Driving path planning method and device, equipment and medium

Abstract

The invention belongs to the technical field of path planning, and discloses a driving path planning method and device, equipment and a medium, and the method comprises the steps: selecting a first driving path under a preset constraint condition, and obtaining a people flow value on the first driving path in a first time interval, the first time interval is determined according to the predicted driving period of the first driving path, and whether to switch to the second driving path or not is determined according to the people flow value. According to the technical scheme provided by the invention, by predicting the people flow condition of the non-driving path and adjusting the path in real time according to the prediction result, long driving time caused by people flow increase is avoided, and real-time monitoring of the road condition is realized. Besides, the optimal path, namely the first driving path, under the preset constraint condition is selected before driving, so that the driving smoothness is ensured, and the driving timeliness is further improved.

Description

Technical Field

[0001] This application belongs to the technical field of path planning, and particularly relates to a method, device, equipment and medium for planning a driving path. Background Art

[0002] With the continuous development of autonomous driving technology and the continuous improvement of technologies such as positioning, perception, fusion and path planning, for example, low-speed unmanned logistics vehicles and food delivery robots have been put into commercial operation.

[0003] In scenarios such as unmanned logistics vehicles delivering express deliveries and goods, the delivery starting point A and the ending point B of the vehicle can be preset in advance, and path planning for a single line from point to point can be carried out, thereby realizing the delivery of goods from point A to point B. Multiple lines can also be set according to the delivery starting point A and the ending point B. If the vehicle encounters sudden accidents such as traffic jams, obstacles or traffic accidents on one of the paths, it can switch to other lines for driving.

[0004] When driving according to the pre-set path above, the vehicle can identify the surrounding environment and plan the path based on its own lidar, camera and other devices, and then realize autonomous control of the vehicle to stop and select other paths for driving. However, when the vehicle determines that a certain section is in the peak period of the flow of people, if it continues to drive along the current path, the timeliness of goods delivery will inevitably be reduced. If it chooses to drive on other pre-set paths, it will take more time and the smoothness of the new path cannot be guaranteed.

[0005] In this regard, a feasible solution is that the operation and maintenance personnel monitor the environment of the vehicle's current driving path in advance through remote monitoring, and then select a suitable path for the vehicle through remote control, which will obviously cause an increase in labor costs and reduce the automation level of the unmanned vehicle.

[0006] Another feasible way is to use the vehicle's running speed and the vehicle's waiting time as measurement indicators, set the control logic for autonomous vehicle path planning, and when the vehicle's running speed and the vehicle's waiting time meet the preset conditions, control the vehicle to select a new route for driving. However, returning to the original path and turning into a new path still cannot guarantee the smoothness of the new path, thereby reducing the timeliness of goods delivery.

[0007] Therefore, it can be seen that how to realize the autonomous planning of the vehicle driving path, predict the flow of people on the path, avoid road congestion caused by the flow of people, ensure the smooth operation of the vehicle, and thus improve the timeliness of vehicle driving is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0008] The purpose of this application is to provide a method, device, equipment and medium for planning a driving route, which is used to realize the autonomous planning of the vehicle driving route, predict the pedestrian flow on the route, avoid road congestion caused by the pedestrian flow, ensure the smooth operation of the vehicle, and thus improve the timeliness of vehicle driving.

[0009] In a first aspect, an embodiment of this application provides a method for planning a driving route, including:

[0010] Select a first driving route under preset constraint conditions;

[0011] Obtain the pedestrian flow value on the first driving route within a first time interval, where the first time interval is determined according to the expected driving period of the first driving route;

[0012] Determine whether to switch to a second driving route according to the pedestrian flow value.

[0013] Therefore, the technical solution provided by this application predicts the pedestrian flow situation of the un-traveled route and adjusts the route in real time according to the prediction result, avoiding long driving time caused by the increase in pedestrian flow and realizing real-time monitoring of the road conditions. In addition, the best route under preset constraint conditions, that is, the first driving route, is selected before driving to ensure the smoothness of driving and thus improve the driving timeliness.

[0014] In a possible implementation manner of the first aspect, the obtaining the pedestrian flow value on the first driving route within the first time interval includes:

[0015] Retrieve the first historical pedestrian flow heat map data on the first driving route within a second time interval from historical data; wherein, the start time of the first time interval differs from the start time of the second time interval by the duration of N first preset periods, the duration of the first preset period is determined according to the correlation between the pedestrian flow value and time, and N is a positive integer;

[0016] Obtain the first pedestrian flow heat map data on the first driving route at the current moment;

[0017] Predict the pedestrian flow within the first time interval according to the first historical pedestrian flow heat map data and the first pedestrian flow heat map data to obtain the pedestrian flow value.

[0018] Therefore, by combining the current pedestrian flow heat map data and historical pedestrian flow heat map data on the first driving route, the pedestrian flow situation on the first driving route is predicted, the prediction accuracy is improved, data support is provided for switching the route, and thus the driving timeliness is improved.

[0019] In a possible implementation of the first aspect, the preset constraint conditions include at least one of the shortest driving duration, the shortest driving mileage, and the least number of traffic lights, and the selected first driving path under the preset constraint conditions includes:

[0020] Obtain a path set by obtaining a target path from all feasible paths from the driving starting point to the driving ending point; wherein, the target path is a path whose difference between the driving distance and the shortest path distance is less than a preset value, and the path set includes the first driving path and the second driving path;

[0021] Select the first driving path under the preset constraint conditions from the path set.

[0022] Thus, select the best path that meets the preset constraint conditions, that is, the first driving path, before driving, which can meet different business requirements while further ensuring the smoothness of the path, and then improve the driving timeliness.

[0023] In a possible implementation of the first aspect, the selecting the first driving path under the preset constraint conditions from the path set includes:

[0024] Obtain the second people flow heat map data at the first moment and the second historical people flow heat map data starting from the second moment; wherein, the first moment and the second moment differ by a duration of M second preset periods, M is a positive integer, and the duration of the second preset period is determined according to the correlation between the people flow value and time on the corresponding target path;

[0025] Determine the driving duration of the target path according to the second people flow heat map data and the second historical people flow heat map data;

[0026] Select the first driving path under the preset constraint conditions from the path set according to the driving duration of each target path.

[0027] Thus, determine the first driving path based on the people flow situation on each target path, ensure the optimal path under the preset constraint conditions before driving, and improve the driving timeliness.

[0028] In a possible implementation of the first aspect, the first time interval is determined according to the expected driving period of the radiation path, and the radiation path refers to the first driving path within the radiation radius, and the radiation radius refers to the scanning radius of the people flow acquisition unit.

[0029] Thus, it provides data support for determining the people flow situation of the path based on the people flow heat map data.

[0030] In a possible implementation of the first aspect, the determining whether to switch to the second driving path according to the people flow value includes:

[0031] Determine whether the pedestrian flow value is greater than a preset threshold;

[0032] If it is greater than the preset threshold, determine whether there is a path that meets the path switching condition in the path set. If so, switch to the second driving path; if not, maintain the first driving path;

[0033] If it is not greater than the preset threshold, return to the step of obtaining the pedestrian flow value on the first driving path within the first time interval until the end condition is triggered.

[0034] Thus, when determining whether to switch to the second driving path according to the pedestrian flow values of each first driving path, when the first driving path is congested due to the pedestrian flow value exceeding the preset threshold, and when it is further determined that other paths in the path set meet the switching conditions, switch from the first driving path to the second driving path, anticipate the path pedestrian flow in advance, avoid congestion caused by the pedestrian flow, and improve the driving timeliness.

[0035] In a possible implementation manner of the first aspect, the path switching condition is that the expected waiting duration is greater than a preset value, and there is at least one first driving duration less than the second driving duration;

[0036] Wherein, the first driving duration is the sum of the driving duration from the current position to the driving starting point and the driving duration of any target path; the second driving duration is the driving duration from the current position to the driving end point.

[0037] Thus, when the first driving path needs to be switched to the second driving path due to a large pedestrian flow, determine whether the expected waiting duration is greater than the preset value, and whether there is at least one sum of the first driving duration and each second driving duration less than the third driving duration, wherein the first driving duration is the driving duration from the current position to the driving starting point, the second driving duration is the driving duration of the second driving path, and the third driving duration is the driving duration from the current position to the driving end point. Thus, determine whether to switch paths according to the driving duration, and further improve the driving timeliness.

[0038] In a second aspect, an embodiment of the present application provides a driving path planning device, including:

[0039] A selection module, configured to select a first driving path under preset constraint conditions;

[0040] An acquisition module, configured to acquire the pedestrian flow value on the first driving path within a first time interval, and the first time interval is determined according to the expected driving period of the first driving path;

[0041] A determination module, configured to determine whether to switch to a second driving path according to the pedestrian flow value.

[0042] In a third aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for planning a driving path according to any one of the above first aspects is implemented.

[0043] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the method for planning a driving path according to any one of the above first aspects is implemented.

[0044] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device is caused to execute the method for planning a driving path according to any one of the above first aspects.

[0045] It should be noted that the beneficial effects of the above second to fifth aspects can be referred to the relevant descriptions in the above first aspect, and will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 A schematic diagram of a vehicle path provided by the present application;

[0048] Figure 2 Another schematic diagram of a vehicle path provided by the present application;

[0049] Figure 3 A flowchart of a method for planning a driving path provided by an embodiment of the present application;

[0050] Figure 4 A schematic diagram of a vehicle path provided by an embodiment of the present application;

[0051] Figure 5 A schematic diagram of the structure of a system for planning a driving path provided by an embodiment of the present application;

[0052] Figure 6 A schematic diagram of the structure of a device for planning a driving path provided by an embodiment of the present application;

[0053] Figure 7A structural schematic diagram of a terminal device provided by an embodiment of the present application;

[0054] The reference numerals are as follows: 50 is a fleet management system, 51 is a domain controller, 52 is an RTK, 53 is a heat map system, 54 is a vehicle body, 70 is a terminal device, 701 is a processor, 702 is a memory, and 703 is a computer program. Specific embodiments

[0055] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0056] It should be understood that when used in the specification of the present application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0057] It should also be understood that the term "and / or" as used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0058] As used in the specification of the present application and the appended claims, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" depending on the context. Similarly, the phrase "if determined" or "if detected [the described condition or event]" can be interpreted as meaning "once determined", "in response to determining", "once detected [the described condition or event]", or "in response to detecting [the described condition or event]" depending on the context.

[0059] In addition, in the description of the specification of the present application and the appended claims, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0060] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that specific features, structures or characteristics described in connection with that embodiment are included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments" etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized.

[0061] To enable those skilled in the art to better understand the solution of this application, the following further details this application in conjunction with the accompanying drawings and specific embodiments.

[0062] Figure 1 A schematic diagram of a vehicle path provided by this application. With the continuous development of autonomous driving technology, for example, unmanned logistics vehicles for delivering express deliveries and goods have been put into commercial operation. Taking an unmanned logistics delivery vehicle as an example, as Figure 1 shown, the delivery starting point A and the ending point B of the vehicle are preset in advance, and path planning for a single line from point to point is carried out, and delivery is carried out according to the pre-planned path. Figure 2 Another schematic diagram of a vehicle path provided by this application, as Figure 2 shown, multiple lines can also be set according to the delivery starting point A and the ending point B. When the vehicle encounters sudden accidents such as traffic jams, obstacles or traffic accidents on one of the paths, it can switch to other paths for driving.

[0063] When driving according to the pre-set path as above, the vehicle can identify the surrounding environment and plan the path based on its own lidar, camera and other devices, and then realize autonomous control of the vehicle to stop and select other paths for driving. However, when a certain section of the current driving path is in the peak period of the flow of people, if the vehicle continues to drive on this path, the timeliness of goods delivery will surely be reduced. If it chooses to drive on other pre-set paths, it will take more time and it is impossible to ensure the smoothness of the new path.

[0064] In response to this, a feasible solution is that the operation and maintenance personnel monitor the environment of the vehicle's current driving path in advance through remote monitoring, and then select a suitable path for the vehicle by means of remote control. Obviously, this will cause an increase in labor costs and reduce the automation level of unmanned vehicles.

[0065] Another feasible way is to use the running speed and waiting time of the vehicle as measurement indicators, set the control logic for autonomous vehicle route planning, and when the running speed and waiting time of the vehicle meet the preset conditions, control the vehicle to choose a new route for driving. However, returning to the original route and turning into a new path still cannot guarantee the smoothness of the new path, thereby reducing the timeliness of goods delivery.

[0066] To solve the above technical problems, achieve autonomous planning of the vehicle driving route, predict the path pedestrian flow, avoid road congestion caused by the pedestrian flow, ensure the smooth operation of the vehicle, and thus improve the timeliness of vehicle driving, this application provides a method for planning a driving route. By obtaining the pedestrian flow value on the path, the pedestrian flow is pre-judged, and whether to switch to a new path is determined according to the pre-judgment result, so as to avoid road congestion caused by excessive pedestrian flow and reduce the driving timeliness.

[0067] Figure 3 The flowchart of a method for planning a driving route provided by an embodiment of this application is as Figure 3 shown. The method includes:

[0068] S30: Select a first driving route under preset constraint conditions;

[0069] It should be noted that the method provided by this application can be applied to technical fields such as robots and unmanned logistics, and this application does not make specific limitations in this regard. For the sake of easy understanding, an unmanned logistics vehicle is taken as an example in the embodiments of this application for illustration.

[0070] In a specific embodiment, the fleet management system selects an available unmanned logistics vehicle for the delivery task and sends the order information to the selected vehicle. Among them, the way of transmitting the order information can be wireless network, etc., and this application does not make specific limitations in this regard. The transmitted order information includes information such as the driving starting point, driving ending point, and loading cargo location of the vehicle, and this application also does not make specific limitations on the specific content of the order information.

[0071] After the vehicle loads the goods according to the order information, it transmits the loading information to the vehicle management system in real time through the wireless communication device on the vehicle. In addition, all feasible paths are obtained according to the driving starting point and driving ending point, and a target path that meets the requirements is obtained from the feasible paths to obtain a target path set. It can be understood that there are countless feasible paths between two points. Therefore, it is necessary to select a target path that meets the requirements from countless paths, that is, select a limited number of them.

[0072] In implementation, when there is only one target path, the target path is used as the first driving path. In fact, there are often multiple target paths. In this case, under the preset constraints, the first driving path is selected from the multiple target paths. It should be noted that, according to the needs of the user, the preset constraints can be the shortest driving time, the shortest driving mileage, the shortest traffic light, etc. Of course, there can also be multiple constraints, and this application does not limit the preset constraints.

[0073] S11: obtaining a passenger flow value on a first driving path within a first time interval, where the first time interval is determined according to an estimated driving period of the first driving path;

[0074] S12: Determine whether to switch to the second driving path according to the passenger flow value.

[0075] Further, the vehicle is controlled to travel according to the selected first travel path. It can be understood that during the continuous travel of the vehicle, the flow of people on the first travel path is constantly changing. In order to avoid congestion on a certain section of the first travel path due to excessive flow of people, which in turn reduces the timeliness of vehicle travel, the flow of people on the first travel path in the first time interval is obtained in real time during the travel process. It should be noted that if there is a large flow of people on the first travel path and there is congestion, it is necessary to return to the travel starting point along the original route, and then select other paths except the first travel path from the travel starting point. Therefore, when obtaining the flow of people on the first travel path, it is necessary to obtain the flow of people on the untraveled section and the flow of people on the untraveled section. Of course, it is also possible to first obtain the flow of people on the untraveled section, and then obtain the flow of people on the traveled section when it is predicted that there is congestion based on the flow of people on the untraveled section. This application does not make specific limitations on this.

[0076] Of course, in an optional embodiment, in order to reduce the amount of data processing and ensure driving timeliness, only the flow of people on the untraveled section can be predicted. Therefore, when obtaining the flow of people, only the flow of people on the untraveled section on the first driving path can be obtained. When congestion is predicted based on the flow of people on the untraveled section, the flow of people on the traveled section is obtained, and this application does not make specific restrictions on this.

[0077] In addition, it should be noted that the method for obtaining the crowd flow value can be to capture it through a camera and perform crowd flow recognition, or to obtain it based on the principle of a heat map, and this application does not make any specific limitations on this.

[0078] The driving mileage that can be determined after obtaining the first driving route can further determine the driving time interval of the vehicle when the vehicle travels at a preset driving speed. For example, starting from the driving starting point at 6:00 and driving at the preset speed, the destination can be reached at 7:00. Therefore, when predicting the flow of people on the driving section, it is necessary to obtain the value of the flow of people within the first time interval, and the first time interval is an interval within future time. At the same time, the first time interval is a part of the total driving time interval of the vehicle on the first driving route. For example, the vehicle starts from the starting point at 6:00 and travels at the preset driving speed. When it reaches half of the journey and the current time is 6:30, the remaining journey is the other half. Any time period between 6:30 and 7:00 can be used as the first time interval.

[0079] It should be noted that if it is determined through the value of the flow of people that the flow of people on the first driving route is too large and the congestion time is long, it is necessary to switch to the second driving route. Among them, the second driving route is one of the target routes except the first driving route, that is to say, the second driving route is one of the target routes that meet the requirements selected from countless routes.

[0080] The driving route planning method provided by the embodiments of the present application includes: selecting a first driving route under preset constraint conditions, and obtaining the value of the flow of people on the first driving route within the first time interval, and then determining whether to switch to the second driving route according to the value of the flow of people. It can be seen that the technical solution provided by the present application predicts the flow of people on the un-traveled route and adjusts the route in real time according to the prediction result, avoiding spending a long time due to the increase in the flow of people and realizing real-time monitoring of the road conditions. In addition, the best route under preset constraint conditions, that is, the first driving route, is selected before driving to ensure smooth driving and thus improve driving timeliness.

[0081] As a preferred embodiment, obtaining the value of the flow of people on the first driving route within the first time interval includes:

[0082] Retrieving the first historical heat map data of the flow of people on the first driving route within the second time interval from historical data; wherein, the start time of the first time interval and the start time of the second time interval differ by the duration of N first preset cycles, the duration of the first preset cycle is determined according to the relevant properties of the value of the flow of people and time, and N is a positive integer;

[0083] Obtaining the first heat map data of the flow of people on the first driving route at the current moment;

[0084] Predicting the flow of people within the first time interval according to the first historical heat map data of the flow of people and the first heat map data of the flow of people to obtain the value of the flow of people.

[0085] In a specific embodiment, to predict the flow of people on the first driving path, the vehicle obtains the first heat map data of the flow of people on the first driving path at the current moment. In the actual application process, to reduce the amount of data processing and the energy consumption of the vehicle, the first heat map data of the flow of people at the current moment can be obtained every preset period. For example, if it is expected that the vehicle departs from the starting point at 6:00 and arrives at the end point at 7:00, then the first heat map data of the flow of people at the current moment is obtained every 10 minutes starting from 6:00, that is, it is obtained at 6:00, 6:10, and so on until the end point is reached.

[0086] It can be understood that the flow of people on the path changes in real time. After predicting the flow value of people on the first driving path in the second time interval in advance, the flow of people may change greatly when the vehicle travels to the predicted section. For example, at 16:30, the vehicle predicts that the flow of people in the section that has not been traveled from 17:00 to 17:10 in the second time interval is small and the road is unobstructed. However, when traveling to this section, it may happen to encounter the evening rush hour, resulting in a dense flow of people and a long waiting time for the vehicle.

[0087] Therefore, to improve the reliability of prediction, in addition to obtaining the first heat map data of the flow of people, the first historical heat map data of the flow of people on the first driving path in the second time interval is retrieved from the historical data, where the starting moment of the second time interval differs from the starting moment of the first time interval in the above embodiment by the duration of N first preset periods, N is a positive integer, and the duration of the first preset period is determined according to the correlation between the flow value of people and time. For the sake of easy understanding, an example will be given below.

[0088] For example, the first time interval is from 7:00 to 8:00, and the first time interval is the time interval on May 1, 2023, Labor Day. Since traffic jams are likely to occur on Labor Day every year, there is a certain correlation between the flow value of people on the first driving path and time. According to this correlation, the duration of the first preset period can be determined to be one year, so the second time interval is from 7:00 to 8:00 on May 1 of previous years. For example, it can be from 7:00 to 8:00 on May 1, 2022, or from 7:00 to 8:00 on May 1, 2021, etc. Obviously, the heat map data of the flow of people from 7:00 to 8:00 on May 1 of each year in the historical data can be used as the first historical heat map data of the flow of people on the first driving path in the second time interval. The specific number of the first historical heat map data retrieved is not specifically limited in this application. It should be noted that, to further improve the reliability of prediction, it is preferable to select a time interval with a smaller number of first preset periods from the starting moment of the first time interval, that is, it is preferable to select a second time interval with a smaller N.

[0089] Further, analyze the pedestrian flow situation in the second time interval based on the first pedestrian flow heat map data and the first historical pedestrian flow heat map data. It should be noted that the steps of obtaining the first pedestrian flow heat map data and retrieving the first historical pedestrian flow heat map data are not in a specific order.

[0090] When predicting the pedestrian flow in the second time interval based on the historical pedestrian flow heat map data and the first pedestrian flow heat map data to obtain the pedestrian flow value, corresponding weights can be set for the first pedestrian flow heat map data and the first historical pedestrian flow heat map data, and then the final pedestrian flow value is calculated according to the weights. The present application does not make specific limitations on the calculation method for calculating the pedestrian flow value.

[0091] It should be noted that the analysis of the pedestrian flow heat map data to determine the pedestrian flow value can be implemented by the vehicle controller or the heat map system. The present application does not make specific limitations in this regard. Taking the heat map system as an example, the heat map system combines the received information with the map, reads and analyzes the pedestrian flow data on the first driving path, and predicts the pedestrian flow data in the second time interval in combination with the previous data (the first historical pedestrian flow heat map data). After the formed prediction result, an image color block and the pedestrian flow result data in the second time interval are formed in the heat map system, and the analysis result is transmitted to the running vehicle through wireless communication.

[0092] In the driving path planning method provided by the embodiments of the present application, when obtaining the pedestrian flow value on the first driving path in the first time interval, retrieve the first historical pedestrian flow heat map data on the first driving path in the second time interval from the historical data, where the start time of the first time interval and the start time of the second time interval differ by the duration of N first preset cycles. The duration of the first preset cycle is determined according to the relevant properties of the pedestrian flow value and time, and N is a positive integer. In addition, obtain the first pedestrian flow heat map data on the first driving path at the current moment, and predict the pedestrian flow in the first time interval according to the first historical pedestrian flow heat map data and the first pedestrian flow heat map data to obtain the pedestrian flow value. Thus, the pedestrian flow situation on the first driving path is predicted by combining the current pedestrian flow heat map data and the historical pedestrian flow heat map data on the first driving path, improving the prediction accuracy, providing data support for switching paths, and further enhancing the driving timeliness.

[0093] In a specific embodiment, there are countless feasible paths from the driving starting point to the driving ending point, that is, there are countless connecting lines between two points, but most of these paths are infeasible.

[0094] Therefore, in the method provided by the present application, when selecting the first driving path under the selected preset constraint conditions, first obtain the target paths from all the feasible paths from the driving starting point to the driving ending point to obtain a path set, where the target path is a path whose difference between the driving distance and the shortest path distance is less than a preset value, that is, driving distance - shortest path distance < preset value.

[0095] It should be noted that the shortest path distance can be the path with the shortest distance among all feasible paths from the driving starting point to the driving ending point, that is, the path with the shortest distance among the paths that the vehicle can travel. It can also be the distance of the straight line connecting the driving starting point and the driving ending point. This application does not make any limitations in this regard. It should be noted that the selected first driving path, and the second driving path switched when the first driving path is congested due to a large flow of people in the above embodiment are both paths in the path set.

[0096] Further, after obtaining the path set, select the first driving path that meets the preset constraint conditions from it. Among them, the preset constraint conditions can be the shortest driving duration, the shortest driving mileage, the least number of traffic lights, or the least energy consumption, etc. Of course, when selecting the first driving path, it can also be selected in the order of the shortest driving duration > the shortest driving mileage > the least number of traffic lights > the least energy consumption. This application does not make specific limitations in this regard and can be set according to actual business requirements.

[0097] In the driving path planning method provided by the embodiment of this application, when selecting the first driving path under the preset constraint conditions, obtain the target path from all feasible paths from the driving starting point to the driving ending point to obtain the path set; among them, the target path is the path whose difference between the driving distance and the shortest path distance is less than the preset value. The path set includes the first driving path and the second driving path, and then select the first driving path from the path set. Thus, select the best path, that is, the first driving path, that meets the preset constraint conditions before driving, which can meet different business requirements while further ensuring the smoothness of the path, thereby improving the driving timeliness.

[0098] Based on the above embodiment, selecting the first driving path under the preset constraint conditions from the path set includes:

[0099] Obtain the second pedestrian flow heat map data at the first moment and the second historical pedestrian flow heat map data starting from the second moment; where the first moment and the second moment differ by a duration of M second preset periods, M is a positive integer, and the duration of the second preset period is determined according to the correlation between the pedestrian flow value and time on the corresponding target path;

[0100] Determine the driving duration of the target path according to the second pedestrian flow heat map data and the second historical pedestrian flow heat map data;

[0101] Select the first driving path under the preset constraint conditions from the path set according to the driving durations of each target path.

[0102] In a specific embodiment, after obtaining the target path whose difference between the driving distance and the shortest path distance is less than the preset value, obtain the second pedestrian flow heat map data of each target path at the first moment.

[0103] It can be understood that the pedestrian flow value on the path changes in real time. It is less reliable to select the first driving path only relying on the current pedestrian flow value of each target path. Therefore, in order to improve the reliability of the selected first driving path, second historical pedestrian flow heat map data since the second moment is obtained, where the first moment and the second moment are separated by a duration of M second preset periods, M is a positive integer, and the duration of the second preset period is determined according to the correlation between the pedestrian flow value and time on the corresponding target path. In implementation, in order to ensure the reliability of the first driving path, the first moment can be selected as the current moment. For the convenience of understanding, the following will be described in detail.

[0104] For example, the first moment is 7:00, and at 7:00 on Monday. According to the correlation between the pedestrian flow value and time on the target path, it can be determined that the morning rush hour is likely to be encountered on Monday morning. Therefore, the second preset period is one week, and the historical pedestrian flow heat map data starting from 7:00 on Monday can be obtained, that is, taking 7:00 on Monday of last week as the second moment, for example, the second historical pedestrian flow heat map data on each target path starting from 7:00 last week.

[0105] It should be noted that when obtaining the second historical pedestrian flow heat map data since the second moment, the duration between the obtained end moment and the second moment is equal to the sum of the duration of driving on the target path at the preset driving speed from the first moment and the preset error value. That is, if the duration between the second moment and the end moment is T0, the duration of driving on the target path at the preset driving speed from the first moment is T1, and the preset error value is x, then T0 = T1 + x.

[0106] It can be understood that there may be errors when determining the end moment of obtaining the second historical pedestrian flow heat map data according to the duration of driving on the target path at the preset driving speed from the first moment. Therefore, in order to eliminate the errors, a part of the historical data within the preset error value can be obtained more, that is, the historical data within the preset error value.

[0107] Furthermore, the driving duration of the target path is determined according to the second pedestrian flow heat map data and the second historical pedestrian flow heat map data.

[0108] As an optional embodiment, when determining the first driving path, it is necessary to compare each target path one by one, and then it is necessary to determine the pedestrian flow situation on each target path. Therefore, the driving time intervals of target paths with different driving mileage are different, that is, each target path corresponds to a driving time interval one by one, and corresponds to a third time interval, and the correlation between the pedestrian flow value and time of each target path is different, then the third time interval corresponds to a second preset period one by one, that is, the second preset periods of different target paths are also different. That is to say, there are corresponding third time intervals and second preset periods for different target paths one by one. For the convenience of understanding, the following will give an example.

[0109] For example, at the same starting point of travel, with a preset travel speed and a starting travel time, the travel time interval for Route 1 is from 6:00 to 7:00, the travel time interval for Route 2 is from 6:00 to 7:10, and the travel time interval for Route 3 is from 6:00 to 7:20. Also, each travel time interval is for the time on May 1st, 2023 (Monday), which is Labor Day. If there is a tourist attraction on Route 1, and since it is prone to congestion during Labor Day every year, then according to the correlation between the pedestrian flow value and time on Route 1, the second preset period for Route 1 can be determined as 1 year. Thus, the third time interval for Route 1 is from 6:00 to 7:00 on May 1st, 2022, or from 6:00 to 7:00 on May 1st, 2021, etc. for Route 1. The present application does not limit the quantity of the second historical pedestrian flow heat map data retrieved for Route 1.

[0110] Similarly, since there are no tourist attractions on Route 2 and Route 3, and the current day is Monday, then according to the correlation between the pedestrian flow value and time, the second preset period for both Route 2 and Route 3 can be one week. Thus, the third time interval for Route 2 is from 6:00 to 7:10 on a historical Monday for Route 2, and the third time interval for Route 3 is from 6:00 to 7:20 on a historical Monday for Route 3. The present application also does not limit the quantity of the second historical pedestrian flow heat map data retrieved for Route 2 and Route 3.

[0111] It should be noted that at least one of the factors such as holidays, weekends, peak commuting hours, and whether there are attractions on the route is used as the basis for determining the correlation between the pedestrian flow value and time. In addition, it should also be noted that multiple target routes can also correspond to the same second preset period, and the present application does not limit this.

[0112] When analyzing the congestion situation of each target route, if it is determined based on the second pedestrian flow heat map data that the current pedestrian flow on the route is large and may cause road congestion, but the result of the second historical pedestrian flow heat map data shows that the pedestrian flow on this route is small and the travel is smooth during the travel interval, then the pedestrian flow value of the route is determined based on the analysis result of the currently obtained second pedestrian flow heat map data, and further the congestion situation of the target route is determined. If it is determined based on the second pedestrian flow heat map data that the current pedestrian flow on the route is small and the travel is smooth, but the result of the second historical pedestrian flow heat map data shows that the pedestrian flow on this route is large and may cause road congestion during the travel interval, then the pedestrian flow value of the route is determined based on the analysis result of the second historical pedestrian flow heat map data. Of course, weights can also be assigned to the second pedestrian flow heat map data and the second historical pedestrian flow heat map data based on a large amount of historical data, and the congestion situation of each target route is calculated based on the pedestrian flow value and the weights. The present application does not specifically limit the method for determining the congestion situation of each target route.

[0113] Further, according to the driving duration of each target path, the first driving path under preset constraints is selected from the path set. It can be understood that when the preset constraint is the shortest driving duration, the greater the flow of people, the more congested the road, and the longer the corresponding driving duration. Therefore, when determining the first driving path, the first driving path is selected according to the flow of people.

[0114] It should be noted that when selecting the first driving path, in addition to based on the second people flow heat map data, each driving time interval, and each second historical people flow heat map data analysis, it can also be comprehensively analyzed according to the vehicle congestion and traffic accident information collected by the GPS system. This application does not make specific limitations on this.

[0115] In the driving path planning method provided by the embodiments of this application, when selecting the first driving path under preset constraints from the path set, the second people flow heat map data at the first moment and the second historical people flow heat map data since the second moment are obtained. Among them, the first moment and the second moment differ by the duration of M second preset cycles, M is a positive integer, and the duration of the second preset cycle is determined according to the correlation between the people flow value and time on the corresponding target path. Further, the driving duration of the target path is determined according to the second people flow heat map data and the second historical people flow heat map data, and the first driving path under preset constraints is selected from the path set according to the driving duration of each target path. Thus, the first driving path is determined based on the people flow situation on each target path, ensuring the optimal path under preset constraints is determined before driving and improving driving timeliness.

[0116] In a specific embodiment, when obtaining the people flow value on the first driving path within the first time interval, the first time interval is a future time interval, thereby realizing the prediction of the people flow value on the first driving path. And the ability of the vehicle to obtain the people flow heat map data is limited, which needs to be determined according to the radiation radius of the heat map search and scanning. For example, if the radiation radius is 3 kilometers, then with the vehicle as the center and 3 kilometers as the radius, the first driving path is scanned to obtain the people flow heat map data.

[0117] And the radiation radius is a straight line, while the first driving path may have a curved situation. Therefore, the first time interval is determined according to the expected driving period of the radiation path, and the radiation path refers to the first driving path within the radiation radius, and the radiation radius refers to the scanning radius of the people flow acquisition unit.

[0118] After confirming the radiation radius and the preset driving speed of the vehicle, the target time interval corresponding to the vehicle driving from the current position to the first driving path within the radiation radius on the first driving path can be determined, and the first time interval is any time interval within the target time interval, or the target time interval can also be used as the first time interval. This application does not make limitations on this. For the sake of understanding, an example will be given below.

[0119] For example, if the radiation radius is 3 kilometers, and the vehicle travels on the first driving path at a preset driving speed within 3 kilometers from the current position, and the corresponding time interval of the first driving path is from 6:00 to 6:20, then the first time interval is any time interval from 6:00 to 6:20. For example, the first time interval is from 6:00 to 6:10.

[0120] That is to say, within the first time interval, there is a corresponding driving section, and this section is within the scanning range of the radiation radius. For example, if the first time interval is from 6:00 to 6:10, then within this time interval, the section that the vehicle is expected to travel is from point A to point B, and the radiation radius is greater than or equal to the distance from point A to point B.

[0121] In a specific embodiment, the first pedestrian flow heat map data or the second pedestrian flow heat map data is data determined according to the current driving position, the driving time interval, and the radiation radius. Among them, the current driving position is determined by the carrier phase differential technology (Real-time kinematic, abbreviated as RTK). After the vehicle determines the first driving path it is currently traveling on, it transmits data such as the current driving position, the driving time interval, and the radiation radius to the heat map system, so that the heat map system can determine the first pedestrian flow heat map data and the second pedestrian flow heat map data.

[0122] It should be noted that the radiation radius refers to the radius for scanning and exploring with the vehicle as the center. The radiation radius can be set according to the vehicle transportation scenario. When the transportation distance is far, the radiation radius can be set larger. On the contrary, when the transportation distance is near, the radiation radius can be set smaller. The present application does not make specific limitations on the size of the radiation radius.

[0123] In the driving path planning method provided by the embodiments of the present application, the first pedestrian flow heat map data and the second pedestrian flow heat map data are data determined according to the current driving position, the driving time interval, and the radiation radius transmitted by the carrier phase differential system, providing data support for determining the path pedestrian flow situation based on the pedestrian flow heat map data.

[0124] As a preferred embodiment, determining whether to switch to the second driving path according to the pedestrian flow value includes:

[0125] Determining whether the pedestrian flow value is greater than a preset threshold;

[0126] If it is greater than the preset threshold, then determine whether there is a path in the path set that meets the path switching condition. If there is, switch to the second driving path. If not, maintain the first driving path;

[0127] If it is not greater than the preset threshold, then return to the step of obtaining the pedestrian flow value on the first driving path within the first time interval until the end condition is triggered and then end.

[0128] In a specific embodiment, when the pedestrian flow value of the un-traveled section on the first driving path within the first time interval exceeds a preset threshold, it can be determined that the pedestrian flow on this section is large and there is congestion. In order to avoid spending a long time waiting and reduce the driving timeliness, it is necessary to predict whether switching to other paths in the path set can reduce the driving duration.

[0129] Specifically, determine whether the pedestrian flow value of the un-traveled section of the first driving path is greater than the preset threshold. If it is greater than the preset threshold, it is determined that there is a congested section in the un-traveled section of the first driving path. Further, determine whether there is a path that meets the path switching condition in the path set, and after taking the path that meets the path switching condition as the second driving path, switch to the second driving path. Of course, if there is no path that meets the path switching condition, the first driving path is maintained.

[0130] It should be noted that when there are multiple paths that meet the path switching condition, further determine the driving duration required for driving on each path that meets the path switching condition, and switch to the path with the shortest driving duration.

[0131] If the pedestrian flow value of the un-traveled section of the first driving path is not greater than the preset threshold, it is determined that the path is unobstructed, and return to the step of obtaining the pedestrian flow heat map data sent by the heat map system, so as to continue to judge the pedestrian flow of the un-traveled section of the first driving path in real time until the vehicle reaches the end point, that is, until the end condition is triggered. For the sake of easy understanding, an example will be given below.

[0132] Figure 4 As shown in the schematic diagram of a vehicle path provided by an embodiment of the present application, Figure 4 As shown, the driving starting point is point A, the driving end point is point B, and the target paths with the difference between the driving distance from point A to point B and the shortest path distance less than the preset value include path 1, path 2, and path 3. And the current first driving path is path 1. After driving on the first driving path for a period of time, it is determined through the pedestrian flow value that there is a congested section on the un-traveled section. At this time, if it is determined that path 2 and / or path 3 meet the path switching condition, the path that meets the preset condition is taken as the second driving path, and switch to the second driving path.

[0133] It can be understood that when both path 2 and path 3 meet the path switching condition, compare the driving durations on path 2 and path 3, and switch from the first driving path to the path with the shorter driving duration.

[0134] When determining whether to switch to the second driving path according to the pedestrian flow values of each first driving path in the driving path planning method provided by the embodiment of the present application, when the first driving path is congested due to the pedestrian flow value exceeding the preset threshold, that is, when it is further determined that other paths in the path set meet the switching conditions, switch from the first driving path to the second driving path, predict the path pedestrian flow in advance, avoid congestion caused by the pedestrian flow, and improve the driving timeliness.

[0135] As another preferred embodiment, the path switching condition is that the predicted waiting time is greater than the preset value, and there is at least one first driving time less than the second driving time, where the first driving time is the sum of the driving time from the current position to the driving starting point and the driving time of any target path; the second driving time is the driving time from the current position to the driving end point.

[0136] Specifically, if the predicted waiting time is t, the preset value is k, the driving time for the vehicle to travel from the current position to the driving starting point is t1, the driving time of any target path is t2, then the first driving time T = t1 + t2, and the driving time for the vehicle to travel from the current position to the driving end point is t3, that is, the second driving time is t3, then the path switching condition is: t ≥ k, and there is at least one T less than t3.

[0137] It should be noted that there are overlapping sections between other paths in the path set and the sections already traveled by the first driving path. When calculating the sum of the first driving time and each second driving time, there is no need to calculate the time for the vehicle to return to the driving starting point, and directly calculate the time from the overlapping point of the return section to the end point and the time from the overlapping point to the end point.

[0138] As Figure 4 shown, when the vehicle determines that there is a congested section on path 1, it is necessary to return to the driving starting point A and switch from the driving starting point A to path 2 or path 3. Before switching, it is necessary to determine whether the predicted waiting time t is greater than or equal to the preset value k, and whether path 2 or path 3 meets the path switching conditions.

[0139] If t ≥ k, and the time T2 spent for the vehicle to travel from the current position to the driving starting point A and then from the driving starting point along path 2 to the end point, and / or the time T3 spent for the vehicle to travel from the current position to the driving starting point A and then from the driving starting point along path 3 to the end point is less than the time t3 for the vehicle to travel from the current position to the driving terminal B, further determine the size of T2 and T3, and take the smaller value as the second driving path for switching.

[0140] Thus, it can be understood that the driving route planning method provided by this application includes three parts: current actual measurement, past speculation, and future prediction. Specifically, the current actual measurement combines the second pedestrian flow heat map data and the second historical pedestrian flow heat map data at the current moment on the target route to determine the optimal route under preset constraints, that is, to determine the first driving route.

[0141] Furthermore, the rationality of the vehicle driving route is judged by combining the first pedestrian flow heat map data on the first driving route with the second historical pedestrian flow heat map data to determine whether to finally switch the route, that is, to determine the rationality of the first driving route by combining past speculation on the basis of current actual measurement.

[0142] Still further, the first pedestrian flow heat map data and the first historical pedestrian flow heat map data are updated and received in real time to speculate on the rationality of the vehicle driving route, and to judge whether the vehicle maintains the existing first driving route or switches to the second driving route to determine the final route plan under preset constraints, that is, to perform future prediction in real time.

[0143] When the driving route planning method provided by the embodiment of this application needs to switch from the first driving route to the second driving route due to a large pedestrian flow, it is judged whether the expected waiting time is greater than a preset value, and there is at least one first driving time less than the second driving time, where the first driving time is the sum of the driving time from the current position to the driving starting point and the driving time of any target route; the second driving time is the driving time from the current position to the driving end point. Thus, it is determined whether to switch the route according to the driving time, further improving the driving timeliness.

[0144] It should be noted that the sequence numbers of the steps in the embodiment of this application do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiment of this application.

[0145] Figure 5 FIG. is a structural schematic diagram of a driving route planning system provided by an embodiment of this application. In order to enable those skilled in the art to better understand the technical solution provided by this application, the following will be combined with Figure 5 Further illustrate the technical solution provided by this application.

[0146] As Figure 5 shown, taking the delivery of express by a driverless logistics vehicle as an example for illustration. In specific implementation, the fleet management system 50 generates a delivery order and generates corresponding order information, where the order information includes the driving starting point and the driving end point, etc. After the fleet management system 50 determines the target vehicle that can execute the task, it sends the order information to the domain controller 51 of the target vehicle, and the domain controller 51 loads the goods at the designated location according to the order information.

[0147] After the goods are loaded, the domain controller 51 determines an optimal path under preset constraint conditions before the vehicle travels according to the starting point and the ending point of the journey, in combination with the map system, that is, determines the first driving path, and controls the vehicle (that is, controls the vehicle body 54) to travel according to the first driving path. Among them, the preset constraint conditions are at least one of the shortest driving duration, the shortest driving mileage, and the least number of traffic lights.

[0148] During the process of traveling on the first driving path, the RTK 52 of the vehicle real-time locates the position of the vehicle, and transmits the current driving position to the heat map system 53 through an in-vehicle wireless transmission device (for example, a 4G router). At the same time, the domain controller on the vehicle real-time transmits data such as the driving time interval and the radiation radius to the heat map system 53.

[0149] The heat map system 53 reads and analyzes the regional population flow data of the first driving path according to the received information (the current driving position, the driving interval, the radiation radius, etc.). Specifically, it combines the heat map data at the current moment with the historical heat map data, and then predicts the population flow value on the first driving path. At the same time, it generates image color blocks according to the prediction result. Then, it sends the prediction result of the population flow value to the domain controller 51 through wireless transmission.

[0150] Further, when traveling on the first driving path, the domain controller 51 real-time plans the path according to the prediction result of the population flow value sent by the heat map system 53, and controls the vehicle (that is, controls the vehicle body 54) to travel according to the planned path. The path planning is specifically that when the predicted population flow value exceeds the preset threshold, it further judges whether a path switch is required. For the specific analysis, refer to the description of the above-mentioned embodiment.

[0151] It should be noted that when planning the path, it can be divided into current actual measurement, past speculation, and future prediction from the time dimension.

[0152] Current actual measurement: Before traveling, according to the population flow value data transmitted by the heat map system 53, determine the initial optimal driving path, that is, the first driving path.

[0153] Past speculation: When traveling according to the optimal path (the first driving path) in the current actual measurement, combine the historical population flow value data to speculate on the rationality of the first driving path.

[0154] Future prediction: On the basis of the past speculation, during the vehicle driving process, through the natural frequency and the prediction result of the population flow value real-time sent by the heat map system 53, speculate on the rationality of the vehicle driving path, and judge whether the vehicle maintains the existing path (the first driving path) or changes to other paths to drive.

[0155] In the above embodiments, the method for planning a driving route has been described in detail. The present application also provides an embodiment corresponding to an apparatus for planning a driving route. It should be noted that the embodiments of the apparatus part of the present application are described from two perspectives, one is from the perspective of functional modules, and the other is from the perspective of hardware structure.

[0156] Figure 6 As shown in the following figure, it is a schematic structural diagram of an apparatus for planning a driving route provided by an embodiment of the present application. Figure 6 As shown in the figure, the apparatus includes:

[0157] A selection module 60, configured to select a first driving route under preset constraint conditions;

[0158] An acquisition module 61, configured to acquire the pedestrian flow value on the first driving route within a first time interval;

[0159] A determination module 62, configured to determine whether to switch to a second driving route according to the pedestrian flow value.

[0160] It should be noted that for the information interaction, execution process, etc. between the above-mentioned device / units, since it is based on the same concept as the method embodiment of the present application, for its specific functions and the technical effects brought, reference can be specifically made to the method embodiment part, and details are not described herein again.

[0161] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above-mentioned division of each functional unit and module is used as an example for illustration. In practical applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit exists physically alone, or two or more units are integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments, and details are not described herein again.

[0162] Figure 7 As shown in the following figure, it is a schematic structural diagram of a terminal device provided by an embodiment of the present application. Figure 7 As shown in the figure, the terminal device 70 includes: at least one processor 701, a memory 702, and a computer program 703 stored in the memory and executable on the at least one processor. When the processor executes the computer program, it implements the steps in any of the above method embodiments.

[0163] An embodiment of the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0164] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal is enabled to implement the steps in the above-mentioned method embodiments when executed.

[0165] 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 computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned method embodiments of the present application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the photographing device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0166] In the above embodiments, the descriptions of the respective embodiments have their own focuses. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0167] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed in this document can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0168] In the embodiments provided in the present application, it should be understood that the disclosed device / network device and method can be implemented in other ways. For example, the device / network device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in electrical, mechanical or other forms.

[0169] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0170] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for planning a driving route, characterized in that, it includes: selecting a first driving route under preset constraint conditions; obtaining the pedestrian flow value on the first driving route within a first time interval, where the first time interval is determined according to the estimated driving period of the first driving route; determining whether to switch to a second driving route according to the pedestrian flow value.

2. The method for planning a driving route according to claim 1, characterized in that, the obtaining the pedestrian flow value on the first driving route within the first time interval includes: retrieving the first historical pedestrian flow heat map data on the first driving route within a second time interval from historical data; wherein, the start time of the first time interval differs from the start time of the second time interval by the duration of N first preset cycles, the duration of the first preset cycle is determined according to the correlation between the pedestrian flow value and time, and N is a positive integer; obtaining the first pedestrian flow heat map data on the first driving route at the current moment; predicting the pedestrian flow within the first time interval according to the first historical pedestrian flow heat map data and the first pedestrian flow heat map data to obtain the pedestrian flow value.

3. The method for planning a driving route according to claim 1, characterized in that, the preset constraint conditions include at least one of the shortest driving duration, the shortest driving mileage, and the least number of traffic lights, and the selecting the first driving route under the preset constraint conditions includes: obtaining a target route from all feasible routes from the driving starting point to the driving ending point to obtain a route set; wherein, the target route is a route whose difference between the driving distance and the shortest route distance is less than a preset value, and the route set includes the first driving route and the second driving route; selecting the first driving route under the preset constraint conditions from the route set.

4. The method for planning a driving route according to claim 3, characterized in that, the selecting the first driving route under the preset constraint conditions from the route set includes: obtaining the second pedestrian flow heat map data at a first moment and the second historical pedestrian flow heat map data starting from a second moment; wherein, the first moment differs from the second moment by the duration of M second preset cycles, M is a positive integer, and the duration of the second preset cycle is determined according to the correlation between the pedestrian flow value and time on the corresponding target route; determining the driving duration of the target route according to the second pedestrian flow heat map data and the second historical pedestrian flow heat map data; selecting the first driving route under the preset constraint conditions from the route set according to the driving durations of the target routes.

5. The method for planning a driving route according to claim 1 or 2, characterized in that, the first time interval is determined according to the estimated driving period of the radiation route, and the radiation route refers to the first driving route within the radiation radius, and the radiation radius refers to the scanning radius of the pedestrian flow collection unit.

6. The method for planning a driving route according to any one of claims 1 to 4, characterized in that, the determining whether to switch to the second driving route according to the pedestrian flow value includes: determining whether the pedestrian flow value is greater than a preset threshold; If it is greater than the preset threshold value, it is determined whether there is a path that meets the path switching condition in the path set. If there is, switch to the second driving path; if not, maintain the first driving path. If it is not greater than the preset threshold value, return to the step of obtaining the pedestrian flow value on the first driving path within the first time interval until the end condition is triggered.

7. The driving path planning method according to claim 6, characterized in that the path switching condition is that the expected waiting time is greater than a preset value, and there is at least one first driving time less than the second driving time; wherein, the first driving time is the sum of the driving time from the current position to the driving starting point and the driving time of any target path; the second driving time is the driving time from the current position to the driving end point.

8. A driving path planning device, characterized in that it includes: a selection module for selecting a first driving path under preset constraint conditions; an acquisition module for acquiring the pedestrian flow value on the first driving path within the first time interval, and the first time interval is determined according to the expected driving period of the first driving path; a determination module for determining whether to switch to a second driving path according to the pedestrian flow value.

9. A terminal device, characterized in that it includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the driving path planning method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the driving path planning method according to any one of claims 1 to 7.

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