CN107738627A - A kind of method and apparatus that rain brush control is carried out in automated driving system - Google Patents
A kind of method and apparatus that rain brush control is carried out in automated driving system Download PDFInfo
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- CN107738627A CN107738627A CN201710792882.3A CN201710792882A CN107738627A CN 107738627 A CN107738627 A CN 107738627A CN 201710792882 A CN201710792882 A CN 201710792882A CN 107738627 A CN107738627 A CN 107738627A
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- information
- control
- rain brush
- road image
- forecasting
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Classifications
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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
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- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Mechanical Engineering (AREA)
- Traffic Control Systems (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
It is an object of the invention to provide a kind of method and apparatus of the control of the rain brush in automated driving system.Comprised the following steps according to the solution of the present invention：User corresponding to a road image information is obtained to the true control information of rain brush；Using the road image information and its corresponding true control information as training data, to be trained to automatic Pilot model, to obtain in automatic driving procedure, control forecasting information corresponding to the road image information, the control forecasting information includes rain brush information of forecasting.This programme advantage is：It is trained by obtaining true control information corresponding with road image information, is particularly based on true control information of the user in sleety weather, the accuracy of the prediction in automatic Pilot for rain brush control can be effectively improved.Avoid due to the faulty operation caused by reason such as the external equipments such as rain sensor are inaccurate, and can adapt to more more complicated ambient conditions.
Description
Technical field
The present invention relates to automatic Pilot field, more particularly to a kind of method that rain brush control is carried out in automated driving system
And device.
Background technology
Along with the development of deep learning, realize that automatic Pilot is automatic Pilot field by deep learning end to end
Main direction of studying.In automatic Pilot end to end, automated driving system is generally inputted with monocular cam, shooting
Head is arranged on automobile inner side, on windshield.Due to the picture quality that camera collects, to safe driving end to end very
It is important, therefore, in sleety weather, rain brush is not turned on, the picture quality of collection will be influenceed, influence driving safety.
The control to rain brush is realized using modes such as rain sensors in the prior art, but, on the one hand, this kind
Mode needs additionally to install the equipment such as rain sensor, and on the other hand, the accuracy of rain sensor is poor, and can not be applicable
In complex situations.For example, when glass is covered by cloud, rain sensor possibly can not sense this change, so that can not
Wiper operation is effectively driven in the case, and the image for influenceing automatic Pilot obtains quality.
The content of the invention
It is an object of the invention to provide a kind of method and apparatus of the control of the rain brush in automated driving system.
According to the solution of the present invention, there is provided a kind of method that rain brush control is carried out in automated driving system, wherein, institute
The method of stating comprises the following steps：
- user corresponding to a road image information is obtained to the true control information of rain brush；
- using the road image information and its corresponding true control information as training data, to automatic Pilot mould
Type is trained, to obtain in automatic driving procedure, control forecasting information corresponding to the road image information, and the control forecasting
Information includes rain brush information of forecasting.
According to the solution of the present invention, there is provided a kind of control device that rain brush control is carried out in automated driving system, its
In, the control device includes：
For obtaining user corresponding to a road image information to the device of the true control information of rain brush；
For using the road image information and its corresponding true control information as training data, to automatic Pilot
Model is trained, to obtain in automatic driving procedure, the device of control forecasting information corresponding to the road image information.
On a memory and it can located according to a kind of computer equipment of the present invention, including memory, processor and storage
The computer program run on reason device, it is characterised in that described method is realized during the computing device described program.
According to a kind of computer-readable storage medium of the present invention, computer program is stored thereon with, it is characterised in that
Described method is realized when the program is executed by processor.
Compared with prior art, the present invention has advantages below：It is corresponding with road image information by obtaining in this programme
True control information be trained, be particularly based on true control information of the user in sleety weather, can effectively improve from
The accuracy of the prediction controlled in dynamic driving for rain brush.Also, without specially installing other equipment in vehicle in this programme,
And it is based only upon the input of the road image information for the camera that automated driving system is connected.Avoid because rainfall senses
The faulty operations caused by reason such as the external equipments such as device are inaccurate, and can adapt to more more complicated ambient conditions.
Brief description of the drawings
By reading the detailed description made to non-limiting example made with reference to the following drawings, of the invention is other
Feature, objects and advantages will become more apparent upon：
Fig. 1 illustrates the side of the rain brush control method in a preferred embodiment of the present invention automated driving system
Method flow chart；
Fig. 2 illustrates the knot of the rain brush control device in a preferred embodiment of the present invention automated driving system
Structure schematic diagram.
Same or analogous reference represents same or analogous part in accompanying drawing.
Embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings.
Wherein, the control device for performing the inventive method is realized by computer equipment.The computer has storage
Device, processor and storage are on a memory and the computer program that can run on a processor, the computing device program are corresponding
Computer program when realize method as performed by access device.In addition, by being stored on computer-readable recording medium
Corresponding computer program, so that the method as corresponding to control device can be realized when causing the computing device program.
Wherein, the computer equipment can carry out numerical value automatically including a kind of according to the instruction for being previously set or storing
Calculate and/or information processing electronic equipment, its hardware include but is not limited to microprocessor, application specific integrated circuit (ASIC), can
Program gate array (FPGA), digital processing unit (DSP), embedded device etc..The computer equipment may include the network equipment and/
Or user equipment.
Preferably, the computer equipment includes the user equipment of controllable vehicle progress automatic Pilot and/or network is set
It is standby.
Wherein, the user equipment, which includes but is not limited to any one, can be embedded in the vehicle and can be with user's touch-control
Mode carries out the electronic product of man-machine interaction, for example, embedded intelligent navigation equipment, tablet personal computer etc..
Wherein, the network equipment includes but is not limited to the service of single network server, multiple webservers composition
Device group or the cloud being made up of a large amount of main frames or the webserver based on cloud computing (Cloud Computing), wherein, cloud computing
It is one kind of Distributed Calculation, a super virtual computer being made up of the computer collection of a group loose couplings.Wherein, according to
The network equipment of the present invention can control vehicle to carry out automatic Pilot by the communication with vehicle.
Wherein, the network residing for the network equipment include but is not limited to internet, wide area network, Metropolitan Area Network (MAN), LAN,
VPN etc..It should be noted that the user equipment, the network equipment and network are only for example, other are existing or from now on
It is likely to occur the network equipment and network is such as applicable to the present invention, should be also included within the scope of the present invention, and to draw
It is incorporated herein with mode.
Reference picture 1, Fig. 1 illustrate the rain brush control in a preferred embodiment of the present invention automated driving system
The method flow diagram of method.
The method according to the invention includes step S1 and step S2.
In step sl, control device obtains true control information corresponding to a road image information.
Wherein, the road image information includes institute's travel that the camera as corresponding to automatic control system was photographed
Image information.
Wherein, the true control information includes but is not limited to the rain brush gear information selected by user.
Preferably, the true control information also includes but is not limited to any one of following：
1) transmits information of vehicle；
2) driving speed information of vehicle.
Wherein, those skilled in the art are, it is to be appreciated that control device can be based on the sensor in vehicle or and vehicle number
The interface of control system, to obtain the true control information, here is omitted.
Then, in step s 2, the control device is by the road image information and its corresponding true control information
As training data, to be trained to automatic Pilot model, to obtain in automatic driving procedure, the road image information is corresponding
Control forecasting information, the control forecasting information includes rain brush information of forecasting.
Wherein, the rain brush information of forecasting includes gear probabilistic information corresponding with each gear of the rain brush.
For example, current rain brush has 0,1,2,3 four gear, 0 grade is pass, and the shake of 1-3 shelves is quick-frozen incremented by successively, and gear is pre-
Measurement information may include the percentage of the respective shared probability of four gears, due to that can only select a gear every time, therefore this four
The probability sum of individual gear is 1.
Wherein, the control forecasting information includes driving the probability of selection for items corresponding with the road image information
Information of forecasting
Preferably, the control forecasting information also includes but is not limited to any one of following：
1) speed prediction information；For example, the probabilistic forecasting of operation is adjusted currently for speed；In another example target velocity
Prediction etc..
2) direction prediction information, for example, subsequent steering wheel angle prediction, or, the adjustment operation of steering wheel angle
Prediction etc..
According to a preferred embodiment of the present invention, the method according to the invention, control device is based on the control obtained
Information of forecasting, to determine automatic Pilot strategy.
Specifically, control device is corresponding to be based on determining in the control forecasting information based on predetermined decision-making mechanism
Automatic Pilot strategy corresponding to road image information.
For example, operation when the maximum information of forecasting of select probability is used as automatic Pilot etc..
Preferably, control device may also be combined with the real-time relevant information related to the rain brush, to determine that automatic Pilot is surveyed
Rain brush control information in amount.
For example, control device obtains the road image information that drop light rain is presented, and determine the rain brush of automatic Pilot model
Information of forecasting is：0 grade 0%, 1 grade 0%, 2 grade 45%, and 3 grade 55%.Then control device obtains current Weather information, and
Based on current real-time weather report：Heavy rain Amber alert, select 3 grades and operated.
According to a preferred embodiment of the present invention, the method according to the invention, control device are based on predetermined filtering condition,
The road image information and its corresponding true control information are screened, to be believed based on the road image after screening
Breath and its corresponding true control information, are trained to the automatic Pilot model.
For example, when the true control information that control device judges to be obtained control generally corresponding with road image information is believed
When the gap of breath is more than predetermined threshold, screened out.
According to another preferred embodiment of the present invention, control device is based on the PREDICTIVE CONTROL information and the true control
Information processed determines the whole loss information of the automatic Pilot model.
Wherein, control device is based on control forecasting information corresponding with a road image information, and road image letter
True control information corresponding to breath, using the modes such as entropy function are intersected, to determine the entirety of current automatic Pilot model
Lose information.
To obtain the control forecasting information that loss information meets pre-provisioning request.
It should be noted that those skilled in the art, it is to be appreciated that can adopt in itself according to the automatic Pilot model of the present invention
Built with various ways, for example, using neural network model etc., the building mode of its model has no effect on the reality of the present invention
Apply.
In this programme, it is trained by obtaining true control information corresponding with road image information, is particularly based on use
Family can effectively improve the accurate of the automatic Pilot of prediction in to(for) rain brush control in the true control information of sleety weather
Property.Also, without specially installing other equipment in vehicle in this programme, and it is based only upon the shooting that automated driving system is connected
The input of the road image information of head.Avoid wrong caused by the reason such as the external equipments such as rain sensor are inaccurate
Maloperation, and can adapt to more more complicated ambient conditions.
Reference picture 2, Fig. 2 illustrate the control of the rain brush in a preferred embodiment of the present invention automated driving system
The structural representation of device processed.
Acquisition device 101 and trainer 102 are included according to the control device of the present invention.
Acquisition device 101 obtains true control information corresponding to a road image information.
Wherein, the road image information includes institute's travel that the camera as corresponding to automatic control system was photographed
Image information.
Wherein, the true control information includes but is not limited to the rain brush gear information selected by user.
Preferably, the true control information also includes but is not limited to any one of following：
1) transmits information of vehicle；
2) driving speed information of vehicle.
Wherein, those skilled in the art are, it is to be appreciated that acquisition device 101 can be based on the sensor in vehicle or and vehicle
The interface of digital control system, to obtain the true control information, here is omitted.
Then, trainer 102 is using the road image information and its corresponding true control information as training data,
To be trained to automatic Pilot model, to obtain in automatic driving procedure, control forecasting corresponding to the road image information is believed
Breath, the control forecasting information include rain brush information of forecasting.
Wherein, the rain brush information of forecasting includes gear probabilistic information corresponding with each gear of the rain brush.
For example, current rain brush has 0,1,2,3 four gear, 0 grade is pass, and the shake of 1-3 shelves is quick-frozen incremented by successively, and gear is pre-
Measurement information may include the percentage of the respective shared probability of four gears, due to that can only select a gear every time, therefore this four
The probability sum of individual gear is 1.
Wherein, the control forecasting information includes driving the probability of selection for items corresponding with the road image information
Information of forecasting
Preferably, the control forecasting information also includes but is not limited to any one of following：
1) speed prediction information；For example, the probabilistic forecasting of operation is adjusted currently for speed；In another example target velocity
Prediction etc..
2) direction prediction information, for example, subsequent steering wheel angle prediction, or, the adjustment operation of steering wheel angle
Prediction etc..
According to a preferred embodiment of the present invention, according to the solution of the present invention, control device is based on the control obtained
Information of forecasting, to determine automatic Pilot strategy.
Specifically, control device is corresponding to be based on determining in the control forecasting information based on predetermined decision-making mechanism
Automatic Pilot strategy corresponding to road image information.
For example, operation when the maximum information of forecasting of select probability is used as automatic Pilot etc..
Preferably, control device may also be combined with the real-time relevant information related to the rain brush, to determine that automatic Pilot is surveyed
Rain brush control information in amount.
For example, control device obtains the road image information that drop light rain is presented, and determine the rain brush of automatic Pilot model
Information of forecasting is：0 grade 0%, 1 grade 0%, 2 grade 45%, and 3 grade 55%.Then control device obtains current Weather information, and
Based on current real-time weather report：Heavy rain Amber alert, select 3 grades and operated.
According to a preferred embodiment of the present invention, predetermined filtering bar is based on according to the solution of the present invention, trainer 102
Part, the road image information and its corresponding true control information are screened, with based on the mileage chart after screening
As information and its corresponding true control information, the automatic Pilot model is trained.
For example, controlled when the true control information and road image information that the judgement of trainer 102 is obtained are generally corresponding
When the gap of information processed is more than predetermined threshold, screened out.
According to another preferred embodiment of the present invention, trainer 102 be based on the PREDICTIVE CONTROL information with it is described true
Real control information determines the whole loss information of the automatic Pilot model.
Wherein, trainer 102 is based on control forecasting information corresponding with a road image information, and the road image
True control information corresponding to information, using the modes such as entropy function are intersected, to determine the whole of current automatic Pilot model
Bulk diffusion information.
So as to obtain the control forecasting information that loss information meets pre-provisioning request.
It should be noted that those skilled in the art, it is to be appreciated that can adopt in itself according to the automatic Pilot model of the present invention
Built with various ways, for example, using neural network model etc., the building mode of its model has no effect on the reality of the present invention
Apply.
In this programme, it is trained by obtaining true control information corresponding with road image information, is particularly based on use
Family can effectively improve the accurate of the automatic Pilot of prediction in to(for) rain brush control in the true control information of sleety weather
Property.Also, without specially installing other equipment in vehicle in this programme, and it is based only upon the shooting that automated driving system is connected
The input of the road image information of head.Avoid wrong caused by the reason such as the external equipments such as rain sensor are inaccurate
Maloperation, and can adapt to more more complicated ambient conditions.
It is obvious to a person skilled in the art that the invention is not restricted to the details of above-mentioned one exemplary embodiment, Er Qie
In the case of without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, embodiment all should be regarded as exemplary, and be nonrestrictive, the scope of the present invention is by appended power
Profit requires rather than described above limits, it is intended that all in the implication and scope of the equivalency of claim by falling
Change is included in the present invention.Any reference in claim should not be considered as to the involved claim of limitation.This
Outside, it is clear that the word of " comprising " one is not excluded for other units or step, and odd number is not excluded for plural number.That is stated in system claims is multiple
Unit or device can also be realized by a unit or device by software or hardware.The first, the second grade word is used for table
Show title, and be not offered as any specific order.
Claims (12)
1. a kind of method that rain brush control is carried out in automated driving system, wherein, it the described method comprises the following steps：
- user corresponding to a road image information is obtained to the true control information of rain brush；
- using the road image information and its corresponding true control information as training data, to enter to automatic Pilot model
Row training, to obtain in automatic driving procedure, control forecasting information corresponding to the road image information, the control forecasting information
Including rain brush information of forecasting.
2. according to the method for claim 1, wherein, each gear that the rain brush information of forecasting includes rain brush corresponds to respectively
Gear probabilistic information.
3. method according to claim 1 or 2, wherein, it the described method comprises the following steps：
- be based on predetermined filtering condition, the road image information and its corresponding true control information are screened, with based on
The road image information and its corresponding true control information after screening, are trained to the automatic Pilot model.
4. according to the method in any one of claims 1 to 3, wherein, methods described is further comprising the steps of：
- based on the PREDICTIVE CONTROL information determine that the whole loss of the automatic Pilot model is believed with the true control information
Breath.
5. method according to any one of claim 1 to 4, wherein, methods described is further comprising the steps of：
- the control forecasting information is based on, it is determined that the driving strategy information for automatic Pilot.
6. a kind of control device that rain brush control is carried out in automated driving system, wherein, the control device includes：
For obtaining user corresponding to a road image information to the device of the true control information of rain brush；
For using the road image information and its corresponding true control information as training data, to automatic Pilot model
It is trained, to obtain in automatic driving procedure, the device of control forecasting information corresponding to the road image information.
7. control device according to claim 6, wherein, the control forecasting information includes rain brush information of forecasting, described
Rain brush information of forecasting includes gear probabilistic information corresponding to each gear difference of rain brush.
8. the control device according to claim 6 or 7, wherein, the control device further comprises：
For based on predetermined filtering condition, being screened to the road image information and its corresponding true control information, with
Based on the road image information after screening and its corresponding true control information, the automatic Pilot model is trained
Device.
9. the control device according to any one of claim 6 to 8, wherein, the control device also includes：
For determining that the overall of the automatic Pilot model damages with the true control information based on the PREDICTIVE CONTROL information
Break one's promise the device of breath.
10. the control device according to any one of claim 6 to 9, wherein, the control device is additionally operable to：
- the control forecasting information is based on, it is determined that the driving strategy information for automatic Pilot.
11. a kind of computer equipment, including memory, processor and storage are on a memory and the meter that can run on a processor
Calculation machine program, it is characterised in that the side as described in any in claim 1-5 is realized during the computing device described program
Method.
12. a kind of computer-readable storage medium, is stored thereon with computer program, it is characterised in that the program is processed
The method as described in any in claim 1-5 is realized when device performs.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710792882.3A CN107738627A (en) | 2017-09-05 | 2017-09-05 | A kind of method and apparatus that rain brush control is carried out in automated driving system |
| PCT/CN2018/098619 WO2019047640A1 (en) | 2017-09-05 | 2018-08-03 | Method and device for controlling windshield wiper in automatic driving system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710792882.3A CN107738627A (en) | 2017-09-05 | 2017-09-05 | A kind of method and apparatus that rain brush control is carried out in automated driving system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107738627A true CN107738627A (en) | 2018-02-27 |
Family
ID=61235239
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710792882.3A Pending CN107738627A (en) | 2017-09-05 | 2017-09-05 | A kind of method and apparatus that rain brush control is carried out in automated driving system |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN107738627A (en) |
| WO (1) | WO2019047640A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019047640A1 (en) * | 2017-09-05 | 2019-03-14 | 百度在线网络技术（北京）有限公司 | Method and device for controlling windshield wiper in automatic driving system |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202518228U (en) * | 2012-04-18 | 2012-11-07 | 长安大学 | Device for controlling windshield wiper of automobile |
| CN102815281A (en) * | 2012-07-25 | 2012-12-12 | 江西好帮手电子科技有限公司 | Automobile windshield wiper control device and control method thereof |
| JP2014024354A (en) * | 2012-07-24 | 2014-02-06 | Tokai Rika Co Ltd | Wiper control device |
| CN104704541A (en) * | 2012-10-04 | 2015-06-10 | 三菱电机株式会社 | Vehicle-mounted information processing device |
| CN104809901A (en) * | 2014-01-28 | 2015-07-29 | 通用汽车环球科技运作有限责任公司 | Method for using street level images to enhance automated driving mode for vehicle |
| CN105699095A (en) * | 2016-01-27 | 2016-06-22 | 常州加美科技有限公司 | A test method for a remotely piloted vehicle (PRV) |
| CN106256643A (en) * | 2015-06-15 | 2016-12-28 | 丰田自动车株式会社 | Servomechanism and controller of vehicle |
| CN106772687A (en) * | 2016-12-06 | 2017-05-31 | 上海博泰悦臻电子设备制造有限公司 | Weather information supplying system and method based on driving vehicle |
| CN106873596A (en) * | 2017-03-22 | 2017-06-20 | 北京图森未来科技有限公司 | A kind of control method for vehicle and device |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3041390B2 (en) * | 1992-03-31 | 2000-05-15 | マツダ株式会社 | Vehicle wiper control device |
| US5453676A (en) * | 1994-09-30 | 1995-09-26 | Itt Automotive Electrical Systems, Inc. | Trainable drive system for a windshield wiper |
| GB2536683A (en) * | 2015-03-26 | 2016-09-28 | Denso Corp | Vehicle equipment control |
| CN106945637B (en) * | 2016-12-23 | 2019-06-07 | 惠州市德赛西威汽车电子股份有限公司 | A kind of rain blowing controlled system and its method based on automobile data recorder |
| CN106945638A (en) * | 2017-03-21 | 2017-07-14 | 重庆长安汽车股份有限公司 | A kind of rain blowing controlled system |
| CN107738627A (en) * | 2017-09-05 | 2018-02-27 | 百度在线网络技术（北京）有限公司 | A kind of method and apparatus that rain brush control is carried out in automated driving system |
-
2017
- 2017-09-05 CN CN201710792882.3A patent/CN107738627A/en active Pending
-
2018
- 2018-08-03 WO PCT/CN2018/098619 patent/WO2019047640A1/en active Application Filing
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202518228U (en) * | 2012-04-18 | 2012-11-07 | 长安大学 | Device for controlling windshield wiper of automobile |
| JP2014024354A (en) * | 2012-07-24 | 2014-02-06 | Tokai Rika Co Ltd | Wiper control device |
| CN102815281A (en) * | 2012-07-25 | 2012-12-12 | 江西好帮手电子科技有限公司 | Automobile windshield wiper control device and control method thereof |
| CN104704541A (en) * | 2012-10-04 | 2015-06-10 | 三菱电机株式会社 | Vehicle-mounted information processing device |
| CN104809901A (en) * | 2014-01-28 | 2015-07-29 | 通用汽车环球科技运作有限责任公司 | Method for using street level images to enhance automated driving mode for vehicle |
| CN106256643A (en) * | 2015-06-15 | 2016-12-28 | 丰田自动车株式会社 | Servomechanism and controller of vehicle |
| CN105699095A (en) * | 2016-01-27 | 2016-06-22 | 常州加美科技有限公司 | A test method for a remotely piloted vehicle (PRV) |
| CN106772687A (en) * | 2016-12-06 | 2017-05-31 | 上海博泰悦臻电子设备制造有限公司 | Weather information supplying system and method based on driving vehicle |
| CN106873596A (en) * | 2017-03-22 | 2017-06-20 | 北京图森未来科技有限公司 | A kind of control method for vehicle and device |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019047640A1 (en) * | 2017-09-05 | 2019-03-14 | 百度在线网络技术（北京）有限公司 | Method and device for controlling windshield wiper in automatic driving system |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2019047640A1 (en) | 2019-03-14 |
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