CN112147615B - Unmanned perception method based on all-weather environment monitoring system - Google Patents
Unmanned perception method based on all-weather environment monitoring system Download PDFInfo
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- 230000008447 perception Effects 0.000 title claims description 13
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- 238000007781 pre-processing Methods 0.000 claims abstract description 19
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
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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
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/86—Combinations of sonar systems with lidar systems; Combinations of sonar systems with systems not using wave reflection
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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
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/88—Sonar systems specially adapted for specific applications
- G01S15/93—Sonar systems specially adapted for specific applications for anti-collision purposes
- G01S15/931—Sonar systems specially adapted for specific applications for anti-collision purposes of land vehicles
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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
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
- G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/02—Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed
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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
Abstract
The invention relates to an all-weather environment monitoring unmanned sensing system and a sensing method, wherein the system comprises a sensing monitoring module, a data preprocessing module and a data decision fusion module; the sensing and monitoring module system comprises a weather monitoring system and an environment sensing system, wherein the weather monitoring system comprises a temperature sensor, a rainfall sensor, a light sensor and a dust sensor; the environment sensing system comprises millimeter wave radar, laser radar, a camera, ultrasonic radar and a vision sensor; the data preprocessing module processes the data acquired by each sensor to realize independent processing and conversion of different data acquired by different sensors; the data decision fusion module forms a decision selection mechanism for the sensor information of the environment sensing system based on the weather environment comprehensive information, selects the information of the sensor which is suitable for the weather environment, carries out multi-source information fusion on the information, adjusts the vehicle state information based on the fused information and sends the information to the vehicle control system.
Description
Technical Field
The invention relates to the field of automatic driving, in particular to an all-weather environment monitoring unmanned sensing system and an all-weather environment monitoring unmanned sensing method.
Background
In view of the characteristics of continuous operation of mining areas around the clock, the mining areas are complex in environment and bad in climate, in order to ensure that the unmanned mining truck can support normal sports cars under all-weather and different weather conditions, the unmanned sensing system of the mining areas needs to acquire road information, surrounding driving environment information, illumination conditions, weather conditions such as rain and fog, smog, rain and snow and the like. Based on road information and weather information, proper sensors can be selected according to different sensor attributes to acquire sensing information, and the effective normal operation of the sensors in different environments is ensured.
Sensing and recognition of different weather is also required to be achieved by corresponding sensor technology. Usually, the distinguishing of sunny days and rainy days can be detected by using a rainfall sensor, and a plurality of sensors integrate the identification of environments such as temperature, light intensity, rain and snow and the like; the wind speed has a large influence on the normal running of the vehicle, the wind speed and the direction can be sensed by a wind speed sensor, and the wind speed caused by the movement of the vehicle or the wind speed caused by the external environment can be distinguished by matching with the running state of the vehicle; the visibility of the thick fog is reduced, and the thick fog can be identified by a camera, a humidity sensor, a light intensity sensor and the like; the detection of the snow is integrated in some rainfall sensors, and can also be detected by the cooperation of a camera and a temperature sensor; day and night perception can be achieved by using a light intensity sensor in combination with local time.
Unmanned techniques have been developed and applied in mining areas and exhibit certain effects. However, due to the fact that the mining area is complex in environment and bad in weather, the sensor arranged on the mine car cannot acquire surrounding driving information due to temperature failure or environmental conditions, and in addition, the defects of stability and reliability of the prior art information can cause that the unmanned mining card driving sensing requirement cannot be met and safe and smooth operation can not be achieved.
Disclosure of Invention
Aiming at the problem of insufficient information stability and reliability in the prior art, the invention designs an unmanned sensing system of multiple sensors such as a laser radar, a millimeter wave radar and the like, which can increase temperature, rainfall and the light sensor to measure the external environment and weather conditions according to the specific mining area environment, and form comprehensive utilization of different sensors in different time periods and different weather conditions according to measurement results. Therefore, under the severe mining area environment, more comprehensive perception information and obstacle information are acquired, so that the driving environment information is more accurate, and the safety and fluency in the whole driving process are ensured.
The invention provides an all-weather environment monitoring unmanned sensing system and an all-weather environment monitoring unmanned sensing method, which are used for realizing the sensing monitoring of an unmanned mining truck on the whole environment such as road environment, driving environment, weather environment and the like, fully utilizing effective information and eliminating the interference of redundant information.
An all-weather environment monitoring unmanned perception system comprises a perception monitoring module, a data preprocessing module and a data decision fusion module;
the sensing and monitoring module system comprises a weather monitoring system and an environment sensing system, wherein the weather monitoring system comprises a temperature sensor, a rainfall sensor, a light sensor and a dust sensor; the environment sensing system comprises millimeter wave radar, laser radar, a camera, ultrasonic radar and a vision sensor;
the data preprocessing module processes the data acquired by each sensor to realize independent processing and conversion of different data acquired by different sensors;
the data decision fusion module forms a decision selection mechanism for the sensor of the environment sensing system based on different weather environment information obtained by the weather monitoring system, selects information of the sensor of the environment sensing system which is suitable for the current real-time weather environment based on the decision mechanism, carries out multi-source information fusion on the information, adjusts vehicle state information based on the fusion information, comprises position, direction and vehicle speed information and sends the information to the vehicle control system.
The sensing method based on the sensing system comprises the following specific steps:
step 1, converting analog quantity acquired by each sensor into digital quantity by utilizing an external temperature acquired by a temperature sensor, external humidity acquired by a rainfall sensor, external illumination intensity acquired by a light sensor and flying dust concentration acquired by a dust sensor in a weather monitoring system, and performing filtering treatment by utilizing a data preprocessing module;
step 2, utilizing the millimeter wave radar in the environment sensing system to acquire speed information of obstacles in the driving environment and utilizing the data preprocessing module to filter clutter false alarms; the size, the position information and the attribute information of the obstacles in the driving environment acquired by the laser radar and the camera are respectively subjected to space and time matching; the vision sensor can also acquire various driving environment information; preprocessing all detected information through a data preprocessing module;
step 3, after the data decision fusion module acquires the information processed by the data preprocessing module, judging based on different sensor thresholds of the weather monitoring system, and integrating the current weather environment information; and forming a selection decision for the sensor of the environment sensing system based on the weather environment information, selecting a sensor combination of the environment sensing system adapting to the current real-time weather environment, removing redundant information of unavailable sensors, carrying out multi-source information fusion on the information of the selected sensor in the environment sensing system, adjusting vehicle state information based on the fused information, including position, direction and vehicle speed information, and sending the information to a vehicle control system.
In step 3, for the selection decision of the information of the sensors in the environment sensing system, a minimum risk decision mode based on the Bayesian principle is adopted, specifically, by setting confidence threshold values of the sensors of different environment sensing systems under different conditions, introducing conditional risk, and selecting a proper sensor of the environment sensing system; the specific method comprises the following steps:
step one: the sensor assembly mode of the environment sensing system under various weather environments is set through experience values, a mode space is formed, and n mode categories exist: omega 1 ，ω 2 ，…，ω n The method comprises the steps of carrying out a first treatment on the surface of the The weather environment information obtained by the sensor of the weather monitoring system forms a decision of which sensor combination mode is selected according to different weather environment information, forms a decision space, and is defined as alpha 1 ，α 2 ，…，α m ；
Defining a pattern class omega j Adopts the decision alpha i The losses caused are noted as:
λ ij ＝λ(α i /ω j )
the risk loss of a sensor module type of an environment sensing system based on weather environment information is called a loss function, wherein lambda is a loss weight;
step two: creating a loss matrix
And a minimum loss criterion is calculated and,
wherein,
selecting the decision with the least risk, i.e
The scheme of the invention has the main effects of sensing the whole unmanned environment and roads to form an intelligent sensing system of the all-weather detection system, processing the currently corresponding available sensor information according to the external environment, removing the unavailable redundant information, avoiding the waste of resources and time for processing the redundant or unavailable information, and improving the intelligence, the high efficiency and the all-weather working capacity of the unmanned sensing system.
The invention has the advantages and positive effects that:
the prior art does not monitor the environmental weather conditions, so that the sensor fails or the sensing capability is lost under a specific environment, and if the dust is large, the laser radar cannot penetrate to cause sensing failure and the like, and the stable operation of the unmanned mine car sensing system cannot be ensured.
1. A plurality of sensors are arranged on the vehicle body, including a temperature sensor, a rainfall sensor, a light intensity sensor and a dust sensor, and the temperature, rainfall or snowfall, illumination intensity and dust concentration of the external environment are monitored; laser radar, millimeter wave radar, camera, ultrasonic radar, etc. perform environmental perception.
2. And according to the environmental monitoring sensor data, selecting a usable sensor in a corresponding scene to perform environmental perception, so as to ensure the safety and the running smoothness of the unmanned mine car. The invention has reasonable design and can be suitable for special environments of mining areas.
Drawings
FIG. 1 is a schematic diagram of an unmanned perception system for all-weather environmental monitoring.
Detailed Description
The invention provides an all-weather environment monitoring unmanned sensing system and a sensing method thereof, which are further described in detail below with reference to the accompanying drawings.
An all-weather environment monitoring unmanned perception system consists of a perception monitoring module, a data preprocessing module and a data decision fusion module;
the weather monitoring module system comprises a weather monitoring system and an environment sensing system, wherein the weather monitoring system comprises a temperature sensor capable of collecting the temperature of the surrounding environment, a rainfall sensor capable of collecting rainfall such as rain and snow, a light sensor capable of detecting illumination intensity and a dust concentration capable of detecting the environment;
the environment sensing system comprises a laser radar, a camera, an ultrasonic radar, a millimeter wave radar and the like; carrying out clutter false alarm filtering processing on speed information of an obstacle in a driving environment acquired by the millimeter wave radar; the size, the position information and the attribute information of the obstacles in the driving environment acquired by the laser radar and the camera are respectively subjected to space and time matching;
the data preprocessing module is a corresponding module for processing the data acquired by each sensor, and realizes independent processing and conversion of different data acquired by different sensors, such as converting the external temperature acquired by the temperature sensor from analog quantity to digital quantity, and performing filtering processing; and carrying out clutter false alarm filtering processing on the speed information of the obstacle in the driving environment acquired by the millimeter wave radar. And processing the data acquired by each sensor and then sending the processed data to a data decision fusion module.
After the data decision fusion module acquires the processed information of the data preprocessing module, the following method is adopted to carry out decision fusion, and the current weather environment information is integrated based on different weather monitoring system sensor threshold judgment mechanisms; forming the sensor information selection decision mechanism of the environment sensing system according to different weather environment information, wherein if the situation of rain and fog or excessive dust emission occurs, the information of the environment sensing system selects the information collected by the vision sensor and the millimeter wave radar under the condition that the laser radar cannot perform normal sensing;
the sensor information selection decision mechanism of the environment sensing system is based on the Bayesian principle, a minimum risk decision mode is adopted, and conditional risks are introduced by setting confidence thresholds of different environment sensing system sensors under different conditions, so that the usability of a result obtained by fusion of the multi-source sensors is increased, and the loss caused by detection errors caused by the environment sensors is reduced. The specific process is as follows:
step one: the sensor assembly mode of the environment sensing system under various weather environments is set through experience values, a mode space is formed, and n mode categories exist: omega 1 ，ω 2 ，…，ω n The method comprises the steps of carrying out a first treatment on the surface of the By weather monitoringWeather environment information acquired by a sensor of the system forms a decision of which sensor combination mode is selected according to different weather environment information, forms a decision space, and is defined as alpha 1 ，α 2 ，…，α m ；
Defining a pattern class omega j Adopts the decision alpha i The losses caused are noted as:
λ ij ＝λ(α i /ω j )
the risk loss of a sensor module type of an environment sensing system based on weather environment information is called a loss function, wherein lambda is a loss weight;
step two: creating a loss matrix
And a minimum loss criterion is calculated and,
wherein,
selecting the decision with the least risk, i.e
Based on the current decision mechanism, multi-source information fusion is carried out on the sensor information of the selected environment sensing system, and the vehicle state information, including the information of position, direction, vehicle speed and the like, is adjusted based on the fused information and is sent to the vehicle control system.
The foregoing is only a preferred embodiment of the present invention, and the scope of the present invention is defined by the claims.
Claims (2)
1. A sensing method of an unmanned sensing system based on all-weather environment monitoring is characterized in that,
the all-weather environment monitoring unmanned perception system comprises a perception monitoring module, a data preprocessing module and a data decision fusion module;
the sensing and monitoring module system comprises a weather monitoring system and an environment sensing system, wherein the weather monitoring system comprises a temperature sensor, a rainfall sensor, a light sensor and a dust sensor; the environment sensing system comprises millimeter wave radar, laser radar, a camera, ultrasonic radar and a vision sensor;
the data preprocessing module processes the data acquired by each sensor to realize independent processing and conversion of different data acquired by different sensors;
the data decision fusion module forms a decision selection mechanism for the sensor of the environment sensing system based on different weather environment information obtained by the weather monitoring system, selects information of the sensor of the environment sensing system which is suitable for the current real-time weather environment based on the decision mechanism, carries out multi-source information fusion on the information, adjusts vehicle state information based on the fusion information, comprises position, direction and vehicle speed information and sends the information to the vehicle control system;
the sensing method comprises the following steps:
step 1, obtaining real-time weather environment information by using a weather monitoring system, wherein the external temperature acquired by a temperature sensor, the external humidity acquired by a rainfall sensor, the external illumination intensity acquired by a light sensor and the dust concentration acquired by a dust sensor are utilized, and converting analog quantity acquired by each sensor into digital quantity by using a data preprocessing module and performing filtering treatment;
step 2, obtaining operation environment information by using an environment sensing system, wherein the speed information of obstacles in the driving environment obtained by the millimeter wave radar is filtered by using a data preprocessing module; the size, the position information and the attribute information of the obstacles in the driving environment acquired by the laser radar and the camera are respectively subjected to space and time matching; the vision sensor can also acquire various driving environment information; preprocessing all detected information through a data preprocessing module;
step 3, after the data decision fusion module acquires the information processed by the data preprocessing module, judging based on different sensor thresholds of the weather monitoring system, and integrating the current weather environment information; and forming a selection decision for the sensor of the environment sensing system based on the weather environment information, selecting a sensor combination of the environment sensing system adapting to the current real-time weather environment, removing redundant information of unavailable sensors, carrying out multi-source information fusion on the information of the selected sensor in the environment sensing system, adjusting vehicle state information based on the fused information, including position, direction and vehicle speed information, and sending the information to a vehicle control system.
2. The sensing method according to claim 1, wherein in step 3, for the decision of selecting the information of the sensors in the environmental sensing system, a minimum risk decision mode based on bayesian principle is adopted, specifically, by setting confidence thresholds of the sensors of different environmental sensing systems under different conditions, introducing conditional risk, and selecting a suitable sensor of the environmental sensing system; the specific method comprises the following steps:
step one: the sensor assembly mode of the environment sensing system under various weather environments is set through experience values, a mode space is formed, and n mode categories exist: omega 1 ,ω 2 ,…,ω n The method comprises the steps of carrying out a first treatment on the surface of the The weather environment information obtained by the sensor of the weather monitoring system forms a decision of which sensor combination mode is selected according to different weather environment information, forms a decision space, and is defined as alpha 1 ,α 2 ,…,α m ；
Defining a pattern class omega j Adopts the decision alpha i The losses caused are noted as:
λ ij ＝λ(α i /ω j )
the risk loss of a sensor module type of an environment sensing system based on weather environment information is called a loss function, wherein lambda is a loss weight;
step two: creating a loss matrix
And a minimum loss criterion is calculated and,
wherein,
selecting the decision with the least risk, i.e
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| CN112947426A (en) * | 2021-02-01 | 2021-06-11 | 南京抒微智能科技有限公司 | Cleaning robot motion control system and method based on multi-sensing fusion |
| CN112959987B (en) * | 2021-03-19 | 2022-02-18 | 东风汽车股份有限公司 | Automatic emergency braking self-adaptive control system and control method thereof |
| CN112849161B (en) * | 2021-03-28 | 2022-06-07 | 重庆长安汽车股份有限公司 | Meteorological condition prediction method and device for automatic driving vehicle, automobile and controller |
| CN113405546A (en) * | 2021-05-18 | 2021-09-17 | 智能移动机器人(中山)研究院 | Global sensing early warning system of intelligent sensing sensor |
| CN115005192B (en) * | 2022-05-16 | 2024-02-23 | 国网吉林省电力有限公司超高压公司 | Data acquisition device for preventing bird damage and application method thereof |
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