Control method and control system of unmanned vehicle, unmanned vehicle and readable storage medium

Abstract

The application discloses a control method of an unmanned vehicle. The method comprises the following steps: obtaining a user's vehicle-riding request, the user's current position and road information; determining at least one candidate pickup point according to the user's vehicle-riding request and the user's current position; performing route planning according to the road information and the candidate pickup point to obtain at least one candidate route; and determining the expected passing time of each candidate route to determine a target pickup point. In the control method of the unmanned vehicle, the user can select a target route from the candidate routes to control the unmanned vehicle to drive to the target pickup point, and the meeting mode of the user and the vehicle is flexible, and the efficiency of the meeting of the user and the vehicle is improved. The application further discloses a control system of an unmanned vehicle, an unmanned vehicle and a readable storage medium.

Description

Technical Field

[0001] The present application relates to the field of intelligent vehicle technology, and in particular to a control method for an unmanned vehicle and its control system, an unmanned vehicle, and a readable storage medium. Background Art

[0002] At present, after users call a driverless taxi, they can only get on the taxi at a fixed station, and it often takes a long time to reach the driverless taxi pick-up point. Therefore, during the actual ride of the driverless taxi, how to make the way of meeting between people and cars more flexible and thus improve the efficiency of the meeting between people and cars is an urgent problem to be solved. Summary of the Invention

[0003] In view of this, the present invention aims to solve at least one of the problems in the related art to a certain extent. To this end, the purpose of this application is to provide a control method and control system of an unmanned vehicle, an unmanned vehicle and a readable storage medium.

[0004] The present application provides a method for controlling an unmanned vehicle. The method includes: obtaining a user's boarding request, the user's current location, and road information; determining at least one alternative boarding point based on the user's boarding request and the user's current location; performing route planning based on the road information and the alternative boarding points to obtain at least one alternative route; and determining an estimated transit time for each of the alternative routes to determine a target boarding point.

[0005] In some embodiments, determining at least one alternative boarding point based on the user's ride request and the user's current location includes: determining the original boarding point based on the user's ride request; if the original boarding point is located after a U-turn section and / or a congested section, selecting a replacement point on a non-U-turn section and / or a non-congested section; calculating the first walking time from the user's current location or the original boarding point to the replacement point, and the first driving time of the unmanned vehicle through the U-turn section and / or the congested section; if the first walking time is less than the first driving time, adding the replacement point as the alternative boarding point.

[0006] In some embodiments, determining at least one alternative boarding point based on the user's ride request and the user's current location includes: determining the original boarding point and the target point based on the user's ride request; selecting a replacement point on a non-U-turn section and / or a non-congested section when a U-turn section and / or a congested section is located after the original boarding point; calculating a second walking time from the user's current location to the replacement point, a second driving time of the unmanned vehicle from the replacement point to the target point, and a third driving time of the unmanned vehicle from the original boarding point to the target point; and when the sum of the second walking time and the second driving time is less than the third driving time, adding the replacement point as the alternative boarding point.

[0007] In some embodiments, determining at least one alternative boarding point based on the user's ride request and the user's current location includes: obtaining historical boarding point data of the user in the area corresponding to the user's current location; performing density analysis on the historical boarding point data to obtain commonly used boarding points; and determining the alternative boarding point based on the commonly used boarding points.

[0008] In some embodiments, the density analysis of the historical boarding point data to obtain the commonly used boarding points includes: determining the latitude and longitude of the historical boarding point data to draw a corresponding coordinate map; aggregating the historical boarding point data within a preset range on the coordinate map to obtain a boarding point cluster; and determining the commonly used boarding points based on the boarding point cluster.

[0009] In some embodiments, determining the commonly used boarding point based on the boarding point cluster includes: using a cluster analysis algorithm to iteratively aggregate the historical boarding point data corresponding to the boarding point cluster to obtain a centroid position; and using the centroid position as the commonly used boarding point.

[0010] In some embodiments, the use of a cluster analysis algorithm to iteratively aggregate the historical boarding point data corresponding to the boarding point cluster to obtain a centroid position includes: obtaining a data creation time corresponding to the historical boarding point data; determining a weighted value of the corresponding historical boarding point data based on the data creation time; and using the cluster analysis algorithm to iteratively aggregate the historical boarding point data corresponding to the boarding point cluster based on the weighted value to obtain a centroid position.

[0011] In some embodiments, the control method includes: automatically calculating an optimal solution to determine the target route without receiving any user operation for the alternative route; and sending a route confirmation message to notify the user and provide path guidance.

[0012] In some embodiments, after obtaining the user's ride request, the user's current location and road information, the control method includes: obtaining the user's route preference; determining at least one alternative boarding point based on the user's ride request and the user's current location includes: determining at least one alternative boarding point based on the user's route preference, the user's ride request and the user's current location.

[0013] In some embodiments, obtaining the user route preference includes: sending preference selection information to the user mobile terminal; and receiving the user's operation in response to the preference selection information to determine the user route preference.

[0014] In some embodiments, obtaining the user route preference includes: obtaining the user's historical travel information, the historical travel information including a preset user tag; and analyzing the historical travel information according to a word frequency calculation algorithm to obtain the user route preference.

[0015] In some embodiments, the preset user tags are learned based on a neural network.

[0016] In certain embodiments, the control method includes: obtaining environmental information surrounding the target boarding point when the unmanned vehicle travels within a first preset distance of the target boarding point; and re-determining the target boarding point and notifying the user when the environmental information surrounding the target boarding point is not suitable for the vehicle to dock.

[0017] In some embodiments, after the unmanned vehicle drives to the target boarding point, the control method includes: controlling the unmanned vehicle to select a parking space near the target boarding point to park when the distance between the user and the target boarding point is greater than a second preset distance or the waiting time is greater than a preset time.

[0018] The present application also provides a control system for an unmanned vehicle. The control system for the unmanned vehicle includes: an acquisition module, a boarding point determination module, a route planning module, a duration determination module, a route determination module, and a control module. The acquisition module is used to obtain a user's boarding request, the user's current location, and road information; the boarding point determination module is used to determine at least one alternative boarding point based on the user's boarding request and the user's current location; the route planning module is used to plan a route based on the road information and the alternative boarding points to obtain at least one alternative route; and the determination module is used to determine the estimated transit time of each of the alternative routes to determine the target boarding point.

[0019] The present application also provides an unmanned vehicle. The unmanned vehicle includes a processor and a memory, wherein the memory is used to store a computer program, and the processor implements any one of the control methods described in the above embodiments when executing the computer program.

[0020] The present application also provides a non-volatile computer-readable storage medium for a computer program. When the computer program is executed by one or more processors, the control method described in any one of the above embodiments is implemented.

[0021] In the control method of the unmanned vehicle of the present application, the user can select a target route from the alternative routes to control the unmanned vehicle to travel to the target boarding point. The way of meeting the driver and the vehicle is flexible, which improves the efficiency of the meeting.

[0022] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become obvious from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0024] Figure 1 is a flow chart of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0025] Figure 2 is a schematic structural diagram of a control system of an unmanned vehicle according to certain embodiments of the present application;

[0026] Figure 3 is a schematic diagram of a scenario of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0027] Figure 4 is a schematic diagram of a scenario of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0028] Figure 5 is a schematic diagram of a scenario of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0029] Figure 6 is a flow chart of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0030] Figure 7 This is a schematic diagram of the structure of a boarding point determination module in a control system of an unmanned vehicle in certain embodiments of the present application;

[0031] Figure 8 is a schematic diagram of a scenario of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0032] Figure 9 is a flow chart of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0033] Figure 10 is a schematic diagram of a scenario of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0034] Figure 11 is a flow chart of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0035] Figure 12 is a schematic structural diagram of a determination unit in a boarding point determination module in certain embodiments of the present application;

[0036] Figure 13 is a flow chart of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0037] Figure 14 is a schematic structural diagram of an analysis unit in a determination unit in certain embodiments of the present application;

[0038] Figure 15 is a flow chart of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0039] Figure 16 is a flow chart of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0040] Figure 17 This is a schematic diagram of the structure of a commonly used boarding point determination unit in the analysis unit of certain embodiments of the present application;

[0041] Figure 18 is a flow chart of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0042] Figure 19 is a schematic structural diagram of a control system of an unmanned vehicle according to certain embodiments of the present application;

[0043] Figure 20 is a flow chart of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0044] Figure 21 is a flow chart of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0045] Figure 22 is a flow chart of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0046] Figure 23 Schematic diagram of the formula of the TF-IDF algorithm in certain embodiments of the present application;

[0047] Figure 24 is a flow chart of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0048] Figure 25 is a schematic structural diagram of a control module in a control system of an unmanned vehicle in certain embodiments of the present application;

[0049] Figure 26 is a flow chart of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0050] Figure 27 is a schematic structural diagram of a control system of an unmanned vehicle according to certain embodiments of the present application;

[0051] Figure 28 is a flow chart of a control method for an unmanned vehicle according to certain embodiments of the present application;

[0052] Figure 29 is a schematic structural diagram of a control system of an unmanned vehicle according to certain embodiments of the present application;

[0053] Figure 30 is a schematic structural diagram of an unmanned vehicle according to certain embodiments of the present application;

[0054] Figure 31 It is a schematic diagram of the structure of a computer-readable storage medium in certain embodiments of the present application. DETAILED DESCRIPTION

[0055] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and should not be understood as limiting the present application.

[0056] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the described features. In the description of this application, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0057] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections, electrical connections, or connections that can communicate with each other; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to internal connectivity between two components or interactions between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on specific circumstances.

[0058] The disclosure below provides many different embodiments or examples for implementing different structures of the present application. In order to simplify the disclosure of the present application, the components and settings of specific examples are described below. Of course, these are merely examples and are not intended to limit the present application. In addition, the present application may repeat reference numbers and / or reference letters in different examples. Such repetition is for the purpose of simplicity and clarity and does not in itself indicate the relationship between the various embodiments and / or settings discussed.

[0059] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and should not be understood as limiting the present application.

[0060] At present, after users call a driverless taxi, they can only get on the taxi at a fixed station, and it often takes a long time to reach the driverless taxi pick-up point. Therefore, during the actual ride of the driverless taxi, how to make the way of meeting between people and cars more flexible and thus improve the efficiency of the meeting between people and cars is an urgent problem to be solved.

[0061] In view of this, please see Figure 1 The present application provides a method for controlling an unmanned vehicle. The method for controlling an unmanned vehicle includes:

[0062] 01: Get the user's ride request, current location, and road information;

[0063] 02: Determine at least one alternative boarding point based on the user's ride request and the user's current location;

[0064] 03: Route planning is performed based on road information and alternative boarding points to obtain at least one alternative route;

[0065] 04: Determine the estimated transit time of each alternative route to determine the target boarding point.

[0066] See also Figure 2 This application also provides a control system 10 for an unmanned vehicle. The control system 10 for the unmanned vehicle includes an acquisition module 11, a boarding point determination module 12, a route planning module 13, and a determination module 14. The control system 10 for the unmanned vehicle can be internally connected to the vehicle or as an external control device. This application uses the example of the control system 10 being internally connected to the vehicle. The control system 10 for the unmanned vehicle can also be part of an online booking platform for unmanned vehicles.

[0067] Step 01 can be implemented by the acquisition module 11, step 02 can be implemented by the boarding point determination module 12, step 03 can be implemented by the route planning module 13, and step 04 can be implemented by the determination module 14. Specifically, the acquisition module 11 is used to obtain the user's boarding request, the user's current location, and road information. The boarding point determination module 12 is used to determine at least one alternative boarding point based on the user's boarding request and the user's current location. The route planning module 13 is used to plan a route based on the road information and the alternative boarding points to obtain at least one alternative route. The determination module 14 is used to determine the estimated transit time of each alternative route to determine the target boarding point.

[0068] Specifically, first, the control system 10 of the unmanned vehicle can obtain the user's ride request, and according to the user's ride request, the user's current location (such as Figure 3 or Figure 4 and Figure 5 The system can also determine the road information near the autonomous vehicle boarding point based on the user's current location and the location of the autonomous vehicle. For example, if the user's ride request is to take an autonomous vehicle from point A to point B, the area near point A is the autonomous vehicle boarding point. The system can obtain road information between the area near point A and point O. This road information includes traffic congestion near point A, whether a U-turn or turn is required, and the frequency of previous driving.

[0069] Then, at least one alternative boarding point is determined based on the user's ride request and the user's current location. The at least one alternative boarding point means that there can be one alternative boarding point A1 or multiple alternative boarding points A2, A3, A4, etc. near point A.

[0070] When there is only one alternative boarding point A1, multiple alternative routes can be obtained by route planning based on road information and the target boarding point, such as Figure 3 As shown in the plan, three alternative routes A1O1, A1O2, and A1O3 are planned.

[0071] When there are multiple alternative boarding points, for example, there are four alternative boarding points A2, A3, A4 and A5, the control system of the unmanned vehicle can perform route planning based on multiple target boarding points to obtain one or more alternative routes corresponding to each target boarding point, thereby obtaining multiple alternative routes.

[0072] In one example, each alternative boarding point corresponds to only one alternative route, that is, the number of alternative boarding points is the same as the number of alternative routes. Figure 4As shown, there can be one alternative route corresponding to alternative boarding point A2, namely alternative route A2O1; one alternative route corresponding to alternative boarding point A3, namely alternative route A3O1; one alternative route corresponding to alternative boarding point A4, namely alternative route A4O1; and one alternative route corresponding to target boarding point A5, namely alternative route A5O1. At this point, route planning for the four target boarding points A2, A3, A4, and A5 yields four alternative routes: A2O1, A3O1, A4O1, and A5O1.

[0073] In another example, each alternative boarding point can be mapped to multiple alternative routes. Figure 5 As shown, for example, there may be three alternative routes corresponding to the alternative boarding point A2, namely alternative routes A2O2, A2O3 and A2O4; there may also be three alternative routes corresponding to the alternative boarding point A3, namely alternative routes A3O2, A3O3 and A3O4; there may also be three alternative routes corresponding to the alternative boarding point A4, namely alternative routes A4O2, A4O3 and A4O4; there may also be three alternative routes corresponding to the alternative boarding point A5, namely alternative routes A5O2, A5O3 and A5O4.

[0074] In other embodiments of the present application, when there are multiple alternative boarding points, one alternative route can be planned based on the target alternative boarding point, and multiple alternative routes can be planned based on the remaining alternative boarding points. The specific planning of the alternative routes is determined based on the actual road conditions.

[0075] Then, the estimated transit time of each alternative route is determined to determine the target boarding point. Specifically, the estimated transit time of each alternative route can be determined first, and the alternative route and the estimated transit time can be sent to the user's mobile terminal. Then, the user's operation on the alternative route is received to determine the target route and the target boarding point. Finally, the unmanned vehicle can be controlled to travel along the target route to the corresponding target boarding point.

[0076] In detail, the estimated passing time of each alternative route can be determined based on the distance of each alternative route and the real-time traffic congestion situation combined with the driving speed of the unmanned vehicle. Figure 3As shown, the estimated passing time of the three alternative routes A1O1, A1O2 and A1O3 is 4 minutes, 5 minutes and 6 minutes respectively. Among them, the alternative route A1O3 is the main traffic road, so the traffic flow is large and the congestion is relatively serious. Therefore, the estimated passing time of the alternative route A1O3 is the longest, which is 6 minutes, including the estimated congestion time of 2 minutes. The alternative route A1O2 may need the unmanned vehicle to turn around or the unmanned vehicle is far away from the user's location, so the estimated passing time of the alternative route A1O2 is relatively long. The alternative route A1O1 may not need the unmanned vehicle to turn around, which is a straight line segment or the unmanned vehicle is close to the user's location, so the estimated passing time of the alternative route A1O1 is the shortest.

[0077] Then, the control system 10 of the unmanned vehicle can receive the user's operation on the alternative routes to determine the target route. For example, the user selects the alternative route A1O1 with the shortest estimated passing time in the alternative routes A1O1, A1O2 and A1O3 as the target route, so as to determine that the target pickup point is A1. Figure 3 The user's operation on the alternative routes can mean that the user touches a certain alternative route as the target route with a finger after seeing the alternative routes and the corresponding estimated passing time on the user's mobile terminal (such as a mobile phone, a smart bracelet, a computer or other mobile devices), or the user selects a certain alternative route as the target route by using a language function.

[0078] Finally, the control system 10 of the unmanned vehicle can control the unmanned vehicle to drive along the target route to the corresponding target pickup point. For example, the control system 10 of the unmanned vehicle can control the unmanned vehicle to drive along the target route A1O1 to the corresponding target pickup point A1.

[0079] The control method of the unmanned vehicle of the present application can determine at least one alternative pickup point according to the user's request for taking the vehicle and the user's current location, then plan at least one alternative route according to the road information and the alternative pickup point, and determine the estimated passing time of each alternative route to determine the target pickup point, which can make the meeting way of the user and the unmanned vehicle more flexible and improve the efficiency of the meeting of the user and the unmanned vehicle.

[0080] Please refer to Figure 6 In some embodiments, step 02 includes:

[0081] 021: determining the original pickup point according to the user's request for taking the vehicle;

[0082] 022: in the case that the original pickup point is located on the turning driving section and / or the congested section, selecting a replacement point on the non-turning driving section and / or the non-congested section;

[0083] 023: calculating the first walking time of the user's current location or the original pickup point to the replacement point, and the first driving time of the unmanned vehicle passing through the turning driving section and / or the congested section;

[0084] 024: When the first walking time is less than the first driving time, the replacement point is added as an alternative boarding point.

[0085] See also Figure 7 In some embodiments, the boarding point determination module 12 includes a determination unit 121 , a replacement point selection unit 122 , a duration calculation unit 123 and a boarding point addition unit 124 .

[0086] Step 021 can be implemented by the determination unit 121, step 022 can be implemented by the replacement point selection unit 122, step 023 can be implemented by the duration calculation unit 123, and step 024 can be implemented by the boarding point adding unit 124. That is, the determination unit 121 is used to determine the original boarding point according to the user's ride request; the replacement point selection unit 122 is used to select a replacement point in a non-U-turn section and / or non-congested section when the original boarding point is located after a U-turn section and / or a congested section; the duration calculation unit 123 is used to calculate the first walking time from the user's current location or the original boarding point to the replacement point, as well as the first driving time of the unmanned vehicle through the U-turn section and / or the congested section; and the boarding point adding unit 124 is used to add the replacement point as an alternative boarding point when the first walking time is less than the first driving time.

[0087] Specifically, for example, Figure 8 As shown, the user's ride request is to take an unmanned vehicle from point A to point B. If the original boarding point determination unit determines that point A1 near point A is the original boarding point, then if the original boarding point A1 is located after a U-turn section and / or a congested section, that is, if the unmanned vehicle needs to pass through a U-turn section and / or a congested section to reach the original boarding point A1, a replacement point can be selected on a non-U-turn section and / or a non-congested section accordingly. The situation where the unmanned vehicle needs to pass through a U-turn section and / or a congested section to reach the original boarding point A1 includes three situations: the unmanned vehicle only needs to pass through a U-turn section to reach the original boarding point A1; the unmanned vehicle only needs to pass through a congested section to reach the original boarding point A1; and the unmanned vehicle needs to pass through both a U-turn section and a congested section to reach the original boarding point A1.

[0088] This application is explained by taking the example of the unmanned vehicle needing to pass through the U-turn section and the congested section to reach the original boarding point A1. Figure 8For example, in the example where both U-turn sections ab and congested sections ad exist, the new alternative boarding point C1 can be selected as a road section near the autonomous vehicle that avoids the U-turn section (section ab) or the congested section (section cd). Alternatively, the first walking time from the user's current location to C1 can be calculated to be less than the first driving time of the autonomous vehicle after the U-turn and congestion. If the first walking time is less than the first driving time, the new alternative boarding point C1 can be selected as the replacement point. Alternatively, if the user has already arrived at the original boarding point A1 but the autonomous vehicle has not yet arrived at the original boarding point A1, the new alternative boarding point C1 can be temporarily selected as the replacement point if the first walking time from the original boarding point to the new alternative boarding point C1 is less than the first driving time of the autonomous vehicle after the U-turn and congestion.

[0089] The scheme where the unmanned vehicle only needs to pass through the U-turn section and the congested section to select a replacement point for boarding in order to reach the original boarding point A1 is based on the same principle as the scheme where the unmanned vehicle needs to pass through the U-turn section and the congested section to select a replacement point for boarding in order to reach the original boarding point A1, except that the first driving time calculated is different. Specifically, if the unmanned vehicle only passes through the U-turn section to reach the original boarding point A1, only the first driving time after the unmanned vehicle completes the U-turn needs to be calculated. If the unmanned vehicle only passes through the congested section to reach the original boarding point A1, only the first driving time after the unmanned vehicle passes through the congested section needs to be calculated. Accordingly, the first driving time is still compared with the first walking time from the user's current location to the selected new boarding point. If the first walking time is less than the first driving time, the selected new boarding point can be used as the replacement point.

[0090] In one example, the control system 10 of the unmanned vehicle calculates the first walking time from the user's current location O to the replacement point C1 as 2.5 minutes through the time calculation unit 123, or the first walking time from the original boarding point A1 to the replacement point C1 as 2 minutes. In addition, the control system 10 of the unmanned vehicle can also calculate the first driving time of the unmanned vehicle through the U-turn driving section and / or congested road section through the time calculation unit 123, for example, Figure 8 The first driving time of the unmanned vehicle passing through the U-turn section and the congested section is 6 minutes. At this time, the first walking time (2 minutes or 2.5 minutes) is less than the first driving time (6 minutes), so the replacement point C1 can be added as an alternative boarding point. In other words, the user can choose to walk from the user's current location O or from the original boarding point A1 to the replacement point C1 to board the vehicle. The unmanned vehicle does not need to pass through the U-turn section or the congested section to reach the boarding point, shortening the time for the vehicle to meet the driver, making the meeting method more flexible, and improving the efficiency of the meeting.

[0091] See also Figure 9 In some embodiments, step 02 includes:

[0092] 025: Determine the original boarding point and destination point based on the user's ride request;

[0093] 026: If the U-turn section and / or congested road section is located after the original boarding point, select an alternative point on the non-U-turn section and / or non-congested road section;

[0094] 027: Calculate the second walking time from the user's current location to the replacement point, the second driving time of the autonomous vehicle from the replacement point to the destination point, and the third driving time of the autonomous vehicle from the original boarding point to the destination point;

[0095] 028: When the sum of the second walking time and the second driving time is less than the third driving time, the replacement point will be added as an alternative boarding point.

[0096] Please combine Figure 7 , step 025 can be implemented by the determination unit 121, step 026 can be implemented by the replacement point selection unit 122, step 027 can be implemented by the duration calculation unit 123, and step 028 can be implemented by the boarding point adding unit 124. That is, the determination unit 121 is used to determine the original boarding point and the destination point according to the user's request; the replacement point selection unit 122 is used to select a replacement point on a non-U-turn section and / or a non-congested section when the U-turn section and / or the congested section is located after the original boarding point; the duration calculation unit 123 is used to calculate the second walking time from the user's current location to the replacement point, the second driving time from the replacement point to the destination point, and the third driving time from the unmanned vehicle to the original boarding point to the destination point; and the boarding point adding unit 124 is used to add the replacement point as an alternative boarding point when the sum of the second walking time and the second driving time is less than the third driving time.

[0097] Specifically, see Figure 10 If the user's ride request is from point A (a supermarket or residential area) to point B, the original boarding point can be determined to be point A1, and the destination point can be determined to be point B. If the U-turn section (ab section) and / or the congested road section (cd section) are located after the original boarding point A1, this means that the unmanned vehicle will pass through the U-turn section (ab section) and / or the congested road section (cd section) after meeting the user at the original boarding point A1. There are three situations in which the unmanned vehicle will pass through the U-turn section (ab section) and / or the congested road section (cd section) after meeting the user at the original boarding point A1: the unmanned vehicle only passes through the U-turn section (ab section) after meeting the user at the original boarding point A1; the unmanned vehicle only passes through the congested road section (cd section) after meeting the user at the original boarding point A1; and the unmanned vehicle will also pass through the U-turn section (ab section) and the congested road section (cd section) after meeting the user at the original boarding point A1.

[0098] This application is based on Figure 10The unmanned vehicle shown in the original pickup point A1 will also pass through the turning driving section (ab section) and the congestion road section (cd section) to meet the user. As shown in Figure 10 , at this time, the time from the original pickup point A1 to the target point B of the unmanned vehicle will consume more time because of the need to turn around and be stuck in traffic, therefore, when selecting the pickup point, the replacement point can be selected on the non-turning driving road section and the non-congestion road section, for example, the replacement point D1 can be selected on another alternative route 2 of the non-turning road section and the non-congestion road section without traffic jam, and the user walks to the replacement point D1 to get on the vehicle, thereby avoiding the situation that the time from the original pickup point A1 to the target point B of the unmanned vehicle is consumed more time because of the need to turn around and be stuck in traffic.

[0099] Further, the second walking time from the current position of the user to the replacement point, the second driving time from the replacement point to the target point of the unmanned vehicle, and the third driving time from the original pickup point to the target point of the unmanned vehicle can be calculated by the time length calculation unit 123, and in the case that the second walking time plus the second driving time is less than the third driving time, the replacement point is added as the target pickup point. For example, the current position of the user is O1, the replacement point is D1, the second walking time from the current position O1 of the user to the replacement point D1 is 2 minutes, and the second driving time from the replacement point D1 (the new target pickup point) to the target point B of the unmanned vehicle is 4 minutes. At this time, since the unmanned vehicle will pass through the turning road section or the congestion road section after picking up the user at the original pickup point A1, the third driving time from the original pickup point A1 to the target point B of the unmanned vehicle is longer, which can be 10 minutes. That is, the second walking time of 2 minutes plus the second driving time of 4 minutes is 6 minutes, which is less than the third driving time of 10 minutes. At this time, the replacement point D1 can be added as the pickup point, thereby shortening the total time required for the unmanned vehicle to meet the user and carry the user to the target point after receiving the user's request for a ride, and improving the driving efficiency of the unmanned vehicle.

[0100] The scheme of selecting the replacement point D1 only through the turning road section and only through the congestion road section after the unmanned vehicle meets the user at the original pickup point A1 is the same as the above principle of needing to pass through the turning road section and the congestion road section at the same time, except that the third driving time from the original pickup point A1 to the target point B of the unmanned vehicle is different because of the different time required for only turning around or only passing through the congestion road section. Here, it will not be repeated.

[0101] Please refer to Figure 11 , step 021 includes:

[0102] 0211: Obtain the historical pickup point data of the user in the area corresponding to the current position of the user;

[0103] 0212: Perform density analysis on the historical pickup point data to obtain the frequently used pickup point;

[0104] 0213: determining the candidate pickup point according to the common pickup point.

[0105] Please refer to Figure 12 , the determining unit 121 can include an obtaining unit 1211, an analyzing unit 1212, and a pickup point determining unit 1213.

[0106] Step 0211 can be implemented by the obtaining unit 1211, step 0212 can be implemented by the analyzing unit 1212, and step 0213 can be implemented by the pickup point determining unit 1213. That is, the obtaining unit 1211 is configured to obtain historical pickup point data of the user in the area corresponding to the current location of the user; the analyzing unit 1212 is configured to perform density analysis on the historical pickup point data to obtain a common pickup point; and the pickup point determining unit 1213 is configured to determine the target pickup point according to the common pickup point.

[0107] Specifically, when selecting a pickup point, the pickup point data of multiple taxis within a certain time period can be obtained to obtain historical pickup point data in the area corresponding to the current location of the user, and density analysis is performed on the historical pickup point data, so as to collect the common pickup point most frequently used by the user near the area corresponding to the current location of the user, and the common pickup point of the user can be plotted on a coordinate map with latitude and longitude as horizontal and vertical coordinates, and then the candidate pickup point is determined according to the common pickup point, for example, the common pickup point can be used as the candidate pickup point.

[0108] Please refer to Figure 13 , step 0212 includes:

[0109] 02121: determining the latitude and longitude of the historical pickup point data to plot a corresponding coordinate map;

[0110] 02122: aggregating the historical pickup point data according to a preset range to obtain a pickup point cluster on the coordinate map;

[0111] 02123: determining the common pickup point according to the pickup point cluster.

[0112] Please refer to Figure 14 , the analyzing unit 1212 includes a map plotting unit 12121, a pickup point cluster obtaining unit 12122, and a common pickup point determining unit 12123.

[0113] Step 02121 can be implemented by the mapping unit 12121, step 02122 can be implemented by the pickup point cluster acquisition unit 12122, and step 02123 can be implemented by the commonly used pickup point determination unit 12123. That is, the mapping unit 12121 is configured to determine the latitude and longitude of the historical pickup point data to draw a corresponding coordinate map; the pickup point cluster acquisition unit 12122 is configured to aggregate the historical pickup point data in the coordinate map according to a preset range to obtain a pickup point cluster; and the commonly used pickup point determination unit 12123 is configured to determine the commonly used pickup point according to the pickup point cluster.

[0114] Specifically, the historical pickup point data includes the pickup points most frequently used by nearby users collected, and then a coordinate map is drawn with latitude and longitude as horizontal and vertical coordinates. The historical pickup point data can be determined based on the selection of other taxi pickup points within a certain time.

[0115] Then, the pickup point data set can be aggregated into several clusters according to a certain activity radius (for example, 10 meters as the activity radius) based on the DBSCAN algorithm, so as to distinguish data clusters in different directions, such as different data clusters on the east or west side of a cell door, and to prevent interference between data clusters with different pickup location preferences. Finally, the data set of each cluster can be used as new input data to obtain several pickup point clusters, so as to determine the commonly used pickup point according to the pickup point cluster.

[0116] Please refer to Figure 15 , step 02123 includes:

[0117] 021231: iteratively aggregating the historical pickup point data corresponding to the pickup point cluster by using a clustering analysis algorithm to obtain a centroid position;

[0118] 021232: taking the centroid position as the commonly used pickup point.

[0119] Please refer to Figure 14 , step 021231 and step 021232 can be implemented by the commonly used pickup point determination unit 12123. That is, the commonly used pickup point determination unit 12123 is configured to iteratively aggregate the historical pickup point data corresponding to the pickup point cluster by using a clustering analysis algorithm to obtain a centroid position; and take the centroid position as the commonly used pickup point.

[0120] Specifically, the clustering analysis algorithm includes a Kmeans algorithm, that is, the iterative aggregation function of the Kmeans algorithm can be used to obtain the position of the centroid as the commonly used pickup point according to the several pickup point clusters.

[0121] Please refer to Figure 16 , step 021231 includes:

[0122] 0212311: obtaining a data creation time corresponding to the historical pickup point data;

[0123] 0212312: Determine the weighted value of the corresponding historical boarding point data based on the data creation time;

[0124] 0212313: Use the cluster analysis algorithm to iteratively aggregate the historical boarding point data corresponding to the boarding point cluster according to the weighted value to obtain the centroid position.

[0125] Please combine Figure 17 The commonly used boarding point determination unit 12123 also includes a center of mass determination unit 121231.

[0126] Steps 0212311, 0212312, and 0212313 can all be implemented by the centroid determination unit 121231. Specifically, the centroid determination unit 121231 is configured to obtain the data creation time corresponding to the historical boarding point data; determine the weighted value of the corresponding historical boarding point data based on the data creation time; and iteratively aggregate the historical boarding point data corresponding to the boarding point cluster using a cluster analysis algorithm based on the weighted value to obtain the centroid position.

[0127] It is understandable that based on the analysis of the selection of other taxi pick-up points within a certain period of time, driverless taxis can be prevented from stopping at pick-up points such as the main entrance of a residential complex where parking is prohibited according to other rules, thereby inconveniencing users to meet driverless taxis.

[0128] Specifically, since the no-parking rules at a particular boarding point may change over time—that is, parking may be prohibited at a certain time period and then permitted again after that time period—time correlation needs to be considered. Therefore, when using the Kmeans algorithm, we can add time tags and weight the corresponding historical boarding point data based on the data creation time. Then, using the clustering analysis algorithm, we iteratively aggregate the historical boarding point data corresponding to the boarding point cluster based on the weighted values ​​to obtain the centroid location.

[0129] The weighted value of the corresponding historical boarding point data is determined according to the data creation time. For example, the weight of data within 1 hour is 1, and the weight of data 1 week ago is 0.1.

[0130] The control method of the present application can achieve cluster analysis of short-term data by weighting the data within a data cluster based on time. In other embodiments of the present application, for example, the weight of the same time period in history can be set to 1, and the weight of other time periods can be set to 0.1, so as to avoid the influence of other historical factors on the boarding point.

[0131] See also Figure 18 , the control method further includes:

[0132] 07: Automatically calculate the optimal solution to determine the target route without receiving any user input on the alternative routes;

[0133] 08: Send a route confirmation message to notify the user and provide route guidance.

[0134] See also Figure 19 The control system 10 of the unmanned vehicle also includes an automatic optimization module 17 and a route confirmation and guidance module 18.

[0135] Step 07 can be implemented by the automatic optimization module 17, and step 08 can be implemented by the route confirmation and guidance module 18. That is, the automatic optimization module 17 is used to automatically calculate the optimal solution to determine the target route without receiving any user operation for the alternative route; the route confirmation and guidance module 18 is used to send a route confirmation message to notify the user and provide route guidance.

[0136] Specifically, if the user who placed the order fails to respond to the selection of the alternative plan in real time, that is, if the unmanned vehicle does not receive the user's operation for the alternative route, the unmanned vehicle can automatically choose to calculate the optimal plan and notify the user and provide route guidance by phone, message sending on the unmanned vehicle online booking platform, etc. before arriving.

[0137] See also Figure 20 After step 01, the control method includes:

[0138] 011: Get user route preference;

[0139] Step 02 includes:

[0140] 021: Determine at least one alternative boarding point based on the user's route preference, the user's ride request, and the user's current location.

[0141] See also Figure 1 Step 011 can be implemented by the acquisition module 11, and step 021 can be implemented by the boarding point determination module 12. That is, the acquisition module 11 is used to obtain the user's route preference; the boarding point determination module 12 is used to determine at least one alternative boarding point based on the user's route preference, the user's boarding request, and the user's current location.

[0142] It is understandable that each user has different preferences and prefers different boarding points. Determining at least one target boarding point based on the user's route preferences can better adapt to the user's preferences and improve the user experience.

[0143] Specifically, when a user places an order on a self-driving car-hailing platform for the first time, a question-and-answer format can be used to collect the user's preferences for the ride. For example, the user can be asked whether they are willing to spend an extra 5 or 10 minutes driving to avoid walking, or whether efficiency is the priority.

[0144] See also Figure 21 , further, step 011 includes:

[0145] 0111: Send preference selection information to the user's mobile terminal;

[0146] 0112: Receive the user's operation for the above preference selection information to determine the user's route preference.

[0147] See also Figure 1 , step 0111 and step 0112 can be implemented by the acquisition module 11. That is, the acquisition module 11 can be specifically used to send preference selection information to the user's mobile terminal; receive the user's operation based on the preference selection information to determine the user's route preference.

[0148] Specifically, preference information can be sent to the user's mobile terminal in the form of questions and answers on the self-driving car ordering platform, or in the form of a questionnaire or other form. The self-driving car can then receive the user's preference information and determine the user's route preference.

[0149] See also Figure 22 , step 011 includes:

[0150] 0113: Get the user's historical travel information, which includes preset user tags;

[0151] 0114: Analyze historical travel information based on the word frequency calculation algorithm to obtain user route preferences.

[0152] See also Figure 1 , step 0113 and step 0114 can be implemented by the acquisition module 11. That is, the acquisition module 11 can be specifically used to obtain the user's historical travel information, the historical travel information including preset user tags; and analyze the historical travel information according to the word frequency calculation algorithm to obtain the user's route preference.

[0153] Specifically, for users with multiple travel histories, analysis can be conducted based on past travel records. For example, it can be determined during which time periods users prefer to save travel time, near which boarding points users prefer to reduce walking distance, etc., to help users generate more appropriate travel plans.

[0154] For example, when analyzing user preferences, the TF-IDF algorithm can be used to analyze the user's travel preferences (such as Figure 23The algorithm formula shown in the figure is as follows, where p is a user identifier used to distinguish different users, and Ti includes all preset user tags of the user identified as P). The control method of the unmanned vehicle of the present application can use preset user tags to mark a user's preferences with the user's preset user tags in a one-to-one correspondence, and can analyze the user's travel preferences.

[0155] Among them, the preset user label can be a yellow triangle logo or other colors or shapes. For example, a yellow triangle logo indicates that the user does not like walking, and a red triangle logo indicates that the user likes to save travel time between 8 and 9 o'clock.

[0156] Specifically, user labels can also be continuously learned through neural networks.

[0157] See also Figure 24 In some embodiments, step 04 includes:

[0158] 041: When the unmanned vehicle travels to within a first preset distance of the target boarding point, obtain the surrounding environment information of the target boarding point;

[0159] 042: When the surrounding environment information of the target boarding point is not suitable for the vehicle to stop, the target boarding point is re-determined and the user is notified.

[0160] Please combine Figure 25 The determination module 14 includes an environment information acquisition unit 141 and a re-determination unit 142.

[0161] Step 041 can be implemented by the environmental information acquisition unit 141, and step 042 can be implemented by the re-determination unit 142. That is, the environmental information acquisition unit 141 is used to obtain environmental information surrounding the target boarding point when the unmanned vehicle travels within a first preset distance of the target boarding point; the re-determination unit 142 is used to re-determine the target boarding point and notify the user if the environmental information surrounding the target boarding point is not suitable for the vehicle to dock.

[0162] Specifically, when the unmanned vehicle drives near the target pick-up point, if the user is a certain distance away, and image analysis finds that there are certain obstacles near the target pick-up point, such as shared bicycles parked, other vehicles parked, and buildings temporarily under maintenance, which are not suitable for the unmanned vehicle to park for a long time, the unmanned vehicle will send this situation to the online car-hailing platform and start to drive slowly around it. After eliminating the target pick-up point, the online car-hailing platform will re-plan the pick-up point in real time and notify the user.

[0163] Replanning the pick-up point and notifying the user can specifically collect road conditions near the pick-up point for the online car-hailing cloud platform, including vehicle congestion, whether a U-turn or a turn is required, previous driving times, etc., analyze the time it takes for the user to arrive at the pick-up point and how long the unmanned vehicle needs to wait, and thus provide other alternative options and the corresponding passing time and send them to the user's mobile terminal.

[0164] See also Figure 26 ,After the unmanned vehicle drives to the target boarding point, the control method includes:

[0165] 051: Obtain user identity information for identity matching;

[0166] 052: After the identity is matched successfully, the user is allowed to get on the bus to complete the task.

[0167] See also Figure 27 The control system 10 of the unmanned vehicle also includes an identity matching module 151.

[0168] Both step 051 and step 052 can be implemented by the identity matching module 151. That is, the identity matching module 151 is used to obtain the user's identity information for identity matching; after the identity matching is successful, the user is allowed to get on the bus to complete the boarding task.

[0169] Specifically, the ride-hailing platform can analyze the distance between the user and the autonomous vehicle in real time. When the autonomous vehicle approaches a pick-up point, if the user is also near the pick-up point, the autonomous vehicle will stop at the selected pick-up point. The user can then scan a QR code for identity matching and board the vehicle. Identity matching is not limited to QR code scanning and can also be performed in other ways, which are not limited here.

[0170] See also Figure 28 ,After the unmanned vehicle drives to the target boarding point, the control method includes:

[0171] 053: When the distance between the user and the target boarding point is greater than a second preset distance or the waiting time is greater than a preset time, the unmanned vehicle is controlled to select a parking space near the target boarding point to park.

[0172] See also Figure 29 The control system 10 of the unmanned vehicle also includes a docking module 153.

[0173] Step 053 can be implemented by the docking module 153, that is, the docking module 153 is used to control the unmanned vehicle to select a parking space near the target boarding point to dock when the distance between the user and the target boarding point is greater than a second preset distance or the waiting time is greater than a preset time.

[0174] Specifically, if the user is far away from the pick-up point or is delayed for a long time due to other things, the unmanned vehicle will choose to park in a parking lot near the pick-up point or a fixed parking space for unmanned vehicles, and wait for the user to go out or walk to the pick-up point before resuming driving.

[0175] See also Figure 30 The present application also provides an unmanned vehicle 100. The unmanned vehicle 100 includes a processor 110 and a memory 120. The memory 120 is used to store a computer program 121. When the processor 110 executes the computer program 121, it implements the control method of the unmanned vehicle described in any of the above embodiments.

[0176] The unmanned vehicle of this application can determine at least one alternative boarding point based on the user's ride request and the user's current location. It then plans at least one alternative route based on road information and the alternative boarding point, and determines the estimated travel time for each alternative route and sends it to the user's mobile device. The user can then select a target route from the alternative routes to control the unmanned vehicle to travel along that target route to the target boarding point, making the meeting between the user and the unmanned vehicle more flexible and improving the efficiency of the meeting.

[0177] See also Figure 31 The present application also provides a non-volatile computer-readable storage medium 200 for a computer program. When the computer program 210 is executed by one or more processors 220, the control method of the unmanned vehicle described in any of the above embodiments is implemented.

[0178] The unmanned vehicle control method and control system, as well as the unmanned vehicle and readable storage medium disclosed herein, determines at least one alternative boarding point based on a user's ride request and the user's current location. It then plans at least one alternative route based on road information and the alternative boarding point, and determines an estimated transit time for each alternative route and sends it to the user's mobile device. The user can select a target route from the alternative routes to control the unmanned vehicle to travel along that target route to the target boarding point, making meeting the user and the unmanned vehicle more flexible and improving the efficiency of the encounter.

[0179] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A control method for an unmanned vehicle, characterized in that: include: Obtain the user's ride request, current location, and road information; Determine at least one alternative boarding point based on the user's boarding request and the user's current location; The determining of at least one alternative boarding point based on the user's ride request and the user's current location includes: determining an original boarding point and a target point based on the user's ride request; selecting a replacement point on a non-U-turn section and / or a non-congested section when a U-turn section and / or a congested section is located after the original boarding point; calculating a second walking time from the user's current location to the replacement point, a second driving time from the unmanned vehicle from the replacement point to the target point, and a third driving time from the unmanned vehicle from the original boarding point to the target point; and if the sum of the second walking time and the second driving time is less than the third driving time, the replacement point is replaced. The point is added as the alternative boarding point; the determining of at least one alternative boarding point according to the user's boarding request and the user's current location further comprises: determining the original boarding point according to the user's boarding request; if the original boarding point is located after a U-turn section and / or a congested section, selecting a replacement point on a non-U-turn section and / or a non-congested section; calculating a first walking time from the user's current location or the original boarding point to the replacement point, and a first driving time for the unmanned vehicle to pass through the U-turn section and / or the congested section; if the first walking time is less than the first driving time, adding the replacement point as the alternative boarding point; Planning a route from the user to the boarding point based on the road information and the alternative boarding point to obtain at least one alternative route; The estimated transit time of each of the alternative routes is determined to determine the target boarding point.

2. The control method according to claim 1, characterized in that: The determining of at least one candidate boarding point according to the user's boarding request and the user's current location includes: Obtain the historical boarding point data of the user in the area corresponding to the current location of the user; Performing density analysis on the historical boarding point data to obtain frequently used boarding points; The alternative boarding point is determined based on the common boarding points.

3. The control method according to claim 2, characterized in that: The density analysis of the historical boarding point data to obtain the frequently used boarding points includes: Determine the latitude and longitude of the historical boarding point data to draw a corresponding coordinate map; Aggregating the historical boarding point data within a preset range on the coordinate map to obtain a boarding point cluster; The frequently used boarding points are determined according to the boarding point clusters.

4. The control method according to claim 3, characterized in that: The determining the frequently used boarding point according to the boarding point cluster includes: Using a cluster analysis algorithm, iteratively aggregate the historical boarding point data corresponding to the boarding point cluster to obtain a centroid position; The centroid position is used as the common boarding point.

5. The control method according to claim 4, characterized in that: The iterative aggregation of the historical boarding point data corresponding to the boarding point cluster using a cluster analysis algorithm to obtain a centroid position includes: Obtain the data creation time corresponding to the historical boarding point data; Determine the weighted value of the corresponding historical boarding point data according to the data creation time; The centroid position is obtained by iteratively aggregating the historical boarding point data corresponding to the boarding point cluster using the cluster analysis algorithm according to the weighted value.

6. The control method according to claim 1, characterized in that: The control method includes: Automatically calculating an optimal solution to determine a target route when no user operation for the alternative route is received; Send a route confirmation message to notify the user and give routing instructions.

7. The control method according to claim 1, characterized in that: After obtaining the user's ride request, the user's current location, and road information, the control method includes: Get user route preferences; The determining of at least one target boarding point according to the user's boarding request and the user's current location includes: At least one of the alternative boarding points is determined according to the user route preference, the user ride request, and the user's current location.

8. The control method according to claim 7, characterized in that: The obtaining of user route preferences includes: Sending preference selection information to the user mobile terminal; The user's operation for the preference selection information is received to determine the user route preference.

9. The control method according to claim 7, characterized in that: The obtaining of user route preferences includes: Obtaining historical travel information of the user, wherein the historical travel information includes a preset user tag; The historical travel information is analyzed according to a word frequency calculation algorithm to obtain the user route preference.

10. The control method according to claim 9, characterized in that: The preset user tags are learned through a neural network.

11. The control method according to claim 1, characterized in that: The control method includes: When the unmanned vehicle travels to within a first preset distance of the target boarding point, obtaining environmental information surrounding the target boarding point; When the surrounding environment information of the target boarding point is not suitable for the vehicle to stop, the target boarding point is re-determined and the user is notified.

12. The control method according to claim 1, characterized in that: After the unmanned vehicle drives to the target boarding point, the control method includes: When the distance between the user and the target boarding point is greater than a second preset distance or the waiting time is greater than a preset time, the unmanned vehicle is controlled to select a parking space near the target boarding point to park.

13. A control system for an unmanned vehicle, characterized in that: include: An acquisition module, which is used to obtain a user's ride request, the user's current location, and road information; a boarding point determination module, configured to determine at least one candidate boarding point based on the user's boarding request and the user's current location; The determining of at least one alternative boarding point based on the user's ride request and the user's current location includes: determining an original boarding point and a target point based on the user's ride request; selecting a replacement point on a non-U-turn section and / or a non-congested section when a U-turn section and / or a congested section is located after the original boarding point; calculating a second walking time from the user's current location to the replacement point, a second driving time from the unmanned vehicle from the replacement point to the target point, and a third driving time from the unmanned vehicle from the original boarding point to the target point; and if the sum of the second walking time and the second driving time is less than the third driving time, the replacement point is replaced. The point is added as the alternative boarding point; the determining of at least one alternative boarding point according to the user's boarding request and the user's current location further comprises: determining the original boarding point according to the user's boarding request; if the original boarding point is located after a U-turn section and / or a congested section, selecting a replacement point on a non-U-turn section and / or a non-congested section; calculating a first walking time from the user's current location or the original boarding point to the replacement point, and a first driving time for the unmanned vehicle to pass through the U-turn section and / or the congested section; if the first walking time is less than the first driving time, adding the replacement point as the alternative boarding point; a route planning module, configured to plan a route from the user to the boarding point based on the road information and the alternative boarding point to obtain at least one alternative route; A determination module is used to determine the estimated passing time of each of the alternative routes to determine the target boarding point.

14. An unmanned vehicle, characterized in that: The method comprises a processor and a memory, wherein the memory is used to store a computer program, and the processor implements the control method according to any one of claims 1 to 12 when executing the computer program.

15. A non-volatile computer-readable storage medium for a computer program, characterized in that: When the computer program is executed by one or more processors, the control method according to any one of claims 1 to 12 is implemented.

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