CN113452904B - Traffic signal lamp detection method and device - Google Patents
Traffic signal lamp detection method and device Download PDFInfo
- Publication number
- CN113452904B CN113452904B CN202110675536.3A CN202110675536A CN113452904B CN 113452904 B CN113452904 B CN 113452904B CN 202110675536 A CN202110675536 A CN 202110675536A CN 113452904 B CN113452904 B CN 113452904B
- Authority
- CN
- China
- Prior art keywords
- target
- signal lamp
- camera
- unmanned equipment
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/695—Control of camera direction for changing a field of view, e.g. pan, tilt or based on tracking of objects
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Traffic Control Systems (AREA)
- Studio Devices (AREA)
Abstract
The specification discloses a traffic signal lamp detection method and a device, wherein unmanned equipment can determine position information of a target signal lamp according to the position of the unmanned equipment, a planned path for executing tasks and a pre-constructed high-precision map. When the unmanned equipment runs to a position close to the target signal lamp, determining an angle adjustment parameter of the target camera according to the latest position of the unmanned equipment, preset camera external parameters and position information of the target signal lamp, rotating the acquisition direction of the target camera according to the angle adjustment parameter, enabling the target camera to face the target signal lamp to acquire images, and detecting the state information of the target signal lamp. Based on the position information of the target signal lamp, the acquisition angle of the target camera is adjusted, the condition that the signal lamp is missed to be detected is avoided, and the accuracy of signal lamp detection is improved.
Description
Technical Field
The application relates to the technical field of unmanned driving, in particular to a traffic signal lamp detection method and device.
Background
In order to ensure the safe driving of the unmanned equipment, the traffic signal lamps in the road need to be detected in real time during the driving process of the unmanned equipment so as to drive according to the instructions. Taking traffic signal lamps as traffic lights as an example, when the unmanned equipment reaches each traffic intersection, the state of the traffic light at the current intersection needs to be detected in real time so as to adjust the driving strategy according to the state of the current traffic light.
However, because the positions and heights of the traffic lights in the roads in different regions are different, for example, cantilever type traffic lights are adopted in part of regions and are arranged at the higher positions above the roads, upright rod type traffic lights are adopted in part of regions and are arranged at the two sides of the roads, and movable traffic lights are adopted and are arranged in the centers of the roads when the road conditions in part of regions are complicated.
The pose of a camera installed in the unmanned equipment is usually fixed, the forward-looking fixed camera can only collect images in a certain visual angle range in front of a road, and when the height of a traffic light in the road is too high or the position of the traffic light deviates seriously and exceeds the visual angle range collected by the camera, the traffic light is missed, so that the driving risk of the unmanned equipment is higher.
Disclosure of Invention
The embodiment of the specification provides a traffic signal lamp detection method and a traffic signal lamp detection device, which are used for partially solving the problems in the prior art.
The embodiment of the specification adopts the following technical scheme:
the present specification provides a traffic signal lamp detection method, including:
monitoring a position of the drone;
determining the position information of a target signal lamp according to the position of the unmanned equipment, a planned path for executing a task and a pre-constructed high-precision map;
when the unmanned equipment is within the preset range of the target signal lamp, determining an angle adjustment parameter of a target camera according to the monitored latest position of the unmanned equipment, preset camera external parameters and the position information of the target signal lamp;
adjusting the acquisition angle of the target camera according to the determined angle adjustment parameter to enable the target camera to face the target signal lamp;
and acquiring an image through the target camera, and detecting the state information of the target signal lamp in the image.
Optionally, determining the position information of the target signal lamp according to the position of the unmanned equipment, the planned path for executing the task and a pre-constructed high-precision map, specifically including:
determining each signal lamp through which the unmanned equipment executes the task according to the planned path of the unmanned equipment executing the task and a pre-constructed high-precision map;
and determining the next signal lamp through which the unmanned equipment passes as a target signal lamp according to the position of the unmanned equipment and the position information of each signal lamp through which the task is executed.
Optionally, the preset range is positively correlated with the driving speed of the unmanned device;
determining angle adjustment parameters of a target camera according to the monitored latest position of the unmanned equipment, preset camera external parameters and the position information of the target signal lamp, wherein the method specifically comprises the following steps:
determining the latest position of a target camera in the unmanned equipment according to the monitored latest position of the unmanned equipment and preset camera external parameters;
and determining angle adjustment parameters of the target camera according to the latest position of the target camera and the position information of the target signal lamp.
Optionally, determining an angle adjustment parameter of the target camera according to the latest position of the target camera and the position information of the target signal lamp specifically includes:
determining a target angle of the target camera towards the target signal lamp according to the latest position of the target camera and the position information of the target signal lamp;
and determining an angle adjustment parameter of the target camera according to the initial angle of the target camera and the target angle.
Optionally, the target camera is a look-around camera;
determining angle adjustment parameters of a target camera according to the monitored latest position of the unmanned equipment, preset camera external parameters and the position information of the target signal lamp, wherein the method specifically comprises the following steps:
determining the latest position of a centre-of-view camera in the unmanned equipment according to the monitored latest position of the unmanned equipment and preset camera external parameters;
determining a target pitch angle of the looking-around camera towards the target signal lamp according to the latest position of the looking-around camera and the height information of the target signal lamp;
and determining an angle adjustment parameter of the looking-around camera according to the initial pitching angle of the looking-around camera and the target pitching angle.
Optionally, the method further comprises:
when the unmanned equipment is within the preset range of the target signal lamp, continuously determining angle adjustment parameters of the target camera according to the monitored latest position of the unmanned equipment, preset camera external parameters and the position information of the target signal lamp;
and continuously adjusting the acquisition angle of the target camera according to the continuously determined angle adjustment parameters.
Optionally, the method further comprises:
and when the unmanned equipment exceeds the preset range of the target signal lamp, recovering the acquisition angle of the target camera to be the initial angle.
This specification provides a traffic signal lamp detection device, includes:
the monitoring module is used for monitoring the position of the unmanned equipment;
the target positioning module is used for determining the position information of a target signal lamp according to the position of the unmanned equipment, a planned path for executing a task and a pre-constructed high-precision map;
the parameter determination module is used for determining an angle adjustment parameter of a target camera according to the monitored latest position of the unmanned equipment, preset camera external parameters and the position information of the target signal lamp when the unmanned equipment is in the preset range of the target signal lamp;
the adjusting module is used for adjusting the acquisition angle of the target camera according to the determined angle adjusting parameter so as to enable the target camera to face the target signal lamp;
and the detection module is used for acquiring images through the target camera and detecting the state information of the target signal lamp in the images.
The present specification provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described traffic signal light detection method.
The unmanned device comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the traffic signal lamp detection method.
The embodiment of the specification adopts at least one technical scheme which can achieve the following beneficial effects:
in this specification, the unmanned aerial vehicle may determine the position information of the target signal lamp according to its own position, a planned path for executing a task, and a high-precision map constructed in advance. When the unmanned equipment runs to a position close to the target signal lamp, determining an angle adjustment parameter of the target camera according to the latest position of the unmanned equipment, preset camera external parameters and position information of the target signal lamp, rotating the acquisition direction of the target camera according to the angle adjustment parameter, enabling the target camera to face the target signal lamp to acquire images, and detecting the state information of the target signal lamp. Based on the position information of the target signal lamp, the acquisition angle of the target camera is adjusted, the condition that the signal lamp is missed to be detected is avoided, and the accuracy of signal lamp detection is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flowchart of a traffic signal light detection method provided in an embodiment of the present specification;
fig. 2a is a schematic diagram of an intersection provided in an embodiment of the present disclosure;
fig. 2b is a schematic diagram of adjusting a capturing angle of a camera according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of dynamically adjusting an acquisition angle according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a traffic signal light detection device provided in an embodiment of the present disclosure;
fig. 5 is a schematic view of an unmanned aerial vehicle implementing a traffic signal light detection method provided in an embodiment of the present specification.
Detailed Description
In order to make the objects, technical solutions and advantages of the present disclosure more apparent, the technical solutions of the present disclosure will be clearly and completely described below with reference to the specific embodiments of the present disclosure and the accompanying drawings. It should be apparent that the described embodiments are only a few embodiments of the present application, and not all embodiments. All other embodiments obtained by a person skilled in the art without making any inventive step based on the embodiments in the description belong to the protection scope of the present application.
In order to ensure safe driving of the unmanned aerial vehicle, a plurality of image sensors (hereinafter, a camera is described as an example) are generally installed in the unmanned aerial vehicle to capture an image of the surrounding environment. The position and the orientation of each camera relative to the unmanned device are fixed and are respectively used for acquiring environment images of the surroundings in all directions so as to carry out obstacle avoidance driving according to the obstacles detected in the environment images.
Taking an unmanned vehicle as an example, since the unmanned vehicle runs on the ground and the obstacle to be avoided mainly includes trees, pedestrians, vehicles, and the like, the range of the view angle of each camera mounted on the unmanned vehicle is generally a region lower than the ground around the unmanned vehicle, and is difficult to be observed in a region higher than the ground. Also, since the range of viewing angles of the cameras is limited, the more marginal areas in the road are also typically blind observation areas.
When the unmanned vehicle passes through the traffic intersection, the position and the height of the traffic signal lamp at each intersection are different, and the traffic signal lamp may be out of the visual angle range acquired by the camera in the unmanned vehicle, so that the unmanned vehicle can run illegally due to the fact that the traffic light is not detected. For example, a cantilever-type signal lamp is adopted at a part of the intersection, and the traffic signal lamp is arranged at a higher position beyond the visual angle range of a camera in the unmanned vehicle.
Based on the existing problems, the present specification provides a traffic signal light detection method, and the following detailed description is provided with reference to the accompanying drawings to describe the technical solutions provided by the embodiments of the present application.
Fig. 1 is a schematic flow chart of a traffic signal light detection method provided in an embodiment of the present specification, which may specifically include the following steps:
s100: the position of the drone is monitored.
S102: and determining the position information of the target signal lamp according to the position of the unmanned equipment, the planned path for executing the task and a pre-constructed high-precision map.
In one or more embodiments of the present disclosure, the unmanned device may monitor its own position in real time during driving, and determine a nearest target signal lamp to pass through according to its own position, so as to adjust an angle at which the camera collects the target signal lamp according to the position information of the target signal lamp, so as to detect a real-time state of the signal lamp.
Specifically, the unmanned equipment can determine a planned path for executing the current task according to the starting point and the end point of the current task to be executed and a pre-constructed high-precision map. And then, the unmanned equipment can determine each signal lamp through which the current task is executed according to the determined planning path and the high-precision map.
It should be noted that the traffic light that passes through during the process of executing the current task in this specification refers to a traffic light that needs to be observed when the vehicle travels along the planned route, and the traffic light may be a traffic light or a lane light (for example, a tidal lane) that adjusts the traveling direction of the lane, which is not limited in this specification. For example, when the unmanned aerial vehicle passes through a traffic intersection, if the planned route is left-turn driving, the traffic light to which the unmanned aerial vehicle needs to adhere is a traffic light corresponding to a left-turn lane in front of the unmanned aerial vehicle.
Then, the unmanned device can determine the next signal lamp through which the unmanned device is to pass as a target signal lamp according to the position of the unmanned device and the position information of each signal lamp through which the task is executed. The pre-constructed high-precision map stores the position information of each signal lamp in advance.
Furthermore, when the unmanned device positions itself, a Global Positioning System (GPS) and an Inertial Measurement Unit (IMU) may be used for fusion Positioning, and laser point cloud Positioning may also be used.
S104: and when the unmanned equipment is in the preset range of the target signal lamp, determining the angle adjustment parameters of the target camera according to the monitored latest position of the unmanned equipment, preset camera external parameters and the position information of the target signal lamp.
In this specification, when the unmanned aerial vehicle is positioned close to a target signal lamp, an adjustment angle of the target camera may be determined according to a position of the target camera in the unmanned aerial vehicle and a position of the target signal lamp, so that the target camera shoots towards the adjacent target signal lamp. The target camera is a camera which is arranged on the unmanned equipment and used for detecting the traffic signal lamp except for a fixed position and a fixed phase of the orientation, and the acquisition angle can be adjusted by driving of the motor.
Specifically, when the unmanned aerial vehicle is determined to be within a preset range near the target signal lamp according to the latest position of the unmanned aerial vehicle and the position information of the target signal lamp, the latest position of the target camera in the unmanned aerial vehicle can be determined according to the latest position of the unmanned aerial vehicle and preset camera external parameters.
The preset camera external parameter is the deviation between a sensor used for positioning on the vehicle and the target camera. When the GPS and IMU fusion positioning is adopted, the camera external parameter can be the deviation between the IMU and the target camera, and when the laser point cloud positioning is adopted, the camera external parameter is the deviation between the laser radar equipment and the target camera. The preset range of the target signal lamp can be set to be a certain distance in front of the target signal lamp, such as a distance range of 100 meters in front of the target signal lamp, and the preset range is positively correlated with the running speed of the unmanned equipment, and the faster the running speed of the unmanned equipment is, the larger the preset range is.
Then, the unmanned aerial vehicle can determine the relative position between the target camera and the target signal lamp according to the position information of the target signal lamp and the latest position of the target camera, and determine the target angle of the target camera towards the target signal lamp according to the relative position between the target camera and the target signal lamp.
And finally, determining an angle adjustment parameter of the target camera, namely the angle required to rotate the target camera according to the initial angle of the target camera and the target angle facing the target signal lamp.
Fig. 2a is a schematic diagram of an exemplary intersection provided in the present specification, in fig. 2a, the unmanned device travels from left to right along the road direction, and when passing through the intersection, the current state of the traffic light at the right intersection needs to be detected to determine whether to stop, wait, or directly pass through. When the unmanned equipment is determined to be in the preset range of the traffic light of the crossroad according to the real-time position of the unmanned equipment, the relative position between the unmanned equipment and the target camera can be determined according to the position of the target camera in the unmanned equipment and the position information of the target traffic light, and the target angle of the camera towards the target traffic light can be determined according to the connection line of the relative positions between the unmanned equipment and the target traffic light.
As shown in fig. 2b, the direction along the dotted line in fig. 2b is the initial angle of the target camera, the direction along the solid line is the target angle formed by the connection line between the target camera and the target traffic light, and the angle difference between the two is the angle adjustment parameter of the target camera. The angle difference in the two-dimensional plane is shown only by way of example, but in actual three-dimensional space, the pitch angle and the rotation angle of the target camera may need to be adjusted.
S106: and adjusting the acquisition angle of the target camera according to the determined angle adjustment parameter to enable the target camera to face the target signal lamp.
S108: and acquiring an image through the target camera, and detecting the state information of the target signal lamp in the image.
In this specification, after determining the angle to be adjusted of the target camera, the acquisition direction of the target camera may be rotated, so that the target camera acquires an image towards the target signal lamp, thereby facilitating detection of the state information of the target signal lamp.
Specifically, the unmanned aerial vehicle can adjust the acquisition angle of the target camera according to the determined angle adjustment parameter, so that the target camera faces the target signal lamp, namely, the target signal lamp is located in the visual field range of the target camera. Then, an image is collected through the target camera, a target signal lamp area in the image is determined, and the state information of the target signal lamp is determined according to the pixel information of the target signal lamp area. The state information of the signal lamp included in the environment image recognition is a mature prior art, and is not improved in the specification, so that the detailed description is omitted.
Of course, after the unmanned device exits the preset range of the target signal lamp, the acquisition angle of the target camera can be restored to the initial angle, so that when the unmanned device approaches the next target signal lamp, angle adjustment is performed again.
Based on the traffic signal lamp detection method shown in fig. 1, the unmanned equipment can determine the position information of the target signal lamp according to the self position, the planned path for executing the task and the pre-constructed high-precision map. When the unmanned equipment runs to a position close to the target signal lamp, determining an angle adjustment parameter of the target camera according to the latest position of the unmanned equipment, preset camera external parameters and position information of the target signal lamp, rotating the acquisition direction of the target camera according to the angle adjustment parameter, enabling the target camera to face the target signal lamp to acquire images, and detecting the state information of the target signal lamp. Based on the position information of the target signal lamp, the acquisition angle of the target camera is adjusted, the condition that the signal lamp is missed to be detected is avoided, and the accuracy of signal lamp detection is improved.
In addition, generally, the unmanned aerial vehicle starts to acquire images and performs traffic light state recognition when approaching a target traffic light, and when the unmanned aerial vehicle travels at a high speed, images including the traffic light often need to be acquired in a short time. Therefore, in the present specification, the latest position of the target camera is determined by the latest position of the unmanned aerial vehicle and the preset camera external reference, and the acquisition orientation of the target camera is accurately adjusted according to the relative position between the target camera and the target signal lamp, so that the target camera on the unmanned aerial vehicle can be positioned to the target signal lamp in time and acquire an image in a short time. Compared with the method that the orientation of the camera is randomly adjusted according to the preset angle step length, the positioning speed is higher and more accurate.
During the driving of the unmanned aerial vehicle, an environmental image in front of the unmanned aerial vehicle is usually captured by a forward-looking fixed camera (a fixed camera observing a driving area in front) mounted on the unmanned aerial vehicle, and the state information of the signal lamp contained therein is recognized based on the environmental image captured by the fixed camera.
Because the signal lamp of not every crossing all sets up in the actual road and is in more partial or higher position, to the upright pole formula signal lamp that sets up in the road, or set up the movable signal lamp at road center, the environmental image that the fixed camera of accessible was gathered directly detects.
Therefore, in the specification, a visible signal lamp and an invisible signal lamp can be marked in a constructed high-precision map in advance, wherein the visible signal lamp is located in the range of the acquisition visual angle of the forward-looking fixed camera, the invisible signal lamp is located out of the range of the acquisition visual angle of the forward-looking fixed camera, and the target camera performs state detection. When the unmanned equipment is close to the visible signal lamp, an environment image can be directly collected by a forward-looking fixed camera of the unmanned equipment, and the current state of the signal lamp is detected based on the collected environment image. When the unmanned equipment is close to the invisible signal lamp, the acquisition angle of the target camera can be adjusted according to the relative position of the target camera and the invisible signal lamp, so that the target camera can acquire images towards the invisible signal lamp for state identification.
In an embodiment of the present disclosure, the target camera may also be a panoramic camera, and since the panoramic camera may observe all view angle ranges of the front area in the horizontal direction, when adjusting the collecting angle of the panoramic camera, the position of the panoramic camera in the unmanned device may be determined according to the position of the unmanned device and a preset camera external parameter. And then, determining a target pitching angle of the looking-around camera towards the target signal lamp according to the position of the looking-around camera and the height information of the target signal lamp, and determining an angle adjusting parameter of the looking-around camera according to the initial pitching angle and the target pitching angle of the target camera so as to adjust the acquisition direction of the looking-around camera according to the angle adjusting parameter.
Furthermore, since the position of the unmanned aerial vehicle changes in real time during driving, the target camera also needs to continuously adjust the collection angle for collecting the target signal lamp, that is, the target signal lamp is tracked in real time for collection. Therefore, in this specification, when the unmanned aerial vehicle is located within a preset range of a target signal lamp, the unmanned aerial vehicle may continuously determine an angle adjustment parameter of the target camera according to the latest position monitored in real time, preset camera external parameters, position information of the target signal, and the like, and continuously adjust the acquisition angle of the target camera according to the continuously determined angle adjustment parameter.
As shown in fig. 3, the position of the target traffic light at the intersection is fixed, but the position of the unmanned device in the driving process changes in real time, so that the relative position between the position of the target camera in the unmanned device and the target traffic light also changes continuously, and the unmanned device can continuously update the angle adjustment parameter of the target camera according to the real-time position of the unmanned device, the preset external parameters of the camera and the position information of the target traffic light, so as to continuously adjust the acquisition angle of the target camera towards the target traffic light.
Based on the traffic signal lamp detection method shown in fig. 1, the embodiment of the present specification further provides a schematic structural diagram of a traffic signal lamp detection device, as shown in fig. 4.
Fig. 4 is a schematic structural diagram of a traffic signal light detection device provided in an embodiment of the present specification, including:
a monitoring module 200 that monitors a position of the drone;
the target positioning module 202 is used for determining the position information of a target signal lamp according to the position of the unmanned equipment, a planned path for executing a task and a pre-constructed high-precision map;
the parameter determining module 204 is used for determining angle adjustment parameters of a target camera according to the monitored latest position of the unmanned equipment, preset camera external parameters and the position information of the target signal lamp when the unmanned equipment is within the preset range of the target signal lamp;
the adjusting module 206 adjusts the acquisition angle of the target camera according to the determined angle adjusting parameter, so that the target camera faces the target signal lamp;
the detection module 208 collects an image through the target camera and detects the state information of the target signal lamp in the image.
Optionally, the target positioning module 202 is specifically configured to determine, according to a planned path of the unmanned device for executing the task and a pre-constructed high-precision map, each signal lamp through which the unmanned device executes the task, and determine, according to the position of the unmanned device and position information of each signal lamp through which the unmanned device executes the task, a next signal lamp through which the unmanned device waits to pass as a target signal lamp.
Optionally, the preset range is positively correlated to a driving speed of the unmanned aerial vehicle, and the parameter determining module 204 is specifically configured to determine a latest position of a target camera in the unmanned aerial vehicle according to the monitored latest position of the unmanned aerial vehicle and a preset camera external parameter, and determine an angle adjustment parameter of the target camera according to the latest position of the target camera and the position information of the target signal lamp.
Optionally, the parameter determining module 204 is specifically configured to determine a target angle of the target camera toward the target signal lamp according to the latest position of the target camera and the position information of the target signal lamp, and determine an angle adjustment parameter of the target camera according to the initial angle of the target camera and the target angle.
Optionally, the target camera is a looking-around camera, and the parameter determining module 204 is specifically configured to determine a latest position of the looking-around camera in the unmanned aerial vehicle according to the monitored latest position of the unmanned aerial vehicle and a preset camera external parameter, determine a target pitch angle of the looking-around camera toward the target signal lamp according to the latest position of the looking-around camera and the height information of the target signal lamp, and determine an angle adjustment parameter of the looking-around camera according to an initial pitch angle of the looking-around camera and the target pitch angle.
Optionally, the adjusting module 206 is further configured to, when the unmanned aerial vehicle is located within a preset range of the target signal lamp, continuously determine an angle adjustment parameter of the target camera according to the monitored latest position of the unmanned aerial vehicle, a preset camera external parameter, and position information of the target signal lamp, and continuously adjust the acquisition angle of the target camera according to the continuously determined angle adjustment parameter.
Optionally, the adjusting module 206 is further configured to recover the collecting angle of the target camera to be an initial angle after the unmanned device exceeds the preset range of the target signal lamp.
Embodiments of the present specification further provide a computer-readable storage medium, where the storage medium stores a computer program, and the computer program may be used to execute the traffic signal light detection method provided in fig. 1.
According to the traffic signal light detection method shown in fig. 1, the embodiment of the present specification further provides a schematic structural diagram of the unmanned device shown in fig. 5. As shown in fig. 5, the drone includes, at the hardware level, a processor, an internal bus, a network interface, a memory, and a non-volatile storage, although it may also include hardware required for other services. The processor reads a corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to implement the traffic light detection method shown in fig. 1.
Of course, besides the software implementation, this specification does not exclude other implementations, such as logic devices or combination of software and hardware, and so on, that is, the execution subject of the following processing flow is not limited to each logic unit, and may be hardware or logic devices.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain a corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD) (e.g., a Field Programmable Gate Array (FPGA)) is an integrated circuit whose Logic functions are determined by a user programming the Device. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and create a dedicated integrated circuit chip. Furthermore, nowadays, instead of manually generating an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as ABEL (Advanced Boolean Expression Language), AHDL (alternate Hardware Description Language), traffic, CUPL (core universal Programming Language), HDCal, jhddl (Java Hardware Description Language), lava, lola, HDL, PALASM, rhyd (Hardware Description Language), and vhigh-Language (Hardware Description Language), which is currently used in most popular applications. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in purely computer readable program code means, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the various elements may be implemented in the same one or more pieces of software and/or hardware in the practice of this description.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises that element.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.
Claims (9)
1. A traffic signal light detection method is characterized by comprising the following steps:
monitoring a position of the drone;
according to a planned path of the unmanned equipment for executing the task and a pre-constructed high-precision map, determining position information of each invisible signal lamp through which the task passes, wherein visible signal lamps and invisible signal lamps of a fixed camera of the unmanned equipment are marked in the high-precision map;
determining the next invisible signal lamp through which the unmanned equipment passes as a target signal lamp according to the position information of each invisible signal lamp and the position of the unmanned equipment, and determining the position information of the target signal lamp;
when the unmanned equipment is within the preset range of the target signal lamp, determining an angle adjustment parameter of a target camera according to the monitored latest position of the unmanned equipment, preset camera external parameters and the position information of the target signal lamp;
adjusting the acquisition angle of the target camera according to the determined angle adjustment parameter to enable the target camera to face the target signal lamp;
and acquiring an image through the target camera, and detecting the state information of the target signal lamp in the image.
2. The method of claim 1, wherein the preset range is positively correlated with a travel speed of the drone;
determining angle adjustment parameters of a target camera according to the monitored latest position of the unmanned equipment, preset camera external parameters and the position information of the target signal lamp, wherein the method specifically comprises the following steps:
determining the latest position of a target camera in the unmanned equipment according to the monitored latest position of the unmanned equipment and preset camera external parameters;
and determining angle adjustment parameters of the target camera according to the latest position of the target camera and the position information of the target signal lamp.
3. The method according to claim 2, wherein determining the angle adjustment parameter of the target camera according to the latest position of the target camera and the position information of the target signal lamp comprises:
determining a target angle of the target camera towards the target signal lamp according to the latest position of the target camera and the position information of the target signal lamp;
and determining an angle adjustment parameter of the target camera according to the initial angle of the target camera and the target angle.
4. The method of claim 1, wherein the target camera is a look-around camera;
determining angle adjustment parameters of a target camera according to the monitored latest position of the unmanned equipment, preset camera external parameters and the position information of the target signal lamp, wherein the method specifically comprises the following steps:
determining the latest position of a centre-of-view camera in the unmanned equipment according to the monitored latest position of the unmanned equipment and preset camera external parameters;
determining a target pitch angle of the looking-around camera towards the target signal lamp according to the latest position of the looking-around camera and the height information of the target signal lamp;
and determining an angle adjustment parameter of the looking-around camera according to the initial pitching angle of the looking-around camera and the target pitching angle.
5. The method of claim 1, wherein the method further comprises:
when the unmanned equipment is in the preset range of the target signal lamp, continuously determining angle adjustment parameters of the target camera according to the monitored latest position of the unmanned equipment, preset camera external parameters and the position information of the target signal lamp;
and continuously adjusting the acquisition angle of the target camera according to the continuously determined angle adjustment parameters.
6. The method of claim 1, wherein the method further comprises:
and when the unmanned equipment exceeds the preset range of the target signal lamp, recovering the acquisition angle of the target camera to be the initial angle.
7. A traffic signal light detection device, comprising:
the monitoring module monitors the position of the unmanned equipment;
the target positioning module is used for determining the position information of each invisible signal lamp through which the task is executed according to a planned path of the unmanned equipment for executing the task and a pre-constructed high-precision map, marking visible signal lamps and invisible signal lamps of a fixed camera of the unmanned equipment in the high-precision map, determining the next invisible signal lamp through which the unmanned equipment is to pass as a target signal lamp according to the position information of each invisible signal lamp and the position of the unmanned equipment, and determining the position information of the target signal lamp;
the parameter determination module is used for determining an angle adjustment parameter of a target camera according to the monitored latest position of the unmanned equipment, preset camera external parameters and the position information of the target signal lamp when the unmanned equipment is in the preset range of the target signal lamp;
the adjusting module is used for adjusting the acquisition angle of the target camera according to the determined angle adjusting parameter so as to enable the target camera to face the target signal lamp;
and the detection module is used for acquiring an image through the target camera and detecting the state information of the target signal lamp in the image.
8. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1 to 6.
9. An unmanned aerial vehicle comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the method of any of claims 1 to 6.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110675536.3A CN113452904B (en) | 2021-06-18 | 2021-06-18 | Traffic signal lamp detection method and device |
| PCT/CN2022/079750 WO2022262327A1 (en) | 2021-06-18 | 2022-03-08 | Traffic signal light detection |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110675536.3A CN113452904B (en) | 2021-06-18 | 2021-06-18 | Traffic signal lamp detection method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113452904A CN113452904A (en) | 2021-09-28 |
| CN113452904B true CN113452904B (en) | 2022-11-04 |
Family
ID=77811640
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110675536.3A Active CN113452904B (en) | 2021-06-18 | 2021-06-18 | Traffic signal lamp detection method and device |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN113452904B (en) |
| WO (1) | WO2022262327A1 (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113452904B (en) * | 2021-06-18 | 2022-11-04 | 北京三快在线科技有限公司 | Traffic signal lamp detection method and device |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106910358A (en) * | 2017-04-21 | 2017-06-30 | 百度在线网络技术（北京）有限公司 | For the attitude determination method and device of unmanned vehicle |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9145140B2 (en) * | 2012-03-26 | 2015-09-29 | Google Inc. | Robust method for detecting traffic signals and their associated states |
| EP2983153B1 (en) * | 2013-04-04 | 2018-12-19 | Nissan Motor Co., Ltd | Signal recognition device |
| CN109492507B (en) * | 2017-09-12 | 2022-09-23 | 阿波罗智能技术(北京)有限公司 | Traffic light state identification method and device, computer equipment and readable medium |
| CN112419768A (en) * | 2019-08-21 | 2021-02-26 | 北京国双科技有限公司 | Vehicle control method, device and system, storage medium and processor |
| KR20210037791A (en) * | 2019-09-27 | 2021-04-07 | 현대모비스 주식회사 | Autonomous driving apparatus and method |
| CN111695546B (en) * | 2020-06-28 | 2023-06-27 | 北京京东乾石科技有限公司 | Traffic signal lamp identification method and device for unmanned vehicle |
| CN112861748B (en) * | 2021-02-22 | 2022-07-12 | 奥特酷智能科技(南京)有限公司 | Traffic light detection system and method in automatic driving |
| CN113452904B (en) * | 2021-06-18 | 2022-11-04 | 北京三快在线科技有限公司 | Traffic signal lamp detection method and device |
-
2021
- 2021-06-18 CN CN202110675536.3A patent/CN113452904B/en active Active
-
2022
- 2022-03-08 WO PCT/CN2022/079750 patent/WO2022262327A1/en active Application Filing
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106910358A (en) * | 2017-04-21 | 2017-06-30 | 百度在线网络技术（北京）有限公司 | For the attitude determination method and device of unmanned vehicle |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2022262327A1 (en) | 2022-12-22 |
| CN113452904A (en) | 2021-09-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR102534353B1 (en) | Navigating a vehicle based on a detected barrier | |
| JP7045628B2 (en) | Vehicle equipment, vehicles, and computer programs for controlling vehicle behavior | |
| JP7334881B2 (en) | Cross-field of view for autonomous vehicle systems | |
| CN112528737B (en) | System and method for detecting objects | |
| JP2022523613A (en) | Lane mapping and navigation | |
| JP2021515286A (en) | Systems and methods for anonymizing navigation information | |
| JP2021517680A (en) | Systems and methods for determining navigation parameters | |
| JP2021088357A (en) | Crowdsourcing and distribution of sparse map and lane measurement values for autonomous vehicle navigation | |
| US11670087B2 (en) | Training data generating method for image processing, image processing method, and devices thereof | |
| RU2639851C1 (en) | Traffic-light recognizer and traffic-light recognizing method | |
| KR20230054911A (en) | systems and methods for navigating lane merges and lane splits | |
| KR20190028359A (en) | Required Image and Location Capture Systems and Methods | |
| JP2015518600A5 (en) | ||
| BR112017000267B1 (en) | TRAFFIC SIGNAL DETECTION DEVICE AND TRAFFIC SIGNAL DETECTION METHOD | |
| JP2021510890A (en) | Systems and methods for estimating lane obstructions | |
| JP4483305B2 (en) | Vehicle periphery monitoring device | |
| CN110660103B (en) | Unmanned vehicle positioning method and device | |
| US10565863B1 (en) | Method and device for providing advanced pedestrian assistance system to protect pedestrian preoccupied with smartphone | |
| CN107728646B (en) | Method and system for automatically controlling camera of automatic driving vehicle | |
| JP7255345B2 (en) | Driving lane recognition device, driving lane recognition method and program | |
| CN113452904B (en) | Traffic signal lamp detection method and device | |
| CN114077252B (en) | Robot collision obstacle distinguishing device and method | |
| JP2019146012A (en) | Imaging apparatus | |
| JP2022040891A (en) | Travel lane planning device and travel lane planning computer program | |
| JP2022006844A (en) | Object detection method and object detection device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |