CN112339693B - Method and device for automatically unlocking vehicle door lock, computer equipment and storage medium - Google Patents
Method and device for automatically unlocking vehicle door lock, computer equipment and storage medium Download PDFInfo
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- CN112339693B CN112339693B CN202011378366.4A CN202011378366A CN112339693B CN 112339693 B CN112339693 B CN 112339693B CN 202011378366 A CN202011378366 A CN 202011378366A CN 112339693 B CN112339693 B CN 112339693B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R21/00—Arrangements or fittings on vehicles for protecting or preventing injuries to occupants or pedestrians in case of accidents or other traffic risks
- B60R21/01—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
- B60Q9/00—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
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Abstract
The application relates to a method, a device, a computer device and a storage medium for automatically unlocking a vehicle door lock. The method comprises the following steps: when a signal that a door of a vehicle is locked is received, opening cameras arranged on the periphery of a vehicle body of the vehicle to acquire images in real time to obtain image information; inputting the image information into a deep learning perception module for collision perception, and outputting a perception result; and when the sensing result is that collision exists, an unlocking signal is sent to the vehicle body controller, so that the vehicle body controller is automatically unlocked. Adopt the camera to gather peripheral information, when the collision is about to take place, send unlocking signal to BCM in advance and open the lock. Therefore, the probability of successful unlocking of the vehicle lock after collision is improved.
Description
Technical Field
The present application relates to the field of automotive technologies, and in particular, to a method and an apparatus for automatically unlocking a door lock, a computer device, and a storage medium.
Background
In the existing vehicle scheme, after an airbag is popped up after a serious collision occurs, an ACU (airbag controller) sends a signal to a BCM (vehicle body controller), and the BCM unlocks a vehicle lock.
After collision occurs, the electronic device needs to be ensured to work normally, and the BCM (body controller) can unlock the vehicle lock. However, after some serious collisions occur, the vehicle electronic devices are often damaged, so that the vehicle lock can not be automatically unlocked after the collision occurs, and the probability of successfully unlocking the vehicle lock after the collision occurs is low.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a computer device and a storage medium for automatically unlocking a vehicle door lock, which can improve the probability of successful unlocking of the vehicle door lock after a collision.
A method of automatically unlocking a vehicle door lock, the method comprising:
when a signal of locking a vehicle door of a vehicle is received, opening cameras arranged around the vehicle body of the vehicle to acquire images in real time to obtain image information;
inputting the image information into a deep learning perception module for collision perception, and outputting a perception result;
and when the sensing result is that collision exists, an unlocking signal is sent to the vehicle body controller, so that the vehicle body controller is automatically unlocked.
In one example, the method further comprises:
and an alarm in the vehicle is started to give an alarm to prompt the driver that danger exists.
In one example, the deep learning perception module is constructed in a manner that:
acquiring an image sample;
marking the image sample to obtain a sample data set;
and training the deep learning perception module to be trained according to the sample data set to obtain the trained deep learning perception module.
In one example, the step of inputting the image information to a deep learning perception module for collision perception and outputting a perception result includes:
inputting the image information into a deep learning perception module for collision perception to obtain specific position information of a target object in the image information;
and analyzing according to the specific position information to determine whether a collision condition exists.
In one example, the step of analyzing according to the specific location information to determine whether there is a collision condition includes:
determining the distance between the target object and the vehicle according to the specific position information;
when the distance between the target object and the vehicle is smaller than or equal to a preset value, determining that a collision condition exists;
and when the distance between the target object and the vehicle is larger than a preset value, determining that no collision exists.
A device for automatically unlocking a door lock of a vehicle, the device comprising:
the system comprises an image acquisition module, a data processing module and a data processing module, wherein the image acquisition module is used for opening cameras arranged around the vehicle body of a vehicle to acquire images in real time to obtain image information when a signal that the door of the vehicle is locked is received;
the collision sensing module is used for inputting the image information into the deep learning sensing module for collision sensing and outputting a sensing result;
and the automatic unlocking module is used for sending an unlocking signal to the vehicle body controller when the sensing result is that the collision exists, so that the vehicle body controller can be automatically unlocked.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method.
According to the method, the device, the computer equipment and the storage medium for automatically unlocking the vehicle door lock, when a signal that the vehicle door of a vehicle is locked is received, the cameras arranged around the vehicle body of the vehicle are started to acquire images in real time, so that image information is obtained; inputting the image information into a deep learning perception module for collision perception, and outputting a perception result; and when the sensing result is that collision exists, an unlocking signal is sent to the vehicle body controller, so that the vehicle body controller is automatically unlocked. Adopt the camera to gather peripheral information, when the collision is about to take place, send unlocking signal to BCM in advance and open the lock. Therefore, the probability of successful unlocking of the vehicle lock after collision is improved.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for automatically unlocking a door lock in one embodiment;
FIG. 2 is a block diagram of a camera mounting configuration in one embodiment;
fig. 3 is a block diagram of a device for automatically unlocking a door lock in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in 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 present application and are not intended to limit the present application.
In one embodiment, as shown in FIG. 1, there is provided a method for automatically unlocking a door lock of a vehicle, comprising the steps of:
and step S220, when a signal that the door of the vehicle is locked is received, opening cameras arranged around the vehicle body of the vehicle to acquire images in real time, and obtaining image information.
The cameras are installed around the body of the vehicle, and as shown in fig. 2, the direction of the head of the vehicle is taken as the advancing direction, and the camera 1 at the tail position of the vehicle, the camera 2 at the left position of the vehicle, the camera 3 at the right position of the vehicle and the camera 4 at the head position of the vehicle are arranged.
And step S240, inputting the image information into the deep learning perception module for collision perception, and outputting a perception result.
In one example, the step of inputting the image information into the deep learning perception module for collision perception and outputting a perception result comprises: inputting the image information into a deep learning perception module for collision perception to obtain specific position information of a target object in the image information; and analyzing according to the specific position information to determine whether the collision exists or not.
In one example, the step of analyzing based on the specific location information to determine whether a collision condition exists includes: determining the distance between the target object and the vehicle according to the specific position information; when the distance between the target object and the vehicle is smaller than or equal to a preset value, determining that a collision condition exists; when the distance between the target object and the vehicle is greater than a preset value, it is determined that there is no collision.
The preset value can be determined according to the braking distance after the vehicle brakes, and the preset value can be 8m, 10m, 19m and the like.
In one example, the deep learning perception module is constructed in the following manner:
acquiring an image sample; marking the image sample to obtain a sample data set; and training the deep learning perception module to be trained according to the sample data set to obtain the trained deep learning perception module.
The acquired image sample can be a picture acquired by a camera for cleaning, namely, a repeated picture is deleted, and the image sample has a diversified image data set. The method comprises the steps of marking an image sample, namely framing a target object in an image data set by using a marking tool, mainly providing reference for subsequent neural network training, training a deep learning perception module to be trained, training the deep learning perception module to be trained on the basis of a marked sample data set, and storing the trained deep learning perception module after full training.
And step S260, when the sensing result is that the collision exists, an unlocking signal is sent to the vehicle body controller, so that the vehicle body controller is automatically unlocked.
In one example, the method of automatically unlocking a door lock further comprises: and an alarm in the vehicle is started to give an alarm to prompt the driver that danger exists.
According to the method for automatically unlocking the vehicle door lock, when a signal that the vehicle door of the vehicle is locked is received, the cameras arranged around the vehicle body of the vehicle are opened to acquire images in real time, and image information is obtained; inputting the image information into a deep learning perception module for collision perception, and outputting a perception result; and when the sensing result is that the collision exists, an unlocking signal is sent to the vehicle body controller, so that the vehicle body controller is automatically unlocked. Adopt the camera to gather information on every side, when the collision is about to take place, send unlocking signal to BCM in advance and open the lock. Therefore, the probability of successful unlocking of the vehicle lock after collision is improved.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 3, there is provided a device for automatically unlocking a door lock of a vehicle, including: an image acquisition module 310, a collision perception module 320, and an automatic unlocking module 330, wherein:
the image acquisition module 310 is used for opening cameras arranged around the vehicle body of the vehicle to acquire images in real time to obtain image information when a signal that the door of the vehicle is locked is received;
the collision sensing module 320 is used for inputting the image information into the deep learning sensing module for collision sensing and outputting a sensing result;
and the automatic unlocking module 330 is configured to send an unlocking signal to the vehicle body controller when the collision is detected, so that the vehicle body controller is automatically unlocked.
In one example, the apparatus for automatically unlocking a door lock further comprises an alarm module: and the alarm is used for starting the alarm in the automobile to alarm and prompting the driver to have danger.
In one example, the deep learning perception module is constructed in a way that: acquiring an image sample; marking the image sample to obtain a sample data set; and training the deep learning perception module to be trained according to the sample data set to obtain the trained deep learning perception module.
In one example, the collision sensing module 320 is further configured to: inputting the image information into a deep learning perception module for collision perception to obtain specific position information of a target object in the image information; and analyzing according to the specific position information to determine whether the collision exists or not.
In one example, the collision sensing module 320 is further configured to: determining the distance between the target object and the vehicle according to the specific position information; when the distance between the target object and the vehicle is smaller than or equal to a preset value, determining that a collision condition exists; when the distance between the target object and the vehicle is greater than a preset value, it is determined that there is no collision.
Specific limitations regarding the means for automatically unlocking the door lock can be found in the above limitations regarding the method for automatically unlocking the door lock, which are not described in detail herein. The modules in the device for automatically unlocking the vehicle door lock can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method for automatically unlocking a vehicle door when executing the computer program.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method for automatically unlocking a vehicle door lock described above.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (5)
1. A method of automatically unlocking a door lock of a vehicle, the method comprising:
when receiving the signal that the door of vehicle was locked, open and install the automobile body of vehicle camera all around carries out real-time acquisition image, obtains image information, the automobile body camera all around includes: the camera at the tail position of the vehicle, the camera at the left position of the vehicle, the camera at the right position of the vehicle and the camera at the head position of the vehicle are arranged;
inputting the image information into a deep learning perception module for collision perception, and outputting a perception result;
when the sensing result is that collision exists, an unlocking signal is sent to the vehicle body controller, so that the vehicle body controller is automatically unlocked;
the step of inputting the image information into the deep learning perception module for collision perception and outputting a perception result comprises the following steps: inputting the image information into a deep learning perception module for collision perception to obtain specific position information of a target object in the image information; analyzing according to the specific position information to determine whether a collision condition exists;
the step of analyzing according to the specific location information and determining whether there is a collision condition includes: determining the distance between the target object and the vehicle according to the specific position information; when the distance between the target object and the vehicle is smaller than or equal to a preset value, determining that a collision condition exists; when the distance between the target object and the vehicle is larger than a preset value, determining that no collision exists, wherein the preset value is determined to be 10m according to the braking distance after the vehicle brakes;
the deep learning perception module is constructed in the following mode:
acquiring an image sample;
marking the image sample to obtain a sample data set;
and training the deep learning perception module to be trained according to the sample data set to obtain the trained deep learning perception module.
2. The method of claim 1, further comprising:
and an alarm in the vehicle is started to give an alarm to prompt the driver that danger exists.
3. A device for automatically unlocking a door lock of a vehicle, the device comprising:
the image acquisition module is used for when the door of receiving the vehicle is locked the signal, opens and installs the camera all around of the automobile body of vehicle carries out the image of gathering in real time, obtains image information, the camera all around of automobile body includes: the camera at the tail position of the vehicle, the camera at the left position of the vehicle, the camera at the right position of the vehicle and the camera at the head position of the vehicle;
the collision sensing module is used for inputting the image information into the deep learning sensing module for collision sensing and outputting a sensing result;
the automatic unlocking module is used for sending an unlocking signal to the vehicle body controller when the sensing result indicates that the collision exists, so that the vehicle body controller automatically unlocks;
the collision sensing module is further configured to: inputting the image information into a deep learning perception module for collision perception to obtain specific position information of a target object in the image information; analyzing according to the specific position information to determine whether a collision condition exists;
the collision sensing module is further configured to: determining the distance between the target object and the vehicle according to the specific position information; when the distance between the target object and the vehicle is smaller than or equal to a preset value, determining that a collision condition exists; when the distance between the target object and the vehicle is larger than a preset value, determining that no collision exists;
the deep learning perception module is constructed in the following mode:
acquiring an image sample;
marking the image sample to obtain a sample data set;
and training the deep learning perception module to be trained according to the sample data set to obtain the trained deep learning perception module.
4. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any one of claims 1 to 2 when executing the computer program.
5. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 2.
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| CN202011378366.4A CN112339693B (en) | 2020-11-30 | 2020-11-30 | Method and device for automatically unlocking vehicle door lock, computer equipment and storage medium |
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| CN202011378366.4A CN112339693B (en) | 2020-11-30 | 2020-11-30 | Method and device for automatically unlocking vehicle door lock, computer equipment and storage medium |
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| CN112339693B true CN112339693B (en) | 2022-09-20 |
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| CN115217373B (en) * | 2022-03-31 | 2024-05-14 | 广州汽车集团股份有限公司 | Pre-crash door lock control method and device, vehicle and storage medium |
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