CN110562264B - Road danger prediction method, device, equipment and medium for unmanned driving - Google Patents
Road danger prediction method, device, equipment and medium for unmanned driving Download PDFInfo
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Abstract
The invention provides a road danger prediction method, a road danger prediction device, road danger prediction equipment and a road danger prediction medium for unmanned driving. The method comprises the following steps: acquiring real-time geographic position coordinate information and a planned path of current running of the unmanned automobile; acquiring the weather on the path according to the planned path, wherein the weather comprises the following steps: the method comprises the following steps of predicting duration of sunny weather and sunny weather, predicting duration of rainy weather and rainy weather, and predicting duration of snowy weather and snowy weather, and dividing a planned path into different weather road sections according to the weather; predicting the road conditions of the meteorological road section according to the weather, wherein the road conditions comprise: drying, wetting and freezing; when the real-time geographic position coordinates of the unmanned automobile are near the dividing point of two different road sections, the road condition prediction result is sent and the automobile is controlled.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to a road hazard prediction method, a road hazard prediction device, road hazard prediction equipment and a road hazard prediction medium for unmanned driving.
Background
With the development of the information era, the unmanned automobile is unlikely to become possible, and the center of gravity of the existing enterprises is gradually transferred to the unmanned automobile, so that the road condition of the front road needs to be judged and reacted in time in the debugging process of the unmanned automobile or the actual running process of the unmanned automobile.
At present, however, when the weather changes during the driving of the unmanned vehicle at the present stage, the risk possibly existing on the road ahead cannot be predicted; secondly, when the weather causes the change of the driving road, the user can only be reminded, the change of the corresponding road condition can not be made, and the safety accident caused by the change of the road condition is prevented.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
In view of the above, the invention provides a road hazard prediction method, a road hazard prediction device, road hazard prediction equipment and a road hazard prediction medium for unmanned driving, and aims to solve the technical problem that the prior art cannot automatically perform corresponding control on a vehicle according to weather changes.
The technical scheme of the invention is realized as follows:
in one aspect, the present invention provides an unmanned road hazard prediction method, including:
acquiring real-time geographic position coordinate information and a planned path of current running of the unmanned automobile;
acquiring meteorological information on a path according to a planned path, wherein the meteorological information comprises: the planned route is divided into different weather road sections according to different weather information;
predicting the road condition of the meteorological road section according to the meteorological information, wherein the road condition comprises the following steps: dry, wet and frozen;
and when the real-time geographic position coordinates of the unmanned automobile are near the dividing point of two different road sections, sending the road condition prediction result and controlling the automobile.
On the basis of the above technical solution, preferably, the method further comprises the following steps of setting a prediction time of the road condition, and predicting the road condition of the meteorological section as a dry road condition when the predicted duration of the meteorological weather in the sunny day is longer than the prediction time; when the predicted duration of the rainy weather is longer than the predicted time, predicting the road condition of the weather road section as a wet road condition; and when the predicted duration of the snowing weather is longer than the predicted time, predicting the road condition of the weather road section as the icing road condition.
On the basis of the technical scheme, the method preferably further comprises the following steps of setting driving modes of different road conditions, and automatically switching the unmanned automobile into different driving modes according to the different road conditions when the unmanned automobile enters dry, wet or icy road conditions.
On the basis of the above technical solution, preferably, the method further includes the following steps of setting three driving modes of a dry road section, a wet road section and an icy road section, and when the unmanned vehicle enters the dry road section, the unmanned vehicle automatically switches to the driving mode of the dry road section, where the driving mode of the dry road section includes: prompting a user to pay attention to the safety of the front road section, acquiring the safe speed of the dry road section, and adjusting the driving speed of the automobile to the safe speed of the dry road section; when the unmanned vehicle enters a wet road section, the unmanned vehicle automatically switches to a wet road section driving mode, wherein the wet road section driving mode comprises the following steps: setting a preset frequency of windshield wiper swing, prompting a user to pay attention to safety of a road section ahead, advising the user to carry out auxiliary control on the automobile, acquiring the swing frequency of the windshield wiper in real time, advising the user to search a safe position for parking when the frequency is greater than the preset frequency, acquiring the safe speed of the wet road section, and adjusting the automobile driving speed to the safe speed of the wet road section; when the unmanned automobile enters the iced road section, the unmanned automobile is automatically switched into an iced road section driving mode, and the iced road section driving mode comprises the following steps: and reminding a user to manually control the automobile, detecting the tire, reminding the user to wind the anti-skid chain when detecting that the tire is not wound with the anti-skid chain, acquiring the safe speed of the iced road section, adjusting the running speed of the automobile to the safe speed of the iced road section, and suggesting the user to avoid the iced road section.
On the basis of the technical scheme, preferably, the method further comprises the following steps of setting recommended safe vehicle distances of the dry road section, the wet road section and the frozen road section, acquiring the vehicle distances between the unmanned vehicle and the front and rear vehicles in the driving process in real time, and adjusting the vehicle distances to the recommended safe vehicle distances.
On the basis of the technical scheme, the method preferably further comprises the following steps of predicting road conditions of different meteorological road sections, and when the real-time geographic position coordinates of the unmanned automobile are located near the dividing point of the two different road sections, sending the predicted road conditions of the next road section to the user.
On the basis of the technical scheme, the method preferably further comprises the following steps of setting safe driving habits of different road conditions, acquiring the predicted road condition of the next road section when the real-time geographic position coordinate of the unmanned automobile is near the dividing point of the two different road sections, and pushing the corresponding safe driving habit to the user according to the predicted road condition of the next road section.
Still further preferably, the unmanned-oriented road risk prediction apparatus includes:
the acquisition module is used for acquiring the current running real-time geographic position coordinate information and a planned path of the unmanned automobile;
the dividing module is used for acquiring meteorological information on the path according to the planned path, wherein the meteorological information comprises: the planned route is divided into different weather road sections according to different weather information;
the prediction module is used for predicting the road condition of the meteorological road section according to the meteorological information, and the road condition comprises: dry, wet and frozen;
setting the prediction time of the road condition, and predicting the road condition of the meteorological section as a dry road condition when the predicted duration of the meteorological weather in the sunny day is longer than the prediction time; when the predicted duration of the rainy weather is longer than the predicted time, predicting the road condition of the weather road section as a wet road condition; when the predicted duration of the snowing weather is longer than the predicted time, predicting the road condition of the weather road section as an icing road condition;
and the processing module is used for warning the danger level of the road section and controlling the vehicle according to the danger level when the real-time geographic position coordinates of the automobile are near the dividing point of two different road sections.
In a second aspect, the unmanned-oriented road risk prediction method further includes an unmanned-oriented road risk prediction terminal device including: a memory, a processor, and an unmanned-oriented road hazard prediction method program stored on the memory and executable on the processor, the unmanned-oriented road hazard prediction method program configured to implement the steps of the unmanned-oriented road hazard prediction method as described above.
In a third aspect, the method for predicting a risk of an unmanned road further includes a storage medium for predicting a risk of an unmanned road, the medium being a computer medium having a program for predicting a risk of an unmanned road stored thereon, the program for predicting a risk of an unmanned road being executed by a processor to implement the steps of the method for predicting a risk of an unmanned road as described above.
Compared with the prior art, the road risk prediction method for unmanned driving has the following beneficial effects:
(1) the method comprises the steps of obtaining meteorological information on a driving path of the unmanned automobile, dividing the path into different road sections according to the meteorological information, predicting road conditions on the road sections through the meteorological information, broadcasting the predicted road conditions to users, reminding the users of paying attention to road safety, and improving safety of the unmanned automobile in the driving process.
(2) By setting the driving modes of the unmanned automobile on dry, wet and icy road conditions, when the unmanned automobile enters a dry, wet or icy road section, the unmanned automobile is automatically switched to the corresponding driving modes, and a user is reminded, and if necessary, the user is reminded to manually control the automobile, so that the safety in the driving process is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of an apparatus in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a first embodiment of the unmanned road hazard prediction method according to the present invention;
FIG. 3 is a functional block diagram of a first embodiment of the unmanned road hazard prediction method according to the present invention;
table 1 shows a reference value table of longitudinal-sliding adhesion coefficients of an automobile facing to the unmanned road risk prediction method of the present invention;
table 2 is a reference value table of the longitudinal sliding adhesion coefficient of the vehicle on the ice and snow road surface of the unmanned road risk prediction method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, the apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the device, and that in actual implementations the device may include more or less components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a medium, may include therein an operating system, a network communication module, a user interface module, and an unmanned-oriented road hazard prediction method program.
In the device shown in fig. 1, the network interface 1004 is mainly used to establish a communication connection between the device and a server storing all data required in the unmanned-oriented road hazard prediction method system; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the unmanned road hazard prediction method apparatus of the present invention may be provided in the unmanned road hazard prediction method apparatus, which calls the unmanned road hazard prediction method program stored in the memory 1005 through the processor 1001 and executes the unmanned road hazard prediction method provided by the present invention.
Referring to fig. 2, fig. 2 is a schematic flowchart of a first embodiment of a road risk prediction method for unmanned driving according to the present invention.
In this embodiment, the road hazard prediction method for unmanned driving includes the following steps:
s10: and acquiring the current running real-time geographic position coordinate information and a planned path of the unmanned automobile.
It should be understood that the real-time geographic position coordinate information of the current driving of the automobile is obtained so as to be capable of accurately positioning to the position of the user, so that the user is provided with accurate service, and the planned path is the total path to be driven selected by the user.
S20: acquiring meteorological information on a path according to a planned path, wherein the meteorological information comprises: the planned route is divided into different weather road sections according to different weather information.
It should be understood that, the weather information on the route is obtained according to the planned route, and the planned route is divided into different weather segments according to different weather information, where different weather information may exist on different routes, for example, if the route is sunny, the route may be divided into an AB weather segment, the route is heavy rain, and the route may be divided into a BC weather segment.
It should be understood that, at the same time, the predicted weather duration is obtained, for example, the AB road section is a sunny weather road section, which is predicted to last for 3 hours, and the road condition of the AB road section is predicted according to the predicted weather duration, and the road condition of the AB road section may be a dry road section.
S30: predicting the road condition of the meteorological road section according to the meteorological information, wherein the road condition comprises the following steps: dry, wet, and frozen.
It should be understood that, the road condition of the weather section is predicted according to the predicted duration of the weather, first, a predicted time of the road condition is set, the predicted time is used for judging the road condition of the weather section, for example, the predicted time is 3 hours, when the unmanned vehicle enters the weather section of the AB sunny day, the predicted duration of the weather section of the AB sunny day is obtained to last for 3 hours, the duration is compared with the predicted time, and when the duration is greater than or equal to the predicted time, the road condition of the road section of the AB sunny day is predicted to be a dry road condition.
S40: and when the real-time geographic position coordinates of the unmanned automobile are near the dividing point of two different road sections, sending the road condition prediction result and controlling the automobile.
It should be understood that when the real-time geographic position coordinates of the unmanned vehicle are located near the dividing point of two different road sections, the predicted road condition of the next road section is sent to the user to remind the user.
It should be understood that recommended safe vehicle distance and safe driving habits of a dry road section, a wet road section and an icy road section are set, when the real-time geographic position coordinates of the unmanned vehicle are obtained in real time and are located near the boundary point of two different road sections, the vehicle distance between the unmanned vehicle and the front and rear vehicles in the driving process is obtained in real time, then the vehicle distance is automatically adjusted to the recommended safe vehicle distance, meanwhile, the driving habits of a driver are obtained through the built-in camera of the vehicle, and when the bad driving habits are found, the driver is reminded.
It should be understood that the unmanned vehicle may set different driving modes for driving on dry road, wet road and icy road in the system, and when the unmanned vehicle enters the dry road, the unmanned vehicle may automatically switch to the dry road driving mode, which may increase the safety of the unmanned vehicle by prompting the user to pay attention to the safety of the road ahead, adjusting the vehicle speed to the safe speed of the dry road, and adjusting the safe distance.
It should be understood that when the unmanned vehicle enters a wet road section, the unmanned vehicle is automatically switched to a wet road section driving mode, the wet road section driving mode can be used for judging the swing frequency of the windshield wiper by prompting a user to pay attention to the safety of the road section in front, when the swing frequency of the windshield wiper is found to be higher than a preset frequency, the judgment is made that the rainfall at the moment is possibly overlarge, the rainwater on the windshield can obscure the sight of the driver, at the moment, the system can suggest the user to search a safe position for parking, adjust the vehicle speed to the safe speed of the wet road section, and adjust the safe vehicle distance to increase the safety of the unmanned vehicle.
It should be understood that when the unmanned vehicle enters the iced road section, the unmanned vehicle is automatically switched to the iced road section driving mode, the iced road section driving mode can remind a user to manually carry out auxiliary control on the vehicle so as to increase driving safety, then whether the tire is wound with the antiskid chain or not can be detected before the vehicle gets on the road, when the unmanned vehicle is detected not to be wound with the antiskid chain, the user can be warned, the user is reminded to wind the antiskid chain, after the unmanned vehicle gets on the road, the vehicle speed can be automatically adjusted to the safe speed of the iced road section, and the user is advised to avoid the iced road section.
It should be understood that, with reference to table 1, when the unmanned vehicle is located on a dry road, a vehicle longitudinal-sliding adhesion coefficient reference value of the dry road is set, and this reference value is extracted from the database by the system, for example, when the unmanned vehicle travels to a dry concrete new road, a real-time speed of the vehicle is obtained, when the speed is below 48km/h, the vehicle longitudinal-sliding adhesion coefficient reference value is in a range of 0.80-1.00, when the speed is above 48km/h, the reference value is in a range of 0.70-0.85, at this time, the vehicle longitudinal-sliding adhesion coefficient of the vehicle is obtained to be compared with the reference value, when the reference value range is met, the vehicle is in a safe state, and can travel according to the current state, and if the speed is not met, the vehicle is in a dangerous state, and needs to be adjusted by deceleration. The wet road condition and the dry road condition are the same in processing method, for the wet road condition, when the speed is below 48km/h, the longitudinal-sliding adhesion coefficient reference value of the automobile is in the range of 0.50-0.80, and when the speed is above 48km/h, the reference value is in the range of 0.40-0.75, so when the automobile is in the wet road condition, the system can remind a user of paying attention to the safety of the road at intervals, and monitor the current speed in real time to ensure that the speed is within the safe speed range.
It should be understood that, with reference to table 2, when the driverless vehicle is located in the icy road section, the longitudinal sliding adhesion coefficient reference value of the vehicle is different due to different states of the icy road section, for example, the longitudinal sliding adhesion coefficient reference value of the vehicle on a snow-covered compacted road condition with fresh snow and near ice is in a range of 0.1-0.2, and the sand and salt spread on the snow is in a range of 0.30-0.45, so when the vehicle enters the icy road section, the driverless vehicle warns the user of the vehicle to pay attention to driving safety, and when the longitudinal sliding adhesion coefficient reference value of the vehicle on the current road condition is too small, danger is likely to occur, and the user is advised to avoid the road condition in time, or the system stops the vehicle to a safe stop point, so as to increase.
TABLE 1 reference value table for longitudinal and sliding adhesion coefficient of automobile
| Status of state | Coefficient of adhesion |
| Fresh snow, compacted snow near ice | 0.1-0.2 |
| Common snow | 0.2-0.25 |
| Coarse snow, snow that begins to dissolve | 0.25-0.30 |
| Spreading salt on accumulated snow | 0.30-0.40 |
| Spreading sand on the accumulated snow | 0.35-0.45 |
| Spreading sand and salt on accumulated snow | 0.30-0.45 |
TABLE 2 reference value table for longitudinal and sliding adhesion coefficient of vehicle on ice and snow road
It should be understood that the present embodiment provides a basic road condition driving mode and an emergency road condition driving mode in addition to the dry road condition driving mode, the wet road condition driving mode and the ice road condition driving mode, and the basic road condition driving mode is directed to the road conditions including: there are a plurality of sharp turns, tunnel, pothole etc. on the road, the road conditions that need the driver to drive carefully, and this kind of basic road conditions driving mode can remind the user to notice the safety of the highway section in the place ahead, and the safety speed is adjusted to the speed of a motor vehicle automatically and the security that increases unmanned automobile and travel, and the road conditions that another kind of urgent road conditions driving mode was directed to include: when extreme road conditions such as road collapse, a front interlinked traffic accident, and too wet and slippery road caused by severe weather are met, the driver can be warned to pay attention to the safety of the vehicle in an emergency road condition driving mode, then a safe parking point nearby can be searched, and after the vehicle speed is adjusted to the safe vehicle speed, the unmanned vehicle can prompt the user to enter the safe parking point nearby to avoid the extreme road condition.
The above description is only for illustrative purposes and does not limit the technical solutions of the present application in any way.
Through the above description, it is easy to find that the present embodiment obtains the current running real-time geographic position coordinate information and the planned path of the unmanned vehicle; acquiring the weather on the path according to the planned path, wherein the weather comprises the following steps: the method comprises the following steps of predicting duration of sunny weather and sunny weather, predicting duration of rainy weather and rainy weather, and predicting duration of snowy weather and snowy weather, and dividing a planned path into different weather road sections according to the weather; predicting the road conditions of the meteorological road section according to the weather, wherein the road conditions comprise: drying, wetting and freezing; when the real-time geographic position coordinates of the unmanned automobile are near the dividing point of two different road sections, the road condition prediction result is sent and the automobile is controlled.
In addition, the embodiment of the invention also provides a road danger prediction device for unmanned driving. As shown in fig. 3, the driverless-oriented road risk prediction device includes: the device comprises an acquisition module 10, a dividing module 20, a prediction module 30 and a control module 40.
The acquiring module 10 is used for acquiring the current running real-time geographic position coordinate information and a planned path of the unmanned automobile;
the dividing module 20 is configured to obtain weather information on a route according to a planned route, where the weather information includes: the planned route is divided into different weather road sections according to different weather information;
a prediction module 30, configured to predict a road condition of the meteorological road segment according to the meteorological information, where the road condition includes: dry, wet and frozen;
setting the prediction time of the road condition, and predicting the road condition of the meteorological section as a dry road condition when the predicted duration of the meteorological weather in the sunny day is longer than the prediction time; when the predicted duration of the rainy weather is longer than the predicted time, predicting the road condition of the weather road section as a wet road condition; when the predicted duration of the snowing weather is longer than the predicted time, predicting the road condition of the weather road section as an icing road condition;
and the control module 40 is used for warning the danger level of the road section when the real-time geographic position coordinates of the automobile are near the dividing point of two different road sections, and controlling the vehicle according to the danger level.
In addition, it should be noted that the above-described embodiments of the apparatus are merely illustrative, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of the modules to implement the purpose of the embodiments according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment may be referred to the driverless road risk prediction method provided in any embodiment of the present invention, and are not described herein again.
Furthermore, an embodiment of the present invention further provides an unmanned road hazard prediction storage medium, where the medium is a computer medium, and the computer medium stores an unmanned road hazard prediction method program, and when executed by a processor, the unmanned road hazard prediction method program implements the following operations:
acquiring real-time geographic position coordinate information and a planned path of current running of the unmanned automobile;
acquiring meteorological information on a path according to a planned path, wherein the meteorological information comprises: the planned route is divided into different weather road sections according to different weather information;
predicting the road condition of the meteorological road section according to the meteorological information, wherein the road condition comprises the following steps: dry, wet and frozen;
and when the real-time geographic position coordinates of the unmanned automobile are near the dividing point of two different road sections, sending the road condition prediction result and controlling the automobile.
Further, when executed by a processor, the unmanned-oriented road hazard prediction method program further implements the following operations:
setting the prediction time of the road condition, and predicting the road condition of the meteorological section as a dry road condition when the predicted duration of the meteorological weather in the sunny day is longer than the prediction time; when the predicted duration of the rainy weather is longer than the predicted time, predicting the road condition of the weather road section as a wet road condition; and when the predicted duration of the snowing weather is longer than the predicted time, predicting the road condition of the weather road section as the icing road condition.
Further, when executed by a processor, the unmanned-oriented road hazard prediction method program further implements the following operations:
and setting driving modes of different road conditions, and automatically switching the unmanned automobile into different driving modes according to the different road conditions when the unmanned automobile enters dry, wet or icy road conditions.
Further, when executed by a processor, the unmanned-oriented road hazard prediction method program further implements the following operations:
setting three driving modes of a dry road section, a wet road section and an icing road section, and automatically switching the unmanned automobile into the driving mode of the dry road section when the unmanned automobile enters the dry road section, wherein the driving mode of the dry road section comprises the following steps: prompting a user to pay attention to the safety of the front road section, acquiring the safe speed of the dry road section, and adjusting the driving speed of the automobile to the safe speed of the dry road section; when the unmanned vehicle enters a wet road section, the unmanned vehicle automatically switches to a wet road section driving mode, wherein the wet road section driving mode comprises the following steps: setting a preset frequency of windshield wiper swing, prompting a user to pay attention to safety of a road section ahead, advising the user to carry out auxiliary control on the automobile, acquiring the swing frequency of the windshield wiper in real time, advising the user to search a safe position for parking when the frequency is greater than the preset frequency, acquiring the safe speed of the wet road section, and adjusting the automobile driving speed to the safe speed of the wet road section; when the unmanned automobile enters the iced road section, the unmanned automobile is automatically switched into an iced road section driving mode, and the iced road section driving mode comprises the following steps: and reminding a user to manually control the automobile, detecting the tire, reminding the user to wind the anti-skid chain when detecting that the tire is not wound with the anti-skid chain, acquiring the safe speed of the iced road section, adjusting the running speed of the automobile to the safe speed of the iced road section, and suggesting the user to avoid the iced road section.
Further, when executed by a processor, the unmanned-oriented road hazard prediction method program further implements the following operations:
and setting recommended safe vehicle distances of the dry road section, the wet road section and the frozen road section, acquiring the vehicle distances between the unmanned automobile and front and rear vehicles in the driving process in real time, and adjusting the vehicle distances to the recommended safe vehicle distances.
Further, when executed by a processor, the unmanned-oriented road hazard prediction method program further implements the following operations:
and predicting road conditions of different meteorological road sections, and when the real-time geographical position coordinates of the unmanned automobile are near a dividing point of two different road sections, sending the predicted road conditions of the next road section to the user.
Further, when executed by a processor, the unmanned-oriented road hazard prediction method program further implements the following operations:
and setting safe driving habits of different road conditions, acquiring the predicted road condition of the next road section when the real-time geographical position coordinate of the unmanned automobile is near the dividing point of the two different road sections, and pushing the corresponding safe driving habit to the user according to the predicted road condition of the next road section.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. A road danger prediction method for unmanned driving is characterized in that: comprises the following steps;
acquiring real-time geographic position coordinate information and a planned path of current running of the unmanned automobile;
acquiring meteorological information on a path according to a planned path, wherein the meteorological information comprises: the planned route is divided into different weather road sections according to different weather information;
predicting the road condition of the meteorological road section according to the meteorological information, wherein the road condition comprises the following steps: dry, wet and frozen;
when the real-time geographic position coordinates of the unmanned automobile are near the dividing point of two different road sections, the road condition prediction result is sent and the automobile is controlled;
setting the prediction time of the road condition, and predicting the road condition of the meteorological section as a dry road condition when the predicted duration of the meteorological weather in the sunny day is longer than the prediction time; when the predicted duration of the rainy weather is longer than the predicted time, predicting the road condition of the weather road section as a wet road condition; and when the predicted duration of the snowing weather is longer than the predicted time, predicting the road condition of the weather road section as the icing road condition.
2. The unmanned-oriented road hazard prediction method of claim 1, wherein: the method further comprises the following steps of setting driving modes of different road conditions, and automatically switching the unmanned automobile into different driving modes according to different road conditions when the unmanned automobile enters dry, wet or icy road conditions.
3. The unmanned-oriented road hazard prediction method of claim 2, wherein: the method further comprises the following steps of setting three driving modes of a dry road section, a wet road section and an icing road section, and when the unmanned automobile enters the dry road section, automatically switching the unmanned automobile into the driving mode of the dry road section, wherein the driving mode of the dry road section comprises the following steps: prompting a user to pay attention to the safety of the front road section, acquiring the safe speed of the dry road section, and adjusting the driving speed of the automobile to the safe speed of the dry road section; when the unmanned vehicle enters a wet road section, the unmanned vehicle automatically switches to a wet road section driving mode, wherein the wet road section driving mode comprises the following steps: setting a preset frequency of windshield wiper swing, prompting a user to pay attention to safety of a road section ahead, advising the user to carry out auxiliary control on the automobile, acquiring the swing frequency of the windshield wiper in real time, advising the user to search a safe position for parking when the frequency is greater than the preset frequency, acquiring the safe speed of the wet road section, and adjusting the automobile driving speed to the safe speed of the wet road section; when the unmanned automobile enters the iced road section, the unmanned automobile is automatically switched into an iced road section driving mode, and the iced road section driving mode comprises the following steps: and reminding a user to manually control the automobile, detecting the tire, reminding the user to wind the anti-skid chain when detecting that the tire is not wound with the anti-skid chain, acquiring the safe speed of the iced road section, adjusting the running speed of the automobile to the safe speed of the iced road section, and suggesting the user to avoid the iced road section.
4. The unmanned-oriented road hazard prediction method of claim 1, wherein: the method further comprises the following steps of setting recommended safe vehicle distances of the dry road section, the wet road section and the icing road section, acquiring the vehicle distances between the unmanned vehicle and front and rear vehicles in the driving process in real time, and adjusting the vehicle distances to the recommended safe vehicle distances.
5. The unmanned-oriented road hazard prediction method of claim 1, wherein: the method also comprises the following steps of predicting the road conditions of different meteorological road sections, and sending the predicted road condition of the next road section to a user when the real-time geographic position coordinates of the unmanned automobile are near the dividing point of the two different road sections.
6. The unmanned-oriented road hazard prediction method of claim 1, wherein: the method comprises the following steps of setting safe driving habits of different road conditions, obtaining the predicted road condition of the next road section when the real-time geographical position coordinate of the unmanned automobile is near the dividing point of the two different road sections, and pushing the corresponding safe driving habits to a user according to the predicted road condition of the next road section.
7. An unmanned road risk prediction apparatus, comprising:
the acquisition module is used for acquiring the current running real-time geographic position coordinate information and a planned path of the unmanned automobile;
the dividing module is used for acquiring meteorological information on the path according to the planned path, wherein the meteorological information comprises: the planned route is divided into different weather road sections according to different weather information;
the prediction module is used for predicting the road condition of the meteorological road section according to the meteorological information, and the road condition comprises: dry, wet and frozen;
setting the prediction time of the road condition, and predicting the road condition of the meteorological section as a dry road condition when the predicted duration of the meteorological weather in the sunny day is longer than the prediction time; when the predicted duration of the rainy weather is longer than the predicted time, predicting the road condition of the weather road section as a wet road condition; when the predicted duration of the snowing weather is longer than the predicted time, predicting the road condition of the weather road section as an icing road condition;
and the control module is used for warning the danger level of the road section and controlling the vehicle according to the danger level when the real-time geographical position coordinates of the automobile are near the dividing point of two different road sections.
8. An unmanned-oriented road hazard prediction terminal device, characterized by comprising: a memory, a processor, and an unmanned-oriented road hazard prediction method program stored on the memory and executable on the processor, the unmanned-oriented road hazard prediction method program configured to implement the steps of the unmanned-oriented road hazard prediction method of any one of claims 1 to 6.
9. An unmanned-oriented road risk prediction storage medium, characterized in that the storage medium is a computer medium on which an unmanned-oriented road risk prediction method program is stored, which when executed by a processor implements the steps of the unmanned-oriented road risk prediction method according to any one of claims 1 to 6.
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| CN112633223A (en) * | 2020-12-30 | 2021-04-09 | 北京航迹科技有限公司 | Unmanned vehicle behavior reminding method and device, unmanned vehicle and storage medium |
| CN114937361B (en) * | 2022-05-19 | 2024-05-28 | 广州新致信息科技有限公司 | Urban traffic service system and operation method |
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