CN111091066A - Method and system for evaluating ground state of automatic driving automobile - Google Patents
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Abstract
The invention discloses a method and a system for evaluating the ground state of an automatic driving automobile, belongs to the technical field of automatic driving automobiles, and solves the problem that the evaluation of the road state in the prior art is not accurate enough. A method for evaluating the ground state of an automatic driving automobile comprises the following steps: acquiring first ground image information, and acquiring ground state characteristics according to the first ground image information; adjusting light rays when the images are obtained, obtaining second ground image information, and obtaining ground state characteristics according to the second ground image information; and comparing whether the ground state features acquired twice are consistent or not, if so, determining the ground state by using the ground state features acquired at any time, otherwise, readjusting the light rays when the images are acquired, acquiring the ground image information, acquiring the ground state features, and determining the ground state by using the ground state features acquired at any time until the ground state features acquired twice are consistent. More accurate evaluation of the road surface state is achieved.
Description
Technical Field
The invention relates to the technical field of automatic driving automobiles, in particular to a method and a system for evaluating the ground state of an automatic driving automobile.
Background
Along with the popularization and the application of the unmanned technology, the unmanned vehicle is gradually popularized and applied, however, when the unmanned vehicle runs, the road surface state has great influence on the safety and reliability of the running of the vehicle, when the matching between the vehicle running state such as the vehicle speed, the steering radius and the like and the road surface state is poor, on one hand, the running comfort of the vehicle is relatively poor, on the other hand, the safety accident is easily caused when the vehicle runs, aiming at the problem, a road condition evaluation method which can match the whole road condition state with the running of the vehicle is lacked at present, therefore, the automatic driving vehicle has a great potential safety hazard during running, so that for the current situation, a method for evaluating the running road surface state of the automatic driving vehicle is needed, the method meets the actual use requirement, and the existing road surface state evaluation method is not accurate enough for evaluating the road surface state.
Disclosure of Invention
The invention aims to overcome at least one technical defect and provides a method and a system for evaluating the ground state of an automatic driving automobile.
In one aspect, the invention provides a method for evaluating the ground state of an automatic driving automobile, which comprises the following steps:
acquiring first ground image information in a vehicle running path, and acquiring ground state characteristics in the vehicle running path according to the first ground image information;
adjusting light rays when the images are obtained, obtaining second ground image information in a vehicle running path, and obtaining ground state characteristics in the vehicle running path according to the second ground image information;
comparing whether the ground state characteristics in the vehicle driving path obtained twice are consistent, if so, obtaining the ground state characteristics in the vehicle driving path at any time, determining the ground state,
otherwise, the light ray when the image is obtained is adjusted again, the ground image information in the vehicle running path is obtained, the ground state feature in the vehicle running path is obtained, and the ground state feature in the vehicle running path is obtained until the ground state features in the vehicle running path obtained twice are consistent, and the ground state feature in the vehicle running path is obtained at any time to determine the ground state.
Further, the adjusting the light ray when the image is obtained specifically includes obtaining illumination intensity information of an area where the vehicle runs, and determining the light ray adjustment size when the image is obtained according to the illumination intensity information.
Further, the step of determining the ground state by obtaining the ground state features in the vehicle driving path at any time specifically includes inputting the ground state features in the vehicle driving path obtained at any time into a convolutional neural network model to determine the ground state.
Further, the method for evaluating the ground state of the automatic driving automobile further comprises the steps of constructing a convolutional neural network model, specifically comprising,
collecting image information data of a test vehicle in the running process of different ground to form a training database; and constructing a convolutional neural network by using the training database, and training the constructed convolutional neural network according to a back propagation algorithm to form a convolutional neural network model based on ground state evaluation.
On the other hand, the invention also provides a system for evaluating the ground state of the automatic driving automobile, which comprises a first ground state characteristic acquisition module, a second ground state characteristic acquisition module and a ground state acquisition module;
the first ground state feature acquisition module is used for acquiring first ground image information in a vehicle running path and acquiring ground state features in the vehicle running path according to the first ground image information;
the second ground state characteristic acquisition module is used for adjusting the light rays when the images are acquired, acquiring second ground image information in the vehicle running path and acquiring the ground state characteristics in the vehicle running path according to the second ground image information;
the ground state acquisition module is used for comparing whether the ground state characteristics in the vehicle running path acquired twice are consistent or not, if so, acquiring the ground state characteristics in the vehicle running path at any time to determine the ground state,
otherwise, the light ray when the image is obtained is adjusted again, the ground image information in the vehicle running path is obtained, the ground state feature in the vehicle running path is obtained, and the ground state feature in the vehicle running path is obtained until the ground state features in the vehicle running path obtained twice are consistent, and the ground state feature in the vehicle running path is obtained at any time to determine the ground state.
Further, the system for evaluating the ground state of the automatic driving automobile further comprises a light ray adjusting module, wherein the light ray adjusting module is used for adjusting light rays when the image is obtained, and specifically comprises the steps of obtaining illumination intensity information of an area where the automobile runs, and determining the size of light ray adjustment when the image is obtained according to the illumination intensity information.
The ground state obtaining module obtains the ground state characteristics in the vehicle running path at any time, and determines the ground state.
Further, the automatic driving automobile ground state evaluation system also comprises a convolutional neural network model construction module, wherein the convolutional neural network model construction module is used for constructing a convolutional neural network model, and specifically comprises,
collecting image information data of a test vehicle in the running process of different ground to form a training database; and constructing a convolutional neural network by using the training database, and training the constructed convolutional neural network according to a back propagation algorithm to form a convolutional neural network model based on ground state evaluation.
Compared with the prior art, the invention has the beneficial effects that: acquiring ground state characteristics in a vehicle driving path according to first ground image information by acquiring the first ground image information in the vehicle driving path; adjusting light rays when the images are obtained, obtaining second ground image information in a vehicle running path, and obtaining ground state characteristics in the vehicle running path according to the second ground image information; and comparing whether the ground state features in the vehicle running paths obtained twice are consistent or not, if so, obtaining the ground state features in the vehicle running paths at any time, and determining the ground state, otherwise, readjusting the light rays when the images are obtained, obtaining the ground image information in the vehicle running paths, obtaining the ground state features in the vehicle running paths, and obtaining the ground state features in the vehicle running paths at any time until the ground state features in the vehicle running paths obtained twice are consistent, and determining the ground state. More accurate evaluation of the road surface state is achieved.
Drawings
Fig. 1 is a schematic flow chart of a method for evaluating the ground condition of an autonomous vehicle according to embodiment 1 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
The invention provides a method for evaluating the ground state of an automatic driving automobile, which comprises the following steps:
acquiring first ground image information in a vehicle running path, and acquiring ground state characteristics in the vehicle running path according to the first ground image information;
adjusting light rays when the images are obtained, obtaining second ground image information in a vehicle running path, and obtaining ground state characteristics in the vehicle running path according to the second ground image information;
comparing whether the ground state characteristics in the vehicle driving path obtained twice are consistent, if so, obtaining the ground state characteristics in the vehicle driving path at any time, determining the ground state,
otherwise, the light ray when the image is obtained is adjusted again, the ground image information in the vehicle running path is obtained, the ground state feature in the vehicle running path is obtained, and the ground state feature in the vehicle running path is obtained until the ground state features in the vehicle running path obtained twice are consistent, and the ground state feature in the vehicle running path is obtained at any time to determine the ground state.
It should be noted that the autonomous vehicle may include at least one camera and may include a plurality of cameras, the adjusting light device when acquiring images may be a light generating device, for example, a Light Emitting Diode (LED), flash, laser, etc. camera may include a device suitable for use with an image recognition application, and the device may be capable of capturing an electronic image and transferring and saving the image to a storage device.
In one specific implementation, the ground state characteristics in the vehicle running path obtained twice are compared to be consistent, and the ground state characteristics in the vehicle running path are obtained at any time to determine the ground state; at the moment, the light ray when the image is obtained does not need to be adjusted again; otherwise, the light ray for acquiring the image needs to be adjusted again to acquire the ground state characteristics in the driving path of the vehicle.
Preferably, the adjusting the light ray when the image is acquired specifically includes acquiring illumination intensity information of an area where the vehicle runs, and determining the light ray adjustment size when the image is acquired according to the illumination intensity information.
Preferably, the obtaining the ground state feature in the vehicle driving path at any time to determine the ground state specifically includes inputting the ground state feature in the vehicle driving path obtained at any time to the convolutional neural network model to determine the ground state.
Preferably, the method for evaluating the ground state of the automatic driving automobile further comprises the steps of constructing a convolutional neural network model, specifically, collecting image information data of a test vehicle in the driving process of different ground surfaces to form a training database; and constructing a convolutional neural network by using the training database, and training the constructed convolutional neural network according to a back propagation algorithm to form a convolutional neural network model based on ground state evaluation.
It should be noted that, the embodiment of the present invention determines the ground state by using a convolutional neural network model, which is a deep feedforward artificial neural network that uses variants of multi-layer perceptrons designed to require minimal preprocessing; convolutional neural networks use relatively little preprocessing compared to other network models, which allows convolutional neural networks to learn the filter. The ground state according to an embodiment of the present invention includes a ground type and a ground condition, and the classifier in the convolutional neural network model may be defined to classify the ground as one of asphalt road, cement road, gravel road, dirt road, pothole, and one of dry, slippery, or snow.
Example 2
The invention also provides a system for evaluating the ground state of the automatic driving automobile, which comprises a first ground state characteristic acquisition module, a second ground state characteristic acquisition module and a ground state acquisition module;
the first ground state feature acquisition module is used for acquiring first ground image information in a vehicle running path and acquiring ground state features in the vehicle running path according to the first ground image information;
the second ground state characteristic acquisition module is used for adjusting the light rays when the images are acquired, acquiring second ground image information in the vehicle running path and acquiring the ground state characteristics in the vehicle running path according to the second ground image information;
the ground state acquisition module is used for comparing whether the ground state characteristics in the vehicle running path acquired twice are consistent or not, if so, acquiring the ground state characteristics in the vehicle running path at any time to determine the ground state,
otherwise, the light ray when the image is obtained is adjusted again, the ground image information in the vehicle running path is obtained, the ground state feature in the vehicle running path is obtained, and the ground state feature in the vehicle running path is obtained until the ground state features in the vehicle running path obtained twice are consistent, and the ground state feature in the vehicle running path is obtained at any time to determine the ground state.
The vehicle-mounted light adjusting device is characterized by further comprising a light adjusting module, wherein the light adjusting module is used for adjusting light when an image is obtained, the light adjusting module specifically comprises the steps of obtaining illumination intensity information of an area where the vehicle runs, and determining the light adjusting size when the image is obtained according to the illumination intensity information.
Preferably, the ground state obtaining module obtains the ground state features in the vehicle driving path at any time to determine the ground state, and specifically includes inputting the ground state features in the vehicle driving path obtained at any time to the convolutional neural network model to determine the ground state.
Preferably, the automatic driving automobile ground state evaluation system further comprises a convolutional neural network model construction module, wherein the convolutional neural network model construction module is used for constructing a convolutional neural network model, and specifically comprises,
collecting image information data of a test vehicle in the running process of different ground to form a training database; and constructing a convolutional neural network by using the training database, and training the constructed convolutional neural network according to a back propagation algorithm to form a convolutional neural network model based on ground state evaluation.
The invention discloses a method and a system for evaluating the ground state of an automatic driving automobile, which are characterized in that the ground state characteristics in a vehicle driving path are obtained according to first ground image information in the vehicle driving path; adjusting light rays when the images are obtained, obtaining second ground image information in a vehicle running path, and obtaining ground state characteristics in the vehicle running path according to the second ground image information; and comparing whether the ground state features in the vehicle running paths obtained twice are consistent or not, if so, obtaining the ground state features in the vehicle running paths at any time, and determining the ground state, otherwise, readjusting the light rays when the images are obtained, obtaining the ground image information in the vehicle running paths, obtaining the ground state features in the vehicle running paths, and obtaining the ground state features in the vehicle running paths at any time until the ground state features in the vehicle running paths obtained twice are consistent, and determining the ground state. More accurate evaluation of the road surface state is achieved.
Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program, which is stored in a computer readable storage medium, to instruct related hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above-described embodiments of the present invention should not be construed as limiting the scope of the present invention. Any other corresponding changes and modifications made according to the technical idea of the present invention should be included in the protection scope of the claims of the present invention.
Claims (8)
1. A method for evaluating the ground state of an automatic driving automobile is characterized by comprising the following steps:
acquiring first ground image information in a vehicle running path, and acquiring ground state characteristics in the vehicle running path according to the first ground image information;
adjusting light rays when the images are obtained, obtaining second ground image information in a vehicle running path, and obtaining ground state characteristics in the vehicle running path according to the second ground image information;
comparing whether the ground state characteristics in the vehicle driving path obtained twice are consistent, if so, obtaining the ground state characteristics in the vehicle driving path at any time, determining the ground state,
otherwise, the light ray when the image is obtained is adjusted again, the ground image information in the vehicle running path is obtained, the ground state feature in the vehicle running path is obtained, and the ground state feature in the vehicle running path is obtained until the ground state features in the vehicle running path obtained twice are consistent, and the ground state feature in the vehicle running path is obtained at any time to determine the ground state.
2. The method according to claim 1, wherein the adjusting the light for obtaining the image comprises obtaining light intensity information of an area where the vehicle is driving, and determining the light adjustment for obtaining the image according to the light intensity information.
3. The method of claim 1, wherein the determining the ground state based on the ground state characteristics of the vehicle along the travel path obtained at any one time comprises inputting the ground state characteristics of the vehicle along the travel path obtained at any one time into a convolutional neural network model to determine the ground state.
4. The automated guided vehicle ground condition assessment method of claim 3, further comprising constructing a convolutional neural network model, in particular comprising,
collecting image information data of a test vehicle in the running process of different ground to form a training database; and constructing a convolutional neural network by using the training database, and training the constructed convolutional neural network according to a back propagation algorithm to form a convolutional neural network model based on ground state evaluation.
5. A system for evaluating the ground state of an automatic driving automobile is characterized by comprising a first ground state feature acquisition module, a second ground state feature acquisition module and a ground state acquisition module;
the first ground state feature acquisition module is used for acquiring first ground image information in a vehicle running path and acquiring ground state features in the vehicle running path according to the first ground image information;
the second ground state characteristic acquisition module is used for adjusting the light rays when the images are acquired, acquiring second ground image information in the vehicle running path and acquiring the ground state characteristics in the vehicle running path according to the second ground image information;
the ground state acquisition module is used for comparing whether the ground state characteristics in the vehicle running path acquired twice are consistent or not, if so, acquiring the ground state characteristics in the vehicle running path at any time to determine the ground state,
otherwise, the light ray when the image is obtained is adjusted again, the ground image information in the vehicle running path is obtained, the ground state feature in the vehicle running path is obtained, and the ground state feature in the vehicle running path is obtained until the ground state features in the vehicle running path obtained twice are consistent, and the ground state feature in the vehicle running path is obtained at any time to determine the ground state.
6. The system according to claim 5, further comprising a light adjustment module, wherein the light adjustment module is configured to adjust light of the captured image, and specifically comprises obtaining illumination intensity information of an area where the vehicle is traveling, and determining a light adjustment size of the captured image according to the illumination intensity information.
7. The system of claim 5, wherein the ground state obtaining module determines the ground state by obtaining the ground state characteristics of the vehicle in the driving path at any one time, and specifically comprises inputting the ground state characteristics of the vehicle in the driving path obtained at any one time into the convolutional neural network model to determine the ground state.
8. The autopilot ground condition assessment system of claim 7, further comprising a convolutional neural network model construction module for constructing a convolutional neural network model, in particular comprising,
collecting image information data of a test vehicle in the running process of different ground to form a training database; and constructing a convolutional neural network by using the training database, and training the constructed convolutional neural network according to a back propagation algorithm to form a convolutional neural network model based on ground state evaluation.
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