Driving route prediction method, electronic equipment and storage medium

Abstract

The invention provides a driving route prediction method, electronic equipment and a storage medium. The method comprises the following steps: acquiring a plurality of historical driving tracks of a target vehicle in historical driving; filtering the plurality of historical driving tracks according to a preset filtering rule to obtain a target historical driving track; acquiring travel information of the target historical driving track; calculating the route probability of the target historical driving track with the same final position and the same vehicle travel time period in the target historical driving track; establishing a conventional route database of the target vehicle based on the route probability and the travel information; and after detecting that the target vehicle is started, determining a first final predicted driving route of the target vehicle according to the habitual route database. According to the method, the future travel of the vehicle can be predicted when the user does not use the vehicle navigation, so that the travel experience of the user is improved.

Description

Technical Field

[0001] The present application relates to the field of automobile technology, and in particular to a driving route prediction method, electronic equipment and storage medium. Background Art

[0002] Currently, the industry is focusing on the research and development of intelligent functions in combination with vehicle navigation information. Navigation road condition information has been widely used, such as using navigation road condition information to optimize the energy management of new energy vehicles.

[0003] However, when users travel without navigation, obtaining road condition information has become a common difficulty faced by the industry. How to record daily regular travel habits and further obtain road condition information for future trips is a bottleneck that the industry needs to focus on breaking through. Summary of the invention

[0004] In view of this, the present application provides a driving route prediction method, an electronic device and a storage medium, which can predict the future itinerary of a vehicle when the user is not using vehicle navigation, thereby improving the user's travel experience.

[0005] An embodiment of the present application provides a driving route prediction method, comprising: obtaining multiple historical driving trajectories of a target vehicle in historical driving; filtering the multiple historical driving trajectories according to a preset filtering rule to obtain a target historical driving trajectory, wherein the preset filtering rule at least includes filtering out historical driving trajectories whose mileage is less than a first preset mileage or greater than a second preset mileage among the multiple historical driving trajectories; obtaining travel information of the target historical driving trajectory, wherein the travel information at least includes an end point position and a vehicle travel period of the target historical driving trajectory; calculating a route probability of the target historical driving trajectory having the same end point position and the same vehicle travel period in the target historical driving trajectory; establishing a common route database for the target vehicle based on the route probability and the travel information; and determining a first final predicted driving route of the target vehicle according to the common route database after detecting that the target vehicle is started.

[0006] Compared with the related art, the embodiment of the present application has at least the following advantages: by filtering multiple historical driving trajectories according to preset filtering rules, it is possible to filter out abnormal driving trajectories in the historical driving trajectories, thereby reducing the amount of calculation in subsequent steps; since users may reach the same destination through different driving trajectories at the same travel time, by calculating the route probability of the target historical driving trajectory with the same terminal position and vehicle travel period in the target historical driving trajectory, it is easy to know the use probability of different routes when the user reaches the same destination in the same travel period, and establish a target vehicle's usual route database based on the route probability and itinerary information. Therefore, after the target vehicle is started, the cloud can select the first final predicted driving route of the target vehicle from the usual route database, which improves the accuracy of the prediction while realizing that the future itinerary of the vehicle can be predicted even when the user does not use the vehicle navigation, thereby improving the user's travel experience.

[0007] In some possible implementations, before determining the first final predicted driving route of the target vehicle based on the habitual route database, it also includes: acquiring the identity information, current first position information and current time of the target vehicle; determining the habitual route database matching the target vehicle based on the identity information; determining the first final predicted driving route of the target vehicle based on the habitual route database includes: selecting an initial predicted driving route that matches the first position information and the current time from the habitual route database, and using the initial predicted driving route with the highest route probability among the initial predicted routes as the first final predicted driving route.

[0008] In some possible implementations, after taking the initial predicted driving route with the highest route probability among the initial predicted routes as the first final predicted driving route of the target vehicle, the method includes: selecting the initial predicted driving route with the highest route probability from the initial predicted routes as the driving route to be determined; detecting whether the driving route of the target vehicle matches the driving route to be determined when the driving time exceeds a second preset time and the driving distance is greater than a preset driving distance; and when it is detected that the driving route matches the driving route to be determined, taking the driving route to be determined as the first final predicted driving route.

[0009] In some possible implementations, after taking the to-be-determined driving route as the first final predicted driving route, the method further includes: detecting whether the target vehicle has deviated from its course at every preset period; acquiring the current second position information of the target vehicle when the target vehicle has been detected to have deviated from its course for M consecutive preset periods, wherein M is an integer greater than 1; and reselecting the second final predicted driving route that matches the second position information from the conventional route database.

[0010] In some possible implementations, filtering the multiple historical driving trajectories according to preset filtering rules includes: detecting whether there is a first historical driving trajectory to be filtered among the multiple historical driving trajectories in which the vehicle speed is 0 and the duration of the vehicle speed at 0 exceeds a first preset duration; when the existence of the first historical driving trajectory to be filtered is detected, filtering the first historical driving trajectory to be filtered among the multiple historical driving trajectories.

[0011] In some possible implementations, filtering the multiple historical driving trajectories according to preset filtering rules includes: detecting whether there is a second historical driving trajectory to be filtered among the multiple historical driving trajectories, in which the mileage for N consecutive days is greater than a third preset mileage, wherein N is an integer greater than 1; when the existence of the second historical driving trajectory to be filtered is detected, filtering the second historical driving trajectory to be filtered among the multiple historical driving trajectories.

[0012] In some possible implementations, after acquiring multiple historical driving trajectories of the target vehicle in historical driving, it also includes: marking feature points of each of the historical driving trajectories, wherein the feature points include the starting point, the end point and the passing point of the historical driving trajectory; performing route correction on each of the historical driving trajectories according to the feature points corresponding to each of the historical driving trajectories; filtering the multiple historical driving trajectories according to preset filtering rules includes: filtering the multiple historical driving trajectories after the route correction according to the preset filtering rules.

[0013] In some possible implementations, each of the historical driving trajectories includes multiple road segments. After performing route correction on each of the historical driving trajectories, it also includes: obtaining the segment names of all the road segments included in each of the historical driving trajectories; filtering the multiple historical driving trajectories after the route correction according to preset filtering rules, including: detecting whether there is a third historical driving trajectory to be filtered in the multiple historical driving trajectories after the route correction, in which the number of occurrences of the segment name is greater than or equal to a preset number; upon detecting the existence of the third historical driving trajectory to be filtered, filtering the third historical driving trajectory to be filtered in the multiple historical driving trajectories after the route correction.

[0014] The second aspect of the present application discloses an electronic device, which includes a processor and a memory, wherein the memory is used to store instructions, and the processor is used to call the instructions in the memory so that the electronic device executes the above-mentioned driving route prediction method.

[0015] The third aspect of the present application discloses a storage medium, including computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the above-mentioned driving route prediction method.

[0016] It can be understood that the electronic device of the second aspect and the storage medium of the third aspect provided above both correspond to the method of the first aspect. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of a driving route prediction method according to an embodiment of the present application.

[0018] Figure 2 It is a flowchart of a driving route prediction method according to an embodiment of the present application.

[0019] Figure 3 It is a flowchart of a driving route prediction method according to an embodiment of the present application.

[0020] Figure 4 It is a flowchart of a driving route prediction method according to an embodiment of the present application.

[0021] Figure 5 It is a flowchart of a driving route prediction method according to an embodiment of the present application.

[0022] Figure 6 It is a schematic diagram of functional modules of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the present application is described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that the implementation methods of the present application and the features in the implementation methods can be combined with each other without conflict.

[0024] In the following description, many specific details are set forth to facilitate a full understanding of the present application. The described implementations are only part of the implementations of the present application, rather than all the implementations.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0026] It should be further noted that, in this article, the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0027] In this application, "at least one" means one or more, and "more" means two or more than two. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and drawings of this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0028] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0029] Please refer to Figure 1 , is a flow chart of a driving route prediction method provided in an embodiment of the present application. This embodiment is applied in the cloud and includes the following steps: Step 101: Acquire multiple historical driving trajectories of the target vehicle during its historical driving.

[0030] In some embodiments, the target vehicle uploads its GPS location to the cloud during historical driving, so that the cloud can obtain the historical driving trajectory.

[0031] In some embodiments, the target vehicle uses navigation software for navigation, and the cloud can also obtain the historical driving trajectory of the target vehicle through the historical records of the navigation software. It is understandable that this embodiment does not specifically limit the method for obtaining the historical driving trajectory, and it can be set according to actual needs.

[0032] Step 102: Filter multiple historical driving trajectories according to preset filtering rules to obtain a target historical driving trajectory, wherein the preset filtering rules at least include filtering out historical driving trajectories whose mileage is less than a first preset mileage or greater than a second preset mileage among the multiple historical driving trajectories.

[0033] In some embodiments, the first preset mileage is less than or equal to 2 km, and the second preset mileage is greater than or equal to 200 km. Since a small mileage indicates that the destination is close, the user does not need navigation services or other services derived from the driving route, and a large mileage indicates that it is not a common destination of the user, that is, it is not a common route of the user, therefore, by setting the first preset mileage and the second preset mileage in this range, the driving trajectories in the historical driving trajectories that the user does not need navigation and the routes that are not common to the user can be filtered out, thereby improving the reliability of the driving route prediction method and reducing the amount of calculation in the subsequent steps.

[0034] In some embodiments, it is also possible to detect whether there is a first historical driving trajectory to be filtered among multiple historical driving trajectories, in which the vehicle speed is 0 and the duration of the vehicle speed being 0 exceeds a first preset duration; when the existence of the first historical driving trajectory to be filtered is detected, the first historical driving trajectory to be filtered among the multiple historical driving trajectories is filtered.

[0035] It can be understood that the first historical driving trajectory to be filtered, in which the vehicle speed is 0 and the duration of the vehicle speed being 0 exceeds the first preset duration, is an abnormal driving trajectory. In this way, the amount of calculation in subsequent steps is further reduced.

[0036] This embodiment does not specifically limit the size of the first preset duration, and can be set according to actual needs. For example, the value range of the first preset duration can be between 10 minutes and 20 minutes. By limiting the size of the first preset duration, the accuracy of the detection of the first historical driving trajectory to be filtered can be ensured.

[0037] In some embodiments, it is also possible to detect whether there is a second historical driving trajectory to be filtered among the multiple historical driving trajectories, in which the mileage for N consecutive days is greater than the third preset mileage, where N is an integer greater than 1; when the existence of the second historical driving trajectory to be filtered is detected, the second historical driving trajectory to be filtered among the multiple historical driving trajectories is filtered.

[0038] It can be understood that if the mileage for N consecutive days is greater than the third preset mileage, it indicates that the target vehicle may be an operating vehicle. In this embodiment, a common route database for operating vehicles is not established.

[0039] In some embodiments, the value range of N may be 15 days to 20 days, and the range of the third preset mileage may be 150 km to 200 km. By setting such a size, the accuracy of the detection of the second historical driving trajectory to be filtered can be ensured.

[0040] Step 103: Acquire the travel information of the target historical driving trajectory, wherein the travel information at least includes the destination position and the vehicle travel period of the target historical driving trajectory.

[0041] In some embodiments, after obtaining multiple historical driving trajectories of the target vehicle in historical driving, the travel information of each historical driving trajectory can be obtained. For example, assuming that the cloud obtains the historical driving trajectory through the GPS position uploaded during the historical driving of the target vehicle, the cloud will record the upload time and the destination of the target vehicle when the target vehicle uploads the GPS position, thereby obtaining the travel information of the target historical driving trajectory.

[0042] Step 104: Calculate the route probability of the target historical driving trajectory with the same end point position and vehicle travel period in the target historical driving trajectory.

[0043] For ease of understanding, the following is a specific example of how to calculate route probability: Assuming that the destination location is terminal A, and there are 10 target historical driving trajectories between 8 am and 9 am, among these 10 target historical driving trajectories, 3 target historical driving trajectories 1 have the same trajectory route, 5 target historical driving trajectories 2 have the same trajectory route, and 2 target historical driving trajectories 3 have the same trajectory route. Then, the route probability 1 of target historical driving trajectory 1 is 3 / 10=30%, the route probability 2 of target historical driving trajectory 2 is 5 / 10=50%, and the route probability 3 of target historical driving trajectory 3 is 2 / 10=20%.

[0044] Find all target historical driving trajectories with the same end point location and vehicle travel period in the target historical driving trajectory, and calculate the route probability of all target historical driving trajectories in the same way as above.

[0045] In some embodiments, the route probability can also be calculated based on the historical driving trajectory of the target vehicle in the previous P days. In this way, the final predicted route can be more in line with the user's current usage habits.

[0046] In some embodiments, the value of P is between 5 days and 7 days. By setting such a range, it is possible to ensure that the route predicted subsequently is more in line with the user's current usage habits while ensuring that the number of historical driving trajectories is sufficient.

[0047] Step 105: Establish a common route database of the target vehicle based on the route probability and the trip information.

[0048] In some embodiments, the conventional database stores the VIN, route probability, corrected GPS track point set, road segment name, total mileage, and travel time of the target vehicle.

[0049] Step 106: After detecting that the target vehicle is started, determine a first final predicted driving route of the target vehicle according to a common route database.

[0050] It should be noted that how to determine the first final predicted driving route is described in detail in subsequent embodiments, and will not be repeated here to avoid repetition.

[0051] Compared with the related art, the embodiment of the present application has at least the following advantages: by filtering multiple historical driving trajectories according to preset filtering rules, it is possible to filter out abnormal driving trajectories in the historical driving trajectories, thereby reducing the amount of calculation in subsequent steps; since users may reach the same destination through different driving trajectories at the same travel time, by calculating the route probability of the target historical driving trajectory with the same terminal position and vehicle travel period in the target historical driving trajectory, it is easy to know the use probability of different routes when the user reaches the same destination in the same travel period, and establish a target vehicle's usual route database based on route probability and travel information. Therefore, after the target vehicle is started, the cloud can determine the first and final predicted driving route of the target vehicle, which improves the accuracy of the prediction while realizing that the future travel of the vehicle can be predicted when the user does not use the car navigation, thereby improving the user's travel experience.

[0052] Please refer to Figure 2 , Figure 2 It is a flow chart of a driving route prediction method provided by an embodiment of the present application. This embodiment is a specific description of the aforementioned embodiment, and mainly explains: how to determine the first final predicted driving route based on the commonly used route database.

[0053] This embodiment is applied to the cloud, and the specific process is as follows Figure 2 As shown, the following steps are included: Step 201: Acquire multiple historical driving trajectories of the target vehicle during its historical driving.

[0054] Step 202: Filter multiple historical driving trajectories according to preset filtering rules to obtain a target historical driving trajectory, wherein the preset filtering rules at least include filtering out historical driving trajectories whose mileage is less than a first preset mileage or greater than a second preset mileage among the multiple historical driving trajectories.

[0055] Step 203: Acquire the travel information of the target historical driving trajectory, wherein the travel information at least includes the destination position and the vehicle travel period of the target historical driving trajectory.

[0056] Step 204: Calculate the route probability of the target historical driving trajectory with the same end point position and vehicle travel period in the target historical driving trajectory.

[0057] Step 205: Establish a common route database of the target vehicle based on the route probability and the trip information.

[0058] Steps 101 to 105 of this embodiment are similar to steps 201 to 205 of the aforementioned embodiment, and are not described again here to avoid repetition.

[0059] Step 206: After detecting that the target vehicle is started, obtain the identity information, the current first location information and the current time of the target vehicle.

[0060] In some embodiments, after the vehicle is started, the vehicle controller will upload the vehicle's identity information, current first location information, and current time to the cloud.

[0061] In some embodiments, the identity information is a vehicle VIN (Vehicle Identification Number), and the first location information is a GPS location.

[0062] Step 207: determine a common route database matching the target vehicle based on the identity information, select an initial predicted driving route matching the first location information and the current time from the common route database, and use the initial predicted driving route with the highest route probability among the initial predicted routes as the first final predicted driving route.

[0063] For ease of understanding, the following is a specific example of how to obtain the first final predicted driving route in this embodiment: The cloud stores a database of common routes for multiple vehicles, wherein each vehicle corresponds to a common route database. After obtaining the identity information of the vehicle, assuming that the identity information a of vehicle A is obtained, the cloud determines the common route database 1 that matches vehicle A through the identity information a. Assuming that the common route database 1 includes 200 historical driving trajectories of vehicle A, assuming that the current time is 8:30 in the morning, and the current GPS position is position 1, according to the current GPS position of vehicle A and the current time, 10 initial predicted driving routes passing through position 1 and the time period between 8 and 9 are determined from the 200 historical driving trajectories, assuming that among these 10 initial predicted driving routes, the maximum route probability of the initial predicted driving route 1 is 60%, then the initial predicted driving route 1 is used as the first final predicted driving route of vehicle A.

[0064] Compared with the related art, the embodiment of the present application has at least the following advantages: by filtering multiple historical driving trajectories according to preset filtering rules, it is possible to filter out abnormal driving trajectories in the historical driving trajectories, thereby reducing the amount of calculation in subsequent steps; since users may reach the same destination through different driving trajectories at the same travel time, by calculating the route probability of the target historical driving trajectory with the same terminal position and vehicle travel period in the target historical driving trajectory, it is easy to know the use probability of different routes when the user reaches the same terminal in the same travel period, and establish a target vehicle's usual route database based on the route probability and itinerary information. Therefore, after the target vehicle is started, the cloud can determine the usual route database based on the identity information of the target vehicle, and select the initial predicted driving route with the largest route probability from the usual route database as the first final predicted driving route of the target vehicle, which improves the accuracy of the prediction while realizing that the future itinerary of the vehicle can be predicted even when the user does not use the car navigation, thereby improving the user's travel experience.

[0065] Please refer to Figure 3 , Figure 3 This is a flow chart of a driving route prediction method provided by an embodiment of the present application. This embodiment is a further improvement on the previous embodiment. The main improvement is that in this embodiment, the route correction of the historical driving trajectory is also performed. In this way, the accuracy of route prediction can be further improved.

[0066] This embodiment is applied to the cloud, and the specific process is as follows Figure 3 As shown, the following steps are included: Step 301: Acquire multiple historical driving trajectories of the target vehicle during its historical driving.

[0067] Step 302: Mark feature points of each historical driving trajectory, wherein the feature points include the starting point, the end point, and the passing point of the historical driving trajectory.

[0068] In some embodiments, the feature points are represented by a GPS array, that is, each feature point includes the longitude and latitude of the vehicle's location.

[0069] In some embodiments, the waypoints are obtained by equally extracting b positions as waypoints according to the total number of frames of the historical driving trajectory. For example, the total number of frames of the historical driving trajectory is 1000 frames, and a waypoint is extracted every 100 frames, so the historical driving trajectory includes a total of 9 waypoints.

[0070] Step 303: perform route correction on each historical driving trajectory according to the feature points corresponding to each historical driving trajectory.

[0071] In some embodiments, the cloud inputs the feature points corresponding to each historical driving trajectory into the API (Application Programming Interface) of the navigation software, so that the navigation software outputs a corrected GPS trajectory point set and the segment names of all road segments.

[0072] Specifically, it can be seen from the description of the above steps that the characteristic points of the historical driving trajectory are the GPS position point set. Since the GPS position points may be offset, the characteristic points are input into the navigation software, and the navigation software corrects the offset GPS position points to obtain the corrected GPS trajectory point set. Since each historical driving trajectory includes multiple road sections, after the characteristic points are input into the navigation software, the navigation software can also simultaneously input the road section name of each road section.

[0073] Step 304: Filter the multiple route-corrected historical driving trajectories according to preset filtering rules to obtain a target historical driving trajectory.

[0074] In some embodiments, it is also possible to detect whether there is a third historical driving trajectory to be filtered in which the number of appearances of the road segment name is greater than or equal to a preset number among multiple historical driving trajectories after route correction; when the existence of the third historical driving trajectory to be filtered is detected, the third historical driving trajectory to be filtered among the multiple historical driving trajectories after route correction is filtered.

[0075] In some embodiments, the value of the preset number of times can be 2 or 3. By setting this preset number of times, it is possible to ensure that the circling driving track is filtered out.

[0076] Step 305: Acquire the travel information of the target historical driving trajectory, wherein the travel information at least includes the destination position and the vehicle travel period of the target historical driving trajectory.

[0077] In some embodiments, the travel information also includes a set of GPS track points, a road segment name, and a total mileage after the target historical track is corrected.

[0078] Step 306: Calculate the route probability of the target historical driving trajectory with the same end point position and vehicle travel period in the target historical driving trajectory.

[0079] Step 307: Establish a common route database of the target vehicle based on the route probability and the trip information.

[0080] Step 308: After detecting that the target vehicle is started, obtain the identity information, the current first position information and the current time of the target vehicle.

[0081] Step 309: determine a common route database that matches the target vehicle based on the identity information, select an initial predicted driving route that matches the first location information and the current time from the common route database, and use the initial predicted driving route with the highest route probability among the initial predicted routes as the first final predicted driving route of the target vehicle.

[0082] Step 301 and step 305 to step 309 of this embodiment are similar to step 201 and step 203 to step 207 of the aforementioned embodiment, and will not be described again here to avoid repetition.

[0083] Compared with the related art, the embodiment of the present application has at least the following advantages: by filtering multiple historical driving trajectories according to preset filtering rules, it is possible to filter out abnormal driving trajectories in the historical driving trajectories, thereby reducing the amount of calculation in subsequent steps; since users may reach the same destination through different driving trajectories at the same travel time, by calculating the route probability of the target historical driving trajectory with the same terminal position and vehicle travel period in the target historical driving trajectory, it is easy to know the use probability of different routes when the user reaches the same terminal in the same travel period, and establish a target vehicle's usual route database based on the route probability and itinerary information. Therefore, after the target vehicle is started, the cloud can determine the usual route database based on the identity information of the target vehicle, and select the initial predicted driving route with the largest route probability from the usual route database as the first final predicted driving route of the target vehicle, which improves the accuracy of the prediction while realizing that the future itinerary of the vehicle can be predicted even when the user does not use the car navigation, thereby improving the user's travel experience.

[0084] Please refer to Figure 4 , Figure 4 This is a flow chart of a driving route prediction method provided by an embodiment of the present application. This embodiment is a further improvement on the previous embodiment. The main improvement is that in this embodiment, the accuracy of the first final predicted driving route is further confirmed. In this way, the accuracy of route prediction can be further improved, thereby improving the user experience.

[0085] This embodiment is applied to the cloud, and the specific process is as follows Figure 4 As shown, the following steps are included: Step 401: Acquire multiple historical driving trajectories of the target vehicle during its historical driving.

[0086] Step 402: Filter multiple historical driving trajectories according to preset filtering rules to obtain a target historical driving trajectory, wherein the preset filtering rules at least include filtering out historical driving trajectories whose mileage is less than a first preset mileage or greater than a second preset mileage among the multiple historical driving trajectories.

[0087] Step 403: Acquire the travel information of the target historical driving trajectory, wherein the travel information at least includes the destination position and the vehicle travel period of the target historical driving trajectory.

[0088] Step 404: Calculate the route probability of the target historical driving trajectory with the same end point position and vehicle travel period in the target historical driving trajectory.

[0089] Step 405: Establish a common route database of the target vehicle based on the route probability and the trip information.

[0090] Step 406: After detecting that the target vehicle is started, obtain the identity information, the current first position information and the current time of the target vehicle.

[0091] Step 407: determine a common route database that matches the target vehicle based on the identity information, select an initial predicted driving route that matches the first location information and the current time from the common route database, and select an initial predicted driving route with the highest route probability from the initial predicted routes as the driving route to be determined.

[0092] Steps 401 to 407 of this embodiment are similar to steps 201 to 207 of the above embodiment, and are not described again here to avoid repetition.

[0093] Step 408, detect whether the driving route of the target vehicle matches the driving route to be determined when the driving time exceeds the second preset time and the driving distance is greater than the preset driving distance, and when it is detected that the driving route matches the driving route to be determined, use the driving route to be determined as the first final predicted driving route.

[0094] In some embodiments, the second preset time ranges from 1 minute to 2 minutes, and the preset driving distance ranges from 500M to 1KM. By setting such a range, the accuracy of the predicted driving route to be determined can be ensured while achieving fast route prediction for the target vehicle, thus avoiding affecting the user experience due to too long prediction time.

[0095] In some embodiments, it is also possible to detect whether the remaining mileage of the to-be-determined driving route is within a preset range. For example, when it is detected that the remaining mileage of the to-be-determined driving route is greater than or equal to the first preset mileage and less than or equal to the second preset mileage, the to-be-determined driving route is used as the first final predicted driving route. In this way, the validity of the predicted first final predicted driving route can be ensured.

[0096] Compared with the related art, the embodiment of the present application has at least the following advantages: by filtering multiple historical driving trajectories according to preset filtering rules, it is possible to filter out abnormal driving trajectories in the historical driving trajectories, thereby reducing the amount of calculation in subsequent steps; since users may reach the same destination through different driving trajectories at the same travel time, by calculating the route probability of the target historical driving trajectory with the same terminal position and vehicle travel period in the target historical driving trajectory, it is easy to know the use probability of different routes when the user reaches the same terminal in the same travel period, and establish a target vehicle's usual route database based on the route probability and itinerary information. Therefore, after the target vehicle is started, the cloud can determine the usual route database based on the identity information of the target vehicle, and select the initial predicted driving route with the largest route probability from the usual route database as the first final predicted driving route of the target vehicle, which improves the accuracy of the prediction while realizing that the future itinerary of the vehicle can be predicted even when the user does not use the car navigation, thereby improving the user's travel experience.

[0097] Please refer to Figure 5 , Figure 5 This is a flow chart of a driving route prediction method provided by an embodiment of the present application. This embodiment is a further improvement on the previous embodiment. The main improvement is that in this embodiment, whether the vehicle deviates during driving is detected, and the driving route of the vehicle is re-predicted after the vehicle deviates. In this way, the user's driving experience can be further improved.

[0098] This embodiment is applied to the cloud, and the specific process is as follows Figure 5 As shown, the following steps are included: Step 501: Acquire multiple historical driving trajectories of the target vehicle during its historical driving.

[0099] Step 502: Filter multiple historical driving trajectories according to preset filtering rules to obtain a target historical driving trajectory, wherein the preset filtering rules at least include filtering out historical driving trajectories whose mileage is less than a first preset mileage or greater than a second preset mileage among the multiple historical driving trajectories.

[0100] Step 503: Acquire the travel information of the target historical driving trajectory, wherein the travel information at least includes the destination position and the vehicle travel period of the target historical driving trajectory.

[0101] Step 504: Calculate the route probability of the target historical driving trajectory with the same end point position and vehicle travel period in the target historical driving trajectory.

[0102] Step 505: Establish a common route database of the target vehicle based on the route probability and the trip information.

[0103] Step 506: After detecting that the target vehicle is started, obtain the identity information, the current first position information and the current time of the target vehicle.

[0104] Step 507: determine a common route database that matches the target vehicle based on the identity information, select an initial predicted driving route that matches the first location information and the current time from the common route database, and use the initial predicted driving route with the highest route probability among the initial predicted routes as the first final predicted driving route of the target vehicle.

[0105] Step 508: Detect whether the target vehicle is yawed at every preset period.

[0106] In some embodiments, the size of the preset period is not specifically limited and can be set according to actual conditions.

[0107] In some embodiments, when the deviation between the real-time position of the target vehicle and the center line of the first final predicted driving route is greater than a certain threshold, the target vehicle is considered to have deviated. The value of the threshold is between 20M and 30M.

[0108] Step 509: when the target vehicle is detected to deviate for M consecutive preset periods, obtain the current second position information of the target vehicle, where M is an integer greater than 1.

[0109] In some embodiments, in order to avoid frequent entry and exit of the route prediction function, taking into account the existence of misjudgment or temporary detours caused by GPS position offset, the target vehicle will be finally confirmed to be deviating only when it continues to deviate for M consecutive preset periods.

[0110] Step 510: Reselect a second final predicted driving route that matches the second location information from the conventional route database.

[0111] In some embodiments, the method for selecting the second final predicted driving route is similar to the method for selecting the first final predicted driving route, and will not be described again here to avoid repetition.

[0112] In some embodiments, if the second final predicted driving route matching the second location information is not found from the common route database, the cloud exits the route prediction function of the target vehicle.

[0113] Please refer to Figure 6 , is a schematic diagram of the hardware structure of the electronic device 1000 provided in the embodiment of the present application. Figure 6As shown, the electronic device 1000 may include a processor 1001 and a memory 1002. The memory 1002 is used to store one or more computer programs 1003. The one or more computer programs 1003 are configured to be executed by the processor 1001. The one or more computer programs 1003 include instructions, and the above instructions can be used to implement the above-mentioned driving route prediction method in the electronic device 1000.

[0114] It is understandable that the structure shown in this embodiment does not constitute a specific limitation on the electronic device 1000. In other embodiments, the electronic device 1000 may include more or fewer components than shown, or combine or separate some components, or arrange the components differently.

[0115] The processor 1001 may include one or more processing units, for example, the processor 1001 may include an application processor (AP), a modem, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0116] The processor 1001 may also be provided with a memory for storing instructions and data. In some embodiments, the memory in the processor 1001 is a cache memory. The memory may store instructions or data that the processor 1001 has just used or circulated. If the processor 1001 needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processor 1001, and thus improves the efficiency of the system.

[0117] In some embodiments, the processor 1001 may include one or more interfaces. The interface may include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a SIM interface, and / or a USB interface, etc.

[0118] In some embodiments, processor 1001 is used to execute acceleration schemes such as single instruction multiple data (SIMD) and very long instruction word (VLIW).

[0119] In some embodiments, memory 1002 may include high-speed random access memory and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0120] This embodiment also provides a storage medium, in which computer instructions are stored. When the instructions are executed on an electronic device, the electronic device executes the above-mentioned related method steps to implement the driving route prediction method in the above-mentioned embodiment.

[0121] Among them, the electronic device and storage medium provided in this embodiment are used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0122] In practical applications, the above functions can be distributed 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.

[0123] In several embodiments provided in the present application, the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are schematic. For example, the division of the modules or units is a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0124] The unit described as a separate component may or may not be physically separated, and the component shown as a unit may be one physical unit or multiple physical units, that is, it may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.

[0125] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0126] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program code.

[0127] The above description is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application.

Claims

1. A driving route prediction method, characterized in that: Applied in the cloud, including: Obtain multiple historical driving trajectories of the target vehicle during its historical driving; Filtering the plurality of historical driving trajectories according to a preset filtering rule to obtain a target historical driving trajectory, wherein the preset filtering rule at least includes filtering out historical driving trajectories whose mileage is less than a first preset mileage or greater than a second preset mileage among the plurality of historical driving trajectories; Acquire the travel information of the target historical driving trajectory, wherein the travel information at least includes the end point position and the vehicle travel time period of the target historical driving trajectory; Calculating the route probability of the target historical driving trajectory in which the end position in the target historical driving trajectory is the same as the vehicle travel period; Establishing a common route database of the target vehicle based on the route probability and the trip information; After detecting that the target vehicle is started, a first final predicted driving route of the target vehicle is determined according to the common route database.

2. The driving route prediction method according to claim 1, characterized in that: Before determining the first final predicted driving route of the target vehicle according to the conventional route database, the method further includes: Obtaining the identity information, current first location information and current time of the target vehicle; Determining the commonly used route database matching the target vehicle according to the identity information; The determining of the first final predicted driving route of the target vehicle according to the conventional route database comprises: An initial predicted driving route that matches the first location information and the current time is selected from the commonly used route database, and the initial predicted driving route with the highest route probability among the initial predicted routes is used as the first final predicted driving route.

3. The driving route prediction method according to claim 2, characterized in that: After taking the initial predicted driving route with the highest route probability among the initial predicted routes as the first final predicted driving route, the method includes: Selecting the initial predicted driving route with the highest route probability from the initial predicted routes as the driving route to be determined; Detecting whether the driving route of the target vehicle when the driving time exceeds a second preset time and the driving distance is greater than a preset driving distance matches the driving route to be determined; When it is detected that the driving route matches the driving route to be determined, the driving route to be determined is used as the first final predicted driving route.

4. The driving route prediction method according to claim 3, characterized in that: After taking the to-be-determined driving route as the first final predicted driving route, the method further includes: Detecting whether the target vehicle deviates at every preset period; When the target vehicle is detected to deviate for M consecutive preset periods, obtaining the current second position information of the target vehicle, wherein M is an integer greater than 1; A second final predicted driving route matching the second position information is reselected from the common route database.

5. The driving route prediction method according to claim 1, characterized in that: The filtering of the plurality of historical driving trajectories according to a preset filtering rule includes: Detecting whether there is a first to-be-filtered historical driving trajectory in which the vehicle speed is 0 and the duration of the vehicle speed being 0 exceeds a first preset duration among the plurality of historical driving trajectories; When the existence of the first historical driving trajectory to be filtered is detected, the first historical driving trajectory to be filtered among the plurality of historical driving trajectories is filtered.

6. The driving route prediction method according to claim 1, characterized in that: The filtering of the plurality of historical driving trajectories according to a preset filtering rule includes: Detecting whether there is a second to-be-filtered historical driving trajectory in which the driving mileage is greater than a third preset mileage for N consecutive days among the plurality of historical driving trajectories, wherein N is an integer greater than 1; When the existence of the second historical driving trajectory to be filtered is detected, the second historical driving trajectory to be filtered among the plurality of historical driving trajectories is filtered.

7. The driving route prediction method according to claim 1, characterized in that: After obtaining a plurality of historical driving trajectories of the target vehicle in historical driving, the method further includes: Marking each of the historical driving trajectories with characteristic points, wherein the characteristic points include a starting point, an end point, and a passing point of the historical driving trajectory; Performing route correction on each of the historical driving trajectories according to the feature points corresponding to each of the historical driving trajectories; The filtering of the plurality of historical driving trajectories according to a preset filtering rule includes: The plurality of historical driving trajectories after the route correction are filtered according to preset filtering rules.

8. The driving route prediction method according to claim 7, characterized in that: Each of the historical driving trajectories includes a plurality of road segments. After performing route correction on each of the historical driving trajectories, the method further includes: Obtaining the names of all the road sections included in each of the historical driving trajectories; The filtering of the plurality of historical driving trajectories after the route correction according to the preset filtering rules includes: Detecting whether there is a third to-be-filtered historical driving track in which the number of occurrences of the road section name is greater than or equal to a preset number of times among the plurality of historical driving tracks after the route correction; When the existence of the third historical driving trajectory to be filtered is detected, the third historical driving trajectory to be filtered among the plurality of historical driving trajectories after the route correction is performed.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, the memory is used to store instructions, and the processor is used to call the instructions in the memory, so that the electronic device executes the driving route prediction method according to any one of claims 1 to 8.

10. A storage medium, characterized in that: It comprises computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the driving route prediction method as claimed in any one of claims 1 to 8.

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