CN115583254A

CN115583254A - Path planning method, device and equipment and automatic driving vehicle

Info

Publication number: CN115583254A
Application number: CN202211202671.7A
Authority: CN
Inventors: 马霖; 夏中谱
Original assignee: Apollo Intelligent Technology Beijing Co Ltd
Current assignee: Apollo Intelligent Technology Beijing Co Ltd
Priority date: 2022-09-29
Filing date: 2022-09-29
Publication date: 2023-01-10

Abstract

The disclosure provides a path planning method, a path planning device, a path planning equipment and an automatic driving vehicle, and relates to the technical field of artificial intelligence, in particular to the technical field of automatic driving. The specific implementation scheme is as follows: performing secondary planning processing on the first path segment to obtain a first planned path, and performing curve fitting processing on the second path segment to obtain a second planned path; generating a plurality of candidate paths based on the first planned path and the second planned path; and selecting a target planning path from the plurality of candidate paths so as to control the vehicle to run from the starting position to the end position along the target planning path. According to the technical scheme provided by the disclosure, the path planning efficiency is ensured, and meanwhile, the path planning accuracy is improved.

Description

Path planning method, device and equipment and automatic driving vehicle

Technical Field

The disclosure relates to the technical field of artificial intelligence, in particular to the technical field of automatic driving, and specifically relates to a path planning method, device, equipment and an automatic driving vehicle.

Background

With the rapid development of artificial intelligence technology, it is a popular research field to use artificial intelligence to automatically drive vehicles. Route planning is currently one of the core technologies of the automatic driving technology, and aims to make a collision-free safe route from a departure place to a destination for a vehicle, so as to ensure the safety and reliability of automatic driving.

Disclosure of Invention

The disclosure provides a path planning method, a path planning device, a path planning equipment and an automatic driving vehicle.

According to one aspect of the present disclosure, a path planning method is provided, which is applied to a process of controlling a vehicle to drive from a starting position to an end position, wherein a path from the starting position to the end position comprises a first path segment and at least one second path segment;

the method comprises the following steps:

performing secondary planning processing on the first path segment to obtain a first planned path, and performing curve fitting processing on the second path segment to obtain a second planned path;

generating a plurality of candidate paths based on the first planned path and the second planned path;

and selecting a target planning path from the plurality of candidate paths to control the vehicle to travel from the starting position to the end position along the target planning path.

According to another aspect of the present disclosure, a path planning apparatus is provided, which is used in a process of controlling a vehicle to travel from a starting position to an end position, wherein a path from the starting position to the end position comprises a first path segment and at least one second path segment;

the device includes:

the processing module is used for carrying out secondary planning processing on the first path segment to obtain a first planned path and carrying out curve fitting processing on the second path segment to obtain a second planned path;

a generating module, configured to generate a plurality of candidate paths based on the first planned path and the second planned path;

and the selection module is used for selecting a target planning path from the plurality of candidate paths so as to control the vehicle to drive from the starting point position to the end point position along the target planning path.

According to another aspect of the present disclosure, there is provided an electronic device including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a path planning method provided by the present disclosure.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to execute a path planning method provided by the present disclosure.

According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the path planning method provided by the present disclosure.

According to another aspect of the present disclosure, an autonomous vehicle is provided, including the electronic device provided by the present disclosure.

According to the technical scheme, in the face of a previous section of path needing important attention in path planning, path planning is carried out in a secondary planning mode with higher flexibility and higher accuracy, a more flexible path can be planned, the scene requirement of the horizontal and vertical height matching of the vehicle can be better met, and a curve fitting mode is adopted for a next section of path planned, the planning efficiency of the next section of path can be improved, and the efficiency of the whole path planning is improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

fig. 1 is a scene diagram illustrating a path planning method according to an embodiment of the present disclosure;

fig. 2 is a schematic flow chart diagram illustrating a path planning method according to an embodiment of the present disclosure;

fig. 3 is a flow chart diagram illustrating a path planning method according to an embodiment of the present disclosure;

FIG. 4 is a schematic diagram illustrating one path division according to an embodiment of the present disclosure;

FIG. 5 is a schematic illustration of a spatial extent of an obstacle shown in accordance with an embodiment of the present disclosure;

FIG. 6 is a schematic flow chart illustrating path planning according to an embodiment of the present disclosure;

FIG. 7 is a schematic diagram illustrating a comparison of a quadratic programming and a template curve according to an embodiment of the disclosure;

fig. 8 is a block diagram illustrating a path planning apparatus according to an embodiment of the present disclosure;

fig. 9 is a block diagram of an electronic device for implementing a path planning method according to an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.

First, description is made for an application scenario related to an embodiment of the present disclosure:

the path planning method provided by the embodiment of the disclosure can be applied to a scene that an automatic driving vehicle needs to be matched with the transverse and longitudinal heights. For example, the intersection is going straight to interact with other vehicles, and the intersection is changing lanes to interact with other vehicles. The horizontal direction refers to the direction of a lane vertical line, and the vertical direction refers to the lane direction. It will be appreciated that accuracy of lateral path planning is often required for such lateral emergencies on roads.

In a possible implementation manner, the path planning method provided by the embodiment of the present disclosure is applied to a transverse path planning part in three-dimensional joint planning, so as to ensure the accuracy of transverse path planning in three-dimensional joint planning. The three-dimensional joint planning refers to that information (such as position) of a spatial dimension and information (such as speed) of a time dimension are considered simultaneously when planning a path. Transverse path planning (or referred to as transverse planning) is a process of planning a driving path of a vehicle based on information of spatial dimensions. It should be understood that a lateral path plan is a plan of one direction, which determines the shape of the path of travel of the vehicle.

In the related art, path planning for an autonomous vehicle is generally performed by curve fitting a path based on a template curve (e.g., a polynomial curve) constructed in advance and state information (e.g., speed or acceleration to a terminal position, etc.) of the vehicle under a specific condition. However, since the line type of the template curve is fixed, on one hand, the expression capability of the template curve is limited, which may cause the problem of line type overshoot (i.e. deviation), and on the other hand, the head and tail connection of the template curve makes the middle line type of the template curve difficult to control, which may cause the problem of vehicle collision and the like due to the deviation.

In the embodiment of the disclosure, a path planning scheme combining a quadratic programming mode and a curve fitting mode is provided, in the face of a previous section of path needing important attention in path planning, path planning is performed by adopting a quadratic programming mode with higher flexibility and higher accuracy, a more flexible path can be planned, the requirement of a scene matched with the transverse and longitudinal height of a vehicle can be better met, and a next section of path planned is aimed at, a curve fitting mode is adopted, the planning efficiency of the next section of path can be improved, and the efficiency of overall path planning is improved.

Fig. 1 is a scene diagram illustrating a path planning method according to an embodiment of the present disclosure. It should be noted that the path planning method provided by the embodiment of the present disclosure is executed by an automatic driving device in an automatic driving vehicle. Referring to fig. 1, the autopilot device may be provided as a terminal device 101 or a server 102 as shown in fig. 1.

The terminal device 101 is at least one of a smart phone, a smart watch, a desktop computer, a laptop computer, a virtual reality terminal, an augmented reality terminal, a wireless terminal, a laptop computer, and the like. In one possible implementation, the terminal device 101 has a communication function and can access a wired network or a wireless network. Terminal device 101 may refer broadly to one of a plurality of terminal devices, and the disclosed embodiments are illustrated with terminal device 101 only. Those skilled in the art will appreciate that the number of terminal devices described above may be greater or fewer.

The server 102 is an independent physical server, or a server cluster or a distributed file system formed by a plurality of physical servers, or at least one of cloud servers providing basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, content distribution network, and big data and artificial intelligence platform, which is not limited in this embodiment of the disclosure. In some embodiments, the server 102 and the terminal device 101 are connected directly or indirectly through wired or wireless communication, which is not limited in this disclosure. In one possible implementation, the number of the servers 102 can be more or less, and the embodiment of the disclosure does not limit this. Of course, the server 102 can also include other functional servers to provide more comprehensive and diverse services.

For the above automatic driving device, in a possible implementation manner, the automatic driving device includes a control module, a positioning module, a sensing module, a decision module, and a planning module.

The control module is used for controlling the motion state of the vehicle. In a possible implementation manner, the vehicle can change the power output by a power generation device such as an engine or an electric motor or change the braking force output by a braking device based on a control signal output by the control module, namely the control module controls the movement speed of the vehicle; or, in another possible implementation manner, the vehicle can change the steering angle of the vehicle based on the control signal output by the control module, that is, the control module controls the moving direction of the vehicle.

The Positioning module is used for Positioning and tracking the vehicle, and for example, the Positioning module may be provided as a Global Positioning System (GPS) device. In a possible implementation mode, the real-time position of the vehicle can be acquired through the positioning module, and the real-time position and the running track of the vehicle are reflected on the electronic map, so that the running track of the vehicle is visualized.

The sensing module is used for detecting object information around the vehicle, such as position, speed, orientation and the like. In one possible implementation, the perception module is provided with road boundary detection, lane line detection, pedestrian detection, vehicle detection, traffic identifier detection, etc.

The decision-making module is used for making decision-making judgment based on the information output by the positioning module and the sensing module so as to determine the driving decision of the vehicle.

The planning module is used for planning the path of the vehicle, namely predicting the future track of the vehicle.

In the embodiment of the disclosure, through mutual cooperation among the control module, the positioning module, the sensing module, the decision module and the planning module, an automatic driving function can be provided for a vehicle.

Fig. 2 is a flowchart illustrating a path planning method applied to a process of controlling a vehicle to travel from a starting point position to an ending point position, where a path from the starting point position to the ending point position includes a first path segment and at least one second path segment, according to an embodiment of the disclosure. The path planning method provided by the embodiment of the disclosure is executed by electronic equipment. In one possible implementation, the electronic device may be provided as the autopilot device described above in connection with fig. 1. As shown in fig. 2, the method includes the following steps.

In step S201, the electronic device performs a secondary planning process on the first path segment to obtain a first planned path, and performs a curve fitting process on the second path segment to obtain a second planned path.

In step S202, the electronic device generates a plurality of candidate paths based on the first planned path and the second planned path.

In step S203, the electronic device selects a target planned route from the candidate routes to control the vehicle to travel from the starting position to the ending position along the target planned route.

The technical scheme provided by the embodiment of the disclosure adopts a quadratic programming mode with higher flexibility and higher accuracy to plan the path in the front section of the path needing important attention in path planning, can plan to obtain a more flexible path, can better adapt to the scene requirement of the horizontal and vertical height matching of the vehicle, and adopts a curve fitting mode to improve the planning efficiency of the rear section of the path aiming at the rear section of the path planning, thereby improving the efficiency of the whole path planning.

Fig. 3 is a schematic flow chart illustrating a path planning method according to an embodiment of the present disclosure, where the path planning method is applied to a process of controlling a vehicle to travel from a starting position to an ending position. The path planning method provided by the embodiment of the disclosure is executed by electronic equipment. In one possible implementation, the electronic device may be provided as the autopilot device described above in connection with fig. 1. As shown in fig. 3, the method includes the following steps.

In step S301, the electronic device divides a path from a start position to an end position into a first path segment and at least one second path segment.

The starting point position is a position where the vehicle starts to travel. In one possible implementation, the electronic device determines that the current position of the vehicle is the starting position. The end position refers to a position to which the vehicle is driven. In one possible implementation, the end position is a predetermined position. For example, the user may upload the end position to the electronic device before triggering the automatic driving function, and the electronic device may be able to acquire the end position.

In an embodiment of the disclosure, the path from the starting position to the ending position comprises a first path segment and at least one second path segment. The first path segment is a path segment which is positioned at the front of the path from the starting position to the end position. The second path segment is a later path segment in the path from the start position to the end position. It should be understood that the first path segment is a path segment that starts from the start position of the vehicle.

In one possible implementation, the first path segment satisfies at least one of the following conditions: taking a position point which is a preset distance away from the starting position as an end point; taking a position point satisfying a heading condition with a terminal position of the vehicle as a terminal, the heading condition including at least one of: the orientation is the same or the orientation angle difference is less than a preset threshold.

The preset distance is a preset distance value, such as 5 meters, 10 meters or other distance values. The predetermined threshold is a predetermined angle difference, such as 15 degrees, 30 degrees or other angle differences. The embodiment of the disclosure does not limit the values of the preset distance and the preset threshold. In this embodiment, the first path segment and the at least one second path segment can be obtained by fast partitioning by setting the position condition that the first path segment needs to satisfy, and the efficiency of path partitioning is improved, thereby ensuring the efficiency of path planning. It should be noted that, in another possible implementation manner, the electronic device can also set other types of location conditions, and then perform path division by using the set location conditions. The embodiments of the present disclosure do not limit this.

Based on the location condition that the first path segment needs to satisfy, correspondingly, the process of obtaining the first path segment and the at least one second path segment by the electronic device includes: in one possible implementation manner, the electronic device determines a position point which is a preset distance away from the starting point position in a path from the starting point position to the end point position, performs division by using the position point as a division point, uses a front path segment obtained through division as the first path segment, and uses a rear path segment obtained through division as the second path segment. Alternatively, in another possible implementation manner, the electronic device determines, in a route from the start position to the end position, a position point satisfying the orientation condition with the end position of the vehicle, divides the route using the position point as a dividing point, takes a preceding route segment obtained by the division as the first route segment, and takes a following route segment obtained by the division as the second route segment.

The above process is taken as an example of obtaining one second path segment by division, and in another possible implementation manner, the electronic device further divides a subsequent path segment obtained by division to obtain a plurality of second path segments. For example, the electronic device equally divides the subsequent path segment to obtain a plurality of second path segments. Of course, the electronic device can also use other ways to divide the latter path segment, which is not limited in this disclosure.

In a possible implementation manner, the electronic device performs spatial sampling in a path from a starting position to an end position to obtain a plurality of sampling points included in the path, and further determines, among the plurality of sampling points, the position point which is a preset distance away from the starting position or the position point which satisfies an orientation condition with the end position of the vehicle. Therefore, the position points meeting the position condition are selected from the plurality of sampling points obtained through space sampling, path division can be realized more quickly, and the efficiency of path division is improved.

For the above spatial sampling process, in a possible implementation manner, the electronic device performs sampling along the lane direction to obtain a plurality of sampling points along the lane direction, and then performs sampling on the lane vertical line where the plurality of sampling points are located, so as to obtain a plurality of sampling points included in the path.

Based on the position condition required to be met by the first path segment, correspondingly, the electronic device selects a sampling point with a preset distance from the starting point position from a plurality of sampling points included in the path, determines the path segment between the starting point position and the sampling point as the first path segment, and determines the path segment between the sampling point and the end point position as the second path segment. Or, in another possible implementation manner, the electronic device determines, among a plurality of sampling points included in the path, a sampling point that satisfies an orientation condition with an end point position of the vehicle, determines a path segment between the start point position and the sampling point as the first path segment, and determines a path segment between the sampling point and the end point position as the second path segment.

Exemplarily, fig. 4 is a schematic diagram of a path division shown according to an embodiment of the present disclosure, and referring to fig. 4, in the path shown in fig. 4, a plurality of sampling points are respectively included in a lane direction and a lane vertical line direction, where the plurality of sampling points shown in a solid line box 401 are a plurality of sampling points included in a first path segment, and the plurality of sampling points shown in a dotted line box 402 are a plurality of sampling points included in a second path segment. In the embodiment of the present disclosure, a quadratic programming manner is adopted for the first path segment to perform path planning, and a curve fitting manner is adopted for the second path segment to perform path planning. The embodiment of the present disclosure subsequently describes a path planning process by taking an example that the first path segment and the second path segment respectively include a plurality of sampling points.

In step S302, the electronic device performs a secondary planning process on the first path segment to obtain a first planned path.

Wherein a Quadratic Programming (QP) process is used to solve a mathematical optimization problem, in particular to solve a linear constraint-based Quadratic optimization problem, i.e. while optimizing (minimizing or maximizing) a Quadratic function of a plurality of variables, also obeying the linear constraints of the plurality of variables. The first planned path is used to refer to a path obtained by path planning of the first path segment. In one possible implementation, the number of the first planned path is at least one.

In a possible implementation manner, the electronic device obtains a feasible region range of the vehicle, and performs secondary planning processing on the first path segment based on the feasible region range to obtain the first planned path.

Wherein the feasible region range is used for indicating a road range in which the vehicle is allowed to travel. In one possible implementation, the range of feasibility is represented by a lane reference line corresponding to the range of feasibility and a lateral distance between a plurality of points in the range of feasibility and the lane reference line. The lateral distance is the normal distance of the sampling point on the lane reference line. For example, the lane reference line may be a lane center line or a lane boundary line.

In the above embodiment, when the secondary planning processing of the first path segment is performed, the feasible region range of the vehicle is referred to, so that a path according with the self state of the vehicle can be planned and obtained, and the accuracy of path planning is improved.

In one possible implementation, the process of acquiring, by the electronic device, the range of the feasible region of the vehicle includes: and determining the feasible region range of the vehicle based on at least one of the boundary range of the road where the vehicle is located, the space range of the dynamic obstacles around the vehicle, the space range of the static obstacles around the vehicle and the circular arc constraint boundary value.

Wherein, the dynamic barrier can be a running vehicle, a running bicycle or a pedestrian. The static obstacle may be a wayside parking vehicle, a wayside parking bicycle, or a wayside device, etc. In one possible implementation, dynamic obstacles or static obstacles around the vehicle are determined based on the current position of the vehicle and an electronic map. The electronic map may be map data of an area where the vehicle is currently located, for example, map data of a lane where the vehicle is located. It should be understood that, by querying the electronic map based on the position information of the vehicle, the obstacle information within a certain range of the position of the vehicle can be acquired.

The spatial extent of the dynamic/static obstacle is used to indicate the activity space of the dynamic/static obstacle. In an embodiment of the disclosure, the spatial range of the dynamic/static obstacle is determined based on decision information and distance information of the vehicle relative to the dynamic/static obstacle. The decision information refers to a decision type of the vehicle relative to the dynamic/static obstacle, such as left-side detour, right-side detour, or non-detour. The distance information refers to a distance between the vehicle and the dynamic/static obstacle. Accordingly, in one possible implementation, the electronic device determines a spatial range of the dynamic obstacle based on the decision information and the distance information of the vehicle relative to the dynamic obstacle. In one possible implementation, the electronic device determines a spatial range of the static obstacle based on the decision information and the distance information of the vehicle relative to the static obstacle.

For example, fig. 5 is a schematic diagram illustrating a spatial range of an obstacle according to an embodiment of the present disclosure, and referring to fig. 5, fig. 5 takes an obstacle vehicle as an example, and uses a solid line to refer to a lane line and uses a trapezoidal dotted line to refer to the spatial range of the obstacle. Taking the obstacle vehicle 501 as an example, the spatial range of the obstacle vehicle is an area above a trapezoidal dashed line 502. Accordingly, if the obstacle vehicle 501 is an obstacle around the vehicle (current vehicle), and the vehicle makes a right-side detour with respect to the obstacle vehicle 501 when the vehicle is determined to make a right-side detour with respect to the obstacle vehicle 501, and the distance between the vehicle and the obstacle vehicle 501 reaches the distance allowing the detour, the range of the vehicle is the region between the trapezoidal broken line 502 and the lower boundary line of the lane.

The arc constraint boundary value is used to constrain the arc of the travel path of the vehicle. In one possible implementation, the arc constraint boundary value is determined based on the path curvature.

It should be noted that, the electronic device may determine the range of the feasible region of the vehicle based on one, two or more of the boundary range of the road where the vehicle is located, the spatial range of the dynamic obstacle around the vehicle, the spatial range of the static obstacle around the vehicle, and the arc constraint boundary value. For example, in one possible implementation, the electronic device determines the range of the feasible region of the vehicle based on the boundary range of the road where the vehicle is located, the spatial range of the dynamic obstacles around the vehicle, and the spatial range of the static obstacles around the vehicle; or, in yet another possible implementation manner, the electronic device may determine the feasible region range of the vehicle based on the boundary range of the road where the vehicle is located and the arc constraint boundary value; or, in another possible implementation manner, the electronic device determines the feasible region range of the vehicle based on the boundary range of the road where the vehicle is located, the spatial range of the dynamic obstacle around the vehicle, the spatial range of the static obstacle around the vehicle, and the arc constraint boundary value. The above three implementations are described as an example of a combination of the determination process of the range of the feasible region of the vehicle. Of course, the electronic device can also use other types of combinations to determine the range of the feasible region of the vehicle, which is not limited by the embodiment of the disclosure.

In the above embodiment, when the feasible region range of the vehicle is determined, rich reference information such as the boundary range of the road, the space range of the obstacle, or the arc constraint boundary value is set, so that the information amount referred by the feasible region range is increased, the accuracy of determining the feasible region range is improved, and by setting the arc constraint boundary value, the vehicle driving experience of the driver can be ensured, and the reliability of path planning is improved.

In a possible implementation manner, the process of the electronic device performing the secondary planning process based on the feasible domain range includes: the method comprises the steps of taking a path curve formed by a plurality of sampling points included in the first path segment, a first derivative of the path curve, a second derivative of the path curve and a third derivative of the path curve to be smooth as expected targets, constructing a quadratic programming objective function, solving the objective function based on the feasible domain range to obtain the transverse positions, the transverse speeds and the transverse accelerations of the plurality of sampling points, and generating the first planning path based on the transverse positions, the transverse speeds and the transverse accelerations of the plurality of sampling points. Wherein the lateral position is represented by the lateral distance of the sample point.

In one possible implementation, the electronic device constructs an S-L coordinate system, and projects the position coordinates of the plurality of sampling points included in the first path segment from an X-Y coordinate system to the S-L coordinate system according to the position coordinates, that is, the position of the sampling point is described by using a variable S and a variable L. For example, the S-L coordinate system has the direction of the center line of the road as the S-axis and the direction perpendicular to the center line of the road as the L-axis. And then, constructing an objective function of quadratic programming based on the S-L coordinate system, solving to obtain information such as the transverse position, the transverse speed, the transverse acceleration and the like of the plurality of sampling points, and determining one or more first planned paths based on the transverse position, the transverse speed and the transverse acceleration of the plurality of sampling points.

In one possible implementation, the objective function of quadratic programming is referred to as the following function (1):

l _i+1 ″＝l _i ″+l″′ _i→i+1 ×Δs (1)

wherein, f represents an objective function, and the objective of the objective function is to expect a path curve formed by a plurality of sampling points, a first derivative of the path curve, a second derivative of the path curve and a third derivative of the path curve to be smooth; i represents sampling points, the number of the sampling points is n-1, wherein i is a positive integer greater than or equal to 0, and n is a positive integer greater than 1; w is a _i Represents a state variable l _i The weight coefficient of (a); state variable l _i Represents the lateral position (which can be understood as the lateral distance) of the sample point i; w is a _i ' represents a state variable l _i ' weight coefficient; state variable l _i ' represents the derivative of the lateral position of the sample point i, i.e. represents the lateral velocity; w is a _i "represents a state variable l _i "is calculated; state variable l _i "represents the second derivative of the lateral position of the sample point i, i.e. represents the lateral acceleration; w is a _i "' indicates the state variable l _i The weight coefficient of ""; state variable l _i "' denotes the third derivative of the lateral position of the sample point i, i.e. represents a constant; l _i+1 Represents the lateral position of the sampling point i + 1; l _i+1 ' represents the derivative of the lateral position of the sample point i +1, i.e. represents the lateral velocity; l _i+1 "represents the second derivative of the lateral position of the sample point i +1, i.e. represents the lateral acceleration; Δ s denotes sampling point i and samplingThe longitudinal distance between samples i + 1.

In one possible implementation manner, a first constraint condition of the objective function is determined based on a feasible region range of the vehicle, and then the objective function is solved based on the first constraint condition of the objective function. Wherein the first constraint condition is used for constraining the range of the feasible region of the vehicle. In one possible implementation, the first constraint of the objective function is referred to in the following constraint (2), where the state variable l _i The constraint (2) shows a range of the lateral position, that is, a range of a feasible region representing the vehicle. Correspondingly, the constraint condition (2) also shows an interval range corresponding to the first derivative of the transverse position, the second derivative of the transverse position and the third derivative of the transverse position based on the interval range of the transverse position, so that the quadratic programming processing is performed according to the interval range corresponding to the first derivative of the transverse position, the second derivative of the transverse position and the third derivative of the transverse position.

In one possible implementation, the electronic device further determines a second constraint condition of the objective function based on the end point position of the vehicle and at least one of the lateral velocity and the lateral acceleration of the vehicle at the end point position, and then solves the objective function based on the constraint condition of the objective function.Wherein the second constraint condition is used for constraining the terminal linetype of the vehicle. In one possible implementation, the second constraint of the objective function is referred to as constraint (3) below, where (l) _t ,l _t ′,l _t ") denotes the end point sampling point,/ _t Indicating the lateral position of the vehicle at time t,/ _t ' represents the lateral speed of the vehicle at time t, /) _t "represents the lateral acceleration of the vehicle at time t, /) _n Indicates the lateral position (lateral distance), l, corresponding to the end position _n ' represents the lateral velocity corresponding to the end position. Therefore, the constraint is set to ensure that the transverse position corresponding to the end point position is the same as the transverse position of the end point sampling point, and further ensure that the transverse speed corresponding to the end point position is the same as the transverse speed of the end point sampling point, so that the path planning of the vehicle can be ensured to meet the expectation that the vehicle can reach the end point position.

(l _t ,l′ _t ,l″ _t ),l _n ＝l _t ,l′ _n ＝l′ _t (3)

In the embodiment, a path curve formed by a plurality of sampling points, a first derivative of the path curve, a second derivative of the path curve and a third derivative of the path curve are taken as an expected target, a quadratic programming target function is constructed, and then by solving the target function, relevant information of the plurality of sampling points meeting the expected target can be output, that is, one or more planned paths meeting the expected target can be determined, so that the accuracy of path programming is improved.

In step S303, the electronic device performs curve fitting processing on the second path segment to obtain a second planned path.

The curve fitting process is a process of fitting a curve of a path based on a preset template curve. In one possible implementation, the template curve is a polynomial spiral curve (polynomial spiral). The second planned path is used to refer to a path obtained by path planning the second path segment. In one possible implementation, the number of the second planned path is at least one.

In a possible implementation manner, the electronic device performs curve fitting processing on the second path segment based on a preset template curve, a start position of the second path segment, and an end position of the second path segment, so as to obtain the second planned path. The corresponding process is as follows: and substituting the preset template curve into the starting point position of the second path segment, the end point position of the second path segment, the speed and the acceleration of the vehicle at the end point position to solve so as to obtain the correlation coefficient of the template curve, and determining the second planned path based on the correlation coefficient of the template curve.

In steps S302 to S303, a secondary planning method with higher flexibility and higher accuracy is adopted to plan a path for a previous path that needs to be focused in path planning, so that a more flexible path can be planned, and a scene requirement matched with the transverse and longitudinal height of a vehicle can be better met. It should be noted that, the above embodiment takes the step S302 and then the step S303 as an example to describe the scheme, and in another possible implementation manner, the electronic device performs the step S303 and then the step S302, or the electronic device performs both the step S302 and the step S303. The execution sequence of step S302 and step S303 is not limited in the embodiment of the present disclosure.

In step S304, the electronic device obtains at least one speed sample of the vehicle.

Wherein one velocity sample corresponds to one velocity value. In one possible implementation, the electronic device obtains the at least one velocity sample by time sampling. Alternatively, in another possible implementation manner, the electronic device obtains the at least one speed sample from an information base associated with a server, where the server maintains at least one preset speed sample.

Further, in a possible implementation manner, after obtaining the at least one speed sample, the electronic device deletes the speed sample which does not meet the speed requirement, and performs the subsequent steps based on the deleted speed sample. The path requirement is a preset requirement, and the path requirement is used for screening out a path sample meeting the vehicle planning requirement. For example, speed samples with a speed value exceeding a speed threshold are deleted, etc. The disclosed embodiments do not impose limitations on the setting of speed requirements. Therefore, by deleting the speed samples which do not meet the speed requirement, the number of the speed samples is reduced, the subsequent calculation amount aiming at the speed samples is reduced, and the efficiency of path planning is improved.

In step S305, the electronic device combines the first planned path and the second planned path to obtain at least one path sample of the vehicle.

In a possible implementation manner, the electronic device combines the first planned path and the second planned path, in which the end point sampling point and the start point sampling point are at the same position or at similar positions, based on the end point sampling point in the first planned path and the start point sampling point in the second planned path, to obtain at least one path sample of the vehicle.

Further, in a possible implementation manner, after obtaining the at least one path sample, the electronic device deletes the path sample that does not meet the path requirement, and performs the subsequent steps based on the deleted path sample. The path requirement is a preset requirement, and the path requirement is used for screening out a path sample meeting the vehicle planning requirement. For example, route samples that are not relevant to the future route of the vehicle are subtracted or route samples in which an obstacle is present in the route are subtracted, etc. The embodiments of the present disclosure do not limit the setting of the path requirements. Therefore, by deleting the path samples which do not meet the path requirement, the number of the path samples is reduced, the subsequent calculation amount aiming at the path samples is reduced, and the efficiency of path planning is improved.

In step S306, the electronic device generates the plurality of candidate paths based on the at least one path sample and the at least one speed sample, wherein different candidate paths correspond to different speed samples.

In one possible implementation, the electronic device combines the at least one path sample and the at least one speed sample to obtain the plurality of candidate paths. Illustratively, taking the number of the at least one path sample as m and the number of the at least one speed sample as n, where m and n are positive integers greater than 0, m × n candidate paths can be obtained by combining the m path samples with the n speed samples.

In this embodiment, after at least one path sample is obtained, by setting different speeds at sampling points included in the path sample, speed tracks with different speeds can be generated, that is, multiple candidate paths can be generated, so that an effect of combining time sampling and space sampling can be achieved, and accuracy of path planning can be improved.

Further, in a possible implementation manner, the electronic device deletes, from the plurality of candidate paths, candidate paths that do not meet the path planning requirement, and performs the process of the subsequent step S307 based on the plurality of deleted candidate paths. The path planning requirement is a preset requirement, and the path planning requirement is used for screening out a path sample meeting the vehicle planning requirement. For example, candidate routes that are not relevant to the future route of the vehicle or candidate routes in which obstacles exist within the route are deleted, and so on. The embodiments of the present disclosure do not limit the setting of the path planning requirements. Therefore, the candidate paths which do not meet the path planning requirement are deleted, the number of the candidate paths is reduced, the subsequent calculation amount aiming at the candidate paths is reduced, and the path planning efficiency is improved.

In the above steps S304 to S306, the electronic device generates multiple candidate paths based on at least one path sample and at least one speed sample formed by the first planned path and the second planned path, so that not only the information of the spatial dimension but also the information of the temporal dimension are considered, and the mutual combination of the temporal sampling and the spatial sampling is realized, that is, the path planning of the mutual coupling in the horizontal and vertical directions is realized, thereby improving the accuracy of the path planning and improving the effect of the path planning.

In step S307, the electronic device selects a target planned path from the candidate paths to control the vehicle to travel from the starting position to the ending position along the target planned path.

Wherein the target planned path is used to refer to a path planned for the vehicle.

In a possible implementation manner, the electronic device determines path cost information of the multiple candidate paths respectively, and selects a candidate path with the minimum path cost information as the target planning path from the multiple candidate paths. Therefore, the path cost information of the multiple candidate paths can be used for evaluating the path cost corresponding to the multiple candidate paths, so that the candidate path with the minimum path cost can be selected, the optimal candidate path can be selected, and the accuracy of path planning is improved.

In a specific embodiment, fig. 6 is a schematic flow chart of path planning according to an embodiment of the present disclosure, and referring to fig. 6, the flow chart of path planning includes: firstly, at least one path sample of the vehicle is obtained through spatial sampling, and path pre-pruning is carried out on the obtained at least one path sample so as to delete the path samples which do not meet the path requirement. Meanwhile, at least one speed sample of the vehicle is obtained through time sampling, and speed pre-pruning is carried out on the obtained at least one speed sample so as to delete the speed samples which do not meet the speed requirement. Then, space-time sampling is performed on the basis of the at least one path sample and the at least one speed sample, that is, the at least one path sample and the at least one speed sample are combined to obtain a plurality of candidate paths of the vehicle, and the plurality of candidate paths are pruned to prune the candidate paths which do not meet the path planning requirement. And then, by calculating the path cost information of the multiple candidate paths, selecting the candidate path with the minimum path cost information, and taking the selected candidate path as the optimal smooth track, namely obtaining the target planning path of the vehicle.

In the embodiment of the disclosure, a path planning scheme with coupled time sampling and space sampling is provided, which not only considers space dimension information, but also considers time dimension information, and realizes the mutual combination of time sampling and space sampling, that is, the path planning with coupled horizontal and vertical directions is realized, the accuracy of path planning is improved, and the effect of path planning is improved. In the related art of path planning, a scheme of horizontal and vertical separation planning is usually adopted, for example, horizontal planning is performed first and then vertical planning is performed, that is, a driving path of a vehicle is planned without considering information of a time dimension, and then vertical planning is performed on the basis of generating the driving path to obtain a series of track points containing speed and time, so that a finished track is formed. Therefore, the transverse and longitudinal planning fit is not good enough due to a transverse and longitudinal separation planning mechanism, and the scene requirement needing the transverse and longitudinal height fit cannot be met.

In the embodiment of the disclosure, a path planning scheme combining a quadratic programming mode and a curve fitting mode is provided, and compared with a curve fitting mode based on a template curve in a direct sampling mode in the related art, the technical scheme provided by the embodiment of the disclosure can improve the accuracy of path planning and ensure the efficiency of path planning. Fig. 7 is a schematic diagram illustrating a comparison between quadratic programming and a template curve according to an embodiment of the present disclosure, and referring to fig. 7, in the road illustrated in fig. 7, a solid line 701 is a planned path determined by a scatter point smoothing method based on the quadratic programming, and a dashed line 702 is a planned path determined by a curve fitting method based on the template curve, it can be found that the line type of the dashed line 702 is not smooth enough, and may cause a vehicle collision or the like due to deviation, while the line type of the solid line 701 is smooth, so that the problem of deviation due to limited expression capability of the template curve can be avoided, and the planned path determined by the scatter point smoothing method based on the quadratic programming better conforms to the expectation of the vehicle, and safety and reliability of vehicle driving can be ensured.

Fig. 8 is a block diagram illustrating a path planning apparatus applied to a process of controlling a vehicle to travel from a starting point position to an end point position, where a path from the starting point position to the end point position includes a first path segment and at least one second path segment according to an embodiment of the disclosure. Referring to fig. 8, the apparatus includes a processing module 801, a generating module 802, and a selecting module 803. Wherein:

a processing module 801, configured to perform secondary planning processing on the first path segment to obtain a first planned path, and perform curve fitting processing on the second path segment to obtain a second planned path;

a generating module 802, configured to generate a plurality of candidate paths based on the first planned path and the second planned path;

the selecting module 803 is configured to select a target planned route from the plurality of candidate routes to control the vehicle to travel from the starting position to the ending position along the target planned route.

According to the technical scheme provided by the embodiment of the disclosure, in the face of a previous section of path needing important attention in path planning, a secondary planning mode with higher flexibility and higher accuracy is adopted for path planning, a more flexible path can be planned, the requirement of a scene matched with the transverse and longitudinal height of a vehicle can be better met, and a curve fitting mode is adopted for a next section of path planned, so that the planning efficiency of the next section of path can be improved, and the efficiency of overall path planning is improved.

In one possible implementation, the first path segment satisfies at least one of the following conditions:

taking a position point which is a preset distance away from the starting position as an end point;

taking a position point satisfying a heading condition with a terminal position of the vehicle as a terminal, the heading condition including at least one of: the orientation is the same or the orientation angle difference is less than a preset threshold.

In one possible implementation, the processing module 801 includes:

the acquisition submodule is used for acquiring a feasible region range of the vehicle, and the feasible region range is used for indicating a road range allowing the vehicle to run;

and the processing submodule is used for carrying out secondary planning processing on the first path segment based on the feasible domain range to obtain the first planned path.

In one possible implementation, the obtaining sub-module is configured to:

determining a feasible region range of the vehicle based on at least one of a boundary range of a road where the vehicle is located, a space range of a dynamic obstacle around the vehicle, a space range of a static obstacle around the vehicle and an arc constraint boundary value;

wherein the spatial range of the dynamic/static obstacle is determined based on decision information and distance information of the vehicle relative to the dynamic/static obstacle; the arc constraint boundary value is determined based on the path curvature.

In one possible implementation, the first path segment includes a plurality of sampling points;

the processing submodule is used for:

constructing a quadratic programming target function by taking a path curve formed by the plurality of sampling points, a first derivative of the path curve, a second derivative of the path curve and a third derivative of the path curve as expected targets;

solving the objective function based on the feasible domain range to obtain the transverse position, the transverse speed and the transverse acceleration of the plurality of sampling points;

the first planned path is generated based on the lateral position, the lateral velocity, and the lateral acceleration of the plurality of sampling points.

In a possible implementation, the system further comprises an obtaining module, configured to obtain at least one speed sample of the vehicle;

the generating module 802 is configured to:

combining the first planned path and the second planned path to obtain at least one path sample of the vehicle;

the plurality of candidate paths are generated based on the at least one path sample and the at least one speed sample, wherein different candidate paths correspond to different speed samples.

In a possible implementation manner, the system further comprises a pruning module, configured to prune, from the multiple candidate paths, a candidate path that does not meet the path planning requirement;

the selecting module 803 is further configured to execute the step of selecting a target planned path from the candidate paths based on the plurality of deleted candidate paths, so as to control the vehicle to travel from the starting point to the ending point along the target planned path.

According to an embodiment of the present disclosure, there is also provided an electronic device, comprising at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the path planning method provided by the present disclosure.

The present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform a path planning method provided by the present disclosure, according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the path planning method provided by the present disclosure.

According to an embodiment of the present disclosure, the present disclosure also provides an autonomous vehicle including the electronic device provided by the present disclosure.

FIG. 9 illustrates a schematic block diagram of an example electronic device 900 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 9, the electronic apparatus 900 includes a computing unit 901 that can perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM) 902 or a computer program loaded from a storage unit 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data necessary for the operation of the electronic apparatus 900 can be stored. The calculation unit 901, ROM 902, and RAM 903 are connected to each other via a bus 904. An input/output (I/O) interface 905 is also connected to bus 904.

A number of components in the electronic device 900 are connected to the I/O interface 905, including: an input unit 906 such as a keyboard, a mouse, and the like; an output unit 907 such as various types of displays, speakers, and the like; a storage unit 908 such as a magnetic disk, optical disk, or the like; and a communication unit 909 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 909 allows the electronic device 900 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.

The computing unit 901 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing Unit 901 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and the like. The computing unit 901 performs various methods and processes described above, such as a process of obtaining a planned path in the path planning method provided by the embodiment of the present disclosure. For example, in some embodiments, the path planning method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 908. In some embodiments, part or all of a computer program may be loaded onto and/or installed onto electronic device 900 via ROM 902 and/or communications unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the path planning method described above may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform the path planning method by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be realized in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Parts (ASSPs), system On Chip (SOC), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access Memory, a Read-Only Memory, an Erasable Programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM or flash Memory), an optical fiber, a Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a Display device (e.g., a Cathode Ray Tube (CRT) or Liquid Crystal Display (LCD) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A path planning method is applied to the process of controlling a vehicle to drive from a starting position to an end position, and a path from the starting position to the end position comprises a first path segment and at least one second path segment;

the method comprises the following steps:

and selecting a target planning path from the plurality of candidate paths to control the vehicle to run from the starting position to the end position along the target planning path.

2. The method of claim 1, wherein the first path segment satisfies at least one of the following conditions:

taking as an end point a position point satisfying an orientation condition with an end point position of the vehicle, the orientation condition including at least one of: the orientation is the same or the orientation angle difference is less than a preset threshold.

3. The method of claim 1, wherein said performing a quadratic planning process on the first path segment to obtain a first planned path comprises:

acquiring a feasible region range of the vehicle, wherein the feasible region range is used for indicating a road range allowing the vehicle to run;

and performing secondary planning processing on the first path segment based on the feasible region range to obtain the first planned path.

4. The method of claim 3, wherein the obtaining the range of feasible regions of the vehicle comprises:

determining a feasible region range of the vehicle based on at least one of a boundary range of a road where the vehicle is located, a spatial range of dynamic obstacles around the vehicle, a spatial range of static obstacles around the vehicle and an arc constraint boundary value;

5. The method of claim 3, wherein the first path segment comprises a plurality of sampling points;

performing secondary planning processing on the first path segment based on the feasible region range to obtain the first planned path, including:

constructing a quadratic programming objective function by taking a path curve formed by the plurality of sampling points, a first derivative of the path curve, a second derivative of the path curve and a third derivative of the path curve as expected targets;

generating the first planned path based on the lateral position, lateral velocity, and lateral acceleration of the plurality of sampling points.

6. The method of claim 1, further comprising:

obtaining at least one speed sample of the vehicle;

generating a plurality of candidate paths based on the first planned path and the second planned path, including:

generating the plurality of candidate paths based on the at least one path sample and the at least one speed sample, wherein different candidate paths correspond to different speed samples.

7. The method of claim 1, further comprising:

deleting candidate paths which do not meet path planning requirements from the multiple candidate paths;

and executing the step of selecting a target planning path from the plurality of candidate paths based on the plurality of deleted candidate paths so as to control the vehicle to travel from the starting position to the end position along the target planning path.

8. A path planning device is applied to the process of controlling a vehicle to drive from a starting position to an end position, and a path from the starting position to the end position comprises a first path segment and at least one second path segment;

the device comprises:

and the selecting module is used for selecting a target planning path from the plurality of candidate paths so as to control the vehicle to drive from the starting point position to the end point position along the target planning path.

9. The apparatus of claim 8, wherein the first path segment satisfies at least one of the following conditions:

10. The apparatus of claim 8, wherein the processing module comprises:

11. The apparatus of claim 10, wherein the acquisition submodule is to:

12. The apparatus of claim 10, wherein the first path segment comprises a plurality of sampling points;

the processing submodule is used for:

taking a path curve formed by the plurality of sampling points, a first derivative of the path curve, a second derivative of the path curve and a third derivative of the path curve as expected targets, and constructing a quadratic programming objective function;

13. The apparatus of claim 8, further comprising an acquisition module to acquire at least one speed sample of the vehicle;

the generation module is configured to:

14. The apparatus of claim 8, further comprising a pruning module configured to prune, among the plurality of candidate paths, candidate paths that do not meet path planning requirements;

the selecting module is further configured to execute a step of selecting a target planned path from the plurality of candidate paths based on the plurality of deleted candidate paths to control the vehicle to travel from the starting position to the ending position along the target planned path.

15. An electronic device, comprising:

at least one processor; and

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

16. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-7.

17. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-7.

18. An autonomous vehicle comprising the electronic device of claim 15.