A test scenario construction method and device

Abstract

The present application relates to a test scenario construction method and apparatus. The method comprises: determining the motion behavior of a traffic moving object required to construct a preset traffic test scenario, wherein the motion behavior is composed of at least one behavioral element; obtaining a set of behavioral data corresponding to each of the at least one behavioral element, wherein the set of behavioral data includes multiple behavioral data with a similarity greater than a preset threshold, and the behavioral data is extracted from real road condition data; and combining the behavioral data from different sets of behavioral data into at least one motion behavior of the traffic moving object.

Description

Technical Field

[0001] The present application relates to the field of unmanned driving technology, and in particular to a test scenario construction method and device. Background Art

[0002] Before autonomous vehicles are allowed on the road, they must undergo multiple simulation tests to ensure their safety and stability. Simulation tests require a large amount of test scenario data to construct the test scenarios. Typical test scenarios include overtaking, passing traffic lights, and driving on curves.

[0003] In the related art, test scenario data is generally acquired manually. Specifically, a worker can replay the data collected by the acquisition vehicle and visually identify and select the required scenario data. After selecting the required scenario data, software tools can automatically convert the scenario data into the data set required for testing. Therefore, the related art method of acquiring test scenario data relies too much on manual processing, which is time-consuming, labor-intensive, and inefficient. The accuracy is also highly dependent on the ability of the worker.

[0004] Therefore, there is an urgent need in the relevant technology for a method of obtaining unmanned driving test scene data with high processing efficiency and high accuracy. Summary of the Invention

[0005] In view of this, a test scenario construction method and device are proposed.

[0006] In a first aspect, an embodiment of the present application provides a test scenario construction method, comprising:

[0007] Determining a motion behavior of a traffic moving object required for constructing a preset traffic test scenario, wherein the motion behavior is composed of at least one behavior element;

[0008] Obtaining a behavior data set corresponding to the at least one behavior element respectively, wherein the behavior data set includes a plurality of behavior data having a similarity higher than a preset threshold, and the behavior data is set to be extracted from real road condition data;

[0009] The behavior data in different behavior data sets are combined into at least one movement behavior of the traffic movement object.

[0010] The test scenario construction method provided in the embodiment of the present application can use the behavioral data extracted from the real road condition data as the material for constructing the movement behavior of the traffic movement object in the process of constructing the preset traffic test scenario. On the one hand, using the real road condition data as the data basis can not only narrow the difference between the test scenario and the real scenario, but also improve the richness of the data. On the other hand, using the method of automatically obtaining the behavioral data set can greatly reduce the cost of obtaining the test data. Based on this, the constructed traffic test scenario not only has a high degree of authenticity, but also can obtain rich test data at a low cost, providing better technical support for testing the performance of intelligent vehicles.

[0011] According to a first possible implementation of the first aspect, the behavior data set is configured to be constructed in the following manner:

[0012] Determine real road condition data;

[0013] Extracting at least one behavior data of at least one traffic moving object from the real road condition data, wherein the behavior data includes at least one motion parameter information;

[0014] For different traffic movement objects, cluster analysis is performed on at least one behavior data of the traffic movement object to generate a behavior data set corresponding to at least one behavior element.

[0015] This embodiment of the present application provides a method for acquiring a behavioral data set. In this method, at least one behavioral data set of at least one traffic object is obtained from real road condition data. Cluster analysis is then performed on the at least one behavioral data set to generate a behavioral data set corresponding to at least one behavioral element. Utilizing the automatically acquired behavioral data in test scenarios not only enhances the realism of the test scenarios but also improves the efficiency of test data acquisition.

[0016] According to a second possible implementation of the first aspect, for different traffic movement objects, cluster analysis is performed on at least one behavior data of the traffic movement object to generate a behavior data set corresponding to at least one behavior element, including:

[0017] For different traffic movement objects, at least one target behavior data that meets the characteristics of the preset behavior element is screened out from at least one behavior data of the traffic movement object;

[0018] A cluster analysis is performed on the at least one target behavior data to generate a behavior data set corresponding to at least one behavior element.

[0019] In an embodiment of the present application, target behavior data required in a test scenario can be screened out from a large amount of real behavior data. Specifically, characteristics of behavior elements can be predefined and the target behavior data can be screened out using the characteristics.

[0020] According to a third possible implementation of the first aspect, combining the behavior data in different behavior data sets into at least one movement behavior of the traffic movement object includes:

[0021] Selecting behavior data from different sets of behavior data respectively;

[0022] The selected behavior data are connected into a motion behavior according to the occurrence order of the at least one behavior element.

[0023] The embodiment of the present application provides a method for automatically combining multiple behavior data into sports behavior.

[0024] According to a fourth possible implementation of the first aspect, connecting the selected behavior data into a movement behavior includes:

[0025] The selected behavior data are smoothly connected according to the changes of motion parameters contained in adjacent behavior data.

[0026] In the embodiment of the present application, the continuity and smoothness between different behavior data can be enhanced.

[0027] According to a fifth possible implementation manner of the first aspect, after combining the behavior data in different behavior data sets into at least one movement behavior of the traffic movement object, the method further includes:

[0028] Acquiring scene data of the preset traffic test scene, the scene data including at least one of road information, traffic facility control information, environmental information, and an initial position of a traffic moving object;

[0029] Constructing the preset traffic test scenario according to the scenario data;

[0030] The at least one motion behavior is respectively set in the preset traffic test scene, the test object is tested, and the test results are obtained.

[0031] The embodiment of the present application sets the movement behavior in a preset traffic test scenario and tests the test object. By applying the movement behavior in the traffic test scenario, more accurate performance data of the test object can be obtained.

[0032] According to a sixth possible implementation of the first aspect, setting at least one movement behavior in the preset traffic test scene, testing the test object, and obtaining the test results includes:

[0033] Determining whether the movement behavior meets the scene characteristics corresponding to the preset traffic test scene;

[0034] When it is determined that the movement behavior meets the scene characteristics, the movement behavior is set in the preset traffic test scene, the test object is tested, and the test results are obtained.

[0035] In the embodiment of the present application, by determining whether the movement behavior conforms to the scene characteristics of the preset traffic test scene, the rationality of the movement behavior set in the preset traffic test scene can be ensured.

[0036] According to the seventh possible implementation method of the first aspect, the behavior data set also includes multiple first behavior data whose similarity with the multiple behavior data is less than a first preset threshold, and the ratio of the number of the multiple behavior data to the multiple first behavior data is not less than a preset ratio threshold.

[0037] In an embodiment of the present application, the behavior data set is configured to include a certain proportion of unconventional behavior data, which satisfies the richness of the behavior data set to a certain extent and enhances the robustness of the test scenario.

[0038] According to the eighth possible implementation method of the first aspect, the behavior data includes at least one of the following motion parameter information: driving direction speed, driving lateral speed, driving acceleration, driving lateral acceleration, driving distance, turning radius, driving time, walking speed, walking acceleration, and walking time.

[0039] According to a ninth possible implementation of the first aspect, the traffic moving objects include at least one of the following: cars, trucks, buses, trams, trains, motorcycles, electric vehicles, bicycles, pedestrians, and animals.

[0040] According to a tenth possible implementation manner of the first aspect, the movement behavior includes at least one of the following: overtaking, going straight in an intersection, turning in an intersection, merging into traffic flow, and pedestrians crossing the sidewalk.

[0041] According to an eleventh possible implementation manner of the first aspect, the behavior elements include at least one of the following: driving in a straight line, switching lanes left, switching lanes right, turning right at a right angle, and parking.

[0042] According to a twelfth possible implementation manner of the first aspect, before determining the motion behavior of the traffic moving object required for constructing the preset traffic test scenario, the method further includes:

[0043] Acquire the preset traffic test scene selected by the user and the configured scene data of the preset traffic test scene.

[0044] The embodiment of the present application provides a method for a user terminal to select a traffic simulation scene and configure scene data.

[0045] In a second aspect, an embodiment of the present application provides a simulation scenario construction method, comprising:

[0046] Obtain real road condition data;

[0047] Extracting at least one behavior data of at least one traffic moving object from the real road condition data, wherein the behavior data includes at least one motion parameter information;

[0048] Constructing a traffic simulation scenario using the at least one behavior data of the at least one traffic moving object. According to a first possible implementation of the second aspect, after extracting at least one behavior data of the at least one traffic moving object from the real road condition data, wherein the at least one behavior data includes at least one motion parameter information, further comprising:

[0049] For different traffic movement objects, cluster analysis is performed on at least one behavior data of the traffic movement object to generate a behavior data set corresponding to at least one behavior element.

[0050] According to a second possible implementation manner of the second aspect, constructing a traffic simulation scenario using the at least one behavior data of the at least one traffic moving object includes:

[0051] Acquire a motion behavior of a traffic moving object required for a traffic test scenario, wherein the motion behavior is composed of at least one behavior element;

[0052] respectively obtaining a set of behavior data corresponding to the at least one behavior element;

[0053] The behavior data in different behavior data sets are combined into at least one movement behavior of the traffic movement object.

[0054] In a third aspect, an embodiment of the present application provides a test scenario construction device, comprising:

[0055] A motion behavior determination module, configured to determine the motion behavior of a traffic motion object required for constructing a preset traffic test scenario, wherein the motion behavior is composed of at least one behavior element;

[0056] a data set acquisition module, configured to respectively acquire a behavior data set corresponding to the at least one behavior element, wherein the behavior data set includes a plurality of behavior data having a similarity higher than a preset threshold, and the behavior data is configured to be extracted from real road condition data;

[0057] The behavior data combination module is used to combine the behavior data in different behavior data sets into at least one movement behavior of the traffic movement object.

[0058] According to a first possible implementation of the third aspect, the behavior data set is configured to be constructed according to the following modules:

[0059] A road condition data determination module, used to determine real road condition data;

[0060] A behavior data extraction module, configured to extract at least one behavior data of at least one traffic moving object from the real road condition data, wherein the behavior data includes at least one movement parameter information;

[0061] The behavior data clustering module is used to perform cluster analysis on at least one behavior data of different traffic movement objects, and generate a behavior data set corresponding to at least one behavior element.

[0062] According to a second possible implementation manner of the third aspect, the behavior data clustering module is specifically configured to:

[0063] For different traffic movement objects, at least one target behavior data that meets the characteristics of the preset behavior element is screened out from at least one behavior data of the traffic movement object;

[0064] A cluster analysis is performed on the at least one target behavior data to generate a behavior data set corresponding to at least one behavior element.

[0065] According to a third possible implementation of the third aspect, the behavior data combination module is specifically configured to:

[0066] Selecting behavior data from different sets of behavior data respectively;

[0067] The selected behavior data are connected into a motion behavior according to the occurrence order of the at least one behavior element.

[0068] According to a fourth possible implementation manner of the third aspect, the behavior data combination module is further configured to:

[0069] The selected behavior data are smoothly connected according to the changes of motion parameters contained in adjacent behavior data.

[0070] According to a fifth possible implementation of the third aspect, the apparatus further includes:

[0071] A scene data acquisition module, configured to acquire scene data of the preset traffic test scene, wherein the scene data includes at least one of road information, traffic facility control information, environmental information, and an initial position of a traffic moving object;

[0072] A test scenario construction module, configured to construct the preset traffic test scenario according to the scenario data;

[0073] The test result acquisition module is used to respectively set the at least one motion behavior in the preset traffic test scene, test the test object, and obtain the test result.

[0074] According to a sixth possible implementation manner of the third aspect, the test result acquisition module is specifically configured to:

[0075] Determining whether the movement behavior meets the scene characteristics corresponding to the preset traffic test scene;

[0076] When it is determined that the movement behavior meets the scene characteristics, the movement behavior is set in the preset traffic test scene, the test object is tested, and the test results are obtained.

[0077] According to the seventh possible implementation method of the third aspect, the behavior data set also includes multiple first behavior data whose similarity with the multiple behavior data is less than a first preset threshold, and the ratio of the number of the multiple behavior data to the multiple first behavior data is not less than a preset ratio threshold.

[0078] According to the eighth possible implementation method of the third aspect, the behavioral data includes at least one of the following motion parameter information: driving direction speed, driving lateral speed, driving acceleration, driving lateral acceleration, driving distance, turning radius, driving time, walking speed, walking acceleration, and walking time.

[0079] According to a ninth possible implementation of the third aspect, the traffic moving objects include at least one of the following: cars, trucks, buses, trams, trains, motorcycles, electric vehicles, bicycles, pedestrians, and animals.

[0080] According to a tenth possible implementation manner of the third aspect, the movement behavior includes at least one of the following: overtaking, going straight in an intersection, turning in an intersection, merging into traffic flow, and pedestrians crossing the sidewalk.

[0081] According to an eleventh possible implementation manner of the third aspect, the behavior elements include at least one of the following: driving in a straight line, switching lanes left, switching lanes right, turning right at a right angle, and parking.

[0082] According to a twelfth possible implementation manner of the third aspect, the apparatus further includes:

[0083] The test scenario acquisition module is used to obtain the preset traffic test scenario selected by the user and the configured scenario data of the preset traffic test scenario.

[0084] In a fourth aspect, an embodiment of the present application provides a simulation scene construction device, comprising:

[0085] A road condition data acquisition module is used to obtain real road condition data;

[0086] A behavior data extraction module, configured to extract at least one behavior data of at least one traffic moving object from the real road condition data, wherein the behavior data includes at least one movement parameter information;

[0087] The scene construction module is used to construct a traffic simulation scene using the at least one behavior data of the at least one traffic motion object.

[0088] According to a first possible implementation of the fourth aspect, the method further includes:

[0089] The behavior data clustering module is used to perform cluster analysis on at least one behavior data of different traffic movement objects, and generate a behavior data set corresponding to at least one behavior element.

[0090] According to a second possible implementation of the fourth aspect, the scenario construction module is specifically configured to:

[0091] Acquire a motion behavior of a traffic moving object required for a traffic test scenario, wherein the motion behavior is composed of at least one behavior element;

[0092] respectively obtaining a set of behavior data corresponding to the at least one behavior element;

[0093] The behavior data in different behavior data sets are combined into at least one movement behavior of the traffic movement object.

[0094] According to a third possible implementation of the fourth aspect, the simulation scene construction device is set in a vehicle or in the cloud.

[0095] In a fifth aspect, an embodiment of the present application provides a device, characterized by comprising:

[0096] processor;

[0097] a memory for storing processor-executable instructions;

[0098] The processor is configured to implement the above-mentioned first aspect or one or more of the multiple possible implementation methods of the first aspect when executing the instruction.

[0099] In the sixth aspect, an embodiment of the present application provides a non-volatile computer-readable storage medium on which computer program instructions are stored, characterized in that when the computer program instructions are executed by a processor, one or more methods of implementing the above-mentioned first aspect or the multiple possible implementation methods of the first aspect are implemented.

[0100] In the seventh aspect, an embodiment of the present application provides a computer program product, characterized in that it includes computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes a method for implementing the above-mentioned first aspect or one or more of the multiple possible implementation methods of the first aspect.

[0101] In the eighth aspect, an embodiment of the present application provides a chip, characterized in that it includes at least one processor, which is used to run a computer program or computer instruction stored in a memory to execute one or several methods of implementing the above-mentioned first aspect or the multiple possible implementation methods of the first aspect.

[0102] These and other aspects of the present application will become more readily apparent from the following description of the embodiment(s). BRIEF DESCRIPTION OF THE DRAWINGS

[0103] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the application and, together with the description, serve to explain the principles of the application.

[0104] Figure 1 A schematic structural diagram of a traffic testing system provided in an embodiment of the present application is shown.

[0105] Figure 2 A schematic diagram of a collection device 101 provided in an embodiment of the present application is shown.

[0106] Figure 3A A schematic structural diagram of an intelligent vehicle 200 provided in an embodiment of the present application is shown.

[0107] Figure 3B A module block diagram of an intelligent vehicle 200 provided in an embodiment of the present application is shown.

[0108] Figure 4 A flow chart showing a test scenario construction method provided in an embodiment of the present application is shown.

[0109] Figure 5A schematic diagram of an application scenario provided by an embodiment of the present application is shown.

[0110] Figure 6 A schematic diagram of an application scenario provided by an embodiment of the present application is shown.

[0111] Figure 7 A schematic diagram of an application scenario provided by an embodiment of the present application is shown.

[0112] Figure 8 A schematic diagram of an application scenario provided by an embodiment of the present application is shown.

[0113] Figure 9 A schematic diagram of an application scenario provided by an embodiment of the present application is shown.

[0114] Figure 10 A schematic diagram of an application scenario provided by an embodiment of the present application is shown.

[0115] Figure 11 A flow chart showing a method for constructing a simulation scenario according to an embodiment of the present application is shown.

[0116] Figure 12 A module schematic diagram of the collection device 101 and the test scenario construction device 103 provided in an embodiment of the present application is shown.

[0117] Figure 13 A schematic structural diagram of a terminal device according to an embodiment of the present application is shown.

[0118] Figure 14 A structural block diagram of a computer program product according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0119] Various exemplary embodiments, features, and aspects of the present application will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0120] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0121] In addition, numerous specific details are provided in the following detailed description to better illustrate the present application. Those skilled in the art will appreciate that the present application can be practiced without certain specific details. In some instances, devices, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main purpose of the present application.

[0122] In the embodiments of this application, " / " can indicate that the associated objects are in an "or" relationship. For example, A / B can mean A or B. "And / or" can be used to describe the existence of three relationships between associated objects. For example, "A and / or B" can mean: A exists alone, A and B exists simultaneously, or B exists alone. A and B can be singular or plural. To facilitate the description of the technical solutions of the embodiments of this application, the words "first" and "second" may be used in the embodiments of this application to distinguish between technical features with the same or similar functions. The words "first" and "second" do not limit the number or order of execution, and the words "first" and "second" do not necessarily mean different. In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. The use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for easier understanding.

[0123] In the embodiments of the present application, for a technical feature, the technical features in the technical feature are distinguished by "first", "second", etc., and there is no order of precedence or size between the technical features described by "first" and "second".

[0124] In addition, numerous specific details are provided in the following detailed description to better illustrate the present application. Those skilled in the art will appreciate that the present application can be practiced without certain specific details. In some instances, devices, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main purpose of the present application.

[0125] In order to facilitate understanding of the embodiments of the present application, the following first describes the structure of one of the traffic test systems on which the embodiments of the present application are based. Figure 1 , Figure 1 This is a structural diagram of a traffic test system provided in an embodiment of the present application, which includes a collection device 101 and a test scenario construction device 103, wherein the collection device 101 and the test scenario construction device 103 can communicate through a network to send the collected data for constructing a test scenario to the test scenario construction device 103, and the test scenario construction device 103 completes the construction of the test scenario.

[0126] The acquisition device 101 can be an electronic device with data acquisition and data transmission and reception capabilities. For example, the acquisition device 101 can be a collection vehicle equipped with one or more sensors, such as a lidar, a camera, a global navigation satellite system (GNSS), and an inertial measurement unit (IMU). The collection vehicle can collect road condition data and extract at least one behavioral data item of at least one traffic object from the road condition data. The at least one behavioral data item can be used as real-world data for constructing a test scenario. The lidar is primarily used to collect point cloud data. Because lidar can accurately reflect location information, it can obtain the location and motion parameters of traffic objects, road surface information, traffic facility information, and other information. The camera is primarily used to collect information such as the type of traffic object (motor vehicle, non-motor vehicle, pedestrian, etc.), road markings, lane markings, etc. The GNSS is primarily used to record the coordinates of the current collection point. The IMU is primarily used to record the angle and acceleration information of the collection vehicle and is used to correct the position and angle of the collection vehicle.

[0127] Or, as Figure 2 As shown, the collection device 101 can also be a roadside unit installed at the intersection. The roadside unit can monitor multiple traffic moving objects in the coverage area and collect the behavior data of each traffic moving object. It should be noted that the behavior data of the traffic moving object can be collected by one roadside unit, or multiple roadside units can cooperate to collect the behavior data of the traffic moving object. This application does not impose any restrictions on this. Among them, the roadside unit can be composed of a high-gain directional beam-controlled read-write antenna and a radio frequency controller. The high-gain directional beam-controlled read-write antenna is a microwave transceiver module, which is responsible for the sending / receiving, modulation / demodulation, encoding / decoding, encryption / decryption of signals and data; the radio frequency controller is a module that controls the transmission and reception of data and processes the information sent and received to the upper computer.

[0128] The collection device 101 can collect behavioral data of various traffic moving objects. The traffic moving objects may include objects moving on the road, including cars, trucks, buses, trams, trains, motorcycles, electric vehicles, bicycles, pedestrians, etc. The behavioral data of traffic objects may include straight-line driving, left lane switching, right lane switching, left right-angle turn, right right-angle turn, and parking. Each behavioral data may include at least one motion parameter and its parameter value. The motion parameter may include driving direction speed, driving lateral speed, driving acceleration, driving lateral acceleration, driving distance, turning radius, driving time, walking speed, walking acceleration, walking time, etc. For example, for a vehicle driving in a straight line, motion parameters such as acceleration, speed, and distance may be included. For another example, for a vehicle switching lanes to the left, motion parameters such as driving lateral speed and driving acceleration may be included. Based on this, the collection device 101 may include a behavior data identification module. This module can analyze and mine road condition data based on pre-set behavior definition rules and the movement information, environmental data, map data, and other information of the collection device 101, thereby identifying the aforementioned various types of behavior data for multiple traffic movement objects within the road condition data. Of course, the collection device 101 may also include a labeling module for labeling the behavior data within the raw road condition data to facilitate its extraction. For example, the start and end times of the behavior data within the raw road condition data can be labeled, along with the corresponding traffic movement object type, the parameter values ​​of the movement parameters, and so on.

[0129] The test scenario construction device 103 can be an electronic device with data processing and data transmission and reception capabilities. It can be a physical device such as a host, rack server, blade server, etc., or a virtual device such as a virtual machine, container, etc. After obtaining multiple behavior data of multiple traffic movement objects sent by the acquisition device 101, the test scenario construction device 103 can cluster the multiple behavior data according to different traffic movement objects to generate multiple behavior data sets. In other words, the similarity of the behavior data in each behavior data set is greater than a preset threshold. Based on the above-mentioned behavior data sets, a traffic test scenario can be constructed. In the traffic test scenario, it is necessary to construct the movement behavior of the traffic movement object. The movement behavior is composed of at least one behavior element, and the movement behavior includes at least one of the following: overtaking, going straight at an intersection, turning at an intersection, merging into traffic flow, pedestrians crossing the sidewalk, etc. For example, in a scenario where the vehicle's ability to respond to the overtaking behavior of surrounding vehicles is tested, it is necessary to arrange the overtaking movement behavior in the test scenario. The overtaking movement behavior can include behavior elements such as left lane change, straight-line acceleration, right lane change, and lane keeping. Based on this, the behavior data sets corresponding to each behavior element can be obtained respectively according to the behavior data sets obtained by clustering. In this way, the behavior data in different behavior data sets can be combined into at least one movement behavior of the traffic movement object.

[0130] In another implementation scenario, such as Figure 3A As shown, an embodiment of the present application provides an intelligent vehicle 200, which may include a collection device 101 and a test scenario construction device 103. The collection device 101 may include one or more sensors such as a lidar, a camera, a global navigation satellite system (GNSS), and an inertial measurement unit (IMU) in the intelligent vehicle 200. The test scenario construction device 103 may be arranged in a processor and a memory in the intelligent vehicle 200. In an embodiment of the present application, the test scenario constructed by the test scenario construction device 103 can be used not only to perform performance testing on the intelligent vehicle 200, but also to simulate traffic scenarios, and the simulated traffic scenarios may include test scenarios that have passed performance testing. In the application scenario of simulating traffic scenarios, such as Figure 3AAs shown, a user (including developers and any ordinary user) can select a traffic scenario to be simulated through the onboard computer 248 of the intelligent vehicle 200, such as simulated overtaking, driving straight through an intersection, merging into traffic flow, and other traffic scenarios. After receiving the user's selection, the intelligent vehicle 200 can display the test scenario constructed by the test scenario construction device 103 to the user, allowing the user to experience the driving state of the intelligent vehicle 200 in the traffic scenario.

[0131] See Figure 3B , Figure 3B This is a functional block diagram of an intelligent vehicle 200 provided in an embodiment of the present application. Here, the intelligent vehicle 200 can be used as an embodiment of the acquisition device 101 in the test scenario construction system architecture, and can also be used as Figure 3A An embodiment of an intelligent vehicle 200. In one embodiment, the intelligent vehicle 200 can be configured for a fully or partially autonomous driving mode. For example, the intelligent vehicle 200 can control itself while in autonomous driving mode, and can determine the current state of the vehicle and its surrounding environment through human operation, determine the possible behavior of at least one other vehicle in the surrounding environment, and determine the confidence level corresponding to the probability of the other vehicle performing the possible behavior, and control the intelligent vehicle 200 based on the determined information. When the intelligent vehicle 200 is in autonomous driving mode, the intelligent vehicle 200 can be set to operate without human interaction.

[0132] The smart vehicle 200 may include various subsystems, such as a travel system 202, a sensor system 204, a control system 206, one or more peripheral devices 208, a power supply 210, a computer system 212, and a user interface 216. Alternatively, the smart vehicle 200 may include more or fewer subsystems, and each subsystem may include multiple components. In addition, each subsystem and component of the smart vehicle 200 may be interconnected via wired or wireless connections.

[0133] Propulsion system 202 may include components that provide powered locomotion for intelligent vehicle 200. In one embodiment, propulsion system 202 may include engine 218, energy source 219, transmission 220, and wheels / tires 221. Engine 218 may be an internal combustion engine, an electric motor, an air compression engine, or other engine combinations, such as a hybrid engine consisting of a gasoline engine and an electric motor, or a hybrid engine consisting of an internal combustion engine and an air compression engine. Engine 218 converts energy source 219 into mechanical energy.

[0134] Examples of energy source 219 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electricity. Energy source 219 can also provide energy for other systems of smart vehicle 200.

[0135] Transmission 220 can transmit mechanical power from engine 218 to wheels 221. Transmission 220 can include a gearbox, a differential, and a drive shaft. In one embodiment, transmission 220 can also include other components, such as a clutch. The drive shaft can include one or more shafts that can be coupled to one or more wheels 221.

[0136] The sensor system 204 may include several sensors that sense information about the environment surrounding the smart vehicle 200. For example, the sensor system 204 may include a global positioning system 222 (the positioning system may be a GPS system, a BeiDou system, or other positioning systems), an inertial measurement unit (IMU) 224, a radar 226, a laser rangefinder 228, and a camera 230. The sensor system 204 may also include sensors of the internal systems of the monitored smart vehicle 200 (e.g., an in-vehicle air quality monitor, a fuel gauge, an oil temperature gauge, etc.). Sensor data from one or more of these sensors may be used to detect objects and their corresponding characteristics (position, shape, direction, speed, etc.). This detection and recognition is a key function for the safe operation of the autonomous smart vehicle 200.

[0137] Positioning system 222 can be used to estimate the geographic location of smart vehicle 200. IMU 224 is used to sense the position and orientation changes of smart vehicle 200 based on inertial acceleration. In one embodiment, IMU 224 can be a combination of an accelerometer and a gyroscope. For example, IMU 224 can be used to measure the curvature of smart vehicle 200.

[0138] Radar 226 can use radio signals to sense objects in the surrounding environment of smart vehicle 200. In some embodiments, in addition to sensing objects, radar 226 can also be used to sense the speed and / or heading of the objects.

[0139] Laser rangefinder 228 can utilize laser light to sense objects in the environment in which smart vehicle 200 is located. In some embodiments, laser rangefinder 228 can include one or more laser sources, a laser scanner, and one or more detectors, among other system components.

[0140] The camera 230 may be used to capture multiple images of the surrounding environment of the smart vehicle 200. The camera 230 may be a still camera or a video camera.

[0141] Control system 206 controls the operation of intelligent vehicle 200 and its components. Control system 206 may include various components, including steering system 232 , throttle 234 , brake unit 236 , sensor fusion algorithm 238 , computer vision system 240 , path control system 242 , and obstacle avoidance system 244 .

[0142] The steering system 232 is operable to adjust the forward direction of the intelligent vehicle 200. For example, in one embodiment, it can be a steering wheel system.

[0143] The throttle 234 is used to control the operating speed of the engine 218 and, in turn, the speed of the intelligent vehicle 200 .

[0144] Braking unit 236 is used to control the deceleration of intelligent vehicle 200. Braking unit 236 can use friction to slow down wheels 221. In other embodiments, braking unit 236 can convert the kinetic energy of wheels 221 into electric current. Braking unit 236 can also take other forms to slow the rotation speed of wheels 221 to control the speed of intelligent vehicle 200.

[0145] The computer vision system 240 can be operated to process and analyze the images captured by the camera 230 in order to identify objects and / or features in the environment surrounding the intelligent vehicle 200. The objects and / or features may include traffic signals, road boundaries, and obstacles. The computer vision system 240 can use object recognition algorithms, structure from motion (SFM) algorithms, video tracking, and other computer vision techniques. In some embodiments, the computer vision system 240 can be used to map the environment, track objects, estimate the speed of objects, and the like.

[0146] The route control system 242 is used to determine the driving route of the smart vehicle 200. In some embodiments, the route control system 242 can combine data from the sensors 238, the GPS 222, and one or more predetermined maps to determine the driving route for the smart vehicle 200.

[0147] The obstacle avoidance system 244 is used to identify, assess, and avoid or otherwise negotiate potential obstacles in the environment of the smart vehicle 200 .

[0148] Of course, in one example, the control system 206 may include additional or alternative components other than those shown and described, or may also include fewer than some of the components shown.

[0149] Smart vehicle 200 interacts with external sensors, other vehicles, other computer systems, or users through peripherals 208. Peripherals 208 may include wireless communication system 246, onboard computer 248, microphone 250, and / or speaker 252.

[0150] In some embodiments, the peripheral device 208 provides a means for the user of the smart vehicle 200 to interact with the user interface 216. For example, the onboard computer 248 can provide information to the user of the smart vehicle 200. The user interface 216 can also operate the onboard computer 248 to receive user input. The onboard computer 248 can be operated via a touch screen. Specifically, when the test scenario constructed by the test scenario construction module 103 is used to simulate a traffic scenario, the user can select the desired simulated traffic scenario or configure scenario data through the onboard computer 248. The onboard computer 248 can also display the driving status of the smart vehicle 200 in the test scenario, allowing the driver to simulate driving in the vehicle and enhance the driving experience. In other cases, the peripheral device 208 can provide a means for the smart vehicle 200 to communicate with other devices located in the vehicle. For example, the microphone 250 can receive audio (e.g., voice commands or other audio input) from the user of the smart vehicle 200. Similarly, the speaker 252 can output audio to the user of the smart vehicle 200.

[0151] The wireless communication system 246 can communicate wirelessly with one or more devices directly or via a communication network. For example, the wireless communication system 246 can use 3G cellular communication, such as CDMA, EVDO, GSM / GPRS, or 4G cellular communication, such as LTE. Or 5G cellular communication. The wireless communication system 246 can use WiFi to communicate with a wireless local area network (WLAN). In some embodiments, the wireless communication system 246 can use an infrared link, Bluetooth, or ZigBee to communicate directly with the device. Other wireless protocols, such as various vehicle communication systems, for example, the wireless communication system 246 may include one or more dedicated short range communications (DSRC) devices, which may include public and / or private data communications between vehicles and / or roadside stations.

[0152] Power supply 210 can provide power to various components of smart vehicle 200. In one embodiment, power supply 210 can be a rechargeable lithium-ion or lead-acid battery. One or more battery packs of such batteries can be configured as a power source to provide power to various components of smart vehicle 200. In some embodiments, power supply 210 and energy source 219 can be implemented together, such as in some all-electric vehicles.

[0153] Some or all functions of the smart vehicle 200 are controlled by a computer system 212. The computer system 212 may include at least one processor 213 that executes instructions 215 stored in a non-transitory computer-readable medium, such as a data storage device 214. The computer system 212 may also be a plurality of computing devices that control individual components or subsystems of the smart vehicle 200 in a distributed manner.

[0154] Processor 213 may be any conventional processor, such as a commercially available CPU. Alternatively, the processor may be a dedicated device such as an ASIC or other hardware-based processor. Although Figure 3B The processor, memory, and other elements of the computer 120 are functionally illustrated as being in the same block, but one of ordinary skill in the art will appreciate that the processor, computer, or memory may actually include multiple processors, computers, or memories that may or may not be stored in the same physical housing. For example, the memory may be a hard drive or other storage medium located in a housing different from that of the computer 120. Thus, references to a processor or computer will be understood to include references to a collection of processors or computers or memories that may or may not operate in parallel. Rather than using a single processor to perform the steps described herein, some components, such as the steering assembly and the deceleration assembly, may each have their own processor that performs only calculations related to the functionality of the component.

[0155] In various aspects described herein, the processor can be located remotely from the vehicle and in wireless communication with the vehicle. In other aspects, some of the processes described herein are performed on a processor disposed within the vehicle while others are performed by a remote processor, including taking the necessary steps to perform a single maneuver.

[0156] In some embodiments, data storage 214 may contain instructions 215 (e.g., program logic) that are executable by processor 213 to perform various functions of smart vehicle 200, including those described above. Data storage 224 may also contain additional instructions, including instructions for sending data to, receiving data from, interacting with, and / or controlling one or more of propulsion system 202, sensor system 204, control system 206, and peripherals 208.

[0157] In addition to instructions 215, memory 214 may also store data such as road maps, route information, the vehicle's location, direction, speed, and other such vehicle data, as well as other information. This information may be used by smart vehicle 200 and computer system 212 during operation of smart vehicle 200 in autonomous, semi-autonomous, and / or manual modes.

[0158] In an embodiment of the present application, the data stored in the memory 214 may also include pre-set behavior definition rules, which may define rules for behavior elements such as straight-line driving, left turns, and right turns. In one embodiment, the behavior definition rules may include features of preset behavior elements. The processor 213 may identify the behavior data in the road condition data based on the behavior definition rules, for example, identify the straight-line driving behavior data, left-turn behavior data, right-turn behavior data, and the like in the road condition data. Of course, after identifying the behavior data, the position of the behavior data in the road condition data, such as the start time and the end time, may also be marked in the road condition data, and the motion parameters and parameter values ​​of the behavior data may be marked. In the case where the intelligent vehicle 200 has both simulator or emulator functions, the memory 214 may also store scene data, motion behavior, and other data required for each traffic scene (i.e., the test scene that has passed the test).

[0159] User interface 216 is used to provide information to or receive information from a user of smart vehicle 200. Optionally, user interface 216 may include one or more input / output devices within the set of peripherals 208, such as wireless communication system 246, onboard computer 248, microphone 250, and speaker 252.

[0160] The computer system 212 can control the functions of the smart vehicle 200 based on input received from various subsystems (e.g., the wireless communication system 246, the travel system 202, the sensor system 204, and the control system 206) and from the user interface 216. For example, the computer system 212 can use input from the wireless communication system 246 to plan lane lines at an intersection that needs to be passed during autonomous driving, and the lane lines can avoid encountering obstacles at the intersection. In some embodiments, the computer system 212 is operable to provide control for many aspects of the smart vehicle 200 and its subsystems.

[0161] Optionally, the computer system 212 can also receive information from other computer systems or transfer information to other computer systems. For example, the computer system 212 can transfer sensor data collected from the sensor system 204 of the smart vehicle 200 to another remote computer system, and have the other computer system process the data. For example, the other computer system can perform data fusion on the data collected by each sensor in the sensor system 204, and then return the fused data or analysis results to the computer system 212. Optionally, the data from the computer system 212 can be transmitted to the cloud-side computer system via a network for further processing. The network and intermediate nodes can include various configurations and protocols, including the Internet, the World Wide Web, an intranet, a virtual private network, a wide area network, a local area network, a private network using one or more companies' proprietary communication protocols, Ethernet, WiFi and HTTP, and various combinations of the foregoing. This communication can be performed by any device capable of transmitting data to and from other computers, such as a modem and a wireless interface.

[0162] As described above, in some possible embodiments, the remote computer system interacting with the computer system 212 in the smart vehicle 200 may include a server having multiple computers, such as a load balancing server cluster, which exchanges information with different nodes of the network for the purpose of receiving, processing, and transmitting data from the computer system 212. The server may have a processor, memory, instructions, data, and the like. For example, in some embodiments of the present application, the data of the server may include providing weather-related information. For example, the server may receive, monitor, store, update, and transmit various weather-related information. This information may include, for example, precipitation, cloud, and / or temperature information in the form of reports, radar information, forecasts, etc. The server data may also include high-precision map data and traffic information on the road ahead (such as real-time traffic congestion and traffic accident conditions, etc.). The server may send this high-precision map data and traffic information to the computer system 212, thereby assisting the smart vehicle 200 in better autonomous driving and ensuring driving safety.

[0163] Alternatively, one or more of the above components may be installed or associated separately from the smart vehicle 200. For example, the data storage device 214 may be partially or completely separate from the smart vehicle 200. The above components may be communicatively coupled together in a wired and / or wireless manner.

[0164] Optionally, the above components are just an example. In actual applications, the components in the above modules may be added or deleted according to actual needs. Figure 3B It should not be understood as limiting the embodiments of the present application.

[0165] An autonomous vehicle traveling on a road, such as intelligent vehicle 200 above, can identify objects in its surroundings to determine adjustments to its current speed. The objects can be other vehicles, traffic control devices, or other types of objects. In some examples, each identified object can be considered independently, and the speed adjustment to be made to the autonomous vehicle can be determined based on its respective characteristics, such as its current speed, acceleration, and distance from the vehicle.

[0166] Optionally, the autonomous vehicle 200 or a computing device associated with the autonomous vehicle 200 (e.g. Figure 3B The computer system 212, the computer vision system 240, the memory 214) can predict the behavior of the identified objects based on the characteristics of the identified objects and the state of the surrounding environment (e.g., traffic, rain, ice on the road, etc.). Optionally, each identified object depends on the behavior of each other, so all the identified objects can also be considered together to predict the behavior of a single identified object. The smart vehicle 200 can adjust its speed based on the predicted behavior of the identified objects. In other words, the self-driving car can determine what stable state the vehicle will need to adjust to (e.g., accelerate, decelerate, or stop) based on the predicted behavior of the objects. In this process, other factors can also be considered to determine the speed of the smart vehicle 200, such as the lateral position of the smart vehicle 200 in the road it is traveling on, the curvature of the road, the proximity of static and dynamic objects, etc.

[0167] In addition to providing instructions to adjust the speed of the autonomous vehicle, the computing device may also provide instructions to modify the steering angle of the intelligent vehicle 200 so that the autonomous vehicle follows a given trajectory and / or maintains a safe lateral and longitudinal distance from objects near the autonomous vehicle (e.g., cars in adjacent lanes on the road).

[0168] The above-mentioned intelligent vehicle 200 can be a car, truck, motorcycle, bus, ship, airplane, helicopter, lawn mower, recreational vehicle, amusement park vehicle, construction equipment, tram, golf cart, train, cart, etc., and the embodiments of the present application do not make special limitations.

[0169] It is understandable that Figure 3B The functional diagram of the smart vehicle 200 is only an exemplary implementation in the embodiment of the present application. The smart vehicle 200 in the embodiment of the present application includes but is not limited to the above structure.

[0170] The test scenario construction method described in this application is described in detail below with reference to the accompanying drawings. Figure 4It is a schematic flow chart of an embodiment of the test scenario construction method provided by the present application. Although the present application provides the method operation steps as shown in the following embodiments or drawings, more or fewer operation steps may be included in the method based on routine or without creative labor. In the steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided by the embodiment of the present application. In the actual test scenario construction process or when the method is executed, the method can be executed in sequence or in parallel (such as a parallel processor or a multi-threaded environment) according to the embodiment or the method shown in the drawings.

[0171] Specifically, an embodiment of the test scenario construction method provided by this application is as follows: Figure 4 As shown, the method may include:

[0172] S401: Determine the motion behavior of a traffic motion object required for constructing a preset traffic test scenario, where the motion behavior is composed of at least one behavior element.

[0173] In an embodiment of the present application, the traffic test is to simulate a traffic test scenario on a terminal device, and test the response capability of the vehicle model in the traffic test scenario, so as to evaluate the performance of the vehicle model in a real traffic environment. The preset traffic test scenario may include traffic moving objects and the movement behavior of the traffic moving objects. As mentioned above, the traffic moving objects may include movable objects on the road, such as cars, trucks, buses, trams, trains, motorcycles, electric vehicles, bicycles, pedestrians, animals, etc. For traffic moving objects, many movement behaviors may occur on the road. For example, a motor vehicle may perform at least one of the following movement behaviors on the road: overtaking, going straight in an intersection, turning in an intersection, merging into traffic flow, and pedestrians crossing the sidewalk. In an embodiment of the present application, the movement behavior may be composed of at least one behavior element. Figure 5 The schematic diagram of the overtaking motion behavior is shown, Figure 5 As shown, the traffic motion object 501 may include behavior elements such as left lane change, straight-line acceleration, right lane change, and lane keeping in its overtaking behavior. For another example, the traffic motion object 501 may include behavior elements such as straight-line deceleration and straight-line acceleration in its straight-line driving behavior. The traffic motion object 501 may include behavior elements such as straight-line deceleration, left-turn deceleration, stop and wait, left turn, and straight-line acceleration in its left-turning behavior.

[0174] It should be noted that the traffic motion objects, the motion behaviors, and the behavior elements are not limited to the above examples. Any motion objects that can appear on roads, garages, and other roads or places where vehicles may travel fall within the protection scope of the embodiments of this application, and any motion behaviors that can be performed by traffic motion objects on roads or places where vehicles may travel also fall within the protection scope of the implementation of this application.

[0175] S403: Obtaining a behavior data set corresponding to the at least one behavior element respectively, wherein the behavior data set includes a plurality of behavior data with a similarity higher than a preset threshold, and the behavior data is set to be extracted from real road condition data.

[0176] In the embodiment of the present application, after obtaining at least one behavioral element of the motion behavior, the behavioral data set corresponding to the at least one behavioral element can be obtained respectively. The motion behavior of overtaking may include left lane change, straight-line acceleration, right lane change, lane keeping and other behavioral elements, such as Figure 6 As shown, a left-turn lane behavior dataset 601, a straight-line acceleration behavior dataset 603, a right lane change 605, and a lane keeping behavior dataset 607 can be obtained. As described above, the acquisition device 101 can collect at least one behavior data of at least one traffic moving object in the real road condition data and send the at least one behavior data to the test scenario construction device 103. Specifically, the behavior data set is configured to be constructed in the following manner:

[0177] S501: Acquire real road condition data;

[0178] S503: extracting at least one behavior data of at least one traffic moving object from the real road condition data, wherein the behavior data includes at least one movement parameter information;

[0179] S505: For different traffic movement objects, perform cluster analysis on at least one behavior data of the traffic movement object to generate a behavior data set corresponding to at least one behavior element.

[0180] In an embodiment of the present application, the acquisition device 101 is capable of acquiring real road condition data, and identifying at least one traffic motion object and at least one behavior data of the traffic motion object from the real road condition data. Real road condition data may include a variety of behavior data, but not all behavior data are suitable for use in test scenarios. Based on this, in one embodiment of the present application, the acquisition device 101 may include pre-set behavior definition rules, and the behavior definition rules may define rules for motion behaviors such as straight-line driving, left turns, and right turns. In one embodiment of the present application, the behavior definition rules may include features of preset behavior elements, where the behavior elements may serve as a category name for a type of behavior data. The difference between the behavior elements and the behavior data is that the behavior data includes specific motion parameters and parameter values ​​of the behavior, and the behavior elements are used to divide behavior data of different categories. According to the behavior definition rules, the acquisition device 101 may mine behavior data that meets the features of the preset behavior elements from the real road condition data. In the process of identifying behavioral data, the operating parameter information corresponding to each behavioral data can also be determined. In one example, the acquisition device 101 identifies the straight-line driving behavior of motor vehicle A from the real road condition data, and the motion parameters of the straight-line driving behavior: acceleration = 10m / s 2 , distance = 500m, identify the left turn behavior of motor vehicle B, and the motion parameters of the left turn behavior: lateral speed = 0.3m / s, acceleration = 5m / s 2 , identifying the straight-line walking behavior of pedestrian C and the motion parameters of the straight-line walking behavior: speed = 5 km / h.

[0181] In an embodiment of the present application, after identifying at least one behavioral data of at least one traffic movement object, the acquisition device 101 can perform cluster analysis on the at least one behavioral data of the traffic movement object for different traffic movement objects, and generate a behavioral data set corresponding to at least one behavioral element. In actual application scenarios, the behavioral characteristics of different traffic movement objects are different. For example, the behavioral characteristics of cars are relatively flexible, while the behavioral characteristics of heavy vehicles (such as trucks) are large inertia and relatively less flexible. Based on this, in an embodiment of the present application, the at least one behavioral data can be clustered according to the traffic movement object. In some embodiments, the clustering algorithm used for clustering the at least one behavioral data may include a K-means clustering algorithm, a mean shift clustering algorithm, a density-based clustering algorithm, a maximum expectation clustering algorithm using a Gaussian mixture model, and the like, which are not limited in this application.

[0182] Figure 7The process of extracting a behavioral data set of at least one behavioral element for a car using real road condition data is shown. After clustering multiple behavioral data of the car, Figure 7 As shown, a behavior data set corresponding to at least one behavior element can be generated. The behavior elements may include, for example, left lane switching, right lane switching, straight driving, left right-angle turn, right right-angle turn, parking, etc., and each behavior element also corresponds to a behavior data set, such as Figure 7 As shown, in the behavior data set corresponding to the left lane change behavior element, each behavior data point has a different lateral speed / acceleration. Straight-line driving can also be divided into behavior elements such as acceleration, deceleration, and lane keeping. Acceleration can also correspond to a behavior data set consisting of multiple behavior data points with different distances / accelerations.

[0183] It should be noted that, in the execution steps S501-S503 above, the acquisition device 101 may execute S501, S501 and S503, or S501-S503, and this application does not impose any restrictions thereon. When the acquisition device 101 executes S501 or S501 and S503, all or part of the remaining steps may be executed by the test scenario construction device 103, or, of course, may be executed by any other device with data processing capabilities, and this application does not impose any restrictions thereon.

[0184] S405: Combining the behavior data in different behavior data sets into at least one movement behavior of the traffic movement object.

[0185] In the embodiment of the present application, after determining at least one behavior element of the motion behavior and the behavior data corresponding to the at least one behavior element, the behavior data in different behavior data sets can be combined into at least one motion behavior of the traffic motion object. Since each behavior data set includes at least one behavior data, then, by combining the behavior data in different behavior data sets, multiple possibilities of the motion behavior can be obtained. For example, Figure 6 As shown, if the left lane change behavior dataset 601 contains M behavior data, the straight-line acceleration behavior dataset 603 contains N behavior data, the right lane change behavior dataset 605 contains P behavior data, and the lane keeping behavior dataset 607 contains Q behavior data, then theoretically, (M×N×P×Q) types of overtaking motion behaviors can be combined to generate rich data for the preset traffic test scenario.

[0186] In the embodiment of the present application, in the process of combining the behavior data in different behavior data sets, such as Figure 6 As shown, it can be combined in the following ways:

[0187] S601: Selecting behavior data from different behavior data sets respectively;

[0188] S603: Connecting the selected behavior data into a motion behavior according to the occurrence order of the at least one behavior element.

[0189] In an embodiment of the present application, the test scenario construction device 103 not only needs to obtain the behavior data set corresponding to the at least one behavior element, but also needs to automatically combine the behavior data in the behavior data set into the motion behavior. Specifically, in the process of combining the behavior data, the test scenario construction device 103 can first select behavior data from different behavior data sets respectively. For example, in the above-mentioned overtaking test scenario, one behavior data can be selected from the left lane change behavior data set 601, the straight-line acceleration behavior data set 603, the right lane change behavior data set 605, and the lane keeping behavior data set 607 respectively. Then, according to the order of occurrence of the corresponding behavior elements, that is, the order of left lane change, straight-line acceleration, right lane change, and lane keeping, the selected behavior data are connected to generate an overtaking motion behavior. As Figure 8 As shown, the behavioral data is visualized as motion trajectories in the test scenario diagram, as shown in Figure 8 As shown, the overtaking test scenario may include four visualized motion trajectories of the left turn lane 801, straight-line acceleration 803, right lane change 805, and lane keeping 807 of the traffic operation object 501. In the process of connecting each behavior data, the tail end of the previous behavior data may be connected to the head end of the next behavior data. For example, the tail end of the visualized motion trajectory of the straight-line acceleration 803 may be connected to the head end of the visualized motion trajectory of the left lane change, until all the behavior data are concatenated into the overtaking motion behavior.

[0190] In an embodiment of the present application, in order to enhance the continuity and smoothness between different behavioral data, the selected behavioral data can be smoothly connected according to the changes in the motion parameters contained in the adjacent behavioral data. In one example, for example, in the process of connecting adjacent behavioral data A and behavioral data B, a smooth transition segment can be generated between the behavioral data A and the behavioral data B, and the smooth transition segment can be generated according to the change process of the motion parameters of the behavioral data A and the behavioral data B. Figure 9 As shown, behavior data A is a left lane change 801, and the motion parameters are lateral speed = 0.3m / s, acceleration = 2m / s 2 , behavior data B is linear acceleration 803, motion parameter is acceleration = 4m / s 2Therefore, a smooth transition section 901 can be added between the left lane change 801 and the straight-line acceleration 803 so that the vehicle can switch smoothly and naturally from the left lane change 801 to the straight-line acceleration 803.

[0191] In the embodiment of the present application, after generating the movement behavior, the movement behavior may be further set in a traffic test scene. Specifically, after combining the behavior data in different behavior data sets into at least one movement behavior of the traffic movement object, the method further includes:

[0192] S701: Acquire scenario data of the preset traffic test scenario, where the scenario data includes at least one of road information, traffic facility control information, environmental information, and initialization information of the test object;

[0193] S703: Constructing the preset traffic test scenario according to the scenario data;

[0194] S705: respectively setting the at least one movement behavior in the preset traffic test scene, testing the test object, and obtaining the test result.

[0195] In an embodiment of the present application, in the process of arranging the preset traffic test scene, the scene data of the preset traffic test scene can be first obtained. Among them, the scene data includes at least one of road information, traffic facility control information, environmental information, and the initial position of traffic motion objects. In some examples, the road information may include the number of lanes, lane types (straight roads, turning roads, etc.), intersections, etc., the traffic facility control information may include traffic lights and their working parameters, speed limit signs, zebra crossings, etc., the environmental information may include weather, obstacles and other information, and the initialization information of the test object may include information such as the initial position and initial speed of the test object. Of course, in other embodiments, the scene data is not limited to the above examples, and any information that may appear in actual traffic scenes falls within the protection scope of the scene data.

[0196] In an embodiment of the present application, after determining the scene data of the preset traffic test scene, the preset traffic test scene can be constructed, and the preset traffic test scene can be initialized using the scene data. Then, the at least one motion behavior can be set in the preset traffic test scene, the test object can be tested, and the test results can be obtained. For example, in the above-mentioned overtaking example, the acquired scene data may include information such as the initial position, initial speed, number of lanes, weather environment, etc. of the test vehicle. Then, the traffic test scene can be arranged according to the scene data, and the at least one motion behavior can be set in the traffic test scene, the test vehicle can be tested, and the test results can be obtained. Of course, the number of the test results matches the at least one motion behavior. In addition, the responsiveness and abnormal conditions of the test object can be obtained based on the test results.

[0197] Figure 10 The visualization interface of the overtaking test scenario is shown. The visualization interface shows the visualization trajectory of each behavior element of the overtaking motion behavior of the traffic motion object 501, that is, it shows multiple possible visualization motion trajectories of left turn lane 801, straight-line acceleration 803, right lane change 805 and lane keeping 807. The visualization interface also shows multiple possible initialization positions 1001 of the test object, such as Figure 10 As shown by the triangle in . It can be seen that, by using the method of constructing the test scenario provided by the embodiment of the present application, multiple test cases can be quickly constructed, thereby obtaining rich test results.

[0198] In actual application scenarios, due to the independence of each behavior data, in at least one movement behavior obtained by combining behavior data from different behavior data sets, not all movement behaviors may meet the scene characteristics of the test scene. Based on this, in one embodiment of the present application, the at least one movement behavior is set in the preset traffic test scene, the test object is tested, and the test results are obtained, including:

[0199] S801: Determine whether the movement behavior meets the scene characteristics corresponding to the preset traffic test scene.

[0200] S803: When it is determined that the movement behavior meets the scene characteristics, the movement behavior is set in the preset traffic test scene, the test object is tested, and the test results are obtained.

[0201] In an embodiment of the present application, in the process of setting the at least one motion behavior in the predicted traffic test scene for testing, it can be first determined whether the motion behavior meets the scene characteristics of the preset traffic test scene. In one example, for example, in the overtaking test scene, generally, the traffic movement object enters the lane and accelerates after switching left, and after accelerating in the lane for 50 meters to 100 meters, it switches right to the original lane. However, if the generated motion behavior is that the traffic behavior switches left, the lane accelerates for 3 kilometers, and then switches right to the original lane. It can be seen from this that the above motion behavior obviously does not meet the scene characteristics of the overtaking test scene. Therefore, such motion behavior does not need to be substituted into the preset traffic test scene for testing. In addition, the scene characteristics of the preset traffic test scene may include behavioral constraints on the motion behavior of the traffic movement object. For example, in the overtaking test scene, the driving distance of the behavior element straight-line acceleration can be set within the range of 50 meters to 100 meters.

[0202] In the embodiment of the present application, by determining whether the movement behavior conforms to the scene characteristics of the preset traffic test scene, the rationality of the movement behavior set in the preset traffic test scene can be ensured.

[0203] In an actual traffic environment, the motion behavior of a traffic moving object may also be unconventional. For example, in an overtaking scenario, the acceleration of a vehicle exceeds the conventional acceleration, but such scenarios are not uncommon on highways. Based on this, in one embodiment of the present application, the behavior data set also includes multiple first behavior data whose similarity with the multiple behavior data is less than a first preset threshold, and the ratio of the number of the multiple behavior data to the multiple first behavior data is not less than a preset ratio threshold. In other words, the behavior data set not only includes multiple behavior data with a similarity higher than the preset threshold, but also includes some multiple first behavior data with a similarity lower than the first preset threshold. Of course, the number of the multiple first behavior data in the behavior data set is not high. For example, the ratio of the number of the multiple behavior data to the multiple first behavior data is not less than 9. In a specific example, the ε-greedy algorithm can be used to set the proportion of the multiple first behavior data in the behavior data set. In the ε-greedy algorithm, a threshold of 0<ε<1 is set, and the number of the multiple first behavior data and the multiple behavior data is set in a ratio of 1-ε:ε, which satisfies the richness of the behavior data set to a certain extent and enhances the robustness of the test scenario.

[0204] On the other hand, the present application also provides a simulation scene construction method from the perspective of a vehicle-mounted simulation scene construction device, such as Figure 11 As shown, the method may include:

[0205] S1101: Acquire real road condition data;

[0206] S1103: Extract at least one behavior data of at least one traffic moving object from the real road condition data, where the behavior data includes at least one movement parameter information.

[0207] In the embodiment of the present application, the simulation scene construction device can collect rich real road condition data, providing a lot of rich and real data basis for the test scene. In addition, the vehicle can also extract the behavior data of different traffic movement objects from the real road condition data, and the behavior data includes at least one motion parameter information. Compared with the manual extraction method used in the related art, the embodiment of the present application can automatically realize the extraction of behavior data, reduce the cost of extracting behavior data, and improve the efficiency of extracting behavior data. The driver can use the simulation scene construction device to simulate driving in the car to enhance the experience.

[0208] Optionally, in one embodiment of the present application, the method further includes:

[0209] For different traffic movement objects, cluster analysis is performed on at least one behavior data of the traffic movement object to generate a behavior data set corresponding to at least one behavior element.

[0210] On the other hand, the present application also provides a test scenario construction device, such as Figure 12 As shown, the test scenario construction device 103 may include:

[0211] A motion behavior determination module 1201 is used to determine the motion behavior of a traffic motion object required for constructing a preset traffic test scenario, wherein the motion behavior is composed of at least one behavior element;

[0212] A data set acquisition module 1203 is configured to respectively acquire a behavior data set corresponding to the at least one behavior element, wherein the behavior data set includes a plurality of behavior data having a similarity greater than a preset threshold, and the behavior data is set to be extracted from real road condition data;

[0213] The behavior data combining module 1205 is configured to combine the behavior data in different behavior data sets into at least one movement behavior of the traffic movement object.

[0214] In the embodiment of the present application, the data processing device 1200 can be set on the server side or in the cloud.

[0215] Optionally, in one embodiment of the present application, the behavior data set is configured to be constructed according to the following modules:

[0216] A road condition data acquisition module is used to obtain real road condition data;

[0217] A behavior data extraction module, configured to extract at least one behavior data of at least one traffic moving object from the real road condition data, wherein the behavior data includes at least one movement parameter information;

[0218] The behavior data clustering module is used to perform cluster analysis on at least one behavior data of different traffic movement objects, and generate a behavior data set corresponding to at least one behavior element.

[0219] Optionally, in one embodiment of the present application, the behavior data clustering module is specifically configured to:

[0220] For different traffic movement objects, at least one target behavior data that meets the characteristics of the preset behavior element is screened out from at least one behavior data of the traffic movement object;

[0221] A cluster analysis is performed on the at least one target behavior data to generate a behavior data set corresponding to at least one behavior element.

[0222] Optionally, in one embodiment of the present application, the behavior data combination module is specifically configured to:

[0223] Selecting behavior data from different sets of behavior data respectively;

[0224] The selected behavior data are connected into a motion behavior according to the occurrence order of the at least one behavior element.

[0225] Optionally, in one embodiment of the present application, the behavior data combination module is further configured to:

[0226] The selected behavior data are smoothly connected according to the changes of motion parameters contained in adjacent behavior data.

[0227] Optionally, in one embodiment of the present application, the device further includes:

[0228] A scene data acquisition module, configured to acquire scene data of the preset traffic test scene, wherein the scene data includes at least one of road information, traffic facility control information, environmental information, and an initial position of a traffic moving object;

[0229] A test scenario construction module, configured to construct the preset traffic test scenario according to the scenario data;

[0230] The test result acquisition module is used to respectively set the at least one motion behavior in the preset traffic test scene, test the test object, and obtain the test result.

[0231] Optionally, in one embodiment of the present application, the test result acquisition module is specifically configured to:

[0232] Determining whether the movement behavior meets the scene characteristics corresponding to the preset traffic test scene;

[0233] When it is determined that the movement behavior meets the scene characteristics, the movement behavior is set in the preset traffic test scene, the test object is tested, and the test results are obtained.

[0234] Optionally, in one embodiment of the present application, the behavior data set also includes multiple first behavior data whose similarity with the multiple behavior data is less than a first preset threshold, and the ratio of the number of the multiple behavior data to the multiple first behavior data is not less than a preset ratio threshold.

[0235] Optionally, in one embodiment of the present application, the behavioral data includes at least one of the following motion parameter information: driving direction speed, driving lateral speed, driving acceleration, driving lateral acceleration, driving distance, turning radius, driving time, walking speed, walking acceleration, and walking time.

[0236] Optionally, in one embodiment of the present application, the traffic moving object includes at least one of the following: a car, a truck, a bus, a tram, a train, a motorcycle, an electric car, a bicycle, a pedestrian, and an animal.

[0237] Optionally, in one embodiment of the present application, the movement behavior includes at least one of the following: overtaking, going straight in an intersection, turning in an intersection, merging into traffic flow, and pedestrians crossing the sidewalk.

[0238] Optionally, in one embodiment of the present application, the behavior element includes at least one of the following: straight driving, left lane switching, right lane switching, left right-angle turn, right right-angle turn, and parking.

[0239] Optionally, in one embodiment of the present application, the device further includes:

[0240] The test scenario acquisition module is used to obtain the preset traffic test scenario selected by the user.

[0241] The embodiment of the present application also provides a collection device 101, such as Figure 12 As shown, the collection device 101 includes:

[0242] The traffic data acquisition module 1301 is used to acquire real traffic data;

[0243] The behavior data extraction module 1303 is configured to extract at least one behavior data of at least one traffic moving object from the real road condition data, wherein the behavior data includes at least one movement parameter information.

[0244] Optionally, in one embodiment of the present application, the method further includes:

[0245] The behavior data clustering module 1305 is used to perform cluster analysis on at least one behavior data of different traffic movement objects, and generate a behavior data set corresponding to at least one behavior element.

[0246] The embodiment of the present application provides a device, such as Figure 13 As shown, it includes: a processor and a memory for storing processor executable instructions; wherein the processor is configured to implement the above-mentioned device when executing the instructions. Device 800 includes a memory 801, a processor 802, a bus 803 and a communication interface 804. The memory 801, the processor 802 and the communication interface 804 communicate with each other via the bus 801. The bus 803 can be a peripheral component interconnect standard (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 13 The bus is represented by only one thick line, but it does not mean that there is only one bus or one type of bus. The communication interface 804 is used for external communication.

[0247] The processor 802 may be a central processing unit (CPU). The memory 801 may include a volatile memory, such as a random access memory (RAM). The memory 801 may also include a non-volatile memory, such as a read-only memory (ROM), a flash memory, a HDD, or an SSD.

[0248] The memory 801 stores executable code, and the processor 802 executes the executable code to perform the aforementioned test scenario construction method.

[0249] An embodiment of the present application provides a non-volatile computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions implement the above-mentioned apparatus when executed by a processor.

[0250] An embodiment of the present application provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above-mentioned device.

[0251] In some embodiments, the disclosed methods may be implemented as computer program instructions encoded in a machine-readable format on a computer-readable storage medium or on other non-transitory media or articles of manufacture. Figure 14 Schematically illustrates a conceptual partial view of an example computer program product, arranged in accordance with at least some embodiments presented herein, comprising a computer program for executing a computer process on a computing device. In one embodiment, the example computer program product 600 is provided using a signal-bearing medium 601. The signal-bearing medium 601 may include one or more program instructions 602 that, when executed by one or more processors, may provide the above-described instructions for executing a computer process. Figure 4 or Figure 11 Thus, for example, reference to Figure 4 In the embodiment shown in , one or more features of blocks 401-405 may be undertaken by one or more instructions associated with signal bearing medium 601. In addition, Figure 6 Program instructions 602 in also describe example instructions.

[0252] In some examples, the signal-bearing medium 601 may include a computer-readable medium 603, such as, but not limited to, a hard drive, a compact disk (CD), a digital video disk (DVD), a digital tape, a memory, a read-only memory (ROM), or a random access memory (RAM), etc. In some embodiments, the signal-bearing medium 601 may include a computer-recordable medium 604, such as, but not limited to, a memory, a read / write (R / W) CD, a R / W DVD, etc. In some embodiments, the signal-bearing medium 601 may include a communication medium 605, such as, but not limited to, a digital and / or analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communication link, a wireless communication link, etc.). Thus, for example, the signal-bearing medium 601 may be conveyed by a wireless form of communication medium 605 (e.g., a wireless communication medium that complies with the IEEE 802.11 standard or other transmission protocol). One or more program instructions 602 may be, for example, computer-executable instructions or logic-implemented instructions. In some examples, such as for Figure 4 or Figure 11The computing device of the computing device described can be configured to, in response to one or more program instructions 602 that are communicated to the computing device by computer-readable medium 603, computer recordable medium 604 and / or communication medium 605, provide various operations, functions or actions.It should be understood that the arrangement described here is only for the purpose of example.Thus, it will be understood by those skilled in the art that other arrangements and other elements (for example, machines, interfaces, functions, sequences, and functional groups, etc.) can be used instead, and some elements can be omitted together according to the desired result.In addition, many of the described elements can be implemented as discrete or distributed components or in any appropriate combination and position to combine the functional entities implemented by other components.

[0253] The flow charts and block diagrams in the accompanying drawings show the possible architecture, function and operation of the devices, systems, devices and computer program products according to multiple embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a part for a module, program segment or instruction, and the part for the module, program segment or instruction comprises one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the function marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be performed substantially in parallel, and they can sometimes also be performed in the opposite order, depending on the function involved.

[0254] It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by hardware that performs the corresponding function or action (such as a circuit or ASIC (Application Specific Integrated Circuit)), or can be implemented by a combination of hardware and software, such as firmware.

[0255] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art can understand and implement other variations of the disclosed embodiments by reviewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple situations. A single processor or other unit can implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0256] The embodiments of the present application have been described above. The above description is illustrative and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or improvements to the technology in the market, or to enable other persons skilled in the art to understand the embodiments disclosed herein.

Claims

1. A test scenario construction method, characterized in that: include: Determining a motion behavior of a traffic moving object required for constructing a preset traffic test scenario, wherein the motion behavior is composed of at least one behavior element; Obtaining a behavior data set corresponding to the at least one behavior element respectively, wherein the behavior data set includes a plurality of behavior data having a similarity higher than a preset threshold, and the behavior data is set to be extracted from real road condition data; Combining the behavior data in different behavior data sets into at least one movement behavior of the traffic movement object includes: selecting behavior data from different behavior data sets respectively; The selected behavior data are connected into a motion behavior according to the occurrence order of the at least one behavior element.

2. The method according to claim 1, characterized in that The behavioral data set is configured to be constructed in the following manner: Determine real road condition data; Extracting at least one behavior data of at least one traffic moving object from the real road condition data, wherein the behavior data includes at least one motion parameter information; For different traffic movement objects, cluster analysis is performed on at least one behavior data of the traffic movement object to generate a behavior data set corresponding to at least one behavior element.

3. The method according to claim 2, characterized in that The step of performing cluster analysis on at least one behavior data of different traffic movement objects to generate a behavior data set corresponding to at least one behavior element includes: For different traffic movement objects, at least one target behavior data that meets the characteristics of the preset behavior element is screened out from at least one behavior data of the traffic movement object; A cluster analysis is performed on the at least one target behavior data to generate a behavior data set corresponding to at least one behavior element.

4. The method according to claim 1, wherein The step of connecting the selected behavior data into a motion behavior includes: The selected behavior data are smoothly connected according to the changes of motion parameters contained in adjacent behavior data.

5. The method according to claim 1, characterized in that After combining the behavior data in different behavior data sets into at least one movement behavior of the traffic movement object, the method further includes: Acquiring scene data of the preset traffic test scene, the scene data including at least one of road information, traffic facility control information, environmental information, and an initial position of a traffic moving object; Constructing the preset traffic test scenario according to the scenario data; The at least one motion behavior is respectively set in the preset traffic test scene, the test object is tested, and the test results are obtained.

6. The method according to claim 5, characterized in that The step of setting the at least one movement behavior in the preset traffic test scene, testing the test object, and obtaining the test result includes: Determining whether the movement behavior meets the scene characteristics corresponding to the preset traffic test scene; When it is determined that the movement behavior meets the scene characteristics, the movement behavior is set in the preset traffic test scene, the test object is tested, and the test results are obtained.

7. The method according to any one of claims 1 to 6, characterized in that The behavior data set also includes a plurality of first behavior data whose similarity with the plurality of behavior data is less than a first preset threshold, and a ratio of the plurality of behavior data to the plurality of first behavior data is not less than a preset ratio threshold.

8. The method according to claim 1, characterized in that The behavior data includes at least one of the following motion parameter information: driving direction speed, driving lateral speed, driving acceleration, driving lateral acceleration, driving distance, turning radius, driving time, walking speed, walking acceleration, and walking time.

9. The method according to claim 1, characterized in that The traffic moving objects include at least one of the following: cars, trucks, trains, motorcycles, bicycles, pedestrians, and animals.

10. The method according to claim 1, characterized in that The movement behavior includes at least one of the following: overtaking, going straight in an intersection, turning in an intersection, merging into traffic flow, and pedestrians crossing the sidewalk.

11. The method according to claim 1, wherein The behavior elements include at least one of the following: driving in a straight line, switching lanes left, switching lanes right, turning right at a right angle, turning left at a right angle, and parking.

12. The method according to claim 1, characterized in that Before determining the motion behavior of the traffic moving object required for constructing the preset traffic test scenario, the method further includes: Acquire the preset traffic test scene selected by the user and the configured scene data of the preset traffic test scene.

13. A simulation scene construction method, characterized in that: include: Obtain real road condition data; Extracting at least one behavior data of at least one traffic moving object from the real road condition data, wherein the behavior data includes at least one motion parameter information; Constructing a traffic simulation scene using the at least one behavior data of the at least one traffic moving object includes: obtaining a movement behavior of the traffic moving object required for the traffic simulation scene, the movement behavior being composed of at least one behavior element; and respectively obtaining a set of behavior data corresponding to the at least one behavior element; The behavior data in different behavior data sets are combined into at least one movement behavior of the traffic movement object.

14. The method according to claim 13, characterized in that After extracting at least one behavior data of at least one traffic moving object from the real road condition data, wherein the behavior data includes at least one motion parameter information, the method further includes: For different traffic movement objects, cluster analysis is performed on at least one behavior data of the traffic movement object to generate a behavior data set corresponding to at least one behavior element.

15. A test scenario construction device, characterized in that: include: A motion behavior determination module, configured to determine the motion behavior of a traffic motion object required for constructing a preset traffic test scenario, wherein the motion behavior is composed of at least one behavior element; a data set acquisition module, configured to respectively acquire a behavior data set corresponding to the at least one behavior element, wherein the behavior data set includes a plurality of behavior data having a similarity higher than a preset threshold, and the behavior data is configured to be extracted from real road condition data; A behavior data combination module is used to combine the behavior data in different behavior data sets into at least one movement behavior of the traffic movement object, including: selecting behavior data from different behavior data sets respectively; The selected behavior data are connected into a motion behavior according to the occurrence order of the at least one behavior element.

16. The device according to claim 15, characterized in that The behavioral data set is configured to be constructed according to the following modules: A road condition data determination module, used to determine real road condition data; A behavior data extraction module, configured to extract at least one behavior data of at least one traffic moving object from the real road condition data, wherein the behavior data includes at least one movement parameter information; The behavior data clustering module is used to perform cluster analysis on at least one behavior data of different traffic movement objects, and generate a behavior data set corresponding to at least one behavior element.

17. The device according to claim 16, characterized in that The behavior data clustering module is specifically used to: For different traffic movement objects, at least one target behavior data that meets the characteristics of the preset behavior element is screened out from at least one behavior data of the traffic movement object; A cluster analysis is performed on the at least one target behavior data to generate a behavior data set corresponding to at least one behavior element.

18. The device according to claim 15, characterized in that The behavior data combination module is further used to: The selected behavior data are smoothly connected according to the changes of motion parameters contained in adjacent behavior data.

19. The device according to claim 15, characterized in that The device further comprises: A scene data acquisition module, configured to acquire scene data of the preset traffic test scene, wherein the scene data includes at least one of road information, traffic facility control information, environmental information, and an initial position of a traffic moving object; A test scenario construction module, configured to construct the preset traffic test scenario according to the scenario data; The test result acquisition module is used to respectively set the at least one motion behavior in the preset traffic test scene, test the test object, and obtain the test result.

20. The device according to claim 19, characterized in that The test result acquisition module is specifically used to: Determining whether the movement behavior meets the scene characteristics corresponding to the preset traffic test scene; When it is determined that the movement behavior meets the scene characteristics, the movement behavior is set in the preset traffic test scene, the test object is tested, and the test results are obtained.

21. The device according to any one of claims 15 to 20, characterized in that The behavior data set also includes a plurality of first behavior data whose similarity with the plurality of behavior data is less than a first preset threshold, and a ratio of the plurality of behavior data to the plurality of first behavior data is not less than a preset ratio threshold.

22. The device according to claim 15, characterized in that The behavior data includes at least one of the following motion parameter information: driving direction speed, driving lateral speed, driving acceleration, driving lateral acceleration, driving distance, turning radius, driving time, walking speed, walking acceleration, and walking time.

23. The device according to claim 15, characterized in that The traffic moving objects include at least one of the following: cars, trucks, trains, motorcycles, bicycles, pedestrians, and animals.

24. The device according to claim 15, characterized in that The movement behavior includes at least one of the following: overtaking, going straight in an intersection, turning in an intersection, merging into traffic flow, and pedestrians crossing the sidewalk.

25. The device according to claim 15, wherein The behavior elements include at least one of the following: driving in a straight line, switching lanes left, switching lanes right, turning right at a right angle, turning left at a right angle, and parking.

26. The device according to claim 15, characterized in that The device further comprises: The test scenario acquisition module is used to obtain the preset traffic test scenario selected by the user and the configured scenario data of the preset traffic test scenario.

27. A simulation scene construction device, characterized in that: include: A road condition data acquisition module is used to obtain real road condition data; A behavior data extraction module, configured to extract at least one behavior data of at least one traffic moving object from the real road condition data, wherein the behavior data includes at least one movement parameter information; A scenario construction module is used to construct a traffic simulation scenario using the at least one behavior data of the at least one traffic moving object, wherein the scenario construction module is specifically used to: Acquire a motion behavior of a traffic moving object required for a traffic test scenario, wherein the motion behavior is composed of at least one behavior element; respectively obtaining a set of behavior data corresponding to the at least one behavior element; The behavior data in different behavior data sets are combined into at least one movement behavior of the traffic movement object.

28. The device according to claim 27, characterized in that Also includes: The behavior data clustering module is used to perform cluster analysis on at least one behavior data of different traffic movement objects, and generate a behavior data set corresponding to at least one behavior element.

29. The device according to claim 27 or 28, characterized in that The simulation scene construction device is arranged in a vehicle or in the cloud.

30. A device, characterized in that include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the method according to any one of claims 1 to 14 when executing the instructions.

31. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 14 is implemented.

32. A computer program product, characterized in that The method comprises a computer-readable code or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the method according to any one of claims 1 to 14.

33. A chip, characterized in that: The system comprises at least one processor configured to execute a computer program or computer instruction stored in a memory to execute the method according to any one of claims 1 to 14.

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