Travel route determination method and device, terminal and medium

Abstract

The application provides a driving path determination method and device, a terminal and a medium. The method comprises: constructing a coordinate system based on the position of an autonomous vehicle and the position of the center line of the road on which the autonomous vehicle is located; determining a drivable area based on the obstacle information of the road in the lateral direction of the coordinate system, the obstacle information of the road after sampling in the longitudinal direction of the coordinate system, the position of the autonomous vehicle and the boundary of the road; and determining the driving path of the autonomous vehicle based on the drivable area and the set conditions met by the driving data of the autonomous vehicle in the driving process. By using the sampled obstacle information in the longitudinal direction and the unsampled obstacle information in the lateral direction, the accuracy of the obstacle information in the lateral direction is improved while reducing the amount of calculation that needs to be processed, thereby improving the accuracy of the determined driving path.

Description

TECHNICAL FIELD

[0001] The present specification relates to the technical field of automatic driving, and in particular, to a driving path determination method and device, a terminal and a medium. BACKGROUND

[0002] As an important approach to improving the intelligent level of road traffic and promoting the transformation and upgrading of the transportation industry, automatic driving technology has gradually become an important research direction.

[0003] In the automatic driving technology, an automatic driving vehicle determines a fast, safe and feasible driving path by integrating information such as perception, positioning, map and vehicle, and then drives according to the determined driving path, so as to realize the automatic driving of the vehicle. Therefore, how to more accurately determine the driving path has become an important issue in the automatic driving technology. SUMMARY

[0004] To more accurately plan a driving path for an automatic driving vehicle, the present specification provides a driving path determination method, device, terminal and medium as follows.

[0005] According to a first aspect of an embodiment of the present specification, a driving path determination method is provided, and the method comprises:

[0006] Based on the position of the automatic driving vehicle and the position of the center line of the road where the automatic driving vehicle is located, a coordinate system is constructed, the longitudinal direction of the coordinate system indicates the center line direction of the road, and the transverse direction of the coordinate system indicates the direction perpendicular to the center line of the road;

[0007] Based on the obstacle information of the road in the transverse direction of the coordinate system, the obstacle information of the road after sampling in the longitudinal direction of the coordinate system, the position of the automatic driving vehicle and the boundary of the road, a drivable area is determined;

[0008] Based on the drivable area and the set conditions met by the driving data of the automatic driving vehicle in the driving process, a driving path of the automatic driving vehicle is determined.

[0009] In some embodiments, the method further comprises:

[0010] In the longitudinal direction of the coordinate system, the obstacle information in the road is sampled.

[0011] In some embodiments, when the coordinate system is constructed, the obstacles in the road are mapped as polygons;

[0012] In the longitudinal direction of the coordinate system, the obstacle information in the road is sampled, comprising:

[0013] The obstacle information of the obstacle corresponding to the polygon is sampled on the side of the polygon in the same direction as the longitudinal direction of the coordinate system.

[0014] In some embodiments, a coordinate system is constructed based on the position of the autonomous vehicle and road information of the road on which the autonomous vehicle is located, including:

[0015] The position of the autonomous vehicle is taken as the coordinate origin of the coordinate system, the tangent direction of the center line of the road is taken as the longitudinal direction of the coordinate system, and the normal direction of the center line of the road is taken as the transverse direction of the coordinate system.

[0016] In some embodiments, the drivable area is determined based on the obstacle information of the road in the transverse direction of the coordinate system, the obstacle information of the road sampled in the longitudinal direction of the coordinate system, the position of the autonomous vehicle, and the boundary of the road, including:

[0017] Based on the obstacle information of the road in the transverse direction of the coordinate system, the obstacle information of the road sampled in the longitudinal direction of the coordinate system, and the boundary of the road, the width of the passable gap between obstacles and between obstacles and the boundary of the road in the driving direction of the autonomous vehicle is obtained.

[0018] The position of the autonomous vehicle is taken as the root node, and the plurality of passable gaps obtained are expanded layer by layer in order of the distance from the obstacle to the position of the autonomous vehicle from small to large, to obtain a first search tree.

[0019] In order of the width corresponding to the plurality of nodes in the first search tree from large to small, a preset number of nodes in the front of the order are retained.

[0020] The drivable area is determined based on the width corresponding to the retained nodes.

[0021] In some embodiments, the drivable area is determined based on the obstacle information of the road in the transverse direction of the coordinate system, the obstacle information of the road sampled in the longitudinal direction of the coordinate system, the position of the autonomous vehicle, and the boundary of the road, including:

[0022] Based on the obstacle information of the road in the transverse direction of the coordinate system, the obstacle information of the road sampled in the longitudinal direction of the coordinate system, and the boundary of the road, the width of the passable gap between obstacles and between obstacles and the boundary of the road in the driving direction of the autonomous vehicle is obtained.

[0023] The position where the autonomous vehicle is located is taken as a root node, and a plurality of passable gaps are traversed in order of distance from the position where the autonomous vehicle is located to the obstacles from small to large, to obtain a plurality of target nodes included in the second search tree, the target node being a node corresponding to the passable gap with the largest width in the passable gap corresponding to the same obstacle as the position where the autonomous vehicle is located;

[0024] The drivable area is determined based on the target node.

[0025] In some embodiments, the driving data includes at least one of a distance between the autonomous vehicle and the obstacle, a distance between the autonomous vehicle and the road center line, a lateral displacement of the autonomous vehicle, a lateral speed of the autonomous vehicle, and a lateral acceleration of the autonomous vehicle.

[0026] In some embodiments, the driving path of the autonomous vehicle is determined based on the drivable area and a set condition met by driving data of the autonomous vehicle during driving, including any one of the following:

[0027] The path formed by the position in the drivable area where the distance between the autonomous vehicle and the obstacle is the largest is determined as the driving path;

[0028] The path formed by the position in the drivable area where the distance between the autonomous vehicle and the road center line is the smallest is determined as the driving path;

[0029] The path formed by the position in the drivable area where the lateral displacement of the autonomous vehicle is the smallest is determined as the driving path;

[0030] The path formed by the position in the drivable area where the change in lateral speed of the autonomous vehicle is the smallest is determined as the driving path;

[0031] The path formed by the position in the drivable area where the lateral acceleration of the autonomous vehicle is the smallest is determined as the driving path.

[0032] According to a second aspect of the embodiments of the present specification, a driving path determination apparatus is provided, the apparatus comprising:

[0033] The construction unit is configured to construct a coordinate system based on the position where the autonomous vehicle is located and the position of the center line of the road where the autonomous vehicle is located, the longitudinal direction of the coordinate system indicating the center line direction of the road, and the lateral direction of the coordinate system indicating the direction perpendicular to the center line of the road;

[0034] The area determination unit is configured to determine the drivable area based on the obstacle information of the road in the lateral direction of the coordinate system, the obstacle information of the road after sampling in the longitudinal direction of the coordinate system, the position where the autonomous vehicle is located, and the boundary of the road;

[0035] A path determination unit is configured to determine a driving path of the autonomous vehicle based on the drivable area and a set condition met by driving data of the autonomous vehicle during driving.

[0036] In some embodiments, the apparatus further comprises:

[0037] A sampling unit is configured to sample obstacle information in the road in a longitudinal direction of the coordinate system.

[0038] In some embodiments, when the coordinate system is constructed, obstacles in the road are mapped as polygons;

[0039] The sampling unit, when sampling obstacle information in the road in the longitudinal direction of the coordinate system, is specifically configured to:

[0040] Sample obstacle information of obstacles corresponding to the polygon on an edge of the polygon in the same direction as the longitudinal direction of the coordinate system.

[0041] In some embodiments, the construction unit, when constructing the coordinate system based on the position of the autonomous vehicle and road information of the road where the autonomous vehicle is located, is specifically configured to:

[0042] Take the position of the autonomous vehicle as the coordinate origin of the coordinate system, take the tangent direction of the center line of the road as the longitudinal direction of the coordinate system, and take the normal direction of the center line of the road as the transverse direction of the coordinate system.

[0043] In some embodiments, the area determination unit, when determining the drivable area based on obstacle information of the road in the transverse direction of the coordinate system, obstacle information of the road sampled in the longitudinal direction of the coordinate system, the position of the autonomous vehicle, and the boundary of the road, is specifically configured to:

[0044] Obtain the width of the passable gap between obstacles and between obstacles and the boundary of the road in the driving direction of the autonomous vehicle based on the obstacle information of the road in the transverse direction of the coordinate system, the obstacle information of the road sampled in the longitudinal direction of the coordinate system, and the boundary of the road.

[0045] Take the position of the autonomous vehicle as the root node, and expand the plurality of passable gaps obtained in layers in order of the distance from the obstacles to the position of the autonomous vehicle from small to large to obtain a first search tree.

[0046] According to the order of the width corresponding to the plurality of nodes in the first search tree from large to small, retain a preset number of nodes in the front of the order.

[0047] Determine the drivable area based on the width corresponding to the retained nodes.

[0048] In some embodiments, the region determining unit, when determining the drivable region based on the obstacle information of the road in the lateral direction of the coordinate system, the obstacle information of the road sampled in the longitudinal direction of the coordinate system, the position of the autonomous vehicle, and the boundary of the road, is specifically configured to:

[0049] obtain the width of the passable gap between the obstacles and the boundary of the road in the driving direction of the autonomous vehicle based on the obstacle information of the road in the lateral direction of the coordinate system, the obstacle information of the road sampled in the longitudinal direction of the coordinate system, and the boundary of the road;

[0050] take the position of the autonomous vehicle as a root node, traverse the plurality of passable gaps in order of the distance from the obstacles to the position of the autonomous vehicle from small to large, and obtain a plurality of target nodes included in the second search tree, the target node being a node corresponding to the passable gap with the maximum width in the passable gap corresponding to the same obstacle to the position of the autonomous vehicle;

[0051] determine the drivable region based on the target node.

[0052] In some embodiments, the driving data includes at least one of the distance between the autonomous vehicle and the obstacle, the distance between the autonomous vehicle and the road center line, the lateral displacement of the autonomous vehicle, the lateral speed of the autonomous vehicle, and the lateral acceleration of the autonomous vehicle.

[0053] In some embodiments, the path determining unit, when determining the driving path of the autonomous vehicle based on the drivable region and the set condition met by the driving data of the autonomous vehicle during driving, is specifically configured to any of the following:

[0054] determine the path formed by the position with the maximum distance between the autonomous vehicle and the obstacle in the drivable region as the driving path;

[0055] determine the path formed by the position with the minimum distance between the autonomous vehicle and the road center line in the drivable region as the driving path;

[0056] determine the path formed by the position with the minimum lateral displacement of the autonomous vehicle in the drivable region as the driving path;

[0057] determine the path formed by the position with the minimum change in the lateral speed of the autonomous vehicle in the drivable region as the driving path;

[0058] determine the path formed by the position with the minimum lateral acceleration of the autonomous vehicle in the drivable region as the driving path.

[0059] According to a third aspect of the embodiments of the present specification, a terminal is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the operations performed by the driving path determination method when executing the computer program.

[0060] According to a fourth aspect of the embodiments of the present specification, a computer readable storage medium is provided, and the computer readable storage medium stores a program, and the program is executed by a processor to implement the operations performed by the driving path determination method.

[0061] According to a fifth aspect of the embodiments of the present specification, a computer program product is provided, comprising a computer program, and the program is executed by a processor to implement the operations performed by the driving path determination method.

[0062] The technical solutions provided by the embodiments of the present specification can include the following beneficial effects:

[0063] In the embodiments of the present specification, by using the sampled obstacle information in the longitudinal direction and using the non-sampled obstacle information in the transverse direction, the accuracy of the obstacle information in the transverse direction is improved while reducing the amount of calculation that needs to be processed, thereby improving the accuracy of the determined driving path.

[0064] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present specification. BRIEF DESCRIPTION OF DRAWINGS

[0065] The accompanying drawings incorporated in the specification and constituting a part of the specification illustrate embodiments consistent with the present specification and serve to explain the principles of the present specification together with the specification.

[0066] Figure 1 is a flowchart of a method according to an exemplary embodiment of the present specification.

[0067] Figure 2 is a schematic diagram of a coordinate system conversion result of an obstacle according to an exemplary embodiment of the present specification.

[0068] Figure 3 is a schematic diagram of a road according to an exemplary embodiment of the present specification.

[0069] Figure 4 is a schematic diagram of a first search tree according to an exemplary embodiment of the present specification.

[0070] Figure 5 is a schematic diagram of a first search tree according to an exemplary embodiment of the present specification.

[0071] Figure 6 is a block diagram of a travel route determination device according to an example embodiment of the present specification.

[0072] Figure 7 is a structural diagram of a terminal according to an example embodiment of the present specification. DETAILED DESCRIPTION

[0073] The example embodiments will be described in detail herein with reference to the attached drawings. In the following description, the same numbers are used to designate the same elements, unless otherwise specified, in different drawings. The embodiments described in the following example embodiments do not represent all the embodiments consistent with the present specification. Rather, they are merely examples of devices and methods consistent with some aspects of the present specification, as detailed in the appended claims.

[0074] The terminology used in the present specification is for the purpose of describing particular embodiments only and is not intended to limit the present specification. As used in the present specification and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0075] It should be understood that although the terms first, second, third, etc. can be used herein to describe various information, these terms are not intended to denote a particular order. These terms are used only to distinguish one piece of information from another. For example, without departing from the scope of the present specification, first information can also be referred to as second information, and similarly, second information can also be referred to as first information. Depending on the context, the word "if' as used herein can be interpreted as meaning "when" or "in response to determining."

[0076] The present application provides a travel route determination method, which can be executed by a terminal. The terminal can be a vehicle-mounted terminal installed on an autonomous vehicle, or the terminal can be a mobile terminal carried by a passenger of an autonomous vehicle, such as a mobile phone, a tablet computer, a game console, a portable computer, etc. The present application does not limit the specific type of terminal.

[0077] In this application, the autonomous vehicle is equipped with various types of sensors, such as camera sensors and radar sensors. The autonomous vehicle collects road condition information through the sensors and then transmits the collected road condition information to the terminal. The terminal processes the received road condition information to determine the driving path of the autonomous vehicle, so that the autonomous vehicle can drive safely and without collisions, reducing the occurrence of collisions between the autonomous vehicle and obstacles on the road.

[0078] The road condition information includes the position of the centerline of the road where the autonomous vehicle is located, the boundary of the road where the autonomous vehicle is located, and the obstacle information on the road where the autonomous vehicle is located, etc. In many possible implementations, the road condition information includes other content, which is not limited in this application.

[0079] The above is merely a brief introduction to the application scenarios of this application. The following section will provide a detailed description of the driving path determination method provided by this application, in conjunction with the embodiments of this specification.

[0080] like Figure 1 As shown, Figure 1 This is a flowchart illustrating a method according to an exemplary embodiment, comprising the following steps:

[0081] In step 101, a coordinate system is constructed based on the location of the autonomous vehicle and the location of the centerline of the road where the autonomous vehicle is located. The longitudinal direction of the coordinate system indicates the direction of the centerline of the road, and the transverse direction of the coordinate system indicates the direction perpendicular to the centerline of the road.

[0082] In the field of autonomous driving, the Cartesian coordinate system (i.e., the latitude and longitude coordinate system) is typically used to represent road condition information of the road where the autonomous vehicle is located. However, the Cartesian coordinate system cannot fully reflect the road structure, resulting in poor representation of road condition information. This application employs a coordinate system transformation method to convert the Cartesian coordinate system into a coordinate system that can more fully reflect the road structure, thereby using the transformed coordinate system to represent the road condition information of the road where the autonomous vehicle is located.

[0083] In one possible implementation, the location of the autonomous vehicle is used as the origin of the coordinate system, the tangent direction of the road's centerline is used as the longitudinal direction of the coordinate system, and the normal direction of the road's centerline is used as the transverse direction of the coordinate system. This completes the construction of the coordinate system, resulting in the Frenet coordinate system (or SL coordinate system) that can more fully reflect the structure of the road.

[0084] In the Cartesian coordinate system, the coordinates (x, y) are used to represent the position of a point in the coordinate system, while in the Frenet coordinate system, the coordinates (s, l) are used to represent the position of a point in the coordinate system. Therefore, after the coordinate system is constructed, the positions of the points in the Frenet coordinate system are determined based on the positions of the points in the Cartesian coordinate system.

[0085] In a possible implementation, for a point (x i , y i ) in the Cartesian coordinate system to be converted, a reference point closest to (x i , y i ) on the road center line (i.e., the reference line) is determined, and the s value at the reference point is the s value of (x i , y i ) in the Frenet coordinate system.

[0086] The l value of (x i , y i ) in the Frenet coordinate system can be determined by the following formula (1):

[0087]

[0088] wherein, is a vector of the reference point in the Cartesian coordinate system, is a vector of (x i , y i ) in the Cartesian coordinate system, is a unit vector of , θ x-r is a direction angle of the vector , θ r is a direction angle of the unit vector , and θ n is a direction angle of the vector .

[0089] Optionally, the smoother the center line of the road where the vehicle is located is, the better the construction effect of the coordinate system is. Therefore, when the coordinate system is constructed, the coordinate system can be constructed based on a map processed by a map editing function, so as to improve the construction effect of the coordinate system.

[0090] In more possible implementations, if the road center line in the map used to construct the coordinate system is not smooth, the road center line can be processed by cubic spline interpolation, so as to improve the smoothness of the road center line.

[0091] In step 102, based on the obstacle information of the road in the lateral direction of the coordinate system, the obstacle information of the road sampled in the longitudinal direction of the coordinate system, the position of the autonomous vehicle, and the boundary of the road, a drivable area is determined.

[0092] Wherein, there is no obstacle in the drivable area, and the autonomous vehicle will not collide when driving at each position in the drivable area.

[0093] Based on the obstacle information of the road in the lateral direction of the coordinate system, the obstacle information of the road sampled in the longitudinal direction of the coordinate system, the position of the autonomous vehicle, and the boundary of the road, the coordinate value of the boundary of the area where the autonomous vehicle will not collide when driving is output, and then based on the output coordinate value, the corresponding boundary is determined, thereby obtaining the drivable area.

[0094] Wherein, the boundary of the drivable area can be represented in the form as shown in formula (2):

[0095]

[0096] Wherein, s0, s1, s2,..., s n is the longitudinal coordinate, d min0, d min1, d min2,..., d min n is the left boundary of the drivable area, d max0, d max1, d max2,..., d max n is the right boundary of the drivable area.

[0097] Since the distance of the road in the lateral direction is small, sampling at this time will cause serious loss of obstacle information in the lateral direction of the road. By using the sampled result only in the longitudinal direction of the obstacle information, and using the unsampled result in the lateral direction of the obstacle information, the accuracy of the obstacle information in the lateral direction is ensured on the basis of reducing the amount of data to be processed, and the accuracy of the determined drivable area is improved.

[0098] In step 103, based on the drivable area and the set condition met by the driving data of the autonomous vehicle in the driving process, the driving path of the autonomous vehicle is determined.

[0099] In one possible implementation, based on the set condition met by the autonomous vehicle in the driving process, a target function is constructed, thereby determining a path that makes the value of the target function minimum from the drivable area as the driving path of the autonomous vehicle.

[0100] The process of constructing the coordinate system in step 101 can be performed in real time, and accordingly, the determination of the drivable area and the determination of the driving path can be performed in real time based on the coordinate system constructed at the current time.

[0101] That is, during the driving of the autonomous vehicle, the coordinate system can be constructed in real time based on the current position of the autonomous vehicle and the position of the center line of the road where the autonomous vehicle is currently located, so that the constructed coordinate system can conform to the driving situation of the autonomous vehicle at each time, thereby enabling the application to accurately represent the road condition information of the road where the autonomous vehicle is located at different times. Accordingly, when the autonomous vehicle drives to any position, the determination of the drivable area and the determination of the driving path are performed based on the coordinate system constructed at the current time, to realize real-time updating of the drivable area and real-time updating of the driving path.

[0102] Optionally, when the drivable area and the driving path determined at the current time are the same as the drivable area and the driving path determined at the previous time, the drivable area and the driving path are not updated, but the drivable area and the driving path determined at the previous time are continued to be used as the drivable area and the driving path at the current time, to reduce the updating frequency and thereby reduce the processing pressure of the terminal, and further improve the determination speed of the drivable area and the driving path.

[0103] In the present application, by not sampling the obstacle information of the road in the transverse direction, the present application can use the discretized result to represent the obstacle information of the road in the longitudinal direction, and use continuous values to represent the obstacle information of the road in the transverse direction, to reduce the amount of calculation that needs to be processed while improving the accuracy of the obstacle information in the transverse direction, and further determine the drivable area in combination with the position of the autonomous vehicle and the boundary of the road, to improve the determination speed of the drivable area and make the determined drivable area more accurate, and thereby reduce the possibility of collision when the autonomous vehicle drives in the drivable area; further, the determination of the driving path is performed based on the drivable area with higher accuracy, so as to reduce the possibility of collision of the determined driving path, and thereby improve the safety of autonomous driving.

[0104] After introducing the basic implementation process of the present application, various non-limiting embodiments of the present application will be specifically introduced below.

[0105] In some embodiments, the process of obtaining the obstacle information of the road after sampling in the longitudinal direction of the coordinate system includes: sampling the obstacle information of the road in the longitudinal direction of the coordinate system.

[0106] By sampling the obstacle information in the longitudinal direction of the road, the amount of calculation in the subsequent determination of the drivable area and the driving path can be reduced, thereby improving the determination speed of the drivable area and the driving path.

[0107] In a possible implementation, when the coordinate system is constructed, the obstacles in the road are mapped as polygons, and correspondingly, the process of sampling the obstacle information in the longitudinal direction of the coordinate system includes:

[0108] The obstacle information of the obstacle corresponding to the polygon is sampled on the side of the polygon in the same direction as the longitudinal direction of the coordinate system.

[0109] Referring to Figure 2 , Figure 2 is a coordinate system conversion result diagram of an obstacle according to an example embodiment, in a Cartesian coordinate system as shown in Figure 2 , the obstacle is a rectangle ABCD, and after the Cartesian coordinate system is converted into a Frenet coordinate system, the obstacle is mapped as a polygon A'B'C'D'. When sampling the obstacle information in the longitudinal direction, sampling can be performed on the side of the polygon in the same direction as the longitudinal direction according to a preset sampling interval, that is, sampling is performed on the side A'D' and the side B'C', thereby achieving sampling of the obstacle information in the longitudinal direction. As shown in Figure 2 , after sampling on the side A'D', three points between the point A' and the point D' are obtained, and after sampling on the side B'C', three points between the point B' and the point C' are obtained.

[0110] Although the shape of the obstacle is deformed when the coordinate system is converted, if the polygon is used to represent the obstacle in the Cartesian coordinate system, the obstacle in the Frenet coordinate system after the coordinate system conversion can still be represented as a polygon, and then the sampling of the obstacle information can be achieved by sampling the sides of the polygon to a certain extent, without sampling each point included in the obstacle, thereby reducing the amount of data to be processed in the sampling process, improving the sampling speed and the sampling efficiency, and reducing the time consumption of the sampling process.

[0111] Optionally, in the sampling process, the sampling interval uses a fixed step length, or the sampling interval uses a non-fixed step length, which is not limited in the present application.

[0112] In some embodiments, when the drivable area is determined based on the obstacle information of the road in the lateral direction of the coordinate system, the obstacle information of the road sampled in the longitudinal direction of the coordinate system, the position of the autonomous vehicle, and the boundary of the road, a plurality of ways can be used, and the determination process of the drivable area will be described based on two example ways.

[0113] In a possible implementation, the width of the passable gap between the obstacles in the driving direction of the autonomous vehicle and between the obstacles and the boundary of the road is obtained based on the obstacle information of the road in the lateral direction of the coordinate system, the obstacle information of the road sampled in the longitudinal direction of the coordinate system, and the boundary of the road; the position of the autonomous vehicle is taken as a root node, and the obtained multiple passable gaps are expanded layer by layer in the order of the distance from the obstacles to the position of the autonomous vehicle from small to large, to obtain a first search tree; the nodes with a preset number of nodes in the front of the order are reserved in the order of the width corresponding to the multiple nodes in the first search tree from large to small; and the drivable area is determined based on the width corresponding to the reserved nodes.

[0114] Referring to Figure 3 , Figure 3 is a schematic diagram of a road according to an example embodiment of the present specification. In the road as shown in Figure 3 , four obstacles 301, 302, 303, and 304 are included, wherein the width of the passable gap between the obstacle 301 and the left boundary of the road is w1, the width of the passable gap between the obstacle 301 and the obstacle 302 is w2, the width of the passable gap between the obstacle 302 and the right boundary of the road is w3, the width of the passable gap between the obstacle 303 and the right boundary of the road is w4, the width of the passable gap between the obstacle 303 and the obstacle 304 is w5, and the width of the passable gap between the obstacle 304 and the left boundary of the road is w6. The present application obtains the widths of the above six passable gaps w1-w6 based on the obstacle information of the road in the lateral direction of the coordinate system, the obstacle information of the road sampled in the longitudinal direction of the coordinate system, and the boundary of the road. The position of the autonomous vehicle (i.e., the position of the coordinate origin in Figure 3 ) is taken as a root node, the passable gaps with the widths w1, w2, and w3 are taken as the child nodes of the root node, and the passable gaps with the widths w4, w5, and w6 are taken as the child nodes of the above respective child nodes, to obtain the first search tree as shown in Figure 4 , referring to Figure 4 , Figure 4 is a schematic diagram of a first search tree according to an example embodiment of the present specification.

[0115] Taking a preset quantity of 2, w1=30, w2=60, w3=25, w4=20, w5=80, w6=15 as an example, from the child nodes w1, w2, and w3 of the root node, the smallest width w3 and its corresponding subtree are deleted, while the larger widths w1 and w2 and their corresponding subtrees are retained. Then, among the child nodes of w1 and w2, the smallest width w6 is deleted, while the larger widths w4 and w5 are retained, thus obtaining the pruned first search tree. Based on the pruned first search tree, multiple optional travel paths are determined. Then, based on the score corresponding to each travel path, the drivable area is determined. Here, the score represents the probability that a vehicle will not collide while traveling on the travel path.

[0116] Optionally, the score for each passable path is determined based on the width of each passable gap within the passable path; alternatively, the score for each passable path is determined based on other phonemes. This application does not limit this to any particular method. Taking the determination of the score for each passable path based on the width of each passable gap within the passable path as an example... Figure 4 The first search tree, after pruning, yields multiple walkable paths including w1→w4, w1→w5, w2→w4, and w2→w5. The path w2→w5 has the widest gaps between walkable segments, meaning it has the highest score. Therefore, the region corresponding to the path w2→w5 is... Figure 3 The area between the left and right drivable boundaries is defined as the drivable area.

[0117] In another possible implementation, based on obstacle information in the horizontal direction of the road in the coordinate system, obstacle information sampled in the vertical direction of the road in the coordinate system, and the road boundary, the width of the passable gaps between obstacles and between obstacles and the road boundary in the driving direction of the autonomous vehicle is obtained; the position of the autonomous vehicle is taken as the root node, and multiple passable gaps are traversed in ascending order of distance between the obstacle and the position of the autonomous vehicle to obtain multiple target nodes included in the second search tree. The target node is the node corresponding to the passable gap with the largest width among the passable gaps corresponding to the obstacle at the same position as the autonomous vehicle; based on the target node, the drivable area is determined.

[0118] Still as Figure 3 Taking the road shown and the values ​​of each passable gap in the road as an example, this application obtains obstacle information based on obstacle information of the road in the horizontal direction of the coordinate system, obstacle information of the road sampled in the vertical direction of the coordinate system, and the road boundary. Figure 3After the widths of the six drivable gaps w1-w6 are determined, the position of the autonomous vehicle is taken as a root node, and the nodes w1, w2, and w3 that can be child nodes of the root node are traversed to determine the node w2 corresponding to the largest width of the drivable gap, and then the nodes w4, w5, and w6 that can be child nodes of the node w2 are traversed to determine the node w5 corresponding to the largest width of the drivable gap. The nodes w2 and w5 are taken as target nodes, and a second search tree is obtained, as shown in FIG. 5. Figure 5 FIG. 5 is a schematic diagram of a second search tree according to an example embodiment of the present disclosure. Based on the second search tree shown in FIG. 5, it can be determined that the target nodes w2 and w5 correspond to the regions Figure 5 Figure 3 The region between the left drivable boundary and the right drivable boundary in FIG. 6 corresponds to the drivable region.

[0119] It should be noted that the above are only two example ways of determining the drivable region, and in other embodiments, other ways can be used to determine the drivable region, which are not limited by the present disclosure.

[0120] In some embodiments, the driving data involved in determining the driving path of the autonomous vehicle includes at least one of the distance between the autonomous vehicle and the obstacle, the distance between the autonomous vehicle and the road center line, the lateral displacement of the autonomous vehicle, the lateral speed of the autonomous vehicle, and the lateral acceleration of the autonomous vehicle. Optionally, the driving data further includes other types of data, which are not limited by the present disclosure.

[0121] Correspondingly, the objective function involved in determining the driving path of the autonomous vehicle includes at least one of a function corresponding to the distance between the autonomous vehicle and the obstacle, a function corresponding to the distance between the autonomous vehicle and the road center line, a function corresponding to the lateral displacement of the autonomous vehicle, a function corresponding to the lateral speed of the autonomous vehicle, and a function corresponding to the lateral acceleration of the autonomous vehicle, or other functions corresponding to the types of driving data.

[0122] Taking the driving data as an example, the process of determining the driving path based on different types of data is described below.

[0123] Taking the driving data as the distance between the autonomous vehicle and the obstacle, the path formed by the position in the drivable region where the distance between the autonomous vehicle and the obstacle is the largest is determined as the driving path.

[0124] Taking the driving data as the distance between the autonomous vehicle and the road center line, the path formed by the position in the drivable region where the distance between the autonomous vehicle and the road center line is the smallest is determined as the driving path. ​

[0125] Taking the lateral displacement of the autonomous vehicle as an example of the driving data, a path formed by positions in the drivable area where the lateral displacement of the autonomous vehicle is the smallest is determined as the driving path.

[0126] Taking the lateral speed of the autonomous vehicle as an example of the driving data, a path formed by positions in the drivable area where the change in the lateral speed of the autonomous vehicle is the smallest is determined as the driving path.

[0127] Taking the lateral acceleration of the autonomous vehicle as an example of the driving data, a path formed by positions in the drivable area where the lateral acceleration of the autonomous vehicle is the smallest is determined as the driving path.

[0128] By determining the driving path based on the driving data, the determined driving path can be more in line with the driving requirements of the vehicle, thereby improving the driving effect of the autonomous vehicle.

[0129] The above are only some example data involved in determining the driving path, and in some other embodiments, the determination of the driving path can also be based on other types of data.

[0130] In addition, the determination of the driving path can also be based on a combination of multiple types of data, for example, in the drivable area, a path formed by positions where the distance to the obstacle is greater than a distance threshold and the lateral displacement of the autonomous vehicle is the smallest is determined as the driving path, and the like. The distance threshold is any positive number, and the application does not limit the value of the distance threshold.

[0131] By combining multiple types of data, the determined driving path can be more in line with the kinematic law of the vehicle on the basis of ensuring driving safety, thereby improving the accuracy and feasibility of the determined driving path.

[0132] Corresponding to the embodiments of the above method, the present specification also provides embodiments of a device and a terminal to which the device is applied.

[0133] Referring to Figure 6 , Figure 6 is a block diagram of a driving path determination device according to an example embodiment, which includes:

[0134] The construction unit 601 is configured to construct a coordinate system based on the position of the autonomous vehicle and the position of the center line of the road where the autonomous vehicle is located. The longitudinal direction of the coordinate system indicates the center line direction of the road, and the lateral direction of the coordinate system indicates the direction perpendicular to the center line of the road.

[0135] The region determination unit 602 is configured to determine a drivable region based on the obstacle information of the road in the lateral direction of the coordinate system, the obstacle information of the road sampled in the longitudinal direction of the coordinate system, the position of the autonomous vehicle, and the boundary of the road.

[0136] The path determination unit 603 is configured to determine a driving path of the autonomous vehicle based on the drivable region and a set condition met by driving data of the autonomous vehicle in the driving process.

[0137] In some embodiments, the driving path determination apparatus further comprises:

[0138] The sampling unit is configured to sample the obstacle information of the road in the longitudinal direction of the coordinate system.

[0139] In some embodiments, when the coordinate system is constructed, the obstacles in the road are mapped as polygons;

[0140] When the sampling unit is configured to sample the obstacle information of the obstacles corresponding to the polygons in the road in the longitudinal direction of the coordinate system, the sampling unit is specifically configured to:

[0141] Sample the obstacle information of the obstacles corresponding to the polygons in the road on the side of the polygon in the same direction as the longitudinal direction of the coordinate system.

[0142] In some embodiments, when the construction unit 601 is configured to construct the coordinate system based on the position of the autonomous vehicle and the road information of the road where the autonomous vehicle is located, the construction unit 601 is specifically configured to:

[0143] Take the position of the autonomous vehicle as the coordinate origin of the coordinate system, take the tangent direction of the center line of the road as the longitudinal direction of the coordinate system, and take the normal direction of the center line of the road as the lateral direction of the coordinate system.

[0144] In some embodiments, when the region determination unit 602 is configured to determine the drivable region based on the obstacle information of the road in the lateral direction of the coordinate system, the obstacle information of the road sampled in the longitudinal direction of the coordinate system, the position of the autonomous vehicle, and the boundary of the road, the region determination unit 602 is specifically configured to:

[0145] Based on the obstacle information of the road in the lateral direction of the coordinate system, the obstacle information of the road sampled in the longitudinal direction of the coordinate system, and the boundary of the road, obtain the width of the passable gap between the obstacles and the boundary of the road in the driving direction of the autonomous vehicle;

[0146] Take the position of the autonomous vehicle as the root node, and expand the plurality of passable gaps obtained in the order of the distance from the obstacles to the position of the autonomous vehicle from small to large to obtain a first search tree;

[0147] reserve a preset number of nodes in a front rank according to an order from large to small of the widths corresponding to the plurality of nodes in the first search tree;

[0148] determine the drivable area based on the widths corresponding to the reserved nodes.

[0149] In some embodiments, the area determination unit 602, when determining the drivable area based on the obstacle information of the road in the lateral direction of the coordinate system, the obstacle information of the road sampled in the longitudinal direction of the coordinate system, the position of the autonomous vehicle, and the boundary of the road, is specifically configured to:

[0150] obtain the width of the passable gap between the obstacles and the boundary of the road in the driving direction of the autonomous vehicle based on the obstacle information of the road in the lateral direction of the coordinate system, the obstacle information of the road sampled in the longitudinal direction of the coordinate system, and the boundary of the road;

[0151] take the position of the autonomous vehicle as a root node, traverse a plurality of passable gaps in an order from small to large of the distances from the obstacles to the position of the autonomous vehicle, and obtain a plurality of target nodes included in a second search tree, the target node being a node corresponding to a passable gap with the largest width in a passable gap corresponding to the same obstacle at the position of the autonomous vehicle;

[0152] determine the drivable area based on the target nodes.

[0153] In some embodiments, the driving data includes at least one of the distance between the autonomous vehicle and the obstacle, the distance between the autonomous vehicle and the road center line, the lateral displacement of the autonomous vehicle, the lateral speed of the autonomous vehicle, and the lateral acceleration of the autonomous vehicle.

[0154] In some embodiments, the path determination unit 603, when determining the driving path of the autonomous vehicle based on the drivable area and a set condition satisfied by the driving data of the autonomous vehicle in the driving process, is specifically configured to any one of the following:

[0155] determine, as the driving path, a path formed by a position in the drivable area with the largest distance between the autonomous vehicle and the obstacle;

[0156] determine, as the driving path, a path formed by a position in the drivable area with the smallest distance between the autonomous vehicle and the road center line;

[0157] determine, as the driving path, a path formed by a position in the drivable area with the smallest lateral displacement of the autonomous vehicle;

[0158] The path formed by the position in the drivable area where the lateral speed of the autonomous vehicle is the least is determined as the driving path.

[0159] The path formed by the position in the drivable area where the lateral acceleration of the autonomous vehicle is the least is determined as the driving path.

[0160] The functions and effects of the units in the device are specifically described in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0161] For the device embodiment, since it basically corresponds to the method embodiment, the related parts can be referred to the part of the method embodiment. The device embodiments described above are only schematic, and the modules described as separate components can or can not be physically separate, and the components displayed as modules can or can not be physical modules, that is, they can be located in one place, or distributed on multiple network modules. Some or all of the modules can be selected to achieve the purpose of the scheme of the present specification according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0162] The present application also provides a terminal, which refers to Figure 7 , Figure 7 is a structural schematic diagram of a terminal according to an exemplary embodiment. As shown in Figure 7 , the terminal includes a processor 710, a memory 720, and a network interface 730, the memory 720 is used to store computer instructions executable on the processor 710, the processor 710 is used to implement the driving path determination method provided by any embodiment of the present application when executing the computer instructions, and the network interface 730 is used to implement the input and output functions. In more possible implementation manners, the terminal can also include other hardware, which is not limited by the present application.

[0163] The present application also provides a computer readable storage medium, which can be in various forms, such as, in different examples, the computer readable storage medium can be: RAM (Radom Access Memory, Random Access Memory), volatile memory, non-volatile memory, flash memory, storage drive (such as hard disk drive), solid state disk, any type of storage disk (such as optical disk, DVD, etc.), or similar storage medium, or a combination thereof. In particular, the computer readable medium can also be paper or other suitable medium capable of printing programs. The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the driving path determination method provided by any embodiment of the present application.

[0164] The application further provides a computer program product comprising a computer program, which, when executed by a processor, implements the driving path determination method provided in any of the embodiments of the application.

[0165] Those skilled in the art will appreciate that one or more embodiments of the disclosure can be provided as a method, apparatus, terminal, computer readable storage medium or computer program product. Therefore, one or more embodiments of the disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, one or more embodiments of the disclosure can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0166] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, for the embodiments corresponding to the terminal, since it is basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiments.

[0167] The above describes specific embodiments of the disclosure. Other embodiments are within the scope of the appended claims. In some cases, the acts or steps recited in the claims can be performed in a different order than those in the embodiments and still achieve desirable results. In addition, the processes depicted in the figures do not necessarily require the particular order shown or sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous or possible.

[0168] Embodiments of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly-embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non-transitory program carrier to execute on or control the operation of data processing apparatus. Alternatively or additionally, the program instructions can be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. A computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.

[0169] The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit), and / or by programmable data processing apparatuses, which can be portion of hardware processing circuitry that executes specific tasks as described. Apparatuses can also be implemented as a combination of special purpose logic circuitry, e.g., an FPGA or an ASIC, and / or one or more programmable data processing apparatuses.

[0170] Computers suitable for the execution of a computer program include, by way of example, general and / or special purpose microprocessors, or any other kind of central processing unit. Generally, a central processing unit will receive instructions and data from a read-only memory and / or a random access memory. The essential elements of a computer are a central processing unit for performing or executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), to name just a few.

[0171] Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0172] While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or of what can be claimed, but as descriptions of features that can be specific to particular embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features can be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination and the claimed combination can be directed to a subcombination or variation of a subcombination.

[0173] Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring such order nor that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing can be advantageous. Moreover, the separation of various system modules and components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0174] Accordingly, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0175] Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the features of the disclosure as set forth in the following claims. Aspects of the present disclosure are intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure that come within known or customary practice in the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with the true scope of the disclosure being indicated by the following claims.

[0176] The above description is intended to be illustrative and not restrictive. Many other embodiments will be apparent to those of skill in the art upon reading and understanding the above description. The scope of the disclosure should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

1. A method for determining a driving path, characterized in that, The method includes: Based on the location of the autonomous vehicle and the location of the centerline of the road where the autonomous vehicle is located, a coordinate system is constructed. The longitudinal direction of the coordinate system indicates the direction of the centerline of the road, and the lateral direction of the coordinate system indicates the direction perpendicular to the centerline of the road. Based on the unsampled obstacle information of the road in the lateral direction of the coordinate system, the sampled obstacle information of the road in the longitudinal direction of the coordinate system, the position of the autonomous vehicle, and the boundary of the road, a drivable area is determined. Based on the drivable area and the driving data of the autonomous vehicle during its driving process, the driving path of the autonomous vehicle is determined. The driving data includes at least one of the following: the distance between the autonomous vehicle and the obstacle, the distance between the autonomous vehicle and the center line of the road, the lateral displacement of the autonomous vehicle, the lateral velocity of the autonomous vehicle, and the lateral acceleration of the autonomous vehicle. The step of determining the drivable area based on unsampled obstacle information of the road in the lateral direction of the coordinate system, sampled obstacle information of the road in the longitudinal direction of the coordinate system, the position of the autonomous vehicle, and the boundary of the road includes: Based on the unsampled obstacle information of the road in the lateral direction of the coordinate system, the sampled obstacle information of the road in the longitudinal direction of the coordinate system, and the boundary of the road, the width of the passable gap between obstacles and between the obstacles and the boundary of the road in the driving direction of the autonomous vehicle is obtained. Using the location of the autonomous vehicle as the root node, the multiple passable gaps are expanded layer by layer in ascending order of the distance between the obstacle and the location of the autonomous vehicle to obtain the first search tree. According to the order of the widths of multiple nodes in the first search tree from largest to smallest, retain a preset number of nodes that are ranked first. Based on the width corresponding to the reserved nodes, multiple usable passage paths and the corresponding scores for each communication path are determined; The drivable area is determined based on the score corresponding to each communication path.

2. The method according to claim 1, characterized in that, The method further includes: Information about obstacles in the road is sampled along the longitudinal direction of the coordinate system.

3. The method according to claim 2, characterized in that, When constructing the coordinate system, obstacles in the road are mapped as polygons; The sampling of obstacle information in the road along the longitudinal direction of the coordinate system includes: On the edges of the polygon that are in the same direction as the longitudinal direction of the coordinate system, obstacle information of the obstacles corresponding to the polygon is sampled.

4. The method according to claim 1, characterized in that, The construction of a coordinate system based on the location of the autonomous vehicle and the road information of the road where the autonomous vehicle is located includes: The location of the autonomous vehicle is taken as the origin of the coordinate system, the tangent direction of the centerline of the road is taken as the longitudinal direction of the coordinate system, and the normal direction of the centerline of the road is taken as the transverse direction of the coordinate system.

5. The method according to claim 1, characterized in that, The determination of the autonomous vehicle's driving path based on the preset conditions satisfied by the drivable area and the driving data of the autonomous vehicle during its operation includes any one of the following: The path formed by the location in the drivable area where the distance between the autonomous vehicle and the obstacle is the greatest is determined as the driving path; The path formed by the position in the drivable area where the distance between the autonomous vehicle and the center line of the road is the smallest is determined as the driving path; The path formed by the position of minimum lateral displacement of the autonomous vehicle in the drivable area is determined as the driving path; The path formed by the position in the drivable area where the lateral speed change of the autonomous vehicle is the smallest is determined as the driving path; The path formed by the position of minimum lateral acceleration of the autonomous vehicle in the drivable area is determined as the driving path.

6. A travel path determination device, characterized in that, The device includes: The construction unit is used to construct a coordinate system based on the position of the autonomous vehicle and the position of the centerline of the road where the autonomous vehicle is located. The longitudinal direction of the coordinate system indicates the direction of the centerline of the road, and the lateral direction of the coordinate system indicates the direction perpendicular to the centerline of the road. The region determination unit is used to determine a drivable area based on the unsampled obstacle information of the road in the lateral direction of the coordinate system, the sampled obstacle information of the road in the longitudinal direction of the coordinate system, the position of the autonomous vehicle, and the boundary of the road. The path determination unit is used to determine the driving path of the autonomous vehicle based on the drivable area and the driving data of the autonomous vehicle during the driving process, wherein the driving data includes at least one of the following: the distance between the autonomous vehicle and the obstacle, the distance between the autonomous vehicle and the center line of the road, the lateral displacement of the autonomous vehicle, the lateral velocity of the autonomous vehicle, and the lateral acceleration of the autonomous vehicle. Specifically, when determining a drivable area based on unsampled obstacle information of the road in the lateral direction of the coordinate system, sampled obstacle information of the road in the longitudinal direction of the coordinate system, the position of the autonomous vehicle, and the boundary of the road, the area determination unit is used for: Based on the unsampled obstacle information of the road in the lateral direction of the coordinate system, the sampled obstacle information of the road in the longitudinal direction of the coordinate system, and the boundary of the road, the width of the passable gap between obstacles and between the obstacles and the boundary of the road in the driving direction of the autonomous vehicle is obtained. Using the location of the autonomous vehicle as the root node, the multiple passable gaps are expanded layer by layer in ascending order of the distance between the obstacle and the location of the autonomous vehicle to obtain the first search tree. According to the order of the widths of multiple nodes in the first search tree from largest to smallest, retain a preset number of nodes that are ranked first. Based on the width corresponding to the reserved nodes, multiple usable passage paths and the corresponding scores for each communication path are determined; The drivable area is determined based on the score corresponding to each communication path.

7. A terminal, characterized in that, The terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the operations performed by the driving path determination method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that is executed by a processor as the driving path determination method as described in any one of claims 1 to 5.

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