certain section of the route, increase the proportion of sections with dangerous behaviors in the subsequent driving practice route, etc.) based on the completion score and the analysis results and provide driving feedback including the suggestions for subsequent driving practice routes to the user.

[0109] Among them, the data of the recorded dangerous behaviors can be stored in the cloud server and pushed to other users with driving internship needs for reference.

[0110] For specific examples of the driving feedback, please refer to Figure 16 , such as Figure 16 shown, the driving feedback includes the total mileage of the trip (such as the 88 km shown in the figure), the total duration of the trip (such as the 3.3 h shown in the figure), and suggestions for the subsequent driving internship route (such as "The speed control at the intersection scene is not smooth enough, and the acceleration start and deceleration stop are not timely enough. The subsequent navigation route will plan more urban intersection scenes for you! Keep up the good work!").

[0111] In one or more embodiments, the route planning system 300 and the navigation system 400 can be combined to obtain a route planning and navigation system for the driving internship scenario.

[0112] In one or more embodiments, the route planning system 300 and the navigation system 400 can be integrated into the in-vehicle navigation system of the vehicle or integrated into the in-vehicle navigation system of the vehicle and the connected cloud server.

[0113] The present invention also provides a vehicle (not shown) including the above-mentioned route planning system 300 and navigation system 400.

[0114] It can be understood that the above specific embodiments are merely exemplary embodiments adopted to illustrate the principles of the present invention and do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A route planning method (100) for a driving practice scenario, characterized in that, The route planning method (100) includes the following steps: Road difficulty classification step (102): Classify and label the roads with different levels of driving practice difficulty based on static and dynamic attributes related to the roads; and Route planning setting step (104): Set a planned route for the driving practice scenario based on the user's driving practice preferences.

2. The route planning method according to claim 1, wherein The road difficulty classification step (102) includes the following steps: Predetermined difficulty coefficient setting step (1022): Set a corresponding plurality of predetermined difficulty coefficients for different static and dynamic attributes of the road; Total difficulty score generation step (1024): Generate a total difficulty score for each section of the road based on the plurality of predetermined difficulty coefficients; And Difficulty level determination step (1026): Determine the level of driving practice difficulty of each section of the road according to the interval in which the total difficulty score of each section of the road lies; And Classification and labeling step (1028): Classify and label each section of the road with the level of driving practice difficulty of each section of the road.

3. The route planning method according to claim 2, wherein The road difficulty classification step (102) further includes a user-defined step (1030) between the predetermined difficulty coefficient setting step (1022) and the total difficulty score generation step (1024): The user customizes one or more of the plurality of predetermined difficulty coefficients to overwrite the corresponding predetermined difficulty coefficients set by the predetermined difficulty coefficient setting step (1022).

4. The route planning method according to claim 1, wherein The route planning setting step (104) includes the following steps: Driving practice preference priority presetting step (1042): The user presets the priority of one or more types of driving practice preferences through the in-vehicle navigation system of the vehicle and stores it in the processor of the in-vehicle navigation system or a cloud server connected to the in-vehicle navigation system; Driving practice requirement input step (1044): For the driving practice scenario, the user inputs specific values of one or more items among the one or more types of driving practice preferences, and the specific values represent the user's driving practice requirements for the driving practice scenario; Planned route calculation step (1046): Based on the priority of the one or more types of driving practice preferences preset by the user and the specific values of one or more items among the one or more types of driving practice preferences input, calculate the corresponding route through the processor of the in-vehicle navigation system or the cloud server and provide the calculation result, and the calculation result shows the proportion of the calculated corresponding route that meets the user's driving practice requirements and / or whether the specific values of each item in the driving practice requirements are met; And Planned route determination step (1048): Based on the calculation result, the user determines whether to accept the calculated corresponding route as the planned route set for the driving practice scenario. If the user accepts, the in-vehicle navigation system starts the navigation service; If the user does not accept, return to the driving practice requirement input step (1044), and the user re-enters the modified specific values of one or more of the one or more types of driving practice preferences for the driving practice scenario.

5. The route planning method according to any one of claims 1-4, characterized in that, The static and dynamic attributes related to the road include, but are not limited to, the number of intersections in the road, the topological complexity of the road and lanes, whether there is a road median, dynamic traffic information, road speed limits, etc.

6. The route planning method according to any one of claims 1-4, characterized in that The different levels of driving practice difficulty include three levels: easy, medium, and difficult. In the road difficulty classification step (102), the road is marked with different colors corresponding to the different levels of driving practice difficulty in the navigation map displayed by the vehicle's in-vehicle navigation system.

7. The route planning method according to any one of claims 1-4, characterized in that In the road difficulty classification step (102), in addition to based on the driving practice difficulty, the road is classified and marked based on one or more of the following: road driving style, road environment, road landscape layout, and other special road conditions.

8. The method according to any one of claims 1 to 4, characterized in that, The driving practice preferences include, but are not limited to, one or more of the following: driving duration, departure location of the route, passing locations, preferred level of driving practice difficulty and corresponding percentage, preferred percentage of driving scenarios, etc.

9. A navigation method (200) for a driving practice scenario, characterized in that, The navigation method (200) includes the following steps: Navigation service step (202): Based on the route planned by the route planning method (100) according to any one of claims 1-8, using the guiding method of a virtual driving instructor, provide the user with guidance on the planned route through audio broadcast and / or video display.

10. The navigation method according to claim 9, characterized in that, The navigation method (200) further includes a driving feedback step (204) after the navigation service step (202): After the driving journey of the route ends, provide the user with various feedback similar to interactive games through the vehicle's in-vehicle navigation system.

11. The navigation method according to claim 10, characterized in that, The driving feedback step (204) includes: Completion score calculation step (2042): Calculate and display the completion score R through the following formula: R = (A * X) - (B * Y), where A is the total score of the driving practice difficulty of the road, X is the mileage correctly driven during the driving journey, B is the error / hazardous driving coefficient, and Y is the mileage wrongly driven during the driving journey; Hazardous behavior analysis step (2044): Record the data of hazardous behaviors during the driving journey, analyze the types and severity of the hazardous behaviors and provide the analysis results; and Driving feedback providing step (2046): Based on the completion score and the analysis results, generate suggestions for subsequent driving practice routes and provide the user with driving feedback including the suggestions for subsequent driving practice routes through the vehicle's in-vehicle navigation system.

12. The navigation method according to claim 11, wherein, In the driving feedback providing step (2046), the recorded data of hazardous behaviors is stored in the cloud server connected to the vehicle's in-vehicle navigation system and pushed to other users with driving practice requirements for reference.

13. A route planning system (300) for a driving practice scenario, characterized in that, The route planning system (300) includes: A road difficulty classification unit (302) for classifying and labeling the road with different levels of driving practice difficulty based on static and dynamic attributes related to the road; and A route planning setting unit (304) for setting a planned route for the driving practice scenario based on the user's driving practice preferences.

14. The route planning system according to claim 13, wherein The road difficulty classification unit (302) includes: A predetermined difficulty coefficient setting unit (3022) for setting a corresponding plurality of predetermined difficulty coefficients for different static or dynamic attributes of the road; A total difficulty score generation unit (3024) for generating a total difficulty score for each section of the road based on the plurality of predetermined difficulty coefficients; A difficulty level determination unit (3026) for determining the level of driving practice difficulty of each section of the road according to the interval in which the total difficulty score of each section of the road lies; and A classification and labeling unit (3028) for classifying and labeling each section of the road with the level of driving practice difficulty of each section of the road.

15. The route planning system according to claim 14, wherein The road difficulty classification unit (302) further includes a user-defined unit (3030) for the user to customize one or more of the plurality of predetermined difficulty coefficients to overwrite the corresponding predetermined difficulty coefficients set by the predetermined difficulty coefficient setting unit (3022).

16. The route planning system according to claim 13, characterized in that, The route planning setting unit (304) includes: A driving practice preference priority presetting unit (3042) for enabling the user to preset the priority of one or more types of driving practice preferences through the in-vehicle navigation system of the vehicle and store it in the cloud server connected to the in-vehicle navigation system; A driving practice requirement input unit (3044) for enabling the user to input specific values of one or more items among the one or more types of driving practice preferences for the driving practice scenario, where the specific values represent the user's driving practice requirements for the driving practice scenario; A planned route calculation unit (3046) for calculating a corresponding route based on the priority of the one or more types of driving practice preferences preset by the user and the specific values of one or more items among the one or more types of driving practice preferences input by the user, and providing a calculation result, where the calculation result shows the proportion of the calculated corresponding route that meets the user's driving practice requirements and / or whether the specific values of each item in the driving practice requirements are met; and A planned route determination unit (3048) for enabling the user to determine whether to accept the calculated corresponding route as the planned route for the driving practice scenario based on the calculation result. If the user accepts, the navigation service unit (306) starts the navigation service; if the user does not accept, the user re-enters the modified specific values of one or more items among the one or more types of driving practice preferences for the driving practice scenario through the driving practice requirement input unit (3044).

17. The route planning system according to any one of claims 13-16, characterized in that, The static and dynamic attributes related to the road include, but are not limited to, the number of intersections in the road, the topological complexity of the road and lanes, whether there is a road median, dynamic traffic information, road speed limits, etc.

18. The route planning system according to any one of claims 13-16, characterized in that, The driving practice difficulties of different levels include three levels: easy, medium, and difficult. The road difficulty classification unit (302) is further configured to mark the road in different colors corresponding to the driving practice difficulties of different levels in the navigation map displayed by the in-vehicle navigation system of the vehicle.

19. The route planning system according to any one of claims 13-16, characterized in that, The road difficulty classification unit (302) is further configured to classify and mark the road based on one or more of the following in addition to the driving practice difficulty: driving style, road environment, road landscape layout, and other special road conditions.

20. The route planning system according to any one of claims 13-16, characterized in that, The driving practice preferences include, but are not limited to, one or more of the following: duration of driving, departure location of the route, passing locations, level and corresponding percentage of the preferred driving practice difficulty, percentage of the preferred driving scenario, etc.

21. A navigation system (400) for a driving practice scenario, characterized in that, The navigation (400) includes: A navigation service unit (402), configured to provide guidance on the planned route to the user by means of audio broadcast and / or video display in the guiding manner of a virtual driving instructor based on the route planned by the route planning system (300) according to any one of claims 13-20.

22. The navigation system according to claim 21, wherein The navigation system (400) further includes a driving feedback unit (404), configured to provide various feedback similar to interactive games to the user through the in-vehicle navigation system of the vehicle after the driving journey of the route ends.

23. The navigation system according to claim 22, characterized in that, The driving feedback unit (404) includes: A completion score calculation unit (4042), configured to calculate and display the completion score R through the following formula: R = (A * X) - (B * Y), where A is the total score of the driving practice difficulty of the road, X is the mileage of correct driving in the driving journey, B is the error / hazardous driving coefficient, and Y is the mileage of incorrect driving in the driving journey; A hazardous behavior analysis unit (4044), configured to record data of hazardous behaviors in the driving journey, analyze the types and severity of the hazardous behaviors, and provide analysis results; and A driving feedback providing unit (4046), configured to generate suggestions for subsequent driving practice routes and provide driving feedback including the suggestions for subsequent driving practice routes to the user based on the completion score and the analysis results.

24. The system according to claim 23, wherein The driving feedback providing unit (404) is further configured to store the recorded data of hazardous behaviors in a cloud server connected to the in-vehicle navigation system of the vehicle and push it to other users with driving practice needs for reference.

25. A vehicle, comprising a route planning system (300) for a driving practice scenario according to any one of claims 13-20 and a navigation system (400) for a driving practice scenario according to any one of claims 21-24.

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