Reminding method and device

Abstract

The embodiment of the invention discloses a reminding method and device. The method comprises the following steps: acquiring driving information of a plurality of vehicles of unknown types in real time; based on the driving information and driving feature information of a preset vehicle type, determining a first target vehicle belonging to the preset vehicle type in the plurality of unknown types of vehicles; determining a second target vehicle within a preset distance of the first target vehicle in real time; and generating prompt information about the first target vehicle and used for being sent to the second target vehicle. According to the technical scheme, effective reminding about the first target vehicle can be accurately performed on the second target vehicle near the first target vehicle, and when the preset vehicle type of the first target vehicle is a large vehicle, real and effective safety early warning of the large vehicle is realized, interference caused by invalid reminding to a user is avoided, and the driving experience of the user is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent transportation technology, and in particular to a reminder method and device. Background Art

[0002] At present, people often travel by car. This mode of travel brings many conveniences to people, but it is also accompanied by a series of safety issues. For example, when a driver's vehicle is traveling with a large vehicle (such as a truck, a bus, etc.), the driver's vehicle often collides with these large vehicles because these large vehicles have large blind spots, long braking distances, and large volumes.

[0003] In the related art, drivers are usually reminded on roads where large vehicles often appear, so as to remind the users to drive carefully and avoid collisions with large vehicles.

[0004] However, the inventors discovered during the process of implementing the present application that this method cannot provide reminders in combination with the actual driving scenario of the driving user, so that the reminders interfere with the user and reduce the user's driving experience. Summary of the invention

[0005] In order to solve the problems in the related art, the embodiments of the present disclosure provide a reminder method and device.

[0006] In a first aspect, a reminder method is provided in an embodiment of the present disclosure.

[0007] Specifically, the reminder method includes: Obtain driving information of multiple unknown types of vehicles in real time; Based on the driving information and driving characteristic information of a preset vehicle type, determining a first target vehicle of the preset vehicle type among the plurality of vehicles of unknown types; Determine in real time a second target vehicle within a preset distance of the first target vehicle; Prompt information about the first target vehicle is generated for transmission to the second target vehicle.

[0008] In a second aspect, an embodiment of the present disclosure provides a reminder device, including: An information acquisition module is configured to acquire driving information of a plurality of vehicles of unknown types in real time; A type determination module is configured to determine a first target vehicle of a preset vehicle type among the plurality of vehicles of unknown types based on the driving information and driving characteristic information of the preset vehicle type; A vehicle determination module, configured to determine in real time a second target vehicle within a preset distance of the first target vehicle; The prompt generation module is configured to generate prompt information about the first target vehicle for sending to the second target vehicle.

[0009] According to the technical solution provided by the embodiment of the present disclosure, the driving information of multiple vehicles of unknown types can be obtained in real time, and based on the driving information and the driving characteristic information of the preset vehicle type, the first target vehicle of the preset vehicle type among the multiple vehicles of unknown types can be determined, and then the second target vehicle within a preset distance of the first target vehicle can be determined in real time, and prompt information about the first target vehicle can be generated for sending to the second target vehicle; in this way, the second target vehicle near the first target vehicle can be accurately and effectively reminded about the first target vehicle, and when the preset vehicle type of the first target vehicle is a large vehicle, a real and effective large vehicle safety warning can be implemented, thus avoiding invalid reminders from causing interference to users and improving the user's driving experience.

[0010] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Other features, objectives and advantages of the present disclosure will become more apparent through the following detailed description of non-limiting embodiments in conjunction with the accompanying drawings. In the accompanying drawings: Figure 1 A flowchart of a reminder method provided by an embodiment of the present disclosure is shown; Figure 2A A schematic diagram of a trajectory point filling process provided by an embodiment of the present disclosure is shown; Figure 2B A schematic diagram of trajectory similarity comparison provided by an embodiment of the present disclosure is shown; Figure 3 A structural block diagram of a reminder device provided by an embodiment of the present disclosure is shown; Figure 4 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown; Figure 5 A schematic diagram showing the structure of a computer system suitable for implementing the method according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0012] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. In addition, for the sake of clarity, parts not related to the description of the exemplary embodiments are omitted in the accompanying drawings.

[0013] In the present disclosure, it should be understood that terms such as "include" or "have" are intended to indicate the presence of features, numbers, steps, behaviors, components, parts, or a combination thereof disclosed in the present specification, and are not intended to exclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or a combination thereof exist or are added.

[0014] It should also be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0015] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0016] As mentioned above, in the related art, drivers are usually reminded on roads where large vehicles often appear, to remind them to drive carefully and avoid collisions with large vehicles. However, the inventors found in the process of implementing this application that this method cannot be combined with the actual driving scene of the driver to remind the driver, so that the reminder causes interference to the user and reduces the user's driving experience.

[0017] The present disclosure provides a reminder method, which can determine a first target vehicle of a preset vehicle type among the multiple unknown vehicles based on driving information of multiple vehicles of unknown types and driving characteristic information of a preset vehicle type, and then determine a second target vehicle within a preset distance of the first target vehicle, so that a reminder message can be sent to the second target vehicle to remind the second target vehicle that there is a first target vehicle near the second target vehicle; in this way, the second target vehicle that is closer to the first target vehicle can be accurately and effectively reminded about the first target vehicle, and when the preset vehicle type of the first target vehicle is a large vehicle, a real and effective large vehicle safety warning can be implemented, thereby avoiding interference to users caused by invalid reminders and improving the user's driving experience.

[0018] Figure 1 A flowchart of a reminder method provided by an embodiment of the present disclosure is shown. Figure 1 As shown, the reminder method includes the following steps S101-S104: In step S101, the driving information of multiple vehicles of unknown types is obtained in real time; In step S102, based on the driving information and driving characteristic information of the preset vehicle type, a first target vehicle belonging to the preset vehicle type among the multiple vehicles of unknown type is determined; In step S103, a second target vehicle within a preset distance of the first target vehicle is determined in real time; In step S104, prompt information about the first target vehicle is generated for sending to the second target vehicle.

[0019] In a possible implementation, the reminder method is applicable to a server-side device such as a computer, a computing device, a server, a server cluster, etc. that can execute driving safety reminders.

[0020] In a possible implementation, the multiple vehicles of unknown types may be various types of vehicles that are currently traveling and whose driving information can be obtained in real time by the server. The driving information here may include the real-time track point position of the vehicle when it is traveling, which can be obtained by the positioning position reported in real time by the positioning device carried on the vehicle; the driving information may also include lane information and speed corresponding to the track point position, and the speed can be reported in real time by the speed sensor carried on the vehicle, and the lane can be determined based on the track point position. It should be noted here that the driving information can be obtained by the server in real time from the vehicles it serves, and / or obtained from other data sources that record the driving information of each vehicle in real time, and there is no limitation here.

[0021] In a possible implementation, on the same road section, the driving characteristic information of different types of vehicles will be different. For example, there are certain differences between large vehicles and ordinary vehicles in vehicle start and stop time (large vehicles start and stop slowly), vehicle speed (large vehicles are slower), vehicle lane (large vehicles will take a dedicated lane), lane when passing through a toll station (large vehicles will take a dedicated toll lane), vehicle lane change time (large vehicles change lanes slowly), etc. Therefore, based on the driving information and the driving characteristic information of the preset vehicle type, the first target vehicle belonging to the preset vehicle type among the multiple unknown type vehicles can be determined. For example, the vehicle start and stop time, vehicle speed, vehicle lane, lane when passing through a toll station, vehicle lane change time and other driving characteristic information of the vehicle can be determined according to the driving information of each vehicle, and then determine whether the driving characteristic information of the vehicle meets the driving characteristic information of the preset vehicle type, and determine the vehicle that meets the driving characteristic information of the preset vehicle type as the first target vehicle. The preset vehicle type in this implementation can be a large vehicle. Of course, in other implementations, the preset vehicle type can also be a specific type actually required, which is not limited here.

[0022] In a possible implementation, the second target vehicle within the preset distance of the first target vehicle can be determined in real time based on the real-time driving information of the vehicle, such as the real-time trajectory point position of the vehicle. When the preset vehicle type of the first target vehicle is a large vehicle, the second target vehicle is another vehicle that is closer to the first target vehicle. In order to ensure the driving safety of the second target vehicle, a prompt message can be generated to be sent to the second target vehicle to prompt that there is a large first target vehicle near the second target vehicle. For example, assuming that the preset distance is 200m, if the large first target vehicle is located in front of the second target vehicle, the reminder message can be "There is a first target vehicle of the preset vehicle type within 200 meters in front, please drive carefully"; if the large first target vehicle is located behind the second target vehicle, the reminder message can be "There is a first target vehicle of the preset vehicle type within 200 meters behind, please drive carefully" and so on.

[0023] This implementation can acquire driving information of multiple vehicles of unknown types in real time, and determine a first target vehicle of a preset vehicle type among the multiple vehicles of unknown types based on the driving information and driving characteristic information of a preset vehicle type, and then determine a second target vehicle within a preset distance of the first target vehicle in real time, and generate prompt information about the first target vehicle to be sent to the second target vehicle; in this way, the second target vehicle near the first target vehicle can be accurately reminded, and when the preset vehicle type of the first target vehicle is a large vehicle, a real and effective safety warning for large vehicles can be implemented, thus avoiding interference to users caused by invalid reminders and improving the user's driving experience.

[0024] In a possible implementation manner, the driving information includes at least: a track point position, lane information corresponding to the track point position, and a speed; The real-time acquisition of driving information of multiple vehicles of unknown types includes: Based on at least two data sources of driving information, real-time acquisition of driving information of multiple vehicles of unknown types; Based on the positions of the track points recorded in the driving information, the driving information of the multiple vehicles of unknown types is deduplicated to obtain deduplicated driving information of the multiple vehicles of unknown types.

[0025] In this embodiment, each vehicle generates a driving track when driving, each driving track is composed of multiple track points, and the driving information of the vehicle includes at least: the track point position in a driving track of the vehicle, the lane information and speed corresponding to the track point position.

[0026] In this implementation, the driving information may have at least two data sources: Data source 1: map application database. The real-time driving information recorded in the map application database may be the driving information reported in real time when the vehicle uses the map application; Data source 2: device positioning database. The real-time driving information recorded in the device positioning database may be the driving information reported in real time by a device (such as a mobile phone or other terminal device) when the vehicle is equipped with the device; Data source 3: the enterprise vehicle database of the booking enterprise. The real-time driving information recorded in the enterprise vehicle database can be the driving information reported in real time when the enterprise vehicle is driving. For example, the freight vehicles of the freight enterprise will report the driving information in real time when operating. Data source 4: The collection vehicle database of the road collection vehicle. During the collection process, the road collection vehicle will take real-time images of various vehicles on the road. Based on the images taken by the road collection vehicle in real time, the real-time driving information of each vehicle can be calculated and recorded in real time in the collection vehicle database.

[0027] In this embodiment, with the authorization of the user, driving information of multiple vehicles of unknown types can be obtained from one or more of the above-mentioned data sources. If the driving information of multiple vehicles of unknown types is obtained from one of the above-mentioned data sources, the first target vehicle of the preset vehicle type among the multiple unknown vehicles can be directly determined based on the driving information and the driving characteristic information of the preset vehicle type; if the driving information of multiple vehicles of unknown types is obtained from two or more of the above-mentioned data sources, there may be repeated driving information corresponding to the same vehicle obtained from different data sources. At this time, it is necessary to deduplicate the driving information of the multiple vehicles of unknown types based on the trajectory point positions recorded in the driving information to obtain the deduplicated driving information of multiple vehicles of unknown types.

[0028] In this embodiment, in the existing real-time driving information, the interval distance between adjacent track point positions of the same vehicle is large, and the interval time is long (usually in the order of seconds). Therefore, in order to more accurately identify the first target vehicle later, the track point positions in the driving information can be filled with track points so that the time interval between adjacent track point positions reaches the millisecond level. Moreover, if the driving information is obtained from two or more data sources mentioned above, considering that the time intervals of the track point positions recorded by different data sources may be different, in order to more accurately identify the first target vehicle later, it is also necessary to fill the track point positions in the driving information with track points so that the frequency of the track point positions in the driving information from different data sources is the same, that is, the driving information from different data sources obtains a track point position in real time at the same interval. For example, for the same vehicle, the map application database obtains a track point position of the vehicle in real time every 2 seconds in the driving information, and the device positioning database obtains a track point position of the vehicle in real time every 5 seconds; in order to make the frequency of track point positions in different data sources the same, it is necessary to fill 1 track point between two adjacent track points of a vehicle in the map application database, and fill 4 track points between two adjacent track points of a vehicle in the device positioning database. In this way, for a vehicle, the driving information of the two data sources both have a track point position every 1 second, and the track point position frequency is the same. For example, Figure 2A A schematic diagram of a trajectory point filling process provided by an embodiment of the present disclosure is shown as follows: Figure 2A As shown, Figure 2A The first, second and third points in the figure are track points in a driving trajectory. The driving trajectory records a track point every 3 seconds. The interval between track points to be reached is 1 second, so it can be as follows: Figure 2A In , two supplementary virtual points are filled between two adjacent trajectory points.

[0029] In this embodiment, when filling the track point positions, track points may be filled between two adjacent track points of a driving track for each driving track. Here, a driving track refers to a driving track of a vehicle from a data source.

[0030] In this embodiment, the trajectory point filling scheme may be: based on the driving information corresponding to the trajectory points in the same driving trajectory whose time or distance to the two adjacent trajectory points is within a first predetermined range, and the driving information corresponding to the trajectory points in other driving trajectories whose distance to the two adjacent trajectory points is within a second predetermined range, fill the trajectory points between the two adjacent trajectory points so that the frequencies of the trajectory point positions in different driving trajectories are the same.

[0031] Specifically, when filling track points between two adjacent track points of a driving track, the driving behavior of the vehicle between the two adjacent track points can be predicted based on the driving information of the vehicle (i.e., the vehicle generating the two adjacent track points) on the current road and the driving information of other vehicles on the current road, and then the track points can be filled between the two adjacent track points. The driving information of other vehicles on the current road includes the driving information of the adjacent vehicles driving on the current road at the same time as the vehicle, and may also include the driving information of vehicles driving on the current road at different times than the vehicle.

[0032] In this embodiment, the driving information of the vehicle on the current road may be the driving information corresponding to a trajectory point in the same driving trajectory whose time interval or distance interval between two adjacent trajectory points is within a first predetermined range. For example, it may be the driving information corresponding to a trajectory point whose distance interval between two adjacent trajectory points is within 1000 meters, or it may be the driving information corresponding to a trajectory point whose time interval between two adjacent trajectory points is within 5 minutes. Other driving information on the current road may be the driving information corresponding to a trajectory point in other driving trajectories whose distance interval between the two adjacent trajectory points is within a second predetermined range.

[0033] This embodiment can obtain driving information of multiple unknown types of vehicles from at least two data sources, which can improve the recognition level of the first target vehicle of a preset vehicle type on the road, can identify more first target vehicles on the road, and then more accurately generate prompt information about the first target vehicle.

[0034] In a possible implementation, deduplicating the driving information of the plurality of vehicles of unknown types based on the track point positions recorded in the driving information to obtain deduplicated driving information of the plurality of vehicles of unknown types includes: The driving information of each unknown type of vehicle is grouped according to the road sections with preset mileage distances; Based on the track point positions recorded in each group of the unknown type of vehicles, the driving information of the multiple unknown type of vehicles is deduplicated to obtain the deduplicated driving information of the multiple unknown type of vehicles.

[0035] In this embodiment, in order to make deduplication more accurate, it is necessary to group the track point positions in the driving information and perform deduplication based on the groups. Here, the road can be divided into sections according to the preset mileage distance, for example, every 25 meters is a section, and the driving information of each unknown type of vehicle can be grouped according to the divided sections, and each unknown type of vehicle can be obtained. Groups, each group corresponds to a section. Based on the track point positions recorded in each group, the repeated groups can be determined more accurately. After removing the driving information recorded in the repeated groups, the deduplicated driving information of multiple unknown types of vehicles can be obtained.

[0036] In a possible implementation, the deduplication of the driving information of the plurality of vehicles of unknown type based on the track point positions recorded in each group of vehicles of unknown type to obtain the deduplication driving information of the plurality of vehicles of unknown type includes: For each group of unknown type vehicles in each data source, cluster the groups with continuous trajectory point positions, and obtain the group corresponding to each unknown type vehicle after clustering as a group set; For multiple grouping sets in at least two data sources, calculate the similarity of trajectory point positions between multiple grouping sets in the same road section and the same time period; The multiple grouping sets are deduplicated according to the similarities to obtain deduplicated driving information of multiple vehicles of unknown types.

[0037] In this implementation, in a data source, an unknown type of vehicle corresponds to a continuous driving track. Usually, the group where the track point position corresponding to the vehicle identification of the unknown type of vehicle is located can be used as the group set corresponding to the unknown type of vehicle. However, in some cases, in order to protect user privacy, an unknown type of vehicle in some data sources will change its vehicle identification at intervals, and each vehicle identification will correspond to a driving track. This will result in the possibility that there are multiple continuous driving tracks corresponding to an unknown type of vehicle in a data source. With the authorization of the user, these multiple continuous driving tracks can be clustered into a driving track of an unknown type of vehicle. Since in each data source, an unknown type of vehicle has only one group of recorded trajectory point positions in a road section, and corresponding trajectory point positions are continuous in time and space in continuous sections, therefore, for each group of unknown type of vehicles in each data source, the continuity of trajectory point positions between different groups on continuous sections can be calculated. For example, the continuity of trajectory point positions between different groups on continuous sections in time and space can be calculated, and then different groups that meet the continuity requirements are clustered into a group set, so that multiple group sets are obtained, each group set corresponds to an unknown type of vehicle, so that the group set corresponding to each unknown type of vehicle can be obtained, and the group set corresponding to each unknown type of vehicle includes all groups of the unknown type of vehicle in a data source, so that the driving information of each unknown type of vehicle in a corresponding data source is obtained.

[0038] In this embodiment, each data source corresponds to multiple grouping sets. Since the trajectory point positions generated by an unknown type of vehicle in the same road section and the same time period can be obtained and recorded by different data sources, an unknown type of vehicle can correspond to a grouping set in different data sources. That is, for multiple grouping sets in at least two data sources, an unknown type of vehicle may correspond to at least two grouping sets. At this time, deduplication is required to retain only one grouping set. Since the trajectory point positions of an unknown type of vehicle recorded by different data sources in the same road section and the same time period are similar, the similarity of the trajectory point positions between multiple grouping sets in the same road section and the same time period can be calculated, and multiple grouping sets from different data sources whose similarity exceeds a predetermined threshold are determined as grouping sets corresponding to an unknown type of vehicle. For example, Figure 2B A schematic diagram of trajectory similarity comparison provided by an embodiment of the present disclosure is shown as follows: Figure 2B As shown, in the same time period on the same road section, the similarity of the trajectory point positions of car A and car B (such as the similarity of trajectory shape and position) is high, and they are determined to be an unknown type of vehicle. The similarity deviation between the trajectory point positions of car A and car C is large and cannot be determined as the same vehicle.

[0039] In this embodiment, after obtaining multiple grouping sets of an unknown type of vehicle in different data sources, you can choose to retain the grouping set in one data source from different data sources, and the grouping sets in other data sources are duplicate records and can be deleted. In this way, a deduplicated grouping set of an unknown type of vehicle is obtained, and the driving information of the unknown type of vehicle is recorded in the grouping set of the unknown type of vehicle. In this way, the deduplicated driving information of multiple unknown types of vehicles is obtained.

[0040] In a possible implementation manner, the driving information further includes one or more of the following: starting time, stopping time, lane changing time, and lane when passing through a toll station.

[0041] In this implementation, the startup time refers to the time required from the start of the vehicle to the time it enters a stable operating state, the vehicle stop time refers to the time required from the start of braking to the complete stop of the vehicle, the lane change time refers to the time required for the vehicle to transfer from one lane to another, and the lane when passing through the toll station refers to the specific lane type selected by the vehicle when passing through the highway toll station. These driving information can be reported by the vehicle or calculated based on the position of the trajectory point and its time.

[0042] In a possible implementation manner, determining a first target vehicle of a preset vehicle type among the multiple vehicles of unknown types based on the driving information and driving characteristic information of a preset vehicle type includes: For each unknown type of vehicle, determining driving characteristic information corresponding to each group according to driving information recorded in each group of the unknown type of vehicle; According to the driving characteristic information corresponding to each group and the driving characteristic information of the preset vehicle type, it is determined whether the unknown type vehicle belongs to the first target vehicle of the preset vehicle type.

[0043] In this implementation, each unknown vehicle corresponds to multiple groups of driving information, and different groups correspond to different road sections. Based on the driving information recorded in each group of the unknown type of vehicle, the driving characteristic information corresponding to each group can be determined concurrently, and then the unknown type of vehicle can be identified as a first target vehicle of a preset vehicle type based on the driving characteristic information corresponding to each group. Grouping the driving information and concurrently determining the driving characteristic information can improve processing efficiency.

[0044] In this embodiment, the driving characteristic information of the preset vehicle type may include one or more of the following characteristics: speed range, start time range, stop time range, lane change time range and lane type when passing through a toll station, the type of lane the vehicle is in, etc.

[0045] In this embodiment, the driving characteristic information corresponding to each group can be determined based on the driving information recorded in each group. Some driving characteristic information can be directly obtained from the driving information, and some driving characteristic information can be calculated based on the driving information. For example, when the driving information includes the vehicle speed and the lane in which the vehicle is located, the driving characteristic information such as the vehicle speed, the lane where the toll station is passed, and the lane in which the vehicle is located can be directly obtained from the driving information; the vehicle lane change time and the vehicle start and stop time can be calculated based on the lane in which the vehicle is located and the position and time of the trajectory points in the driving information.

[0046] In this embodiment, determining whether the unknown type vehicle belongs to the first target vehicle of the preset vehicle type may include the following two methods: one method is to first score the driving characteristic information corresponding to each group based on the driving characteristic information of the preset vehicle type, and then determine the vehicle type corresponding to the unknown type vehicle according to the vehicle type scores corresponding to each group of the unknown type vehicle; the other method is to score the driving characteristic information corresponding to each group based on the driving characteristic information of the preset vehicle type, and directly obtain the corresponding vehicle type of the unknown type vehicle. It should be noted here that the above-mentioned vehicle type scoring can be scored using a pre-trained scoring model, or it can be directly compared and scored with the driving characteristic information of the preset vehicle type, and there is no limitation here.

[0047] In a possible implementation manner, the real-time determination of a second target vehicle within a preset distance of the first target vehicle includes: Determine in real time the target position of the first target vehicle after a target duration; A second target vehicle is determined to be within a preset distance of the first target vehicle when the first target vehicle is at the target location.

[0048] In this embodiment, based on the driving information of the first target vehicle acquired in real time, the driving habits of the first target vehicle can be analyzed to predict the future driving trajectory of the first target vehicle within a target time period, such as 2 minutes, and then determine the target position of the first target vehicle after the target time period.

[0049] In this embodiment, after determining the target position of the first target vehicle, the driving habits of vehicles on nearby roads can be analyzed to predict the position of vehicles on nearby roads after the target time, and then determine from these vehicles the second target vehicle within a preset distance of the first target vehicle when the first target vehicle is at the target position. The second target vehicle is a vehicle that will appear near the first target vehicle in the future, so that the second target vehicle near the first target vehicle after the target time is predicted in real time in advance, so that the second target vehicle can be prompted about the first target vehicle in advance, so that the user of the second target vehicle can make accurate preparations in advance and improve the user's driving experience.

[0050] In a possible implementation manner, the real-time determination of the target position of the first target vehicle after a target duration includes: Acquire historical driving information of the first target vehicle, and / or historical driving information of vehicles that have historically passed through a target road on the target road, wherein the target road is the road on which the first target vehicle is currently traveling; The target position of the first target vehicle after a target time period is determined based on at least one of the historical driving information of the first target vehicle and the historical driving information of vehicles passing through the target road in history, as well as the real-time driving information of the first target vehicle.

[0051] In this embodiment, the historical driving information of the first target vehicle refers to the driving information of the first target vehicle at a historical time. The historical driving information of the first target vehicle can reflect the driving characteristics of the vehicle on various roads. For example, it may be various behavioral information such as frequent lane changes when a vehicle appears in front, and very little deviation during driving.

[0052] In this embodiment, the historical driving information of the historical vehicles passing through the target road on the target road can reflect the driving characteristics of various vehicles when passing through the target road, such as the vehicle speed on the target road in various time periods every day, vehicle lane change information, etc.

[0053] In this embodiment, when predicting the future driving trajectory of the first target vehicle, the driving characteristics of the first target vehicle on a road similar to the target road can be determined based on the historical driving information of the first target vehicle. For example, if the target road is a highway, the historical driving information of the first target vehicle on the highway is obtained to determine the driving characteristics of the first target vehicle on the highway; the general driving characteristics of the target road in the same time period can be determined based on the historical driving information on the target road, for example, the first target vehicle generally drives at a low speed at night; then, with reference to the driving information obtained in real time by the first target vehicle (such as the average speed, maximum speed, lane and other driving information of the current 10 most recent trajectory points obtained in real time), the driving characteristics of the first target vehicle on a road similar to the target road and the general driving characteristics of the target road in the same time period are used to calculate the future driving trajectory of the first target vehicle in the future period of time, and determine the target position of the first target vehicle after the target duration.

[0054] In this embodiment, vehicles that will appear near the first target vehicle after the target duration will usually be within a predetermined range from the first target vehicle. Therefore, in order to reduce resource waste and improve processing speed, only the future driving trajectory of user vehicles that are within a predetermined range from the first target vehicle can be predicted. The prediction process is the same as the prediction process of the future driving trajectory of the first target vehicle, and is also based on the historical driving information of the user vehicle and / or the historical driving information of historical vehicles passing through the road where the user vehicle is located to determine the position of the user vehicle after the target duration; the details will not be repeated here.

[0055] In this embodiment, based on the target position of the first target vehicle after the target time and the position of the user vehicle after the target time, a second target vehicle within a preset distance of the first target vehicle can be obtained when the first target vehicle is at the target position.

[0056] This embodiment not only takes into account the real-time driving information of the vehicle when predicting the future driving trajectory of the vehicle, but also takes into account the historical driving information of the vehicle and / or the historical driving information of vehicles that have passed through the target road on the target road, so that the predicted future driving trajectory is more accurate.

[0057] In a possible implementation, the method further includes: Acquire relative relationship information and environmental scene information, wherein the relative relationship information includes a relative position and / or relative speed between the first target vehicle and the second target vehicle, and the environmental scene information includes at least one of a weather scene type, a road scene type, and a time scene type; The generating of prompt information about the first target vehicle for sending to the second target vehicle includes: Prompt information about the first target vehicle is generated for sending to the second target vehicle based on the relative relationship information and the environmental scene information.

[0058] In this embodiment, the relative relationship information includes the relative position and / or relative speed of the first target vehicle and the second target vehicle. The weather scene type can be normal weather or abnormal weather such as rain, snow, fog, etc. The road scene type can be road scenes such as tunnels, downhill, uphill, etc. The time scene type can be daytime or nighttime. For different relative relationship information and environmental scene information, prompt information sent to the second target vehicle can be generated at different time points. For example, during the day, when the first target vehicle is driving fast in front of the second target vehicle, a reminder message "50m behind the first target vehicle is approaching quickly" can be sent at a distance of 50m; at night, when there is a fast-moving first target vehicle behind the second target vehicle, a reminder message "100m behind the first target vehicle is approaching quickly" should be sent at a distance of 100m. Or, for example, for abnormal weather such as rain, snow, fog, etc., it is necessary to generate reminder information earlier so that the user of the second target vehicle can prepare early. For complex road scenes such as tunnels, downhill, uphill, etc., it is also necessary to generate reminder information earlier so that the user of the second target vehicle can prepare early, and so on.

[0059] This implementation can provide a more refined reminder to the second target vehicle according to the relative relationship information and environmental scene information.

[0060] Figure 3 FIG. 1 is a block diagram of a reminder device provided by an embodiment of the present disclosure. The device may be implemented as part or all of an electronic device through software, hardware, or a combination of both. Figure 3 As shown, the reminder device includes: The information acquisition module 301 is configured to acquire driving information of multiple vehicles of unknown types in real time; The type determination module 302 is configured to determine a first target vehicle of a preset vehicle type among the plurality of vehicles of unknown types based on the driving information and driving characteristic information of the preset vehicle type; The vehicle determination module 303 is configured to determine in real time a second target vehicle within a preset distance of the first target vehicle; The prompt generating module 304 is configured to generate prompt information about the first target vehicle for sending to the second target vehicle.

[0061] In a possible implementation manner, the driving information includes at least: a track point position, lane information corresponding to the track point position, and a speed; The information acquisition module is configured as follows: Based on at least two data sources of driving information, real-time acquisition of driving information of multiple vehicles of unknown types; Based on the positions of the track points recorded in the driving information, the driving information of the multiple vehicles of unknown types is deduplicated to obtain deduplicated driving information of the multiple vehicles of unknown types.

[0062] In a possible implementation manner, the part of the information acquisition module that deduplicates the driving information of the plurality of vehicles of unknown types based on the positions of the trajectory points recorded in the driving information to obtain the deduplicated driving information of the plurality of vehicles of unknown types is configured as follows: The driving information of each unknown type of vehicle is grouped according to the road sections with preset mileage distances; Based on the track point positions recorded in each group of the unknown type of vehicles, the driving information of the multiple unknown type of vehicles is deduplicated to obtain the deduplicated driving information of the multiple unknown type of vehicles.

[0063] In a possible implementation manner, the driving information further includes one or more of the following: starting time, stopping time, lane changing time, and lane when passing through a toll station.

[0064] In a possible implementation manner, the vehicle determination module is configured to: Determine in real time the target position of the first target vehicle after a target duration; A second target vehicle is determined to be within a preset distance of the first target vehicle when the first target vehicle is at the target location.

[0065] In a possible implementation, the information acquisition module removes duplicate driving information of the plurality of vehicles of unknown type based on the track point positions recorded in each group of vehicles of unknown type to obtain the deduplicated driving information of the plurality of vehicles of unknown type, and is configured as follows: For each group of unknown type vehicles in each data source, cluster the groups with continuous trajectory point positions, and obtain the group corresponding to each unknown type vehicle after clustering as a group set; For multiple grouping sets in at least two data sources, calculate the similarity of trajectory point positions between multiple grouping sets in the same road section and the same time period; The multiple grouping sets are deduplicated according to the similarities to obtain deduplicated driving information of multiple vehicles of unknown types.

[0066] In a possible implementation, the type determination module is configured to: For each unknown type of vehicle, determining driving characteristic information corresponding to each group according to driving information recorded in each group of the unknown type of vehicle; According to the driving characteristic information corresponding to each group and the driving characteristic information of the preset vehicle type, it is determined whether the unknown type vehicle belongs to the first target vehicle of the preset vehicle type.

[0067] In a possible implementation manner, the part of the vehicle determination module that determines in real time the target position of the first target vehicle after a target duration is configured as follows: Determine a target position of the first target vehicle after a target time period based on at least one of historical driving information of the first target vehicle and historical driving information of vehicles passing through the target road on the target road, and real-time driving information of the first target vehicle; The target position of the first target vehicle after the target duration is determined based on the historical driving information of the first target vehicle and / or the historical driving information of vehicles that have historically passed through the target road.

[0068] In a possible implementation, the device further includes: A scene acquisition module is configured to acquire relative relationship information and environmental scene information, wherein the relative relationship information includes a relative position and / or relative speed between the first target vehicle and the second target vehicle, and the environmental scene information includes at least one of a weather scene type, a road scene type, and a time scene type; The prompt generation module is also configured to: Prompt information about the first target vehicle is generated for sending to the second target vehicle based on the relative relationship information and the environmental scene information.

[0069] The technical terms and technical features mentioned in the implementation manner of this device are the same as or similar to the technical terms and technical features mentioned in the implementation manner of the above method. For the explanation and description of the technical terms and technical features involved in this device, reference may be made to the explanation and description of the implementation manner of the above method, and no further details will be given here.

[0070] The present disclosure also discloses an electronic device, Figure 4 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0071] like Figure 4As shown, the electronic device 400 includes a memory 401 and a processor 402, wherein the memory 401 is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor 402 to implement the method according to an embodiment of the present disclosure.

[0072] Figure 5 A schematic diagram showing the structure of a computer system suitable for implementing the method according to an embodiment of the present disclosure is shown.

[0073] like Figure 5 As shown, the computer system 500 includes a processing unit 501, which can perform various processes in the above-mentioned embodiments according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 to a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the computer system 500 are also stored. The processing unit 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0074] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage section 508 as needed. Among them, the processing unit 501 can be implemented as a processing unit such as a CPU, a GPU, a TPU, an FPGA, an NPU, etc.

[0075] In particular, according to an embodiment of the present disclosure, the method described above can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes computer instructions, and the computer instructions are executed by a processor to implement the method steps described above. In such an embodiment, the computer program product can be downloaded and installed from a network through the communication part 509, and / or installed from a removable medium 511.

[0076] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they may sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0077] The units or modules involved in the embodiments described in the present disclosure may be implemented by software or programmable hardware. The units or modules described may also be set in a processor, and the names of these units or modules do not constitute limitations on the units or modules themselves in some cases.

[0078] As another aspect, the present disclosure further provides a computer-readable storage medium, which may be a computer-readable storage medium included in the electronic device or computer system in the above embodiment; or a computer-readable storage medium that exists independently and is not assembled into a device. The computer-readable storage medium stores one or more programs, and the programs are used by one or more processors to execute the method described in the present disclosure.

[0079] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present disclosure is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other.

Claims

1. A reminder method, characterized in that: include: Obtain driving information of multiple unknown types of vehicles in real time; Based on the driving information and driving characteristic information of a preset vehicle type, determining a first target vehicle of the preset vehicle type among the plurality of vehicles of unknown types; Determine in real time a second target vehicle within a preset distance of the first target vehicle; Prompt information about the first target vehicle is generated for transmission to the second target vehicle.

2. The method according to claim 1, characterized in that The driving information includes at least: the position of the track point, the lane information and the speed corresponding to the position of the track point; The real-time acquisition of driving information of multiple vehicles of unknown types includes: Based on at least two data sources of driving information, real-time acquisition of driving information of multiple vehicles of unknown types; Based on the positions of the track points recorded in the driving information, the driving information of the multiple vehicles of unknown types is deduplicated to obtain deduplicated driving information of the multiple vehicles of unknown types.

3. The method according to claim 2, characterized in that The deduplication of the driving information of the plurality of vehicles of unknown types based on the track point positions recorded in the driving information to obtain the deduplication driving information of the plurality of vehicles of unknown types includes: The driving information of each unknown type of vehicle is grouped according to the road sections with preset mileage distances; Based on the track point positions recorded in each group of the unknown type of vehicles, the driving information of the multiple unknown type of vehicles is deduplicated to obtain the deduplicated driving information of the multiple unknown type of vehicles.

4. The method according to claim 3, characterized in that The method of deduplicating the driving information of the plurality of vehicles of unknown type based on the track point positions recorded in each group of vehicles of unknown type to obtain deduplicated driving information of the plurality of vehicles of unknown type includes: For each group of unknown type vehicles in each data source, cluster the groups with continuous trajectory point positions, and obtain the group corresponding to each unknown type vehicle after clustering as a group set; For multiple grouping sets in at least two data sources, calculate the similarity of trajectory point positions between multiple grouping sets in the same road section and the same time period; The multiple grouping sets are deduplicated according to the similarities to obtain deduplicated driving information of multiple vehicles of unknown types.

5. The method according to claim 3, characterized in that: The determining, based on the driving information and the driving characteristic information of the preset vehicle type, a first target vehicle of the plurality of vehicles of unknown types belonging to the preset vehicle type comprises: For each unknown type of vehicle, determining driving characteristic information corresponding to each group according to driving information recorded in each group of the unknown type of vehicle; According to the driving characteristic information corresponding to each group and the driving characteristic information of the preset vehicle type, it is determined whether the unknown type vehicle belongs to the first target vehicle of the preset vehicle type.

6. The method according to claim 2, characterized in that The driving information also includes one or more of the following: starting time, stopping time, lane changing time and lane when passing through a toll station.

7. The method according to claim 1, characterized in that The real-time determination of a second target vehicle within a preset distance of the first target vehicle includes: Determine in real time the target position of the first target vehicle after a target duration; A second target vehicle is determined to be within a preset distance of the first target vehicle when the first target vehicle is at the target location.

8. The method according to claim 7, characterized in that The real-time determination of the target position of the first target vehicle after the target time period includes: Acquire historical driving information of the first target vehicle, and / or historical driving information of vehicles that have historically passed through a target road on the target road, wherein the target road is the road on which the first target vehicle is currently traveling; The target position of the first target vehicle after a target time period is determined based on at least one of the historical driving information of the first target vehicle and the historical driving information of vehicles passing through the target road in history, as well as the real-time driving information of the first target vehicle.

9. The method according to claim 1, characterized in that: The method further comprises: Acquire relative relationship information and environmental scene information, wherein the relative relationship information includes a relative position and / or relative speed between the first target vehicle and the second target vehicle, and the environmental scene information includes at least one of a weather scene type, a road scene type, and a time scene type; The generating of prompt information about the first target vehicle for sending to the second target vehicle includes: Prompt information about the first target vehicle is generated for sending to the second target vehicle based on the relative relationship information and the environmental scene information.

10. A reminder device, characterized in that: include: An information acquisition module is configured to acquire driving information of a plurality of vehicles of unknown types in real time; A type determination module is configured to determine a first target vehicle of a preset vehicle type among the plurality of vehicles of unknown types based on the driving information and driving characteristic information of the preset vehicle type; A vehicle determination module, configured to determine in real time a second target vehicle within a preset distance of the first target vehicle; The prompt generation module is configured to generate prompt information about the first target vehicle for sending to the second target vehicle.

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