CN116660934A - Vehicle, rain/snow weather identification method, vehicle control method and system - Google Patents
Vehicle, rain/snow weather identification method, vehicle control method and system Download PDFInfo
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- CN116660934A CN116660934A CN202211552096.3A CN202211552096A CN116660934A CN 116660934 A CN116660934 A CN 116660934A CN 202211552096 A CN202211552096 A CN 202211552096A CN 116660934 A CN116660934 A CN 116660934A
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- 238000000034 method Methods 0.000 title claims abstract description 60
- 238000001514 detection method Methods 0.000 claims abstract description 41
- 238000002592 echocardiography Methods 0.000 claims abstract description 10
- 230000015654 memory Effects 0.000 claims description 18
- 238000011217 control strategy Methods 0.000 claims description 9
- 238000012216 screening Methods 0.000 claims description 5
- 238000012549 training Methods 0.000 abstract description 5
- 238000009826 distribution Methods 0.000 description 13
- 230000009286 beneficial effect Effects 0.000 description 10
- 238000013135 deep learning Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 230000000149 penetrating effect Effects 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
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- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/95—Lidar systems specially adapted for specific applications for meteorological use
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
- B60W60/0015—Planning or execution of driving tasks specially adapted for safety
- B60W60/0018—Planning or execution of driving tasks specially adapted for safety by employing degraded modes, e.g. reducing speed, in response to suboptimal conditions
- B60W60/00182—Planning or execution of driving tasks specially adapted for safety by employing degraded modes, e.g. reducing speed, in response to suboptimal conditions in response to weather conditions
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
- B60W2420/408—Radar; Laser, e.g. lidar
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Remote Sensing (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Electromagnetism (AREA)
- Automation & Control Theory (AREA)
- Human Computer Interaction (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Traffic Control Systems (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
The invention belongs to the technical field of automatic driving, and particularly relates to a vehicle, a rain/snow weather identification method, a vehicle control method and a vehicle control system. Firstly, acquiring point cloud data detected by a laser radar, and identifying point cloud noise points in the point cloud data; then, judging: if the number of the point clouds with secondary and more echoes in all the point cloud data exceeds a set point cloud number threshold value and the point cloud noise points are basically and uniformly distributed in the detection range of the laser radar, judging that rain/snow weather occurs in the detection range of the laser radar; and finally, controlling the vehicle. According to the method, the rain/snow weather is identified by directly utilizing the characteristics of the laser radar point cloud of the rain/snow, the identification method is simple but high in efficiency, the work of data marking, data training and the like which take a lot of time as in the prior art is not needed, and the weather detection sensor is not needed to be added, so that the effective and accurate identification of the rain/snow weather is simply and efficiently realized. Further, the vehicle is controlled after the weather of rain and snow is identified, so as to prevent accidents.
Description
Technical Field
The invention belongs to the technical field of automatic driving, and particularly relates to a vehicle, a rain/snow weather identification method, a vehicle control method and a vehicle control system.
Background
With the development of artificial intelligence technology, more and more vehicles are beginning to be equipped with driving assistance functions that can alleviate the driving load of the driver to some extent and make timely countermeasures against emergency situations that the driver does not notice. With the development and propulsion of technology, autonomous vehicles have also begun to appear. The automatic driving vehicle realizes the automatic and intelligent control of the vehicle by utilizing the technologies of environment sensing, map navigation and positioning, motion root track planning, target tracking and the like.
However, autonomous vehicles are faced with a number of challenges that need to be overcome. Particularly, when facing severe climatic environments (such as rainy and snowy weather), the tires are easy to slip, so that the phenomena of vehicle rollover, increase of braking distance, deviation from a planned track and the like are caused; the detection accuracy of partial sensors on the vehicle is reduced in rainy and snowy weather, for example, the camera on the vehicle is difficult to continuously and accurately identify the obstacle and the lane line information, a large number of noise points can be generated by the laser radar in rainy and snowy weather, so that the sensing system cannot accurately acquire the environment information, and the like, which can cause that the automatic driving vehicle cannot normally pass under the rainy and snowy weather condition. Therefore, it is extremely necessary to automatically control the driving vehicle under severe weather conditions, and a set of control strategies different from those under normal weather conditions need to be used under severe weather conditions, but the precondition of these studies is that whether the vehicle is in severe weather conditions such as rainy and snowy weather or not can be accurately perceived.
To solve this problem, it is common to sense the weather of rain and snow by providing a weather detection type sensor from the hardware aspect, but this approach requires an additional weather detection type sensor; in terms of software, the most common method is to use a deep learning method, and although a better effect can be obtained, the deep learning often needs to carry out data labeling, data training and other works, so that the cost and the time consumption are high, and the weather of rain and snow cannot be simply, quickly and effectively identified.
Disclosure of Invention
The invention aims to provide a rain/snow weather identification method for solving the problem of high cost and time consumption caused by using a deep learning method for rain/snow weather identification, and also provides a vehicle control method based on the rain/snow weather identification method, a system for realizing the vehicle control method and a vehicle comprising the system.
In order to solve the technical problems, the invention provides a vehicle control method based on rain and snow weather identification, which comprises the following steps: 1) Acquiring point cloud data detected by a laser radar, and identifying point cloud noise points in the point cloud data; 2) And if the number of the point clouds with secondary and more echoes in all the point cloud data exceeds the set point cloud number threshold value and the point cloud noise points are basically and uniformly distributed in the detection range of the laser radar, judging that rain/snow weather occurs in the detection range of the laser radar.
The beneficial effects are as follows: the invention adopts the multi-echo characteristic and the distribution characteristic of point clouds in rainy and snowy weather to identify rainy/snowy weather. The multi-echo characteristic is that laser pulse is beaten on rain/snow to form a first echo, and penetrating the rain/snow is beaten on the body to form a second echo, namely, more secondary and more echo conditions are generated, and rain/snow weather is likely to occur; by "distribution characteristics of point clouds in rainy and snowy weather" is meant that the point cloud noise points formed by laser pulses striking on the rain/snow are substantially uniformly distributed within the detection range of the laser radar. The method directly utilizes the characteristics of the laser radar point cloud of the rain/snow to identify the rain/snow weather, is simple but has higher efficiency, does not need to take a lot of time to carry out data marking, data training and other works like the prior art, does not need to increase a weather detection sensor, and simply and efficiently realizes the effective and accurate identification of the rain/snow weather.
As a further improvement of the rain/snow weather identification method, the point cloud belonging to the point cloud noise point satisfies the following condition: the reflection intensity is lower than a set reflection intensity threshold, and the size of the point cloud cluster is smaller than the set cluster threshold and is in a suspended state.
The beneficial effects are as follows: by utilizing the characteristics that the reflection intensity of rain/snow is low, the corresponding point cloud cluster is small and the rain/snow is generally in the air, the point cloud which is likely to be the rain/snow is accurately identified.
As a further improvement of the rain/snow weather identification method, in step 2), the following method is adopted to determine whether the cloud noise points are substantially uniformly distributed in the detection range of the laser radar: performing grid division, and performing grid projection on the obtained point cloud data; screening grids where all the point cloud noise points are located; and respectively solving standard deviations of the abscissa and the ordinate of the screened grids, and judging that the point cloud noise points are not basically uniformly distributed in the detection range of the laser radar when the standard deviations of the abscissa and the ordinate are larger than a set standard deviation threshold value, otherwise, judging that the point cloud noise points are basically uniformly distributed in the detection range of the laser radar.
The beneficial effects are as follows: and judging whether the point cloud noise points are basically and uniformly distributed in the detection range of the laser radar by utilizing grid division and the distribution positions of grids where all the point cloud noise points are located, wherein the method is simple and effective.
In order to solve the technical problem, the invention also provides a vehicle control method, which comprises the following steps: 1) Acquiring point cloud data detected by a laser radar, and identifying point cloud noise points in the point cloud data; 2) If the number of the point clouds with secondary and more echoes in all the point cloud data exceeds a set point cloud number threshold value and the point cloud noise points are basically and uniformly distributed in the detection range of the laser radar, judging that rain/snow weather occurs in the detection range of the laser radar; 3) The vehicle is controlled in accordance with the identified weather.
The beneficial effects are as follows: the invention adopts the multi-echo characteristic and the distribution characteristic of point clouds in rainy and snowy weather to identify rainy/snowy weather. The multi-echo characteristic is that laser pulse is beaten on rain/snow to form a first echo, and penetrating the rain/snow is beaten on the body to form a second echo, namely, more secondary and more echo conditions are generated, and rain/snow weather is likely to occur; by "distribution characteristics of point clouds in rainy and snowy weather" is meant that the point cloud noise points formed by laser pulses striking on the rain/snow are substantially uniformly distributed within the detection range of the laser radar. The method directly utilizes the characteristics of the laser radar point cloud of the rain/snow to identify the rain/snow weather, is simple but has higher efficiency, does not need to take a lot of time to carry out data marking, data training and other works like the prior art, does not need to increase a weather detection sensor, and simply and efficiently realizes the effective and accurate identification of the rain/snow weather. On the basis, after the rainy and snowy weather is identified, the vehicle is controlled to prevent accidents and improve the running safety of the vehicle.
As a further improvement of the vehicle control method, the point cloud belonging to the point cloud noise point satisfies the following condition: the reflection intensity is lower than a set reflection intensity threshold, and the size of the point cloud cluster is smaller than the set cluster threshold and is in a suspended state.
The beneficial effects are as follows: by utilizing the characteristics that the reflection intensity of rain/snow is low, the corresponding point cloud cluster is small and the rain/snow is generally in the air, the point cloud which is likely to be the rain/snow is accurately identified.
As a further improvement of the vehicle control method, in step 2), the following method is adopted to determine whether the cloud noise points are substantially uniformly distributed in the detection range of the laser radar: performing grid division, and performing grid projection on the obtained point cloud data; screening grids where all the point cloud noise points are located; and respectively solving standard deviations of the abscissa and the ordinate of the screened grids, and judging that the point cloud noise points are not basically uniformly distributed in the detection range of the laser radar when the standard deviations of the abscissa and the ordinate are larger than a set standard deviation threshold value, otherwise, judging that the point cloud noise points are basically uniformly distributed in the detection range of the laser radar.
The beneficial effects are as follows: and judging whether the point cloud noise points are basically and uniformly distributed in the detection range of the laser radar by utilizing grid division and the distribution positions of grids where all the point cloud noise points are located, wherein the method is simple and effective.
As a further improvement of the vehicle control method, in step 3), the identified point cloud noise is filtered when the vehicle is controlled.
The beneficial effects are as follows: after the rain/snow point cloud is accurately identified, the rain/snow point cloud can be filtered, and only real obstacle information is reserved, so that the vehicle can be controlled by using the real obstacle information, and safe and reliable operation of the vehicle is ensured.
As a further improvement of the vehicle control method, the control strategy adopted in step 3) when controlling the vehicle includes at least one of the following control strategies: control strategy one: controlling to reduce the vehicle speed; and a control strategy II: sensing of the surroundings of the vehicle is performed using sensors that are not affected by rain/snow weather, including millimeter wave radar.
The beneficial effects are as follows: after the weather is identified, the safety performance of the vehicle running in severe weather conditions can be improved by controlling the vehicle speed to decrease and/or using sensors that are not affected by the weather.
In order to solve the technical problems, the invention also provides a system which comprises a memory and a processor, wherein the processor is used for executing the computer instructions stored in the memory to realize the vehicle control method described above and correspondingly achieve the same beneficial effects as the vehicle control method.
In order to solve the technical problems, the invention also provides a vehicle which comprises the laser radar for sensing the surrounding environment of the vehicle, and the system described above, and achieves the same beneficial effects as the system.
Drawings
FIG. 1 is a flow chart of a vehicle control method of the present invention;
fig. 2 is a structural diagram of the vehicle control system of the present invention.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
Vehicle control method embodiment:
the embodiment of the vehicle control method is shown in a flow chart in fig. 1, and has the main concept that the method accurately identifies the rain and snow weather by utilizing the multi-echo characteristic of the laser radar and the distribution characteristic of point clouds in the rain and snow weather, and ensures the normal passing of the automatic driving vehicle in the rain and snow weather by adopting a noise filtering method suitable for the rain and snow weather and a vehicle passing strategy after the rain and snow weather is identified.
The multi-echo characteristic of the laser radar refers to that multiple reflections occur when laser pulses encounter obstacles with different distances from a laser emission source in the propagation process, and the distance measured by the reflections can be recorded and obtained as long as the strength of echo signals is enough to be accepted and the distance between the echo signals meets a certain condition. In rainy and snowy weather, laser pulses often form first echoes on the rainy and snowy weather, and then the pulses penetrate through the rainy and snowy weather to form second echoes on the body. With this feature, when more secondary echoes or more occur, it is possible to use rainy and snowy weather.
The distribution characteristic of the point cloud in the rainy and snowy weather means that the point cloud noise points formed on the rainy and snowy weather are basically and evenly distributed in the detection range of the self-vehicle laser radar, and the point cloud clusters formed by the rainy and snowy weather are small and suspended in the air, so that the self-vehicle laser radar can be obviously different from real objects. The point cloud data with low reflection intensity, small point cloud clusters and suspension is regarded as point cloud noise points.
Meanwhile, when the multi-echo characteristic and the point cloud distribution characteristic of the laser radar in rainy and snowy weather are met, the current rainy and snowy weather can be judged.
The vehicle control method based on the analysis comprises the following steps:
step one, acquiring point cloud data in a laser radar detection range installed on a vehicle.
And secondly, aiming at the characteristics of low reflection intensity of the rain/snow point cloud and small and suspended point cloud clusters, the point cloud noise points which are the rain/snow point cloud are screened from the obtained point cloud data, namely, the point cloud with the reflection intensity lower than a set reflection intensity threshold value and the point cloud cluster size smaller than a set cluster threshold value and in a suspended state (the suspended state can be judged through Gao Chenglai of the point cloud, for example, gao Chengda of the point cloud can be judged as the suspended state at a set elevation threshold value) is identified as the point cloud noise points. The values of the several thresholds, namely, the set reflection intensity threshold, the set cluster threshold and the set elevation threshold, are different due to different performances of different lidars. For example, a certain 16-line laser radar is used as an example, the reflection intensity threshold value is set to be 3, the cluster threshold value is set to be 3, and the elevation threshold value is set to be 0.8 meter, namely, the laser radar is suspended.
Step three, judging the point cloud data according to the multi-echo characteristic and the point cloud distribution characteristic: and if the number of the point clouds with secondary and more echoes in all the point cloud data exceeds the set point cloud number threshold value and the point cloud noise points are basically and uniformly distributed in the detection range of the laser radar, judging that rain/snow weather occurs in the detection range of the laser radar.
The specific judgment mode of basically uniform distribution is as follows:
(1) dividing grids in a certain range, wherein the size of each grid is 0.5m by 0.5m; the total range is generally the detection range of the laser radar, if the detection distance of the laser radar is 100 meters, the total range can be set to be 100 meters by 40 meters, the grid size can be set according to the specific conditions of different laser radars, 0.5 meter by 0.5 meter is generally selected, and the number of the divided grids is 200 by 80;
(2) performing grid projection on the laser radar point cloud in the range;
(3) screening all grids where the point clouds (i.e. the point cloud noise points screened in the second step) which meet the characteristics of the rain and the snow are located;
(4) and respectively solving standard deviations of the transverse coordinates and the longitudinal coordinates of the grids, and when the standard deviations of the transverse coordinates and the longitudinal coordinates are larger than a certain threshold value, indicating that the points are scattered more discretely and accord with the distribution characteristics of rain and snow weather.
Step four, after recognizing that the current environment is in rainy/snowy weather, firstly, point cloud noise points can be filtered from all point cloud data, and obstacle recognition and judgment are carried out on the filtered point cloud data so as to control the vehicle (including obstacle avoidance, path planning and the like); secondly, the vehicle can be controlled according to the identified rain/snow weather, and the method specifically comprises the following steps: (1) the laser radar can generate more noise points, and at the moment, the perception confidence of the laser radar is reduced, and the perception capability is weakened, so that the running speed of the vehicle needs to be reduced, and the normal running of the automatic driving vehicle is ensured; (2) the adjustment of the degree of dependence on other sensors means that under normal weather, mainly a laser radar is used as a main sensor, millimeter wave radar and vision are used as assistance, and under rainy and snowy weather, the sensor which is not easily affected by the weather such as millimeter wave radar can be used as the main sensor.
Thus, the vehicle control after the rain/snow weather is identified and the rain/snow weather is identified can be completed.
In summary, the invention has the following characteristics:
1) The rain and snow weather is identified by utilizing the multi-echo characteristic and the point cloud distribution characteristic of the laser radar in the rain and snow weather, so that the rain and snow weather can be identified by utilizing the laser radar sensor of the automatic driving vehicle, a weather detection sensor is not required, and the hardware cost is reduced.
2) The noise filtering algorithm and the traffic strategy suitable for the weather are used in the rainy and snowy weather, so that the point cloud noise formed by the rainy and snowy weather can be filtered without deep learning, and the marking, training time and cost are saved; and the passing strategy corresponding to weather is used in rainy and snowy weather, so that the safety and normal operation of the automatic driving vehicle can be ensured.
Rain/snow weather identification method embodiment:
the invention relates to an embodiment of a rain/snow weather identification method, which mainly aims at utilizing the multi-echo characteristic and the distribution characteristic of point clouds under the rain/snow weather to identify the rain/snow weather, namely: and if the number of the point clouds with secondary and more echoes in all the point cloud data exceeds the set point cloud number threshold value and the point cloud noise points are basically and uniformly distributed in the detection range of the laser radar, judging that rain/snow weather occurs in the detection range of the laser radar. The specific steps are consistent with the implementation steps of the steps one to three of the vehicle control method described in the vehicle control method embodiment, and are not described herein.
Vehicle control system embodiment:
an embodiment of a vehicle control system of the present invention, as shown in fig. 2, includes a memory, a processor, and an internal bus, where the processor and the memory complete communication and data interaction with each other through the internal bus. The memory includes at least one software functional module stored in the memory, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory to implement a vehicle control method as described in the vehicle control method embodiments of the present invention.
The processor may be a microprocessor MCU, a programmable logic device FPGA, or other processing device. The memory may be various memories for storing information by using electric energy, such as RAM, ROM, etc.; the magnetic storage device can also be various memories for storing information by utilizing a magnetic energy mode, such as a hard disk, a floppy disk, a magnetic tape, a magnetic core memory, a bubble memory, a U disk and the like; various memories for optically storing information, such as CDs, DVDs, etc.; of course, other types of memory are also possible, such as quantum memory, graphene memory, etc.
Vehicle embodiment:
a vehicle embodiment of the invention includes a vehicle body and a lidar and vehicle control system for sensing the environment surrounding the vehicle, which may implement a vehicle control method as described in the vehicle control method embodiment. The specific vehicle control system has been described in detail in the vehicle control system embodiment, the specific vehicle control method has been described in detail in the vehicle control method embodiment, and the vehicle embodiment will not be described again.
Claims (10)
1. A method of identifying rain/snow weather, comprising the steps of:
1) Acquiring point cloud data detected by a laser radar, and identifying point cloud noise points in the point cloud data;
2) And if the number of the point clouds with secondary and more echoes in all the point cloud data exceeds the set point cloud number threshold value and the point cloud noise points are basically and uniformly distributed in the detection range of the laser radar, judging that rain/snow weather occurs in the detection range of the laser radar.
2. The rain/snow weather identification method according to claim 1, wherein the point cloud belonging to the point cloud noise satisfies the following condition: the reflection intensity is lower than a set reflection intensity threshold, and the size of the point cloud cluster is smaller than the set cluster threshold and is in a suspended state.
3. The method for recognizing rain/snow weather according to claim 1, wherein in the step 2), it is determined whether the cloud noise points are substantially uniformly distributed within the detection range of the lidar by:
performing grid division, and performing grid projection on the obtained point cloud data;
screening grids where all the point cloud noise points are located;
and respectively solving standard deviations of the abscissa and the ordinate of the screened grids, and judging that the point cloud noise points are not basically uniformly distributed in the detection range of the laser radar when the standard deviations of the abscissa and the ordinate are larger than a set standard deviation threshold value, otherwise, judging that the point cloud noise points are basically uniformly distributed in the detection range of the laser radar.
4. A vehicle control method characterized by comprising the steps of:
1) Acquiring point cloud data detected by a laser radar, and identifying point cloud noise points in the point cloud data;
2) If the number of the point clouds with secondary and more echoes in all the point cloud data exceeds a set point cloud number threshold value and the point cloud noise points are basically and uniformly distributed in the detection range of the laser radar, judging that rain/snow weather occurs in the detection range of the laser radar;
3) The vehicle is controlled in accordance with the identified weather.
5. The vehicle control method according to claim 4, characterized in that the point cloud belonging to the point cloud noise point satisfies the following condition: the reflection intensity is lower than a set reflection intensity threshold, and the size of the point cloud cluster is smaller than the set cluster threshold and is in a suspended state.
6. The method according to claim 4, wherein in step 2), it is determined whether the cloud noise points are substantially uniformly distributed within the detection range of the lidar by:
performing grid division, and performing grid projection on the obtained point cloud data;
screening grids where all the point cloud noise points are located;
and respectively solving standard deviations of the abscissa and the ordinate of the screened grids, and judging that the point cloud noise points are not basically uniformly distributed in the detection range of the laser radar when the standard deviations of the abscissa and the ordinate are larger than a set standard deviation threshold value, otherwise, judging that the point cloud noise points are basically uniformly distributed in the detection range of the laser radar.
7. The method according to any one of claims 4 to 6, characterized in that in step 3), the identified point cloud noise is filtered when the vehicle is controlled.
8. The vehicle control method according to any one of claims 4 to 6, characterized in that the control strategy employed in step 3) in controlling the vehicle includes at least one of the following control strategies:
control strategy one: controlling to reduce the vehicle speed;
and a control strategy II: sensing of the surroundings of the vehicle is performed using sensors that are not affected by rain/snow weather, including millimeter wave radar.
9. A vehicle control system comprising a memory and a processor for executing computer instructions stored in the memory to implement the vehicle control method of any one of claims 4-8.
10. A vehicle comprising a lidar for sensing the environment surrounding the vehicle, further comprising the vehicle control system of claim 9.
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| CN202211552096.3A CN116660934A (en) | 2022-12-05 | 2022-12-05 | Vehicle, rain/snow weather identification method, vehicle control method and system |
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| CN202211552096.3A CN116660934A (en) | 2022-12-05 | 2022-12-05 | Vehicle, rain/snow weather identification method, vehicle control method and system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117647852A (en) * | 2024-01-29 | 2024-03-05 | 吉咖智能机器人有限公司 | Weather state detection method and device, electronic equipment and storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117647852A (en) * | 2024-01-29 | 2024-03-05 | 吉咖智能机器人有限公司 | Weather state detection method and device, electronic equipment and storage medium |
| CN117647852B (en) * | 2024-01-29 | 2024-04-09 | 吉咖智能机器人有限公司 | Weather state detection method and device, electronic equipment and storage medium |
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