Geofence-based unmanned logistics vehicle intersection scheduling method and storage medium

Abstract

The invention provides an unmanned logistics vehicle intersection scheduling method based on a geofence and a storage medium, and belongs to the field of automatic driving. According to the method, a geo-fence is arranged at an intersection, a monitoring range and a scheduling range are set according to the geo-fence, the area of the monitoring range is larger than that of the scheduling range, and the area of the scheduling range is larger than that of the geo-fence; if the unmanned logistics vehicle enters the monitoring range, adding the unmanned logistics vehicle into a monitoring vehicle queue; if the unmanned logistics vehicles in the monitoring vehicle queue enter the scheduling range, adding the unmanned logistics vehicles into a scheduling vehicle queue; and setting a conflict queue according to the distance from each unmanned logistics vehicle in the scheduling vehicle queue to the geo-fence, and scheduling the unmanned logistics vehicles according to the conflict queue. The vehicles are reasonably dispatched in advance, the passing efficiency can be improved, and meanwhile the situation that the intersection is blocked by multiple vehicles is avoided.

Description

Technical Field

[0001] The present invention belongs to the field of autonomous driving, and in particular relates to an intersection dispatching method for unmanned logistics vehicles based on geographic fences and a storage medium. Background Art

[0002] With the development of autonomous driving technology, unmanned logistics vehicles can rely on single-vehicle intelligence such as perception, positioning, control, and decision-making to complete the transportation of goods from point A to point B in the park. However, in actual operation, when multiple vehicles pass through the intersection at the same time, there is often a delay in the recognition and obstacle avoidance of a single vehicle, which leads to a collision. Therefore, a cloud-based intersection dispatching solution was born.

[0003] The conventional intersection dispatching scheme is to calculate the distance from the center of the intersection to the vehicle, and only allow one vehicle to enter within a certain distance, while other vehicles wait. However, this scheme has the following problems:

[0004] (1) For asymmetric intersections, the distance from the vehicle to the intersection is not very accurate when the intersection center and the vehicle position are used to calculate the distance, which results in incorrect arrangement of the vehicle traffic order.

[0005] (2) Based on a single threshold, the decision is made only when the distance information is less than the threshold, without considering issues such as scheduling control delays;

[0006] (3) The order of passage is determined by dividing the distance by the speed to obtain the time, which is easily affected by speed fluctuations, resulting in the passage order possibly changing multiple times.

[0007] In summary, the current intersection dispatching scheme for unmanned logistics vehicles only uses the distance from the vehicle to the center point of the intersection to calculate the distance, which has the problem of inaccurate distance calculation for irregular intersections; and it is based on a single distance threshold, which has the problem of dispatching delay. Summary of the invention

[0008] The purpose of the present invention is to solve the difficulties existing in the above-mentioned prior art, and to provide an unmanned logistics vehicle intersection scheduling method and storage medium based on geographic fences. Relying on high-precision maps, geographic fences are set according to the shape of the intersection to automatically schedule unmanned logistics vehicles at intersections, so as to solve the problems of untimely scheduling of vehicles and inaccurate parking points when vehicles are about to meet at an intersection.

[0009] The present invention is achieved through the following technical solutions:

[0010] A first aspect of the present invention provides a method for dispatching unmanned logistics vehicles at intersections based on geo-fences, wherein the method sets a geo-fence at an intersection, and sets a monitoring range and a dispatching range according to the geo-fence, wherein the area of ​​the monitoring range is larger than the area of ​​the dispatching range, and the area of ​​the dispatching range is larger than the area of ​​the geo-fence;

[0011] If an unmanned logistics vehicle enters the monitoring range, the unmanned logistics vehicle will be added to the monitoring vehicle queue; if an unmanned logistics vehicle in the monitoring vehicle queue enters the dispatching range, the unmanned logistics vehicle will be added to the dispatching vehicle queue;

[0012] A conflict queue is set according to the distance from each unmanned logistics vehicle in the dispatch vehicle queue to the geographic fence, and the unmanned logistics vehicles are dispatched according to the conflict queue.

[0013] like Figure 2 As shown, the method includes:

[0014] (1) Import the same set of high-precision maps into the vehicle and the cloud;

[0015] (2) Draw a geographic fence and set the dispatch range and monitoring range;

[0016] (3) For each HD map segment, the cloud determines whether the HD map segment has an intersection with the geographic fence. If there is an intersection, the cloud finds the two intersections closest to the unmanned logistics vehicle and connects the two intersections with a straight line. The midpoint of the line between the two intersections is taken as the parking point.

[0017] (4) When the unmanned logistics vehicle travels from the starting point to the end point, it reports the list of serial numbers of the traveled sections to the cloud;

[0018] (5) The cloud periodically determines whether the unmanned logistics vehicle has entered the monitoring range. If so, the unmanned logistics vehicle is added to the monitoring vehicle queue;

[0019] (6) The cloud periodically determines whether the unmanned logistics vehicle in the monitoring vehicle queue has entered the dispatch range. If so, the unmanned logistics vehicle is added to the dispatch vehicle queue;

[0020] (7) Setting a conflict queue based on the distance from each unmanned logistics vehicle in the dispatching vehicle queue to the parking point;

[0021] (8) Dispatch unmanned logistics vehicles according to conflicting queues.

[0022] A further improvement of the present invention is:

[0023] In step (1), when the cloud imports the high-precision map, it will obtain the longitude and latitude information of each segment of the high-precision map;

[0024] The driving trajectory of an unmanned logistics vehicle is a line. The left line is formed by moving the driving trajectory to the left by a set distance, and the right line is formed by moving the driving trajectory to the right by the same set distance. The left line and the right line enclose a section of high-precision map, and the driving trajectory is the center line of this section of high-precision map; the left line and the right line are composed of multiple points, and the latitude and longitude information of each section of high-precision map includes the longitude and latitude of each point on the left line and the right line of this section of high-precision map.

[0025] A further improvement of the present invention is:

[0026] In step (2), a geographic fence is drawn in the cloud; the monitoring range, the dispatching range and the geographic fence are concentric similar polygons, and the area of ​​the monitoring range is larger than the area of ​​the dispatching range, and the area of ​​the dispatching range is larger than the area of ​​the geographic fence.

[0027] A further improvement of the present invention is:

[0028] The driving section serial number list information in step (4) includes: the serial numbers of all high-precision maps that the unmanned logistics vehicle must pass from the starting point to the end point, and the serial numbers are arranged in sequence from the starting point to the end point.

[0029] A further improvement of the present invention is:

[0030] The operation of step (7) includes:

[0031] If there is only one unmanned logistics vehicle in the dispatch vehicle queue, the conflict queue is empty, and the cloud sends a high priority to the unmanned logistics vehicle;

[0032] If the number of unmanned logistics vehicles in the dispatching vehicle queue is greater than or equal to 2, the distances from the unmanned logistics vehicles in the dispatching vehicle queue to the parking point along the driving trajectory are sorted in real time in ascending order to obtain a conflict queue.

[0033] A further improvement of the present invention is:

[0034] The operation of step (8) includes:

[0035] (81) The cloud sends a high priority to the first vehicle in the conflict queue. At this time, the first vehicle has a high priority.

[0036] (82) Perform the following operations on the second to the last vehicle in the conflict queue:

[0037] Take the unmanned logistics vehicle as the target vehicle, and determine whether there is a vehicle with high priority among the unmanned logistics vehicles before the target vehicle in the conflict queue. If not, the cloud sends a high priority to the target vehicle, and the target vehicle has a high priority at this time; if so, determine whether the target vehicle and the unmanned logistics vehicle with high priority have an intersection. If not, the cloud sends a high priority to the target vehicle, and the target vehicle has a high priority at this time; if so, the cloud sends a low priority to the target vehicle, and the target vehicle has a low priority at this time, and the target vehicle slows down and drives to its corresponding parking point.

[0038] A further improvement of the present invention is:

[0039] In step (82), the operation of determining whether the target vehicle and the unmanned logistics vehicle with a high priority have an intersection includes:

[0040] If there are four intersection points between the left and right lines of the high-precision map of the target vehicle and the high-precision map of the unmanned logistics vehicle with a high priority, the midpoint of the quadrilateral formed by the four intersection points is taken as the intersection point of the two vehicles, and it is determined that the vehicle has an intersection with the unmanned logistics vehicle with a high priority.

[0041] A further improvement of the present invention is:

[0042] The method further comprises:

[0043] (9) Perform the following operations before the target vehicle reaches the parking point:

[0044] Determine whether all unmanned logistics vehicles with high priority that have an intersection with the target vehicle have passed the intersection. If not, the target vehicle stops at the parking point; if so, the cloud changes the low priority of the target vehicle to a high priority and continues to run along its driving trajectory.

[0045] A further improvement of the present invention is:

[0046] In step (9), the operation of determining whether all the unmanned logistics vehicles with a high priority that have an intersection with the target vehicle have passed the intersection includes:

[0047] Perform the following operations on each unmanned logistics vehicle that has an intersection with the target vehicle and has a high priority:

[0048] The distance from the unmanned logistics vehicle to the intersection is calculated, and it is determined whether the distance from the unmanned logistics vehicle to the intersection is less than or equal to 0. If so, it is determined that the unmanned logistics vehicle has passed the intersection. If not, it is determined that the unmanned logistics vehicle has not passed the intersection.

[0049] A further improvement of the present invention is:

[0050] The operations for calculating the distance from the unmanned logistics vehicle to the intersection point include:

[0051] Taking the starting point of the current high-precision map of the unmanned logistics vehicle as the reference point, the distance from the unmanned logistics vehicle to the intersection is calculated using the following formula:

[0052] The distance from the unmanned logistics vehicle to the intersection = the distance from the intersection to the reference point - the distance from the unmanned logistics vehicle to the reference point.

[0053] According to a second aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one computer-executable program, and when the at least one program is executed by the computer, the computer executes the steps in the unmanned logistics vehicle intersection scheduling method based on geographic fence.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] The present invention relies on the geographic fence at the intersection to obtain the driving route information of the unmanned logistics vehicle and the intersection with the geographic fence. For any intersection, the distance from the vehicle to the intersection can be accurately obtained, and the monitoring range and the dispatching range can be divided. The vehicle can be reasonably dispatched in advance, which can improve the traffic efficiency and avoid multiple vehicles blocking the intersection. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A flowchart of the steps of the unmanned logistics vehicle intersection dispatching method based on geographic fences of the present invention;

[0057] Figure 2 The geographic fence, monitoring range and dispatch range of the intersection drawn in the method of the present invention;

[0058] Figure 3 A schematic diagram of the distance between a vehicle and the intersection point of two vehicles in the method of the present invention. DETAILED DESCRIPTION

[0059] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0060] The present invention provides an unmanned logistics vehicle intersection dispatching method based on geographic fences. The method sets a geographic fence at an intersection, and sets a monitoring range and a dispatching range according to the geographic fence, wherein the area of ​​the monitoring range is larger than the area of ​​the dispatching range, and the area of ​​the dispatching range is larger than the area of ​​the geographic fence; if an unmanned logistics vehicle enters the monitoring range, the unmanned logistics vehicle is added to a monitoring vehicle queue; if an unmanned logistics vehicle in the monitoring vehicle queue enters the dispatching range, the unmanned logistics vehicle is added to the dispatching vehicle queue; a conflict queue is set according to the distance from each unmanned logistics vehicle in the dispatching vehicle queue to the geographic fence, and the unmanned logistics vehicle is dispatched according to the conflict queue.

[0061] like Figure 1 As shown, the method includes:

[0062] (1) Import the same set of high-precision maps on the vehicle and in the cloud:

[0063] The vehicle and the cloud use the same set of high-precision maps, and when the cloud imports the high-precision maps, it will obtain the longitude and latitude information of each section of the high-precision maps.

[0064] The driving track of each unmanned logistics vehicle corresponds to multiple high-precision maps connected in sequence. Each high-precision map corresponds to two lines on the left and right sides of the vehicle's driving track, that is, the driving track of an unmanned logistics vehicle is a line (straight or curved), and the driving track is moved to the left by a set distance to form the left line, and the driving track is moved to the right by the same set distance to form the right line. In this way, the left line and the right line enclose a high-precision map, and the driving track is the center line of the high-precision map. The left line and the right line are composed of multiple points, each of which has longitude and latitude. The longitude and latitude information of each high-precision map includes the longitude and latitude of each point on the left line and the right line of the high-precision map.

[0065] For example, an unmanned logistics vehicle wants to turn right through an intersection. The left and right lines of the current straight driving trajectory (straight line) constitute the first section of the high-precision map, the left and right lines of the right-turn driving trajectory (arc) constitute the second section of the high-precision map, and the left and right lines of the driving trajectory (straight line) after the turn constitute the third section of the high-precision map. In this way, the unmanned logistics vehicle has to go through three sections of high-precision maps.

[0066] (2) Draw a geographic fence and set the dispatch range and monitoring range:

[0067] Draw geo-fences in the cloud. Geographic fences can be manually set to any shape based on the shape of the intersection. Figure 2 The geo-fence in the illustrated embodiment is a square frame consisting of four points, and the drawing of the geo-fence is achieved by manually setting the four points. The geo-fence can also be set as an irregular polygon.

[0068] The monitoring range and dispatch range are also manually set in the cloud. The monitoring range, dispatch range and geo-fence are concentric similar polygons, that is, the three are three polygons with the same center and are enlarged in equal proportions. The area of ​​the monitoring range is larger than the area of ​​the dispatch range, and the area of ​​the dispatch range is larger than the area of ​​the geo-fence. For example, Figure 2 As shown, the geofence is a rectangular shape (such as Figure 2 The dispatch range is also a rectangle (such as Figure 2 The length and width of the rectangle are 40 meters larger than the length and width of the geographic fence, and the monitoring range is also a rectangle (such as Figure 2The length and width of the geofence are 100 meters greater than the length and width of the geofence, that is, Figure 2 The distance between the double arrows next to "Dispatch range" is 20 meters, and the distance between the double arrows next to "Monitoring range" is 50 meters.

[0069] (3) For each HD map segment, the cloud determines whether the HD map segment has an intersection with the geo-fence (the intersection can be determined by traversing the longitude and latitude of the left and right lines of each HD map segment and the longitude and latitude of each point of the geo-fence. If there are points with the same longitude and latitude, it can be determined that there is an intersection. This can be achieved using existing technologies and will not be described in detail here). If there is an intersection, the two closest intersections to the unmanned logistics vehicle are found (from the driving direction, the two closest intersections to the unmanned logistics vehicle are the locations where the vehicle arrives first. These two intersections can be used to determine whether the vehicle will enter the geo-fence), and the two intersections are connected by a straight line. The midpoint of the line between the two intersections is taken as the parking point. The parking point is also the point for calculating the distance from the vehicle to the geo-fence. Figure 2 Point P in the figure represents the midpoint of the line connecting the intersection of a section of the high-precision map and the geographic fence, i.e., the parking point. Point P1 represents the midpoint of the line connecting the intersection of another section of the high-precision map and the geographic fence, i.e., the parking point. It can be seen that the midpoints of the lines connecting the intersections of different sections of the high-precision map and the geographic fence are different, i.e., different high-precision maps have different parking points P.

[0070] (4) When the unmanned logistics vehicle travels from the starting point to the end point, it reports the list of serial numbers of the traveled sections to the cloud through the https protocol.

[0071] The driving section sequence number list information includes the sequence numbers of all high-precision maps that the unmanned logistics vehicle needs to pass from the starting point to the end point, and the sequence numbers are arranged in order from the starting point to the end point. For example, if the sequence number of the first high-precision map that a vehicle needs to pass from the starting point to the end point is 5, the sequence number of the second high-precision map is 2, and the sequence number of the third high-precision map is 4, then the driving section sequence number list information is 5, 2, 4.

[0072] (5) The cloud periodically determines (once at a set time, for example, it can be manually set to determine once every 200 milliseconds) whether the unmanned logistics vehicle has entered the monitoring range (the unmanned logistics vehicle is regarded as a point, and the monitoring range is a polygon. The existing algorithm for determining whether a point is within the polygon can be used to achieve this determination. For example, the detailed algorithm disclosed at https: / / www.cnblogs.com / charlee44 / p / 10704156.html can be used). If so, the unmanned logistics vehicle is added to the monitoring vehicle queue corresponding to the geographic fence (each geographic fence corresponds to a monitoring vehicle queue). When the unmanned logistics vehicle leaves the monitoring range, it is deleted from the monitoring vehicle queue. If all unmanned logistics vehicles have not entered the monitoring range or have left the monitoring range, the monitoring vehicle queue is empty.

[0073] (6) The cloud performs a scheduled judgment (judgment every set time, for example, it can be manually set to judge every 200 milliseconds) to monitor whether the unmanned logistics vehicles in the vehicle queue have entered the dispatch range (the vehicle is regarded as a point, and the dispatch range is a polygon. The existing algorithm for judging whether a point is within the polygon can be used to implement this judgment. For example, the detailed algorithm disclosed on https: / / www.cnblogs.com / charlee44 / p / 10704156.html can be used). If so, the unmanned logistics vehicle is added to the dispatch vehicle queue corresponding to the geographic fence (each geographic fence corresponds to a dispatch vehicle queue). When the unmanned logistics vehicle leaves the dispatch range, it is deleted from the dispatch vehicle queue. If all unmanned logistics vehicles have not entered the dispatch range or have left the dispatch range, the dispatch vehicle queue is empty.

[0074] (7) Set up a conflict queue based on the distance between each unmanned logistics vehicle in the dispatching vehicle queue and the parking point:

[0075] If there is only one unmanned logistics vehicle in the dispatch vehicle queue, the conflict queue is empty, and the cloud sends a high priority to the unmanned logistics vehicle through the MQTT protocol, allowing it to pass directly; if the number of unmanned logistics vehicles in the dispatch vehicle queue is greater than or equal to 2, the distance from the unmanned logistics vehicles in the dispatch vehicle queue along the driving trajectory to the parking point (the parking points of different high-precision maps are different, and the distance from the unmanned logistics vehicle along the driving trajectory to its corresponding parking point, that is, the distance from the unmanned logistics vehicle to the geographic fence) is sorted in real time in ascending order to obtain a conflict queue.

[0076] Specifically, the first vehicle from front to back in the conflict queue is the vehicle closest to its corresponding parking point along its driving trajectory, that is, the vehicle that will enter the geo-fence the earliest. The second vehicle is the vehicle second closest to the parking point along its driving trajectory, and so on. The last vehicle is the vehicle farthest from the parking point along its driving trajectory, that is, the vehicle that will enter the geo-fence the latest. There is only the first vehicle before the second vehicle, and there are the first and second vehicles before the third vehicle, and so on.

[0077] When multiple unmanned logistics vehicles enter the dispatching range, it is necessary to consider whether a conflict will occur. Therefore, the present invention examines the situation of conflict by setting a conflict queue.

[0078] (8) Dispatch the unmanned logistics vehicles according to the conflict queues. The specific operations include:

[0079] (81) The cloud sends a high priority to the first car in the conflict queue through the MQTT protocol, allowing the first car to pass directly; at this time, the first car has a high priority;

[0080] (82) Perform the following operations on the second to the last vehicle in the conflict queue:

[0081] Take the unmanned logistics vehicle as the target vehicle, and determine whether there is a vehicle with high priority among the unmanned logistics vehicles before the target vehicle in the conflict queue. If not, the cloud sends a high priority to the target vehicle through the MQTT protocol. At this time, the target vehicle has a high priority, that is, it is allowed to pass. If it does, determine whether the target vehicle has an intersection with the unmanned logistics vehicle with high priority. If not (that is, there is no intersection with each unmanned logistics vehicle with high priority), the cloud sends a high priority to the target vehicle through the MQTT protocol. At this time, the target vehicle has a high priority, that is, it is allowed to pass, which greatly improves the traffic efficiency; if yes (that is, there is an intersection with any vehicle with high priority), the cloud sends a low priority to the target vehicle through the MQTT protocol. At this time, the target vehicle has a low priority, and the target vehicle slows down and drives to its corresponding parking point.

[0082] For example, for the second car in the conflict queue, it is only necessary to determine whether the car has an intersection with the first car; for the third car, it is first necessary to determine whether the car has an intersection with the first car, and secondly, if the second car needs to stop (that is, the second car does not have a high priority), there is no need to determine whether the third car has an intersection with the second car; if the second car can pass through the first car (that is, the second car also has a high priority), it is also necessary to determine whether the third car has an intersection with the second car; and so on for the fourth, fifth, and so on.

[0083] More specifically, the operation of determining whether the target vehicle and the unmanned logistics vehicle with a high priority have an intersection is as follows:

[0084] If there are four intersection points between the left and right lines of the high-precision map of the target vehicle and the high-precision map of the unmanned logistics vehicle with a high priority, the midpoint of the quadrilateral formed by the four intersection points is taken as the intersection point of the two vehicles, and it is determined that the vehicle has an intersection with the vehicle with a high priority.

[0085] For example, for each vehicle, they have their own multi-segment high-precision map, and each segment of the high-precision map includes two side lines, namely the left side line and the right side line. The intersection of the two vehicles is actually the intersection of the high-precision maps of the two vehicles. If the two side lines of vehicle A and the two side lines of vehicle B intersect, four intersections will be generated. The midpoint of the quadrilateral surrounded by the four intersections can be taken as the intersection of the two vehicles. For example, there is no intersection when going straight at the intersection and going straight in the opposite direction, and there is no intersection when turning right and going straight in the opposite direction. These belong to the existing technology and will not be repeated here. The distance from the unmanned logistics vehicle to the intersection is the distance from the current position of the vehicle to the intersection along the driving trajectory.

[0086] The method further comprises:

[0087] (9) Before the target vehicle reaches the parking point, the following operations can be performed:

[0088] It is determined whether all the unmanned logistics vehicles with high priority that have an intersection with the target vehicle have passed the intersection. If not (that is, at least one vehicle that has an intersection with the target vehicle has not passed the intersection), the target vehicle stops normally at the parking point; if yes (that is, all vehicles that have an intersection with the target vehicle have passed the intersection), the cloud modifies the low priority of the target vehicle to a high priority through the MQTT protocol, that is, the target vehicle does not need to stop at the parking point, but is allowed to pass, and continues to run along its driving trajectory after passing the parking point, which can also improve the traffic efficiency.

[0089] Specifically, the operation of determining whether all the unmanned logistics vehicles with a high priority that have an intersection with the target vehicle have passed the intersection includes:

[0090] Perform the following operations on each unmanned logistics vehicle that has an intersection with the target vehicle and has a high priority:

[0091] The distance from the unmanned logistics vehicle to the intersection is calculated, and it is determined whether the distance from the unmanned logistics vehicle to the intersection is less than or equal to 0. If so, it is determined that the unmanned logistics vehicle has passed the intersection. If not, it is determined that the unmanned logistics vehicle has not passed the intersection.

[0092] More specifically, the operation of calculating the distance from the unmanned logistics vehicle to the intersection point includes:

[0093] Taking the starting point of the current high-precision map of the unmanned logistics vehicle (i.e. the high-precision map of the current section of the unmanned logistics vehicle) as the reference point, the distance from the unmanned logistics vehicle to the intersection is calculated using the following formula:

[0094] The distance from the unmanned logistics vehicle to the intersection = the distance from the intersection to the reference point - the distance from the unmanned logistics vehicle to the reference point.

[0095] It can be seen that when the unmanned logistics vehicle passes the intersection, the "distance from the unmanned logistics vehicle to the intersection" is negative. This distance can also be understood as displacement, that is, the intersection is the 0 point. When the vehicle is between the intersection and the starting point of the high-precision map (i.e. the reference point), the distance from the vehicle to the intersection is positive. When the vehicle reaches the intersection, the distance from the vehicle to the intersection is 0. When the vehicle is between the intersection and the end point of the high-precision map, the distance from the vehicle to the intersection is negative. Figure 3 As shown in the figure, the black straight line and the red straight line respectively represent a section of the high-precision map where the two vehicles are currently located, and the green dot represents the intersection point 3 of the two vehicles (the midpoint of the quadrilateral formed by the four points where the two black straight lines and the two red straight lines intersect). Taking the intersection point 3 as the 0 point, the vehicle passes through the position 1 represented by the blue dot, the intersection point 3 represented by the green dot, and the position 2 represented by the pink dot from right to left along the driving trajectory. In this way, when the vehicle is at position 1, the distance from the vehicle to the intersection point 3 is positive; when the vehicle is at the green dot 3, the distance from the vehicle to the intersection point is 0, and when the vehicle is at position 2, the distance from the intersection point to the vehicle is negative.

[0096] In summary, the present invention calculates the distance from the vehicle to the intersection based on the drawn geographic fence, which is more accurate and can cover intersections in any scenario; moreover, the present invention sets a scheduling range according to the geographic fence, which can achieve early scheduling and avoid delays; in addition, the parking point calculated by the present invention using the intersection of the geographic fence and the road section in the high-precision map is very accurate.

[0097] The above technical solution is only one implementation mode of the present invention. For those skilled in the art, it is easy to make various types of improvements or modifications based on the principles disclosed in the present invention, and it is not limited to the technical solution described in the above specific embodiments of the present invention. Therefore, the above description is only preferred and does not have a restrictive meaning.

Claims

1. A method for dispatching unmanned logistics vehicles at intersections based on geographic fences, characterized by: The method sets a geographic fence at the intersection, and sets a monitoring range and a dispatching range according to the geographic fence, and the area of ​​the monitoring range is larger than the area of ​​the dispatching range, and the area of ​​the dispatching range is larger than the area of ​​the geographic fence; If an unmanned logistics vehicle enters the monitoring range, the unmanned logistics vehicle will be added to the monitoring vehicle queue; If the unmanned logistics vehicle in the monitoring vehicle queue enters the dispatch range, the unmanned logistics vehicle is added to the dispatch vehicle queue; A conflict queue is set according to the distance from each unmanned logistics vehicle in the dispatch vehicle queue to the geographic fence, and the unmanned logistics vehicles are dispatched according to the conflict queue.

2. The method for dispatching unmanned logistics vehicles at intersections based on geographic fences according to claim 1, characterized in that: The method comprises: (1) Import the same set of high-precision maps into the vehicle and the cloud; (2) Draw a geographic fence and set the dispatch range and monitoring range; (3) For each HD map segment, the cloud determines whether the HD map segment has an intersection with the geographic fence. If there is an intersection, the cloud finds the two intersections closest to the unmanned logistics vehicle and connects the two intersections with a straight line. The midpoint of the line between the two intersections is taken as the parking point. (4) When the unmanned logistics vehicle travels from the starting point to the end point, it reports the list of serial numbers of the traveled sections to the cloud; (5) The cloud periodically determines whether the unmanned logistics vehicle has entered the monitoring range. If so, the unmanned logistics vehicle is added to the monitoring vehicle queue; (6) The cloud periodically determines whether the unmanned logistics vehicle in the monitoring vehicle queue has entered the dispatch range. If so, the unmanned logistics vehicle is added to the dispatch vehicle queue; (7) Setting a conflict queue based on the distance from each unmanned logistics vehicle in the dispatching vehicle queue to the parking point; (8) Dispatch unmanned logistics vehicles according to conflicting queues.

3. The method for dispatching unmanned logistics vehicles at intersections based on geographic fences according to claim 2, characterized in that: In step (1), when the cloud imports the high-precision map, it will obtain the longitude and latitude information of each segment of the high-precision map; The driving trajectory of an unmanned logistics vehicle is a line. The left line is formed by moving the driving trajectory to the left by a set distance, and the right line is formed by moving the driving trajectory to the right by the same set distance. The left line and the right line enclose a section of high-precision map, and the driving trajectory is the center line of this section of high-precision map; the left line and the right line are composed of multiple points, and the latitude and longitude information of each section of high-precision map includes the longitude and latitude of each point on the left line and the right line of this section of high-precision map.

4. The method for dispatching unmanned logistics vehicles at intersections based on geographic fences according to claim 2, characterized in that: In step (2), a geographic fence is drawn in the cloud; the monitoring range, the scheduling range and the geographic fence are concentric similar polygons.

5. The method for dispatching unmanned logistics vehicles at intersections based on geographic fences according to claim 2, characterized in that: The driving section serial number list information in step (4) includes: the serial numbers of all high-precision maps that the unmanned logistics vehicle must pass from the starting point to the end point, and the serial numbers are arranged in sequence from the starting point to the end point.

6. The method for dispatching unmanned logistics vehicles at intersections based on geographic fences according to claim 2, characterized in that: The operation of step (7) includes: If there is only one unmanned logistics vehicle in the dispatch vehicle queue, the conflict queue is empty, and the cloud sends a high priority to the unmanned logistics vehicle; If the number of unmanned logistics vehicles in the dispatching vehicle queue is greater than or equal to 2, the distances from the unmanned logistics vehicles in the dispatching vehicle queue to the parking point along the driving trajectory are sorted in real time in ascending order to obtain a conflict queue.

7. The method for dispatching unmanned logistics vehicles at intersections based on geographic fences according to claim 2, characterized in that: The operation of step (8) includes: (81) The cloud sends a high priority to the first vehicle in the conflict queue. At this time, the first vehicle has a high priority. (82) Perform the following operations on the second to the last vehicle in the conflict queue: Take the unmanned logistics vehicle as the target vehicle, and determine whether there is a vehicle with high priority among the unmanned logistics vehicles before the target vehicle in the conflict queue. If not, the cloud sends a high priority to the target vehicle, and the target vehicle has a high priority at this time; if so, determine whether the target vehicle and the unmanned logistics vehicle with high priority have an intersection. If not, the cloud sends a high priority to the target vehicle, and the target vehicle has a high priority at this time; if so, the cloud sends a low priority to the target vehicle, and the target vehicle has a low priority at this time, and the target vehicle slows down and drives to its corresponding parking point.

8. The method for dispatching unmanned logistics vehicles at intersections based on geographic fences according to claim 7, characterized in that: In step (82), the operation of determining whether the target vehicle and the unmanned logistics vehicle with a high priority have an intersection includes: If there are four intersection points between the left and right lines of the high-precision map of the target vehicle and the high-precision map of the unmanned logistics vehicle with a high priority, the midpoint of the quadrilateral formed by the four intersection points is taken as the intersection point of the two vehicles, and it is determined that the vehicle has an intersection with the unmanned logistics vehicle with a high priority.

9. The method for dispatching unmanned logistics vehicles at intersections based on geographic fences according to claim 2, characterized in that: The method further comprises: (9) Perform the following operations before the target vehicle reaches the parking point: Determine whether all unmanned logistics vehicles with high priority that have an intersection with the target vehicle have passed the intersection. If not, the target vehicle stops at the parking point; if so, the cloud changes the low priority of the target vehicle to a high priority and continues to run along its driving trajectory.

10. The method for dispatching unmanned logistics vehicles at intersections based on geographic fences according to claim 9, characterized in that: In step (9), the operation of determining whether all the unmanned logistics vehicles with a high priority that have an intersection with the target vehicle have passed the intersection includes: Perform the following operations on each unmanned logistics vehicle that has an intersection with the target vehicle and has a high priority: The distance from the unmanned logistics vehicle to the intersection is calculated, and it is determined whether the distance from the unmanned logistics vehicle to the intersection is less than or equal to 0. If so, it is determined that the unmanned logistics vehicle has passed the intersection. If not, it is determined that the unmanned logistics vehicle has not passed the intersection.

11. The method for dispatching unmanned logistics vehicles at intersections based on geographic fences according to claim 10, characterized in that: The operations for calculating the distance from the unmanned logistics vehicle to the intersection point include: Taking the starting point of the current high-precision map of the unmanned logistics vehicle as the reference point, the distance from the unmanned logistics vehicle to the intersection is calculated using the following formula: The distance from the unmanned logistics vehicle to the intersection = the distance from the intersection to the reference point - the distance from the unmanned logistics vehicle to the reference point.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one computer-executable program, and when the at least one program is executed by the computer, the computer executes the steps in the geo-fence-based unmanned logistics vehicle intersection scheduling method as described in any one of claims 1 to 11.

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