CN112147615A - Unmanned sensing method based on all-weather environment monitoring system - Google Patents
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- 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
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Abstract
The invention relates to an all-weather environment monitoring unmanned sensing system and a sensing method, comprising a sensing monitoring module, a data preprocessing module and a data decision fusion module; the perception monitoring module system comprises a weather monitoring system and an environment perception system, wherein the weather monitoring system comprises a temperature sensor, a rainfall sensor, a light sensor and a dust sensor; the environment perception system comprises a millimeter wave radar, a laser radar, a camera, an ultrasonic radar and a visual 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; and the data decision fusion module forms a decision selection mechanism for the sensor information of the environment perception system based on the comprehensive information of the weather environment, selects the information of the sensor which is adaptive to the weather environment, performs 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 a sensing method.
Background
In view of the characteristics of continuous operation of a mining area day and night, complex mining area environment and bad weather, in order to ensure that the unmanned mining truck can support normal roadster under all weather and different weather conditions, the unmanned sensing system of the mining area needs to acquire road information and peripheral driving environment information and also needs to acquire illumination conditions and weather conditions such as rain, fog, smog, rain, snow and the like. Based on the road information and the weather information, a proper sensor can be selected according to different sensor attributes to acquire sensing information, and the effective normal work of the sensor in different environments is guaranteed.
The perception and identification of different weather conditions also needs to be achieved by corresponding sensor technologies. Generally, the distinction in sunny and rainy days can be detected by a rainfall sensor, and a plurality of sensors integrate the identification of environments such as temperature, light intensity, rain, snow and the like; the wind speed has a great influence on the normal operation of the vehicle, the wind speed and the direction can be sensed through the wind speed sensor, and the wind speed caused by the motion of the vehicle or the wind speed caused by the external environment can be distinguished by matching the operation state of the vehicle; the visibility is reduced due to the thick fog, and the visibility can be identified through a camera, a humidity and light intensity sensor and the like; the detection of snow is integrated in some rainfall sensors, and can also be detected by the cooperation of a camera and a temperature sensor; the perception of day and night can be used for distinguishing daytime by using a light intensity sensor in cooperation with local time.
The unmanned technology is developed and applied in mining areas and shows certain effects. However, due to the complex mining area environment and the bad weather, the sensors arranged on the mine car can not acquire the peripheral driving information due to temperature failure or environmental conditions, and in addition, the sensing requirements of the unmanned mine car driving and safe and smooth operation can not be met due to the insufficient stability and reliability of the information in the prior art.
Disclosure of Invention
Aiming at the problems of insufficient information stability and reliability in the prior art, the invention aims to design a multi-sensor unmanned sensing system, which can increase temperature and rainfall and can measure the external environment and weather conditions by light sensors aiming at specific mining area environments, and can form comprehensive utilization of laser radar, millimeter wave radar and the like under different periods and different weather conditions by different sensors according to the measurement result. Therefore, more comprehensive perception information and barrier information are obtained under the severe mining area environment, driving environment information is more accurate, and safety and smoothness in the whole driving process are guaranteed.
The invention provides an all-weather environment monitoring unmanned sensing system and method, which are used for sensing and monitoring the whole environment such as road environment, driving environment, weather environment and the like by an unmanned mining truck, fully utilizing effective information and eliminating the interference of redundant information.
An all-weather environment monitoring unmanned sensing system comprises a sensing monitoring module, a data preprocessing module and a data decision fusion module;
the perception monitoring module system comprises a weather monitoring system and an environment perception system, wherein the weather monitoring system comprises a temperature sensor, a rainfall sensor, a light sensor and a dust sensor; the environment perception system comprises a millimeter wave radar, a laser radar, a camera, an ultrasonic radar and a visual 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 sensors of the environment sensing system based on different weather environment information obtained by the weather monitoring system, selects the information of the environment sensing system sensors which are adaptive to the current real-time weather environment based on the decision mechanism, performs multi-source information fusion on the information, adjusts vehicle state information including position, direction and speed information based on the fusion information, and sends the information to the vehicle control system.
A perception method based on the perception system comprises the following specific steps:
step 1, converting analog quantity acquired by each sensor into digital quantity by using an external temperature acquired by a temperature sensor, external humidity acquired by a rainfall sensor, external illumination intensity acquired by a light sensor and raised dust concentration acquired by a dust sensor in a weather monitoring system, and performing filtering processing;
step 2, the speed information of the obstacles in the driving environment is acquired by a millimeter wave radar in the environment perception system, and clutter false alarms are filtered by a data preprocessing module; respectively carrying out space and time matching on the size, position information and attribute information of the obstacle in the driving environment acquired by the laser radar and the camera; the vision sensor can also acquire various driving environment information; preprocessing all the 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 sensors of the environment sensing system based on the weather environment information, selecting a sensor combination of the environment sensing system suitable for the current real-time weather environment, removing redundant information of unavailable sensors, performing multi-source information fusion on the selected information of the sensors in the environment sensing system, adjusting vehicle state information including position, direction and speed information based on the fused 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 environmental perception system, a minimum risk decision mode based on the Bayesian principle is adopted, specifically, condition risks are introduced by setting confidence thresholds of the sensors of different environmental perception systems under different conditions, and a proper sensor of the environmental perception system is selected; the specific method comprises the following steps:
the method comprises the following steps: the sensor combination mode of the environmental perception system under various weather environments is set through empirical values, a mode space is formed, and n types of mode categories exist: omega1，ω2，…，ωn(ii) a The weather environment information acquired by the sensors of the weather monitoring system forms a decision of which sensor combination mode is selected according to different weather environment information to form a decision space defined as alpha1，α2，…，αm；
Defining a mode class omegajUsing the decision alphaiThe resulting loss is noted as:
λij＝λ(αi/ωj)
namely, the risk loss of the sensor combination mode of the environment perception system based on the weather environment information is called as a loss function, wherein lambda is a loss weight;
step two: creating a loss matrix
And a minimum loss criterion is calculated,
wherein the content of the first and second substances,
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 road to form the intelligent sensing system of the all-weather detection system, processing the currently corresponding available sensor information according to the external environment, removing unavailable redundant information, avoiding the waste of resources and time for processing the redundant or unavailable information and improving the intelligence, high efficiency and all-weather working capacity of the unmanned sensing system.
The invention has the advantages and positive effects that:
the existing sensing technology does not monitor the environmental weather conditions, so that the sensor fails or the sensing capability is lost in a specific environment, if 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. Various sensors are arranged on a vehicle body, including a temperature sensor, a rainfall sensor, a light intensity sensor and a dust sensor, to monitor the temperature, rainfall or snowfall, illumination intensity and dust concentration of the external environment; laser radar, millimeter wave radar, camera, ultrasonic radar, etc. for environmental perception.
2. According to the data of the environment monitoring sensors, the sensors which can be used in corresponding scenes are selected for environment sensing, and the safety and the smooth driving of the unmanned mine car are guaranteed. The scheme of the invention is reasonable in design and can be suitable for special environments of mining areas.
Drawings
FIG. 1 is a schematic diagram of an all-weather environmental monitoring unmanned sensing system.
Detailed Description
The following will explain in detail an unmanned sensing system and sensing method for all-weather environmental monitoring according to the present invention with reference to the accompanying drawings.
An all-weather environment monitoring unmanned sensing system comprises a sensing 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 ambient temperature, a rainfall sensor capable of collecting rainfall such as rain, snow and the like, a light sensor capable of detecting the illumination intensity and a dust concentration in the environment;
the environment sensing system comprises a laser radar, a camera, an ultrasonic radar, a millimeter wave radar and the like; clutter false alarm filtering processing is carried out on the speed information of the obstacles in the driving environment acquired by the millimeter wave radar; respectively carrying out space and time matching on the size, position information and attribute information of the obstacle in the driving environment acquired by the laser radar and the camera;
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 carrying out filtering processing; and carrying out clutter false alarm filtering processing on the speed information of the obstacles 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 decision fusion is carried out by adopting the following method, and the current weather environment information is integrated based on different weather monitoring system sensor threshold judgment mechanisms; forming a sensor information selection decision mechanism of the environment sensing system according to different weather environment information, wherein information collected by a visual sensor and a millimeter wave radar is selected by the information of the environment sensing system if rain and fog conditions occur or dust is too heavy and the laser radar cannot sense the rain and fog conditions normally;
the method is characterized in that a decision mechanism for selecting sensor information of the environmental perception system is based on a Bayesian principle, a minimum risk decision mode is adopted, and conditional risks are introduced by setting confidence thresholds of different environmental perception system sensors under different conditions, so that usability of results obtained by fusing the multi-source sensors is increased, and loss caused by detection errors brought by the environmental sensors is reduced. The specific process is as follows:
the method comprises the following steps: the sensor combination mode of the environmental perception system under various weather environments is set through empirical values, a mode space is formed, and n types of mode categories exist: omega1，ω2，…，ωn(ii) a The weather environment information acquired by the sensors of the weather monitoring system forms a decision of which sensor combination mode is selected according to different weather environment information to form a decision space defined as alpha1，α2，…，αm；
Defining a mode class omegajUsing the decision alphaiThe resulting loss is noted as:
λij＝λ(αi/ωj)
namely, the risk loss of the sensor combination mode of the environment perception system based on the weather environment information is called as a loss function, wherein lambda is a loss weight;
step two: creating a loss matrix
And a minimum loss criterion is calculated,
wherein the content of the first and second substances,
selecting the decision with the least risk, i.e.
And based on a current decision-making mechanism, carrying out multi-source information fusion on the selected sensor information of the environment perception system, adjusting vehicle state information including information such as position, direction and speed based on the fused information, and sending the information to a vehicle control system.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is defined by the appended claims.
Claims (3)
1. An all-weather environment monitoring unmanned sensing system is characterized by comprising a sensing monitoring module, a data preprocessing module and a data decision fusion module;
the perception monitoring module system comprises a weather monitoring system and an environment perception system, wherein the weather monitoring system comprises a temperature sensor, a rainfall sensor, a light sensor and a dust sensor; the environment perception system comprises a millimeter wave radar, a laser radar, a camera, an ultrasonic radar and a visual 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 sensors of the environment sensing system based on different weather environment information obtained by the weather monitoring system, selects the information of the environment sensing system sensors which are adaptive to the current real-time weather environment based on the decision mechanism, performs multi-source information fusion on the information, adjusts vehicle state information including position, direction and speed information based on the fusion information, and sends the information to the vehicle control system.
2. The perception method of the unmanned perception system for all-weather environmental monitoring based on claim 1 is characterized by comprising the following specific steps:
step 1, obtaining real-time weather environment information by using a weather monitoring system, wherein the outside temperature acquired by a temperature sensor, the outside humidity acquired by a rainfall sensor, the outside illumination intensity acquired by a light sensor and the flying dust concentration acquired by a dust sensor, converting analog quantity acquired by each sensor into digital quantity by using a data preprocessing module, and filtering;
step 2, obtaining running environment information by using an environment sensing system, wherein the speed information of obstacles in the driving environment is obtained by a millimeter wave radar, and clutter false alarms are filtered by using a data preprocessing module; respectively carrying out space and time matching on the size, position information and attribute information of the obstacle in the driving environment acquired by the laser radar and the camera; the vision sensor can also acquire various driving environment information; preprocessing all the 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 sensors of the environment sensing system based on the weather environment information, selecting a sensor combination of the environment sensing system suitable for the current real-time weather environment, removing redundant information of unavailable sensors, performing multi-source information fusion on the selected information of the sensors in the environment sensing system, adjusting vehicle state information including position, direction and speed information based on the fused information, and sending the information to a vehicle control system.
3. The sensing method according to claim 2, wherein in step 3, a minimum risk decision manner based on a bayesian principle is adopted for the selection decision of the information of the sensors in the environmental sensing system, specifically, a condition risk is introduced by setting confidence thresholds of the sensors of different environmental sensing systems under different conditions, and a sensor of a proper environmental sensing system is selected; the specific method comprises the following steps:
the method comprises the following steps: by passingEmpirical values are set in sensor combination modes of an environment sensing system under various weather environments to form a mode space, and n types of mode categories exist: omega1,ω2,…,ωn(ii) a The weather environment information acquired by the sensors of the weather monitoring system forms a decision of which sensor combination mode is selected according to different weather environment information to form a decision space defined as alpha1,α2,…,αm；
Defining a mode class omegajUsing the decision alphaiThe resulting loss is noted as:
λij＝λ(αi/ωj)
namely, the risk loss of the sensor combination mode of the environment perception system based on the weather environment information is called as a loss function, wherein lambda is a loss weight;
step two: creating a loss matrix
And a minimum loss criterion is calculated,
wherein the content of the first and second substances,
selecting the decision with the least risk, i.e.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112849161A (en) * | 2021-03-28 | 2021-05-28 | 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 |
| 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 |
| CN112959987A (en) * | 2021-03-19 | 2021-06-15 | 东风汽车股份有限公司 | Automatic emergency braking self-adaptive control system and control method thereof |
| 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 |
| CN115005192A (en) * | 2022-05-16 | 2022-09-06 | 国网吉林省电力有限公司超高压公司 | Data acquisition device for preventing bird damage and using method thereof |
| CN115447593A (en) * | 2022-09-28 | 2022-12-09 | 中汽创智科技有限公司 | Perception data acquisition method and device for automatic driving vehicle and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150253775A1 (en) * | 2014-03-04 | 2015-09-10 | Cybernet Systems Corporation | All weather autonomously driven vehicles |
| CN110406544A (en) * | 2019-08-06 | 2019-11-05 | 阿尔法巴人工智能(深圳)有限公司 | Vehicle sensory perceptual system and method under misty rain scene |
| CN111413983A (en) * | 2020-04-08 | 2020-07-14 | 江苏盛海智能科技有限公司 | Environment sensing method and control end of unmanned vehicle |
| CN111551938A (en) * | 2020-04-26 | 2020-08-18 | 北京踏歌智行科技有限公司 | Unmanned technology perception fusion method based on mining area environment |
-
2020
- 2020-09-08 CN CN202010934040.9A patent/CN112147615B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150253775A1 (en) * | 2014-03-04 | 2015-09-10 | Cybernet Systems Corporation | All weather autonomously driven vehicles |
| CN110406544A (en) * | 2019-08-06 | 2019-11-05 | 阿尔法巴人工智能(深圳)有限公司 | Vehicle sensory perceptual system and method under misty rain scene |
| CN111413983A (en) * | 2020-04-08 | 2020-07-14 | 江苏盛海智能科技有限公司 | Environment sensing method and control end of unmanned vehicle |
| CN111551938A (en) * | 2020-04-26 | 2020-08-18 | 北京踏歌智行科技有限公司 | Unmanned technology perception fusion method based on mining area environment |
Non-Patent Citations (1)
| Title |
|---|
| 钟陈志鹏: "无人驾驶智能伦理决策研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》, no. 4, pages 35 - 44 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN112959987A (en) * | 2021-03-19 | 2021-06-15 | 东风汽车股份有限公司 | Automatic emergency braking self-adaptive control system and control method thereof |
| 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 |
| CN112849161A (en) * | 2021-03-28 | 2021-05-28 | 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 |
| CN115005192A (en) * | 2022-05-16 | 2022-09-06 | 国网吉林省电力有限公司超高压公司 | Data acquisition device for preventing bird damage and using method thereof |
| 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 |
| CN115447593A (en) * | 2022-09-28 | 2022-12-09 | 中汽创智科技有限公司 | Perception data acquisition method and device for automatic driving vehicle and storage medium |
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