An intelligent driving hardware-in-the-loop simulation test scene building method, system, device and medium

Abstract

The application provides a kind of intelligent driving hardware-in-loop simulation test scene building method, system, equipment and medium, belongs to automatic driving simulation test technical field, the method includes: establishing road scene element library, environmental condition element library, dynamic target scene element library, static target scene element library and test vehicle scene element library;Road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, test vehicle scene elements are selected from each element library, and their attribute settings are carried out;The elements that complete attribute setting are combined according to the order of road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements and test vehicle scene elements.It improves the efficiency and flexibility of scene construction.

Description

Technical Field

[0001] This invention belongs to the field of autonomous driving simulation testing technology, and in particular relates to a method, system, equipment and medium for building intelligent driving hardware-in-the-loop simulation testing scenarios. Background Technology

[0002] With the rapid development of automotive intelligent technology, the functions of Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS) are becoming increasingly complex. Comprehensive, efficient, and safe testing and verification of these systems has become a crucial aspect of industry development. While real-vehicle road testing can accurately reflect system performance, it has inherent limitations such as long testing cycles, high costs, significant safety risks, and difficulty in reproducing extreme scenarios. To overcome these shortcomings, Hardware-in-the-Loop (HIL) simulation testing technology has emerged. By constructing highly realistic virtual test scenarios in a laboratory environment, it enables repeatable, efficient, safe, and reliable testing and verification of controllers, and has become an indispensable tool in the intelligent driving development process.

[0003] Currently, in the field of HIL simulation testing for intelligent driving, common scenario construction methods mostly rely on pre-defined fixed scenarios or scripted editing. These methods control the timing and behavioral logic of each element in the scenario by pre-defining a complete test scenario structure or writing detailed script instructions, enabling the construction of test scenarios under specific conditions.

[0004] However, the drawback of this approach is that the scenario construction process is cumbersome and lacks scalability. When building complex interactive scenarios, users need to repeatedly define similar elements, making it difficult to achieve element-level reuse and flexible combination. This not only reduces the efficiency of scenario building but also limits the diversity and coverage of test scenarios, failing to fully meet the needs of intelligent driving systems for the efficient construction of massive test scenarios, especially multi-target interactive scenarios. Summary of the Invention

[0005] This invention provides a method, system, device, and medium for building hardware-in-the-loop simulation test scenarios for intelligent driving, so as to at least solve the problems of low efficiency and poor flexibility in scenario construction in the prior art.

[0006] In a first aspect, embodiments of this application provide a method for building a hardware-in-the-loop simulation test scenario for intelligent driving, the method comprising: Step S1: Establish a road scene element library containing road scene elements, an environmental condition element library containing environmental condition elements, a dynamic target scene element library containing dynamic target scene elements, a static target scene element library containing static target scene elements, and a test vehicle scene element library containing test vehicle scene elements. Step S2: Select road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, and test vehicle scene elements from the element libraries, and set their attributes; Step S3: Combine the elements with completed attribute settings in the following order: road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, and test vehicle scene elements.

[0007] Furthermore, the method also includes: Step S4: Deploy the combined hardware-in-the-loop simulation test scenario to the HIL simulation platform; Step S5: The HIL simulation platform runs the hardware-in-the-loop simulation test scenario, generates corresponding sensor simulation signals, and sends them to the intelligent driving controller under test. Step S6: Receive the control command generated by the intelligent driving controller based on the sensor simulation signal, and feed it back to the vehicle dynamics model in the HIL simulation platform to drive the virtual test vehicle to run in the test scenario, forming a closed-loop test.

[0008] Furthermore, in step S2, the attributes of the road scene elements include: lane start position, lane end position, number of lanes, lane lateral slope, lane longitudinal slope, lane width, lane line color, lane line type, lane line clarity, lane line length, lane line width, lane curvature, and whether there is an emergency lane. The attributes of environmental condition elements include: the starting position of the environmental setting, the ending position of the environmental setting, the intensity of sunlight, the angle of sunlight, rainfall, snowfall, hail size, hail speed, fog concentration, cloud cover, cloud shape, wind classification, and wind direction. The attributes of dynamic target scene elements include: the size of the dynamic target, the size of the dynamic target, the starting position of the dynamic target, the ending position of the dynamic target, the lateral velocity of the dynamic target, the longitudinal velocity of the dynamic target, the tilt angle of the dynamic target, the acceleration of the dynamic target, and the deceleration of the dynamic target. The attributes of static target scene elements include: static target size, static target dimensions, static target position, static target quantity, static target tilt angle, static target spacing, static target dimensions, static target upright / fallen / flipped state, and static target label; The attributes of the test vehicle scene elements include: the appearance parameters of the test vehicle, the load of the test vehicle, whether the test vehicle is attached or not, the initial position of the test vehicle, the tilt angle of the test vehicle, and the speed of the test vehicle.

[0009] Furthermore, the method also includes an element library update step: adding new elements to each element library based on actual traffic scenario data and testing requirements.

[0010] Secondly, embodiments of this application also provide a system for building a hardware-in-the-loop simulation test scenario for intelligent driving as described in the above aspects, the system comprising: The scene element library construction module is used to build a road scene element library containing road scene elements, an environmental condition element library containing environmental condition elements, a dynamic target scene element library containing dynamic target scene elements, a static target scene element library containing static target scene elements, and a test vehicle scene element library containing test vehicle scene elements. The scene element attribute configuration module is used to select road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, and test vehicle scene elements from various element libraries, and set their attributes. The hardware-in-the-loop simulation test scenario building module is used to combine the elements with completed attribute settings in the following order: road scenario elements, environmental condition elements, dynamic target scenario elements, static target scenario elements, and test vehicle scenario elements.

[0011] Furthermore, the system also includes: The scenario deployment and execution module is used to deploy the combined hardware-in-the-loop simulation test scenarios to the HIL simulation platform; The signal excitation module is used by the HIL simulation platform to run the hardware-in-the-loop simulation test scenario, generate corresponding sensor analog signals and send them to the intelligent driving controller under test. The closed-loop feedback module is used to receive control commands generated by the intelligent driving controller based on the sensor simulation signals and feed them back to the vehicle dynamics model in the HIL simulation platform to drive the virtual test vehicle to run in the test scenario, forming a closed-loop test.

[0012] Furthermore, in the scene element attribute configuration module, the attributes of road scene elements include: lane start position, lane end position, number of lanes, lane lateral slope, lane longitudinal slope, lane width, lane line color, lane line type, lane line clarity, lane line length, lane line width, lane curvature, and whether there is an emergency lane. The attributes of environmental condition elements include: the starting position of the environmental setting, the ending position of the environmental setting, the intensity of sunlight, the angle of sunlight, rainfall, snowfall, hail size, hail speed, fog concentration, cloud cover, cloud shape, wind classification, and wind direction. The attributes of dynamic target scene elements include: the size of the dynamic target, the size of the dynamic target, the starting position of the dynamic target, the ending position of the dynamic target, the lateral velocity of the dynamic target, the longitudinal velocity of the dynamic target, the tilt angle of the dynamic target, the acceleration of the dynamic target, and the deceleration of the dynamic target. The attributes of static target scene elements include: static target size, static target dimensions, static target position, static target quantity, static target tilt angle, static target spacing, static target dimensions, static target upright / fallen / flipped state, and static target label; The attributes of the test vehicle scene elements include: the appearance parameters of the test vehicle, the load of the test vehicle, whether the test vehicle is attached or not, the initial position of the test vehicle, the tilt angle of the test vehicle, and the speed of the test vehicle.

[0013] Furthermore, the system also includes an element library update module, which is used to add new elements to each element library based on actual traffic scenario data and testing requirements.

[0014] Thirdly, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the intelligent driving hardware-in-the-loop simulation test scenario construction method as described in the above aspects.

[0015] Fourthly, a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the intelligent driving hardware-in-the-loop simulation test scenario construction method as described in the above aspects.

[0016] As can be seen from the above technical solutions, the present invention has the following advantages: The intelligent driving hardware-in-the-loop simulation test scenario construction method provided in this application realizes the modular construction of test scenarios by establishing an independent element library containing road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, and test vehicle scene elements. Users can select the required elements from each library and set their attributes, and then combine them in a predetermined order, which improves the efficiency and flexibility of scenario construction, reduces the dependence on preset scripts, and enables testers to quickly respond to different test requirements.

[0017] By establishing a standardized scenario element library through classification, it supports the fine-grained and independent configuration of the attributes of various scenario elements. Lane parameters, environmental conditions, dynamic target behavior, etc. can all be adjusted individually according to actual test requirements, thereby enabling the construction of test scenarios with high realism and close fit to the real traffic environment, effectively improving the accuracy and effectiveness of simulation testing.

[0018] The modular architecture allows each scenario element library to be maintained and expanded independently. As real traffic data and new testing requirements continue to emerge, new element types can be easily added to each element library, ensuring the continuous enrichment and evolution of the test scenario library and overcoming the scalability bottleneck caused by the rigid structure of traditional methods. Attached Figure Description

[0019] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of the method for building a hardware-in-the-loop simulation test scenario for intelligent driving as described in this invention. Detailed Implementation

[0021] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0022] This application provides a method, system, device, and medium for building a hardware-in-the-loop simulation test scenario for intelligent driving, addressing the urgent technical problem of improving the accuracy and scalability of simulation testing.

[0023] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0024] Figure 1 This is a flowchart illustrating a method for building a hardware-in-the-loop simulation test scenario for intelligent driving, provided in an embodiment of this application. Figure 1 As shown in the figure, the method for building a hardware-in-the-loop simulation test scenario for intelligent driving provided in this application embodiment specifically includes the following steps: Step S1: Establish a road scene element library containing road scene elements, an environmental condition element library containing environmental condition elements, a dynamic target scene element library containing dynamic target scene elements, a static target scene element library containing static target scene elements, and a test vehicle scene element library containing test vehicle scene elements. Each element library stores the corresponding type of standardized scene element model. It should be noted that each element library here refers to the road scene element library, environmental condition element library, dynamic target scene element library, static target scene element library, and test vehicle scene element library. Step S2: Select road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, and test vehicle scene elements from the element libraries, and set their attributes; Step S3: Combine the elements with completed attribute settings in the following order: road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, and test vehicle scene elements to obtain the hardware-in-the-loop simulation test scenario. It should be noted that the elements here refer to the road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, and test vehicle scene elements.

[0025] It should be noted that in step S1, the road scene element library includes straight roads, curved roads, curved roads cutting into straight roads, straight roads cutting into curved roads, ramps, main road entering ramps, ramps merging into main roads, tunnels, road merging sections, road separating sections, and forking road sections. The environmental condition element library includes sunny, cloudy, overcast, rain, snow, hail, fog, wind, daytime, nighttime, dawn, dusk, noon, and midnight; The dynamic target scene element library includes cars, vans, commercial vehicles, buses, two-wheeled motorcycles, three-wheeled motorcycles, tanker trucks, construction vehicles, police cars, special vehicles, vans, extra-long trailers, extra-wide trailers, adults, children, camels, sheep, horses, and dogs; The static target scene element library includes trees, flower beds, streetlights, traffic cones, water-filled barriers, ground nails, tire covers, manhole covers, cardboard boxes, gantry frames, guardrails, vehicle identification signs, construction signs, giant signs, roadside speed limit signs, and warning triangles; The test vehicle scenario element library includes sedans, SUVs, pickup trucks, sports cars, vans, commercial vehicles, heavy trucks, and light trucks.

[0026] It should be noted that the road scene element library is used to store road scene elements such as straight roads, curved roads, curved roads cutting into straight roads, straight roads cutting into curved roads, ramps, main road entering ramps, ramps merging into main roads, tunnels, road merging sections, road separating sections, and forking road sections. After selecting a road scene element from the road scene element library, you can set its attributes. The environmental condition element library is used to store environmental condition elements such as sunny, cloudy, overcast, rain, snow, hail, fog, wind, daytime, nighttime, dawn, dusk, noon, and midnight. After selecting an environmental condition element from the environmental condition element library, you can set its attributes. The dynamic target scene element library is used to store dynamic target scene elements such as cars, vans, commercial vehicles, buses, two-wheeled motorcycles, three-wheeled motorcycles, oil tankers, construction vehicles, police cars, special vehicles, vans, extra-long trailers, extra-wide trailers, adults, children, camels, sheep, horses, and dogs. After selecting a dynamic target scene element from the dynamic target scene element library, you can set its attributes. The static target scene element library is used to store static target scene elements such as trees, flower beds, street lights, cones, water-filled barriers, ground nails, tire covers, manhole covers, cardboard boxes, gantry frames, guardrails, vehicle identification signs, construction signs, giant signs, roadside speed limit signs, and warning triangles. After selecting a static target scene element from the static target scene element library, you can set its attributes. The test vehicle scene element library is used to store test vehicle scene elements such as sedans, SUVs, pickup trucks, sports cars, vans, commercial vehicles, heavy trucks, and light trucks. After selecting a test vehicle scene element from the test vehicle scene element library, its attributes can be set.

[0027] In an exemplary embodiment, in step S2, the attributes of the road scene element include: lane start position, lane end position, number of lanes, lane lateral slope, lane longitudinal slope, lane width, lane line color, lane line type, lane line clarity, lane line length, lane line width, lane curvature, and whether there is an emergency lane. The attributes of environmental condition elements include: the starting position of the environmental setting, the ending position of the environmental setting, the intensity of sunlight, the angle of sunlight, rainfall, snowfall, hail size, hail speed, fog concentration, cloud cover, cloud shape, wind classification, and wind direction. The attributes of dynamic target scene elements include: the size of the dynamic target, the size of the dynamic target, the starting position of the dynamic target, the ending position of the dynamic target, the lateral velocity of the dynamic target, the longitudinal velocity of the dynamic target, the tilt angle of the dynamic target, the acceleration of the dynamic target, and the deceleration of the dynamic target. The attributes of static target scene elements include: static target size, static target dimensions, static target position, static target quantity, static target tilt angle, static target spacing, static target dimensions, static target upright / fallen / flipped state, and static target label; The attributes of the test vehicle scene elements include: the appearance parameters of the test vehicle, the load of the test vehicle, whether the test vehicle is attached or not, the initial position of the test vehicle, the tilt angle of the test vehicle, and the speed of the test vehicle.

[0028] It should be noted that in the road scene element library, different roads have different settable attributes, and the settable attributes are set according to the characteristics of the road type. In the environmental condition element library, different environmental conditions have different configurable attributes, and the configurable attributes are set according to the characteristics of the environmental condition. In the dynamic target scene element library, different dynamic targets have different settable attributes, and the settable attributes are set according to the characteristics of the dynamic target. In the static target scene element library, different static targets have different settable attributes, and the settable attributes are set according to the characteristics of the static target. In the test vehicle scene element library, different test vehicles have different configurable attributes, which are set according to the characteristics of the test vehicle.

[0029] Select appropriate scene elements from the above element library and configure their attributes to assemble complex test scenarios with multi-objective interactions in a "building block" manner, specifically including: Road infrastructure setup: First, select a basic road type from the road scene element library. For example, to construct a highway ramp merging scene, you need to select "Straight Road" (as the main road) and "Ramp Merging into Main Road Section". Then, set the attributes of the selected road, including lane start position, lane end position, number of lanes, road surface slope, lane width, lane line color, lane line type, lane line clarity, lane line length, lane line width, lane curvature, and whether there is an emergency lane, thereby generating the basic road topology environment.

[0030] Environmental Condition Settings: Based on the established road type, select one or more environmental conditions from the environmental condition element library. For example, to set a sunny and breezy weather environment at noon, select "Sunny" and "Noon". Then, set the start and end positions of this weather on the road, the intensity and angle of sunlight, cloud cover, cloud shape, wind type and direction, etc., thereby setting the environmental conditions such as weather and lighting for this section of road; Dynamic target injection: Based on the existing road structure, select one or more dynamic targets from the dynamic target scene element library. For example, add a car traveling at a constant speed on the main road and a truck on the ramp. Then, set the attributes of each dynamic target, including the target's size, starting position, ending position, lateral velocity, longitudinal velocity, tilt angle, acceleration, and deceleration, to simulate real traffic flow and interaction behavior.

[0031] Static target placement: Select the required static elements from the static target scene element library, such as speed limit signs on the roadside and cones in the construction area, and set their attributes, such as the location of the static target, the number of static targets, the tilt angle of the static targets, the spacing of the static targets, the size of the static targets, the upright / fallen / overturned state of the static targets, and the labels of the static targets, to enhance the realism and complexity of the scene.

[0032] Test vehicle configuration: Select the vehicle model to be tested from the test vehicle element scene library, such as a heavy truck, and configure it as the initial state attributes of the vehicle, including the appearance parameters of the test vehicle, the load of the test vehicle, whether the test vehicle has a trailer or not, the initial position of the test vehicle, the tilt angle of the test vehicle, and the speed of the test vehicle.

[0033] After the above configuration is completed, a complete scenario for hardware-in-the-loop simulation testing of intelligent driving is set up.

[0034] In one embodiment, the method further includes: Step S4: Deploy the combined hardware-in-the-loop simulation test scenario to the HIL simulation platform; Step S5: The HIL simulation platform runs the hardware-in-the-loop simulation test scenario, generates corresponding sensor simulation signals, and sends them to the intelligent driving controller under test. Step S6: Receive the control command generated by the intelligent driving controller based on the sensor simulation signal, and feed it back to the vehicle dynamics model in the HIL simulation platform to drive the virtual test vehicle to run in the test scenario, forming a closed-loop test.

[0035] According to another embodiment of the present invention, the method further includes an element library update step: adding new elements to each element library based on actual traffic scenario data and testing requirements.

[0036] This invention also provides a hardware-in-the-loop simulation test scenario building system for intelligent driving, the system comprising: The scene element library construction module is used to build a road scene element library containing road scene elements, an environmental condition element library containing environmental condition elements, a dynamic target scene element library containing dynamic target scene elements, a static target scene element library containing static target scene elements, and a test vehicle scene element library containing test vehicle scene elements. The scene element attribute configuration module is used to select road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, and test vehicle scene elements from various element libraries, and set their attributes. The hardware-in-the-loop simulation test scenario building module is used to combine the elements with completed attribute settings in the order of road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, and test vehicle scene elements to obtain the hardware-in-the-loop simulation test scenario.

[0037] It should be noted that in the scene element library construction module, the road scene element library includes straight roads, curved roads, curved road sections cutting into straight roads, straight road sections cutting into curved roads, ramps, main road sections entering ramps, ramps merging into main roads, tunnels, road merging sections, road separating sections, and forking road sections. The environmental condition element library includes sunny, cloudy, overcast, rain, snow, hail, fog, wind, daytime, nighttime, dawn, dusk, noon, and midnight; The dynamic target scene element library includes cars, vans, commercial vehicles, buses, two-wheeled motorcycles, three-wheeled motorcycles, tanker trucks, construction vehicles, police cars, special vehicles, vans, extra-long trailers, extra-wide trailers, adults, children, camels, sheep, horses, and dogs; The static target scene element library includes trees, flower beds, streetlights, traffic cones, water-filled barriers, ground nails, tire covers, manhole covers, cardboard boxes, gantry frames, guardrails, vehicle identification signs, construction signs, giant signs, roadside speed limit signs, and warning triangles; The test vehicle scenario element library includes sedans, SUVs, pickup trucks, sports cars, vans, commercial vehicles, heavy trucks, and light trucks.

[0038] For example, in the scene element attribute configuration module, the attributes of the road scene element include: lane start position, lane end position, number of lanes, lane lateral slope, lane longitudinal slope, lane width, lane line color, lane line type, lane line clarity, lane line length, lane line width, lane curvature, and whether there is an emergency lane. The attributes of environmental condition elements include: the starting position of the environmental setting, the ending position of the environmental setting, the intensity of sunlight, the angle of sunlight, rainfall, snowfall, hail size, hail speed, fog concentration, cloud cover, cloud shape, wind classification, and wind direction. The attributes of dynamic target scene elements include: the size of the dynamic target, the size of the dynamic target, the starting position of the dynamic target, the ending position of the dynamic target, the lateral velocity of the dynamic target, the longitudinal velocity of the dynamic target, the tilt angle of the dynamic target, the acceleration of the dynamic target, and the deceleration of the dynamic target. The attributes of static target scene elements include: static target size, static target dimensions, static target position, static target quantity, static target tilt angle, static target spacing, static target dimensions, static target upright / fallen / flipped state, and static target label; The attributes of the test vehicle scene elements include: the appearance parameters of the test vehicle, the load of the test vehicle, whether the test vehicle is attached or not, the initial position of the test vehicle, the tilt angle of the test vehicle, and the speed of the test vehicle.

[0039] For example, the system further includes: The scenario deployment and execution module is used to deploy the combined hardware-in-the-loop simulation test scenarios to the HIL simulation platform; The signal excitation module is used by the HIL simulation platform to run the hardware-in-the-loop simulation test scenario, generate corresponding sensor analog signals and send them to the intelligent driving controller under test. The closed-loop feedback module is used to receive control commands generated by the intelligent driving controller based on the sensor simulation signals and feed them back to the vehicle dynamics model in the HIL simulation platform to drive the virtual test vehicle to run in the test scenario, forming a closed-loop test.

[0040] In one embodiment, the system further includes an element library update module, used to add new elements to each element library based on actual traffic scenario data and testing requirements.

[0041] The method for building intelligent driving hardware-in-the-loop simulation test scenarios provided in this application can be applied to electronic devices. Those skilled in the art will understand that the electronic device structures involved in the embodiments of this invention do not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, electronic devices include, but are not limited to, laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0042] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0043] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device. In other embodiments of this application, the electronic device may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0044] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0045] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0046] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0047] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0048] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0049] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.

[0050] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0051] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0052] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.

[0053] Electronic devices can achieve display functions through GPUs, displays, and application processors.

[0054] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0055] A display screen is used to display images, videos, etc. A display screen includes a display panel.

[0056] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0057] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0058] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, apparatuses, or units, or they may be electrical, mechanical, or other forms of connection.

[0059] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a full understanding of embodiments of the invention. However, those skilled in the art will recognize that the technical solutions of the invention can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of the invention.

[0060] The aforementioned electronic device realizes the establishment of the intelligent driving hardware-in-the-loop simulation test scenario construction method of this application, which includes a road scene element library containing road scene elements, an environmental condition element library containing environmental condition elements, a dynamic target scene element library containing dynamic target scene elements, a static target scene element library containing static target scene elements, and a test vehicle scene element library containing test vehicle scene elements. It selects road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, and test vehicle scene elements from each element library and sets their attributes. The elements with completed attribute settings are then combined in the order of road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, and test vehicle scene elements to form a hardware-in-the-loop simulation test scenario, thus improving the efficiency and flexibility of scenario construction.

[0061] The storage medium provided in this application stores a program product capable of implementing a method for building a hardware-in-the-loop simulation test scenario for intelligent driving.

[0062] The method for building a hardware-in-the-loop simulation test scenario for intelligent driving includes: Step S1: Establishing a road scenario element library containing road scenario elements, an environmental condition element library containing environmental condition elements, a dynamic target scenario element library containing dynamic target scenario elements, a static target scenario element library containing static target scenario elements, and a test vehicle scenario element library containing test vehicle scenario elements. Step S2: Select road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, and test vehicle scene elements from the element libraries, and set their attributes; Step S3: Combine the elements with completed attribute settings in the following order: road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, and test vehicle scene elements to form a hardware-in-the-loop simulation test scenario.

[0063] This invention enables modular construction of test scenarios by establishing an independent element library containing road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, and test vehicle scene elements. Users can select the required elements from each library, set their attributes, and then combine them in a predetermined order, which improves the efficiency and flexibility of scenario construction, reduces the dependence on preset scripts, and enables testers to quickly respond to different test requirements.

[0064] By establishing a standardized scenario element library through classification, it supports the fine-grained and independent configuration of the attributes of various scenario elements. Lane parameters, environmental conditions, dynamic target behavior, etc. can all be adjusted individually according to actual test requirements, thereby enabling the construction of test scenarios with high realism and close fit to the real traffic environment, effectively improving the accuracy and effectiveness of simulation testing.

[0065] The modular architecture allows each scenario element library to be maintained and expanded independently. As real traffic data and new testing requirements continue to emerge, new element types can be easily added to each element library, ensuring the continuous enrichment and evolution of the test scenario library and overcoming the scalability bottleneck caused by the rigid structure of traditional methods.

[0066] In some possible implementations, the intelligent driving hardware-in-the-loop simulation test scenario construction method of this disclosure can be implemented as a program product, which includes program code. When the program product is run on a terminal device, the program code is used to cause the terminal device to perform the steps described in the "Exemplary Methods" section above according to various exemplary embodiments of this disclosure.

[0067] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0068] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0069] For those skilled in the art, designing different forms of control circuits according to the teachings of this invention does not require creative effort. Changes, modifications, substitutions, and variations made to the embodiments without departing from the principles and spirit of this invention still fall within the scope of protection of this invention.

Claims

1. A method for building a hardware-in-the-loop simulation test scenario for intelligent driving, characterized in that, The method includes: Step S1: Establish a road scene element library containing road scene elements, an environmental condition element library containing environmental condition elements, a dynamic target scene element library containing dynamic target scene elements, a static target scene element library containing static target scene elements, and a test vehicle scene element library containing test vehicle scene elements. Step S2: Select road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, and test vehicle scene elements from the element libraries, and set their attributes; Step S3: Combine the elements with completed attribute settings in the following order: road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, and test vehicle scene elements.

2. The method as described in claim 1, characterized in that, The method further includes: Step S4: Deploy the combined hardware-in-the-loop simulation test scenario to the HIL simulation platform; Step S5: The HIL simulation platform runs the hardware-in-the-loop simulation test scenario, generates corresponding sensor simulation signals, and sends them to the intelligent driving controller under test. Step S6: Receive the control command generated by the intelligent driving controller based on the sensor simulation signal, and feed it back to the vehicle dynamics model in the HIL simulation platform to drive the virtual test vehicle to run in the test scenario, forming a closed-loop test.

3. The method as described in claim 1, characterized in that, In step S2, the attributes of the road scene elements include: lane start position, lane end position, number of lanes, lane lateral slope, lane longitudinal slope, lane width, lane line color, lane line type, lane line clarity, lane line length, lane line width, lane curvature, and whether there is an emergency lane. The attributes of environmental condition elements include: the starting position of the environmental setting, the ending position of the environmental setting, the intensity of sunlight, the angle of sunlight, rainfall, snowfall, hail size, hail speed, fog concentration, cloud cover, cloud shape, wind classification, and wind direction. The attributes of dynamic target scene elements include: the size of the dynamic target, the size of the dynamic target, the starting position of the dynamic target, the ending position of the dynamic target, the lateral velocity of the dynamic target, the longitudinal velocity of the dynamic target, the tilt angle of the dynamic target, the acceleration of the dynamic target, and the deceleration of the dynamic target. The attributes of static target scene elements include: static target size, static target dimensions, static target position, static target quantity, static target tilt angle, static target spacing, static target dimensions, static target upright / fallen / flipped state, and static target label; The attributes of the test vehicle scene elements include: the appearance parameters of the test vehicle, the load of the test vehicle, whether the test vehicle is attached or not, the initial position of the test vehicle, the tilt angle of the test vehicle, and the speed of the test vehicle.

4. The method as described in claim 3, characterized in that, The method also includes an element library update step: adding new elements to each element library based on actual traffic scenario data and testing requirements.

5. A system applied to the method for building a hardware-in-the-loop simulation test scenario for intelligent driving as described in any one of claims 1-4, characterized in that, The system includes: The scene element library construction module is used to build a road scene element library containing road scene elements, an environmental condition element library containing environmental condition elements, a dynamic target scene element library containing dynamic target scene elements, a static target scene element library containing static target scene elements, and a test vehicle scene element library containing test vehicle scene elements. The scene element attribute configuration module is used to select road scene elements, environmental condition elements, dynamic target scene elements, static target scene elements, and test vehicle scene elements from various element libraries, and set their attributes. The hardware-in-the-loop simulation test scenario building module is used to combine the elements with completed attribute settings in the following order: road scenario elements, environmental condition elements, dynamic target scenario elements, static target scenario elements, and test vehicle scenario elements.

6. The system as described in claim 5, characterized in that, The system also includes: The scenario deployment and execution module is used to deploy the combined hardware-in-the-loop simulation test scenarios to the HIL simulation platform; The signal excitation module is used by the HIL simulation platform to run the hardware-in-the-loop simulation test scenario, generate corresponding sensor analog signals and send them to the intelligent driving controller under test. The closed-loop feedback module is used to receive control commands generated by the intelligent driving controller based on the sensor simulation signals and feed them back to the vehicle dynamics model in the HIL simulation platform to drive the virtual test vehicle to run in the test scenario, forming a closed-loop test.

7. The system as described in claim 6, characterized in that, In the scene element attribute configuration module, the attributes of road scene elements include: lane start position, lane end position, number of lanes, lane lateral slope, lane longitudinal slope, lane width, lane line color, lane line type, lane line clarity, lane line length, lane line width, lane curvature, and whether there is an emergency lane. The attributes of environmental condition elements include: the starting position of the environmental setting, the ending position of the environmental setting, the intensity of sunlight, the angle of sunlight, rainfall, snowfall, hail size, hail speed, fog concentration, cloud cover, cloud shape, wind classification, and wind direction. The attributes of dynamic target scene elements include: the size of the dynamic target, the size of the dynamic target, the starting position of the dynamic target, the ending position of the dynamic target, the lateral velocity of the dynamic target, the longitudinal velocity of the dynamic target, the tilt angle of the dynamic target, the acceleration of the dynamic target, and the deceleration of the dynamic target. The attributes of static target scene elements include: static target size, static target dimensions, static target position, static target quantity, static target tilt angle, static target spacing, static target dimensions, static target upright / fallen / flipped state, and static target label; The attributes of the test vehicle scene elements include: the appearance parameters of the test vehicle, the load of the test vehicle, whether the test vehicle is attached or not, the initial position of the test vehicle, the tilt angle of the test vehicle, and the speed of the test vehicle.

8. The system as described in claim 7, characterized in that, The system also includes an element library update module, which is used to add new elements to each element library based on actual traffic scenario data and testing requirements.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1-4.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-4.

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