CN114118904A

CN114118904A - Unmanned vehicle distribution method and device

Info

Publication number: CN114118904A
Application number: CN202111293756.6A
Authority: CN
Inventors: 卢二宝; 李光宇; 姜訢; 常鑫
Original assignee: Beijing Sankuai Online Technology Co Ltd
Current assignee: Beijing Sankuai Online Technology Co Ltd
Priority date: 2021-11-03
Filing date: 2021-11-03
Publication date: 2022-03-01

Abstract

The present specification discloses a method and an apparatus for delivering an unmanned vehicle, wherein a risk area in a task path of a task executed by the unmanned vehicle is determined as a target risk area by determining a position of each risk area and a remote control apparatus corresponding to each risk area, and when it is determined that the unmanned vehicle has a risk according to position information of the unmanned vehicle and the target risk area, a stop instruction is sent to the unmanned vehicle, so that the unmanned vehicle sends a control request to the remote control apparatus corresponding to the target risk area after receiving the stop instruction, and the remote control apparatus remotely controls the unmanned vehicle to pass through the target risk area after receiving the control request. According to the method, the target risk area in the driving path can be predetermined through the determined position of the risk area and the driving path of the unmanned vehicle, and the unmanned vehicle is controlled to be connected with the remote control device corresponding to the target risk area when the risk of the unmanned vehicle is determined, so that a safety worker is not required to control the unmanned vehicle to stop emergently, and the safety of the unmanned vehicle is ensured.

Description

Unmanned vehicle distribution method and device

Technical Field

The specification relates to the technical field of computers, in particular to a method and a device for unmanned vehicle distribution.

Background

At present, with the progress of technology and the maturity of unmanned technology, unmanned equipment has successfully realized application in the delivery field, and is often applied to scenes such as take-out, express delivery and the like. In the unmanned vehicle distribution process, since risk areas may be encountered, such as roads without lane lines, roads with road surfaces damaged to be repaired, and the like, how to ensure driving safety in the process of carrying out the distribution task by the unmanned vehicle is one of the problems to be solved by the service provider.

A common unmanned vehicle distribution method is realized based on a remote control device. Specifically, the service provider is provided with a safety guard for each unmanned vehicle, and the safety guard closely follows the unmanned vehicle and observes road conditions in front of the unmanned vehicle in the process of executing distribution tasks by the unmanned vehicle. When observing that unmanned vehicle front road is the risk zone, the security personnel accessible remote control unit, control unmanned vehicle stops to pass through this risk zone by remote control unit control unmanned vehicle. Finally, the security officer can control the unmanned vehicle to switch to the automatic driving mode through the remote control device after the unmanned vehicle passes through the risk area.

However, in the prior art, when the delivery is performed, a security officer needs to timely control the unmanned vehicle to stop when finding that a risk area exists, and the security officer may not timely control the unmanned vehicle to stop, so that potential safety hazards exist.

Disclosure of Invention

The present disclosure provides a method and a device for unmanned vehicle distribution, which partially solve the above problems in the prior art.

The technical scheme adopted by the specification is as follows:

the present specification provides an unmanned vehicle distribution method, including:

determining the position of each risk area and the remote control device corresponding to each risk area, wherein different remote control devices correspond to each risk area;

determining task information of tasks distributed to the unmanned vehicle, wherein the task information at least comprises task paths for executing the tasks, and taking risk areas existing in the task paths as target risk areas according to the task paths of the unmanned vehicle and the positions of the risk areas;

judging whether the unmanned vehicle has risks or not according to the position information of the unmanned vehicle and the target risk area;

if yes, sending a stop instruction to the unmanned vehicle, so that the unmanned vehicle sends a control request to a remote control device corresponding to the target risk area after receiving the stop instruction, and the remote control device remotely controls the unmanned vehicle to pass through the target risk area after receiving the control request.

Optionally, determining the position of each risk area specifically includes:

acquiring the driving track of an unmanned vehicle in each historical task;

determining a part of the running speed which is less than a preset speed threshold value from the running tracks as a risk track;

and determining each risk area according to the preset risk distance and each risk track.

Optionally, determining whether the unmanned vehicle has a risk according to the position information of the unmanned vehicle and the target risk area, specifically including:

determining an area before the unmanned vehicle enters the target risk area along the task path according to a preset first range, and using the area as a queuing area;

and judging whether the unmanned vehicle is in the queuing area or not according to the position information of the unmanned vehicle.

Optionally, the sending a stop instruction to the unmanned vehicle, so that the unmanned vehicle sends a control request to the remote control device corresponding to the target risk area after receiving the stop instruction, specifically including:

sending the stop instruction to the unmanned vehicle to enable the unmanned vehicle to stop running according to the stop instruction;

and after the unmanned vehicle is determined to stop running, sending a communication instruction to the unmanned vehicle, so that the unmanned vehicle sends a control request to a remote control device corresponding to the target risk area according to the communication instruction.

Optionally, the method further comprises:

judging whether the unmanned vehicle passes through the target risk area or not according to the position information of the unmanned vehicle and the target risk area;

if so, sending an automatic driving instruction to the unmanned vehicle, enabling the unmanned vehicle to continue to execute the task according to the automatic driving instruction and the task path, and sending a control stopping instruction to the remote control device, and enabling the remote control device corresponding to the target risk area to stop remotely controlling the unmanned vehicle.

Optionally, determining whether the unmanned vehicle passes through the target risk area according to the position information of the unmanned vehicle and the target risk area, specifically including:

determining an area after the unmanned vehicle passes through the target risk area along the task path according to a preset second range, and taking the area as a recovery area;

and judging whether the unmanned vehicle reaches the recovery area under the control of the remote control device according to the position information of the unmanned vehicle.

Optionally, an automatic driving instruction is sent to the unmanned vehicle, so that the unmanned vehicle continues to execute a task according to the automatic driving instruction and the task path, specifically including:

and after the unmanned vehicle stops running, sending a recovery instruction to the unmanned vehicle, enabling the unmanned vehicle to re-determine the position information of the unmanned vehicle according to the recovery instruction, and continuing to execute the task according to the task path and the position information of the unmanned vehicle.

Optionally, the method further comprises:

receiving an automatic driving request sent by a remote control device corresponding to the target risk area, wherein the automatic driving request is sent after the remote control device remotely controls the unmanned vehicle to pass through the target risk area;

and sending an automatic driving instruction to the unmanned vehicle according to the automatic driving request, so that the unmanned vehicle continues to execute the task according to the automatic driving instruction and the task path.

determining the risk of other unmanned vehicles according to the position information of other unmanned vehicles and the target risk area;

determining the priority of the unmanned vehicle and the other unmanned vehicles with risks according to the state information of the unmanned vehicle and the other unmanned vehicles with risks;

determining the time when the unmanned vehicle passes through the target risk area according to the determined priority;

and sending a stop instruction carrying the time to the unmanned vehicle, so that the unmanned vehicle sends a control request to a remote control device corresponding to the target risk area according to the time after receiving the stop instruction.

Optionally, the status information includes at least one of a type of a delivery of a task performed by the unmanned vehicle, a remaining delivery time of the task, a length of a remaining task route of the task, and a congestion status of the remaining task route, the remaining delivery time and the priority are positively correlated, the length of the remaining task route and the priority are negatively correlated, and the congestion status of the remaining task route and the priority are positively correlated.

This specification provides an unmanned vehicle distribution device, includes:

the first determining module is used for determining the positions of the risk areas and the remote control devices corresponding to the risk areas respectively, wherein different remote control devices correspond to each risk area;

the second determining module is used for determining task information of tasks distributed to the unmanned vehicle, wherein the task information at least comprises task paths for executing the tasks, and risk areas existing in the task paths are used as target risk areas according to the task paths of the unmanned vehicle and the positions of the risk areas;

the judging module is used for judging whether the unmanned vehicle has risks or not according to the position information of the unmanned vehicle and the target risk area;

and if so, sending a stop instruction to the unmanned vehicle so that the unmanned vehicle sends a control request to a remote control device corresponding to the target risk area after receiving the stop instruction, and enabling the remote control device to remotely control the unmanned vehicle to pass through the target risk area after receiving the control request.

The present specification provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described unmanned vehicle delivery method.

The specification provides an unmanned vehicle, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the unmanned vehicle distribution method.

The technical scheme adopted by the specification can achieve the following beneficial effects:

in the unmanned vehicle delivery method provided by the present specification, the position of each risk area and the remote control device corresponding to each risk area are determined, the risk area in the task path of the task executed by the unmanned vehicle is determined as the target risk area, and when it is determined that the unmanned vehicle has a risk according to the position information of the unmanned vehicle and the target risk area, a stop instruction is sent to the unmanned vehicle, so that the unmanned vehicle sends a control request to the remote control device corresponding to the target risk area after receiving the stop instruction, and the remote control device receives the control request and remotely controls the unmanned vehicle to pass through the target risk area.

According to the method, the target risk area in the driving path can be determined in advance through the determined position of the risk area and the driving path of the unmanned vehicle, and the unmanned vehicle is controlled to be connected with the remote control device corresponding to the target risk area when the risk of the unmanned vehicle is determined, so that a security officer is not required to control the unmanned vehicle to stop emergently, and the safe distribution of the unmanned vehicle is ensured

Drawings

The accompanying drawings, which are included to provide a further understanding of the specification and are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description serve to explain the specification and not to limit the specification in a non-limiting sense. In the drawings:

fig. 1 is a schematic flow chart of an unmanned vehicle delivery method provided in the present specification;

FIG. 2 is a schematic illustration of determining risk zones provided herein;

FIG. 3 is a schematic illustration of updating risk areas;

FIG. 4 is a schematic illustration of a queuing area provided herein;

FIG. 5 is a schematic illustration of determining a queuing area as provided herein;

FIG. 6 is a schematic illustration of a recovery area provided herein;

FIG. 7 is a schematic illustration of determining a recovery area provided herein;

FIG. 8 is a schematic view of an unmanned vehicle dispensing apparatus provided herein;

fig. 9 is a schematic diagram of an electronic device corresponding to fig. 1 provided in the present specification.

Detailed Description

In order to make the objects, technical solutions and advantages of the present disclosure more clear, the technical solutions of the present disclosure will be clearly and completely described below with reference to the specific embodiments of the present disclosure and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort belong to the protection scope of the present specification.

The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.

Generally, when delivery is performed, an individual remote control device and an individual security officer need to be equipped for each unmanned vehicle, and during the driving process of the unmanned vehicles, the security officer needs to follow the unmanned vehicles at a short distance and constantly observe whether a road in front of the unmanned vehicles is a risk area where the unmanned vehicles cannot safely drive, and if so, the security officer can control the unmanned vehicles to stop driving through the remote control device and remotely control the unmanned vehicles to pass through the risk area.

However, in the prior art, the characteristics of a security guard are configured for each unmanned vehicle, so that the distribution cost of the unmanned vehicles is high, and the prior art is more suitable for the test stage of the unmanned vehicles in the closed area. The prior art is not suitable for the process that the unmanned vehicle actually executes the distribution task.

In addition, a security officer closely follows the unmanned vehicle to observe whether the road in front of the unmanned vehicle contains the characteristics of a risk area or not, so that the security officer can not consider the safety of the security officer and has potential safety hazards.

Fig. 1 is a schematic flow chart of an unmanned vehicle distribution method provided in an embodiment of the present specification.

S100: and determining the position of each risk area, and determining the remote control device corresponding to each risk area, wherein different remote control devices correspond to each risk area.

In the field of unmanned delivery, a server of a service provider generally assigns a task to an unmanned vehicle and transmits a task path corresponding to the task to the unmanned vehicle. And executing the task by the unmanned vehicle according to the received task path and the self-positioning. The unmanned vehicle distribution method provided by the specification is also applied to a scene that the unmanned vehicle executes the distribution task.

The present specification provides a new unmanned vehicle delivery method, such that a target risk region existing in a task path can be determined based on predetermined positions of each risk region and the task path allocated to a task of an unmanned vehicle, and further, based on the target risk region and the position of the unmanned vehicle, when it is determined that the unmanned vehicle has a risk, the unmanned vehicle is remotely controlled by a remote control device corresponding to the target risk region to pass through the target risk region, without a security guard following the unmanned vehicle, thereby reducing the potential safety hazard.

Based on this, the server can first determine the position of each risk area and the remote control device corresponding to each risk area.

Specifically, a high-precision map indicating the positions of the risk areas may be prestored in the server, and the server may determine the positions of the risk areas from the high-precision map during the task execution of the unmanned vehicle. The server can also determine the identification of the remote control device corresponding to each risk area according to the stored risk area-remote control device record table aiming at each risk area. Wherein, the position of each risk area in the high-precision map can be artificially marked.

Further, in the process of executing the task by the unmanned vehicle, if a risk area exists in the task path of the unmanned vehicle, the unmanned vehicle cannot travel in the risk area due to lack of lane lines and the like, so that the staying time is too long. According to the mode of the prior art, the unmanned vehicle needs to be stopped at the risk area, and then is remotely controlled by a security guard to pass through the risk area, and then is stopped and switched into an automatic driving state. Obviously, the unmanned vehicle remains in the risk area for a significantly longer time than in other areas of the unmanned vehicle's mission path. Thus, the server may determine the location of each risk area based on the travel trajectory of each unmanned vehicle in the historical mission.

Specifically, the server may first obtain the driving trajectory of the unmanned vehicle in each historical task. Wherein each historical task is a task which is historically distributed to the unmanned vehicle by the server. For each driving track, the driving track comprises positions where the unmanned vehicle passes and speeds corresponding to the positions when the unmanned vehicle executes the distribution task corresponding to the driving track, and the positions can be determined according to a preset time interval or a preset length.

Then, the server may determine, from the travel tracks, a portion of the travel track having a travel speed less than a preset speed threshold as a risk track according to the preset speed threshold.

Finally, the server may determine, for each risk trajectory, the number of risk trajectories within a range of a length from the risk region as a risk distance according to a preset risk distance, and if the number of risk trajectories exceeds a preset first number threshold, it may be determined that the risk trajectory belongs to the risk region. As shown in fig. 2.

Fig. 2 is a schematic diagram of determining risk regions provided in the present specification. In the figure, a black dotted line is a track of the unmanned vehicle when the unmanned vehicle executes a task, a dotted circle is a range determined according to a preset risk distance l, and black

solid lines

1, 2, 3, and 4 are determined risk tracks, respectively. Obviously, for each risk track, according to the preset risk distance, when the number of risk tracks within the dashed circle is greater than the preset first number threshold, it may be determined that the risk track belongs to a risk area. Taking the first number threshold as 1 as an example, the risk regions are corresponding to black

solid lines

1, 2, 3, and 4, respectively.

Of course, when determining the risk areas, there may be a case that the determined risk areas are communicated, and the server may update the risk areas according to the communicated risk areas. As shown in fig. 3.

Fig. 3 is a schematic diagram of updating risk areas, in the diagram, black solid lines b, c, and d are history tracks of tasks executed by the unmanned vehicles, respectively, and white matrixes 1, 2, and 3 are determined risk areas, respectively, so that the server may determine a triangular area enclosed by dotted lines as a risk area according to the determined risk areas with overlapping portions, that is, connected risk areas. That is, the risk regions corresponding to the white matrices 1, 2, and 3 are updated to the risk regions corresponding to the triangular regions surrounded by the dotted lines.

Of course, if the identified risk areas are not connected, but the identified risk areas are in close proximity, the server may divide the risk areas in close proximity into the same risk area.

Furthermore, the server can divide the high-precision map into a plurality of grids, project each risk track into the grids, and determine the grids in which the risk tracks fall as risk areas.

Certainly, the step of dividing the high-precision map into grids may be performed by the server according to a preset grid length, or may be performed by the server according to a preset area type, such as a sidewalk, a roadway, a cell, and the like, and a specific division principle may be set as required, which is not limited by the comparison in this specification.

In addition, the risk track is composed of position points belonging to the risk track. Therefore, in order to more accurately determine the position of each risk area, the server may determine, as a risk position, a position where the travel speed is less than a preset speed threshold value for each history track from the unmanned vehicle.

Then, the server may determine, for each risk position, a number of other risk positions within a range where a distance from the risk position is a preset risk distance according to the preset risk distance, and if the number is greater than a preset second number threshold, may determine that the risk position belongs to a risk region.

It should be noted that, in the step of determining each risk area according to the preset risk distance and each risk trajectory, the distance between each two risk trajectories may also be determined, and if the distance is smaller than the preset risk distance, it is determined that the risk trajectories corresponding to the distance belong to the same risk area.

S102: determining task information of tasks allocated to the unmanned vehicle, wherein the task information at least comprises task paths for executing the tasks, and taking risk areas existing in the task paths as target risk areas according to the task paths of the unmanned vehicle and the positions of the risk areas.

In one or more embodiments provided in this specification, as described above, when a risk area exists in a task path of an unmanned vehicle, whether the unmanned vehicle has a risk may be determined according to a position of the unmanned vehicle and a position of the risk area existing in the task path. Based on this, the server may first determine risk areas present in the task path of the unmanned vehicle.

Specifically, the server may first determine task information for tasks assigned to the unmanned vehicle. The task information at least comprises a task path of the unmanned vehicle for executing the distribution task. Of course, the job information may include the type of the delivered material corresponding to the job, the delivery time corresponding to the job, and the like.

Then, the server can determine a task path corresponding to the task executed by the unmanned vehicle according to the determined task information of the unmanned vehicle.

Finally, the server may determine whether a risk area exists in the task path according to the position of each risk area determined in step S100 and the task path of the unmanned vehicle. If the risk area in the task path exists, the server may perform subsequent steps based on the target risk area, taking the risk area in the task path as the target risk area. If the unmanned vehicle does not exist, the server can determine that the unmanned vehicle does not have risks, and the unmanned vehicle can execute the task according to the received task path.

S104: and judging whether the unmanned vehicle has risks or not according to the position information of the unmanned vehicle and the target risk area.

In one or more embodiments provided in this specification, the unmanned vehicle delivery method provided in this specification is to determine whether the unmanned vehicle needs to pass through any risk area during the task performed by the unmanned vehicle, and after determining that the unmanned vehicle reaches the risk area, remotely control the unmanned vehicle to pass through the risk area by using a remote control device corresponding to the risk area, so that the unmanned vehicle can smoothly perform the task. Therefore, after determining the target risk area in the task path of the unmanned vehicle for executing the task, the server may determine whether the unmanned vehicle is at risk based on the position information of the unmanned vehicle and the determined target risk area.

Specifically, the server may first determine the location information of the unmanned vehicle. The position information can be uploaded to the server by the unmanned vehicle according to a preset time interval.

Then, the server can determine the distance between the unmanned vehicle and the target risk area according to the determined position information of the unmanned vehicle and the determined position of the target risk area.

And finally, the server can judge whether the distance is smaller than a preset distance threshold value according to the determined distance and the preset distance threshold value, and further determine whether the unmanned vehicle has risks according to a judgment result.

Further, when a roundabout road condition exists near the target risk area, if it is determined whether the unmanned vehicle has a risk according to the determined distance between the unmanned vehicle and the target risk area, it may happen that the distance between the unmanned vehicle and the target risk area is smaller than a distance threshold, but the unmanned vehicle does not reach the vicinity of the target area, so that an error exists when it is determined whether the unmanned vehicle has a risk according to the determination result, and therefore, the server may set a queuing area near the target risk area for each target risk area, and determine whether the unmanned vehicle is at a risk based on the queuing area and the position of the unmanned vehicle.

Specifically, the server may determine, from a task path corresponding to the unmanned vehicle, an area before the unmanned vehicle enters the target risk area along the task path, as a queuing area, according to a preset first range. As shown in fig. 4.

Fig. 4 is a schematic diagram of a queuing area provided herein. In the figure, a black rectangle is a target risk region, a white rectangle z is a task path of the unmanned vehicle, and a traveling direction of the unmanned vehicle is a direction indicated by an arrow. The server may determine, according to the preset first range, that the rectangular area surrounded by the dotted line is the queuing area.

After the queuing area is determined, the server can judge whether the unmanned vehicle has risks according to whether the position of the unmanned vehicle is in the queuing area.

Of course, in order to relieve the calculation pressure of the server, the server may also determine, for each risk area, each queuing area around the risk area according to each queuing area determined when each unmanned vehicle performs each task and passes through the risk area. Then, when it is determined that the unmanned vehicle is located within the queuing area according to the position information of the unmanned vehicle, it may be determined that the unmanned vehicle is at risk. As shown in fig. 5.

Fig. 5 is a schematic diagram of determining a queuing area provided in the present specification. The black rectangles are target risk areas, the rectangles surrounded by the dotted frames are queuing areas determined according to the target risk areas when tasks are executed by the unmanned vehicles, and the white rectangles are roads. The queuing area corresponding to the target risk area can be determined as the area corresponding to the rectangle enclosed by the dotted lines. When the unmanned vehicle executes a task, the server can determine that the unmanned vehicle has a risk according to each queuing area corresponding to the determined target risk area and the position information of the unmanned vehicle.

S106: if yes, sending a stop instruction to the unmanned vehicle, so that the unmanned vehicle sends a control request to a remote control device corresponding to the target risk area after receiving the stop request, and the remote control device remotely controls the unmanned vehicle to pass through the target risk area after receiving the control request.

In one or more embodiments provided in this specification, as described above, when there is a risk in the unmanned vehicle, if the unmanned vehicle continues to perform a task by automatic driving, the unmanned vehicle may not be able to run, so that the task cannot be performed and completed, or the unmanned vehicle may run forward according to a motion strategy when there is no risk, which may result in a traffic accident. Therefore, when it is determined that the unmanned vehicle is at risk, the server may send a stop instruction to the unmanned vehicle, so that the unmanned vehicle sends a control request to the remote control device after receiving the stop instruction, and the remote control device remotely controls the unmanned vehicle to pass through the target risk area. Wherein, the remote control device is a remote control device corresponding to the target risk area.

Further, because there is a potential safety hazard when the unmanned vehicle is switched from the automatic driving state to the remote control state during the driving process, the server may send a stop instruction to the unmanned vehicle when determining that the unmanned vehicle is at risk.

Specifically, when it is determined that the unmanned vehicle has a risk according to the position information of the unmanned vehicle and the target risk area, the server may send a stop instruction to the unmanned vehicle, so that the unmanned vehicle stops traveling according to the stop instruction. And after the unmanned vehicle stops running, sending a communication instruction to the unmanned vehicle, so that the unmanned vehicle sends a control request to the remote control device corresponding to the target risk area according to the communication instruction.

After receiving the control request, the remote control load corresponding to the target risk area can send a control instruction to the unmanned vehicle so as to control the unmanned vehicle to pass through the target risk area. Wherein, the communication instruction comprises the identification of the remote control device corresponding to the target risk area.

Furthermore, compared with the risk area, the safety hazard is lower when the unmanned vehicle is located in the first area, so that the server can send a stop instruction for decelerating the unmanned vehicle and stopping driving in the queuing area to the unmanned vehicle when determining that the unmanned vehicle is at risk.

In addition, after the unmanned vehicle enters the queuing area, because other social vehicles may exist, when the unmanned vehicle stops only according to the stop command sent by the server, potential safety hazards may exist. Therefore, the server can acquire the image data acquired by the unmanned vehicle and send a stop request to the unmanned vehicle according to the image data.

Specifically, the server may first obtain image data collected by the unmanned vehicle.

Then, the server may determine a stop position of the unmanned vehicle within the queuing area and a queuing path to which the unmanned vehicle travels to the stop position, based on the image data.

Finally, the server can send a stop instruction to the unmanned vehicle according to the queuing path and the stop position, so that the unmanned vehicle can drive to the stop position according to the stop instruction and stop driving at the stop position.

Based on the unmanned vehicle delivery method shown in fig. 1, the positions of the risk areas and the remote control devices corresponding to the risk areas are determined, the risk area in the task path of the task executed by the unmanned vehicle is determined to be the target risk area, and when the unmanned vehicle is determined to have a risk according to the position information of the unmanned vehicle and the target risk area, a stop instruction is sent to the unmanned vehicle, so that the unmanned vehicle sends a control request to the remote control device corresponding to the target risk area after receiving the stop instruction, and the remote control device receives the control request and remotely controls the unmanned vehicle to pass through the target risk area. According to the method, the target risk area existing in the driving path can be determined in advance through the determined position of the risk area and the driving path of the unmanned vehicle, and the unmanned vehicle is controlled to be connected with the remote control device corresponding to the target risk area when the risk of the unmanned vehicle is determined, so that a safety worker is not required to control the unmanned vehicle to stop emergently, and the efficiency and safety of unmanned vehicle distribution are guaranteed.

Further, after the unmanned vehicle passes through the target risk area, the server can control the unmanned vehicle to recover the automatic driving state so that the unmanned vehicle can continuously execute the task.

Specifically, the server may obtain the position information of the unmanned vehicle according to a preset time interval.

Then, the server may determine whether the unmanned vehicle passes through the target risk area according to the position information of the unmanned vehicle and the target risk area.

Finally, after the unmanned vehicle is determined to pass through the target risk area, the server can send an automatic driving instruction to the unmanned vehicle, so that the unmanned vehicle continues to execute the task according to the automatic driving instruction and the task path. And sending a control termination instruction to the remote control device to enable the remote control device of the target risk area to stop remotely controlling the unmanned vehicle.

Furthermore, when determining whether the unmanned vehicle passes through the target risk area according to the position information of the unmanned vehicle and the position information of the target risk area, the distance between the unmanned vehicle and the target risk area may be determined according to the position information of the unmanned vehicle and the position information of the target risk area, and then whether the unmanned vehicle passes through the target risk area may be determined according to a preset distance threshold.

In addition, when it is determined whether or not the unmanned vehicle passes through the target risk area based on the distance, there is a possibility that the determination may be erroneous on a road of a circle island type. Therefore, the server may set a recovery area around the target risk area so that the unmanned vehicle can recover the autonomous driving state within the recovery area.

Specifically, the server may determine, according to a preset second range, an area where the unmanned vehicle passes through the target risk area along the task path from the task path corresponding to the unmanned vehicle, as a recovery area, as shown in fig. 6.

Fig. 6 is a schematic diagram of a recovery area provided herein. In the figure, a black rectangle is a target risk region, a white rectangle z is a task path of the unmanned vehicle, and a traveling direction of the unmanned vehicle is a direction indicated by an arrow. The server may determine a rectangular area surrounded by the dotted line as the recovery area according to a preset second range.

After the recovery area is determined, the server can judge whether the unmanned vehicle passes through the target risk area according to whether the position of the unmanned vehicle is in the recovery area. The specific shape and size of the first range and the second range can be set according to needs, and the specification does not limit the specific shape and size.

Of course, in order to relieve the calculation pressure of the server, the server may also determine, for each risk area, each recovery area around the risk area according to each recovery area determined when each unmanned vehicle performs each task and passes through the risk area. Then, when it is determined that the unmanned vehicle is located within the recovery area according to the position information of the unmanned vehicle, it may be determined that the unmanned vehicle has passed through the target risk area. As shown in fig. 7.

Fig. 7 is a schematic diagram of determining a recovery area provided in the present specification. The black rectangles are target risk areas, the rectangles surrounded by the dotted frames are queuing areas determined according to the target risk areas when tasks are executed by the unmanned vehicles, the white rectangles are roads, and the areas filled with oblique lines are recovery areas determined according to the target risk areas when the tasks are executed by the unmanned vehicles.

When the unmanned vehicle executes a task, the server can determine that the unmanned vehicle passes through the target risk area according to each recovery area corresponding to the determined target risk area and the position information of the unmanned vehicle.

Further, since a potential safety hazard may occur when the unmanned vehicle is directly switched from the remote control state to the automatic driving state during the driving process, the server may first control the unmanned vehicle to stop and resume the automatic driving state.

Specifically, after the unmanned vehicle is determined to have passed through the target risk area according to the position information of the unmanned vehicle and the target risk area, the server may send a stop instruction to the unmanned vehicle, so that the unmanned vehicle stops traveling according to the stop instruction. And after the unmanned vehicle stops running, sending a recovery instruction to the unmanned vehicle, enabling the unmanned vehicle to re-determine the position information of the unmanned vehicle according to the recovery instruction, and continuing to execute the task according to the task path of the task executed by the unmanned vehicle and the position information of the unmanned vehicle. Wherein the recovery command is one of the automatic driving commands.

Of course, the step of determining that the unmanned vehicle has passed through the target risk area may also be a step of determining whether the unmanned vehicle is not in the target risk area according to the self-positioning and the target risk area, and reporting the determination result to the server according to the determination result. Or, the unmanned vehicle can judge whether the unmanned vehicle is in the target risk area according to self positioning and skin area, and reports the judgment result to the server for determination. Specifically, how to determine that the unmanned vehicle has passed through the target risk area may be set as needed, and this specification does not limit this.

Furthermore, after the unmanned vehicle passes through the target risk area, the position of the unmanned vehicle deviates relative to the task path, so that the unmanned vehicle can travel to the task path according to the current position of the unmanned vehicle when performing automatic driving, and then execute the task.

Specifically, the unmanned vehicle can report the position of the unmanned vehicle to the server, and the server judges whether the unmanned vehicle deviates from the task path.

If so, the server can plan a planned road of the unmanned vehicle driving to the task path according to the current position of the unmanned vehicle and the task path. So that the unmanned vehicle can continue to perform tasks based on the task path.

If not, the server can directly send an automatic driving instruction to the unmanned vehicle.

Of course, the degree of deviation of the unmanned vehicle from the mission path may be too high, so that the unmanned vehicle may spend a lot of time in driving to the mission path according to the planned road. Therefore, the server can update the task path according to the position information reported by the unmanned vehicle and the information such as the distribution end point in the task path, and send a recovery instruction to the unmanned vehicle according to the updated task path. The updated task path is planned by taking the position information reported by the unmanned vehicle as a starting point.

In addition, the step of determining that the unmanned vehicle passes through the target risk area may be further configured to send an automatic driving request to a server after the remote control device remotely controls that the unmanned vehicle passes through the target risk area, and the step is determined by the server according to the automatic driving request. The server may determine an identifier of the unmanned vehicle passing through the target risk area according to the stored correspondence between the remote control device and each unmanned vehicle, and send an automatic driving instruction to the unmanned vehicle according to the identifier of the unmanned vehicle, where each unmanned vehicle in the correspondence between the remote control device and each unmanned vehicle is the unmanned vehicle remotely controlled by the remote control device, and the time at which each unmanned vehicle is remotely controlled by the remote control device is stored in the correspondence.

Of course, the corresponding relationship between the remote control device and the unmanned vehicle may be only the corresponding relationship between the remote control device and the unmanned vehicle currently being remotely controlled, or the corresponding relationship between the remote control device and the last unmanned vehicle remotely controlled by the remote control device when the remote control device is in an idle state.

In addition, the same risk area may be included in the driving paths of the multiple unmanned vehicles, and if the multiple unmanned vehicles arrive near the risk area at the same time, the server may determine the priority of each unmanned vehicle, and then control each unmanned vehicle to pass through the risk area according to the priority.

Specifically, for each unmanned vehicle, the server may determine that the other unmanned vehicle is at risk according to the position information of the other unmanned vehicle and the target risk area.

Secondly, the server can determine the priority of the unmanned vehicle and other unmanned vehicles with risks according to the state information of the unmanned vehicle and other unmanned vehicles.

The server may then determine a time at which the unmanned vehicle passes through the target risk area based on the determined priority.

Finally, the server can send a stop instruction carrying the time when the unmanned vehicle passes through the target risk area to the unmanned vehicle, so that the unmanned vehicle sends a control request to the remote control device corresponding to the target risk area according to the time carried in the stop instruction after receiving the stop instruction.

The unmanned vehicle can stop after receiving the stop request, and sends a control request to the remote control device corresponding to the target risk area when the time arrives

The status information includes at least one of a type of a distribution object of a task performed by the unmanned vehicle, a remaining distribution time of the task, a length of a remaining task route of the task, and a congestion state of the remaining task route. The remaining delivery time is positively correlated with the priority, the length of the remaining task path is negatively correlated with the priority, the congestion condition of the remaining task path is positively correlated with the priority, the types of the delivered objects comprise fresh goods, takeaway goods, medicines and the like, and the priority corresponding to each type can be set.

Of course, the different types of status information may be set as different types of priority factors, the server may determine the weight corresponding to the priority factor for each type of priority factor, and when determining the priority corresponding to each unmanned vehicle, may determine the score of each priority factor according to the status information of each unmanned vehicle, and perform weighted summation according to the score of each priority factor and the weight corresponding to each priority factor to determine the priority of each unmanned vehicle.

When a plurality of risk regions are present in the task route of the unmanned vehicle, the plurality of risk regions present in the task route may be determined as the first risk regions. Then, from the first risk areas, the first risk area which is not passed by the unmanned vehicle and is closest to the unmanned vehicle is determined as a target risk area.

One or more embodiments of the specification provide an unmanned vehicle distribution method, and based on the same idea, the specification further provides a corresponding unmanned vehicle distribution device, as shown in fig. 8.

Fig. 8 is an unmanned vehicle dispensing apparatus provided herein, comprising:

the first determining module 200 is configured to determine the positions of the risk areas and the remote control devices corresponding to the risk areas, where for each risk area, a different remote control device corresponds to each risk area.

A second determining module 202, configured to determine task information of a task allocated to an unmanned vehicle, where the task information at least includes a task path for executing the task, and set a risk area existing in the task path as a target risk area according to the task path of the unmanned vehicle and a position of each risk area.

And the judging module 204 is configured to judge whether the unmanned vehicle has a risk according to the position information of the unmanned vehicle and the target risk area.

And the control module 206 is configured to send a stop instruction to the unmanned vehicle, so that the unmanned vehicle sends a control request to the remote control device corresponding to the target risk area after receiving the stop instruction, and the remote control device receives the control request and then remotely controls the unmanned vehicle to pass through the target risk area.

Optionally, the first determining module 200 is configured to obtain a driving track of the unmanned vehicle in each historical task, determine, from the driving tracks, a part of the driving speed that is less than a preset speed threshold as a risk track, and determine each risk area according to a preset risk distance and each risk track.

Optionally, the determining module 204 is configured to determine, according to a preset first range, an area before the unmanned vehicle enters the target risk area along the task path, and determine, as a queuing area, whether the unmanned vehicle is in the queuing area according to the position information of the unmanned vehicle.

Optionally, the control module 206 is configured to send the stop instruction to the unmanned vehicle, so that the unmanned vehicle stops running according to the stop instruction, and after it is determined that the unmanned vehicle stops running, send a communication instruction to the unmanned vehicle, so that the unmanned vehicle sends a control request to the remote control device corresponding to the target risk area according to the communication instruction.

Optionally, the control module 206 is configured to determine whether the unmanned vehicle passes through the target risk area according to the position information of the unmanned vehicle and the target risk area, if so, send an automatic driving instruction to the unmanned vehicle, enable the unmanned vehicle to continue to execute a task according to the automatic driving instruction and the task path, and send a termination control instruction to the remote control device, enable the remote control device corresponding to the target risk area to stop remotely controlling the unmanned vehicle.

Optionally, the control module 206 is configured to determine, according to a preset second range, an area where the unmanned vehicle passes through the target risk area along the task path, as a recovery area, and determine, according to the position information of the unmanned vehicle, whether the unmanned vehicle reaches the recovery area under the control of the remote control device.

Optionally, the control module 206 is configured to send the stop instruction to the unmanned vehicle, so that the unmanned vehicle stops running according to the stop instruction, and after it is determined that the unmanned vehicle stops running, send a recovery instruction to the unmanned vehicle, so that the unmanned vehicle re-determines the position information of the unmanned vehicle according to the recovery instruction, and continues to execute the task according to the task path and the position information of the unmanned vehicle.

Optionally, the control module 206 is configured to receive an automatic driving request sent by a remote control device corresponding to the target risk area, where the automatic driving request is sent after the remote control device remotely controls the unmanned vehicle to pass through the target risk area, and send an automatic driving instruction to the unmanned vehicle according to the automatic driving request, so that the unmanned vehicle continues to execute a task according to the automatic driving instruction and the task path.

Optionally, the control module 206 is configured to determine that another unmanned vehicle has a risk according to the position information of the another unmanned vehicle and the target risk area, determine priorities of the unmanned vehicle and the another unmanned vehicle having the risk according to the state information of the unmanned vehicle and the another unmanned vehicle having the risk, determine a time when the unmanned vehicle passes through the target risk area according to the determined priorities, and send a stop instruction carrying the time to the unmanned vehicle, so that the unmanned vehicle sends a control request to the remote control device corresponding to the target risk area according to the time after receiving the stop instruction.

The present specification also provides a computer-readable storage medium storing a computer program for executing the unmanned vehicle distribution method provided in fig. 1.

This specification also provides a schematic block diagram of the electronic device shown in fig. 9. As shown in fig. 9, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, but may also include hardware required for other services. The processor reads a corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to implement the unmanned vehicle distribution method described in fig. 1. Of course, besides the software implementation, the present specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may be hardware or logic devices.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.

Claims

1. An unmanned vehicle distribution method, comprising:

2. The method of claim 1, wherein determining the location of each risk zone specifically comprises:

acquiring the driving track of an unmanned vehicle in each historical task;

3. The method according to claim 1, wherein determining whether the unmanned vehicle has a risk according to the position information of the unmanned vehicle and the target risk area specifically comprises:

4. The method according to claim 1, wherein sending a stop instruction to the unmanned vehicle so that the unmanned vehicle sends a control request to a remote control device corresponding to the target risk area after receiving the stop instruction, specifically comprises:

5. The method of claim 1, wherein the method further comprises:

6. The method according to claim 5, wherein determining whether the unmanned vehicle passes through the target risk area according to the position information of the unmanned vehicle and the target risk area specifically comprises:

7. The method according to claim 5, wherein sending an automatic driving instruction to the unmanned vehicle to enable the unmanned vehicle to continue to execute a task according to the automatic driving instruction and the task path comprises:

8. The method of claim 1, wherein the method further comprises:

9. The method according to claim 1, wherein sending a stop instruction to the unmanned vehicle so that the unmanned vehicle sends a control request to a remote control device corresponding to the target risk area after receiving the stop instruction, specifically comprises:

10. The method according to claim 9, wherein the status information includes at least one of a type of a delivery of a task performed by the unmanned vehicle, a remaining delivery time of the task, a length of a remaining task route of the task, and a congestion status of the remaining task route, the remaining delivery time being positively correlated with the priority, the length of the remaining task route being negatively correlated with the priority, and the congestion status of the remaining task route being positively correlated with the priority.

11. An unmanned vehicle dispensing device, comprising:

12. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1 to 10.

13. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 10 when executing the program.