CN110780358A - Method, system, computer-readable storage medium and vehicle for autonomous driving weather environment recognition - Google Patents
Method, system, computer-readable storage medium and vehicle for autonomous driving weather environment recognition Download PDFInfo
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- CN110780358A CN110780358A CN201911009632.3A CN201911009632A CN110780358A CN 110780358 A CN110780358 A CN 110780358A CN 201911009632 A CN201911009632 A CN 201911009632A CN 110780358 A CN110780358 A CN 110780358A
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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
- G01W1/06—Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed giving a combined indication of weather conditions
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- 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 discloses a method, a system, a computer readable storage medium and a vehicle for automatically recognizing a weather environment, wherein the method comprises the following steps of 1, acquiring weather forecast information in real time, and simultaneously monitoring a rainfall signal output by a rainfall sensor, an illumination signal output by a sunshine sensor and a current-time sun azimuth angle output by a sun azimuth angle sensor in real time; acquiring environmental information output by a visual sensor in real time, extracting environmental image features through a neural network, and outputting visibility information and whether snow information exists or not after classification; step 2, estimating a backlight state; presume the state of rainstorm; presume whether there is snow on the road surface; determining visibility; step 3, judging the current weather state: if one or more conditions of a backlight state, rainstorm, poor visibility and accumulated snow on the road surface occur, outputting a weather state which is poor; and if the weather condition is good, returning to the step 1. The invention can accurately sense the current weather environment.
Description
Technical Field
The invention belongs to the technical field of automobile weather environment cognition, and particularly relates to a method and a system for automatically recognizing a weather environment, a computer-readable storage medium and a vehicle.
Background
The weather environment is one of the important factors affecting driving safety. There is data display, traditional car accident quantity accounts for 22% of total accident quantity under bad weather, and bad weather such as highlight, sleet, fog and haze all can cause the wrong judgement of driver to the environment. For autonomous vehicles, severe weather can cause the performance of the sensing system to degrade significantly. Strong light, rain and snow can cause certain difficulty for the identification of markers and the extraction of characteristics of the camera, and although the laser radar has strong robustness to illumination and color, the laser radar still has no strategy under the interference of rain, snow, fog and smoke. Compared with a laser radar, the millimeter wave radar has longer wavelength and stronger capability of penetrating fog, smoke and dust, so the influence of severe weather is less, but different objects cannot be distinguished.
Therefore, there is a need to develop a new method, system, computer readable storage medium, and vehicle for autonomous driving weather environment awareness.
Disclosure of Invention
The invention aims to provide a method, a system, a computer readable storage medium and a vehicle for automatically recognizing a driving weather environment, which can automatically recognize severe weather environments such as rainstorm, road snow, low visibility, backlight and the like.
The invention relates to a method for automatically recognizing a weather environment for driving, which comprises the following steps:
step 1, weather forecast information is acquired in real time, wherein the weather forecast information comprises whether the rain is rainy or not, whether snow is accumulated or not and visibility information, and meanwhile, rainfall signals output by a rainfall sensor, illumination signals output by a sunshine sensor and the current-time sun azimuth angle output by a sun azimuth angle sensor are monitored in real time;
acquiring environmental information output by a visual sensor in real time, extracting environmental image features through a neural network, and outputting visibility information and whether snow information exists or not after classification;
step 2, estimating a backlight state: whether the vehicle is in a backlight environment or not is presumed according to the current running course, the current sun azimuth angle and whether the illumination is greater than a threshold value Y or not;
presume the rainstorm state: whether the vehicle is in a rainstorm environment or not is presumed according to real-time rainfall information output by weather forecast and whether the rainfall signal is larger than a threshold value X or not;
presume whether the road surface has snow: comprehensively presuming whether snow is accumulated on the road according to weather forecast information and information acquired by a visual sensor;
determining visibility: outputting a visibility reference value according to weather forecast information and information acquired by a visual sensor, and judging whether the vehicle is in a low-visibility environment;
step 3, judging the current weather state: if one or more conditions of a backlight state, rainstorm, poor visibility and accumulated snow on the road surface occur, the weather is considered to be poor, and the weather state is output to be poor; and if the weather condition is good, returning to the step 1.
Further, the step 2 of estimating the backlight state specifically includes: the current running course angle theta of the vehicle relative to the true north direction
1Azimuth angle theta of sun relative to true north direction at present moment
2If theta
1-θ
2Less than 15 degrees and the current sunlight illumination>And the threshold value Y is used for judging that the vehicle is in the backlight state, otherwise, judging that the vehicle is not in the backlight state.
Further, the estimation of the rainstorm state in the step 2 specifically includes:
if (A2-X) A1>0, judging that the vehicle is in a rainstorm environment, otherwise, judging that the vehicle is in a non-rainstorm environment;
wherein:
a1 is the real-time rain information output by weather forecast, if it is rainstorm and above, the A1 value is 1, otherwise, A1 is 0,
a2 is a rainfall signal output by the rainfall sensor, and X is a preset rainfall threshold.
Further, the step 2 of presuming whether the road surface has accumulated snow specifically includes:
if B2 XB
δ×B1>0.9, judging that the road has accumulated snow, or else, judging that the road has no accumulated snow;
wherein B1 is dayIf the real-time snowfall quantity output by the air forecast is equal to or above the middle snow, the B1 value is 1, otherwise, the B1 value is 0; b2 shows whether the output of the vision sensor is snowing, if so, B2 shows 1; if the snow does not fall, B2 is 0; b is
δThe confidence of B2.
Further, the step 2 of determining the visibility specifically includes:
if C2 is C
δ*C1>0.9, the visibility is judged to be poor;
wherein: c1 is real-time visibility output by weather forecast, according to the visibility grade output by weather forecast, when the visibility is less than 0.2km, the C1 value is 1, otherwise, the visibility is 0;
c2 is a visual sensor that outputs visibility levels, which can output three levels of visibility: clear, light, heavy, when visibility is heavy, C2 is 1, otherwise 0; c
δConfidence of C2.
The invention relates to an automatic driving weather environment cognition system which comprises a controller, a drop sensor, an sunshine sensor and a sun azimuth angle sensor, wherein the drop sensor, the sunshine sensor and the sun azimuth angle sensor are respectively connected with the controller; the controller is programmed to perform the steps of the method of autonomous driving weather environment awareness according to the invention.
The invention provides a computer readable storage medium, which stores a computer readable program, wherein the computer readable program can realize the steps of the method for automatically recognizing the weather environment for driving according to the invention when being called and executed by a controller.
The invention relates to an automatic driving vehicle, which adopts the automatic driving weather environment cognition system.
The invention has the following advantages: the invention not only depends on the sensing capability of the automatic driving sensing system, but also fully utilizes the information of a plurality of sensors such as weather forecast information, a rainfall sensor, a sunshine sensor and the like, can comprehensively judge the weather state of the current driving environment in an all-round way, and is more accurate compared with the traditional method which only depends on a vision sensor.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention will be further explained with reference to the drawings.
As shown in fig. 1, a method for autonomous driving weather environment recognition includes the following steps:
step 1, acquiring weather forecast information in real time, wherein the weather forecast information comprises information of whether the weather is rainy or snowy and visibility; and simultaneously monitoring a rainfall signal output by the rainfall sensor, an illumination signal output by the sunshine sensor and a current solar azimuth output by the solar azimuth sensor in real time.
The method comprises the steps of acquiring environmental information output by a visual sensor in real time, extracting environmental image features through a neural network, and outputting visibility information and whether snow information exists or not after classification. In this embodiment, the visibility information refers to a visibility reference value and a confidence thereof; whether snow information exists or not means whether snow exists or not and key information such as confidence coefficient of the snow.
Step 2, estimating a backlight state: and estimating whether the vehicle is in a backlight environment or not according to the current running course, the current sun azimuth angle and whether the illumination is greater than a threshold value Y or not.
Presume the rainstorm state: and estimating whether the vehicle is in a rainstorm environment or not according to the real-time rainfall information output by the weather forecast and whether the rainfall signal is greater than a threshold value X or not.
Presume whether the road surface has snow: and comprehensively estimating whether the snow is accumulated on the road according to the weather forecast information and the information acquired by the vision sensor.
Determining visibility: outputting a visibility reference value according to weather forecast information and information acquired by a visual sensor, and judging whether the vehicle is in a low-visibility environment;
step 3, judging the current weather state: if one or more conditions of a backlight state, rainstorm, poor visibility and accumulated snow on the road surface occur, the weather is considered to be poor, and the weather state is output to be poor; and if the weather condition is good, returning to the step 1.
In this embodiment, the output result of the poor weather condition is sent to the automatic driving controller, the automatic driving controller is notified to make a risk minimization strategy, and meanwhile, the automatic driving user is reminded to observe the road environment and respond to the take-over request of the automatic driving system at any time, so that traffic accidents are avoided.
In this embodiment, the step 2 of estimating the backlight state specifically includes: the current running course angle theta of the vehicle relative to the true north direction
1Azimuth angle theta of sun relative to true north direction at present moment
2If theta
1-θ
2Less than 15 degrees and the current sunlight illumination>And the threshold value Y is used for judging that the vehicle is in a backlight state, otherwise, judging that the vehicle is not in the backlight state which can influence the performance of the automatic driving sensor.
In this embodiment, the estimation of the rainstorm state in step 2 specifically includes: and if the (A2-X) A1>0, judging that the vehicle is in a rainstorm environment, otherwise, judging that the vehicle is in a non-rainstorm environment. Wherein: a1 is real-time rain information output by weather forecast, if the information is rainstorm and above, the value of A1 is 1, otherwise, A1 is 0; a2 is a rainfall signal output by the rainfall sensor, and X is a preset rainfall threshold.
In this embodiment, the step 2 of estimating whether the road surface has snow specifically includes: if B2 XB
δ×B1>And 0.9, judging that the road has the accumulated snow, or else, judging that the road has no accumulated snow. B1 is the real-time snowfall quantity output by the weather forecast, if the real-time snowfall quantity is at or above the middle snow, the B1 value is 1, otherwise, B1 is 0; b2 shows whether the output of the vision sensor is snowing, if so, B2 shows 1; if the snow does not fall, B2 is 0; b is
δThe confidence of B2.
In this embodiment, the visibility determination in step 2 is specifically that if C2 × C
δ*C1>0.9, the visibility is judged to be poor. Wherein: and C1 outputs real-time visibility for the weather forecast, and according to the visibility grade output by the weather forecast, the value of C1 is 1 when the visibility is less than 0.2km, otherwise, the visibility is 0. C2 is a visual sensor that outputs visibility levels, which can output three levels of visibility: clear, light, heavy, when ableWhen visibility is heavy, C2 is 1, otherwise, it is 0; c
δConfidence of C2.
In this embodiment, an automatic driving weather environment recognition system includes a controller, and a drop sensor, an sunshine sensor and a sun azimuth sensor respectively connected to the controller, where the rainfall sensor is configured to output a rainfall signal, the sunshine sensor is configured to output an illuminance signal, and the sun azimuth sensor is configured to output a sun azimuth at a current time; the controller is programmed to perform the steps of the method of autonomous driving weather environment awareness as described in the present embodiment.
In this embodiment, a computer readable storage medium stores a computer readable program, and when the computer readable program is called by a controller, the steps of the method for automatically recognizing the weather environment for driving are implemented as described in this embodiment.
In this embodiment, an autonomous vehicle adopts the system for autonomous weather environment recognition according to the present invention.
Claims (8)
1. A method of autonomous driving weather environment awareness, comprising the steps of:
step 1, weather forecast information is acquired in real time, wherein the weather forecast information comprises whether the rain is rainy or not, whether snow is accumulated or not and visibility information, and meanwhile, rainfall signals output by a rainfall sensor, illumination signals output by a sunshine sensor and the current-time sun azimuth angle output by a sun azimuth angle sensor are monitored in real time;
acquiring environmental information output by a visual sensor in real time, extracting environmental image features through a neural network, and outputting visibility information and whether snow information exists or not after classification;
step 2, estimating a backlight state: whether the vehicle is in a backlight environment or not is presumed according to the current running course, the current sun azimuth angle and whether the illumination is greater than a threshold value Y or not;
presume the rainstorm state: whether the vehicle is in a rainstorm environment or not is presumed according to real-time rainfall information output by weather forecast and whether the rainfall signal is larger than a threshold value X or not;
presume whether the road surface has snow: comprehensively presuming whether snow is accumulated on the road according to weather forecast information and information acquired by a visual sensor;
determining visibility: outputting a visibility reference value according to weather forecast information and information acquired by a visual sensor, and judging whether the vehicle is in a low-visibility environment;
step 3, judging the current weather state: if one or more conditions of a backlight state, rainstorm, poor visibility and accumulated snow on the road surface occur, the weather is considered to be poor, and the weather state is output to be poor; and if the weather condition is good, returning to the step 1.
2. The method of autonomous driving weather environment awareness of claim 1, wherein: the step 2 of estimating the backlight state specifically comprises the following steps: the current running course angle theta of the vehicle relative to the true north direction
1Azimuth angle theta of sun relative to true north direction at present moment
2If theta
1-θ
2Less than 15 degrees and the current sunlight illumination>And the threshold value Y is used for judging that the vehicle is in the backlight state, otherwise, judging that the vehicle is not in the backlight state.
3. The method of autonomous driving weather environment awareness of claim 1 or 2, wherein: the step 2 of presuming the rainstorm state specifically comprises the following steps:
if (A2-X) A1>0, judging that the vehicle is in a rainstorm environment, otherwise, judging that the vehicle is in a non-rainstorm environment;
wherein:
a1 is the real-time rain information output by weather forecast, if it is rainstorm and above, the A1 value is 1, otherwise, A1 is 0,
a2 is a rainfall signal output by the rainfall sensor, and X is a preset rainfall threshold.
4. The automated driving weather environment awareness method of claim 3, wherein: the step 2 of presuming whether the road surface has accumulated snow specifically comprises the following steps:
if B2 XB
δ×B1>0.9, judging that the road has accumulated snow, or else, judging that the road has no accumulated snow;
b1 is the real-time snowfall quantity output by the weather forecast, if the real-time snowfall quantity is at or above the middle snow, the B1 value is 1, otherwise, B1 is 0; b2 shows whether the output of the vision sensor is snowing, if so, B2 shows 1; if the snow does not fall, B2 is 0; b is
δThe confidence of B2.
5. The method of autonomous driving weather environment awareness of claim 1, 2 or 4, wherein: the step 2 of determining visibility specifically includes:
if C2 is C
δ*C1>0.9, the visibility is judged to be poor;
wherein: c1 is real-time visibility output by weather forecast, according to the visibility grade output by weather forecast, when the visibility is less than 0.2km, the C1 value is 1, otherwise, the visibility is 0;
c2 is a visual sensor that outputs visibility levels, which can output three levels of visibility: clear, light, heavy, when visibility is heavy, C2 is 1, otherwise 0; c
δConfidence of C2.
6. An automatic driving weather environment cognition system comprises a controller, a drop sensor, an sunshine sensor and a sun azimuth sensor, wherein the drop sensor, the sunshine sensor and the sun azimuth sensor are respectively connected with the controller; the method is characterized in that: the controller is programmed to perform the steps of the method for autonomous driving weather environment awareness of any of claims 1 to 5.
7. A computer-readable storage medium characterized by: stored with a computer readable program enabling the implementation of the steps of the method for autonomous driving weather environment recognition according to any of claims 1 to 5 when invoked by a controller.
8. An autonomous vehicle, characterized by: a system employing autonomous driving weather environment awareness as claimed in claim 6.
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| CN111953789A (en) * | 2020-08-17 | 2020-11-17 | 广西云森科技有限公司 | Voice recognition-based network car booking abnormal driving environment monitoring system and method |
| CN112036389A (en) * | 2020-11-09 | 2020-12-04 | 天津天瞳威势电子科技有限公司 | Vehicle three-dimensional information detection method, device and equipment and readable storage medium |
| 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 |
| CN113276882A (en) * | 2021-04-22 | 2021-08-20 | 清华大学苏州汽车研究院（相城） | Control method and control system for automatic driving vehicle and calculation method for target speed |
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Application publication date: 20200211 |