CN116176493A - Control method and system for automatic windshield wiper - Google Patents

Control method and system for automatic windshield wiper Download PDF

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Publication number
CN116176493A
CN116176493A CN202211589306.6A CN202211589306A CN116176493A CN 116176493 A CN116176493 A CN 116176493A CN 202211589306 A CN202211589306 A CN 202211589306A CN 116176493 A CN116176493 A CN 116176493A
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obstacle
area
wiper
threshold value
type
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吴丹玲
李广宇
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Chery New Energy Automobile Co Ltd
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Chery New Energy Automobile Co Ltd
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Priority to CN202211589306.6A priority Critical patent/CN116176493A/en
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60S—SERVICING, CLEANING, REPAIRING, SUPPORTING, LIFTING, OR MANOEUVRING OF VEHICLES, NOT OTHERWISE PROVIDED FOR
    • B60S1/00—Cleaning of vehicles
    • B60S1/02—Cleaning windscreens, windows or optical devices
    • B60S1/04—Wipers or the like, e.g. scrapers
    • B60S1/06—Wipers or the like, e.g. scrapers characterised by the drive
    • B60S1/08—Wipers or the like, e.g. scrapers characterised by the drive electrically driven
    • B60S1/0818—Wipers or the like, e.g. scrapers characterised by the drive electrically driven including control systems responsive to external conditions, e.g. by detection of moisture, dirt or the like
    • B60S1/0822—Wipers or the like, e.g. scrapers characterised by the drive electrically driven including control systems responsive to external conditions, e.g. by detection of moisture, dirt or the like characterized by the arrangement or type of detection means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • 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
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30248—Vehicle exterior or interior
    • G06T2207/30252—Vehicle exterior; Vicinity of vehicle
    • G06T2207/30261—Obstacle

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Multimedia (AREA)
  • Health & Medical Sciences (AREA)
  • Mechanical Engineering (AREA)
  • Artificial Intelligence (AREA)
  • Automation & Control Theory (AREA)
  • Computing Systems (AREA)
  • Databases & Information Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Geometry (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

The invention belongs to the field of windshield wiper control, and particularly discloses a control method and a system of an automatic windshield wiper, wherein an image video of a front windshield obstacle of a vehicle is collected, a video frame sequence is trained by a deep learning algorithm to distinguish the type and the area of the obstacle, and the speed and the cleaning mode of the automatic windshield wiper are determined according to the area and the type of the obstacle; in addition, when the types of the obstacles are distinguished, the types of the obstacles are divided in detail through analysis of the attributes of the obstacles, so that different obstacles can be identified more efficiently, and the control of the automatic windshield wiper can be completed by adopting different control modes.

Description

Control method and system for automatic windshield wiper
Technical Field
The invention belongs to the field of windshield wiper control, and particularly relates to a control method and a control system of an automatic windshield wiper.
Background
The windshield wiper is used as a basic component of an automobile, and is a tool for cleaning the front windshield of the automobile to prevent rainwater and other dirty objects from affecting the sight. The traditional windshield wiper is manually started and controls the movement gear of the windshield wiper because the driver is required to judge the conditions of the clear and rain-proof of the windshield of the bicycle, and the like, so that the driver is easy to be distracted, and traffic accidents are caused. Meanwhile, the existing electric automobile is provided with an ADAS advanced auxiliary system, which has higher requirements on the definition of the vehicle-mounted camera.
Therefore, automatic wiper control systems based on a rain sensor are emerging on the market. The automatic windshield wiper control system is mainly realized by the following principle: the rainfall is detected by the rainfall sensor, the detection value is converted into an electric signal, and then the time interval of the windshield wiper is controlled according to the size of the electric signal, so that the automatic control of the windshield wiper is realized. Currently, the control system sensors applied to automatic rain wipers in the market mainly comprise the following three types: piezoelectric sensors, electrostatic capacitance sensors, sensors of light intensity variation. The piezoelectric sensor and the electrostatic capacitance sensor are arranged outside the vehicle, and raindrops directly realize rainfall detection at the sensor; the sensor for the change of the light intensity is characterized in that a rain sensor is arranged in a windshield of a cockpit, and the rain amount is detected by sensing caused by the change of the light intensity reflected by rain falling on the windshield. Through practical use and analysis, the rain sensor has the defects of limited measurement range, environmental influence on detection of key factors and the like.
In order to overcome the above drawbacks, a wiper control method and system are proposed in the patent publication CN114312672 a. Collecting video images of a front windshield of a vehicle through a front camera of a vehicle recorder; obtaining the area of the obstacle by carrying out image analysis on the video image; acquiring the swinging frequency of the windshield wiper required for removing the obstacle according to the area of the obstacle; and controlling the windshield wiper driving motor to control the windshield wiper to swing at the windshield wiper swing frequency. Another patent publication No. CN112109664a proposes a control method by collecting an image of a front windshield; processing the image to obtain the shielding degree and the type of the obstacle of the front windshield; the front windshield is cleaned based on the degree of shielding and the type of obstacle. According to the scheme, although the limitation that the operation of the windshield wiper is controlled only according to the rainfall sensed by the rainfall sensor in the prior art can be solved, and the windshield wiper can be automatically controlled to swing according to the area of the obstacle to improve the intelligence of the windshield wiper control at a proper frequency, the characteristics of the obstacle are not considered in the scheme, the swinging frequency of the windshield wiper is controlled only according to the area and the general type of the obstacle, and when a plurality of types of obstacles appear on the front windshield, corresponding processing cannot be performed carefully according to the specific type of the obstacle.
Therefore, an automatic wiper control scheme that is highly accurate, efficient, and effective in reducing extraneous environmental impact becomes necessary.
Disclosure of Invention
In view of the above problems, the present invention discloses a control method of an automatic wiper, the control method comprising the steps of:
collecting an image video of a front windshield of a vehicle and forming a video frame sequence;
detecting the area and the type of the obstacle in sequence according to the video frame sequence, wherein the type of the obstacle comprises cleanable obstacles and uncleanable obstacles;
the speed and the cleaning mode of the automatic windshield wiper are determined according to the area and the type of the obstacle.
Further, before sequentially detecting the area and the type of the obstacle according to the video frame sequence, judging whether the obstacle exists or not according to the video frame sequence;
judging the type of the obstacle if the obstacle exists; if no obstacle exists, continuing to acquire the image video of the front windshield of the vehicle.
Further, the cleanable barrier includes rain, snow, and/or foliage;
the uncleanable barrier comprises soil and/or large area bird droppings.
Further, composing the video frame sequence includes: and setting a certain time interval to extract pictures in the image video, and forming a video frame sequence by a plurality of pictures extracted in the interval time.
Further, detecting the area and the type of the obstacle according to the video frame sequence sequentially comprises the following steps:
calculating an obstacle area in a video frame sequence and judging whether the obstacle area exceeds a first area threshold;
if the area of the obstacle exceeds a first area threshold, inputting the current frame of the video frame sequence into a deep learning model;
and judging the type of the obstacle based on the deep learning model.
Further, if the obstacle area does not exceed the first area threshold, discarding the video of the current frame and continuously collecting the image video of the front windshield of the vehicle.
Further, determining the speed and the sweeping mode of the automatic windshield wiper according to the area and the type of the obstacle comprises the following steps:
judging the type of the obstacle, if the obstacle is an uncleanable obstacle, not starting the windshield wiper to move, and pre-warning a driver to stop the vehicle by the side to manually clear the obstacle;
and if the obstacle is a cleanable obstacle, calculating the area of the obstacle, and determining the movement frequency required by the windshield wiper to scrape the obstacle according to the area of the obstacle.
Further, determining the movement frequency required by the windshield wiper to scrape the obstacle according to the obstacle area comprises the following steps:
if the area of the obstacle exceeds the first area threshold value and is smaller than or equal to the second area threshold value, the windshield wiper speed is set to be F1;
if the area of the obstacle exceeds the second area threshold value and is equal to or less than the third area threshold value, the wiper speed is set to F2.
Further, the control method further comprises the step of further judging whether an obstacle exists or not after the windshield wiper moves for more than a certain time;
if the obstacle still exists, readjusting the movement rate of the windshield wiper according to the relation between the area of the residual obstacle and the first area threshold value, the second area threshold value and the third area threshold value;
if no obstacle exists, continuing to collect the image video of the front windshield of the vehicle.
In another aspect, the present invention provides a control system for an automatic wiper, the control system comprising:
the acquisition unit is used for acquiring an image video of a front windshield of the vehicle and forming a video frame sequence;
the detection unit is used for sequentially detecting the area and the type of the obstacle according to the video frame sequence, wherein the type of the obstacle comprises cleanable obstacles and uncleanable obstacles;
and the determining unit is used for determining the speed and the cleaning mode of the automatic windshield wiper according to the area and the type of the obstacle.
Further, the detection unit executes the following logic to sequentially detect the area and the type of the obstacle:
calculating an obstacle area in a video frame sequence and judging whether the obstacle area exceeds a first area threshold;
if the area of the obstacle exceeds a first area threshold, inputting the current frame of the video frame sequence into a deep learning model; if the area of the obstacle does not exceed the first area threshold, discarding the video of the current frame and continuously collecting the image video of the front windshield of the vehicle;
and judging the type of the obstacle based on the deep learning model.
Further, the determination unit performs the following logic to determine the speed and sweep pattern of the automatic wiper:
judging the type of the obstacle, if the obstacle is an uncleanable obstacle, not starting the windshield wiper to move, and pre-warning a driver to park and manually clear the obstacle;
if the obstacle is a cleanable obstacle, calculating the area of the obstacle, and determining the movement frequency required by the windshield wiper to scrape the obstacle according to the area of the obstacle; if the area of the obstacle exceeds the first area threshold value and is smaller than or equal to the second area threshold value, the windshield wiper speed is set to be F1; if the area of the obstacle exceeds the second area threshold value and is equal to or less than the third area threshold value, the wiper speed is set to F2.
Further, the control system further comprises a follow-up judging unit, which is used for further judging whether the obstacle exists after the windshield wiper moves for a certain time; if the obstacle still exists, readjusting the movement rate of the windshield wiper according to the relation between the area of the residual obstacle and the first area threshold value, the second area threshold value and the third area threshold value; if no obstacle exists, continuing to collect the image video of the front windshield of the vehicle.
The invention has the beneficial effects that:
according to the invention, through collecting an image video of a front windshield obstacle of a vehicle, training a video frame sequence by using a deep learning algorithm to distinguish the type and the area of the obstacle, and determining the speed and the cleaning mode of an automatic windshield wiper according to the area and the type of the obstacle; in addition, when the types of the obstacles are distinguished, the types of the obstacles are divided in detail through analysis of the attributes of the obstacles, so that different obstacles can be identified more efficiently, and the control of the automatic windshield wiper is completed by adopting different control modes.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows a main flow chart of a control method in an embodiment of the invention;
fig. 2 shows a detailed flowchart of a control method in an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Based on the prior art, the self properties of the obstacles are not considered, the swinging frequency of the windshield wiper is controlled only by the area and the approximate type of the obstacles, and when the front windshield presents a plurality of types of obstacles, the corresponding processing can not be intelligently carried out according to the types of the obstacles. The invention provides a control method of an automatic windshield wiper based on the type of the obstacle and the self-properties of each obstacle, as shown in fig. 1, and the specific scheme is as follows:
collecting an image video of a front windshield of a vehicle and forming a video frame sequence;
the method comprises the steps of sequentially detecting the area and the type of the obstacle according to the video frame sequence, wherein the type of the obstacle comprises cleanable obstacles and uncleanable obstacles;
the speed and the cleaning mode of the automatic windshield wiper are determined according to the area and the type of the obstacle.
The specific flow is shown in fig. 2:
s1, performing image video acquisition on a front windshield of a vehicle through a front-view camera to acquire a video frame sequence.
For the acquired image video, a certain time interval can be used for extracting pictures to form a video frame sequence. The specific time interval can be designed by the staff by himself, so that better acquisition effect can be achieved.
S2, performing obstacle detection on the video frame sequence.
Detection of the acquired sequence of video frames includes detecting the area and type of obstacle. The method comprises the following specific steps:
s21, judging whether an obstacle exists or not;
s22, judging the type of the obstacle if the obstacle exists; if no obstacle exists, continuing to acquire the vehicle front windshield image video through the front-view camera.
Wherein, step S21 is implemented by the following method for judging whether an obstacle exists or not: based on image features such as definition, contrast, brightness and the like, a deep learning algorithm locates an obstacle in a frame image, calculates an obstacle area, and judges whether the obstacle area exceeds a first area threshold. The first area threshold may be set by a worker. If the obstacle area exceeds the first area threshold, then the obstacle is considered to be present.
In step S22, the deep learning model can detect obstacles in two main categories: the obstacle can be cleared and the obstacle cannot be cleared.
Cleanable obstructions include, but are not limited to, rain, snow, and foliage, among others, that can be cleaned by the wiper, for which the wiper should be activated to clean.
Non-cleanable obstructions include, but are not limited to, obstructions such as soil, large area bird droppings, and the like. The image features of the uncleanable obstacle may be: higher blur, lower brightness, large contrast with background, color features, etc. And compared with the cleanable barrier, the change rate of the image features along with the scraping action of the windscreen wiper is small, namely the windscreen wiper can not remove the barrier through the scraping action, so that the picture becomes clear. The obstacle is difficult to clean through the windshield wiper, even the movement of the windshield wiper can lead to the expansion of the shielding area, so that the movement of the windshield wiper is stopped immediately, and meanwhile, the driver is warned to stop the vehicle by the side, and the obstacle is manually cleared.
The deep learning algorithm is trained by inputting an image dataset containing obstacles such as raindrops, rain lines, soil, resin and the like, images of each obstacle can be photographed by a front-view camera placed behind a front windshield, and the obstacles can be intelligently classified after training.
Further, factors such as weather, environment, etc. may be added in the construction of the image dataset. For example, the position of the obstacle may be determined by the wind direction when the raindrop falls.
S3, respectively acquiring and adopting corresponding processing schemes according to the types of the obstacles.
Specifically, if the obstacle is a cleanable obstacle, the wiper works normally. The speed of the wiper can be selected by the area of the obstacle, for example: if the area of the obstacle exceeds the first area threshold value and is smaller than or equal to the second area threshold value, the wiper speed can be set to be F1; if the obstacle area exceeds the second area threshold value and is equal to or less than the third area threshold value, the wiper speed may be set to F2. The windshield wiper speed gear and the specific speed can be set by staff according to actual conditions.
After the windshield wiper moves for a period of time, whether the obstacle exists or not is further judged, and the movement rate of the windshield wiper is readjusted according to the relation between the area of the residual obstacle and the first area threshold value, the second area threshold value and the third area threshold value.
If the obstacle is an uncleanable obstacle, the early warning unit of the system should be activated. The early warning unit firstly broadcasts voice to early warn a driver to manually clear an obstacle, and then sends a request to a BCM (system braking unit) to stop driving the wiper motor. At the moment, a driver can intervene in the early warning unit through a voice command or a manual deflector rod to control the windscreen wiper to be continuously opened. The voice command may be: "continue to open the wiper". After the system receives the intervention operation of the driver, the frame images with the obstacles and the corresponding operation are transmitted to the cloud platform, so that the subsequent optimization work of the research and development personnel is facilitated.
And meanwhile, the driver is warned to stop by the side, and the obstacle is manually cleared.
Based on the method, the invention provides a control system of an automatic windshield wiper, which comprises an acquisition unit, a detection unit and a determination unit;
wherein, the liquid crystal display device comprises a liquid crystal display device,
the acquisition unit is used for acquiring an image video of a front windshield of the vehicle and forming a video frame sequence; similar to a front-view camera on a front windshield;
the detection unit is used for sequentially detecting the area and the type of the obstacle according to the video frame sequence;
a determining unit for determining the speed and the cleaning mode of the automatic windshield wiper according to the area and the type of the obstacle;
and the early warning unit is used for early warning the driver to manually clear the obstacle.
The control system further comprises a follow-up judging unit, a follow-up judging unit and a control unit, wherein the follow-up judging unit is used for further judging whether an obstacle exists after the windshield wiper moves for a certain time;
if the obstacle still exists, readjusting the movement rate of the windshield wiper according to the relation between the area of the residual obstacle and the first area threshold value, the second area threshold value and the third area threshold value;
if no obstacle exists, continuing to collect the image video of the front windshield of the vehicle.
Although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (12)

1. A control method of an automatic wiper, characterized by comprising the steps of:
collecting an image video of a front windshield of a vehicle and forming a video frame sequence;
detecting the area and the type of the obstacle in sequence according to the video frame sequence, wherein the type of the obstacle comprises cleanable obstacles and uncleanable obstacles;
the speed and the cleaning mode of the automatic windshield wiper are determined according to the area and the type of the obstacle.
2. The control method of automatic wiper according to claim 1, wherein before sequentially detecting the area and type of the obstacle according to the video frame sequence, further comprising judging whether the obstacle exists according to the video frame sequence;
judging the type of the obstacle if the obstacle exists; if no obstacle exists, continuing to acquire the image video of the front windshield of the vehicle.
3. The control method of an automatic wiper according to claim 1, wherein,
the composing of the video frame sequence comprises: setting a certain time interval to extract pictures in the image video, and forming a video frame sequence by a plurality of pictures extracted in the interval time;
the cleanable barrier includes rain, snow, and/or foliage;
the uncleanable barrier comprises soil and/or large area bird droppings.
4. The control method of an automatic wiper according to claim 1, wherein,
detecting the area and the type of the obstacle according to the video frame sequence in sequence comprises the following steps:
calculating an obstacle area in a video frame sequence and judging whether the obstacle area exceeds a first area threshold;
if the area of the obstacle exceeds a first area threshold, inputting the current frame of the video frame sequence into a deep learning model;
and judging the type of the obstacle based on the deep learning model.
5. The method for controlling an automatic wiper according to claim 4, wherein,
and if the area of the obstacle does not exceed the first area threshold value, discarding the video of the current frame and continuously collecting the image video of the front windshield of the vehicle.
6. The control method of an automatic wiper according to claim 1, wherein,
the method for determining the speed and the cleaning mode of the automatic windshield wiper according to the area and the type of the obstacle comprises the following steps:
judging the type of the obstacle, if the obstacle is an uncleanable obstacle, not starting the windshield wiper to move, and pre-warning a driver to stop the vehicle by the side to manually clear the obstacle;
and if the obstacle is a cleanable obstacle, calculating the area of the obstacle, and determining the movement frequency required by the windshield wiper to scrape the obstacle according to the area of the obstacle.
7. The control method of an automatic wiper according to claim 6, wherein,
determining the movement frequency required by the windshield wiper to scrape the obstacle according to the obstacle area comprises the following steps:
if the area of the obstacle exceeds the first area threshold value and is smaller than or equal to the second area threshold value, the windshield wiper speed is set to be F1;
if the area of the obstacle exceeds the second area threshold value and is equal to or less than the third area threshold value, the wiper speed is set to F2.
8. The control method of an automatic wiper according to any one of claims 1 to 7, wherein,
the control method further comprises the step of further judging whether an obstacle exists or not after the windshield wiper moves for a certain time;
if the obstacle still exists, readjusting the movement rate of the windshield wiper according to the relation between the area of the residual obstacle and the first area threshold value, the second area threshold value and the third area threshold value;
if no obstacle exists, continuing to collect the image video of the front windshield of the vehicle.
9. A control system for an automatic windshield wiper, the control system comprising:
the acquisition unit is used for acquiring an image video of a front windshield of the vehicle and forming a video frame sequence;
the detection unit is used for sequentially detecting the area and the type of the obstacle according to the video frame sequence, wherein the type of the obstacle comprises cleanable obstacles and uncleanable obstacles;
and the determining unit is used for determining the speed and the cleaning mode of the automatic windshield wiper according to the area and the type of the obstacle.
10. The automatic wiper control system according to claim 9, wherein,
the detection unit executes the following logic to sequentially detect the area and the type of the obstacle:
calculating an obstacle area in a video frame sequence and judging whether the obstacle area exceeds a first area threshold;
if the area of the obstacle exceeds a first area threshold, inputting the current frame of the video frame sequence into a deep learning model; if the area of the obstacle does not exceed the first area threshold, discarding the video of the current frame and continuously collecting the image video of the front windshield of the vehicle;
and judging the type of the obstacle based on the deep learning model.
11. The automatic wiper control system according to claim 9, wherein,
the determination unit performs the following logic to determine the speed and sweep pattern of the automatic wiper:
judging the type of the obstacle, if the obstacle is an uncleanable obstacle, not starting the windshield wiper to move, and pre-warning a driver to park and manually clear the obstacle;
if the obstacle is a cleanable obstacle, calculating the area of the obstacle, and determining the movement frequency required by the windshield wiper to scrape the obstacle according to the area of the obstacle; if the area of the obstacle exceeds the first area threshold value and is smaller than or equal to the second area threshold value, the windshield wiper speed is set to be F1; if the area of the obstacle exceeds the second area threshold value and is equal to or less than the third area threshold value, the wiper speed is set to F2.
12. The automatic wiper control system according to any one of claims 9 to 11, wherein,
the control system further comprises a follow-up judging unit, a follow-up judging unit and a control unit, wherein the follow-up judging unit is used for further judging whether an obstacle exists after the windshield wiper moves for a certain time;
if the obstacle still exists, readjusting the movement rate of the windshield wiper according to the relation between the area of the residual obstacle and the first area threshold value, the second area threshold value and the third area threshold value;
if no obstacle exists, continuing to collect the image video of the front windshield of the vehicle.
CN202211589306.6A 2022-12-09 2022-12-09 Control method and system for automatic windshield wiper Pending CN116176493A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211589306.6A CN116176493A (en) 2022-12-09 2022-12-09 Control method and system for automatic windshield wiper

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211589306.6A CN116176493A (en) 2022-12-09 2022-12-09 Control method and system for automatic windshield wiper

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CN116176493A true CN116176493A (en) 2023-05-30

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CN202211589306.6A Pending CN116176493A (en) 2022-12-09 2022-12-09 Control method and system for automatic windshield wiper

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