CN115675528A

CN115675528A - Automatic driving method and vehicle based on similar scene mining

Info

Publication number: CN115675528A
Application number: CN202211429893.2A
Authority: CN
Inventors: 苟少帅
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2022-11-15
Filing date: 2022-11-15
Publication date: 2023-02-03

Abstract

The disclosure provides an automatic driving method and a vehicle based on similar scene mining, and relates to the technical field of data processing, in particular to the technical field of automatic driving. The implementation scheme is as follows: obtaining current driving scene data of the vehicle, wherein the current driving scene data comprises environment information of the vehicle; obtaining at least one similar driving scene data corresponding to the current driving scene data from a data set, wherein the data set comprises a plurality of driving scene data corresponding to a plurality of vehicles respectively, each driving scene data in the plurality of driving scene data indicates environmental information of the corresponding vehicle when the corresponding vehicle runs on the corresponding driving track, and the similarity between each similar driving scene data in the at least one similar driving scene data and the current driving scene data is greater than a preset value; and obtaining a recommended track based on a plurality of driving tracks corresponding to the similar driving scenes so as to recommend the recommended track to the vehicle.

Description

Automatic driving method and vehicle based on similar scene mining

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a method and apparatus for data processing automatic driving based on similar scene mining, a vehicle, an electronic device, a computer-readable storage medium, and a computer program product.

Background

Artificial intelligence is the subject of research that causes computers to simulate certain human mental processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), both at the hardware level and at the software level. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, a machine learning/deep learning technology, a big data processing technology, a knowledge map technology and the like.

Data processing based on artificial intelligence has been widely applied in various fields. In the field of automatic driving, data are processed based on artificial intelligence, and reasonable driving tracks can be planned for vehicles.

The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, the problems mentioned in this section should not be considered as having been acknowledged in any prior art, unless otherwise indicated.

Disclosure of Invention

The present disclosure provides an automatic driving method, apparatus, vehicle, electronic device, computer-readable storage medium, and computer program product based on similar scene mining.

According to an aspect of the present disclosure, there is provided an automatic driving method based on similar scene mining for automatic driving based on similar scene mining, including: obtaining current driving scene data of a vehicle, wherein the current driving scene data comprises environmental information of the vehicle; obtaining at least one similar driving scene data corresponding to the current driving scene data from a data set, wherein the data set comprises a plurality of driving scene data corresponding to a plurality of vehicles respectively, each driving scene data in the plurality of driving scene data indicates environmental information of the corresponding vehicle when the corresponding vehicle runs on a corresponding driving track, and the similarity between each similar driving scene data in the at least one similar driving scene data and the current driving scene data is greater than a preset value; and determining an automatic driving recommendation track for the host vehicle based on a plurality of driving tracks corresponding to the plurality of similar driving scenes.

According to another aspect of the present disclosure, there is provided an automatic driving method including: receiving a recommended track obtained according to the automatic driving method based on similar scene mining, wherein the recommended track is obtained according to the automatic driving method based on similar scene mining; obtaining a target driving track based on the recommended track; and performing automatic driving based on the target driving track.

According to another aspect of the present disclosure, there is provided a similar scene mining-based automatic driving apparatus for automatic driving based on similar scene mining, including: the driving scene data acquisition unit is configured to acquire current driving scene data of the vehicle, wherein the current driving scene data comprises environmental information of the vehicle; a retrieval unit configured to obtain at least one similar driving scene data corresponding to the current driving scene data from a data set, the data set including a plurality of driving scene data corresponding to a plurality of vehicles, respectively, each of the plurality of driving scene data indicating environmental information of the corresponding vehicle when the corresponding vehicle travels on a corresponding driving track, a similarity between each of the at least one similar driving scene data and the current driving scene data being greater than a preset value; and a recommended trajectory acquisition unit configured to determine an automatic driving recommended trajectory for the host vehicle, for a plurality of driving trajectories corresponding to the plurality of similar driving scenes.

According to another aspect of the present disclosure, there is provided an automatic driving apparatus including: a receiving unit configured to receive a recommended trajectory obtained according to the automatic driving method based on similar scene mining according to an embodiment of the present disclosure; a target driving track obtaining unit configured to obtain a target driving track based on the recommended track; and a driving unit configured to perform automatic driving based on the target driving trajectory.

According to another aspect of the present disclosure, there is provided a vehicle including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of embodiments of the present disclosure.

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 memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method described in embodiments of 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 the computer to perform the method according to the embodiments of the present disclosure.

According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program realizes the method of an embodiment of the present disclosure when executed by a processor.

According to one or more embodiments of the disclosure, an automatic driving track can be recommended for a vehicle based on a driving track of the vehicle in a driving scene similar to a current driving scene of the vehicle by understanding the driving scene, so that the recommended track obtained by the vehicle is suitable for the current driving scene of the vehicle, accuracy of the obtained recommended track is improved, an accurate recommended track can be obtained for various different scenes according to the automatic driving method based on similar scene mining, and scene generalization capability in a path planning and track recommendation process is improved.

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

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the embodiments and, together with the description, serve to explain the exemplary implementations of the embodiments. The illustrated embodiments are for purposes of example only and do not limit the scope of the claims. Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.

FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, according to an embodiment of the present disclosure;

FIG. 2 shows a flow diagram of a method of automatic driving based on similar scene mining according to an embodiment of the present disclosure;

fig. 3 shows a flowchart of a process of obtaining a plurality of similar driving scenario data corresponding to current driving scenario data from a dataset in an automatic driving method based on similar scenario mining according to an embodiment of the present disclosure;

fig. 4 is a flowchart illustrating a process of obtaining a recommended trajectory based on a plurality of driving trajectories corresponding to a plurality of similar driving scenes in the automatic driving method based on similar scene mining according to an embodiment of the present disclosure;

fig. 5 shows a flowchart of a process of obtaining a recommended trajectory based on a plurality of first driving trajectories in an automatic driving method based on similar scene mining according to an embodiment of the present disclosure;

fig. 6 shows a flowchart of a process of obtaining a recommended trajectory based on a plurality of third driving trajectories in the automatic driving method based on similar scene mining according to the embodiment of the present disclosure;

FIG. 7 shows a flow diagram of an automated driving method based on similar scene mining according to an embodiment of the disclosure;

FIG. 8 shows a flow chart of an autonomous driving method according to an embodiment of the disclosure;

FIG. 9 shows a block diagram of an autopilot based on similar scene mining in accordance with an embodiment of the present disclosure;

FIG. 10 illustrates a block diagram of an autopilot device according to an embodiment of the present disclosure; and

FIG. 11 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments 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 of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the present disclosure, unless otherwise specified, the use of the terms "first", "second", and the like to describe various elements is not intended to limit the positional relationship, the temporal relationship, or the importance relationship of the elements, and such terms are used only to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, based on the context, they may also refer to different instances.

The terminology used in the description of the various described examples in this disclosure is for the purpose of describing the particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the elements may be one or more. Furthermore, the term "and/or" as used in this disclosure is intended to encompass any and all possible combinations of the listed items.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods and apparatus described herein may be implemented in accordance with embodiments of the present disclosure. Referring to fig. 1, the system 100 includes a motor vehicle 110, a server 120, and one or more communication networks 130 coupling the motor vehicle 110 to the server 120.

In embodiments of the present disclosure, motor vehicle 110 may include a computing device and/or be configured to perform a method in accordance with embodiments of the present disclosure.

The server 120 may run one or more services or software applications that enable the information processing method to be performed. In some embodiments, the server 120 may also provide other services or software applications that may include non-virtual environments and virtual environments. In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof, which may be executed by one or more processors. A user of motor vehicle 110 may, in turn, utilize one or more client applications to interact with server 120 to take advantage of the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from system 100. Accordingly, fig. 1 is one example of a system for implementing the various methods described herein, and is not intended to be limiting.

The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture involving virtualization (e.g., one or more flexible pools of logical storage that may be virtualized to maintain virtual storage for the server). In various embodiments, the server 120 may run one or more services or software applications that provide the functionality described below.

The computing units in server 120 may run one or more operating systems including any of the operating systems described above, as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

In some embodiments, server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from motor vehicle 110. Server 120 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of motor vehicle 110.

Network 130 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. By way of example only, one or more networks 110 may be a satellite communication network, a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (including, e.g., bluetooth, wiFi), and/or any combination of these and other networks.

The system 100 may also include one or more databases 150. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 150 may be used to store information such as audio files and video files. The data store 150 may reside in various locations. For example, the data store used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The data store 150 may be of different types. In certain embodiments, the data store used by the server 120 may be a database, such as a relational database. One or more of these databases may store, update, and retrieve data to and from the databases in response to the commands.

In some embodiments, one or more of the databases 150 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key-value stores, object stores, or regular stores supported by a file system.

Motor vehicle 110 may include sensors 111 for sensing the surrounding environment. The sensors 111 may include one or more of the following sensors: visual cameras, infrared cameras, ultrasonic sensors, millimeter wave radar, and laser radar (LiDAR). Different sensors may provide different detection accuracies and ranges. The camera may be mounted in front of, behind, or otherwise of the vehicle. The visual camera may capture conditions inside and outside the vehicle in real time and present to the driver and/or passengers. In addition, by analyzing the picture captured by the visual camera, information such as traffic light indication, intersection situation, other vehicle running state, and the like can be acquired. The infrared camera can capture objects under night vision conditions. The ultrasonic sensors can be arranged on the periphery of the vehicle and used for measuring the distance between an object outside the vehicle and the vehicle by utilizing the characteristics of strong ultrasonic directionality and the like. The millimeter wave radar may be installed in front of, behind, or other positions of the vehicle for measuring the distance of an object outside the vehicle from the vehicle using the characteristics of electromagnetic waves. The lidar may be mounted in front of, behind, or otherwise of the vehicle for detecting object edges, shape information, and thus object identification and tracking. The radar apparatus can also measure the velocity change of the vehicle and the moving object due to the doppler effect.

Motor vehicle 110 may also include a communication device 112. The communication device 112 may include a satellite positioning module capable of receiving satellite positioning signals (e.g., beidou, GPS, GLONASS, and GALILEO) from the satellites 141 and generating coordinates based on these signals. The communication device 112 may also comprise modules for communicating with a mobile communication base station 142, and the mobile communication network may implement any suitable communication technology, such as current or evolving wireless communication technologies (e.g. 5G technologies) like GSM/GPRS, CDMA, LTE, etc. The communication device 112 may also have a Vehicle-to-Vehicle (V2X) networking or Vehicle-to-anything (V2X) module configured to enable, for example, vehicle-to-Vehicle (V2V) communication with other vehicles 143 and Vehicle-to-Infrastructure (V2I) communication with Infrastructure 144. Further, the communication device 112 may also have a module configured to communicate with a user terminal 145 (including but not limited to a smartphone, tablet, or wearable device such as a watch), for example, via wireless local area network using IEEE802.11 standards or bluetooth. Motor vehicle 110 may also access server 120 via network 130 using communication device 112.

Motor vehicle 110 may also include a control device 113. The control device 113 may include a processor, such as a Central Processing Unit (CPU) or a Graphics Processing Unit (GPU), or other special purpose processor, etc., in communication with various types of computer-readable storage devices or media. The control device 113 may include an autopilot system for automatically controlling various actuators in the vehicle. The autopilot system is configured to control a powertrain (not shown), a steering system, and a braking system, etc., of a motor vehicle 110 (not shown) via a plurality of actuators in response to inputs from a plurality of sensors 111 or other input devices to control acceleration, steering, and braking, respectively, without or with limited human intervention. Part of the processing functions of the control device 113 may be realized by cloud computing. For example, some processing may be performed using an onboard processor while other processing may be performed using the computing resources in the cloud. The control device 113 may be configured to perform a method according to the present disclosure. Furthermore, the control apparatus 113 may be implemented as one example of a computing device on the motor vehicle side (client) according to the present disclosure.

The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with the present disclosure.

In the related art, a path or a track of a vehicle is planned based on a map to obtain a navigation path or a recommended track of the vehicle, different rules are designed for different types of driving scenes in the planning process of the path or the track based on the map, and the navigation path is obtained according to the rules based on the corresponding types of the scenes. For example, for a left turn scene, a navigation path or a recommended trajectory is obtained from a map based on a starting position corresponding to the left turn scene. In the process of obtaining the navigation path or the recommended track by the method, the generalization capability of the scene is often not strong, and the accurate navigation path or the recommended track cannot be obtained under the condition that a new type of scene appears.

According to one aspect of the disclosure, an automatic driving method based on similar scene mining is provided. As shown in fig. 2, a similar scenario mining based autonomous driving method 200 according to the present disclosure includes:

step S210: obtaining current driving scene data of a host vehicle, wherein the current driving scene data comprises environmental information of the host vehicle;

step S220: obtaining a plurality of similar driving scene data corresponding to the current driving scene data from a data set, wherein the data set comprises a plurality of driving scene data corresponding to a plurality of vehicles respectively, each driving scene data in the plurality of driving scene data indicates environmental information of the corresponding vehicle when the corresponding vehicle runs on a corresponding driving track, and the similarity between each similar driving scene data in the plurality of similar driving scene data and the current driving scene data is greater than a preset value; and

step S230: determining an automatic driving recommendation trajectory for the host vehicle based on a plurality of driving trajectories corresponding to the plurality of similar driving scene data.

The recommended trajectory of the host vehicle is obtained by obtaining a plurality of similar driving scene data corresponding to the current driving scene data from the data set based on the current driving scene data of the host vehicle, and obtaining a plurality of driving trajectories corresponding to the plurality of similar driving scene data based on the recommended trajectory of the host vehicle. In the process of obtaining the recommended track, after the current driving scene data in the driving scene where the vehicle is located and the scene data in the driving scene where other vehicles are located in the data set are understood, the recommended track is obtained by obtaining the driving track of the vehicle under the driving scene similar to the driving scene where the vehicle is located, the driving scene where the vehicle is located is understood, and processing is not required to be performed according to the rules, so that the obtained recommended track can be suitable for the current driving scene, the accuracy is high, the driving track obtained by the vehicle based on the recommended track is suitable for the current driving scene, and the accuracy is high.

Meanwhile, according to the automatic driving method based on similar scene mining disclosed by the invention, the driving scene where the vehicle is located is directly understood without being converted into a scene type for understanding, so that the scene generalization capability in the process of obtaining the recommended track can be improved.

In some embodiments, the host vehicle is a vehicle that is ready to run or is running for which a path plan or recommended trajectory is desired.

In some embodiments, the current driving scenario data may include data collected by a data collection device on the host vehicle. For example, a data acquisition device on the host vehicle may include a camera device; the current driving scene data may include, but is not limited to, a video captured by a camera device.

In some embodiments, the current driving scenario data may also include data collected from roadside devices at the location of the host vehicle.

In some embodiments, the current driving scenario data may be data that has an understanding of data collected by a data collection device on the host vehicle. For example, after the video frames captured by the camera device on the host vehicle are understood by using the image recognition model, the instances in the environment where the host vehicle is located and the distances of the instances relative to the host vehicle are obtained. For example, the instances may be pedestrians, obstacles, transportation facilities, roads, and the like.

In some embodiments, the environment information of the host vehicle includes road information, traffic facility information, obstacle information, and the like, which are not limited herein.

In some embodiments, the current driving scene data is obtained in step S210 by receiving data collected by the data collection device on the host vehicle sent by the host vehicle.

In some embodiments, the data set may be pre-collected driving scenario data from a plurality of vehicles. The plurality of vehicles respectively collect driving scene data in the driving process and send the driving scene data to the server side, so that the server side stores the driving scene data, and the data set is obtained.

In some embodiments, in the process of obtaining the data set at the server, a driving track of the vehicle corresponding to each driving scene data in the data set is also obtained, and each driving scene data is associated with the corresponding driving track for storage.

In some embodiments, the plurality of similar driving scenario data is obtained by comparing the current driving scenario data with each of the driving scenario data in the data set in step S220. For example, for each driving scene data in the data set, the similarity between the current driving scene data and each driving scene data is obtained by comparing the road information and the transportation facility information in the driving scene data with the road information and the transportation facility information in the current driving scene data, respectively.

In some embodiments, the data set includes a feature vector corresponding to each of the plurality of driving scenario data, and as shown in fig. 3, the step S220 of obtaining a plurality of similar driving scenario data corresponding to the current driving scenario data from the data set includes:

step S310: processing the current driving scene data to obtain a feature vector corresponding to the current driving scene data; and

step S320: obtaining the plurality of similar driving scene data based on a vector corresponding to the current driving scene data and a feature vector of each of the plurality of driving scene data.

By storing the driving scene data by the characteristic vectors, the similarity among the scene data is directly obtained by obtaining the characteristic vectors of the current driving scene data and calculating based on the similarity among the characteristic vectors in the process of obtaining a plurality of similar scene data, the data processing amount for obtaining the similar driving scene data can be reduced, and the calculation speed is improved.

According to the embodiment of the disclosure, a plurality of similar scene data are obtained in a vector retrieval (ANN) mode, the calculation complexity is reduced, the processing efficiency is improved, and meanwhile, as the feature vectors in the data set are expressed by cutting the scene data, the driving scene can be understood and indexed in the vector dimension, and the understanding and indexing of the scene are strengthened.

In some embodiments, in step S310, a feature vector of the current driving scenario data is obtained by inputting the current driving scenario data to the convolutional neural network CNN.

In some embodiments, in step S310, a feature vector of the current driving scene data is obtained by inputting the current driving scene data to a vector network (VectorNet). The vector network models the relation among different examples by extracting the example characteristics in the driving scene data to obtain the characteristic vector of the driving scene data, so that the driving scene data can be more accurately understood.

In some embodiments, in step S230, each of the plurality of driving trajectories is taken as a recommended trajectory to be recommended to the host vehicle, so that the host vehicle selects one driving trajectory from the plurality of driving trajectories as a target driving trajectory for autonomous driving, wherein the target driving trajectory is along which the host vehicle travels during the autonomous driving.

In some embodiments, as shown in fig. 4, the step S230 of determining the automatic driving recommendation trajectory for the host vehicle based on the plurality of driving trajectories corresponding to the plurality of similar driving scenes comprises:

step S410: determining a safety index for each of the plurality of driving trajectories;

step S420: obtaining a plurality of first driving trajectories of the plurality of driving trajectories, each of the plurality of first driving trajectories having a safety index greater than a second driving trajectory, the second driving trajectory being distinct from each of the first driving trajectories; and

step S430: obtaining the recommended trajectory based on the plurality of first driving trajectories.

In the process of obtaining the recommended track, firstly, the safety of the driving track in each similar scene is considered, so that the obtained recommended track is a track with high safety, and the safety of the target recommended track obtained based on the recommended track is improved.

In some embodiments, in step S410, the safety index is obtained by comparing the curvature of each of the plurality of driving trajectories on the respective segments, wherein the driving trajectory having the larger curvature has a lower safety index.

In some embodiments, each of the plurality of driving scenario data further includes driving behavior data of the respective vehicle while traveling on the respective driving trajectory, the driving behavior data including first behavior data indicating whether the respective vehicle has a collision, second behavior data indicating whether the respective vehicle has sudden braking, third behavior data indicating whether the respective vehicle has sudden acceleration or sudden deceleration, or fourth behavior data indicating whether the respective vehicle has a jerk;

wherein the step S410 of determining the safety index of each of the plurality of driving trajectories includes:

and based on the driving behavior data in each of the plurality of driving scene data, performing safety scoring on the corresponding driving track of the driving scene data to obtain a safety index of the corresponding driving track.

The safety of the corresponding driving track is scored according to the driving behavior data of each driving scene data to obtain the safety index of the driving track, so that the obtained safety index is carried out on the basis of the actual driving process carried out on the driving track, and the accuracy and the reliability of the obtained safety index are improved.

In some embodiments, in step S420, a preset number of first driving trajectories are obtained, wherein the predicted number of first driving trajectories are driving trajectories with a higher safety index among the driving trajectories.

In some embodiments, in step S420, a plurality of first driving trajectories are obtained from the plurality of driving trajectories based on a preset safety index threshold, wherein the safety index of each first driving trajectory is not less than the safety index threshold.

In some embodiments, in step S430, each of the plurality of first driving trajectories is taken as a recommended trajectory to be recommended to the host vehicle, so that the host vehicle selects one first driving trajectory from the plurality of first driving trajectories as a target driving trajectory for automatic driving.

In some embodiments, as shown in fig. 5, the step S430 of obtaining the recommended trajectory based on the plurality of first driving trajectories includes:

step S510: determining a comfort index for each of the plurality of first driving trajectories;

step S520: obtaining a plurality of third driving trajectories of the plurality of first driving trajectories, each third driving trajectory of the plurality of third driving trajectories having a comfort index greater than a fourth driving trajectory, the fourth driving trajectory being distinct from each of the third driving trajectories; and

step S530: obtaining the recommended trajectory based on the plurality of third driving trajectories.

In the process of obtaining the recommended track, the comfort of the driving track corresponding to each piece of similar scene data is also considered, so that the obtained recommended track is a track with high safety and high comfort in the driving process based on the recommended track, and the comfort of the vehicle in automatic driving based on the obtained recommended track is improved.

In some embodiments, in step S510, a comfort index is obtained by comparing the curvature of each of the plurality of first driving trajectories on the respective segment, wherein the comfort index of the driving trajectory in which the segment with the larger curvature is located is lower.

In some embodiments, in step S510, for each of a plurality of first driving trajectories, a dynamic model of how well the host vehicle is traveling on the first driving trajectory is obtained based on the first driving trajectory, and a comfort score is performed based on the dynamic model to obtain a comfort index of the first driving trajectory.

In some embodiments, in step S520, a preset number of third driving trajectories are obtained, wherein the predicted number of third driving trajectories are the first driving trajectories with a higher comfort index among the first driving trajectories.

In some embodiments, in step S520, a plurality of third driving trajectories are obtained from the plurality of first driving trajectories based on a preset comfort index threshold, wherein the comfort index of each third driving trajectory is not less than the comfort index threshold.

In some embodiments, in step S530, each of the plurality of third driving trajectories is taken as a recommended trajectory to be recommended to the host vehicle, so that the host vehicle selects one third driving trajectory from the plurality of third driving trajectories as a target driving trajectory for automatic driving.

In some embodiments, as shown in fig. 6, the obtaining the recommended trajectory based on the plurality of third driving trajectories in step S530 includes:

step S610: obtaining a comprehensive score corresponding to each of the plurality of third driving trajectories by using a simulation driving system, wherein the comprehensive score indicates a score considering driving efficiency, safety and comfort when the vehicle runs according to the third driving trajectory;

step S620: obtaining a fifth driving track with the highest comprehensive score in the plurality of third driving tracks; and

step S630: obtaining the recommended trajectory based on the fifth driving trajectory.

For each of the plurality of third driving trajectories, a comprehensive score considering the driving efficiency, safety and comfort of the vehicle when the vehicle runs according to the third driving trajectory is obtained through the simulation driving system, so that the obtained recommended trajectory is an optimal trajectory considering various factors, the target driving trajectory obtained by the vehicle based on the recommended trajectory better meets the requirements of the vehicle, and the accuracy of the obtained target driving trajectory is further improved.

In some embodiments, in step S630, the fifth trajectory is taken as a recommended trajectory to be recommended to the host vehicle.

In some embodiments, as shown in fig. 7, the automatic driving method based on similar scene mining according to the present disclosure further includes:

step S710: pushing the recommended track to the vehicle to enable the vehicle to obtain a target driving track based on the recommended track, and automatically driving based on the target driving track; and

step S720: and obtaining a driving track of the vehicle after automatic driving based on the target driving track, and storing the driving track and the current driving scene data into the data set.

By pushing the recommended track to the current vehicle and updating the data set based on the actual running track of the current vehicle and the current driving scene data, after the current driving scene data of other vehicles are subsequently obtained, the automatic driving method based on similar scene mining disclosed by the invention has more data when the data set is used for searching, so that the capability of subsequently obtaining an accurate recommended track is improved.

According to the embodiment of the disclosure, the driving scene data and the driving track of the vehicle are continuously collected, the capability of obtaining similar driving scene data can be continuously obtained based on retrieval, and the capability of recommending an accurate recommended track for the vehicle can be further improved.

In some embodiments, in step S710, the recommended trajectory is sent to the host vehicle through a wireless network.

In some embodiments, the host vehicle directly performs the navigation travel using the recommended trajectory as the target driving trajectory.

In some embodiments, the host vehicle corrects a navigation path or trajectory obtained based on a map by the recommended trajectory, and takes the corrected path or trajectory as a target driving trajectory for navigation driving.

In some embodiments, in step S720, the traveling trajectory transmitted by the host vehicle is received through the wireless network.

According to another aspect of the present disclosure, there is also provided a navigation method, as shown in fig. 8, the method 800 includes:

step S810: receiving a recommended track, wherein the recommended track is obtained according to the automatic driving method based on similar scene mining of the embodiment of the disclosure;

step S820: obtaining a target driving track based on the recommended track; and

step S830: and carrying out automatic driving based on the target driving track.

According to the automatic driving method based on similar scene mining disclosed by the invention, in the process of obtaining the recommended track, after the current driving scene data in the driving scene where the vehicle is located and the scene data in the driving scene where other vehicles are located in the data set are understood, the recommended track is obtained by obtaining the driving track of the vehicle under the driving scene similar to the driving scene where the vehicle is located, the recommended track is understood according to the driving scene where the vehicle is located, and the processing is not required according to the rules, so that the obtained target driving track can be suitable for the current driving scene, the accuracy is high, and the target driving track obtained by the automatic driving method disclosed by the invention is suitable for the current driving scene, and the accuracy is high.

Meanwhile, according to the automatic driving method based on similar scene mining disclosed by the invention, the driving scene where the vehicle is located is directly understood, the driving scene is not converted into the scene type for understanding, the scene generalization capability in the process of obtaining the recommended track can be improved, the scene generalization capability in the process of obtaining the target driving track can be improved according to the automatic driving method disclosed by the invention, and accurate navigation and automatic driving can be carried out based on the obtained target driving track.

In some embodiments, in step S820, the recommended trajectory is taken as the target driving trajectory.

In some embodiments, in step S820, the navigation path or trajectory obtained based on the map is modified by the recommended trajectory, and the modified path or trajectory is taken as the target driving trajectory.

According to another aspect of the present disclosure, there is also provided an automatic driving apparatus based on similar scene mining, as shown in fig. 9, the automatic driving apparatus 900 based on similar scene mining includes: a current driving scene data obtaining unit 910 configured to obtain current driving scene data of a host vehicle, where the current driving scene data includes environmental information of the host vehicle; a retrieving unit 920, configured to obtain a plurality of similar driving scene data corresponding to the current driving scene data from a data set, where the data set includes a plurality of driving scene data corresponding to a plurality of vehicles, respectively, each of the plurality of driving scene data indicates environmental information of a corresponding vehicle when the corresponding vehicle travels on a corresponding driving track, and a similarity between each of the plurality of similar driving scene data and the current driving scene data is greater than a preset value; and a recommended trajectory acquisition unit 930 configured to determine an automatic driving recommended trajectory for the host vehicle based on a plurality of driving trajectories corresponding to the plurality of similar driving scene data.

In some embodiments, the data set includes a feature vector corresponding to each of the plurality of driving scenario data, and the retrieving unit 920 includes: the characteristic vector acquisition unit is configured to process the current driving scene data to acquire a characteristic vector corresponding to the current driving scene data; and a retrieval subunit configured to obtain the plurality of similar driving scene data based on a vector corresponding to the current driving scene data and a feature vector of each of the plurality of driving scene data.

In some embodiments, the recommended trajectory obtaining unit 930 includes: a first determination unit configured to determine a safety index for each of the plurality of driving trajectories; a first acquisition unit configured to acquire a plurality of first driving trajectories among the plurality of driving trajectories, each of the plurality of first driving trajectories having a safety index greater than a second driving trajectory, the second driving trajectory being different from each of the first driving trajectories; and a first recommended trajectory acquisition subunit configured to acquire the recommended trajectory based on the plurality of first driving trajectories.

In some embodiments, each of the plurality of driving scenario data further includes driving behavior data of the respective vehicle while traveling on the respective driving trajectory, the driving behavior data including first behavior data indicating whether the respective vehicle has a collision, second behavior data indicating whether the respective vehicle has sudden braking, third behavior data indicating whether the respective vehicle has sudden acceleration or sudden deceleration, or fourth behavior data indicating whether the respective vehicle has a jerk; wherein the first determination unit includes: a safety scoring unit configured to perform safety scoring on a corresponding driving track of each of the plurality of driving scene data based on the driving behavior data in the driving scene data to obtain a safety index of the corresponding driving track.

In some embodiments, the first recommended trajectory acquisition subunit includes: a second determination unit configured to determine a comfort index for each of the plurality of first driving trajectories; a second obtaining unit configured to obtain a plurality of third driving trajectories among the plurality of first driving trajectories, each of the plurality of third driving trajectories having a comfort index greater than a fourth driving trajectory, the fourth driving trajectory being different from each of the third driving trajectories; and a second recommended trajectory acquisition subunit configured to acquire the recommended trajectory based on the plurality of third driving trajectories.

In some embodiments, the second recommended trajectory acquisition subunit includes: a simulation processing unit configured to obtain, using a simulation driving system, a composite score corresponding to each of the plurality of third driving trajectories, the composite score indicating a score that takes into account driving efficiency, safety, and comfort when the host vehicle travels according to the third driving trajectory; a third acquisition unit configured to acquire a fifth driving trajectory having a highest composite score among the plurality of third driving trajectories; and a third recommended trajectory acquisition subunit configured to acquire the recommended trajectory based on the fifth driving trajectory.

In some embodiments, the apparatus 900 further comprises: pushing the recommended track to the vehicle to enable the vehicle to obtain a target driving track based on the recommended track, and automatically driving based on the target driving track; and obtaining a driving track of the vehicle after automatic driving based on the target driving track, and storing the driving track and the current driving scene data into the data set.

According to another aspect of the present disclosure, there is also provided a navigation device, as shown in fig. 10, the device 1000 includes: a receiving unit 1010 configured to receive a recommended trajectory obtained according to the automatic driving method based on similar scene mining according to an embodiment of the present disclosure; a target driving trajectory obtaining unit 1020 configured to obtain a target driving trajectory based on the recommended trajectory; and a driving unit 1030 configured to perform automatic driving based on the target driving trajectory.

According to another aspect of the present disclosure, there is also provided a vehicle including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method according to embodiments of the present disclosure.

According to an embodiment of the present disclosure, an electronic device, a readable storage medium, and a computer program product are also provided.

Referring to fig. 11, a block diagram of a structure of an electronic device 1100, which may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular telephones, 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 intended to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 11, the electronic device 1100 includes a computing unit 1101, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 1102 or a computer program loaded from a storage unit 1108 into a Random Access Memory (RAM) 1103. In the RAM 1103, various programs and data necessary for the operation of the electronic device 1100 may also be stored. The calculation unit 1101, the ROM 1102, and the RAM 1103 are connected to each other by a bus 1104. An input/output (I/O) interface 1105 is also connected to bus 1104.

A number of components in electronic device 1100 are connected to I/O interface 1105, including: an input unit 1106, an output unit 1107, a storage unit 1108, and a communication unit 1109. The input unit 1106 may be any type of device capable of inputting information to the electronic device 1100, and the input unit 1106 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and/or a remote control. Output unit 1107 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. Storage unit 1108 may include, but is not limited to, a magnetic or optical disk. The communication unit 1109 allows the electronic device 1100 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, 802.11 devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.

The computing unit 1101 can be a variety of general purpose and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 1101 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated 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 so forth. The computing unit 1101 performs the various methods and processes described above, such as the method 200 or the method 800. For example, in some embodiments, the method 200 or the method 800 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 1108. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 1100 via the ROM 1102 and/or the communication unit 1109. When loaded into RAM 1103 and executed by computing unit 1101, may perform one or more of the steps of method 200 described above. Alternatively, in other embodiments, the computing unit 1101 may be configured by any other suitable means (e.g., by means of firmware) to perform the method 200 or the method 800.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), 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 similar context mining-based autopilot device such that the program codes, when executed by the processor or controller, cause the functions/acts 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 (RAM), a read-only memory (ROM), an 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 CRT (cathode ray tube) or LCD (liquid crystal display) 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 may 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 combining a blockchain.

It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical aspects of the present disclosure can be achieved.

Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the above-described methods, systems and apparatus are merely exemplary embodiments or examples and that the scope of the present invention is not limited by these embodiments or examples, but only by the claims as issued and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced with equivalents thereof. Further, the steps may be performed in an order different from that described in the present disclosure. Further, various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced with equivalent elements that appear after the present disclosure.

Claims

1. An automatic driving method based on similar scene mining comprises the following steps:

obtaining current driving scene data of a vehicle, wherein the current driving scene data comprises environmental information of the vehicle;

obtaining a plurality of similar driving scene data corresponding to the current driving scene data from a data set, wherein the data set comprises a plurality of driving scene data corresponding to a plurality of vehicles respectively, each driving scene data in the plurality of driving scene data indicates environmental information of the corresponding vehicle when the corresponding vehicle runs on a corresponding driving track, and the similarity between each similar driving scene data in the plurality of similar driving scene data and the current driving scene data is greater than a preset value; and

and determining an automatic driving recommendation track for the host vehicle based on a plurality of driving tracks corresponding to the plurality of similar driving scene data.

2. The method of claim 1, wherein the data set includes a feature vector corresponding to each of the plurality of driving scenario data, and the obtaining a plurality of similar driving scenario data corresponding to the current driving scenario data from the data set includes:

processing the current driving scene data to obtain a feature vector corresponding to the current driving scene data; and

obtaining the plurality of similar driving scene data based on the vector corresponding to the current driving scene data and the feature vector of each of the plurality of driving scene data.

3. The method of claim 1, wherein the determining an autodrive recommended trajectory for the host vehicle based on a plurality of driving trajectories corresponding to the plurality of similar driving scenarios comprises:

determining a safety index for each of the plurality of driving trajectories;

obtaining a plurality of first driving trajectories of the plurality of driving trajectories, each of the plurality of first driving trajectories having a safety index greater than a second driving trajectory, the second driving trajectory being distinct from each of the first driving trajectories; and

obtaining the recommended trajectory based on the plurality of first driving trajectories.

4. The method of claim 3, wherein each of the plurality of driving scenario data further comprises driving behavior data while the respective vehicle is traveling on a respective driving trajectory, the driving behavior data comprising first behavior data indicative of whether the respective vehicle is in a collision, second behavior data indicative of whether the respective vehicle is in sharp braking, third behavior data indicative of whether the respective vehicle is in sharp acceleration or sharp deceleration, or fourth behavior data indicative of whether the respective vehicle is in sharp frustration;

wherein the determining a safety index for each of the plurality of driving trajectories comprises:

5. The method of claim 3, wherein the obtaining the recommended trajectory based on the plurality of first driving trajectories comprises:

determining a comfort index for each of the plurality of first driving trajectories;

obtaining a plurality of third driving trajectories among the plurality of first driving trajectories, each third driving trajectory of the plurality of third driving trajectories having a comfort index greater than a fourth driving trajectory, the fourth driving trajectory being different from each third driving trajectory of the plurality of third driving trajectories; and

obtaining the recommended trajectory based on the plurality of third driving trajectories.

6. The method of claim 4, wherein the obtaining the recommended trajectory based on the plurality of third driving trajectories comprises:

obtaining a comprehensive score corresponding to each of the plurality of third driving tracks by adopting a simulation driving system, wherein the comprehensive score indicates a score considering driving efficiency, safety and comfort when the vehicle runs according to the third driving track;

obtaining a fifth driving track with the highest comprehensive score in the plurality of third driving tracks; and

obtaining the recommended trajectory based on the fifth driving trajectory.

7. The method of claim 1, further comprising:

pushing the recommended track to the vehicle to enable the vehicle to obtain a target driving track based on the recommended track, and automatically driving based on the target driving track; and

and obtaining a driving track of the vehicle after automatic driving based on the target driving track, and storing the driving track and the current driving scene data into the data set.

8. An autonomous driving method comprising:

receiving a recommended trajectory, the recommended trajectory obtained according to the method of claim 1;

obtaining a target driving track based on the recommended track; and

and carrying out automatic driving based on the target driving track.

9. An automatic driving device based on similar scene mining, comprising:

the driving scene data acquisition unit is configured to acquire current driving scene data of the vehicle, wherein the current driving scene data comprises environmental information of the vehicle;

a retrieval unit configured to obtain a plurality of similar driving scene data corresponding to the current driving scene data from a data set, the data set including a plurality of driving scene data corresponding to a plurality of vehicles, respectively, each of the plurality of driving scene data indicating environmental information when the corresponding vehicle travels on a corresponding driving track, a similarity between each of the plurality of similar driving scene data and the current driving scene data being greater than a preset value; and

a recommended trajectory acquisition unit configured to determine an automatic driving recommended trajectory for the host vehicle based on a plurality of driving trajectories corresponding to the plurality of similar driving scene data.

10. The apparatus of claim 9, wherein the data set includes a feature vector for each of the plurality of driving scenario data, the retrieving unit comprising:

the characteristic vector acquisition unit is configured to process the current driving scene data to acquire a characteristic vector corresponding to the current driving scene data; and

a retrieval subunit configured to obtain the plurality of similar driving scene data based on a vector corresponding to the current driving scene data and a feature vector of each of the plurality of driving scene data.

11. The apparatus according to claim 10, wherein the recommended trajectory acquisition unit includes:

a first determination unit configured to determine a safety index for each of the plurality of driving trajectories;

a first obtaining unit configured to obtain a plurality of first driving trajectories among the plurality of driving trajectories, each of the plurality of first driving trajectories having a safety index greater than a second driving trajectory, the second driving trajectory being different from each of the first driving trajectories; and

a first recommended trajectory acquisition subunit configured to acquire the recommended trajectory based on the plurality of first driving trajectories.

12. The apparatus of claim 11, wherein each of the plurality of driving scenario data further comprises driving behavior data of the respective vehicle while traveling on the respective driving trajectory, the driving behavior data comprising first behavior data indicating whether the respective vehicle has a collision, second behavior data indicating whether the respective vehicle has hard braking, third behavior data indicating whether the respective vehicle has hard acceleration or hard deceleration, or fourth behavior data indicating whether the respective vehicle has a jerk;

wherein the first determination unit includes:

a safety scoring unit configured to perform safety scoring on a corresponding driving track of each of the plurality of driving scene data based on the driving behavior data in the driving scene data to obtain a safety index of the corresponding driving track.

13. The apparatus of claim 11, wherein the first recommended trajectory acquisition subunit comprises:

a second determination unit configured to determine a comfort index for each of the plurality of first driving trajectories;

a second obtaining unit configured to obtain a plurality of third driving trajectories among the plurality of first driving trajectories, each of the plurality of third driving trajectories having a comfort index greater than a fourth driving trajectory, the fourth driving trajectory being different from each of the third driving trajectories; and

a second recommended trajectory acquisition subunit configured to acquire the recommended trajectory based on the plurality of third driving trajectories.

14. The apparatus of claim 13, wherein the second recommended trajectory acquisition subunit comprises:

a simulation processing unit configured to obtain, using a simulation driving system, a composite score corresponding to each of the plurality of third driving trajectories, the composite score indicating a score that takes into account driving efficiency, safety, and comfort when the host vehicle travels according to the third driving trajectory;

a third acquisition unit configured to acquire a fifth driving trajectory having a highest composite score among the plurality of third driving trajectories; and

a third recommended trajectory acquisition subunit configured to acquire the recommended trajectory based on the fifth driving trajectory.

15. The apparatus of claim 9, further comprising:

and obtaining a running track of the vehicle after automatic driving is carried out on the basis of the target driving track, and storing the running track and the current driving scene data into the data set.

16. An autopilot device comprising:

a receiving unit configured to receive a recommended trajectory obtained according to the method of claim 1;

a target driving track obtaining unit configured to obtain a target driving track based on the recommended track; and

a driving unit configured to perform automatic driving based on the target driving trajectory.

17. A vehicle, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of claim 8.

18. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein

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 1-8.

19. 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 1-8.

20. A computer program product comprising a computer program, wherein the computer program realizes the method of any one of claims 1-8 when executed by a processor.