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WO2019047643A1 - Procédé et dispositif de commande destinés à un véhicule aérien sans pilote - Google Patents

Procédé et dispositif de commande destinés à un véhicule aérien sans pilote Download PDF

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Publication number
WO2019047643A1
WO2019047643A1 PCT/CN2018/098630 CN2018098630W WO2019047643A1 WO 2019047643 A1 WO2019047643 A1 WO 2019047643A1 CN 2018098630 W CN2018098630 W CN 2018098630W WO 2019047643 A1 WO2019047643 A1 WO 2019047643A1
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WIPO (PCT)
Prior art keywords
obstacle avoidance
unmanned vehicle
parameter
learning model
sensors
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.)
Ceased
Application number
PCT/CN2018/098630
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English (en)
Chinese (zh)
Inventor
郑超
郁浩
闫泳杉
唐坤
张云飞
姜雨
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Baidu Online Network Technology Beijing Co Ltd
Original Assignee
Baidu Online Network Technology Beijing Co Ltd
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Filing date
Publication date
Application filed by Baidu Online Network Technology Beijing Co Ltd filed Critical Baidu Online Network Technology Beijing Co Ltd
Publication of WO2019047643A1 publication Critical patent/WO2019047643A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
    • G05D1/024—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors in combination with a laser
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0242—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using non-visible light signals, e.g. IR or UV signals
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar

Definitions

  • the present application relates to the field of computer technology, and in particular to the field of Internet technologies, and in particular, to a control method and apparatus for an unmanned vehicle.
  • An unmanned vehicle is a type of intelligent car, also known as a wheeled mobile robot. It mainly relies on a computer-based intelligent pilot in the car to achieve the goal of unmanned driving.
  • the unmanned vehicle can detect the road surface condition through the sensor.
  • a single sensor is used for detection, and the detection result is easily affected by the surrounding environment, and the stability is poor.
  • the purpose of the present application is to propose an improved control method and apparatus for an unmanned vehicle to solve the technical problems mentioned in the background section above.
  • an embodiment of the present application provides a control method for an unmanned vehicle, the method comprising: installing, by the unmanned vehicle, at least two sensors, the method comprising: acquiring data collected by at least two sensors; The data is input into a pre-trained obstacle avoidance learning model, wherein the obstacle avoidance deep learning model is used to characterize the correspondence between the data collected by the sensor and the obstacle avoidance parameter of the unmanned vehicle; and the unmanned vehicle that obtains the obstacle avoidance depth learning model output The obstacle avoidance parameter controls the unmanned vehicle based on the obstacle avoidance parameter.
  • the obstacle avoidance parameter includes a brake parameter and/or a steering parameter.
  • the at least two sensors include a camera, a lidar, and a millimeter wave radar.
  • the obstacle avoidance depth learning model is trained in an end-to-end manner.
  • the method before acquiring data collected by the at least two sensors, the method further comprises: acquiring data collected by the at least two sensors, and acquiring an current obstacle avoidance parameter of the unmanned vehicle, the obstacle avoidance parameter being driven by the user The behavior is generated; the acquired data and the current obstacle avoidance parameters are respectively used as input and output of the obstacle avoidance deep learning model to train the obstacle avoidance deep learning model.
  • the embodiment of the present application provides a control device for an unmanned vehicle, the device includes: the unmanned vehicle is equipped with at least two sensors, and the device includes: an acquiring unit configured to acquire at least two sensor acquisitions Data; an input unit configured to input the acquired data into a pre-trained obstacle avoidance deep learning model, wherein the obstacle avoidance depth learning model is used to characterize data collected by the sensor and obstacle avoidance parameters of the unmanned vehicle a control unit configured to acquire an obstacle avoidance parameter of the unmanned vehicle outputted by the obstacle avoidance deep learning model, to control the unmanned vehicle based on the obstacle avoidance parameter.
  • the obstacle avoidance parameter includes a brake parameter and/or a steering parameter.
  • the at least two sensors include a camera, a lidar, and a millimeter wave radar.
  • the obstacle avoidance depth learning model is trained in an end-to-end manner.
  • the apparatus further includes: a parameter acquisition unit configured to acquire data collected by the at least two sensors, and acquire an current obstacle avoidance parameter of the unmanned vehicle, wherein the obstacle avoidance parameter is generated by the driving behavior of the user;
  • the unit is configured to use the acquired data and the current obstacle avoidance parameter as input and output of the obstacle avoidance deep learning model, respectively, to train the obstacle avoidance deep learning model.
  • an embodiment of the present application provides an unmanned vehicle, including: one or more processors; and a storage device for storing one or more programs when one or more programs are used by one or more processors Executing, such that one or more processors implement a method as in any of the embodiments of the control method for an unmanned vehicle.
  • an embodiment of the present application provides a computer readable storage medium having stored thereon a computer program, the program being executed by a processor to implement a method as in any one of the methods for controlling an unmanned vehicle.
  • the control method and device for an unmanned vehicle provided by the embodiment of the present application, the unmanned vehicle is installed with at least two sensors, and the method comprises: first acquiring data collected by at least two sensors. Then, the acquired data is input into a pre-trained obstacle avoidance deep learning model, wherein the obstacle avoidance deep learning model is used to characterize the correspondence between the data collected by the sensor and the obstacle avoidance parameter of the unmanned vehicle. Finally, the obstacle avoidance parameters of the unmanned vehicle output from the obstacle avoidance deep learning model are obtained to control the unmanned vehicle based on the obstacle avoidance parameter. In the embodiment of the present application, the obstacle avoidance parameter is obtained through the obstacle avoidance deep learning model, and the driving of the unmanned vehicle is controlled.
  • FIG. 1 is an exemplary system architecture diagram to which the present application can be applied;
  • FIG. 2 is a flow chart of one embodiment of a method for controlling an unmanned vehicle according to the present application
  • FIG. 3 is a schematic diagram of an application scenario of a method for controlling an unmanned vehicle according to the present application
  • FIG. 4 is a flow chart of still another embodiment of a method for controlling an unmanned vehicle according to the present application.
  • Figure 5 is a schematic structural view of an embodiment of a control device for an unmanned vehicle according to the present application.
  • FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing an unmanned vehicle of an embodiment of the present application.
  • FIG. 1 shows an exemplary system architecture 100 of an embodiment of a control method for an unmanned vehicle or a control device for an unmanned vehicle to which the present application can be applied.
  • system architecture 100 can include unmanned vehicle 101, network 102, and server 103.
  • the network 102 is used to provide a medium for communication links between the unmanned vehicle 101 and the server 103.
  • Network 102 can include a variety of connection types, such as wired, wireless communication links, fiber optic cables, and the like.
  • the user can use the unmanned vehicle 101 to interact through the network 102 server 103 to receive or send messages and the like.
  • Various communication client applications can be installed on the unmanned vehicle 101.
  • the unmanned vehicle 101 may be various electronic devices that support image acquisition and capable of image processing, and may be an unmanned vehicle or the like.
  • the server 103 may be a server that provides various services.
  • the server 103 can perform processing such as analysis and feed back the processing result to the unmanned vehicle.
  • the image processing method for an unmanned vehicle provided by the embodiment of the present application is generally performed by the unmanned vehicle 101. Accordingly, the image processing apparatus for the unmanned vehicle is generally disposed in the unmanned vehicle 101.
  • terminal devices, networks, and servers in Figure 1 is merely illustrative. Depending on the implementation needs, there can be any number of terminal devices, networks, and servers.
  • the control method for the unmanned vehicle includes the following steps:
  • Step 201 Acquire data collected by at least two sensors.
  • the unmanned vehicle is equipped with at least two sensors, and the unmanned vehicle on which the control method for the unmanned vehicle runs can obtain the above from a local or other electronic device through a wired connection or a wireless connection. Data collected by at least two sensors.
  • the number of each sensor may be one or two or more.
  • the at least two sensors may include a camera, a lidar, and a millimeter wave radar.
  • the camera can acquire image data or video stream data.
  • the laser radar uses a laser for detection, and the returned data is also a laser signal.
  • the millimeter wave radar uses millimeter waves for detection, and the collected data is millimeter wave signals.
  • Step 202 Input the acquired data into a pre-trained obstacle avoidance learning model.
  • the unmanned vehicle can input the acquired data into the pre-trained obstacle avoidance depth learning model, so that the obstacle avoidance depth learning model outputs according to the input data.
  • the obstacle avoidance depth learning model is used to characterize the correspondence between the data collected by the sensor and the obstacle avoidance parameters of the unmanned vehicle.
  • the obstacle avoidance parameter is the parameter involved in the unmanned vehicle avoiding the obstacle.
  • the obstacle avoidance parameter here is the data that the unmanned vehicle uses immediately.
  • the above-mentioned obstacle avoidance deep learning model may be trained by a Classifier such as a Support Vector Machine (SVM) or a Naive Bayesian Model (NBM) model.
  • SVM Support Vector Machine
  • NBM Naive Bayesian Model
  • the image processing model described above may also be pre-trained based on certain classification functions (eg, softmax functions, etc.).
  • the obstacle avoidance deep learning model is trained in an end-to-end manner.
  • the end-to-end training model can take the data collected by the sensor as an input and output the obstacle avoidance parameters to be adopted by the unmanned vehicle.
  • the obstacle avoidance deep learning model is a deep neural network, which can directly generate obstacle avoidance parameters of vehicles based on the collected data. Specifically, in the training process of the model, what kind of data is used as the input and output for training, in the application process of the model, the corresponding output data can be obtained according to the input data.
  • the obstacle avoidance deep learning model includes:
  • the feature extraction component extracts image features of the image acquired by the camera, extracts first data features of the data collected by the laser radar, and extracts second data features of the data acquired by the millimeter wave radar.
  • the feature extraction may be performed in the following manner, extracting image features from the image acquired by the camera, extracting the first data feature from the laser data collected by the laser radar, and extracting from the millimeter wave data collected by the millimeter wave radar Second data feature.
  • Step 203 Acquire an obstacle avoidance parameter of the unmanned vehicle outputted by the obstacle avoidance depth learning model, so as to control the unmanned vehicle based on the obstacle avoidance parameter.
  • the unmanned vehicle acquisition obstacle avoidance learning model controls the unmanned vehicle based on the obstacle avoidance parameter according to the obstacle avoidance parameter of the unmanned vehicle outputted according to the input data.
  • the obstacle avoidance parameter includes a brake parameter and/or a steering parameter.
  • the braking parameter is a parameter used by the brake, and may include the magnitude of the deceleration of the unmanned vehicle, and may also include the direction of the deceleration of the unmanned vehicle. In general, the direction of deceleration of an unmanned vehicle is opposite to the direction of travel.
  • the speed of the driving can be controlled by the braking parameters.
  • the steering parameter is a parameter for the unmanned vehicle to turn, and may be a steering angle such as a steering wheel angle or the like.
  • the steering direction can be controlled by steering parameters.
  • the above-mentioned unmanned vehicle can perform the obstacle avoiding operation by limiting or adjusting any of the above two obstacle avoidance parameters.
  • FIG. 3 is a schematic diagram of an application scenario of a control method for an unmanned vehicle according to the present embodiment.
  • the unmanned vehicle 301 acquires data 302 collected by at least two sensors installed in the unmanned vehicle. Thereafter, the unmanned vehicle inputs the acquired data into a pre-trained obstacle avoidance learning model, and the obstacle avoidance deep learning model is used to characterize the correspondence between the data collected by the sensor and the obstacle avoidance parameter of the unmanned vehicle.
  • the obstacle avoidance parameter 303 of the unmanned vehicle outputted by the obstacle avoidance deep learning model is acquired to control the unmanned vehicle 304 based on the obstacle avoidance parameter.
  • the method provided by the above embodiment of the present application obtains the obstacle avoidance parameter through the obstacle avoidance deep learning model, and realizes the control of driving.
  • the process 400 for the control method of the unmanned vehicle includes the following steps:
  • Step 401 Acquire data collected by at least two sensors, and obtain current obstacle avoidance parameters of the unmanned vehicle.
  • the model can be trained prior to applying the model.
  • the unmanned vehicle can acquire data collected by at least two sensors and obtain current obstacle avoidance parameters.
  • the above braking parameters are generated by the driving behavior of the user, that is, the obstacle avoidance parameters of the vehicle generated by the braking behavior taken by the user while driving the unmanned vehicle.
  • the unmanned vehicle here can be a designated unmanned vehicle or an arbitrary unmanned vehicle for model training. At least two sensors here are mounted on the unmanned vehicle.
  • Step 402 The acquired data and the current obstacle avoidance parameter are respectively used as input and output of the obstacle avoidance depth learning model to train the obstacle avoidance deep learning model.
  • the unmanned vehicle uses the data acquired in step 401 and the current obstacle avoidance parameter as the input and output of the obstacle avoidance depth learning model, respectively, to train the obstacle avoidance depth learning model.
  • Step 403 Acquire data collected by at least two sensors.
  • the unmanned vehicle is equipped with at least two sensors, and the unmanned vehicle on which the control method for the unmanned vehicle runs can obtain the above from a local or other electronic device through a wired connection or a wireless connection. Data collected by at least two sensors.
  • the number of each sensor may be one or two or more.
  • the at least two sensors may include a camera, a lidar, and a millimeter wave radar.
  • the camera can acquire image data or video stream data.
  • the laser radar uses a laser for detection, and the returned data is also a laser signal.
  • the millimeter wave radar uses millimeter waves for detection, and the collected data is millimeter wave signals.
  • step 404 the acquired data is input into a pre-trained obstacle avoidance learning model.
  • the unmanned vehicle can input the acquired data into the pre-trained obstacle avoidance depth learning model, so that the obstacle avoidance depth learning model outputs according to the input data.
  • the obstacle avoidance depth learning model is used to characterize the correspondence between the data collected by the sensor and the obstacle avoidance parameters of the unmanned vehicle.
  • the obstacle avoidance parameter is the parameter involved in the unmanned vehicle avoiding the obstacle.
  • the obstacle avoidance parameter here is the data that the unmanned vehicle uses immediately.
  • the above-mentioned obstacle avoidance deep learning model may be trained by a Classifier such as a Support Vector Machine (SVM) or a Naive Bayesian Model (NBM) model.
  • SVM Support Vector Machine
  • NBM Naive Bayesian Model
  • the image processing model described above may also be pre-trained based on certain classification functions (eg, softmax functions, etc.).
  • the obstacle avoidance deep learning model is trained in an end-to-end manner.
  • the end-to-end training model can take the data collected by the sensor as an input and output the obstacle avoidance parameters to be adopted by the unmanned vehicle.
  • the obstacle avoidance deep learning model is a deep neural network, which can directly generate obstacle avoidance parameters of vehicles based on the collected data. Specifically, in the training process of the model, what kind of data is used as the input and output for training, in the application process of the model, the corresponding output data can be obtained according to the input data.
  • Step 405 Acquire an obstacle avoidance parameter of the unmanned vehicle outputted by the obstacle avoidance depth learning model, so as to control the unmanned vehicle based on the obstacle avoidance parameter.
  • the unmanned vehicle acquisition obstacle avoidance learning model controls the unmanned vehicle based on the obstacle avoidance parameter according to the obstacle avoidance parameter of the unmanned vehicle outputted according to the input data.
  • the obstacle avoidance parameter includes a brake parameter and/or a steering parameter.
  • the braking parameter is a parameter used by the brake, and may include the magnitude of the deceleration of the unmanned vehicle, and may also include the direction of the deceleration of the unmanned vehicle. In general, the direction of deceleration of an unmanned vehicle is opposite to the direction of travel.
  • the steering parameter is a parameter for the unmanned vehicle to turn, and may be a steering angle such as a steering wheel angle or the like. The above-mentioned unmanned vehicle can perform the obstacle avoiding operation by limiting or adjusting any of the above two obstacle avoidance parameters.
  • the obstacle avoidance depth learning model is trained end-to-end, and the obstacle avoidance parameter can be accurately obtained in the application process of the model.
  • the present application provides an embodiment of a control device for an unmanned vehicle, the device embodiment corresponding to the method embodiment shown in FIG.
  • the device can be specifically applied to various electronic devices.
  • the control device 500 for an unmanned vehicle of the present embodiment includes an acquisition unit 501, an input unit 502, and a control unit 503.
  • the acquiring unit 501 is configured to acquire data collected by at least two sensors
  • the input unit 502 is configured to input the acquired data into a pre-trained obstacle avoidance learning model, wherein the obstacle avoidance deep learning model is used.
  • the control unit 503, configured to acquire the obstacle avoidance parameter of the unmanned vehicle output of the obstacle avoidance learning model, based on the The obstacle avoidance parameter controls the unmanned vehicle.
  • the acquiring unit 501 can acquire data collected by the at least two sensors from a local or other electronic device by using a wired connection manner or a wireless connection manner.
  • the number of each sensor may be one or two or more.
  • the input unit 502 can input the acquired data into the pre-trained obstacle avoidance depth learning model, so that the obstacle avoidance depth learning model outputs according to the input data.
  • the obstacle avoidance depth learning model is used to characterize the correspondence between the data collected by the sensor and the obstacle avoidance parameters of the unmanned vehicle.
  • the obstacle avoidance parameter is the parameter involved in the unmanned vehicle avoiding the obstacle.
  • the obstacle avoidance parameter here is the data that the unmanned vehicle uses immediately.
  • control unit 503 acquires the obstacle avoidance parameter of the unmanned vehicle output according to the input data by the obstacle avoidance depth learning model, and controls the unmanned vehicle based on the obstacle avoidance parameter.
  • the obstacle avoidance parameter includes a brake parameter and/or a steering parameter.
  • the at least two sensors include a camera, a lidar, and a millimeter wave radar.
  • the obstacle avoidance deep learning model is trained in an end-to-end manner.
  • the device further includes: a parameter acquiring unit configured to acquire data collected by the at least two sensors, and acquire an current obstacle avoidance parameter of the unmanned vehicle, where the obstacle avoidance parameter is determined by the user The driving behavior is generated; the training unit is configured to use the acquired data and the current obstacle avoidance parameter as input and output of the obstacle avoidance deep learning model, respectively, to train the obstacle avoidance deep learning model.
  • a parameter acquiring unit configured to acquire data collected by the at least two sensors, and acquire an current obstacle avoidance parameter of the unmanned vehicle, where the obstacle avoidance parameter is determined by the user The driving behavior is generated
  • the training unit is configured to use the acquired data and the current obstacle avoidance parameter as input and output of the obstacle avoidance deep learning model, respectively, to train the obstacle avoidance deep learning model.
  • FIG. 6 a block diagram of a computer system 600 suitable for use in implementing an unmanned vehicle of an embodiment of the present application is shown.
  • the unmanned vehicle shown in Fig. 6 is merely an example and should not impose any limitation on the function and scope of use of the embodiments of the present application.
  • computer system 600 includes a central processing unit (CPU) 601 that can be loaded into a program in random access memory (RAM) 603 according to a program stored in read only memory (ROM) 602 or from storage portion 608. And perform various appropriate actions and processes.
  • RAM random access memory
  • ROM read only memory
  • RAM random access memory
  • various programs and data required for the operation of the system 600 are also stored.
  • the CPU 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604.
  • An input/output (I/O) interface 605 is also coupled to bus 604.
  • the following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), and the like, and a storage portion 608 including a hard disk or the like. And a communication portion 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the Internet.
  • Driver 610 is also coupled to I/O interface 605 as needed.
  • a removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory or the like, is mounted on the drive 610 as needed so that a computer program read therefrom is installed into the storage portion 608 as needed.
  • an embodiment of the present disclosure includes a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for executing the method illustrated in the flowchart.
  • the computer program can be downloaded and installed from the network via communication portion 609, and/or installed from removable media 611.
  • the computer program is executed by the central processing unit (CPU) 601
  • the above-described functions defined in the method of the present application are performed.
  • the computer readable medium of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two.
  • the computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of computer readable storage media may include, but are not limited to, electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the foregoing.
  • a computer readable storage medium may be any tangible medium that can contain or store a program, which can be used by or in connection with an instruction execution system, apparatus or device.
  • a computer readable signal medium may include a data signal that is propagated in the baseband or as part of a carrier, carrying computer readable program code. Such propagated data signals can take a variety of forms including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • the computer readable signal medium can also be any computer readable medium other than a computer readable storage medium, which can transmit, propagate, or transport a program for use by or in connection with the instruction execution system, apparatus, or device.
  • Program code embodied on a computer readable medium can be transmitted by any suitable medium, including but not limited to wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
  • each block of the flowchart or block diagram can represent a module, a program segment, or a portion of code that includes one or more of the logic functions for implementing the specified.
  • Executable instructions can also occur in a different order than that illustrated in the drawings. For example, two successively represented blocks may in fact be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts can be implemented in a dedicated hardware-based system that performs the specified function or operation. Or it can be implemented by a combination of dedicated hardware and computer instructions.
  • the units involved in the embodiments of the present application may be implemented by software or by hardware.
  • the described unit may also be provided in the processor, for example, as a processor including an acquisition unit, an input unit, and a control unit.
  • the names of these units do not constitute a limitation on the unit itself under certain circumstances.
  • the acquisition unit may also be described as “a unit that acquires data collected by at least two sensors”.
  • the present application also provides a computer readable medium, which may be included in the apparatus described in the above embodiments, or may be separately present and not incorporated into the apparatus.
  • the computer readable medium carries one or more programs, when the one or more programs are executed by the device, causing the device to: acquire data collected by at least two sensors; and input the acquired data into a pre-trained obstacle avoidance
  • the deep learning model wherein the obstacle avoidance deep learning model is used to characterize the correspondence between the data collected by the sensor and the obstacle avoidance parameter of the unmanned vehicle; and the obstacle avoidance parameter of the unmanned vehicle output obtained by the obstacle avoidance deep learning model is obtained based on the avoidance
  • the obstacle parameter controls the unmanned vehicle.

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Abstract

L'invention concerne un procédé et un dispositif de commande destinés à un véhicule aérien sans pilote. Le procédé de commande consiste : à acquérir des données recueillies par au moins deux capteurs (201) ; à saisir les données acquises dans un modèle d'apprentissage profond d'évitement d'obstacle pré-entraîné (202), ledit modèle étant destiné à caractériser une relation de correspondance entre les données recueillies par les capteurs et des paramètres d'évitement d'obstacle du véhicule sans pilote (101) ; et à acquérir des paramètres d'évitement d'obstacle du véhicule sans pilote fournis par le modèle d'apprentissage profond d'évitement d'obstacle puis à commander le véhicule sans pilote (101) sur la base des paramètres d'évitement d'obstacle (203). L'invention fait appel à au moins deux capteurs de manière à améliorer la stabilité de détection et à commander l'entraînement du véhicule sans pilote (101).
PCT/CN2018/098630 2017-09-05 2018-08-03 Procédé et dispositif de commande destinés à un véhicule aérien sans pilote Ceased WO2019047643A1 (fr)

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CN201710791661.4A CN107515607A (zh) 2017-09-05 2017-09-05 用于无人车的控制方法和装置

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CN107515607A (zh) * 2017-09-05 2017-12-26 百度在线网络技术（北京）有限公司 用于无人车的控制方法和装置
CN110298219A (zh) 2018-03-23 2019-10-01 广州汽车集团股份有限公司 无人驾驶车道保持方法、装置、计算机设备和存储介质
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