Automatic driving simulation scene construction method and device

Abstract

The present disclosure provides an automatic driving simulation scene construction method and device, relates to the field of automatic driving, and particularly relates to the field of automatic driving scene simulation technology. The implementation scheme is as follows: obtaining a first vehicle and a vehicle attribute of the first vehicle, the vehicle attribute at least indicating a motion state of the first vehicle; obtaining at least one traffic element and an element attribute and a state machine of each traffic element in the at least one traffic element, the element attribute at least indicating a position of the corresponding traffic element, and the state machine indicating that the corresponding traffic element performs a corresponding action under a preset trigger condition; and constructing a simulation scene based on the vehicle attribute and the element attribute and the state machine of each traffic element in the at least one traffic element.

Description

Technical Field

[0001] This disclosure relates to the field of autonomous driving technology, and more particularly to the field of autonomous driving scenario simulation technology, specifically to an autonomous driving simulation scenario construction method, apparatus, electronic device, computer-readable storage medium, and computer program product. Background Technology

[0002] As the most important data asset in an autonomous driving simulation system, "autonomous driving test scenario use cases" provide the most direct input for simulation testing. Research shows that autonomous vehicles need to accumulate hundreds of millions of kilometers of testing to prove the safety of their systems. A more efficient way to achieve this is through large-scale concurrent execution in the simulation system, as the simulation system also requires hundreds of millions of scenario test cases.

[0003] How to improve the efficiency of constructing autonomous driving simulation scenarios is a topic that researchers in the autonomous driving industry are always concerned about.

[0004] The methods described in this section are not necessarily methods that had been previously conceived or adopted. Unless otherwise specified, no method described in this section should be assumed to be prior art simply because it is included in this section. Similarly, unless otherwise specified, the issues mentioned in this section should not be considered to be accepted in any prior art. Summary of the Invention

[0005] This disclosure provides a method, apparatus, electronic device, computer-readable storage medium, and computer program product for constructing autonomous driving simulation scenarios.

[0006] According to one aspect of this disclosure, a method for constructing an autonomous driving simulation scenario is provided, comprising: obtaining a first vehicle and vehicle attributes of the first vehicle, wherein the vehicle attributes at least indicate the motion state of the first vehicle; obtaining at least one traffic element and element attributes and a state machine for each of the at least one traffic element, wherein the element attributes at least indicate the position of the corresponding traffic element, and the state machine indicates that the corresponding traffic element performs a corresponding action under preset triggering conditions; and constructing a simulation scenario based on the vehicle attributes and the element attributes and state machine of each of the at least one traffic element.

[0007] According to another aspect of this disclosure, an autonomous driving simulation scene construction apparatus is provided, comprising: a first vehicle acquisition unit configured to acquire a first vehicle and vehicle attributes of the first vehicle, the vehicle attributes indicating at least the motion state of the first vehicle; a traffic element acquisition unit configured to acquire at least one traffic element and element attributes and a state machine of each of the at least one traffic element, the element attributes indicating at least the position of the corresponding traffic element, the state machine indicating that the corresponding traffic element performs a corresponding action under preset triggering conditions; and a construction unit configured to construct a simulation scene based on the vehicle attributes and the element attributes and state machine of each of the at least one traffic element.

[0008] According to another aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method according to embodiments of this disclosure.

[0009] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described according to embodiments of this disclosure.

[0010] According to another aspect of this disclosure, a computer program product is provided, including a computer program, wherein the computer program, when executed by a processor, implements the method described according to embodiments of this disclosure.

[0011] According to one or more embodiments of this disclosure, the efficiency of constructing autonomous driving simulation scenarios can be improved.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0013] The accompanying drawings exemplify embodiments and form part of the specification, serving together with the textual description to explain exemplary implementations of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0014] Figure 1 A schematic diagram of an exemplary system in which the various methods described herein may be implemented according to embodiments of the present disclosure is shown;

[0015] Figure 2 A flowchart of a method for constructing an autonomous driving simulation scenario according to an embodiment of the present disclosure is shown;

[0016] Figure 3A and Figure 3B A schematic diagram of the framework of the traffic element state machine during scene construction in the autonomous driving simulation scene construction method according to an embodiment of the present disclosure is shown.

[0017] Figure 4 A flowchart is shown of the process of obtaining at least one traffic element and the element attributes and state machine of each of the at least one traffic element in the autonomous driving simulation scene construction method according to an embodiment of the present disclosure.

[0018] Figure 5 A flowchart is shown of the process of determining a first traffic element from a preset set of traffic elements in an autonomous driving simulation scenario construction method according to an embodiment of the present disclosure;

[0019] Figure 6 A schematic diagram is shown of an interface for displaying a preset set of traffic elements in an autonomous driving simulation scene construction method according to an embodiment of the present disclosure;

[0020] Figure 7 A flowchart is shown showing the process of obtaining a node combination between at least one trigger node in the set of trigger nodes corresponding to a first traffic element and at least one action node in the set of action nodes in the autonomous driving simulation scene construction method according to an embodiment of the present disclosure.

[0021] Figure 8 A flowchart of a method for constructing an autonomous driving simulation scenario according to an embodiment of the present disclosure is shown;

[0022] Figure 9 A flowchart of a method for constructing an autonomous driving simulation scenario according to an embodiment of the present disclosure is shown;

[0023] Figure 10 A flowchart illustrating the process of generalizing the constructed scenario in an autonomous driving simulation scenario construction method according to an embodiment of the present disclosure is shown.

[0024] Figure 11 A structural block diagram of an autonomous driving simulation scenario construction apparatus according to an embodiment of the present disclosure is shown;

[0025] Figure 12 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure is shown. Detailed Implementation

[0026] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0027] In this disclosure, unless otherwise stated, the use of terms such as "first," "second," etc., to describe various elements is not intended to limit the positional, temporal, or importance relationships of these elements; such terms are merely used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of that element, while in other cases, based on the context, they may refer to different instances.

[0028] The terminology used in the description of the various examples described in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context explicitly indicates otherwise, an element may be one or more unless the number of elements is specifically limited. Furthermore, the term "and / or" as used in this disclosure covers any one of the listed items and all possible combinations thereof.

[0029] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0030] Figure 1 A schematic diagram of an exemplary system 100 in which the various methods and apparatus described herein can be implemented according to embodiments of this disclosure is shown. Reference Figure 1 The system 100 includes a motor vehicle 110, a server 120, and one or more communication networks 130 that couple the motor vehicle 110 to the server 120.

[0031] In embodiments of this disclosure, the motor vehicle 110 may include a computing device according to embodiments of this disclosure and / or be configured to perform a method according to embodiments of this disclosure.

[0032] Server 120 may run one or more services or software applications that enable methods for constructing autonomous driving simulation scenarios. In some embodiments, server 120 may also provide other services or software applications, which may include non-virtual environments and virtual environments. Figure 1In the configuration shown, 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 combinations thereof that can be executed by one or more processors. A user of motor vehicle 110 may sequentially interact with server 120 using one or more client applications to utilize the services provided by these components. It should be understood that various different system configurations are possible and may differ from system 100. Therefore, Figure 1 This is an example of a system used to implement the various methods described herein, and is not intended to be limiting.

[0033] Server 120 may include one or more general-purpose computers, special-purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running a virtual operating system, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that can be virtualized to maintain virtual storage devices for servers). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0034] The computing unit in server 120 can run one or more operating systems, including any of the aforementioned operating systems and any commercially available server operating system. Server 120 can also run any of a variety of additional server applications and / or middleware applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0035] In some implementations, server 120 may include one or more applications to analyze and merge data feeds and / or event updates received from 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 vehicle 110.

[0036] Network 130 can be any type of network well known to those skilled in the art, and can support data communication 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 130 can be satellite communication networks, local area networks (LANs), Ethernet-based networks, token ring networks, wide area networks (WANs), the Internet, virtual networks, virtual private networks (VPNs), intranets, extranets, blockchain networks, public switched telephone networks (PSTNs), infrared networks, wireless networks (including, for example, Bluetooth, WiFi), and / or any combination of these with other networks.

[0037] 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 repository 150 may reside in various locations. For example, a data repository used by server 120 may be local to server 120, or it may be located away from server 120 and may communicate with server 120 via a network-based or dedicated connection. The data repository 150 may be of different types. In some embodiments, the data repository used by server 120 may be a database, such as a relational database. One or more of these databases may store, update, and retrieve data from and from the database in response to commands.

[0038] In some embodiments, one or more of the databases 150 may also be used by an application to store application data. The databases used by the application may be of different types, such as key-value stores, object stores, or regular stores supported by a file system.

[0039] Motor vehicle 110 may include sensors 111 for sensing the surrounding environment. Sensors 111 may include one or more of the following sensors: a visual camera, an infrared camera, an ultrasonic sensor, a millimeter-wave radar, and a lidar (LiDAR). Different sensors can provide different detection accuracy and range. Cameras may be mounted in front of, behind, or at other locations on the vehicle. Visual cameras can capture the situation inside and outside the vehicle in real time and present it to the driver and / or passengers. In addition, by analyzing the images captured by the visual cameras, information such as traffic light indications, intersection conditions, and the operating status of other vehicles can be obtained. Infrared cameras can capture objects in night vision conditions. Ultrasonic sensors may be mounted around the vehicle to measure the distance of objects outside the vehicle using the strong directionality of ultrasound. Millimeter-wave radar may be mounted in front of, behind, or at other locations on the vehicle to measure the distance of objects outside the vehicle using the characteristics of electromagnetic waves. LiDAR may be mounted in front of, behind, or at other locations on the vehicle to detect the edges and shape information of objects, thereby performing object recognition and tracking. Due to the Doppler effect, the radar device can also measure the speed changes of the vehicle and moving objects.

[0040] The 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 satellite 141 and generating coordinates based on these signals. The communication device 112 may also include a module for communicating with a mobile communication base station 142. The mobile communication network can implement any suitable communication technology, such as current or emerging wireless communication technologies (e.g., 5G technology) like GSM / GPRS, CDMA, and LTE. The communication device 112 may also have a vehicle-to-everything (V2X) module, configured to enable vehicle-to-the-world communication, for example, vehicle-to-vehicle (V2V) communication with other vehicles 143 and vehicle-to-infrastructure (V2I) communication with infrastructure 144. Furthermore, the communication device 112 may also have a module configured to communicate with a user terminal 145 (including but not limited to smartphones, tablets, or wearable devices such as watches) via, for example, a wireless local area network conforming to the IEEE 802.11 standard or Bluetooth. Using the communication device 112, the motor vehicle 110 can also access the server 120 via the network 130.

[0041] The motor vehicle 110 may also include a control unit 113. The control unit 113 may include a processor, such as a central processing unit (CPU) or a graphics processing unit (GPU), or other dedicated processors, that communicates with various types of computer-readable storage devices or media. The control unit 113 may include an autonomous driving system for automatically controlling various actuators in the vehicle. The autonomous driving system is configured to control the powertrain, steering system, and braking system of the motor vehicle 110 (not shown) via multiple actuators in response to inputs from multiple sensors 111 or other input devices to control acceleration, steering, and braking respectively, without human intervention or with limited human intervention. Some processing functions of the control unit 113 can be implemented via cloud computing. For example, some processing can be performed using an onboard processor while other processing can be performed using cloud computing resources. The control unit 113 may be configured to perform methods according to this disclosure. Furthermore, the control unit 113 may be implemented as an example of a computing device on the motor vehicle side (client) according to this disclosure.

[0042] Figure 1 The system 100 can be configured and operated in various ways to enable the application of the various methods and apparatus described in this disclosure.

[0043] In related technologies, autonomous driving simulation scenarios are constructed by writing code using a text editor. For example, in the process of scene editing based on OpenScenario, simulation tools and content editors are used to edit XML files to achieve automatic construction of simulation scenarios. However, the large amount of text to be edited and the tedious editing process often result in low efficiency in constructing simulation scenarios.

[0044] According to one aspect of this disclosure, a method for constructing an autonomous driving simulation scenario is provided. See also... Figure 2 According to some embodiments of this disclosure, the method for constructing an autonomous driving simulation scenario includes:

[0045] Step S210: Obtain the first vehicle and its vehicle attributes, wherein the vehicle attributes at least indicate the motion state of the first vehicle;

[0046] Step S220: Obtain at least one traffic element and the element attributes and state machine of each of the at least one traffic element, wherein the element attributes at least indicate the position of the corresponding traffic element, and the state machine indicates that the corresponding traffic element performs a corresponding action under preset triggering conditions; and

[0047] Step S230: Construct a simulation scenario based on the vehicle attributes and the element attributes and state machine of each of the at least one traffic element.

[0048] By obtaining the vehicle attributes of the first vehicle and the element attributes and state machine of each of at least one traffic element, the interaction design between the first vehicle and the traffic elements is realized, and a simulation scene is constructed based on the obtained interaction design. The whole process does not require text editing, thus improving the efficiency of simulation scene construction.

[0049] In some embodiments, the first vehicle is the master vehicle in the simulation scenario, and the interaction between the master vehicle and other traffic elements is simulated with the master vehicle as the center.

[0050] In embodiments according to this disclosure, the vehicle attributes of the first vehicle at least indicate the motion mechanism of the first vehicle, such as the starting position, transit position, ending position, or initial speed of the first vehicle.

[0051] In some embodiments, the vehicle attributes of the first vehicle may also include the vehicle's size, etc., which are not limited herein.

[0052] In some embodiments, at least one traffic element can be any entity capable of interacting with the first vehicle. For example, other vehicles, pedestrians, bicycles, parking barriers, traffic cones, following traffic, or unknown obstacles.

[0053] In embodiments according to this disclosure, each traffic element has element attributes that at least indicate the position of the traffic element. For example, element attributes may include the coordinates of the traffic element (horizontal and vertical coordinates in a two-dimensional scene, and spatial coordinates in a three-dimensional scene). Element attributes may also include the size of the element. For example, when the traffic element is a vehicle, the element coordinates may also include the length, width, and height of the vehicle.

[0054] In embodiments according to this disclosure, each traffic element includes a state machine that instructs the traffic element to perform a corresponding action under preset triggering conditions.

[0055] In some embodiments, the state machine may include preset free states in which traffic elements perform corresponding actions according to preset conditions. For example, the free state of a following traffic flow is when it travels at a preset distance from the first vehicle.

[0056] See Figure 3A and Figure 3B This diagram illustrates a framework of the traffic element state machine during scene construction in an autonomous driving simulation scene construction method according to some embodiments of the present disclosure. Figure 3A This diagram illustrates the process of scene construction, from the beginning to the end of the scene, where the state machine of traffic elements transitions from a controlled state to a free state, and the scene is constructed through a driving engine. Figure 3B The diagram illustrates the process by which a traffic element, in control mode, enters an action node after being triggered by a trigger to complete the action and thus complete the control mode.

[0057] In some embodiments, a step of constructing a static scene is included before obtaining the first vehicle. The static scene provides an environment for the first vehicle and at least one traffic element to interact. For example, the static scene is obtained by obtaining a map. In some embodiments, the map may be a user-defined map; in other embodiments, the map may be a map obtained from a map storage device.

[0058] In some embodiments, the autonomous driving simulation scenario construction method according to the present disclosure includes: obtaining a high-precision map; and obtaining a static scene based on the high-precision map.

[0059] Because high-precision maps provide rich lane-level real road network data, simulation scenarios built on static scenes based on high-precision maps are more realistic and can simulate more accurate autonomous driving scenarios.

[0060] In some embodiments, the first vehicle is obtained by obtaining the position of the first vehicle in a static scene.

[0061] For example, the first vehicle can be obtained by receiving the location of the first vehicle set by the user in a static scene.

[0062] In some embodiments, the state machine includes control states, such as... Figure 4 As shown, obtaining at least one traffic element and the element attributes and state machine of each of the at least one traffic element includes:

[0063] Step S410: Determine a first traffic element from a preset traffic element set. Each traffic element set in the preset traffic element set corresponds to a preset trigger node set and a preset action node set. Each trigger node in the preset trigger node set has a corresponding set of trigger condition parameters to be determined, and each preset action node in the preset action node set has a corresponding set of action parameters to be determined; and

[0064] Step S420: Obtain a node combination formed by at least one trigger node in the preset trigger node set corresponding to the first traffic element and at least one action node in the preset action node set, wherein each trigger condition parameter in the at least one trigger condition parameter set corresponding to the at least one trigger node and each action parameter in the at least one action parameter set corresponding to the at least one action node are determined; and

[0065] Step S430: Based on the node combination, obtain the control state of the first traffic element.

[0066] By obtaining a node combination consisting of at least one trigger node and at least one action node from the preset set of trigger nodes and preset set of action behaviors corresponding to traffic elements, the interaction design between the element and the first vehicle can be obtained, further improving the convenience of users in building simulation scenarios.

[0067] The preset trigger node set and preset action node set can be preset in the system according to the needs of traffic elements and simulation scenarios.

[0068] In some embodiments, the preset trigger node set includes at least one of the following: a scene time trigger node, a distance trigger node, a collision time trigger node, a following distance trigger node, and a collision detection trigger node; and

[0069] The action nodes include at least one of the following: stationary node, tracking node, lane change node, road-following node, and following node.

[0070] In some embodiments, after determining each trigger condition parameter in the set of trigger condition parameters corresponding to the trigger node, the functionality of the trigger node is implemented by calling an atomic function. For example,

[0071] For the scene time trigger node, the parameters include: time_threshold (time threshold); the calculation method is: return true when "current scene time" >= "time threshold", otherwise return false; the corresponding atomic function is: booltime_condition(double time_threshold).

[0072] For distance trigger nodes, the parameters include: agent_id ("-1" represents the master vehicle; "other" represents obstacles), master_agent_id ("-1" represents the master vehicle; "other" represents obstacles), type (distance calculation model, including CENTER, POLYGON, HEAD_REAR (front of non-main element -> rear of main element)) and axis ("coordinate system, including EULER, HORIZONTAL, VERTICAL"). The calculation methods include: <Euclidean distance> - the straight-line distance between master_agent and agent, <lateral distance> - the coordinate system is established with the position of master_agent, and the direction perpendicular to the direction master_agent is the lateral direction (positive if the agent is on the right, negative if the agent is on the left), and <vertical distance> - the coordinate system is established with the position of master_agent, and the direction master_agent is facing is the vertical direction (positive if the agent is in front, negative if the agent is behind). The atomic function is: doubledistance(int agent_id, int master_agent_id, string type, string axis).

[0073] For the remaining collision time trigger node, the parameters include: agent_id ("-1" represents the master vehicle; "other" represents obstacles) and master_agent_id ("-1" represents the master vehicle; "other" represents obstacles); the return value is: the remaining time for master_agent to catch up with agent; the calculation method is: straight-line distance between the two objects / relative instantaneous velocity in the distance direction; the atomic function is: double ttc(int agent_id, int master_agent_id).

[0074] For the following distance trigger node, the parameters include: agent_id ("-1" represents the master vehicle; "other" represents obstacles), master_agent_id ("-1" represents the master vehicle; "other" represents obstacles); the return value is: the remaining time for master_agent to reach the current horizontal position of the agent; the calculation method is: the projection of the distance between the bounding boxes of the two objects in the vertical direction of master_agent / the instantaneous velocity of master_agent (establishing a coordinate system with the position of master_agent, and the orientation of master_agent is the vertical direction); the atomic function is: double thw(int agent_id, int master_agent_id).

[0075] For the collision detection trigger node, the parameters include: agent_id ("-1" represents the master vehicle; "other" represents the obstacle), master_agent_id ("-1" represents the master vehicle; "other" represents the obstacle), and check_duration (the collision detection time period, i.e. whether a collision will occur within check_duration s after the current time). The return value is: whether a collision will occur (true indicates that a collision will occur). The calculation method is: whether the front of the agent_id vehicle will collide with the bounding box of the master_agent_id vehicle within check_duration. The atomic function is: bool check_collision(int agent_id, int master_agent_id, double check_duration).

[0076] It is understood that the parameter settings and atomic function settings of the above trigger nodes are merely exemplary. Those skilled in the art will understand that other trigger node machines can be set with corresponding parameters and atomic functions as needed, and this is not a limitation.

[0077] In some embodiments, a user-defined composite trigger node can also be obtained, for example, by using function nesting. In one example, the atomic function of the composite trigger node is: ttl(2,_,_)-ttl(-1,_,_)>=3&&keep_true(ego.speed==0,1)&&in_range(thw(2,-1),2,5).

[0078] In some embodiments, such as Figure 5 As shown, determining the first traffic element from the preset set of traffic elements includes:

[0079] Step S510: Display the preset traffic element set; and

[0080] Step S520: In response to receiving a selection instruction for the first traffic element, display the first traffic element in a first preset area.

[0081] By displaying a set of traffic elements and determining the first traffic element based on the user's commands to the traffic elements, a visual interactive experience is achieved during the simulation scene construction process, further improving the efficiency of simulation scene construction.

[0082] See Figure 6 The diagram illustrates an interface displaying a preset set of traffic elements in an autonomous driving simulation scene construction method according to some embodiments of the present disclosure.

[0083] Among them, such as Figure 6 Area 601 displays a preset set of traffic elements, which includes: main vehicle, vehicle, unknown obstacle, pedestrian, bicycle, parking barrier, cone, and following traffic flow. Upon receiving a selection instruction for the first traffic element 601a, the first traffic element 601a is displayed in the first preset area 602.

[0084] In some embodiments, the user's selection instruction for the first traffic element may be an instruction issued by the user regarding an operation performed on the first traffic element among the displayed preset traffic elements.

[0085] In other embodiments, user operations on the first traffic element include: moving, selecting, rotating, copying, pasting, undoing, and redoing. These commands enable users to visually edit the first traffic element within the simulation scenario, providing a WYSIWYG user experience.

[0086] In some embodiments, advanced assistance functions are provided to support user operations, such as compass, distance measurement, full-view switching, perspective / orthographic camera switching, lane and coordinate positioning, etc., to further simplify user operations and improve the efficiency of simulation scene construction.

[0087] According to some embodiments of this disclosure, when constructing a static scene based on a map, the process of constructing an autonomous driving simulation scene further includes: identifying traffic lights with the same semantics within the map area and synchronizing interactive data to further simplify the steps for users to set traffic elements and streamline the operation process.

[0088] In some embodiments, such as Figure 7 As shown, obtaining the node combination formed by at least one trigger node in the trigger node set corresponding to the first traffic element and at least one action node in the action node set includes:

[0089] Step S710: Display the preset trigger node set and preset action node set corresponding to the first traffic element;

[0090] Step S720: In response to receiving a selection instruction for a first trigger node in the preset trigger node set and a setting value for each trigger condition parameter in the trigger condition parameter set corresponding to the first trigger node, the first trigger node is displayed in the second preset area.

[0091] Step S730: In response to receiving a selection instruction for a first action node in the preset action node set and a setting value for each action parameter in the action set corresponding to the first action node, the first action node is displayed in the second preset area;

[0092] Step S740: In response to receiving a first operation for the first trigger node and the first action node, display the connection between the first trigger node and the first action node in the second preset area to combine the first trigger node and the first action node; and

[0093] Step S750: Obtain the node combination based on the combination of the first trigger node and the first action node.

[0094] By displaying a set of preset trigger nodes and a set of preset action nodes corresponding to traffic elements, and in response to the user's selection instruction for the first trigger node in the set of preset trigger nodes, the first trigger node is displayed in a second preset area. In response to the user's selection instruction for the first action node in the set of preset action nodes, the second trigger node is displayed in the second preset area. In response to obtaining a first operation for the first trigger node and the first action node, the connection between the first trigger node and the first action node is displayed in a second prediction area, thereby obtaining a combination of the first trigger node and the first action node. Based on the combination of the first trigger node and the first action node, a node combination is obtained, realizing the interaction design between the first traffic element and the first vehicle. The entire process realizes the user's visual programming of the interaction design. After inputting parameters and connecting lines, the interaction behavior design of traffic elements can be completed, further improving the efficiency of simulation scene construction.

[0095] Continue reading Figure 6 The combination of the first trigger node and the first action node is displayed in the second preset area 603. In some embodiments, after obtaining the vehicle attributes and the element attributes and state machine of each of at least one traffic element, a new simulation scene is created.

[0096] See Figure 8The diagram illustrates a method for constructing a new simulation scene according to some embodiments of the present disclosure.

[0097] like Figure 8 As shown, in step S810, the user places the main vehicle in the scene;

[0098] In step S820, the user sets the main vehicle attributes, adds the destination and waypoints;

[0099] In step S830, the user places other traffic elements;

[0100] In step S840, the user sets the state machine of other traffic elements, including step S841a, setting trigger nodes, step S841b, setting action nodes to edit the control state, and step S842, setting the free state.

[0101] When a user needs to set multiple traffic elements, after placing each traffic element and completing the state machine setting for that traffic element, the next traffic element is placed and its state machine is set. Finally, the scene is constructed by saving the scene.

[0102] In some embodiments, such as Figure 8 As shown, before saving the scene, you can also perform step S850, preview the existing scene, and step S850, adjust the attributes of traffic elements, trigger nodes and action nodes based on the preview.

[0103] In some embodiments, the method for constructing an autonomous driving scenario according to this disclosure further includes: obtaining road data acquired by an autonomous vehicle, the road data including motion state data of the autonomous vehicle and environmental data perceived by the autonomous vehicle, wherein obtaining a first vehicle and vehicle attributes of the first vehicle includes:

[0104] The autonomous vehicle is identified as the first vehicle, and the vehicle attributes of the first vehicle are obtained based on the motion state data; wherein obtaining at least one traffic element and the element attributes and state machine of each of the at least one traffic element includes:

[0105] Based on the environmental data, obtain the at least one traffic element and the element attributes and state machine of each of the at least one traffic element.

[0106] By obtaining the first vehicle and its vehicle attributes, as well as the element attributes and state machine of at least one traffic element and each of the at least one traffic element through road survey data, a simulation scenario can be constructed. This enables the generalization of more simulation scenarios based on real road survey data, thereby avoiding the problem of inaccurate parameters set manually.

[0107] In some embodiments, the constructed scenario can be generalized to select the simulation scenario that meets the expectations.

[0108] See Figure 9 The autonomous driving simulation scenario construction method according to some embodiments of this disclosure further includes:

[0109] Step S910: Obtain the trigger generalization parameter corresponding to each trigger condition parameter in the at least one set of trigger condition parameters and the action generalization parameter corresponding to each action parameter in the at least one set of action parameters, wherein the trigger fuzzy parameter indicates the range corresponding to the corresponding trigger condition parameter, and the action generalization parameter indicates the range corresponding to the corresponding action parameter; and

[0110] Step S920: Obtain the generalization constraints corresponding to the at least one set of triggering condition parameters and the at least one set of action parameters;

[0111] Step S930: Based on the trigger generalization parameter corresponding to each trigger condition parameter in the at least one set of trigger condition parameters, the action generalization parameter corresponding to each action parameter in the at least one set of action parameters, the generalization constraint, and the simulation scenario, perform scenario generalization to obtain the target scenario.

[0112] By obtaining the trigger generalization parameters corresponding to each trigger condition parameter, the action generalization statement and generalization constraints corresponding to each action parameter, parameter-level scene generalization can be achieved, multi-dimensional scene generalization can be realized, and the generalization efficiency in the scene construction process can be improved.

[0113] In related technologies, defining the expected interactive behaviors of each element in a scene under certain parameters is difficult and inefficient. According to embodiments of this disclosure, by defining trigger condition parameters and action parameters with a wider range through generalization parameters, the scene is generalized, and scenes that meet the expectations (generalization constraints) are selected, greatly improving the efficiency of obtaining the expected simulation scene.

[0114] In some embodiments, users can obtain trigger condition generalization parameters and action generalization parameters through two methods: equidistant and enumeration. For example, during the process of obtaining trigger condition generalization parameters through equidistant methods, a minimum value, a maximum value, and a step size can be set as trigger condition generalization parameters.

[0115] See Figure 10 The diagram illustrates a flowchart of the process of generalizing the constructed scenario in an autonomous driving simulation scenario construction method according to some embodiments of the present disclosure.

[0116] like Figure 10 As shown, firstly, in step S1001, the generalization parameters (including action generalization parameters and trigger generalization parameters) are obtained. In some examples, the generalization parameters are input by the user.

[0117] Next, in step S1002, a parameter table is generated based on the generalization parameters. The parameter table is displayed as multiple parameter combinations consisting of different trigger condition parameters and different action parameters.

[0118] Next, in step S1003, generalization constraints are obtained, and multiple parameter combinations in the parameter table are sequentially filtered based on the generalization constraints. Specifically, multiple parameter combinations in the parameter table generated in step S1002 are input cyclically for filtering.

[0119] Next, in step S1004, the filtered parameter combinations are parsed. If the parsing fails, they are added to the restricted combinations and counted in step S1005; if the parsing succeeds, they are added to the restricted table in step S1006.

[0120] Next, in step S1007, the parameter combinations in the restricted table are substituted into the user-created scenario;

[0121] Next, in step S1008, the created scene is copied to generalize the scene;

[0122] Next, in step S1009, multiple generalized scenarios are submitted for testing simultaneously. When a scenario test is successful, in step S1010, it is added to the successful scenario set and counted. When a scenario test fails, in step S1011, it is retested. When the retest is successful, in step S1010, it is added to the successful scenario set and counted. When the retest fails, in step S1012, it is added to the failed scenario set and counted.

[0123] In some embodiments, the autonomous driving simulation scenario construction method according to some embodiments of the present disclosure further includes: obtaining a target metric; and determining whether the target scenario has achieved the expected purpose of the prediction based on the target metric.

[0124] In some embodiments, in response to a user selecting a first metric from a preset set of metrics, the first metric is determined as the target metric.

[0125] In some embodiments, the preset metric set includes multiple grouping types, each grouping type includes multiple metrics, and the target metric includes at least one metric from the multiple metrics corresponding to the multiple grouping types.

[0126] In some embodiments, a user-defined metric is obtained and determined as the target metric. By obtaining the user-defined metric and then the target metric, the metric configuration capability is maximized to meet the metric requirements of different scenarios.

[0127] According to another aspect of this disclosure, an apparatus for constructing autonomous driving simulation scenarios is also provided, such as... Figure 11 As shown, the device 1100 includes: a first vehicle acquisition unit 1110, configured to acquire a first vehicle and vehicle attributes of the first vehicle, the vehicle attributes indicating at least the motion state of the first vehicle; a traffic element acquisition unit 1120, configured to acquire at least one traffic element and element attributes and a state machine of each of the at least one traffic element, the element attributes indicating at least the position of the corresponding traffic element, the state machine indicating that the corresponding traffic element performs a corresponding action under preset triggering conditions; and a construction unit 1130, configured to construct a simulation scene based on the vehicle attributes and the element attributes and state machine of each of the at least one traffic element.

[0128] In some embodiments, the state machine includes a control state, and the at least one traffic element acquisition unit 1120 includes: a first determining unit configured to determine a first traffic element from a preset traffic element set, wherein each traffic element set in the preset traffic element set corresponds to a preset trigger node set and a preset action node set, each trigger node in the preset trigger node set has a corresponding set of trigger condition parameters to be determined, and each preset action node in the preset action node set has a corresponding set of action parameters to be determined; a node combination acquisition unit configured to obtain a node combination formed between at least one trigger node in the preset trigger node set corresponding to the first traffic element and at least one action node in the preset action node set, wherein each trigger condition parameter in at least one trigger condition parameter set corresponding to the at least one trigger node and each action parameter in at least one action parameter set corresponding to the at least one action node are determined; and a first acquisition unit configured to obtain the control state of the first traffic element based on the node combination.

[0129] In some embodiments, the first determining unit includes: a first display unit configured to display the preset traffic element set; and a first response unit configured to display the first traffic element in a first preset area in response to receiving a selection instruction for the first traffic element.

[0130] In some embodiments, the node combination acquisition unit includes: a second display unit configured to display a preset set of trigger nodes and a preset set of action nodes corresponding to the first traffic element; a second response unit configured to display the first trigger node in a preset area in response to receiving a selection instruction for a first trigger node in the preset set of trigger nodes and a set value for each trigger condition parameter in the set of trigger condition parameters corresponding to the first trigger node; a third response unit configured to display the first action node in the preset area in response to receiving a selection instruction for a first action node in the preset set of action nodes and a set value for each action parameter in the set of actions corresponding to the first action node; a fourth response unit configured to combine the first trigger node and the first action node in response to obtaining a connection between the first trigger node and the first action node; and a node combination acquisition unit configured to obtain the node combination based on the combination of the first trigger node and the first action node.

[0131] In some embodiments, the apparatus 1100 further includes: a generalization parameter acquisition unit, configured to acquire a trigger generalization parameter corresponding to each trigger condition parameter in the at least one set of trigger condition parameters and an action generalization parameter corresponding to each action parameter in the at least one set of action parameters, wherein the trigger fuzzy parameter indicates the range corresponding to the corresponding trigger condition parameter and the action generalization parameter indicates the range corresponding to the corresponding action parameter; a constraint condition acquisition unit, configured to acquire generalization constraint conditions corresponding to the at least one set of trigger condition parameters and the at least one set of action parameters; and a scene generalization unit, configured to perform scene generalization based on the trigger generalization parameter corresponding to each trigger condition parameter in the at least one set of trigger condition parameters, the action generalization parameter corresponding to each action parameter in the at least one set of action parameters, the generalization constraint conditions, and the simulation scene, to obtain a target scene.

[0132] In some embodiments, the preset trigger node set includes at least one of the following: scene time trigger node, distance trigger node, collision time trigger node, following distance trigger node, and collision detection trigger node; and the action node includes at least one of the following: stationary node, tracking node, lane change node, road-following node, and following node.

[0133] In some embodiments, the apparatus 1100 further includes: a high-precision map acquisition unit configured to acquire a high-precision map; a static scene acquisition unit configured to acquire a static scene based on the high-precision map; and wherein the construction unit includes: a dynamic scene acquisition unit configured to acquire a dynamic scene based on the vehicle attributes and the element attributes and state machine of each of the at least one traffic element; and a construction subunit configured to acquire the simulation scene based on the dynamic scene and the static scene.

[0134] In some embodiments, the apparatus 1100 further includes: a road data acquisition unit configured to acquire road data obtained by an autonomous vehicle, the road data including motion state data of the autonomous vehicle and environmental data perceived by the autonomous vehicle, wherein the first vehicle acquisition unit includes: a second determination unit configured to determine the autonomous vehicle as the first vehicle and obtain vehicle attributes of the first vehicle based on the motion state data; and wherein the traffic element acquisition unit includes: a traffic element acquisition subunit configured to obtain the at least one traffic element and the element attributes and state machine of each of the at least one traffic element based on the environmental data.

[0135] According to embodiments of this disclosure, an electronic device, a readable storage medium, and a computer program product are also provided.

[0136] refer to Figure 12 The present invention describes a structural block diagram of an electronic device 1200 that can serve as a server or client of the present disclosure, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0137] like Figure 12As shown, the electronic device 1200 includes a computing unit 1201, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1202 or a computer program loaded from a storage unit 1208 into a random access memory (RAM) 1203. The RAM 1203 may also store various programs and data required for the operation of the electronic device 1200. The computing unit 1201, ROM 1202, and RAM 1203 are interconnected via a bus 1204. An input / output (I / O) interface 1205 is also connected to the bus 1204.

[0138] Multiple components in electronic device 1200 are connected to I / O interface 1205, including: input unit 1206, output unit 1207, storage unit 1208, and communication unit 1209. Input unit 1206 can be any type of device capable of inputting information to electronic device 1200. Input unit 1206 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device, and may include, but is not limited to, a mouse, keyboard, touchscreen, trackpad, trackball, joystick, microphone, and / or remote control. Output unit 1207 can be any type of device capable of presenting information, and may include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 1208 may include, but is not limited to, a hard disk and an optical disk. The communication unit 1209 allows the electronic device 1200 to exchange information / data with other devices through computer networks 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™ devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices and / or the like.

[0139] The computing unit 1201 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1201 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1201 performs the various methods and processes described above, such as method 200. For example, in some embodiments, method 200 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 1208. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1200 via ROM 1202 and / or communication unit 1209. When the computer program is loaded into RAM 1203 and executed by the computing unit 1201, one or more steps of method 200 described above may be performed. Alternatively, in other embodiments, the computing unit 1201 may be configured to execute method 200 by any other suitable means (e.g., by means of firmware).

[0140] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0141] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0142] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0143] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the 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 sound input, voice input, or tactile input).

[0144] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0145] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0146] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0147] While embodiments or examples of this disclosure have been described with reference to the accompanying drawings, it should be understood that the methods, systems, and devices described above are merely exemplary embodiments or examples, and the scope of the invention is not limited by these embodiments or examples, but only by the granted claims and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. Furthermore, the steps may be performed in a different order than that described in this disclosure. Further, various elements in the embodiments or examples may be combined in various ways. Importantly, as the technology evolves, many elements described herein can be replaced by equivalents that appear after this disclosure.

Claims

1. A method for constructing an autonomous driving simulation scenario, comprising: Obtain a first vehicle and its vehicle attributes, wherein the vehicle attributes at least indicate the motion state of the first vehicle; Obtain at least one traffic element and the element attributes and state machine of each of the at least one traffic element, wherein the element attributes at least indicate the position of the corresponding traffic element, and the state machine indicates that the corresponding traffic element performs a corresponding action under preset triggering conditions; as well as Based on the vehicle attributes and the element attributes and state machine of each of the at least one traffic element, a simulation scenario is constructed. The state machine includes a control state, and obtaining at least one traffic element and the element attributes and state machine of each of the at least one traffic element includes: A first traffic element is determined from a preset set of traffic elements. Each set of traffic elements in the preset set corresponds to a preset set of trigger nodes and a preset set of action nodes. Each trigger node in the preset set of trigger nodes has a corresponding set of trigger condition parameters to be determined, and each preset action node in the preset set of action nodes has a corresponding set of action parameters to be determined. Obtain a node combination formed by at least one trigger node in a preset set of trigger nodes corresponding to the first traffic element and at least one action node in a preset set of action nodes, wherein each trigger condition parameter in at least one set of trigger condition parameters corresponding to the at least one trigger node and each action parameter in at least one set of action parameters corresponding to the at least one action node are determined; and Based on the node combination, the control state of the first traffic element is obtained, wherein obtaining the node combination formed by at least one trigger node in the trigger node set corresponding to the first traffic element and at least one action node in the action node set includes: Displays the set of preset trigger nodes and the set of preset action nodes corresponding to the first traffic element; In response to receiving a selection instruction for a first trigger node in the preset trigger node set and a set value for each trigger condition parameter in the trigger condition parameter set corresponding to the first trigger node, the first trigger node is displayed in the second preset area. In response to receiving a selection instruction for a first action node in the preset action node set and a setting value for each action parameter in the action set corresponding to the first action node, the first action node is displayed in the second preset area; In response to receiving a first operation for the first trigger node and the first action node, a connection between the first trigger node and the first action node is displayed in the second preset area to combine the first trigger node and the first action node; and The node combination is obtained based on the combination of the first trigger node and the first action node.

2. The method according to claim 1, wherein determining the first traffic element from the preset set of traffic elements comprises: Display the preset set of traffic elements; as well as In response to receiving a selection instruction for the first traffic element, the first traffic element is displayed in a first preset area.

3. The method according to claim 1, further comprising: Obtain the trigger generalization parameter corresponding to each trigger condition parameter in the at least one set of trigger condition parameters and the action generalization parameter corresponding to each action parameter in the at least one set of action parameters, wherein the trigger generalization parameter indicates the range corresponding to the corresponding trigger condition parameter and the action generalization parameter indicates the range corresponding to the corresponding action parameter; as well as Obtain the generalization constraints corresponding to the at least one set of triggering condition parameters and the at least one set of action parameters; Based on the trigger generalization parameter corresponding to each trigger condition parameter in the at least one set of trigger condition parameters, the action generalization parameter corresponding to each action parameter in the at least one set of action parameters, the generalization constraint, and the simulation scenario, scenario generalization is performed to obtain the target scenario.

4. The method according to claim 1, wherein the preset trigger node set includes at least one of the following: scene time trigger node, distance trigger node, collision time trigger node, following distance trigger node, and collision detection trigger node; and The action nodes include at least one of the following: stationary node, tracking node, lane change node, road-following node, and following node.

5. The method according to any one of claims 1-4, further comprising: Obtain high-precision maps; as well as Based on the high-precision map, a static scene is obtained; And wherein, the construction of the simulation scenario based on the vehicle attributes and the element attributes and state machine of each of the at least one traffic element includes: Based on the vehicle attributes and the element attributes and state machine of each of the at least one traffic element, a dynamic scene is obtained; and The simulation scene is obtained based on the dynamic scene and the static scene.

6. The method according to any one of claims 1-4, further comprising: Obtaining road data acquired by an autonomous vehicle, the road data including the autonomous vehicle's motion state data and the environmental data perceived by the autonomous vehicle, wherein obtaining the first vehicle and its vehicle attributes includes: The autonomous vehicle is identified as the first vehicle, and the vehicle attributes of the first vehicle are obtained based on the motion state data; wherein obtaining at least one traffic element and the element attributes and state machine of each of the at least one traffic element includes: Based on the environmental data, obtain the at least one traffic element and the element attributes and state machine of each of the at least one traffic element.

7. An autonomous driving simulation scenario construction device, comprising: The first vehicle acquisition unit is configured to acquire a first vehicle and vehicle attributes of the first vehicle, wherein the vehicle attributes at least indicate the motion state of the first vehicle. A traffic element acquisition unit is configured to acquire at least one traffic element and the element attributes and state machine of each of the at least one traffic element, wherein the element attributes at least indicate the position of the corresponding traffic element, and the state machine indicates that the corresponding traffic element performs a corresponding action under preset triggering conditions. as well as The construction unit is configured to construct a simulation scenario based on the vehicle attributes and the element attributes and state machine of each of the at least one traffic element. The state machine includes a control state, and the at least one traffic element acquisition unit includes: A first determining unit is configured to determine a first traffic element from a preset set of traffic elements. Each set of traffic elements in the preset set of traffic elements corresponds to a preset set of trigger nodes and a preset set of action nodes. Each trigger node in the preset set of trigger nodes has a corresponding set of trigger condition parameters to be determined, and each preset action node in the preset set of action nodes has a corresponding set of action parameters to be determined. The node combination acquisition unit is configured to obtain a node combination formed between at least one trigger node in a preset trigger node set corresponding to the first traffic element and at least one action node in a preset action node set, wherein each trigger condition parameter in at least one trigger condition parameter set corresponding to the at least one trigger node and each action parameter in at least one action parameter set corresponding to the at least one action node are determined. The first acquisition unit is configured to obtain the control state of the first traffic element based on the node combination. Furthermore, the node combination acquisition unit includes: The second display unit is configured to display a preset set of trigger nodes and a preset set of action nodes corresponding to the first traffic element; The second response unit is configured to display the first trigger node in a preset area in response to receiving a selection instruction for a first trigger node in the preset trigger node set and a set value for each trigger condition parameter in the trigger condition parameter set corresponding to the first trigger node. The third response unit is configured to display the first action node in the preset area in response to receiving a selection instruction for a first action node in the preset action node set and a setting value for each action parameter in the action set corresponding to the first action node. A fourth response unit is configured to combine the first trigger node with the first action node in response to obtaining a connection between the first trigger node and the first action node; and The node combination acquisition subunit is configured to obtain the node combination based on the combination of the first trigger node and the first action node.

8. The apparatus according to claim 7, wherein the first determining unit comprises: The first display unit is configured to display the preset set of traffic elements; as well as A first response unit is configured to display the first traffic element in a first preset area in response to receiving a selection instruction for the first traffic element.

9. The apparatus according to claim 7, further comprising: The generalization parameter acquisition unit is configured to obtain a trigger generalization parameter corresponding to each trigger condition parameter in the at least one set of trigger condition parameters and an action generalization parameter corresponding to each action parameter in the at least one set of action parameters, wherein the trigger generalization parameter indicates the range corresponding to the corresponding trigger condition parameter and the action generalization parameter indicates the range corresponding to the corresponding action parameter. as well as The constraint acquisition unit is configured to obtain the generalized constraint conditions corresponding to the at least one set of triggering condition parameters and the at least one set of action parameters. The scene generalization unit is configured to perform scene generalization based on the trigger generalization parameter corresponding to each trigger condition parameter in the at least one set of trigger condition parameters, the action generalization parameter corresponding to each action parameter in the at least one set of action parameters, the generalization constraint, and the simulation scene, so as to obtain the target scene.

10. The apparatus according to claim 7, wherein the preset trigger node set includes at least one of the following: a scene time trigger node, a distance trigger node, a collision time trigger node, a following distance trigger node, and a collision detection trigger node; and The action nodes include at least one of the following: stationary node, tracking node, lane change node, road-following node, and following node.

11. The apparatus according to any one of claims 7-10, further comprising: The high-precision map acquisition unit is configured to acquire high-precision maps. The static scene acquisition unit is configured to acquire a static scene based on the high-precision map; And wherein the building unit includes: A dynamic scene acquisition unit is configured to acquire a dynamic scene based on the vehicle attributes and the element attributes and state machine of each of the at least one traffic element; and A sub-unit is constructed and configured to obtain the simulation scene based on the dynamic scene and the static scene.

12. The apparatus according to any one of claims 7-10, further comprising: A road data acquisition unit is configured to acquire road data obtained by an autonomous vehicle, the road data including motion state data of the autonomous vehicle and environmental data perceived by the autonomous vehicle, wherein the first vehicle acquisition unit includes: The second determining unit is configured to determine the autonomous vehicle as the first vehicle and obtain the vehicle attributes of the first vehicle based on the motion state data; and wherein the traffic element acquisition unit includes: The traffic element acquisition subunit is configured to obtain, based on the environmental data, the at least one traffic element and the element attributes and state machine of each of the at least one traffic element.

13. An electronic device, comprising: At least one processor; as well as A memory that is communicatively connected to the at least one processor; in The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

15. A computer program product comprising a computer program, wherein, The computer program, when executed by a processor, implements the method of any one of claims 1-6.

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