WO2021056556A1 - Vehicle-mounted autonomous driving test system and test method - Google Patents

Abstract

A vehicle-mounted autonomous driving test system, comprising: a virtual scene overlay module (100). In the working state, the virtual scene overlay module (100) is used for obtaining traveling data of an autonomous vehicle (200), obtaining the external environment data of the autonomous vehicle (200), adding, according to the traveling data of the autonomous vehicle (200), additional data to the external environment data to generate modified external environment data, and sending the modified external environment data to the control module (160) onboard the autonomous vehicle. Also disclosed is a corresponding autonomous vehicle test method. The autonomous driving test system and method can be applied in the 4G network environment, but the autonomous driving test system and method have higher requirements for network delay and data transmission speed and are more suitable for use in the 5G network environment.

Description

Vehicle-mounted automatic driving test system and test method Technical field

This application relates to the field of automotive electronics technology, and in particular to a vehicle-mounted automatic driving test system and method.

technical background

With the development of science and technology, self-driving vehicles have become an important development direction for future automobiles. Autonomous vehicles can not only guarantee people's safe travel and comfortable experience, but also greatly improve the efficiency of people's travel. More and more controllers related to autopilot systems are deployed in mass-produced models, which is of great significance for improving driving safety and reducing traffic accidents.

The information perception system, decision-making system, and automobile execution system require perfect coordination to complete the entire autonomous driving process. During the testing of autonomous vehicles, it is necessary to constantly change the environment to verify whether the autonomous vehicle is perceiving correctly and to judge whether the autonomous driving system makes correct decisions and executes on the basis of perception. However, if the autonomous vehicle is allowed to drive directly on the actual road, it will not only bring safety hazards, but also need to prepare various props and scenes, such as dummy, fake car and fake animal, in order to simulate different environments.

Therefore, a vehicle-mounted automatic driving test system and method capable of superimposing virtual scenes are needed to solve the above technical problems.

Summary of the invention

The present application discloses a vehicle-mounted automatic driving test system, including: a virtual scene superimposing module, in a working state, the virtual scene superimposing module obtains driving data of an automatic driving vehicle; and obtains the exterior of the automatic driving vehicle Environment data; add virtual data to the external environment data according to the driving data of the autonomous vehicle to generate modified external environment data; transmit the modified external environment data to the on-board control module of the autonomous vehicle .

On the one hand, the present application provides an automatic driving vehicle test method, which is applied to a test device of an automatic driving vehicle. The automatic driving vehicle test method includes: acquiring driving data of the automatic driving vehicle; acquiring the external environment of the automatic driving vehicle Data; add virtual data to the external environment data according to the driving data of the autonomous vehicle to generate modified external environment data; transmit the modified external environment data to the on-board control module of the autonomous vehicle.

The invention in this application requires relatively high network delay and data transmission speed. For example, the technology disclosed in the present invention can be applied to a 4G network environment, but is more suitable for a 5G network environment.

Description of the drawings

The following drawings describe in detail the exemplary embodiments disclosed in this application. The same reference numerals indicate similar structures in several views of the drawings. Those of ordinary skill in the art will understand that these embodiments are non-limiting and exemplary embodiments. The drawings are only for illustration and description purposes, and are not intended to limit the scope of the application. Embodiments in other ways are also possible. The same is true of the intention of the invention in this application. among them:

Fig. 1 is a block diagram of an exemplary vehicle with automatic driving capability in the present application;

2A-2D are schematic diagrams of a coupling mode of a virtual scene overlay module and a visual sensor in this application;

Fig. 3 is an exemplary flow chart of an on-vehicle automatic driving test system in the present application;

Fig. 4 is a schematic diagram of a modified external environment data scenario in this application.

detailed description

This application discloses a vehicle-mounted automatic driving test system and method. A virtual scene can be added to external environment data through a virtual scene overlay module to generate modified external environment data, which is used to test and verify the driving performance of an autonomous vehicle on the road. Safety, to provide guidance for the technology research and development of autonomous vehicles, road test permits and product access certification.

Since this application relates to automatic driving, the technology involved in this application requires relatively high network delay and data transmission speed. For example, the technology disclosed in this application can be applied to a 4G network environment, but is more suitable for a 5G network environment. The data transmission rate of 4G is on the order of 100Mbps, the delay is 30-50ms, the maximum number of connections per square kilometer is on the order of 10,000, and the mobility is about 350KM/h, while the transmission rate of 5G is on the order of 10Gbps, and the delay is 1ms, the maximum number of connections per square kilometer is in the order of millions, and the mobility is about 500km/h. 5G has a higher transmission rate, shorter delay, more square kilometers of connections, and higher speed tolerance. Another change in 5G is the change in the transmission path. In the past, when we make calls or send photos, the signal must be relayed through the base station, but after 5G, the device and the device can be directly transmitted without passing through the base station. Therefore, although the present invention is also suitable for 4G environment, it will get better technical performance when running in 5G environment and reflect higher commercial value.

In order to provide those of ordinary skill in the art with a thorough understanding of the relevant disclosures, specific details of the present application are illustrated by examples in the following detailed description. However, the content disclosed in this application should be understood as consistent with the protection scope of the claims, and not limited to the specific invention details. For example, it is obvious for a person of ordinary skill in the art to make various modifications to the embodiments disclosed in this application; and without departing from the spirit and scope of the application, those of ordinary skill in the art can define here The general principle is applied to other embodiments and applications. For another example, if these details are not disclosed below, those of ordinary skill in the art can also practice this application without knowing these details. On the other hand, in order to avoid unnecessarily obscuring the content of this application, this application provides a general summary of well-known methods, processes, systems, components and/or circuits without detailed descriptions. Therefore, the content disclosed in this application is not limited to the illustrated embodiment, but is consistent with the scope of the claims.

The terms used in this application are only used for the purpose of describing specific example embodiments, and are not restrictive. For example, unless the context clearly indicates otherwise, if a singular form is used for a certain element in this application (for example, "a", "an" and/or equivalent description), a plurality of such elements may also be included. The terms "including" and/or "including" used in this application refer to an open concept. For example, A includes/includes B only means that there is a feature of B in A, but it does not exclude the possibility that other elements (such as C) exist or be added to A.

It should be understood that the terms used in this application, such as "system", "unit", "module" and/or "block", are used to distinguish different components, elements, components, parts, or components at different levels. Kind of method. However, if other terms can achieve the same purpose, the other terms may also be used in this application to replace the above-mentioned terms.

The modules (or units, blocks, units) described in this application can be implemented as software and/or hardware modules. Unless the context clearly dictates otherwise, when a unit or module is described as being “connected”, “connected to” or “coupled to” another unit or module, the expression may mean that the unit or module is directly connected or linked Or coupled to the other unit or module, it may also mean that the unit or module is indirectly connected, connected, or coupled to the other unit or module in some form. In this application, the term "and/or" includes any and all combinations of one or more related listed items.

In this application, the terms "self-driving vehicle" and "self-driving car" may refer to the ability to perceive its environment and automatically perceive the external environment without human input and/or intervention (eg, driver, pilot, etc.) , Judge and then make a decision-making vehicle. The terms "self-driving vehicle", "self-driving car" and "vehicle" can be used interchangeably. The term "autopilot" may refer to the ability to make intelligent judgments and navigate the surrounding environment without input from a person (for example, a driver, a pilot, etc.).

In consideration of the following description, the operation and function of these and other features of the present application, as well as related elements of the structure, as well as the combination of components and the economics of manufacturing can be significantly improved. With reference to the drawings, all of these form a part of this application. However, it should be clearly understood that the drawings are only for illustration and description purposes, and are not intended to limit the scope of the application. It should be understood that the drawings are not drawn to scale.

The flowchart used in this application shows the operations implemented by the system according to some embodiments in this application. It should be clearly understood that the operations of the flowchart can be implemented out of order. Instead, the operations can be performed in reverse order or simultaneously. In addition, one or more other operations can be added to the flowchart. One or more operations can be removed from the flowchart.

In addition, although the circuit and method in this application mainly describe the on-vehicle automatic driving test system and method, it should be understood that this is only an exemplary embodiment. The device and method of the present application can also be applied to other types of systems. For example, the system or method of the present application can be applied to driving tests of transportation systems in different environments, including land, sea, aerospace, etc., or any combination thereof. The autonomous vehicles of the transportation system may include taxis, private cars, trailers, buses, trains, bullet trains, high-speed railways, subways, ships, airplanes, spacecraft, hot air balloons, unmanned vehicles, etc., or any combination thereof. In some embodiments, the system or method may find applications in, for example, logistics warehouses, military affairs (such as pilots' flight simulations or automated flight tests of drones).

Fig. 1 is a block diagram of an automated driving test system disclosed according to some embodiments. The system includes the whole or any part of an exemplary vehicle 200 having automatic driving capabilities and automatic driving test capabilities. The vehicle 200 with automatic driving capability may include a control module 160, a sensor module 150, a memory 140, an instruction module 130, a controller area network (CAN) 120, an actuator 110, a communication module 180, an automatic driving test device 190, a test A module 192, a planning control module, and a virtual scene superimposing module 100. The control module 160 of the autonomous vehicle and the virtual scene overlay module 100 are both connected to the network 170 through the communication module 180. The virtual scene overlay module 100 may be an independent hardware module, or may be a hardware or software module subordinate to the control module 160.

In some embodiments, the control module 160 may include one or more central processors (for example, single-core processors or multi-core processors). For example only, the control module may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction-set processor (ASIP), and graphics Processing unit (graphics processing unit, GPU), physical processing unit (physics processing unit, PPU), digital signal processor (digital signal processor, DSP), field programmable gate array (field programmable gate array, FPGA), programmable logic Device (programmable logic device, PLD), controller, microcontroller unit, reduced instruction-set computer (RISC), microprocessor (microprocessor), etc., or any combination thereof.

The memory 140 (local memory, remote memory) can store data and/or instructions. In some embodiments, the memory 140 may store data obtained from sensors of an autonomous vehicle. In some embodiments, the memory may store data and/or instructions that can be executed or used by the control module to perform the exemplary methods described in the present disclosure. For example, the instruction may include the virtual scene superimposing module 100 and so on. Of course, the virtual scene superimposing module 100 may also be an independent hardware module. In some embodiments, the memory may include a mass memory, a removable memory, a volatile read-and-write memory (volatile read-and-write memory), a read-only memory (ROM), etc., or any combination thereof. As an example, for example, mass storage may include magnetic disks, optical disks, solid-state drives, etc.; for example, removable storage may include flash drives, floppy disks, optical disks, memory cards, zipper disks, and magnetic tapes; for example, volatile read-write memory may include random access Memory (RAM); for example, RAM can include dynamic RAM (DRAM), double data rate synchronous dynamic RAM (DDR SDRAM), static RAM (SRAM), thyristor RAM (T-RAM) and zero capacitor RAM (Z-RAM) ); For example, ROM may include mask ROM (MROM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), compact disk ROM (CD-ROM), and Digital universal disk ROM, etc. In some embodiments, storage can be implemented on a cloud platform. For example only, the cloud platform may include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, inter-cloud cloud, multi-cloud, etc., or any combination thereof.

In some embodiments, the storage 140 may be a local storage, that is, the storage 140 may be a part of the autonomous vehicle 200. In some embodiments, the storage 140 may also be a remote storage. The central processing unit may connect to the remote storage through the network 170 to communicate with one or more components of the autonomous vehicle 200 (for example, the sensor module 150 and the virtual scene overlay module 100). One or more components in the autonomous vehicle 200 may access data or instructions remotely stored in a remote storage via the network 170. In some embodiments, the memory 140 may be directly connected to or communicate with one or more components in the autonomous vehicle 200 (for example, the control module 160, the sensor module 150).

The virtual scene overlay module 100 is responsible for receiving the data from the sensor module 150, and according to the needs of the automated driving vehicle test, part or all of the virtual scene is embedded in the sensor module 150 to transmit the data, and then the sensor module 150 The incoming data is sent to the planning control module for planning control of automatic driving.

The planning control module performs automatic driving planning decision information and vehicle control information based on the external environment image data, combined with maps and other information sensed and received by the autonomous driving vehicle 100. For example, the planning decision information and control information may include planning control information such as vehicle path, lane change information, acceleration and deceleration information, and turning information. Then the planning control module sends the control information to the instruction module 130. The planning control module can also share the planning decision information with other vehicles through the communication data processing module.

The network 170 may facilitate the exchange of information and/or data. In some embodiments, one or more components in the autonomous vehicle 200 (for example, the control module 160, the sensor 150) may send information and/or data to the autonomous vehicle 200 via the network 170 Other components. E.g. The control module 160 may obtain/acquire dynamic conditions of the vehicle and/or environmental information around the vehicle via the network 170. In some embodiments, the network 170 may be any type of wired or wireless network, or a combination thereof. For example only, the network 170 may include a wired network, a wired network, an optical fiber network, a telecommunication network, an intranet, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), and a metropolitan area network (MAN) , Wide Area Network (WAN), Public Switched Telephone Network (PSTN), Bluetooth network, ZigBee network, Near Field Communication (NFC) network, 3G/4G/5G network, etc., or any combination thereof. In some embodiments, the network 170 may include one or more network access points. For example, the network 170 may include wired or wireless network access points. One or more components of the autonomous vehicle 200 may be connected to the network 170 to exchange data and/or information.

The actuator 110 may include, but is not limited to, the driving execution of the accelerator, the engine, the brake, and the steering system (including the steering of the tire and/or the operation of the turn signal). The steering system can steer the autonomous vehicle 200. In some embodiments, the steering system may manipulate the autonomous vehicle 200 based on a control signal sent from the control module. The control signal may include information related to turning direction, turning position, turning angle, turn signal, etc., or any combination thereof. For example, when the control signal sent by the control module is to turn the front wheel of the self-driving vehicle 45° counterclockwise, the steering system may guide the self-driving vehicle 200 to turn left based on the control signal sent from the control module, And the front wheel turning angle is 45°.

The braking system can control the motion state of the autonomous vehicle 200. For example, the braking system may decelerate the autonomous vehicle 200. In some embodiments, the braking system may stop the autonomous vehicle 200 from moving forward in one or more road conditions (e.g., downhill). In some embodiments, the braking system can keep the autonomous vehicle 200 at a constant speed when driving downhill. The braking system may include mechanical control components, hydraulic units, power units (for example, vacuum pumps), execution units, etc., or any combination thereof. Mechanical control components can include pedals, hand brakes, etc. The hydraulic unit may include hydraulic oil, hydraulic hoses, brake pumps, etc. The execution unit may include brake calipers, brake pads, brake discs, and so on.

The engine system can determine the engine performance of the autonomous vehicle 200. In some embodiments, the engine system may determine the engine performance of the autonomous vehicle 200 based on the control signal from the control module. E.g. The engine system may determine the engine performance of the autonomous vehicle 200 based on the control signal associated with the acceleration from the control module. The engine system may include a plurality of sensors and at least one microprocessor. The plurality of sensors may be configured to detect one or more physical signals and convert the one or more physical signals into electrical signals for processing. In some embodiments, the plurality of sensors may include various temperature sensors, air flow sensors, throttle position sensors, pump pressure sensors, speed sensors, oxygen sensors, load sensors, knock sensors, etc., or any combination thereof. The one or more physical signals may include, but are not limited to, engine temperature, engine air intake, cooling water temperature, engine speed, etc., or any combination thereof. The microprocessor may determine engine performance based on a plurality of engine control parameters. The microprocessor may determine multiple engine control parameters based on multiple electrical signals, and may determine multiple engine control parameters to optimize engine performance. The plurality of engine control parameters may include ignition timing, fuel delivery, idling air flow, etc., or any combination thereof.

The throttle system can change the driving speed of the autonomous vehicle 200. In some embodiments, the throttle system can maintain the driving speed of the autonomous vehicle 200 under one or more road conditions. In some embodiments, the throttle system can increase the driving speed of the autonomous vehicle 200 when acceleration is required. For example, if the self-driving vehicle 200 surpasses the preceding vehicle when conditions permit, the accelerator system may accelerate the self-driving vehicle 200.

The instruction module 130 receives the information from the control module 160, converts it into an instruction to drive the actuator, and sends it to the Controller Area Network (CAN) bus 120. For example, the control module 160 sends the driving strategy (acceleration, deceleration, turning, etc.) of the autonomous vehicle 200 to the instruction module 130, and the instruction module 130 receives the driving strategy and converts it into a pair of actuators. Drive instructions (drive instructions for the accelerator, brake mechanism, and steering mechanism). At the same time, the instruction module 130 then issues the finger to the actuator through the CAN bus 120. The execution of the instructions by the actuator 110 is then detected by the vehicle component sensors and fed back to the control module 16, thereby completing the closed-loop control and driving of the autonomous vehicle 200.

The CAN bus 120 is a reliable vehicle bus standard (for example, a message-based protocol), which allows microcontrollers (for example, the control module 150) and devices (for example, engine systems, braking systems, and steering systems) And/or throttle system, etc.) communicate with each other in applications that do not have a host computer. The CAN bus 120 may be configured to connect the control module 150 with multiple ECUs (for example, an engine system, a braking system, a steering system, and a throttle system).

The sensor module 150 may include one or more sensors. The plurality of sensors may include various internal and external sensors that provide data to the autonomous vehicle 200. For example, as shown in FIG. 1, the plurality of sensors may include vehicle component sensors and environmental sensors. The vehicle component sensor is connected to the actuator of the vehicle 200, and can detect the operating status and parameters of each component of the actuator.

The environmental sensor allows the vehicle to understand and potentially respond to its environment, so as to help the autonomous vehicle 200 perform navigation, route planning, and ensure the safety of passengers and people or property in the surrounding environment. The environmental sensors can also be used to identify, track and predict the movement of objects, such as pedestrians and other vehicles. The environmental sensor may include a position sensor and an external object sensor.

The position sensor may include a GPS receiver, an accelerometer and/or a gyroscope, and a receiver. The location sensor can sense and/or determine the geographic location and orientation of the autonomous vehicle 200. For example, determine the latitude, longitude and altitude of the vehicle.

The external object sensor can detect objects outside the autonomous vehicle 200, such as other vehicles, obstacles in the road, traffic signals, signs, trees, and so on. The external object sensor may include a vision sensor, a lidar, a sonar sensor, a climate sensor, an acceleration sensor, and/or other detection devices, and/or any combination thereof.

The lidar may be located on the top, front and rear of the autonomous vehicle 200, and either side of the front bumper. Lidar may include, but is not limited to, single-line lidar, 4-line lidar, 16-line lidar, 32-line lidar, 64-line lidar, etc., or any combination thereof. In some embodiments, the lidar collects laser beams reflected by objects in the environment in different directions to form a lattice of the surrounding environment.

In addition to using lidar to determine the relative position of external objects, other types of radar can also be used for other purposes, such as traditional speed detectors. Shortwave radar can be used to determine the depth of snow on the road and determine the location and condition of the road.

The data collected by the lidar can be further added with virtual data. In some embodiments, based on the same principles in FIGS. 2A-2C, the virtual image superimposition module can also be connected to the lidar or other types of radar, so as to add the signal of the virtual object to the detection signal of the radar.

The sonar sensor can detect the distance between the autonomous vehicle 200 and surrounding obstacles. For example, the sonar may be an ultrasonic rangefinder. The ultrasonic rangefinder is installed on both sides and the back of the autonomous vehicle 200 and is turned on when parking to detect obstacles around the parking space and the distance between the autonomous vehicle 200 and the obstacle. The data detected by the sonar sensor may be further added with virtual data (for example, virtual obstacles). In some embodiments, based on the same principles of FIGS. 2A-2C, the virtual image overlay module can also be connected to the sonar sensor, so as to add the data of the virtual object to the detection data of the sonar sensor.

The climate sensor can detect weather information outside the autonomous vehicle 200. The weather information includes, but is not limited to, wind, frost, rain, snow, temperature, light, etc., or any combination thereof. For example, the climate sensor detects that the exterior of the autonomous vehicle 200 is night. The data detected by the climate sensor may be further added with virtual data (for example, it is snowing, raining). In some embodiments, based on the same principles in FIGS. 2A-2C, the virtual image overlay module can also be connected to the climate sensor, so as to add virtual weather data to the actual weather data.

The acceleration sensor can detect the acceleration of an object external to the autonomous vehicle 200. The external object may be static or movable. The data detected by the acceleration sensor can be further added with virtual data. In some embodiments, the virtual data is added by the virtual scene overlay module 100.

The vision sensor can capture a visual image around the autonomous vehicle 200 and extract content therefrom. The vision sensor may include, but is not limited to, a monocular or binocular ordinary camera, a monocular or binocular wide-angle camera (such as a fisheye camera), a laser scanner, a linear CCD camera, an area CCD camera, a TV camera [MOU1 ], digital camera, etc., or any combination thereof. For example, the visual sensor can take pictures of street signs on both sides of the road, and use the control module 160 to recognize the meaning of these signs. For example, the visual sensor is used to determine the speed limit of the road. The self-driving vehicle 200 can also calculate the distance of surrounding objects from the self-driving vehicle 200 through the parallax of different images taken by multiple vision sensors. In some embodiments, the visual image captured by the visual sensor may also be used to judge the surrounding environment such as obstacles, pedestrians, vehicles, and weather. For example, the captured visual image shows that there are dark clouds ahead, and the control module 160 analyzes that there may be thunderstorms ahead. In some embodiments, the captured visual image can also be superimposed with the virtual data output by the virtual scene superimposing module 100, and the superimposed data can be sent to the control module 160.

In some embodiments, the vision sensor may be a monocular or binocular ordinary camera, a monocular or binocular wide-angle camera (such as a fisheye camera), a linear CCD camera, an area CCD camera, a TV camera, a digital camera, etc. Any kind of camera, or any combination. As shown in FIG. 2A, the camera may include a lens 212, an image sensor 214, an output module 216, etc., or any combination thereof. In the working state, the camera lens 212 captures the light of the external scenery, and projects the light onto the imaging surface of the image sensor 214 (such as an imager) to expose the photosensitive array. The photosensitive array converts the exposure into electric charges. At the end of the timed exposure, the image sensor 214 converts the accumulated electric charges into a continuous analog signal for output or digitized output. After the conversion is completed, the camera will be reset to start the exposure of the next video frame. The electrical signal output from the image sensor 214 is input to the output module 216, and is scanned in the output module as a frame of image output.

In some embodiments, for different sensors, the virtual data can be represented in different forms. For example, when adding virtual data to the lidar, the virtual data can be point cloud information or point cloud sequence; when adding information to a visual sensor , Virtual data can include pixel information, and different obstacles can be expressed through pixel information. For different sensors, virtual data expresses the content that needs to be added in different forms, such as one or more of virtual obstacles, virtual vehicles, virtual pedestrians, virtual roads, and different virtual weather.

The automatic driving test device 190 may be a vehicle-mounted hardware device, or may be combined with the test module 192 to become a software module subordinate to the control module 160. For example, when the automated driving vehicle 200 needs to perform an automated driving test, the automated driving test device may be connected to the sensor module 150 and/or the control module 160 of the automated driving vehicle 200 for use. In some embodiments, the automatic driving test device may include a test module 192. The test module 192 may be a hardware module and is connected to the sensor module 150 and/or the control module 160 of the autonomous vehicle 200 to measure when the virtual scene overlay module 100 inputs virtual data to the autonomous vehicle 200 to generate part or all of the virtual driving Under the environment, the autonomous vehicle 200 responds accordingly. Thus, the performance of the automatic driving system of the automatic driving vehicle 200 (the response speed of the algorithm, accuracy, etc.) is tested.

In some embodiments, the automatic driving test device 190 may include a mounting structure. The installation structure may be a mechanical structure for fixing the virtual scene superimposing module 100 on the autonomous vehicle. For example, the mounting structure in question can include screws, bases, and so on. The automatic driving test device 190 will be fixed on the base, and then fixed on the automatic driving vehicle 200 with screws.

In some embodiments, the illustrated automated driving test device 190 may include an automated driving vehicle 200. That is, in some embodiments, the autonomous driving vehicle 200 may be an autonomous driving vehicle 200 for testing that includes a virtual scene superimposing module 100 and a testing module 192.

In some embodiments, the illustrated automatic driving test device 190 may also be located on a cloud server. The cloud server is used for unified scheduling and monitoring of the autonomous vehicle. In some embodiments, during the vehicle test, the cloud server may send virtual data to the sensor module 150 or the control module 160 of the autonomous vehicle 200 through the automatic driving test device 190 to generate part or all of the virtual driving environment, so as to obtain The test result of the autonomous vehicle 200.

Just to illustrate the problem, only one processor is described in the control module 160 in this application. However, it should be noted that the control module 160 in this application may also include multiple processors. Therefore, the operations and/or method steps disclosed in this application can be executed by one processor as described in this application, or Jointly executed by multiple processors. For example, if the central processing unit of the control module 160 described in this application performs step A and step B, it should be understood that step A and step B can also be performed jointly or separately by two different processors in information processing (for example, , The first processor executes step A, the second processor executes step B, or the first and second processors execute steps A and B) together.

2A-2D are schematic diagrams of a coupling manner of a virtual scene overlay module and the external object sensor in this application. It should be pointed out that the virtual scene overlay module 100 may be an independent hardware module, or may be a hardware or software module subordinate to the control module 160. The external object sensor may be any one or more of the above-mentioned external object sensors in the autonomous vehicle. For the convenience of description, a visual sensor is mainly used as an example to illustrate the technical points of the present disclosure. However, those skilled in the art can easily understand that this technical point can be directly applied to other forms of external object sensors. For example, this technology can be directly applied to lidar.

As shown in FIG. 2A, the virtual scene superimposing module 100 is connected to the output terminal of the output module 216. The virtual scene superimposing module 100 adds virtual data to the data output by the camera output module 216 to generate modified data. Specifically, the virtual data is data corresponding to the virtual image. The virtual scene superimposing module 100 is connected to the output terminal of the output module 216, and receives a real image at the output terminal of the output module. Then, the virtual scene superimposing module 100 superimposes the pixels of the virtual image to the position corresponding to the real image. For example, assuming that the real image is the image of the road ahead when the autonomous vehicle 200 is driving, the virtual data may be a virtual traffic light image in front of the road, images of other virtual vehicles driving on the road, and/or virtual pedestrian images crossing the road. . According to a preset setting, the aforementioned virtual image will appear at a predetermined position in front of the autonomous vehicle 200. Correspondingly, the aforementioned virtual image will appear in the corresponding predetermined position in the real image. Therefore, the virtual scene superimposing module 100 can superimpose the pixels of the virtual image on a predetermined position of the real image. The predetermined position and the size, angle of view, etc. of the virtual image will change as the autonomous vehicle 200 moves forward, and its effect is equivalent to the appearance of the corresponding position and moving objects in the real world after being photographed by the camera. The state in the real image.

The virtual scene superimposing module 100 superimposing the pixels of the virtual image on the real image may be performed by weighting the pixel values of the virtual image and the pixel values of the real image to obtain the modified The image data; it can also be that the pixels planned to be added to the virtual image in the output image are deleted first, and the pixels of the virtual image are added. The image data output in this way also includes the image data of the real scene and the image data of the artificially added virtual data, so that the virtual image is added to the real image.

Based on the same principle, the virtual scene superimposing module 100 can also be connected to the output end of the lidar. By embedding the lidar dot matrix data of the virtual object into the real surrounding environment dot matrix collected by the lidar, the virtual scene superimposing module 100 can generate the modified data of the lidar.

Similarly, the virtual scene superimposing module 100 can also be connected to the sonar and/or climate sensor, and the control module 160 can modify the driving environment by modifying the data of the sonar and/or climate sensor. "Cognition".

In some embodiments, the virtual scene superimposing module 100 may be coupled with the camera in other ways. For example, as shown in FIG. 2B, the virtual scene superimposing module 100 may be connected between the camera lens 212 and the camera image sensor 214. Therefore, before the image information (or the light of the scenery) passes through the camera lens 212 but has not reached the camera image sensor 214, the virtual scene superimposing module 100 can add virtual data representing the virtual image to the data collected by the camera lens 212 To generate the modified data.

For example, the virtual scene superimposing module 100 may be a set of instructions in the control module 160, which receives and modifies the signal from the camera 212 by means of software and transmits the modified signal back to the camera for processing; it may also be a set of instructions. Kind of projection device or equivalent projection device. On the one hand, the projection device projects an image of a virtual object onto the imaging surface of the image sensor 214, and on the other hand blocks the light from the lens 212 corresponding to the image. In this way, from the stage of incident light, the scene captured by the camera is modified. Then, the modified data is collected by the camera image sensor 214, and the output module 216 generates image data. The image data includes image data of a real scene and image data of artificially added virtual data, so that the virtual image is added to the real image.

For another example, as shown in FIG. 2C, the virtual scene superimposing module 100 may be connected between the camera image sensor 214 and the camera output module 216. Therefore, after the image information (or the light of the scenery) reaches the imaging surface of the camera image sensor 214 through the camera lens 212 and is output as an electrical signal, but before being input to the camera output module 216, this part of the electrical signal is input to the virtual scene superimposing module 100. Then, the virtual scene superimposing module 100 can cut off a part of the electrical signal and add the electrical signal corresponding to the virtual object, and then input the modified electrical signal into the output module 216 to scan to generate image data. The image data also includes the image data of the real scene and the image data of the artificially added virtual data, so that the virtual image is added to the real image. The virtual scene superimposing module 100 may be a set of instructions in the control module 160, which receives and modifies the signal from the image sensor 214 through software, and transmits the modified signal to the output module 216 for further processing; or It is a hardware module embedded between the image sensor 214 and the output module 216 of the camera.

For another example, as shown in FIG. 2D, the virtual scene overlay module 100 adds virtual data to the data output by the camera output module 216 to generate modified data. Specifically, the virtual scene superimposing module 100 is electrically connected to the output module 216 and controls the output module to scan the signal from the camera image sensor 214. At the pixel points of the virtual image planned to be added to the output image, the virtual scene superimposing module 100 can block the normal scanning of the output module and replace it with pixels of the virtual image. The image data output in this way also includes the image data of the real scene and the image data of the artificially added virtual data, so that the virtual image is added to the real image. The virtual scene superimposing module 100 may be a set of instructions in the control module 160, which receives and modifies the signal acquired during scanning by the output module 216 through software and transmits the modified signal back to the camera for processing; or It is a hardware module embedded in the output module 216 of the camera.

The virtual data may be image data of a virtual object, or other virtual data, such as temperature, wind speed, and so on. The virtual data may be data stored in the virtual scene overlay module 100, or data received in real time via the network 170. The object corresponding to the data may be an object actually photographed, or an object synthesized by a computer. The time for adding virtual data to the data detected by the real sensor can be predetermined according to the scene, or it can be random.

After receiving the information sensed by the multiple sensors, the control module 160 may process information and/or data related to vehicle driving (for example, automatic driving) to perform one or more functions described in the present disclosure. In some embodiments, the control module 160 may be configured to autonomously drive the vehicle. For example, the control module 160 may output multiple control signals. The multiple control signals may be configured to be received by one or more electronic control units (ECU) to control the driving of the vehicle. In some embodiments, the control module may output a control signal based on the external environment information of the vehicle and the superimposed virtual scene.

The technology disclosed in the present invention can be applied in a 4G network environment. However, since the invention in this application requires high network delay and data transmission speed, the 5G network environment is more suitable. The data transmission rate of 4G is on the order of 100Mbps, the delay is 30-50ms, the maximum number of connections per square kilometer is on the order of 10,000, and the mobility is about 350KM/h, while the transmission rate of 5G is on the order of 10Gbps, and the delay is 1ms, the maximum number of connections per square kilometer is in the order of millions, and the mobility is about 500km/h. 5G has a higher transmission rate, shorter delay, more square kilometers of connections, and higher speed tolerance. Therefore, although the present invention is also suitable for 4G environment, it will get better technical performance when running in 5G environment and reflect higher commercial value.

As described above, the virtual scene overlay module 100 can modify the external environment data of the autonomous vehicle 200. The external environment data of the autonomous vehicle 200 can be acquired through the sensor module 150. The external environment data may include, but is not limited to, location information, weather information, traffic sign information, pedestrian information, vehicle information, driving lane information, obstacle information, signal light information, lighting information, etc.

In some embodiments, the external environmental data may be collected by environmental sensors. For example, the external environment data may collect obstacles (for example, rocks, falling objects, etc.) in the driving road of the autonomous vehicle 200 through external object sensors. For another example, the external environment data may collect geographic location information such as longitude and latitude of the autonomous vehicle 200 through a location sensor.

In some embodiments, the sensor module 150 of the self-driving vehicle 200 may or may not obtain null value data, and the external environment data may be partially or completely provided by the virtual scene overlay module 100. For example, the autonomous driving vehicle 200 performs an autonomous driving road test. When the autonomous driving vehicle 200 is in a stopped state, the data collected by the external object sensor may not obtain data. The virtual scene overlay module 100 will provide a simulated road test. Relevant external environmental data. For another example, the self-driving vehicle 200 performs an indoor automatic driving test, the data collected by the external object sensor can be indoor environment data, and the virtual scene overlay module 100 will provide the relevant external environment data of the simulated drive test instead of collecting it. Indoor environmental data. In some embodiments, the virtual scene superimposing module 100 may provide the relevant external environment corresponding to the calibration execution mechanism 110. For example, when the autonomous vehicle 200 is performing an autonomous driving road test, the virtual scene overlay module 100 can provide initial calibration of the actuator. In order to calibrate the right turn indicator of the steering system, the virtual scene overlay module 100 provides crossroads.

In some embodiments, the virtual scene superimposing module 100 may be connected to one or more sensors in the sensor module 150. For example, the virtual scene superimposing module 100 may be connected to an external object sensor (for example, a vision sensor, a climate sensor, a sonar sensor, etc.), and receive the external environment data collected by the external object sensor in real time. For another example, the virtual scene superimposing module 100 may be connected to a position sensor, and receive position data collected by the visual sensor in real time.

In some embodiments, the virtual scene superimposing module 100 may be connected to the input terminals of one or more sensors in the sensor module 150. In some embodiments, the virtual scene overlay module 100 may send virtual data to one or more sensors in the sensor module 150. The virtual data may be stationary objects appearing on the driving path of the autonomous vehicle 200, or moving objects, such as pedestrians, other vehicles in motion, and so on. For example, in the real-time collected external environment, there are no traffic lights on the road on which the autonomous vehicle 200 is traveling, and the virtual scene superimposing module 100 may add virtual traffic lights on the road image on which the autonomous vehicle 200 is traveling. For example, the virtual scene superimposing module 100 may add a traffic signal 100 meters in front of the autonomous vehicle 200. For another example, on the path of an autonomous vehicle, a pedestrian crossing the road or a vehicle changing lanes is virtually added. The virtual added object is virtual data.

The virtual data may be an environment difference from the real environment collected by the sensor module 150. The environmental difference may include, but is not limited to, pedestrians, vehicles, obstacles, weather factors (for example, wind, frost, rain, snow, temperature, light), etc., or any combination thereof. The pedestrian can walk across the road at a normal speed, or run into the road. For example, the self-driving vehicle 200 is running normally on the test road, and there are no pedestrians in the real environment. To test the self-driving vehicle 200 when a pedestrian suddenly enters the road, the pedestrians who suddenly rushed into the road corresponding to the virtual data will be added In the external environment data. The vehicle includes, but is not limited to, motor vehicles, non-motor vehicles, etc., or any combination thereof. The motor vehicle may include, but is not limited to, an automobile, an electric automobile, a motorcycle, an electric motorcycle, etc., or any combination thereof. The non-motor vehicle may include, but is not limited to, bicycles, tricycles, scooters, electric bicycles, etc., or any combination thereof. In some embodiments, the obstacle may be dynamic (e.g., animal). In some embodiments, the obstacle may be static (for example, the road pavement has a concave pit, and the road pavement has a raised boulder). In some embodiments, weather factors can make the sensor's signal different from the effect of testing normal weather. For example, rainy days may cause blurring of the vision sensor, and the collected data will be biased. In some embodiments, the environmental difference may be an environmental difference of a geographic location. For example, the road on which the autonomous vehicle 200 is traveling during the test is straight, the target scene of the test may be a winding mountain road with a constantly changing altitude, and the environmental difference is a change in altitude.

In some embodiments, the virtual data may be preset according to the scene. For example, the virtual data may include one and/or more different scenes, and the one and/or more different scenes may be stored in a memory. In some embodiments, the virtual data may also be scenes encountered by manned and/or unmanned vehicles through autonomous learning. For example, in the blind spot at the rear of a man-driving vehicle, a child squats down to play, and the driver may not find it and backs up, and then hits the child; the virtual data can be a child squatting down and playing in the rear of the vehicle. When testing an autonomous vehicle When the vehicle 200 is reversing, the virtual data will be added to the external environment data of the autonomous vehicle 200.

In some embodiments, when the virtual scene superimposing module 100 is an independent hardware structure, it can be connected to the control module 160. For example, the virtual scene superimposing module 100 is connected to the input/output end of the control module 160, and the virtual scene superimposing module 100 can obtain/acquire the virtual data from the control module 160. For another example, the virtual scene superimposing module 100 may store the virtual data and receive a control signal from the control module 160, and the control signal may be used to select virtual data corresponding to the current test. In some embodiments, after the virtual scene overlay module 100 adds virtual data to the external environment data, it can generate modified external environment data, and the modified external environment data is sent to the on-board control of the autonomous vehicle. Module.

The virtual scene superimposing module 100 may include a timer. The timer may be inside the virtual scene superimposing module 100 or outside the virtual scene superimposing module 100 (for example, the control module 160, the sensor module 150). In some embodiments, the timer can be used to count down and issue an instruction to add dummy data. The clock cycle of the countdown may be preset or randomly generated. For example, when the countdown clock cycle is preset to 30s, the virtual data will be added to the external environment data after 30s. For another example, when the countdown clock period is randomly generated, the dummy data will be added to the external environment data after the randomly generated clock period.

Fig. 3 is an exemplary flow chart of a vehicle-mounted automatic driving test device in the present application. As shown in FIG. 3, an on-vehicle automatic driving test device is applied. The on-vehicle automatic driving test device includes a virtual scene overlay module 100. The method includes the following steps:

In step 310, the virtual scene overlay module 100 obtains driving data of the autonomous vehicle 200. In some embodiments, the driving data of the autonomous vehicle may include, but is not limited to, vehicle speed, acceleration, weather parameters, steering, etc., or any combination thereof. In some embodiments, when the weather is a snowy day, the driving data of the autonomous vehicle 200 acquired by the virtual scene overlay module 100 is related to a snowy day. For example, the driving data related to a snowy day may be that the autonomous vehicle 200 is traveling at a slower speed, or may be a weaker braking ability, or a combination thereof.

In step 320, the virtual scene overlay module 100 obtains real-time driving data of the autonomous vehicle 200. The real-time driving data may include driving state data and/or external environment data of the autonomous vehicle 200. In some embodiments, the external environment data may be obtained through real-time collection of external object sensors, and the driving state data may be obtained through vehicle component sensors. In some embodiments, the external object sensor is connected to the virtual scene overlay module 100. For example, the virtual scene superimposition module 100 can obtain driving state data such as the driving speed, acceleration, and direction of the autonomous vehicle 200, and/or external environment data such as temperature, light, and wind speed around the autonomous vehicle 200.

In step 330, based on the real-time driving data of the autonomous vehicle 200, virtual data is added to the external environment data to generate modified external environment data. The driving data of the autonomous vehicle includes a vehicle speed, the virtual data changes with time, and the change is related to the vehicle speed. For example, in some embodiments, as shown in FIG. 4, when the test location of the autonomous vehicle 200 is on an urban road, the real-time driving data may include the driving speed. If the virtual data can be the zebra crossing on the road ahead on which the autonomous vehicle 200 is traveling and pedestrians crossing the road. When the virtual scene superimposing module 100 adds virtual data to the external environment data, it can adjust the virtual data according to the driving speed and add the virtual data to the scene to generate corresponding modified external environment data. For example, when an autonomous vehicle is slowly driving toward a pedestrian at a zebra crossing, the image of the pedestrian in the camera becomes larger and larger. The speed at which the pedestrian size increases should match the speed of the autonomous vehicle, causing the autonomous vehicle to continue to drive. Close to the perspective effect of pedestrians. In some embodiments, when the autonomous vehicle 200 is tested, the acceleration of the driving is relatively large, the external environment is sunny, and the virtual data is thunderstorms. When the virtual scene superimposing module 100 adds virtual data to the external environment data, it can adjust the amount of rain of the thundershower according to the acceleration of driving and add it to the sunny day accordingly to generate the corresponding modified external environment data, causing the vehicle 200 to The effect of amplifying the amount of rain produced by a faster vehicle speed.

In step 340, the modified external environment data is transmitted to the on-board control module of the autonomous vehicle 200. In some embodiments, the modified external environment data may be transmitted to the control module 160 by the virtual scene overlay module 100. In some embodiments, the modified external environment data may be sent to the sensor module 150 by the virtual scene overlay module 100, and then transmitted to the control module 160 through the sensor module 150.

In step 350, the driving test module 192 collects the response of the autonomous vehicle 200 to the modified external environment data, and evaluates the performance of the autonomous driving system of the autonomous vehicle 200 based on the response. The response refers to a change in the driving state of the autonomous vehicle 200 due to the addition of virtual data, such as a change in parameters such as vehicle speed, braking, lights, turning, and wipers.

In summary, after reading this detailed disclosure, those skilled in the art can understand that the foregoing detailed disclosure may be presented by way of example only, and may not be restrictive. Although there is no clear description here, those skilled in the art can understand that this application intends to cover various reasonable changes, improvements and modifications to the embodiments. These changes, improvements and modifications are intended to be proposed by this application and are within the spirit and scope of the exemplary embodiments of this application.

In addition, certain terms in this application have been used to describe the embodiments of this application. For example, "one embodiment", "an embodiment" and/or "some embodiments" mean that a specific feature, structure or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present application. Therefore, it can be emphasized and should be understood that two or more references to "an embodiment" or "one embodiment" or "alternative embodiment" in various parts of this specification do not necessarily all refer to the same embodiment. In addition, specific features, structures, or characteristics can be appropriately combined in one or more embodiments of the present application.

It should be understood that in the foregoing description of the embodiments of the present application, in order to help understand a feature, for the purpose of simplifying the present application, the present application sometimes combines various features in a single embodiment, drawings, or descriptions thereof. Or, the present application disperses various features in multiple embodiments of the present application. However, this does not mean that the combination of these features is necessary. It is entirely possible for those skilled in the art to extract some of the features as a separate embodiment for understanding when reading this application. That is to say, the embodiments in this application can also be understood as an integration of multiple sub-embodiments. It is also true that the content of each sub-embodiment is less than all the features of a single aforementioned disclosed embodiment.

In some embodiments, numbers expressing quantities or properties used to describe and claim certain embodiments of this application should be understood as modified by the terms "about", "approximately" or "substantially" in some cases. For example, unless otherwise stated, "about", "approximately" or "substantially" may mean a ±20% variation of the value described. Therefore, in some embodiments, the numerical parameters listed in the written description and appended claims are approximations, which may vary according to the desired properties that a particular embodiment is attempting to achieve. In some embodiments, the numerical parameters should be interpreted based on the number of significant figures reported and by applying common rounding techniques. Although some embodiments described in this application list a wide range of numerical ranges and the parameters are approximate values, the specific examples all list numerical values as accurate as possible.

Each patent, patent application, patent application publication and other materials cited herein, such as articles, books, specifications, publications, documents, articles, etc., may be incorporated herein by reference. All content used for all purposes, except for any related file history of the prosecution, may be inconsistent with or conflict with this document, or any identical prosecution file that may have restrictive effects on the broadest scope of the claims. history. Associate with this document now or in the future. For example, if there is any inconsistency or conflict between the description, definition, and/or use of terms related to any contained material The terminology shall prevail.

Finally, it should be understood that the embodiment of the application disclosed herein is an explanation of the principle of the embodiment of the application. Other modified embodiments are also within the scope of this application. Therefore, the embodiments disclosed in this application are merely examples rather than limitations. Those skilled in the art can adopt alternative configurations according to the embodiments of the present application to implement the invention of the present application. Therefore, the embodiments of the present application are not limited to those exactly described in the application.

Claims (18)

1. A vehicle-mounted automatic driving test system includes: a virtual scene superimposing module. In a working state, the virtual scene superimposing module:

   Obtain driving data of autonomous vehicles;

   Acquiring external environment data of the autonomous vehicle;

   Adding virtual data to the external environment data according to the driving data of the autonomous vehicle to generate modified external environment data;

   The modified external environment data is transmitted to the on-board control module of the self-driving vehicle.
2. The vehicle-mounted automatic driving vehicle test system as claimed in claim 1, further comprising:

   The test module, in the working state, collects the response of the autonomous vehicle to the modified external environment data, and the response includes the response of the autonomous vehicle caused by the addition of the virtual data The change in the driving state.
3. The vehicle-mounted automatic driving vehicle test system according to claim 1, wherein the driving data of the automatic driving vehicle includes a vehicle speed, the virtual data changes with time, and the change is related to the vehicle speed.
4. The test system for an on-vehicle self-driving vehicle according to claim 1, wherein the virtual scene overlay module obtains the virtual data through a 5G network;

   The adding virtual data to the external environment data to generate the modified external environment data is randomly generated in time.
5. The vehicle-mounted automatic driving vehicle test system as claimed in claim 1, further comprising:

   The external object sensor of the self-driving vehicle is connected to the virtual scene overlay module and collects the external environment data in real time.
6. The test system for an in-vehicle autonomous vehicle according to claim 5, wherein the external object sensors of the autonomous vehicle include a vision sensor, a lidar, a sonar sensor, a climate sensor, an acceleration sensor, and an ultrasonic sensor.
7. The vehicle-mounted automatic driving vehicle test system according to claim 1, wherein the virtual data includes at least one of virtual vehicles, virtual obstacles, virtual pedestrians, virtual lanes, virtual weather and other virtual data; wherein, based on Different types of sensors have different manifestations of the virtual data.
8. The test system for an in-vehicle self-driving vehicle according to claim 1, wherein:

   The external object sensor of the autonomous vehicle includes a visual sensor, and the visual sensor includes:

   Lens to capture ambient light,

   The image sensor includes a photosensitive array that receives ambient light from the lens and converts the ambient light into electrical signals,

   The output module receives the electrical signal and converts the electrical signal into external data;

   The virtual scene superimposing module is connected to the image sensor;

   The adding virtual data to the external environment data includes: the virtual scene superimposing module adds virtual data to the electrical signal.
9. The test system for an in-vehicle self-driving vehicle according to claim 1, wherein:

   The external object sensor of the autonomous vehicle includes a visual sensor, and the visual sensor includes

   Lens to capture ambient light,

   The image sensor includes a photosensitive array that receives ambient light from the lens and converts the ambient light into electrical signals,

   The output module receives the electrical signal and converts the electrical signal into external data;

   The virtual scene superimposing module is connected to the output module;

   The adding virtual data to the external environment data includes: adding virtual data to the external data when the electrical signal is converted into an image.
10. The test system for an in-vehicle automatic driving vehicle according to claim 1, wherein:

    The external object sensor of the autonomous vehicle includes a visual sensor;

    The virtual scene superimposing module is connected to the output terminal of the visual sensor;

    The adding virtual data to the external environment data includes: adding the virtual data to the data output by the world sensor.
11. A test method for an automatic driving vehicle is applied to a test system of an automatic driving vehicle, and the test method for an automatic driving vehicle includes:

    Obtain driving data of autonomous vehicles;

    Acquiring external environment data of the autonomous vehicle;

    Adding virtual data to the external environment data according to the driving data of the autonomous vehicle to generate modified external environment data;

    The modified external environment data is transmitted to the on-board control module of the self-driving vehicle.
12. The method for testing an autonomous vehicle according to claim 11, further comprising:

    Collecting the response of the self-driving vehicle to the modified external environment data, the response including the change of the driving state of the self-driving vehicle caused by the addition of the virtual data.
13. The method for testing an autonomous vehicle according to claim 11, wherein the driving data of the autonomous vehicle includes a vehicle speed, the virtual data changes with time, and the change is related to the vehicle speed.
14. The method for testing an autonomous vehicle according to claim 11, wherein the virtual data is obtained through a 5G network;

    The adding virtual data to the external environment data to generate the modified external environment data is randomly generated in time.
15. The method for testing an autonomous vehicle according to claim 11, wherein the virtual data includes at least one of virtual vehicles, virtual obstacles, virtual pedestrians, virtual lanes, virtual weather and other virtual data; wherein, based on different Different types of sensors have different manifestations of the virtual data. .
16. The self-driving vehicle testing method according to claim 11, wherein:

    The autonomous vehicle includes an external object sensor, the external object sensor includes a visual sensor, and the visual sensor includes:

    Lens to capture ambient light,

    The image sensor includes a photosensitive array that receives ambient light from the lens and converts the ambient light into electrical signals,

    The output module receives the electrical signal and converts the electrical signal into external data;

    The virtual scene superimposing module is connected to the image sensor;

    The adding virtual data to the external environment data includes: adding virtual data to the electrical signal.
17. The self-driving vehicle testing method according to claim 11, wherein:

    The autonomous vehicle includes an external object sensor, the external object sensor includes a visual sensor, and the visual sensor includes:

    Lens to capture ambient light,

    The image sensor includes a photosensitive array that receives ambient light from the lens and converts the ambient light into electrical signals,

    The output module receives the electrical signal and converts the electrical signal into external data;

    The virtual scene superimposing module is connected to the output module;

    The adding virtual data to the external environment data includes: adding virtual data to the external data when the electrical signal is converted into an image.
18. The self-driving vehicle testing method according to claim 11, wherein:

    The external object sensor of the autonomous vehicle includes a visual sensor;

    The virtual scene superimposing module is connected to the output terminal of the visual sensor;

    The adding virtual data to the external environment data includes: adding the virtual data to the data output by the world sensor.

Images