A method, apparatus, vehicle and storage medium for recommending a navigation path

Abstract

This application provides a method, apparatus, vehicle, and storage medium for recommending navigation routes. The method, applied in the field of route navigation technology, includes: determining the target motion scenario currently occupied by the vehicle from multiple preset motion scenarios; dividing the preset motion scenarios based on the user's exercise activities; determining a comprehensive score for each candidate navigation path among multiple candidate navigation paths based on the target motion scenario; and selecting and recommending the target navigation path from among the multiple candidate navigation paths based on the comprehensive score. This method can score candidate navigation paths based on the vehicle's current target motion scenario to achieve navigation route recommendation, meeting users' personalized route needs in different driving scenarios, ensuring that the recommended navigation path matches actual usage needs, and improving the user experience.

Description

Technical Field

[0001] This application relates to the field of route navigation technology, and more specifically, to a method, apparatus, vehicle, and storage medium for recommending navigation routes in the field of route navigation technology. Background Technology

[0002] Existing navigation systems typically prioritize minimizing driving distance or time when planning routes, focusing primarily on basic factors like distance and time. They fail to adequately consider differentiated route planning based on actual user scenarios, resulting in route recommendations that are limited in scope and poor adaptability to different scenarios.

[0003] Therefore, existing navigation systems struggle to meet users' personalized route needs in different driving scenarios, often resulting in a mismatch between recommended routes and actual usage requirements, leading to a poor user experience. Summary of the Invention

[0004] This application provides a method, apparatus, vehicle, and storage medium for recommending navigation routes. The method can meet users' personalized route needs in different driving scenarios, ensure that the recommended navigation routes match actual usage needs, and improve the user experience.

[0005] Firstly, a method for recommending navigation routes is provided, which includes: determining the target motion scenario where the vehicle is currently located from multiple preset motion scenarios; the preset motion scenarios are obtained based on the motion activities performed by the user; based on the target motion scenario, determining the comprehensive score of each candidate navigation path among multiple candidate navigation paths; and based on the comprehensive score, selecting the target navigation path from the multiple candidate navigation paths for recommendation.

[0006] In the above technical solution, multiple preset motion scenarios are pre-defined based on the user's exercise activities. When the user uses the vehicle, the target motion scenario of the vehicle is determined. Based on the target motion scenario, a comprehensive score of multiple candidate navigation paths is determined. The comprehensive score takes into account the impact of the motion scenario on navigation path planning. The target navigation path is determined based on the comprehensive score to realize navigation path recommendation. This meets the user's personalized path needs in different driving scenarios, ensures that the recommended navigation path matches the actual usage needs, and improves the user experience.

[0007] In conjunction with the first aspect, in some possible implementations, based on the target motion scene, the comprehensive score of each candidate navigation path among multiple candidate navigation paths is determined, including: based on the target motion scene, determining the weights corresponding to multiple evaluation dimensions respectively; for each candidate navigation path among multiple candidate navigation paths, calculating the scores corresponding to multiple evaluation dimensions respectively; and based on the weights corresponding to multiple evaluation dimensions and the scores corresponding to each candidate navigation path in multiple evaluation dimensions, determining the comprehensive score corresponding to each candidate navigation path.

[0008] In the above technical solution, the weights corresponding to each evaluation dimension are dynamically determined by the target motion scene. Based on the weights and the scores corresponding to each evaluation dimension, the comprehensive score of each candidate navigation path is calculated. This makes the comprehensive score take into account the user's needs in the target motion scene and dynamically adjust it based on the target motion scene, ensuring that the subsequent navigation path recommendation based on the comprehensive score is safer and more reliable.

[0009] In combination with the first aspect and the above implementation methods, in some possible implementation methods, the target motion scenario in which the vehicle is currently located is determined from multiple preset motion scenarios, including: detecting whether the vehicle is equipped with sports equipment; and if the vehicle is equipped with sports equipment, determining the target motion scenario in which the vehicle is currently located from multiple preset motion scenarios based on the sports equipment.

[0010] In the above technical solution, considering that users usually carry corresponding sports equipment when they travel for exercise, the system can detect whether the vehicle is carrying sports equipment and accurately predict the user's travel purpose based on the sports equipment carried, thereby efficiently and accurately determining the target sports scene where the vehicle is located from the preset sports scene.

[0011] Combining the first aspect and the above implementation methods, in some possible implementation methods, each preset motion scenario includes at least one motion item. Based on the motion equipment, the target motion scenario in which the vehicle is currently located is determined from multiple preset motion scenarios, including: determining the target motion item corresponding to the motion equipment; and determining the preset motion scenario that includes the target motion item from among the multiple preset motion scenarios as the target motion scenario.

[0012] In the above technical solution, the target sports activity that the user will be engaged in can be accurately determined through sports equipment, and the preset sports scene that includes the target sports activity can be determined as the target sports scene, which can efficiently and accurately determine the target sports scene.

[0013] Combining the first aspect and the above implementation methods, in some possible implementation methods, multiple evaluation dimensions include at least two of the following: time dimension, distance dimension, comfort dimension, safety dimension, economy dimension, and scenery dimension. Scores are calculated for each of the multiple evaluation dimensions, including: when the multiple evaluation dimensions include the time dimension, calculating a first score corresponding to the time dimension; when the multiple evaluation dimensions include the distance dimension, calculating a second score corresponding to the distance dimension; when the multiple evaluation dimensions include the comfort dimension, calculating a third score corresponding to the comfort dimension; when the multiple evaluation dimensions include the safety dimension, calculating a fourth score corresponding to the safety dimension; when the multiple evaluation dimensions include the economy dimension, calculating a fifth score corresponding to the economy dimension; and when the multiple evaluation dimensions include the scenery dimension, calculating a sixth score corresponding to the scenery dimension.

[0014] Combining the first aspect and the above implementation methods, in some possible implementation methods, the fourth score corresponding to the safety dimension is calculated, including: obtaining vehicle speed planning information, road quality information and road weight limit information corresponding to the candidate navigation path; and determining the fourth score corresponding to the safety dimension based on one or more of the vehicle speed planning information, road quality information and road weight limit information.

[0015] In the above technical solution, considering the speed limit of the vehicle when it is equipped with sports equipment, speed planning information, road quality information and road weight limit information are obtained to determine the fourth score of the safety dimension. This makes the safety dimension score take into account the user's travel needs in the target scenario and ensure that the subsequent recommended navigation route can better meet the user's needs.

[0016] Combining the first aspect and the above implementation methods, in some possible implementation methods, the sixth score corresponding to the scenery dimension is calculated, including: obtaining information on attractions along the route, road greening information, and driving visibility information corresponding to the candidate navigation path; and determining the sixth score corresponding to the scenery dimension based on one or more of the information on attractions along the route, road greening information, and driving visibility information.

[0017] In the above technical solution, the sixth score corresponding to the scenery dimension is determined based on information on attractions along the way, road greenery, and driving visibility. This takes into account the user's various needs for attractions along the way, road greenery, and driving visibility, making the candidate navigation recommendations more able to meet the user's needs.

[0018] Secondly, a device for recommending navigation routes is provided. The device includes: a determining module for determining the target motion scenario where the vehicle is currently located from multiple preset motion scenarios; the preset motion scenarios are obtained based on the motion activities performed by the user; a scoring module for determining a comprehensive score for each candidate navigation path among multiple candidate navigation paths based on the target motion scenario; and a recommending module for selecting a target navigation path from the multiple candidate navigation paths for recommendation based on the comprehensive score.

[0019] In conjunction with the second aspect, in some possible implementations, the scoring module is specifically used to: determine the weights corresponding to multiple evaluation dimensions based on the target motion scene; calculate the scores corresponding to multiple evaluation dimensions for each candidate navigation path among multiple candidate navigation paths; and determine the comprehensive score corresponding to each candidate navigation path based on the weights corresponding to multiple evaluation dimensions and the scores corresponding to each candidate navigation path in multiple evaluation dimensions.

[0020] Combining the second aspect and the above implementation methods, in some possible implementation methods, the determining module is specifically used to: detect whether the vehicle is equipped with sports equipment; and if the vehicle is equipped with sports equipment, determine the target sports scene where the vehicle is currently located from multiple preset sports scenes based on the sports equipment.

[0021] Combining the second aspect and the above implementation methods, in some possible implementation methods, the determining module is specifically used to: determine the target sport corresponding to the sports equipment; and determine the preset sports scene that includes the target sport from multiple preset sports scenes as the target sports scene.

[0022] Combining the second aspect and the above implementation methods, in some possible implementation methods, the multiple evaluation dimensions include at least two of the following: time dimension, distance dimension, comfort dimension, safety dimension, economy dimension, and scenery dimension. The scoring module is specifically used to: calculate the first score corresponding to the time dimension when the multiple evaluation dimensions include the time dimension; calculate the second score corresponding to the distance dimension when the multiple evaluation dimensions include the distance dimension; calculate the third score corresponding to the comfort dimension when the multiple evaluation dimensions include the comfort dimension; calculate the fourth score corresponding to the safety dimension when the multiple evaluation dimensions include the safety dimension; calculate the fifth score corresponding to the economy dimension when the multiple evaluation dimensions include the economy dimension; and calculate the sixth score corresponding to the scenery dimension when the multiple evaluation dimensions include the scenery dimension.

[0023] Combining the second aspect and the above implementation methods, in some possible implementation methods, the scoring module is specifically used to: obtain vehicle speed planning information, road quality information, and road weight limit information corresponding to the candidate navigation path; and determine the fourth score corresponding to the safety dimension based on one or more of the vehicle speed planning information, road quality information, and road weight limit information.

[0024] Combining the second aspect and the above implementation methods, in some possible implementation methods, the scoring module is specifically used to: obtain information on scenic spots along the route, road greening information, and driving visibility information corresponding to the candidate navigation path; and determine the sixth score corresponding to the scenery dimension based on one or more of the scenic spot information, road greening information, and driving visibility information.

[0025] Thirdly, a vehicle is provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, causing the vehicle to perform the methods of the first aspect or any possible implementation thereof.

[0026] Fourthly, a computer program product is provided, comprising: computer program code, which, when run on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof.

[0027] Fifthly, a computer-readable storage medium is provided that stores computer program code, which, when executed on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof. Attached Figure Description

[0028] Figure 1 This is a schematic flowchart illustrating a method for recommending navigation paths provided in an embodiment of this application.

[0029] Figure 2 This is a schematic diagram illustrating the division of a preset motion scene provided in an embodiment of this application.

[0030] Figure 3 This is a flowchart of another method for recommending navigation paths provided in an embodiment of this application.

[0031] Figure 4 This is a flowchart of a real-time path optimization method provided in an embodiment of this application.

[0032] Figure 5 This is a schematic diagram of a device for recommending navigation paths provided in an embodiment of this application.

[0033] Figure 6 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. Detailed Implementation

[0034] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0035] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0036] Current navigation systems typically prioritize minimizing distance or time during route planning, focusing primarily on basic factors like distance and time. They fail to adequately consider differentiated route planning based on actual user driving scenarios, resulting in simplistic route recommendations and poor scenario adaptability. Consequently, existing navigation systems struggle to meet users' personalized route needs across various driving situations, often leading to mismatches between recommended routes and actual usage requirements, resulting in a poor user experience.

[0037] For example, if a user is a sports enthusiast who needs to participate in multiple sports, the user usually needs to drive to the target location where the sports can be carried. In some cases, the vehicle also needs to carry sports equipment. The travel needs of different sports vary greatly. For example, the transportation of ski equipment needs to take into account the road load-bearing capacity, while the running route itself requires good scenic roads. The transportation of heavy sports equipment affects the vehicle's handling performance, but the existing system has failed to take into account the above factors and provide corresponding safe navigation suggestions.

[0038] Based on this, this application proposes a method for recommending navigation routes, which takes into account the impact of motion scenarios on navigation route planning. Based on the target motion scenario in which the vehicle is currently located, candidate navigation routes are scored to achieve navigation route recommendation, thereby meeting the personalized route needs of users in different driving scenarios, ensuring that the recommended navigation routes match the actual usage needs, and improving the user experience.

[0039] Figure 1 This is a schematic flowchart illustrating a method for recommending navigation paths provided in an embodiment of this application.

[0040] For example, such as Figure 1 As shown, the method 100 includes: Step 101: Determine the target motion scenario where the vehicle is currently located from multiple preset motion scenarios; the preset motion scenarios are obtained based on the motion activities performed by the user. Step 102: Based on the target motion scene, determine the comprehensive score of each candidate navigation path among multiple candidate navigation paths; Step 103: Based on the comprehensive score, select the target navigation path from multiple candidate navigation paths for recommendation.

[0041] exist Figure 1 In the illustrated embodiment, multiple preset motion scenarios are pre-defined based on the user's exercise activities. When the user uses the vehicle, the target motion scenario in which the vehicle is currently located is determined. Based on the target motion scenario, a comprehensive score of multiple candidate navigation paths is determined. The comprehensive score takes into account the impact of the motion scenario on navigation path planning. The target navigation path is determined based on the comprehensive score to realize navigation path recommendation. This meets the user's personalized path needs in different driving scenarios, ensures that the recommended navigation path matches the actual usage needs, and improves the user experience.

[0042] The following is about Figure 1 The specific implementation methods of each step in the illustrated embodiments are explained in detail below: In step 101, multiple preset sports scenarios are obtained based on the sports activities that users can perform.

[0043] The aforementioned sports activities refer to the types, forms, or behaviors of sports related to the user's travel purpose. Specifically, these include various sports activities, fitness activities, and outdoor activities that the user plans to engage in, is currently engaging in, or expects to engage in while driving or before traveling, including but not limited to running, cycling, swimming, ball sports, rock climbing, etc.

[0044] Specifically, various sports activities that users can participate in can be categorized in advance, resulting in multiple groups of sports activities. Each group of sports activity can be considered a preset sports scenario, leading to multiple preset sports scenarios. When a user is driving a vehicle, the target sports scenario corresponding to the current vehicle can be determined. The target sports scenario can be any one of the multiple preset sports scenarios.

[0045] Figure 2 This is a schematic diagram illustrating the division of a preset motion scene provided in an embodiment of this application.

[0046] For example, such as Figure 2 As shown, the preset motion scenarios include: the first motion scenario, the second motion scenario, the third motion scenario, the fourth motion scenario, and the fifth motion scenario.

[0047] The first sports scenario can be a scenario categorized based on extreme sports. The sports corresponding to the first sports scenario can include extreme sports such as skiing, skateboarding, rock climbing, and surfing. In some embodiments, the first sports scenario may also be referred to as an extreme sports scenario.

[0048] The second sports scenario can be a scenario derived from outdoor field sports. The sports corresponding to the second sports scenario can include outdoor field sports such as football, rugby, baseball, and track and field. In some embodiments, the second sports scenario may also be referred to as an outdoor field scenario.

[0049] The third sports scenario can be a scenario derived from indoor venue sports. The sports corresponding to the third sports scenario can include indoor venue sports such as badminton, table tennis, tennis, squash, basketball, and volleyball. In some embodiments, the third sports scenario may also be referred to as an indoor venue scenario.

[0050] The fourth sports scenario can be a scenario based on cycling or running sports. The sports corresponding to the fourth sports scenario can include: road cycling, mountain biking, and road running.

[0051] The aforementioned running activities specifically refer to running exercises that do not require dedicated sports venues and can be conducted in ordinary public environments such as outdoor roads, parks, and residential areas, unlike track and field sports that require professional track and field facilities. These running activities include, but are not limited to: road running, jogging, morning runs, evening runs, and outdoor free running.

[0052] The fifth sports scenario can be a scenario derived from water sports. The sports corresponding to the fifth sports scenario can include water sports such as swimming, sailing, kayaking, and diving. In some embodiments, the fifth sports scenario may also be referred to as a water sports scenario.

[0053] Understandable, Figure 2 This is merely an example of dividing preset motion scenarios; the embodiments of this application do not specifically limit the division of preset motion scenarios or the number of preset motion scenarios.

[0054] In some embodiments, determining the target motion scenario where the vehicle is currently located from a plurality of preset motion scenarios includes: detecting whether the vehicle is equipped with sports equipment; if the vehicle is equipped with sports equipment, determining the target motion scenario where the vehicle is currently located from a plurality of preset motion scenarios based on the sports equipment.

[0055] The aforementioned sports equipment specifically refers to the equipment, apparatus, clothing, or auxiliary devices required by a user to participate in a particular sport, including but not limited to sports shoes, sportswear, rackets, balls, skateboards, skis, diving equipment, swimming equipment, bicycles, etc. Different sports typically correspond to different sports equipment.

[0056] The vehicle can be equipped with sensors to detect whether sports equipment is loaded in the vehicle.

[0057] The aforementioned sensors may include multiple sensors such as those installed on the vehicle roof, the vehicle trunk, and the vehicle rear. Specifically, the roof sensor detects whether sports equipment is mounted on the vehicle roof, the rear sensor detects whether sports equipment is mounted on the rear of the vehicle, and the trunk sensor detects whether sports equipment is mounted in the vehicle trunk.

[0058] Understandably, suitable locations for carrying sports equipment in a vehicle typically include the roof, trunk, and rear. The roof is usually suitable for larger items like ski racks, while the rear can accommodate smaller items such as bicycles (e.g., a bicycle rack can be installed at the rear). The trunk can hold smaller items like athletic shoes, sportswear, rackets, balls, skateboards, diving equipment, and swimming gear. Therefore, sensors need to be installed on the roof, trunk, and rear to detect whether sports equipment is being carried in the vehicle.

[0059] The aforementioned sensor can specifically be an in-vehicle camera, which can capture images of the roof, rear, and trunk of the vehicle, and use image recognition technology to identify the captured images to determine whether the images contain sports equipment.

[0060] If any of the aforementioned sensors detects motion equipment, it can be determined that the vehicle is equipped with motion equipment. At this point, based on the detected motion equipment, the target motion scenario in which the vehicle is located can be determined from multiple preset motion scenarios.

[0061] In the above method, considering that users usually carry corresponding sports equipment when they travel for exercise, the method can detect whether the vehicle is carrying sports equipment and accurately predict the user's travel purpose based on the sports equipment carried, thereby accurately determining the target sports scene where the vehicle is located from the preset sports scene.

[0062] In some embodiments, each preset motion scenario includes at least one sport. Based on the sports equipment, determining the target motion scenario where the vehicle is currently located from multiple preset motion scenarios includes: determining the target sport corresponding to the sports equipment; and determining the preset motion scenario that includes the target sport among the multiple preset motion scenarios as the target motion scenario.

[0063] Different sports require different equipment, so the target sport can be determined based on the equipment. For example, if the detected equipment is a ski rack, then the target sport corresponding to the ski rack can be determined to be skiing.

[0064] Preset sports scenarios are scenarios pre-defined based on the sports activities that users can perform. Therefore, each preset sports scenario corresponds to at least one sports activity. After obtaining the target sports activity, the sports activities included in each preset sports scenario can be determined. Then, the sports activities included in each preset sports scenario are compared with the target sports activity. Preset sports scenarios where the sports activities included in the preset sports scenario are the same as the target sports activity are identified as the target sports scenario.

[0065] like Figure 2 As shown, the preset sports scenarios include: the first sports scenario, the second sports scenario, the third sports scenario, the fourth sports scenario, and the fifth sports scenario. The sports corresponding to the first sports scenario include: skiing, skateboarding, rock climbing, and surfing. Therefore, if the sports equipment is determined to be any one of skiing equipment, rock climbing equipment, skateboarding equipment, or surfing equipment, the target sports scenario can be determined as the first sports scenario.

[0066] The sports corresponding to the second sports scenario include outdoor field sports such as football, rugby, baseball, and track and field. Therefore, if the sports equipment is determined to be any one of football equipment, rugby equipment, baseball equipment, or track and field equipment, the target sports scenario can be determined as the second sports scenario.

[0067] The sports corresponding to the third sports scenario include indoor sports such as badminton, table tennis, tennis, squash, basketball, and volleyball. Therefore, if the sports equipment is determined to be any one of badminton equipment, table tennis equipment, tennis equipment, squash equipment, basketball equipment, or volleyball equipment, the target sports scenario can be determined as the third sports scenario.

[0068] The sports activities corresponding to the fourth sports scenario include road cycling, mountain biking, and road running. Therefore, if the sports equipment is determined to be any one of road cycling equipment, mountain biking equipment, or road running equipment, the target sports scenario can be determined to be the fourth sports scenario.

[0069] The sports activities corresponding to the fifth sports scenario include water sports such as swimming, sailing, kayaking, and diving. Therefore, if the sports equipment is determined to be any one of swimming equipment, sailing equipment, kayaking equipment, or diving equipment, the target sports scenario can be determined as the fifth sports scenario.

[0070] In the above method, the target sport that the user will be engaged in can be accurately determined by sports equipment, and the preset sports scene that includes the target sport can be determined as the target sports scene, which can efficiently and accurately determine the target sports scene.

[0071] In some embodiments, determining the target motion scenario in which the vehicle is currently located from multiple preset motion scenarios includes: predicting the vehicle's travel purpose based on at least one of the vehicle's navigation data, time data, and historical behavior data; and determining the target motion scenario in which the vehicle is currently located based on the destination.

[0072] The navigation data includes destination information, which can be used to predict the vehicle's travel purpose. For example, if the destination information includes a ski resort, the purpose of the trip can be determined to be skiing. In this case, since skiing is an extreme sport, the target sports scene is determined as the first sports scene. Or, if the destination information includes a stadium, the target sports scene can be determined as the third sports scene, corresponding to indoor stadium sports.

[0073] The aforementioned time data can be either a season or a time period. Seasons include the four seasons of spring, summer, autumn, and winter, while time periods can refer to different time intervals within a day, such as the morning rush hour, evening rush hour, daytime, nighttime, and early morning.

[0074] When time and navigation data are obtained, they can be combined to predict the purpose of travel. For example, if the destination information in the navigation data includes a ski resort and the season in the time data is winter, then the combination of the two can determine that the purpose of the vehicle's trip is to ski, and thus determine the target sports scene as the first sports scene.

[0075] Historical behavior data can include a user's historical driving routes and historical travel destinations. When it is determined that the vehicle's current driving route is a route in the historical driving routes, the travel destination corresponding to that historical driving route can be determined as the current travel destination of the vehicle.

[0076] For example, if the vehicle's current driving route corresponds to historical driving route 1, and the user participated in water sports activities under historical driving route 1, then the purpose of the vehicle's trip can be determined to be water sports activities based on historical behavior data, and the target sports scenario can be determined to be the fifth sports scenario.

[0077] In some embodiments, after pre-dividing multiple preset motion scenarios, multiple options corresponding to multiple preset motion scenarios can be configured in the vehicle, with one option corresponding to one preset motion scenario. When driving the vehicle, the user can select any of the multiple options through the selection interface. The system can detect the target option selected by the user and determine the preset motion scenario corresponding to the target option as the target motion scenario.

[0078] For example, five preset motion scenarios are pre-defined: the first motion scenario, the second motion scenario, the third motion scenario, the fourth motion scenario, and the fifth motion scenario. Five options are correspondingly set: the first option for the first motion scenario, the second option for the second preset scenario, the third option for the third motion scenario, the fourth option for the fourth motion scenario, and the fifth option for the fifth motion scenario. Assuming the user's selected option is the third option, then the third motion scenario corresponding to the third option can be determined as the target motion scenario.

[0079] In step 102, the multiple candidate navigation paths are multiple passable paths planned by the navigation system based on the vehicle's current location and destination. The multiple candidate navigation paths have the same starting point and ending point, but they differ in terms of route direction, roads passed through, travel distance, travel time, and road attributes.

[0080] Understandably, when driving, users typically use a navigation system and input their destination into it. The navigation system then pre-plans multiple candidate navigation routes based on the vehicle's current location and the destination, selecting the optimal route that best suits the user's current scenario for recommendation.

[0081] Once the target motion scenario is determined, multiple candidate navigation paths can be scored individually based on the target motion scenario to obtain a comprehensive score for each candidate navigation path. The comprehensive score can be used to represent the score of each candidate navigation path in the target motion scenario; the higher the score, the more the candidate navigation path meets the requirements of the target motion scenario.

[0082] In some embodiments, determining the comprehensive score of each candidate navigation path among multiple candidate navigation paths based on the target motion scene includes: determining the weights corresponding to multiple evaluation dimensions based on the target motion scene; calculating the scores corresponding to multiple evaluation dimensions for each candidate navigation path among multiple candidate navigation paths; and determining the comprehensive score corresponding to each candidate navigation path based on the weights corresponding to multiple evaluation dimensions and the scores corresponding to each candidate navigation path in multiple evaluation dimensions.

[0083] Multiple evaluation dimensions refer to the various assessment dimensions used to rate the navigation route. These dimensions can be determined based on the user's travel needs. For example, multiple evaluation dimensions may include: time, distance, comfort, safety, cost-effectiveness, and scenery.

[0084] The time dimension mentioned above is an evaluation dimension used to assess the degree of matching between the travel time of the candidate navigation route and the user's needs. The higher the score of the time dimension, the more the estimated travel time of the candidate navigation route meets the user's needs.

[0085] The distance dimension mentioned above is an evaluation dimension used to assess the degree of matching between the driving distance of the candidate navigation route and the user's needs. The higher the score of the distance dimension, the more the expected driving distance of the candidate navigation route matches the user's needs.

[0086] The aforementioned comfort dimension is an evaluation dimension used to assess the degree of matching between the comfort of candidate navigation routes and user needs. The higher the score of the comfort dimension, the more the comfort of the candidate navigation route meets the user's needs.

[0087] The aforementioned safety dimension is an evaluation dimension used to assess the degree of matching between the safety of candidate navigation routes and user needs. The higher the score of the safety dimension, the more the driving safety of the candidate navigation route meets the user's needs.

[0088] The aforementioned economic dimension is an evaluation dimension used to assess the degree of matching between the cost of candidate navigation routes and user needs. The higher the score of the economic dimension, the more the cost of the candidate navigation route matches the user's needs.

[0089] The scenery dimension mentioned above is an evaluation dimension used to assess the degree of matching between the scenery of the candidate navigation route and the user's needs. The higher the score of the scenery dimension, the more the scenery of the candidate navigation route matches the user's needs.

[0090] After obtaining the target motion scene, the weights corresponding to multiple evaluation dimensions can be determined based on the target motion scene. The vehicle can store mapping relationships between different preset motion scenes and the corresponding weights of multiple evaluation dimensions. This mapping relationship can be looked up based on the target motion scene to determine the weights corresponding to multiple evaluation dimensions under the target motion scene. The mapping relationship can be shown in Table 1 below: Table 1

[0091] In Table 1 above, x represents the preset sports scenario, and y represents the evaluation dimension. The preset sports scenarios include: the first sports scenario, the second sports scenario, the third sports scenario, the fourth sports scenario, and the fifth sports scenario. The evaluation dimensions include six dimensions: time, distance, comfort, safety, economy, and scenery.

[0092] As shown in Table 1, in the mapping relationship, under the first motion scenario, the weights of the six dimensions of time, distance, comfort, safety, economy, and scenery are 0.15, 0.1, 0.15, 0.3, 0.15, and 0.15, respectively.

[0093] Understandably, the first sports scenario is based on extreme sports, including climbing. Climbing typically requires vehicles to carry heavy climbing equipment, which severely impacts vehicle stability. Therefore, the first sports scenario places a high demand on vehicle safety. Consequently, the safety dimension in the first sports scenario has a maximum weight of 0.3. Other dimensions, such as time, comfort, safety, economy, and scenery, take a backseat to safety and can all be assigned lower weights, such as 0.15. Extreme sports venues are usually far away, and distance is not a primary consideration for drivers. Therefore, in the first sports scenario, the distance dimension has the lowest weight, for example, it can be set to 0.1.

[0094] In the second sports scenario, the weights of the six evaluation dimensions—time, distance, comfort, safety, economy, and scenery—are 0.2, 0.15, 0.15, 0.15, 0.15, and 0.2, respectively.

[0095] Understandably, the second sports scenario is a scenario derived from outdoor sports activities, matching the characteristics of outdoor sports. Users typically want to enjoy the natural environment; therefore, the scenery dimension has a higher weight in the second sports scenario, which can be set to 0.2. Outdoor sports also require a significant amount of time, so the time dimension also has a relatively high weight in the second sports scenario, which can be set to 0.2. Other evaluation dimensions can be assigned lower weights, for example, all set to 0.15.

[0096] In the third sports scenario, the weights of the six dimensions—time, distance, comfort, safety, economy, and scenery—are 0.25, 0.15, 0.15, 0.1, 0.15, and 0.2, respectively.

[0097] Understandably, the third sports scenario is based on indoor sports venues. Venues typically have fixed operating hours, requiring precise arrival at the destination, thus placing high demands on time. Therefore, in the third sports scenario, the time dimension has the highest weight, specifically 0.25. Users usually have expectations regarding the scenery along the route, so the scenery dimension also has a high weight, for example, 0.2. Venues are usually located in cities, where roads are typically paved, resulting in lower road risks. Therefore, users have lower safety requirements, and in the third sports scenario, the safety dimension has the lowest weight, specifically 0.1. All other dimensions have low weights, for example, all set to 0.15.

[0098] In the fourth sports scenario, the weights of the six dimensions—time, distance, comfort, safety, economy, and scenery—are 0.25, 0.15, 0.1, 0.1, 0.1, and 0.3, respectively.

[0099] Understandably, the fourth sports scenario is based on indoor cycling or running activities. Since cycling and running are both outdoors, users typically need to enjoy the scenery while exercising, making scenery a high priority. Therefore, the scenery dimension has the highest weight in the fourth sports scenario, specifically 0.3. Cycling and running usually require matching exercise plans to specific time periods, also placing a high demand on time. Therefore, the time dimension has a slightly lower weight than the scenery dimension in the fourth sports scenario, for example, 0.25. Comfort, safety, and cost-effectiveness are typically less important in cycling and running; therefore, the comfort, safety, and cost-effectiveness dimensions have the lowest weights in the fourth sports scenario, for example, all 0.1. The distance dimension has a slightly higher weight than the lowest weight, specifically 0.15.

[0100] In the fifth sports scenario, the weights of the six dimensions—time, distance, comfort, safety, economy, and scenery—are 0.15, 0.1, 0.15, 0.25, 0.15, and 0.2, respectively.

[0101] Understandably, the fifth sports scenario is based on water sports, which typically require specific equipment like surfboards. This equipment is usually large and can impact vehicle safety, leading to a higher safety requirement in the fifth sports scenario (though not higher than for extreme sports). Therefore, the safety dimension has the highest weight in the fifth sports scenario, specifically 0.25 (slightly lower than the safety dimension weight in the first preset scenario). Water sports are usually held at high altitudes, and coastal routes are inherently scenic; therefore, the scenery dimension can also have a slightly higher weight in the fifth preset scenario, such as 0.2. Water sports venues are usually far away, and distance is not a primary consideration for drivers; therefore, the distance dimension has the lowest weight in the fifth sports scenario, such as 0.1. Time, comfort, and economy can all be assigned relatively low weights, such as 0.15.

[0102] The preset motion scenarios, evaluation dimensions, and weight values ​​of each evaluation dimension in different scenarios in Table 1 above are only examples, and this application does not make any specific limitations on them.

[0103] For each candidate navigation path, a score can be obtained for each of the above evaluation dimensions. Based on the determined weights and scores for each evaluation dimension, a comprehensive score can be calculated for each candidate navigation path.

[0104] Specifically, the scores of each evaluation dimension can be multiplied by the weight of each evaluation dimension and summed to calculate the weighted average score of each candidate navigation path, thus obtaining the comprehensive score of each candidate navigation path.

[0105] In some embodiments, the multiple evaluation dimensions include at least two of the following: time dimension, distance dimension, comfort dimension, safety dimension, economy dimension, and scenery dimension. Calculating scores for each of the multiple evaluation dimensions includes: calculating a first score for the time dimension when the multiple evaluation dimensions include the time dimension; calculating a second score for the distance dimension when the multiple evaluation dimensions include the distance dimension; calculating a third score for the comfort dimension when the multiple evaluation dimensions include the comfort dimension; calculating a fourth score for the safety dimension when the multiple evaluation dimensions include the safety dimension; calculating a fifth score for the economy dimension when the multiple evaluation dimensions include the economy dimension; and calculating a sixth score for the scenery dimension when the multiple evaluation dimensions include the scenery dimension.

[0106] As in the above embodiment, the evaluation dimensions include six dimensions: time, distance, comfort, safety, economy, and scenery. Scores are calculated for each of the multiple evaluation dimensions, including: calculating the first score for the time dimension, calculating the second score for the distance dimension, calculating the third score for the comfort dimension, calculating the fourth score for the safety dimension, calculating the fifth score for the economy dimension, and calculating the sixth score for the scenery dimension.

[0107] The calculation of the first score corresponding to the time dimension includes: for each candidate navigation path, obtaining the estimated travel time of the current candidate navigation path, and calculating the first score based on the estimated travel time and the shortest travel time.

[0108] Specifically, the first ratio between the shortest travel time and the estimated travel time can be calculated. Multiplying this first ratio by 100 yields the first score. The formula for calculating the first score is: First Score = (Shortest Travel Time / Estimated Travel Time) 100.

[0109] The aforementioned shortest duration can specifically be the shortest of the estimated travel times for multiple candidate navigation paths.

[0110] The calculation of the second score corresponding to the distance dimension includes: for each candidate navigation path, obtaining the actual distance of the current candidate navigation path, and calculating the second score based on the actual distance and the shortest distance.

[0111] Specifically, a second ratio between the shortest distance and the actual distance can be calculated. Multiplying this second ratio by 100 yields the second score. The formula for the second score is: Second Score = (Shortest Distance / Actual Distance) 100.

[0112] The aforementioned shortest distance can specifically be the shortest distance among the actual distances corresponding to multiple candidate navigation paths.

[0113] The calculation of the third score corresponding to the comfort dimension includes: for each candidate navigation route, calculating the slope score based on the slope and the road condition score based on the road condition; and calculating the third score based on the slope score and the road condition score.

[0114] The slope score is calculated as follows: For each candidate navigation path, determine the length of road segments in the candidate navigation path whose absolute slope is greater than a preset slope (pre-defined, for example, 5%), and calculate the ratio between the road segment length and the actual distance (total distance) of the candidate navigation path to obtain the slope score. Slope score = Road segment length (total length of road segments whose absolute slope is greater than the preset slope) / Total distance.

[0115] The road condition score is calculated as follows: road conditions are divided into three levels: good, average, and poor. For each candidate navigation route, the lengths of the good, average, and poor road conditions in the candidate navigation route are determined, and the road condition score of the candidate navigation route is calculated based on the lengths of the good, average, and poor road conditions.

[0116] Specifically, a corresponding coefficient is set for each road condition level in advance. The length of each road condition is multiplied by the corresponding coefficient and the values ​​are added together. The ratio between the sum and the actual distance (total distance) is used as the road condition score.

[0117] For example, if the coefficient for good road conditions is set to 1, the coefficient for average road conditions is set to 1.2, and the coefficient for poor road conditions is set to 1.5, then the road condition score = (length of good road conditions) / (length of good road conditions) = 1.5. 1+ length of normal road conditions 1.2+ length of poor road conditions 1.5) / Total distance.

[0118] The vehicle can also pre-set coefficients corresponding to the gradient score and road condition score. The calculation of the third score based on the gradient score and road condition score specifically includes: multiplying the gradient score by the coefficient corresponding to the gradient score to obtain the first value, multiplying the road condition score by the coefficient corresponding to the road condition score to obtain the second value, and subtracting the first and second values ​​from 100 to obtain the third score.

[0119] For example, if the coefficient for the slope score is 30 and the coefficient for the road condition score is 20, then the third score = 100 - slope score. 30-Road Condition Rating 20.

[0120] It is understandable that the reason for setting the coefficient for the gradient score to 30, which is greater than the coefficient for the road condition score to 20, is that the gradient has an exponential effect on heavy-duty vehicles: the engine load increases dramatically when going uphill, the braking system is under great pressure when going downhill, and the loading of sports equipment (such as skis or bicycles) will change the vehicle's center of gravity, making the gradient have a more significant impact on vehicle safety. Therefore, designing the coefficient for the gradient score to be a high coefficient of 30 can ensure that the system prioritizes avoiding steep slopes and avoids difficulties in vehicle handling.

[0121] The reason why the coefficient corresponding to the road condition rating is lower than that of the slope rating is that the vehicle's suspension system can partially mitigate the impact of ordinary road conditions, but for vehicles carrying precision sports equipment (such as surfboards and rock climbing equipment), bumps can still lead to the risk of equipment damage. Setting the road condition rating to a coefficient of 20 lower than that of the slope rating can balance "completely avoiding all uneven road sections" with "actual drivability".

[0122] In some embodiments, calculating the fourth score corresponding to the safety dimension includes: obtaining vehicle speed planning information, road quality information, and road weight limit information corresponding to the candidate navigation path; and determining the fourth score corresponding to the safety dimension based on one or more of the vehicle speed planning information, road quality information, and road weight limit information.

[0123] For each of the multiple candidate navigation paths, road speed limit information, road weight limit information, and road quality information can be obtained; based on the road speed limit information, road weight limit information, and road quality information, the corresponding score for the safety dimension can be calculated.

[0124] The safety dimension includes: speed limit factor, road quality factor, and road load-bearing factor. The fourth score corresponding to the safety dimension is calculated by: obtaining vehicle speed planning information, road quality information, and road weight limit information for candidate navigation paths; determining the score corresponding to the speed limit factor based on the speed planning information; determining the score corresponding to the road quality factor based on the road quality information; determining the score corresponding to the road load-bearing factor based on the road weight limit information; and determining the fourth score corresponding to the safety dimension based on the scores corresponding to the speed limit factor, road quality factor, and road load-bearing factor.

[0125] The speed limit factor mentioned above is an assessment factor used to evaluate the degree of impact of speed limits on vehicle safety. Generally, the higher the score of the speed limit factor, the more significant the adverse impact of the speed limit on vehicle safety on the road section, and the lower the fourth score of the safety dimension.

[0126] The aforementioned road quality factors are assessment factors used to evaluate the impact of uneven road surfaces on vehicle safety. The higher the score of the road quality factor, the worse the road surface quality of that section, the more significant the adverse impact on vehicle safety, and the lower the fourth score of the safety dimension.

[0127] The aforementioned road load-bearing factor is an assessment factor used to evaluate the degree of impact of road load-bearing capacity on vehicle safe driving. The higher the score of the road load-bearing factor, the lower the load-bearing capacity of the road section, the more significant the adverse impact on vehicle safe driving, and the lower the fourth score of the safety dimension.

[0128] Specifically, based on vehicle speed planning information, the scores corresponding to the vehicle speed limit factors are determined, including: when a vehicle is detected to be equipped with sports equipment, a target vehicle speed threshold is determined based on the sports equipment; and based on the target vehicle speed threshold and vehicle speed planning information, the scores corresponding to the vehicle speed limit factors are determined.

[0129] The target speed threshold can be defined as the maximum speed at which a vehicle can travel without damaging the sports equipment. The speed threshold varies depending on the type of sports equipment. When sports equipment is detected on a vehicle, the target speed threshold can be determined based on the currently loaded sports equipment.

[0130] It is understandable that sports equipment affects the maximum speed of a vehicle. To ensure that the sports equipment is not damaged, the vehicle speed cannot exceed the speed threshold corresponding to the sports equipment. Therefore, when sports equipment is detected on a vehicle, the target speed threshold can be determined based on the sports equipment.

[0131] For each candidate navigation path, its corresponding vehicle speed planning information, road quality information, and road weight limit information can be obtained. Based on the target vehicle speed threshold and vehicle speed planning information, the score corresponding to the vehicle speed limit factor is determined; based on the road quality information, the score corresponding to the road quality factor is determined; and based on the road weight limit information, the score corresponding to the road load-bearing factor is determined.

[0132] For each candidate navigation path, the speed planning information includes the expected speed of each segment in the candidate navigation path (i.e., the planned speed in the navigation route). The target segments in the candidate navigation path with expected speeds greater than the target speed threshold can be identified. Based on the length of the target segments and the actual distance (total distance) of the candidate navigation path, the score corresponding to the speed limit factor is calculated.

[0133] Specifically, the ratio of the length of the target road segment to the actual distance (total distance) of the candidate navigation path can be used as the score corresponding to the speed limit factor. The score corresponding to the speed limit factor = length of the target road segment / total distance.

[0134] Based on road quality information, the scores corresponding to road quality factors are determined, including: determining the length of uneven road surfaces in the candidate navigation path based on road quality information, and calculating the scores corresponding to road quality factors based on the length of uneven road surfaces and the actual distance (total distance) of the candidate navigation path.

[0135] Specifically, the ratio of the length of the uneven road surface to the actual distance (total distance) of the candidate navigation path can be used as the score corresponding to the road quality factor. The road quality factor score = length of uneven road surface / total distance.

[0136] The aforementioned uneven road surface refers to road conditions with potholes, cracks, bulges, damage, bumps, etc., that affect the smooth driving of vehicles. Such road surface has poor smoothness and can easily have an adverse effect on the driving stability and safety of vehicles.

[0137] Road quality information can include road surface smoothness information, road surface damage information, uneven road surface location information, and uneven road surface length information. Based on the road quality information, the length of uneven road surface in the target candidate navigation path can be determined.

[0138] Based on road weight limit information, determine the score corresponding to the road load-bearing factor, including: based on road weight limit information, identify road segments with insufficient load-bearing capacity in the candidate navigation path, and calculate the score corresponding to the road load-bearing factor based on the road segments with insufficient load-bearing capacity.

[0139] The road weight limit information can include the load-bearing capacity of bridge sections in the candidate navigation path. The aforementioned insufficient load-bearing sections refer to bridge sections whose load-bearing capacity is less than or equal to the preset weight. The preset weight can be pre-calibrated or determined based on the vehicle's current curb weight.

[0140] For example, if the current curb weight of a vehicle is M1, a preset weight of M2 can be determined based on the curb weight. M2 is greater than M1. In this case, bridges in the candidate navigation path with a load-bearing capacity less than or equal to M2 are identified as sections with insufficient load-bearing capacity.

[0141] Specifically, the score corresponding to the road load-bearing factor can be determined based on the difference between the load-bearing mass and the preset mass. The larger the difference, the greater the risk of the bridge, and the higher the score corresponding to the determined road load-bearing factor.

[0142] After obtaining the scores corresponding to the speed limit factor, road quality factor, and road load-bearing factor, a fourth score corresponding to the safety dimension can be determined based on these scores.

[0143] Specifically, corresponding coefficients are pre-set for the vehicle's speed limit factor, road quality factor, and road load-bearing factor. The score for the speed limit factor is multiplied by its corresponding coefficient to obtain the third value; the score for the road quality factor is multiplied by its corresponding coefficient to obtain the fourth value; and the score for the road load-bearing factor is multiplied by its corresponding coefficient to obtain the fifth value. Subtracting the third, fourth, and fifth values ​​from 100 yields the fourth score.

[0144] For example, the coefficient corresponding to the speed limit factor is 25, the coefficient corresponding to the road quality factor is 25, and the coefficient corresponding to the road load-bearing factor is 20; the scoring formula for calculating the fourth score is: Fourth score = 100 - Score corresponding to the speed limit factor 25 - Score corresponding to road quality factor 25 - Score corresponding to road load-bearing factor 20.

[0145] Understandably, the reason for setting the speed limit factor and road quality factor to the highest coefficients is that speed is a decisive factor in the severity of accidents. Kinetic energy is proportional to the square of speed. When transporting sports equipment, the vehicle's center of gravity changes, and the risk of high-speed cornering increases exponentially. A high coefficient of 25 ensures that the system strictly adheres to speed limits and does not take risks to save time. The reason for giving the road quality factor the same weight as the speed limit is that wet / damaged roads significantly reduce tire grip, increase the braking distance of heavy vehicles, and amplify the impact of road quality, especially in skiing / water sports scenarios. Icy roads in winter and slippery roads in summer rain require equal attention.

[0146] The coefficient for the road load-bearing factor is set to 20, which is slightly lower than the original values ​​for the speed limit factor and road quality factor mentioned above. Bridge load-bearing problems only occur on specific road sections and are not a factor affecting the entire route, but they have a significant impact on the transportation of heavy equipment. A coefficient of 20 can ensure that the system avoids the route when it detects bridge load-bearing problems, but does not excessively adjust the overall route due to a very small number of bridges.

[0147] In the above method, considering the speed limit of the vehicle when it is equipped with sports equipment, speed planning information, road quality information and road weight limit information are obtained to determine the fourth score of the safety dimension. This makes the safety dimension score take into account the user's travel needs in the target scenario and ensure that the subsequent recommended navigation route can better meet the user's needs.

[0148] The calculation of the fifth score corresponding to the economic dimension includes: for each candidate navigation path, obtaining the actual cost of the current candidate navigation path, and calculating the fifth score based on the actual cost and the base cost.

[0149] Specifically, the third ratio between actual cost and basic cost can be calculated. Multiplying this third ratio by a preset coefficient yields the sixth value. Subtracting the sixth value from 100 gives the fifth score. Assuming the preset coefficient is 50, the formula for calculating the fifth score is: Fifth Score = 100 - (Actual Cost / Basic Cost) 50.

[0150] The aforementioned basic cost can be pre-set based on user needs, or it can be the lowest cost among the actual costs corresponding to multiple candidate navigation paths.

[0151] The reason for setting a coefficient of 50 for the ratio of operating costs to base costs is to ensure that the score drops to 25 points when the cost reaches 1.5 times the benchmark, and resets to zero when it reaches 2 times. This reflects the principle of "acceptable premium": for active travel, people are willing to pay a reasonable premium for safety / scenery, but reject excessive costs. The coefficient of 50 ensures that economic efficiency does not dominate the decision (compared to comfort, the maximum deduction is 50 points), which aligns with the "experience-first" characteristic of active travel.

[0152] The sixth score corresponding to the scenery dimension is calculated as follows: for each candidate navigation path, the environmental information of the candidate navigation path is obtained, the attractions, green coverage and openness of the view along the candidate navigation path are determined based on the environmental information, and the sixth score is determined based on this.

[0153] In some embodiments, calculating the sixth score corresponding to the scenery dimension includes: obtaining information on attractions along the route, road greening information, and driving visibility information corresponding to the candidate navigation path; and determining the sixth score corresponding to the scenery dimension based on one or more of the information on attractions along the route, road greening information, and driving visibility information.

[0154] The scenery dimension specifically includes: landscape quality factor, green coverage factor, and field of vision factor. Based on one or more of the following: information on scenic spots along the route, roadside greenery, and driving visibility, a sixth score is determined for the scenery dimension. This includes: determining the score for the landscape quality factor based on information on scenic spots along the route; determining the score for the green coverage factor based on information on roadside greenery; determining the score for the field of vision factor based on driving visibility; and calculating the sixth score for the scenery dimension based on the scores for the landscape quality factor, green coverage factor, and field of vision factor.

[0155] The aforementioned information on attractions along the route can include the number and quality of attractions along the candidate navigation path. Specifically, the quality can be the quality rating of each attraction on the platform, typically ranging from 0 to 5 points. A landscape quality factor rating can be determined based on the number and quality of attractions.

[0156] As a real-time method, a fourth ratio between the number of attractions and the number of basic attractions can be determined, and this fourth ratio can be used as one of the scoring criteria for the landscape quality factor. At the same time, combined with the attraction quality score, the score corresponding to the landscape quality factor is calculated comprehensively. The more attractions there are and the higher the attraction quality score, the higher the score corresponding to the landscape quality factor.

[0157] The aforementioned basic number of attractions can be the minimum number of attractions determined based on user preferences, or it can be the minimum number of attractions among multiple candidate navigation paths.

[0158] For example, the formula for calculating the score corresponding to the landscape quality factor can be: Score corresponding to the landscape quality factor = Fourth ratio Average quality rating of attractions.

[0159] The road greening information can include the length of roads covered by greenery in the candidate navigation path. The score corresponding to the greening coverage factor can be calculated based on the length of roads covered by greenery and the actual distance (total distance) of the candidate navigation path. Specifically, the ratio of the length of roads covered by greenery to the actual distance of the candidate navigation path can be determined as the score corresponding to the greening coverage factor. The score corresponding to the greening coverage factor = length of roads covered by greenery / total distance.

[0160] The driving field of vision information can include the number of obstacles within the driving field of vision along the candidate navigation path. The degree of driving field of vision along the candidate navigation path can be determined based on the number of obstacles, and this degree of field of vision can be assigned as a score corresponding to the field of vision openness factor. Specifically, the number of obstacles can be compared with a preset base number of obstacles to determine the ratio between the two. A larger ratio indicates more obstacles within the driving field of vision, a lower degree of driving field of vision, and a lower score for the field of vision openness factor; a smaller ratio indicates fewer obstacles within the driving field of vision, a higher degree of driving field of vision, and a higher score for the field of vision openness factor.

[0161] The vehicle can pre-store the coefficients corresponding to the landscape quality factor, green coverage factor, and field of vision factor. After obtaining the scores for the landscape quality factor, green coverage factor, and field of vision factor, the scores for each factor are multiplied by their corresponding coefficients to obtain the seventh value; the scores for the green coverage factor are multiplied by their corresponding coefficients to obtain the eighth value; and the scores for the field of vision factor are multiplied by their corresponding coefficients to obtain the ninth value. The seventh, eighth, and ninth values ​​are then added together to obtain the sixth score for the landscape dimension.

[0162] For example, if the coefficient for the landscape quality factor is 40, the coefficient for the green coverage factor is 30, and the coefficient for the field of view factor is 30, then the formula for calculating the sixth score for the landscape dimension is: Sixth score = Score corresponding to the landscape quality factor. Scores corresponding to 40+ green coverage factors Scores corresponding to 30+ field of view factors 30.

[0163] Understandably, the reason for setting the landscape quality factor to the highest coefficient of 40 is that scenic spots, landmarks, and distinctive buildings directly reflect the core of the user's visual experience, and a high weight of 40 ensures that the system prioritizes routes with clear landscape value. This aligns with the requirement in cycling or running scenarios that "scenery is part of the exercise experience."

[0164] The reason for setting the coefficient for green coverage rate to have the same weight as that for field of vision is that greening is a fundamental element of urban landscape, affecting visual comfort and perceived air quality. A coefficient of 30 can prevent the system from excessively pursuing "pure natural landscapes" and ignoring the value of urban greenways. Especially for users in indoor venues, urban greenways are more practical than wilderness routes.

[0165] The field of vision factor is given equal weight to the green coverage rate because a wide field of vision reduces the feeling of oppression while driving, which is crucial for long-distance travel. It avoids recommending routes that cause psychological discomfort, such as "under elevated bridges" or "between dense high-rise buildings." The coefficient of 30 ensures that the system balances landscape quality and spatial experience, and does not focus on a single attraction.

[0166] In the above method, the sixth score corresponding to the scenery dimension is determined based on information on attractions along the way, road greenery, and driving visibility. This takes into account the user's various needs for attractions along the way, road greenery, and driving visibility, making the candidate navigation recommendations more able to meet the user's needs.

[0167] As described in the above embodiment, the scores corresponding to the multiple evaluation dimensions include: a first score S1 for the time dimension, a second score S2 for the distance dimension, a third score S3 for the comfort dimension, a fourth score S4 for the safety dimension, a fifth score S5 for the economy dimension, and a sixth score S6 for the scenery dimension. The target exercise scenario is the first exercise scenario, and the weights corresponding to the six dimensions—time, distance, comfort, safety, economy, and scenery—are 0.15, 0.15, 0.15, 0.3, 0.15, and 0.15, respectively. The formula for calculating the comprehensive score is: Comprehensive Score = 0.15 S1+0.1 S2+0.15 S3+0.3 S4+0.15 S5+0.15 S6.

[0168] In the above method, the weights corresponding to each evaluation dimension are dynamically determined by the target motion scene. Based on the weights and the scores corresponding to each evaluation dimension, the comprehensive score of each candidate navigation path is calculated. This ensures that the comprehensive score takes into account the user's needs in the target motion scene and is dynamically adjusted based on the target motion scene, thus ensuring that the subsequent navigation path recommendation based on the comprehensive score is safer and more reliable.

[0169] In step 103, after obtaining the comprehensive score corresponding to each candidate navigation path, the multiple candidate navigation paths can be sorted based on the comprehensive score, and the target navigation path can be selected from the multiple candidate navigation paths for recommendation based on the sorting.

[0170] Specifically, multiple candidate navigation paths can be sorted from highest to lowest based on their overall score. After sorting, the top N candidate navigation paths are selected as the target navigation paths for recommendation. N is an integer greater than or equal to 1. N can be pre-set based on user needs; when N is 1, the selected target navigation path is the candidate navigation path with the highest overall score.

[0171] Figure 3 This is a flowchart of another method for recommending navigation paths provided in an embodiment of this application.

[0172] For example, such as Figure 3 As shown, the method 300 includes: Step 301: Obtain the set of candidate navigation paths.

[0173] The above set of candidate navigation paths includes multiple candidate navigation paths.

[0174] Step 302: Calculate the time score, distance score, comfort score, safety score, cost-effectiveness score, and scenery score.

[0175] Among them, the time score is the first score corresponding to the time dimension, the distance score is the second score corresponding to the distance dimension, the comfort score is the third score corresponding to the comfort dimension, the safety score is the fourth score corresponding to the safety dimension, the economy score is the fifth score corresponding to the economy dimension, and the scenery score is the sixth score corresponding to the scenery dimension.

[0176] Step 303: Identify the type of motion scene.

[0177] Identifying the motion scene category means determining the target motion scene where the vehicle is currently located from multiple preset motion scenes in the above embodiments.

[0178] Step 304: Query the preset weight configuration.

[0179] After identifying the target motion scene by classifying the motion scene, the mapping relationship shown in Table 1 can be looked up based on the target motion scene to obtain the preset weight configuration for each evaluation dimension.

[0180] Step 305: Fine-tune according to user preferences.

[0181] In some embodiments, after obtaining the weights corresponding to multiple evaluation dimensions, the weights can be adjusted based on user preferences to obtain adjusted weights. Then, based on the adjusted weights and the scores of each dimension, a comprehensive score for the candidate navigation path is calculated.

[0182] The aforementioned user preferences specifically refer to dimensions that reflect a user's preferences across multiple evaluation dimensions. If a user's preferred dimension is a key focus, then that dimension needs to be considered in advance when recommending navigation paths. Therefore, the weight of that dimension can be appropriately increased based on user preferences. For example, if a user's preference is for time, then the weight of the time dimension among the determined evaluation dimensions can be increased based on this preference, resulting in an adjusted weight.

[0183] Step 306: Weighted summation of scores for each dimension.

[0184] Step 307 yields the overall path score.

[0185] The time rating is S1, distance rating is S2, comfort rating is S3, safety rating is S4, economy rating is S5, and scenery rating is S6. The weights of the six dimensions—time, distance, comfort, safety, economy, and scenery—are P1, P2, P3, P4, P5, and P6, respectively. The formula for calculating the overall score is: Overall Score = P1 S1+P2 S2+P3 S3+P4 S4+P5 S5+P6 S6.

[0186] Step 308: Sort candidate navigation paths.

[0187] Step 309: Select the optimal path recommendation.

[0188] As in the above embodiment, multiple candidate navigation paths are sorted from largest to smallest based on the comprehensive score. After sorting, the top N candidate navigation paths are determined as the target navigation paths and recommended.

[0189] In some embodiments, after recommending a target navigation path, traffic conditions can be continuously monitored and the recommended target navigation path can be dynamically optimized.

[0190] Specifically, after determining that the user has initiated the navigation service based on the recommended target navigation path, various parameters such as road conditions, the fixed status of sports equipment, and weather conditions corresponding to the target navigation path can be obtained. Based on these parameters, the function of replanning the navigation route is triggered if the vehicle meets any of the following conditions: Traffic condition indicator increases the congestion index ahead of the target navigation route by a preset amount (pre-calibrated, for example, 20%). The sports equipment fixation status indicator shows an abnormal fixation of the sports equipment. Weather conditions indicate weather changes and their impact on road conditions.

[0191] For example, if an accident occurs ahead of the target navigation path during driving, causing congestion, the traffic condition indicator will increase the congestion index ahead of the target navigation path by a preset amount. For example, if sports equipment falls off or collides with a vehicle, causing damage to the vehicle body and the equipment, the indicator will determine that the equipment is improperly secured. For example, if the weather changes from sunny to snowy during driving, reducing the road surface adhesion coefficient ahead of the navigation path and making it easier to slip, the weather condition indicator will determine that the weather change affects road conditions.

[0192] Once the vehicle triggers the route replanning function, multiple new candidate navigation routes can be replanned based on the vehicle's current location and destination. These new candidate navigation routes are then scored to obtain a comprehensive score for each new candidate navigation route. Based on the comprehensive score, a new target navigation route is selected from the multiple new candidate navigation routes and recommended.

[0193] Figure 4 This is a flowchart of a real-time path optimization method provided in an embodiment of this application.

[0194] For example, such as Figure 4 As shown, the method 400 includes: Step 401: Start the navigation service.

[0195] Step 402: Monitor real-time road conditions, the fixed status of sports equipment, and weather conditions.

[0196] Step 403: Determine whether the vehicle meets the conditions for rerouting. If not, proceed to step 404; if yes, proceed to step 405.

[0197] If any of the following conditions are met: the congestion index ahead of the target navigation route is increased by a preset amount; the sports equipment fixation status is indicated as abnormal; or the weather status is indicated as a weather change affecting the road conditions, then the vehicle is deemed to meet the conditions for rerouting.

[0198] Step 404: Continue the current navigation.

[0199] Step 405: Replan new candidate navigation paths.

[0200] Step 406: Evaluate new candidate navigation paths.

[0201] Step 407: Determine if the new path is better; if yes, proceed to step 408; if no, proceed to step 404.

[0202] Step 408: Update navigation guide.

[0203] Step 409: Notify the user to change the navigation.

[0204] Step 410: Confirm that the user has arrived at their destination.

[0205] After evaluating the new candidate navigation paths, a new target navigation path can be determined. It can be determined whether the new target navigation path or the current navigation path is better. If the new target navigation path is determined to be better, step 408 is executed. If the current navigation path is determined to be better, step 404 is executed.

[0206] Specifically, the overall scores of the new target navigation path and the current navigation path can be compared. If the overall score of the new target navigation path is higher than that of the current navigation path, the new target navigation path is determined to be better.

[0207] Figure 5 This is a schematic diagram of a device for recommending navigation paths provided in an embodiment of this application.

[0208] For example, such as Figure 5 As shown, the device 500 includes: The determination module 501 is used to determine the target motion scene where the vehicle is currently located from multiple preset motion scenes; the preset motion scenes are obtained based on the motion items performed by the user. The scoring module 502 is used to determine the comprehensive score of each candidate navigation path among multiple candidate navigation paths based on the target motion scene. The recommendation module 503 is used to select a target navigation path from multiple candidate navigation paths for recommendation based on a comprehensive score.

[0209] In some embodiments, the scoring module 502 is specifically used to: determine the weights corresponding to multiple evaluation dimensions based on the target motion scene; calculate the scores corresponding to multiple evaluation dimensions for each candidate navigation path among multiple candidate navigation paths; and determine the comprehensive score corresponding to each candidate navigation path based on the weights corresponding to multiple evaluation dimensions and the scores corresponding to each candidate navigation path in multiple evaluation dimensions.

[0210] In some embodiments, the determining module 501 is specifically used to: detect whether the vehicle is equipped with sports equipment; and if the vehicle is equipped with sports equipment, determine the target sports scene in which the vehicle is currently located from multiple preset sports scenes based on the sports equipment.

[0211] In some embodiments, the determining module 501 is specifically used to: determine the target sport corresponding to the sports equipment; and determine the preset sports scene that includes the target sport from a plurality of preset sports scenes as the target sports scene.

[0212] In some embodiments, the multiple evaluation dimensions include at least two of the following: time dimension, distance dimension, comfort dimension, safety dimension, economy dimension, and scenery dimension. The scoring module 502 is specifically used to: calculate a first score corresponding to the time dimension when the multiple evaluation dimensions include the time dimension; calculate a second score corresponding to the distance dimension when the multiple evaluation dimensions include the distance dimension; calculate a third score corresponding to the comfort dimension when the multiple evaluation dimensions include the comfort dimension; calculate a fourth score corresponding to the safety dimension when the multiple evaluation dimensions include the safety dimension; calculate a fifth score corresponding to the economy dimension when the multiple evaluation dimensions include the economy dimension; and calculate a sixth score corresponding to the scenery dimension when the multiple evaluation dimensions include the scenery dimension.

[0213] In some embodiments, the scoring module 502 is specifically used to: obtain vehicle speed planning information, road quality information, and road weight limit information corresponding to the candidate navigation path; and determine a fourth score corresponding to the safety dimension based on one or more of the vehicle speed planning information, road quality information, and road weight limit information.

[0214] In some embodiments, the scoring module 502 is specifically used to: obtain information on scenic spots along the route, road greening information, and driving visibility information corresponding to the candidate navigation path; and determine a sixth score corresponding to the scenery dimension based on one or more of the information on scenic spots along the route, road greening information, and driving visibility information.

[0215] Figure 6 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application.

[0216] For example, such as Figure 6 As shown, the vehicle 600 includes a memory 601 and a processor 602. The memory 601 stores executable program code 6011, and the processor 602 is used to call and execute the executable program code 6011 to perform a method for recommending a navigation path.

[0217] Furthermore, embodiments of this application also protect an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to perform a method for recommending a navigation path provided in embodiments of this application.

[0218] This embodiment can divide the device into functional modules based on the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0219] When the functional modules are divided according to their respective functions, the device may also include a determination module, a scoring module, and a recommendation module. It should be noted that all relevant content regarding the steps involved in the above method embodiments can be referenced from the functional descriptions of the corresponding functional modules, and will not be repeated here.

[0220] It should be understood that the apparatus provided in this embodiment is used to execute the above-described method for recommending a navigation path, and therefore can achieve the same effect as the above-described implementation method.

[0221] When using an integrated unit, the device may include a processing module and a storage module. When the device is applied to a vehicle, the processing module can be used to control and manage the vehicle's movements. The storage module can be used to support the vehicle in executing relevant program code.

[0222] The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits shown in conjunction with the disclosure of this application. The processor may also be a combination of functions that implement computing capabilities, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0223] In addition, the apparatus provided in the embodiments of this application may specifically be a chip, component or module. The chip may include a connected processor and a memory. The memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute a method for recommending a navigation path provided in the above embodiments.

[0224] This embodiment also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, it causes the computer to execute the above-described related method steps to implement a method for recommending a navigation path provided in the above embodiment.

[0225] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement a method for recommending navigation paths provided in the above embodiment.

[0226] In this embodiment, the device, computer-readable storage medium, computer program product, or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0227] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0228] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0229] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for recommending navigation routes, characterized in that, The method includes: The target motion scenario currently in which the vehicle is located is determined from multiple preset motion scenarios; the preset motion scenarios are obtained based on the motion activities performed by the user. Based on the target motion scene, a comprehensive score is determined for each candidate navigation path among multiple candidate navigation paths; Based on the comprehensive score, a target navigation path is selected from multiple candidate navigation paths for recommendation.

2. The method according to claim 1, characterized in that, The step of determining a comprehensive score for each candidate navigation path among multiple candidate navigation paths based on the target motion scene includes: Based on the target motion scenario, determine the weights corresponding to multiple evaluation dimensions respectively; For each candidate navigation path among multiple candidate navigation paths, calculate the score corresponding to each of the multiple evaluation dimensions; Based on the weights corresponding to multiple evaluation dimensions and the scores corresponding to each candidate navigation path on each of the multiple evaluation dimensions, a comprehensive score is determined for each candidate navigation path.

3. The method according to claim 1 or 2, characterized in that, Determining the target motion scenario where the vehicle is currently located from multiple preset motion scenarios includes: To detect whether the vehicle is equipped with sports equipment; If the vehicle is found to be equipped with sports equipment, the target sports scene in which the vehicle is currently located is determined from multiple preset sports scenes based on the sports equipment.

4. The method according to claim 3, characterized in that, Each of the preset motion scenarios includes at least one sport, and determining the target motion scenario currently in which the vehicle is located from among the multiple preset motion scenarios based on the sports equipment includes: Determine the target sport corresponding to the sports equipment; The target sports scenario is determined from among the multiple preset sports scenarios that include the target sports project.

5. The method according to claim 2, characterized in that, Multiple evaluation dimensions include at least two of the following: time, distance, comfort, safety, cost-effectiveness, and scenery. The calculation of scores corresponding to multiple evaluation dimensions includes: When multiple evaluation dimensions include the time dimension, calculate the first score corresponding to the time dimension; When multiple evaluation dimensions include the distance dimension, calculate the second score corresponding to the distance dimension; When multiple evaluation dimensions include the comfort dimension, calculate the third score corresponding to the comfort dimension; When multiple evaluation dimensions include the security dimension, calculate the fourth score corresponding to the security dimension; When multiple evaluation dimensions include the economic dimension, calculate the fifth score corresponding to the economic dimension; When multiple evaluation dimensions include the landscape dimension, calculate the sixth score corresponding to the landscape dimension.

6. The method according to claim 5, characterized in that, The calculation of the fourth score corresponding to the security dimension includes: Obtain vehicle speed planning information, road quality information, and road weight limit information corresponding to candidate navigation routes; Based on one or more of the vehicle speed planning information, the road quality information, and the road weight limit information, a fourth score corresponding to the safety dimension is determined.

7. The method according to claim 5, characterized in that, The calculation of the sixth score corresponding to the landscape dimension includes: Obtain information on attractions, roadside greenery, and driver visibility along the candidate navigation routes; Based on one or more of the information on scenic spots along the way, the information on road greenery, and the information on driving visibility, a sixth score corresponding to the scenery dimension is determined.

8. A device for recommending navigation routes, characterized in that, The device includes: The determination module is used to determine the target motion scenario in which the vehicle is currently located from multiple preset motion scenarios; the preset motion scenarios are obtained based on the motion activities performed by the user. The scoring module is used to determine the comprehensive score of each candidate navigation path among multiple candidate navigation paths based on the target motion scene. The recommendation module is used to select a target navigation path from multiple candidate navigation paths and recommend it based on the comprehensive score.

9. A vehicle, characterized in that, The vehicles include: Memory, used to store executable program code; A processor for calling and running the executable program code from the memory, causing the vehicle to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed, implements the method as described in any one of claims 1 to 7.

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