Method, device, equipment, medium and program product for recommending vehicle travel route

Abstract

The application discloses a vehicle driving route recommendation method, device, equipment, medium and program product, and relates to the technical field of vehicles. The method comprises the following steps: determining a target score corresponding to each candidate driving route according to feature data corresponding to each scene feature of a vehicle; determining a first score corresponding to each candidate driving route under each intelligent driving adaptation feature; determining an intelligent driving adaptation degree score corresponding to each candidate driving route according to the first score corresponding to each candidate driving route under each intelligent driving adaptation feature; and finally outputting a route recommendation signal containing two types of scores. In the route recommendation process, the method can simultaneously take into account multi-scene personalized travel demand and vehicle intelligent driving adaptation capability, improve the rationality, pertinence and practicability of the recommended route, enhance driving safety and driving experience, and provide a route selection basis that is more in line with actual travel and intelligent driving operation conditions for users.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to methods, apparatus, devices, media, and program products for recommending vehicle driving routes. Background Technology

[0002] As vehicle travel demands become increasingly diversified, traditional route recommendation methods often focus on basic indicators such as traffic efficiency, making it difficult to fully adapt to the actual needs of different travel scenarios. At the same time, insufficient consideration is given to compatibility with vehicle intelligent driving systems, resulting in recommended routes often failing to balance driving comfort, driving safety, and the effective use of intelligent driving functions. Therefore, there is an urgent need for a route recommendation method that is more in line with actual travel conditions and vehicle driving capabilities. Summary of the Invention

[0003] The main objective of this application is to provide a method, apparatus, device, medium, and program product for recommending vehicle driving routes, aiming to solve the technical problem that recommended routes often fail to simultaneously ensure driving comfort, driving safety, and the effective functioning of intelligent driving.

[0004] To achieve the above objectives, this application proposes a method for recommending vehicle travel routes, the method comprising: Based on the feature data corresponding to the vehicle under each scenario feature, the target score corresponding to each candidate driving route is determined. The scenario features include at least one of the following: time features, passenger features, road condition features, in-vehicle environment features, and driver physiological condition features. Determine the first score corresponding to each candidate driving route under each intelligent driving adaptation feature, wherein the intelligent driving adaptation feature includes at least one of intelligent driving road adaptability, intelligent driving function coverage, and environmental adaptability; Based on the first score corresponding to each candidate driving route under each intelligent driving adaptation feature, determine the intelligent driving adaptation score corresponding to each candidate driving route; The output route recommendation signal includes the target score and the intelligent driving adaptation score corresponding to each candidate driving route.

[0005] Furthermore, to achieve the above objectives, this application also proposes a vehicle route recommendation device, which includes: The first determining module is used to determine the target score corresponding to each candidate driving route based on the feature data corresponding to the vehicle under each scene feature. The scene features include at least one of time features, passenger features, road condition features, in-vehicle environment features, and driver physiological condition features. The second determining module is used to determine the first score corresponding to each candidate driving route under each intelligent driving adaptation feature, wherein the intelligent driving adaptation feature includes at least one of intelligent driving road adaptability, intelligent driving function coverage, and environmental adaptability. The third determining module is used to determine the intelligent driving adaptation score corresponding to each candidate driving route based on the first score corresponding to each candidate driving route under each intelligent driving adaptation feature. The fourth determination module is used to output a route recommendation signal, which includes the target score and the intelligent driving adaptation score corresponding to each candidate driving route.

[0006] Furthermore, to achieve the above objectives, this application also proposes a vehicle route recommendation device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the vehicle route recommendation method as described above.

[0007] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the vehicle route recommendation method as described above.

[0008] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the vehicle route recommendation method as described above.

[0009] One or more technical solutions proposed in this application have at least the following technical effects: The application determines the target score for each candidate driving route based on the feature data corresponding to the vehicle under each scenario feature, and determines the first score for each candidate driving route under each intelligent driving adaptation feature. Based on the first score, the intelligent driving adaptation score for each candidate driving route is determined. Finally, a route recommendation signal containing both types of scores is output. This allows for simultaneous consideration of personalized travel needs in multiple scenarios and the vehicle's intelligent driving adaptation capabilities during the route recommendation process, improving the rationality, relevance, and practicality of the recommended routes, enhancing driving safety and the driving experience, and providing users with a route selection basis that is more in line with actual travel and intelligent driving operation conditions. Attached Figure Description

[0010] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating the recommended method for the vehicle's driving route in this application (Example 1); Figure 2 A flowchart illustrating the recommended method for the vehicle's driving route in this application, as shown in Embodiment 2. Figure 3 A flowchart illustrating the recommended method for the vehicle's driving route in this application, as provided in Embodiment 3; Figure 4 A flowchart illustrating the recommended method for the vehicle's driving route in this application, embodiment four; Figure 5 This is a schematic diagram of the module structure of the vehicle travel route recommendation device according to an embodiment of this application; Figure 6 This is a schematic diagram of the hardware operating environment involved in the vehicle driving route recommendation method in the embodiments of this application.

[0013] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0014] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0015] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0016] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, a vehicle route recommendation device, etc. The following description uses a vehicle route recommendation device as an example to illustrate this embodiment and the subsequent embodiments.

[0017] This application provides a method for recommending vehicle driving routes, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the method for recommending vehicle travel routes according to this application. Figure 1 As shown, the recommended method for vehicle driving routes includes steps S10 to S40: Step S10: Determine the target score for each candidate driving route based on the feature data corresponding to the vehicle under each scene feature. The scene features include at least one of the following: time features, passenger features, road condition features, in-vehicle environment features, and driver physiological condition features.

[0018] Among them, scene features are multi-dimensional scene-related attributes that affect the choice of vehicle driving routes, and are the core basis for evaluating route scene adaptability.

[0019] Among them, feature data consists of quantifiable and analyzable specific data collected for each scene feature, used to reflect the actual state of the feature.

[0020] Among them, the feature data corresponding to the time feature can be at least one of the following: the current time is a weekday, a holiday, the morning rush hour, or the evening rush hour.

[0021] Among them, passenger characteristics can be characteristics related to the people riding in the vehicle, and the associated characteristic data can be the number of passengers, their identities, travel purposes, special needs (such as mothers and infants, rest), etc.

[0022] Among them, road condition features are those related to the condition of the road, and the associated feature data include congestion, road scenery, road smoothness, road type (expressway / national highway / urban road), traffic density, etc.

[0023] Among them, the in-vehicle environment characteristics are those related to the vehicle's internal environment, and the associated characteristic data can include in-vehicle temperature and humidity, air quality, noise level, etc.

[0024] Among them, the physiological characteristics of the driver can be characteristics related to the driver's physical condition, and the associated characteristic data can be heart rate, fatigue level, blinking frequency, etc.

[0025] The candidate driving routes can be a preset number of routes that meet preset conditions and can be selected based on the vehicle's origin, destination, and navigation road network data. The preset conditions may include: (1) the road type matches the current intelligent driving system's supported level (Level 2 intelligent driving selects paved roads, Level 3-L4 intelligent driving is compatible with urban expressways / highways); (2) the route travel time deviation is within 20%; (3) there are no static traffic obstacles such as road construction or traffic control. The preset number can be 3, 5, etc.

[0026] The target score can be a quantitative score obtained by comprehensively evaluating each candidate route based on feature data of multiple scenarios. It reflects the overall adaptability of the route to the current scenario features. The higher the score, the stronger the adaptability.

[0027] Step S20: Determine the first score corresponding to each candidate driving route under each intelligent driving adaptation feature, wherein the intelligent driving adaptation feature includes at least one of intelligent driving road adaptability, intelligent driving function coverage, and environmental adaptability.

[0028] Among them, intelligent driving adaptation features can be a set of features that reflect the degree of matching between candidate driving routes and vehicle intelligent driving systems, including intelligent driving road adaptability, intelligent driving function coverage, and environmental adaptability.

[0029] Among them, intelligent driving road adaptability can be defined as the degree of matching between the road attributes of the candidate route and the perception and decision-making capabilities of the intelligent driving system. Intelligent driving road adaptability can also include sub-features such as lane line clarity, road sign integrity, and turning curvature.

[0030] In some embodiments, scores corresponding to lane clarity, road sign integrity, and turning curvature of the candidate driving route can be determined. Then, based on the weights corresponding to the sub-features such as lane clarity, road sign integrity, and turning curvature, the corresponding scores are weighted and fused to obtain a first score corresponding to intelligent driving road adaptability. Specifically, the clearer the lane, the more complete the road signs, and the smaller the turning curvature, the higher the score.

[0031] The intelligent driving function coverage rate refers to the percentage of road segments in the candidate driving route where the vehicle's intelligent driving functions can be activated. The higher the percentage, the stronger the adaptability, and the higher the score. Intelligent driving functions include Adaptive Cruise Control (ACC), Lane Centering Control (LCC), and Navigation on Autopilot (NOA).

[0032] Among them, environmental adaptability can be the degree of influence of the external environment of the candidate driving route (such as weather, visibility, traffic density, etc.) on the operation of the intelligent driving system. The more the external driving environment is adapted to the operation of the intelligent driving system, the stronger the adaptability and the higher the first score.

[0033] Step S30: Determine the intelligent driving adaptation score for each candidate driving route based on the first score corresponding to each intelligent driving adaptation feature for each candidate driving route.

[0034] In some embodiments, when there are two or more intelligent driving adaptation features, the weight corresponding to each intelligent driving adaptation feature is determined. Based on the weight corresponding to each intelligent driving adaptation feature, the first scores corresponding to each candidate driving route under each intelligent driving adaptation feature are fused to determine the intelligent driving adaptation score corresponding to each candidate driving route.

[0035] For example, the weight of intelligent driving road adaptability is 0.4, the weight of intelligent driving function coverage is 0.3, and the weight of environmental adaptability is 0.3. The calculation formula for the intelligent driving adaptability score corresponding to the candidate driving route is: Intelligent driving adaptability score = first score corresponding to intelligent driving road adaptability × 0.4 + first score corresponding to intelligent driving function coverage × 0.3 + first score corresponding to environmental adaptability × 0.3.

[0036] In some possible implementations, when there are two or more intelligent driving adaptation features, the average of the first scores corresponding to each candidate driving route under each intelligent driving adaptation feature can be determined as the intelligent driving adaptation score. Alternatively, the sum of the first scores corresponding to each candidate driving route under each intelligent driving adaptation feature can be determined as the intelligent driving adaptation score for each candidate driving route.

[0037] In some possible implementations, when there is only one intelligent driving adaptation feature, the first score corresponding to the intelligent driving adaptation feature is determined as the first score corresponding to the intelligent driving adaptation feature of the candidate driving path.

[0038] Step S40: Output a route recommendation signal, which includes the target score and the intelligent driving adaptation score corresponding to each candidate driving route.

[0039] In some embodiments, the route recommendation signal can be output to an in-vehicle display unit (such as an instrument panel, central control screen, etc.) or to a user's terminal device (such as a mobile phone, tablet, etc.).

[0040] This embodiment determines the target score for each candidate driving route based on the feature data corresponding to the vehicle under each scenario feature, and determines the first score for each candidate driving route under each intelligent driving adaptation feature. Based on the first score, it determines the intelligent driving adaptation score for each candidate driving route, and finally outputs a route recommendation signal containing both types of scores. This allows for simultaneous consideration of personalized travel needs in multiple scenarios and the vehicle's intelligent driving adaptation capabilities during the route recommendation process, improving the rationality, relevance, and practicality of the recommended routes, enhancing driving safety and the driving experience, and providing users with a route selection basis that is more in line with actual travel and intelligent driving operation conditions.

[0041] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 . Figure 2 This is a flowchart illustrating the second embodiment of the method for recommending vehicle travel routes in this application, as shown below. Figure 2As shown, step S30 is followed by steps S31 to S34: Step S31: Determine the basic label corresponding to the candidate driving route under each scene feature.

[0042] The basic label corresponding to the time feature is the time label, which can include at least one of weekday / holiday, morning peak / evening peak / off-peak. The time label reflects when the candidate driving route is suitable to travel. For example, if the time label for a candidate driving route is holiday, it means that the candidate driving route is suitable to travel during holidays.

[0043] Among these, the basic label corresponding to the passenger feature is the passenger label, which can include any of the following: solo, family, or group. The passenger label reflects the type of passengers in the vehicle that the candidate route is suitable for. For example, if the passenger label for a candidate route is "solo," it means that the candidate route is suitable for solo driving.

[0044] Among them, the basic label corresponding to the road condition feature is the road condition label, which can include road type (e.g., highway, national highway, urban road, etc.) and road congestion level (e.g., smooth traffic, slow traffic, congested, etc.). The road condition label can reflect the actual road conditions of the candidate driving route.

[0045] Among them, the basic label corresponding to the in-vehicle environment characteristics is the environment label, which can be either comfortable or uncomfortable. The environment label can reflect whether the environment of the candidate driving route can make the in-vehicle environment comfortable.

[0046] Among them, the basic label corresponding to the driver's physiological condition characteristics is the physiological label, which can be either fatigued or normal. The physiological label can reflect whether the candidate driving route is suitable for driving when the driver is fatigued or in a normal physiological state.

[0047] Step S32: Determine the fusion label corresponding to each candidate driving route based on the basic label corresponding to each scene feature.

[0048] In some embodiments, a fusion tag may be determined based on multiple or all of the base tags. Fusion tags may include efficient commuting, scenic comfort, and easy driving.

[0049] In some embodiments, multiple labels that are more compatible with the feature data can be selected to determine the fusion label. For example, if the feature data is "single person" and "driver fatigue", and the basic labels corresponding to the candidate driving route also include "single person", "driver fatigue", and "high speed", then the fusion label "easy driving" can be determined based on "single person", "driver fatigue", and "high speed".

[0050] Step S33: Determine the scene adaptation label corresponding to the candidate driving route based on the basic label and the fused label.

[0051] In some embodiments, multiple or all basic tags and fusion tags are determined as scene adaptation tags. For example, scene adaptation tags can be "single person - fatigue - highway - easy driving"; or, "weekday - morning rush hour - city road - efficient commuting"; "holiday - family - national highway - comfortable scenery", etc.

[0052] Step S34: Add the scene adaptation label corresponding to each candidate driving route to the route recommendation signal.

[0053] In some embodiments, the route recommendation signal is upgraded by structurally encapsulating the scenario adaptation label of each candidate route, and combining it with the original target score and intelligent driving adaptation score to form a new route recommendation signal.

[0054] In this embodiment, by generating scene-adaptive tags consisting of basic tags and fusion tags for candidate driving routes and adding them to the route recommendation signal, an intuitive, accurate and comprehensive expression of the overall scene attributes of candidate routes is achieved, solving the problem that quantitative scores cannot intuitively reflect scene attributes.

[0055] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3 This is a flowchart illustrating the third embodiment of the method for recommending vehicle travel routes in this application, as shown below. Figure 3 As shown, after step S40, steps S50 to S80 are also included: Step S50: Receive the target driving route selected by the driver from the candidate driving routes.

[0056] Among them, the target driving route is the final driving route that the driver chooses autonomously from the candidate driving routes, and it is the core basis for subsequent intelligent driving status determination and multimedia settings.

[0057] In some embodiments, the driver or passengers in the vehicle can select the target driving route through the in-vehicle central control screen, the instrument panel touch operation, voice commands, etc.

[0058] In other embodiments, if the driver or passengers do not select a route within a preset time, the system can automatically determine the candidate route with the highest target score and intelligent driving suitability score as the target route, and simultaneously inform the driver via voice feedback (e.g., "The optimal suitable route has been selected for you"). Alternatively, the system can perform a weighted fusion of the target score and intelligent driving suitability score for each candidate route based on their respective weights, obtaining a total score for each candidate route, and then determine the route with the highest total score as the target route.

[0059] Step S60: When the vehicle is currently in intelligent driving state, determine the second score corresponding to each intelligent driving credibility feature based on the feature data corresponding to each intelligent driving credibility feature in the current intelligent driving scenario. The intelligent driving credibility feature includes at least one of the following: intelligent driving perception system status, road condition complexity, vehicle operating status, driver physiological condition features, and in-vehicle environment features.

[0060] Among them, intelligent driving status refers to the driving status of the vehicle when intelligent driving functions (ACC, LCC, NOA, etc.) are activated.

[0061] Among them, the intelligent driving credibility feature is the core dimension used to determine the credibility of the intelligent driving system's working status.

[0062] Among them, the intelligent driving perception system status refers to the working status of the intelligent driving system's perception hardware. The secondary indicators included in the intelligent driving perception system status can include the working status of cameras, millimeter-wave radar, lidar, positioning modules, data transmission latency, etc.

[0063] In the specific implementation, a score is determined for each type of sensing hardware based on the number of faults and the magnitude of data transmission delay. Finally, based on the weight of each sensing hardware, the scores for each type of sensing hardware are merged to obtain a second score. The more faults, the lower the second score; the greater the data transmission delay, the lower the score. For example, if all sensing hardware is fault-free, unobstructed, and the data transmission delay is ≤50ms, the score is 10 points; if one sensing hardware is slightly obstructed, the data transmission delay is between 50 and 100ms, and it does not affect the overall sensing, the score is 7 to 9 points; if two or more hardware are obstructed and the data transmission delay is >100ms, the score is 6 points or less.

[0064] Among them, road condition complexity refers to the real-time complexity of road conditions during vehicle travel; the more complex the road conditions, the lower the second score. The secondary indicators of road condition complexity can include road congestion, traffic density, road construction, pedestrian interference, weather conditions, etc.

[0065] For example, if the road is clear, traffic density is less than 10 vehicles / km, there is no construction, no pedestrian interference, and no obstruction of vision on a sunny day, the second score is 10 points; if the road is slow, traffic density is 10 to 20 vehicles / km, there is slight pedestrian interference, and visibility is slightly obstructed in light rain or fog, the second score is 7 to 9 points; if the road is congested, traffic density is greater than 20 vehicles / km, there is serious construction, serious pedestrian interference, heavy rain or fog, or serious obstruction of vision at night without streetlights, the second score is 6 points or less.

[0066] Among them, vehicle operating status refers to the working status of the vehicle's core systems. Secondary indicators of vehicle operating status may include the working status of the powertrain, braking system, steering system, intelligent driving execution module, command response latency, etc.

[0067] In the specific implementation, a score is determined for each system type based on whether it is faulty and the magnitude of the instruction response delay. Finally, based on the weight of each system, the scores for each system type are combined to obtain a second score. For example, if the system is fault-free and the instruction response delay is ≤100ms, the score is 10 points; if the system is fault-free and the instruction response delay is 100 to 200ms, the score is 7 to 9 points; if the system has a fault code or the instruction response delay is >200ms, the score is 6 points or less.

[0068] In some embodiments, the more normal the feature data corresponding to the driver's physiological condition characteristics, the higher the second score.

[0069] In some embodiments, the more comfortable the feature data corresponding to the in-vehicle environment characteristics, the higher the second score.

[0070] The second score is the quantitative score under the intelligent driving credibility feature, which is the basis for calculating the intelligent driving credibility score.

[0071] Step S70: Determine the intelligent driving credibility score based on the second score corresponding to each of the intelligent driving credibility features.

[0072] In some embodiments, when there are two or more intelligent driving credibility features, the weight corresponding to each intelligent driving credibility feature is determined. Then, the weights corresponding to each intelligent driving credibility feature are combined with the second scores corresponding to each intelligent driving credibility feature to determine the intelligent driving credibility score.

[0073] For example, the weight of the intelligent driving perception system state is 0.4, the weight of road condition complexity is 0.3, the weight of vehicle operating state is 0.2, the weight of driver physiological condition characteristics is 0.05, and the weight of the in-vehicle environment characteristics is 0.05. Then, the intelligent driving credibility score = the second score of the intelligent driving perception system state × 0.4 + the second score of road condition complexity × 0.3 + the second score of vehicle operating state × 0.2 + the second score of driver physiological condition characteristics × 0.05 + the second score of in-vehicle environment × 0.05.

[0074] In other embodiments, when there is only one intelligent driving credibility feature, the second score corresponding to the intelligent driving credibility feature is determined as the intelligent driving credibility score.

[0075] Step S80: Determine the setting parameters of the vehicle multimedia based on the scenario adaptation tag and the intelligent driving credibility score.

[0076] Among them, vehicle multimedia refers to the multimedia interaction module inside the vehicle, including the display of the in-vehicle central control screen and instrument panel, music, white noise playback, voice feedback, etc.

[0077] The settings parameters can be the operating parameters of the vehicle's multimedia system, including display interface style (efficiency / leisure / alert), color scheme, screensaver style, music type, voice feedback content, and alert tone intensity.

[0078] In some embodiments, basic multimedia settings parameters are first matched according to the scene adaptation tags (e.g., for commuting scenes, an efficiency-oriented display interface, a simple blue tone, and enlarged key information are matched; for holiday scenes, a leisure-oriented display interface, a scenic background, and large icons are matched); then, a credibility level is determined according to the intelligent driving credibility score, and the basic parameters are dynamically adjusted based on the credibility level.

[0079] For example, a smart driving credibility score of ≥8.0 is considered high level, a smart driving credibility score of 6.0 to 7.9 is considered medium level, and a smart driving credibility score <6.0 is considered low level. High level credibility is indicated by a starry sky / custom landscape screensaver + soothing music / white noise + voice feedback "High smart driving credibility, drive with confidence", medium level credibility is indicated by a standard display interface + gentle prompts, and low level credibility is indicated by a warning display interface (red border, key road conditions highlighted) + voice reminder "Please pay attention to road conditions".

[0080] In this embodiment, by receiving the target driving route selected by the driver, and combining the intelligent driving scenario to quantify and score each intelligent driving credibility feature, the intelligent driving credibility score is obtained. Finally, the vehicle multimedia setting parameters are determined based on the scenario adaptation label and the intelligent driving credibility score. This realizes the quantitative judgment and visual feedback of the intelligent driving system's credibility, allowing users to intuitively perceive the intelligent driving working status, greatly enhancing users' trust and acceptance of the intelligent driving system. At the same time, it realizes deep dual linkage between vehicle multimedia, driving scenario, and real-time intelligent driving status.

[0081] Based on the first embodiment of this application, in the fourth embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 4 , Figure 4 This is a flowchart illustrating the fourth embodiment of the method for recommending vehicle travel routes in this application, as shown below. Figure 4 As shown, step S10 further includes steps S11 to S13: Step S11: Determine the target weight corresponding to each scene feature based on the feature data corresponding to each scene feature and the historical behavior data of the vehicle driver.

[0082] Among them, historical behavior data can be a record of the driver's behavior during past driving, including route selection, UI style switching, voice adjustment history, etc., which is stored in the user behavior feedback database and is the core data for realizing behavior feedback self-optimization.

[0083] The target weight is the real-time weight of each scene feature when calculating the target score.

[0084] In one possible implementation, step S11 may include steps S111 to S113: Step S111: Determine the first weight correction coefficient corresponding to each scene feature based on the feature data corresponding to each scene feature.

[0085] The first weight correction coefficient is a weight correction coefficient determined based on whether the feature data of the scene characteristics are suitable for the user's driving needs. In some embodiments, the value range of the first weight correction coefficient can be [0.8, 1.2].

[0086] In some embodiments, a first weight correction coefficient can be determined based on whether the feature data corresponding to the scene features is suitable for driving needs. If the feature data is suitable for the user's driving needs, the first weight correction coefficient is 1; if it is not suitable, a value greater than 1, such as 1.2, can be used to increase the weight of this dimension and prioritize the route suitability of this dimension; if it is over-suitable, a value less than 1, such as 0.8, can be used to reduce the weight of this dimension. For example, if the driver's physiological state is fatigued (heart rate > 100 beats / minute, abnormal blinking frequency), the first weight correction coefficient corresponding to the physiological state feature is 1.2, and the road condition dimension is also 1.2; if the in-vehicle environment is uncomfortable (temperature > 25℃, noise > 50dB), the first weight correction coefficient corresponding to the in-vehicle environment feature is 1.2. Over-suitability can be understood as the current state of a certain scene feature already fully meets the needs of the driver / driving, or even exceeds the basic needs. In this case, there is no need to give this feature a higher weight when scoring the route, and its importance can be appropriately reduced.

[0087] Step S112: Determine the second weight correction coefficient corresponding to each scene feature based on the historical behavior data.

[0088] The second weighting correction coefficient is a weighting correction coefficient determined based on the driver's historical behavior data, and its value ranges from 0.8 to 1.2.

[0089] For example, if a driver has selected a certain type of route three or more times under a certain scenario feature, the second weight correction coefficient corresponding to that scenario feature will be a value greater than 1, such as 1.2. For instance, if a driver repeatedly chooses the shortest route in a commuting scenario, the second weight correction coefficient corresponding to the time feature will be 1.2; if a driver repeatedly chooses scenic routes in a family travel scenario, the second weight correction coefficient corresponding to the road condition feature will be 1.2; if a driver rarely pays attention to a certain scenario feature, the scenario feature will be a value less than 1, such as 0.8.

[0090] In some embodiments, historical behavioral data is analyzed based on machine learning algorithms to uncover drivers’ potential preferences (such as drivers often choosing routes with good road conditions on rainy days), and a second weight correction coefficient is matched to the corresponding scene features to achieve accurate preference discovery and dynamic weight adjustment.

[0091] Step S113: Determine the target weight corresponding to each scene feature based on the initial weight, the first weight correction coefficient, and the second weight correction coefficient corresponding to each scene feature.

[0092] The initial weights are preset base weights for each scene feature. For example, the initial weight for time feature is 0.2, the initial weight for passenger feature is 0.2, the initial weight for road condition feature is 0.3, the initial weight for in-vehicle environment feature is 0.15, and the initial weight for driver physiological condition feature is 0.15.

[0093] In some embodiments, the target weight corresponding to each scene feature can be determined by multiplying the initial weight, the first weight correction coefficient, and the second weight correction coefficient corresponding to each scene dimension.

[0094] By combining real-time data of scene features to determine the first weight correction coefficient and the second weight correction coefficient based on the driver's historical behavior data, and then calculating the target weight of each scene feature by multiplying the initial weight and the double correction coefficient, the dynamic and precise adjustment of scene feature weights is achieved. This allows the weight allocation to not only meet the current real-time driving scenario requirements of the vehicle, but also match the driver's historical behavior preferences, laying a core weight foundation for the accurate calculation of the subsequent target score.

[0095] Step S12: Based on the attribute data associated with each of the candidate driving routes and each of the scene features, determine the third score corresponding to each candidate driving route under each of the scene features.

[0096] Among them, attribute data is quantitative data that associates candidate driving routes with the features of each scenario. It is the basis for calculating the third score, such as the travel time corresponding to time features, the service facility configuration corresponding to passenger features, and the congestion degree corresponding to road condition features.

[0097] In one possible implementation, a third score for the candidate driving route under the time feature is determined based on the ratio between the travel time corresponding to the candidate driving route and the minimum travel time, wherein the ratio is negatively correlated with the score, and the minimum travel time is the minimum value among the travel times corresponding to all candidate driving routes.

[0098] In practical implementation, a quantification rule can be set between the ratio and the third score. For example, when the ratio is ≤1, the third score is 10 points, and the score decreases by 1 point for every 0.1 increase in the ratio, until it reaches 0 points. If the minimum travel time is 30 minutes, and the travel time of a candidate route is 30 minutes, then the third score is 10 points; if the travel time of the candidate route is 33 minutes, the ratio is 1.1, then the third score is 9 points.

[0099] In one possible implementation, a third score for the candidate route under the passenger characteristics is determined based on the degree of matching between the service facility configuration of the candidate route and the feature data corresponding to the passenger characteristics.

[0100] In specific implementation methods, a matching rule between service facility configuration and passenger characteristics can be preset. For example, when matching family travel passenger characteristics with routes that include rest areas, restrooms, and parking lots, the higher the matching degree, the higher the third-party score.

[0101] In one possible implementation, a third score is determined for the candidate driving route based on the congestion level, road scenery, and road smoothness corresponding to the road condition features.

[0102] In a specific implementation, congestion, road scenery, and road smoothness can each be assigned a weight of 1 / 3. Each of these factors is scored on a scale of 0-10, and the scores are then weighted and summed to obtain a third score. The more congested the road, the lower the score; the worse the road scenery, the lower the score; and the more uneven the road, the lower the score.

[0103] In one possible implementation, a third score corresponding to the in-vehicle environment feature is determined based on the matching degree between the environmental data along the candidate driving route and the feature data corresponding to the in-vehicle environment feature.

[0104] In the specific implementation, environmental data along the route can include outdoor weather, road noise, and dust. Combined with the capabilities of the vehicle's air conditioning and sound insulation systems, it is determined whether the interior temperature can be maintained at ≤25℃ and the noise level at ≤50dB throughout the entire route. The higher the matching degree, the higher the third-party score.

[0105] In one possible implementation, a third score corresponding to the driver's physiological condition characteristics is determined based on the degree of matching between the driving intensity of the candidate driving route and the feature data corresponding to the driver's physiological condition characteristics.

[0106] In specific implementation methods, driving intensity can include the number of turns, gradient, lane change frequency, etc. When the driver's physiological condition is fatigued, routes with fewer turns, more straight sections, and gentler gradients are matched. The higher the matching degree, the higher the third score.

[0107] Step S13: Determine the target score for each candidate driving route based on the third score and target weight corresponding to each candidate driving route under each scene feature.

[0108] In the specific implementation, the product of the third score and the target weight corresponding to each scenario feature of the candidate driving route is determined, and the sum of the products corresponding to all scenario features is determined as the target score corresponding to the candidate driving route.

[0109] In this embodiment, the target weight corresponding to each scene feature is first determined by the feature data corresponding to each scene feature and the historical behavior data of the vehicle driver. Then, based on the attribute data associated with each scene feature and the candidate driving route, the third score corresponding to each candidate driving route under each scene feature is determined. Finally, the target score is calculated by combining the target weight and the third score through a weighted summation algorithm. The entire process fully integrates multimodal scene feature data and driver historical behavior data, realizing a scientific, accurate and personalized quantitative evaluation of the scene adaptability of candidate driving routes. This allows the target score to truly reflect the degree of adaptability of the candidate route to the current scene and user needs, providing a core quantitative basis for subsequent accurate route recommendations.

[0110] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the recommended method of vehicle driving route in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0111] This application also provides a vehicle route recommendation device, please refer to... Figure 5 The vehicle route recommendation device includes: The first determining module 501 is used to determine the target score corresponding to each candidate driving route based on the feature data corresponding to the vehicle under each scene feature. The scene features include at least one of time features, passenger features, road condition features, in-vehicle environment features, and driver physiological condition features. The second determining module 502 is used to determine the first score corresponding to each candidate driving route under each intelligent driving adaptation feature, wherein the intelligent driving adaptation feature includes at least one of intelligent driving road adaptability, intelligent driving function coverage, and environmental adaptability. The third determining module 503 is used to determine the intelligent driving adaptation score corresponding to each candidate driving route based on the first score corresponding to each candidate driving route under each intelligent driving adaptation feature. The fourth determining module 504 is used to output a route recommendation signal, which includes the target score and the intelligent driving adaptation score corresponding to each candidate driving route.

[0112] In one possible implementation, a first processing module is also included, for: Determine the basic label corresponding to the candidate driving route under each scene feature; Based on the basic labels corresponding to each scene feature of the candidate driving routes, determine the fusion label corresponding to each candidate driving route; Based on the base tags and the fused tags, determine the scene adaptation tags corresponding to the candidate driving routes; Add the scene adaptation label corresponding to each candidate driving route to the route recommendation signal.

[0113] In one possible implementation, a second processing module is also included, for: Receive the target driving route selected by the driver from the candidate driving routes; When the vehicle is currently in an intelligent driving state, a second score is determined for each intelligent driving credibility feature based on the feature data corresponding to each intelligent driving credibility feature in the current intelligent driving scenario. The intelligent driving credibility feature includes at least one of the following: intelligent driving perception system status, road condition complexity, vehicle operating status, driver physiological condition features, and in-vehicle environment features. The intelligent driving credibility score is determined based on the second score corresponding to each of the intelligent driving credibility features; Based on the scenario adaptation tags and the intelligent driving credibility score, the setting parameters of the vehicle multimedia are determined.

[0114] In one possible implementation, a first processing module 501 is also included, for: Based on the feature data corresponding to each scenario feature and the historical behavior data of the vehicle driver, determine the target weight corresponding to each scenario feature; Based on the attribute data associated with each of the candidate driving routes and each of the scene features, determine the third score corresponding to each candidate driving route under each of the scene features; The target score for each candidate driving route is determined based on the third score and target weight corresponding to each scenario feature.

[0115] In one possible implementation, a first processing module 501 is further included, which is used to: determine a first weight correction coefficient corresponding to each scene feature based on the feature data corresponding to each scene feature; Based on the historical behavior data, determine the second weight correction coefficient corresponding to each scene feature; The target weight for each scene feature is determined based on the initial weight, the first weight correction coefficient, and the second weight correction coefficient corresponding to each scene feature.

[0116] In one possible implementation, a first processing module 501 is further included, configured to: determine the third score of the candidate driving route under the time feature based on the ratio between the travel time corresponding to the candidate driving route and the minimum travel time, wherein the minimum travel time is the minimum value among the travel times corresponding to all candidate driving routes; Based on the matching degree between the service facility configuration of the candidate driving route and the feature data corresponding to the passenger characteristics, the third score of the candidate driving route under the passenger characteristics is determined; Based on the congestion, scenery, and smoothness of the candidate driving routes, a third score is determined for each candidate driving route under the given road conditions. Based on the matching degree between the environmental data along the candidate driving route and the feature data corresponding to the in-vehicle environmental features, the third score corresponding to the candidate driving route under the in-vehicle environmental features is determined. Based on the degree of matching between the driving intensity of the candidate driving route and the feature data corresponding to the driver's physiological condition characteristics, a third score corresponding to the candidate driving route under the driver's physiological condition characteristics is determined.

[0117] The vehicle route recommendation device provided in this application, employing the vehicle route recommendation method described in the above embodiments, can solve the technical problem that recommended routes often fail to simultaneously consider driving comfort, driving safety, and the effective functioning of intelligent driving. Compared with the prior art, the beneficial effects of the vehicle route recommendation device provided in this application are the same as those of the vehicle route recommendation method provided in the above embodiments, and other technical features in the vehicle route recommendation device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0118] This application provides a vehicle route recommendation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the vehicle route recommendation method in Embodiment 1 above.

[0119] The following is for reference. Figure 6 The diagram illustrates a structural schematic of a recommended device suitable for implementing vehicle driving routes in the embodiments of this application. The recommended device for vehicle driving routes in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6The recommended device for the vehicle driving route shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0120] like Figure 6 As shown, the vehicle route recommendation device may include a processing unit 601 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 603 into a random access memory (RAM) 604. The RAM 604 also stores various programs and data required for the operation of the vehicle route recommendation device. The processing unit 601, ROM 602, and RAM 604 are interconnected via a bus 605. An input / output (I / O) interface 606 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 606: input devices 607 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 608 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 603 including, for example, magnetic tape, hard disk, etc.; and communication devices 609. Communication device 609 allows the vehicle route recommendation device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows vehicle route recommendation devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0121] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 603, or installed from ROM 602. When the computer program is executed by processing device 601, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0122] The vehicle route recommendation device provided in this application, employing the vehicle route recommendation method described in the above embodiments, can solve the technical problem that recommended routes often fail to simultaneously consider driving comfort, driving safety, and the effective functioning of intelligent driving. Compared with the prior art, the beneficial effects of the vehicle route recommendation device provided in this application are the same as those of the vehicle route recommendation method provided in the above embodiments, and other technical features in this vehicle route recommendation device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0123] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0124] 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.

[0125] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the recommended method for vehicle driving routes in the above embodiments.

[0126] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0127] The aforementioned computer-readable storage medium may be included in the recommended device for the vehicle travel route; or it may exist independently and not be installed in the recommended device for the vehicle travel route.

[0128] The aforementioned computer-readable storage medium carries one or more programs that, when executed by a vehicle route recommendation device, enable the vehicle route recommendation device to implement a vehicle route recommendation method.

[0129] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0130] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0131] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0132] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described method for recommending vehicle driving routes. This solves the technical problem that recommended routes often fail to simultaneously ensure driving comfort, driving safety, and the effective functioning of intelligent driving capabilities. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the vehicle driving route recommendation method provided in the above embodiments, and will not be elaborated upon here.

[0133] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the vehicle driving route recommendation method as described above.

[0134] The computer program product provided in this application can solve the technical problem that recommended routes often fail to simultaneously ensure driving comfort, driving safety, and the effective functioning of intelligent driving features. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the vehicle driving route recommendation method provided in the above embodiments, and will not be repeated here.

[0135] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

[0136] It should be noted that the data collection, tag management, rule setting, and push decision-making processes involved in this application are designed to work with other technical features to solve technical problems. They do not involve or support any illegal activities. Any data processing that may violate laws and regulations (such as unauthorized collection of privacy data, generation of discriminatory tags, setting unfair rules, or pushing illegal information) is not within the scope of protection of this application's technical solution. Of course, the user data in this application will be encrypted, anonymized, or de-identified before storage to ensure user data security.

Claims

1. A method for recommending vehicle travel routes, characterized in that, The method includes: Based on the feature data corresponding to the vehicle under each scenario feature, the target score corresponding to each candidate driving route is determined. The scenario features include at least one of the following: time features, passenger features, road condition features, in-vehicle environment features, and driver physiological condition features. Determine the first score corresponding to each candidate driving route under each intelligent driving adaptation feature, wherein the intelligent driving adaptation feature includes at least one of intelligent driving road adaptability, intelligent driving function coverage, and environmental adaptability; Based on the first score corresponding to each candidate driving route under each intelligent driving adaptation feature, determine the intelligent driving adaptation score corresponding to each candidate driving route; The output route recommendation signal includes the target score and the intelligent driving adaptation score corresponding to each candidate driving route.

2. The method according to claim 1, characterized in that, Before the output route recommendation signal, the following is also included: Determine the basic label corresponding to the candidate driving route under each scene feature; Based on the basic labels corresponding to each scene feature of the candidate driving routes, determine the fusion label corresponding to each candidate driving route; Based on the base tags and the fused tags, determine the scene adaptation tags corresponding to the candidate driving routes; Add the scene adaptation label corresponding to each candidate driving route to the route recommendation signal.

3. The method according to claim 1 or 2, characterized in that, Following the output route recommendation signal, the following is also included: Receive the target driving route selected by the driver from the candidate driving routes; When the vehicle is currently in an intelligent driving state, a second score is determined for each intelligent driving credibility feature based on the feature data corresponding to each intelligent driving credibility feature in the current intelligent driving scenario. The intelligent driving credibility feature includes at least one of the following: intelligent driving perception system status, road condition complexity, vehicle operating status, driver physiological condition features, and in-vehicle environment features. The intelligent driving credibility score is determined based on the second score corresponding to each of the intelligent driving credibility features; Based on the scenario adaptation tags and the intelligent driving credibility score, the setting parameters of the vehicle multimedia are determined.

4. The method according to claim 1, characterized in that, The step of determining the target score for each candidate driving route based on the feature data corresponding to the vehicle under each scene feature includes: Based on the feature data corresponding to each scenario feature and the historical behavior data of the vehicle driver, determine the target weight corresponding to each scenario feature; Based on the attribute data associated with each of the candidate driving routes and each of the scene features, determine the third score corresponding to each candidate driving route under each of the scene features; The target score for each candidate driving route is determined based on the third score and target weight corresponding to each scenario feature.

5. The method according to claim 4, characterized in that, The step of determining the target weight corresponding to each scene feature based on the feature data corresponding to each scene feature and the historical behavior data of the vehicle driver includes: Based on the feature data corresponding to each scene feature, determine the first weight correction coefficient corresponding to each scene feature; Based on the historical behavior data, determine the second weight correction coefficient corresponding to each scene feature; The target weight for each scene feature is determined based on the initial weight, the first weight correction coefficient, and the second weight correction coefficient corresponding to each scene feature.

6. The method according to claim 4, characterized in that, The step of determining the third score for each candidate driving route under each scene feature based on the attribute data associated with the candidate driving route and each scene feature includes at least one of the following: The third score of the candidate driving route under the time feature is determined based on the ratio between the travel time corresponding to the candidate driving route and the minimum travel time, wherein the minimum travel time is the minimum value among the travel times corresponding to all candidate driving routes. Based on the matching degree between the service facility configuration of the candidate driving route and the feature data corresponding to the passenger characteristics, the third score of the candidate driving route under the passenger characteristics is determined; Based on the congestion, road scenery, and road smoothness of the candidate driving routes, a third score is determined for each candidate driving route under the given road condition characteristics. Based on the matching degree between the environmental data along the candidate driving route and the feature data corresponding to the in-vehicle environmental features, the third score corresponding to the candidate driving route under the in-vehicle environmental features is determined. Based on the degree of matching between the driving intensity of the candidate driving route and the feature data corresponding to the driver's physiological condition characteristics, a third score corresponding to the candidate driving route under the driver's physiological condition characteristics is determined.

7. A vehicle route recommendation device, characterized in that, The device for recommending vehicle routes includes: The first determining module is used to determine the target score corresponding to each candidate driving route based on the feature data corresponding to the vehicle under each scene feature. The scene features include at least one of time features, passenger features, road condition features, in-vehicle environment features, and driver physiological condition features. The second determining module is used to determine the first score corresponding to each candidate driving route under each intelligent driving adaptation feature, wherein the intelligent driving adaptation feature includes at least one of intelligent driving road adaptability, intelligent driving function coverage, and environmental adaptability. The third determining module is used to determine the intelligent driving adaptation score corresponding to each candidate driving route based on the first score corresponding to each candidate driving route under each intelligent driving adaptation feature. The fourth determination module is used to output a route recommendation signal, which includes the target score and the intelligent driving adaptation score corresponding to each candidate driving route.

8. A vehicle route recommendation device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the method for recommending vehicle travel routes as claimed in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the method for recommending vehicle driving routes as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the method for recommending vehicle driving routes as described in any one of claims 1 to 6.

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