CN116238527B - Intelligent driving auxiliary protection method and device, electronic equipment and medium - Google Patents
Intelligent driving auxiliary protection method and device, electronic equipment and medium Download PDFInfo
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- CN116238527B CN116238527B CN202310519373.9A CN202310519373A CN116238527B CN 116238527 B CN116238527 B CN 116238527B CN 202310519373 A CN202310519373 A CN 202310519373A CN 116238527 B CN116238527 B CN 116238527B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/0098—Details of control systems ensuring comfort, safety or stability not otherwise provided for
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
Abstract
The embodiment of the application relates to the technical field of automatic driving, and particularly provides an intelligent driving auxiliary protection method, an intelligent driving auxiliary protection device, electronic equipment and a medium. The method comprises the following steps: determining a preset protection strategy level of intelligent driving assistance of the current vehicle; wherein each preset protection policy level corresponds to a recovery mechanism; detecting a target protection strategy level of the current vehicle based on a preset protection strategy level, and indicating the current vehicle to execute a target protection action corresponding to the target protection strategy level; after the preset time, when the current vehicle is detected to meet the preset recovery condition of the target recovery mechanism corresponding to the target protection strategy level, the current vehicle is instructed to execute the target recovery action corresponding to the target recovery mechanism, the target protection strategy level of the current vehicle is detected according to the current environment, the auxiliary driving of the current vehicle is set as the target protection strategy level, and the driving safety of the user is further improved under the condition of reducing the operation burden of a driver.
Description
Technical Field
The application relates to the technical field of automatic driving, in particular to an intelligent driving auxiliary protection method, an intelligent driving auxiliary protection device, electronic equipment and a readable storage medium.
Background
The intelligent automobile driving technology is one of the important development directions of the modern automobile industry, and along with the continuous progress of technology, the intelligent automobile driving technology is also continuously developed and perfected, and most of vehicles today are provided with intelligent driving auxiliary functions to reduce the operation burden of drivers. Not only can the driving safety performance be improved, but also the driving comfort and convenience can be greatly improved. Intelligent driving technology is still in the development stage and has many defects and shortcomings. For example, in severe weather and under complex road conditions, the intelligent driving assistance function still has a certain limitation in identifying obstacles and judging road conditions. These problems not only affect the safety of driving, but also increase the sense of unsafe for the driver. Furthermore, intelligent driving assistance functions require a driver to have a certain technical literacy when in use. For example, the driver is required to effectively control the operation of the vehicle, the planning of the route, the monitoring of the state, etc., otherwise misoperation and dangerous situations may occur. The use of intelligent driving assistance can present significant concerns and concerns to drivers who do not have the related art literacy.
Therefore, a protection method capable of deciding whether to use the intelligent driving assistance function is needed to improve the experience and safety of the driver.
Disclosure of Invention
The main objective of the embodiments of the present application is to provide an intelligent driving assistance protection method, apparatus, electronic device and readable storage medium, which are aimed at solving the problem that in the existing intelligent driving technology, the intelligent driving assistance function still has a certain limitation in identifying obstacles and judging road conditions, and seriously affects the safety problem of a driver in the intelligent driving process, and deciding whether to use the intelligent driving assistance function according to a protection strategy, so as to further improve the experience and safety of the driver.
In a first aspect, an embodiment of the present application provides an intelligent driving assistance protection method, including:
determining a preset protection strategy level of intelligent driving assistance of the current vehicle; wherein each preset protection policy level corresponds to a recovery mechanism;
detecting a target protection strategy level of a current vehicle based on the preset protection strategy level, and indicating the current vehicle to execute a target protection action corresponding to the target protection strategy level;
after the preset time, when the current vehicle is detected to meet the preset recovery condition of the target recovery mechanism corresponding to the target protection strategy level, the current vehicle is instructed to execute the target recovery action corresponding to the target recovery mechanism.
In a second aspect, embodiments of the present application further provide an intelligent driving assistance protection device, including:
the determining module is used for determining a preset protection strategy level of intelligent driving assistance of the current vehicle; wherein each preset protection policy level corresponds to a recovery mechanism;
the first detection module is used for detecting the target protection strategy level of the current vehicle based on the preset protection strategy level and indicating the current vehicle to execute the target protection action corresponding to the target protection strategy level;
and the second detection module is used for indicating the current vehicle to execute the target recovery action corresponding to the target recovery mechanism when detecting that the current vehicle meets the preset condition of the target recovery mechanism corresponding to the target protection strategy level after the preset time.
In a third aspect, embodiments of the present application also provide an electronic device comprising a processor, a memory, a computer program stored on the memory and executable by the processor, and a data bus for enabling a connection communication between the processor and the memory, wherein the computer program, when executed by the processor, implements the steps of any of the intelligent driving assistance protection methods as provided in the present application.
In a fourth aspect, embodiments of the present application further provide a storage medium for computer-readable storage, the storage medium storing one or more programs executable by one or more processors to implement the steps of the intelligent driving assistance protection method as provided in any one of the present application specifications.
The embodiment of the application provides an intelligent driving assistance protection method, an intelligent driving assistance protection device, electronic equipment and a readable storage medium, wherein the method comprises the steps of determining a preset protection strategy level of intelligent driving assistance of a current vehicle; wherein each preset protection policy level corresponds to a recovery mechanism; detecting a target protection strategy level of the current vehicle based on a preset protection strategy level, and indicating the current vehicle to execute a target protection action corresponding to the target protection strategy level; after the preset time, when the current vehicle is detected to meet the preset recovery condition of the target recovery mechanism corresponding to the target protection strategy level, the current vehicle is instructed to execute the target recovery action corresponding to the target recovery mechanism, the target protection strategy level of the current vehicle is detected according to the current environment, the auxiliary driving of the current vehicle is set to be the target protection strategy level, further, the problem that the safety of a driver is seriously affected in the intelligent driving process due to the fact that the existing intelligent driving technology still has a certain limitation in the aspects of identifying obstacles and judging road conditions is solved, whether the intelligent driving auxiliary function is used or not is decided according to the protection strategy, the experience of the driver is further improved, and the driving safety of the user is further improved under the condition that the operation burden of the driver is lightened.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an intelligent driving assistance protection method provided in an embodiment of the present application;
fig. 2 is a flow chart of a recovery mechanism corresponding to each of preset protection strategy levels in an intelligent driving assistance protection method according to an embodiment of the present application;
FIG. 3 is a flowchart corresponding to one embodiment of step S2 in FIG. 1;
fig. 4 is a schematic block diagram of an intelligent driving assistance protection device according to an embodiment of the present application;
fig. 5 is a schematic block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
It is to be understood that the terminology used in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
Today, most vehicles are equipped with intelligent driving assistance functions to reduce the operational burden on the driver. However, due to the limited capacity of the sensor, when the situation of severe environment is met, the intelligent driving assistance function of the intelligent driving assistance device can directly exit or prompt the driver to take over, and whether the function is forbidden or exits can not be judged according to the severe degree of the current environment, and the intelligent driving assistance device can not be used until the next ignition period, so that the experience of the driver is poor, namely the intelligent driving assistance function of the intelligent driving assistance device is not intelligent.
Accordingly, there is a need for a protection method that can determine whether to use an intelligent driving assistance function, and that can improve the feeling of experience and safety of the driver.
The embodiment of the application provides an intelligent driving assistance protection method, an intelligent driving assistance protection device, electronic equipment and a readable storage medium. The intelligent driving assistance protection method can be applied to electronic equipment.
The method provided by the embodiment of the application can be executed by the electronic device, can be executed by a chip in the electronic device, can be executed by a device connected with the electronic device, and can be a server or a server cluster, for example, the electronic device can be communicated with the server, for example, an Application (APP) corresponding to the server can be installed on the electronic device, and a user can operate the APP in the electronic device to trigger the electronic device and the server to establish communication connection. The user can trigger reporting of the environmental information of the current vehicle to the server in the APP of the electronic equipment, so that the server executes the intelligent driving assistance protection method provided by the embodiment of the application.
The embodiment of the application provides an intelligent driving assistance protection method, an intelligent driving assistance protection device, electronic equipment and a readable storage medium, wherein the method comprises the steps of determining a preset protection strategy level of intelligent driving assistance of a current vehicle; wherein each preset protection policy level corresponds to a recovery mechanism; detecting a target protection strategy level of the current vehicle based on a preset protection strategy level, and indicating the current vehicle to execute a target protection action corresponding to the target protection strategy level; after the preset time, when the current vehicle is detected to meet the preset recovery condition of the target recovery mechanism corresponding to the target protection strategy level, the current vehicle is instructed to execute the target recovery action corresponding to the target recovery mechanism, the target protection strategy level of the current vehicle is detected according to the current environment, the auxiliary driving of the current vehicle is set to be the target protection strategy level, and further, the problem that the safety of a driver is seriously influenced in the intelligent driving process due to the fact that the intelligent driving auxiliary function still has a certain limitation in the aspects of identifying obstacles and judging road conditions in the existing intelligent driving technology is solved, whether the intelligent driving auxiliary function is used or not is decided according to the protection strategy, the experience of the driver is further improved, and the driving safety of the user is further improved under the condition that the operation load of the driver is reduced.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a flow chart of an intelligent driving assistance protection method according to an embodiment of the present application.
As shown in fig. 1, the intelligent driving assistance protection method includes steps S1 to S3.
Step S1: determining a preset protection strategy level of intelligent driving assistance of the current vehicle; wherein each preset protection policy level corresponds to a recovery mechanism.
The preset protection policy level is a safety mechanism, that is, when the current vehicle uses the intelligent driving assistance function, when the current vehicle detects that the current vehicle encounters a situation of rain, shielding, glare, fog and the like through a camera sensor in the current vehicle, objects in the visual field of the current vehicle such as roads, vehicles, pedestrians and the like are affected, in order to ensure the use safety of driving, the protection policy level under different environments and a recovery mechanism corresponding to the protection policy level can be set in advance, and then the setting of the protection policy level under different subsequent environments is supported, so that the driving of the current vehicle is realized.
In some embodiments, the determining the preset protection policy level of the intelligent driving assistance of the current vehicle includes: acquiring test data of the current vehicle adopting intelligent driving assistance in different driving environments; and dividing the test data according to preset dividing conditions to obtain the preset protection strategy level of the intelligent driving assistance of the current vehicle.
By way of example, different driving environments are set, intelligent driving assistance tests are conducted according to different driving strategies, test data of the current vehicle adopting intelligent driving assistance in the different driving environments are obtained, the test data are divided according to test results in the test data, and then preset protection strategy levels of the intelligent driving assistance of the current vehicle are obtained.
For example, 4 conventional driving environments are set, and weather, thunderstorm weather and ground wet skid are set, and the intelligent driving assistance level is set to three levels of simply assisting the driver by using the intelligent assistance strategy, frequently assisting the driver by using the intelligent assistance strategy and basically replacing the driver by using the intelligent assistance strategy. The three grades were used to run under 4 conventional driving environments, respectively, and test data were obtained as shown in table 1.
Table 1 patterns of test data
| Tianqing | Tianyin | Thunderstorm weather | Ground wet skid | |
| Simple auxiliary driver | Secure | Secure | Secure | Secure |
| Frequent driver assistance | Secure | Secure | General danger | More dangerous |
| Basically replace the driver | Secure | Secure | More dangerous | Is very dangerous |
Therefore, as can be seen from table 1, the simple auxiliary driver is safe in all of the 4 conventional driving environments, so the simple auxiliary driver is suitable for all of the 4 conventional driving environments, the frequent auxiliary driver is safe in the sunny and cloudy days, but is generally dangerous in the thunderstorm weather, and is dangerous in the slippery ground, so the intelligent strategy can be set as the frequent auxiliary driver in sunny or cloudy days. The basic alternative driver and the frequent auxiliary driver should be set in sunny and cloudy days, although the corresponding test data results are different in thunderstorm weather and ground slippery.
It will be appreciated that the above-described division of table 1 is only one embodiment of the present application.
Optionally, the preset protection policy level may be calibrated through a real vehicle test according to the level degree corresponding to the specific scene. For example, ten scenarios are divided as follows: (1) detecting a foggy scene when a high beam is turned on; (2) Micro scratches or wiper marks on the windshield, resulting in intense glare for the user; (3) When the road is wet, the vehicle can splash water so as to blur the bottom (or all) of the target vehicle; (4) The images are not clear when the surrounding images are collected, mainly due to weather conditions such as rain, fog, low sun and the like; (5) The dazzling sunlight can interfere with the image when collecting the surrounding image; (6) When the surrounding images are collected, heavy rain makes the light spot size of each frame look different, and a smeared light source is detected in the detection process; (7) Low visibility due to partial occlusion of the lens when surrounding images are acquired, due to blurred images and partially transparent occlusion; (8) The lens/windshield is blocked from view of the target; (9) May be triggered by dirty/oil/frozen windshields and sometimes also by fog; (10) The fog affecting the appearance of the spots is calibrated and preset protection strategy grades are calibrated according to real vehicle tests of different scenes.
For example, the preset protection policy Level includes 4 levels, namely Level 0, level 1, level2 and Level 3. Wherein Level 0 represents the first Level of intelligent driving assistance protection strategy, i.e. the intelligent driving assistance function remains active in this Level state, without disabling or exiting the intelligent driving assistance function. Level 1 represents the second Level of intelligent driving assistance protection strategy, i.e., the intelligent driving assistance function, in this Level state, will exit the intelligent driving assistance function but will not be disabled. If a small rain environment is met and a driver opens the slow wiper, the protection strategy can exit the intelligent driving auxiliary function to remind the driver of taking over, so that the situation that the follow-up rainy environment is heavy is avoided, and the detection of the sensor is possibly limited. However, the driver may continue to use the intelligent driving assistance function when attempting to reactivate because the current environmental sensor may correctly identify the surrounding environment. Level2 represents a third Level of intelligent driving assistance protection strategy, i.e., the intelligent driving assistance function, in this Level state, will exit the intelligent driving assistance function while disabling its function. If a haze environment is met and a driver turns on a fog lamp, the protection strategy can exit the intelligent driving auxiliary function, the driver cannot be reactivated, and the driver can reactivate the intelligent driving auxiliary function after the haze is removed. Level3 represents a fourth Level of intelligent driving assistance protection strategy, i.e., the intelligent driving assistance function, in this Level state, will exit the intelligent driving assistance function while disabling its function. If heavy storm is met and the driver starts the quick wiper and the double flashing, the protection strategy can exit the intelligent driving auxiliary function, the driver cannot be reactivated, and even if the environmental conditions are met, the driver does not have heavy storm, the intelligent driving auxiliary function can be reactivated and used only after the next ignition period.
In addition, each of the preset protection policy levels corresponds to a recovery mechanism.
Wherein, level 0 will not exit or disable the intelligent driving assistance function, therefore, there is no recovery mechanism; after the function exits, the Level 1 needs to wait for a time (a calibratable value) of T1, and then the intelligent driving assistance function can be activated again. After the function of Level 2 exits and is disabled, for example, after the intelligent driving sensor judges that the environment is good, the intelligent driving assistance function can be re-activated after waiting for the time of T2 (the calibratable value). After the function of Level3 is withdrawn and disabled, the intelligent driving sensor cannot recover even if the intelligent driving sensor judges that the environment is good, and the intelligent driving auxiliary function can be activated again only after the next ignition period. Therefore, when the preset protection policy Level includes Level 0, level 1, level 2, and Level3, the relationship between the recovery mechanisms corresponding to Level 0, level 1, level 2, and Level3 respectively is shown in fig. 2.
Step S2: and detecting the target protection strategy grade of the current vehicle based on the preset protection strategy grade, and indicating the current vehicle to execute the target protection action corresponding to the target protection strategy grade.
After the preset protection strategy level is obtained, detecting surrounding information of the current vehicle, matching corresponding condition information in the preset protection strategy level according to the surrounding information, and further taking the protection strategy level corresponding to the condition information in the preset protection strategy level as a target protection strategy level of the current vehicle, so that the current vehicle executes a corresponding intelligent driving strategy according to the target protection strategy level, namely, the current vehicle executes a target protection action corresponding to the target protection strategy level.
In some embodiments, as shown in fig. 3, step S2 includes steps S21 to S25.
S21, obtaining candidate local environment information around the running environment where the current vehicle is located;
illustratively, the current vehicle adopts the intelligent driving strategy and is related to the candidate local environment information around the running environment where the current vehicle is located, and the better the candidate local environment information around the running environment is, the better the experience of the current vehicle adopting the intelligent driving strategy is.
For example, the candidate local environment information of the current vehicle is asphalt road, and the road is flat, weather wind and daily, and so on, so that the road condition information, weather information, traffic/flow information of the running environment of the current vehicle can be obtained as the candidate local environment information.
S22, preprocessing the candidate local environment information to obtain target local environment information;
for example, when obtaining the candidate local environment information, if the weather is worse at this time, more interference information exists in the obtained candidate local environment information, which will affect the selection and judgment of the target protection policy level, so that preprocessing, such as data screening, data denoising, data fusion, etc., needs to be performed on the candidate local environment information, so as to obtain the target local environment information, so that the accurate selection of the subsequent target protection policy level can be facilitated.
For example, the candidate local environment information includes sensor data, but when the sensor data is obtained, the sensor is shielded by the outside, and thus the obtained sensor data contains noise data, so when the sensor data is obtained, data analysis needs to be performed on the sensor data to determine whether the sensor data contains noise data, and then the noise data is removed or corrected, so that the target local environment information is obtained.
In some embodiments, the candidate local environment information includes candidate picture information, and the target local environment information includes target picture information; preprocessing the candidate local environment information to obtain target local environment information, wherein the method comprises the following steps: detecting whether a target object in the candidate picture information is clear or not; screening out candidate picture information with clear target objects, and taking the candidate picture information with clear target objects as target picture information.
Illustratively, the candidate local environment information includes candidate picture information, which is preprocessed to obtain the target picture information. And carrying out target identification according to the candidate picture information to obtain a target object, further judging whether the target object is clear or not, and taking the candidate picture information corresponding to the target object as target picture information if the target object is clear.
For example, when acquiring candidate picture information, the target object is low in visibility due to partial coverage of a lens in an image pickup apparatus that captures the candidate picture information, which is caused by a blurred image and a partially transparent occlusion, or the lens/windshield is occluded from view of the target object, and therefore, it is necessary to screen out candidate picture information in which the target object is clear as the target picture information.
S23, determining target weather information according to the target local environment information;
for example, weather information of the current environment of the vehicle is obtained according to the target local environment information, for example, when weather is converted into weather and rain in a sunny condition, after the target picture information in the obtained target local environment information, the target weather information can be obtained according to the picture quality of the target picture information.
For example, according to the color saturation in the target picture information, the picture color saturation contained in the target picture information is higher when the weather is clear, the picture color saturation contained in the target picture information is lower when the weather is overcast and rainy, and the picture color saturation contained in the target picture information is lower when the weather is severe, therefore, the mapping table of the weather information and the picture color saturation can be obtained according to the picture color saturation dividing range, and the corresponding target weather information is determined according to the picture color saturation corresponding to the target picture information in the mapping table of the weather information and the picture color saturation.
In some embodiments, the target local environment information includes target picture information and target sensor information; determining target weather information according to the target local environment information, including: performing feature extraction analysis on the target picture information to obtain first weather information of the running environment of the current vehicle; obtaining second weather information of the running environment of the current vehicle according to the target sensor information; and fusing the first weather information and the second weather information based on evidence theory, and determining the target weather information.
The target local environment information includes target picture information and target sensor information, wherein the target sensor information can obtain control information of a user on a current vehicle, for example, when the user rains in the sky, the user can use a windshield wiper, visual interference of rainwater on the windshield is reduced, and the larger the rainwater is, the more frequently the windshield wiper is used. Therefore, the target picture information and the target sensor information can be fused to judge the target weather information.
The image feature extraction model is used for feature extraction of the target picture information to obtain feature information corresponding to the target picture information, and the feature information is used for first weather classification to obtain first weather information of the running environment of the current vehicle, wherein the first weather information comprises first weather type and probability information corresponding to the first weather type. And establishing a corresponding relation between the sensor using frequency and the weather category according to the target sensor, acquiring second weather information of the current running environment of the vehicle according to the corresponding relation between the target sensor information and the sensor using frequency and the weather category, wherein the second weather information comprises the second weather category and frequency information corresponding to the second weather category, and converting the frequency information corresponding to the second weather category into probability information.
After the first weather information and the second weather information are obtained, the first weather information and the second weather information are fused by utilizing an evidence theory, and then the weather category with the largest fusion probability is obtained, namely the target weather information.
In some embodiments, performing feature extraction analysis on the target picture information to obtain first weather information of the current running environment of the vehicle, where the first weather information includes: performing feature extraction on the target picture information according to a preset feature extraction network to obtain a feature vector corresponding to the target picture information; and carrying out fuzzy detection on the feature vector to obtain a fuzzy region in the target picture information, and obtaining first weather information of the running environment of the current vehicle according to the fuzzy region.
For example, when weather is bad, some fuzzy objects usually appear in the shot target picture information, for example, when the ground is muddy, mud points or rain points can be splashed in the running process of the vehicle, so that some fuzzy areas appear in the target picture information, and the more the ground is muddy, the larger the fuzzy areas are, so that feature extraction can be performed on the target picture information according to a preset feature extraction network to obtain feature vectors corresponding to the target picture information; and carrying out fuzzy detection on the feature vector to obtain a fuzzy region in the target picture information, and obtaining first weather information of the running environment of the current vehicle according to the distribution rule of the fuzzy region in the target picture information or the duty ratio of the fuzzy region in the target picture information.
For example, the more messy the distribution rule of the blurred region in the target picture information, the worse the first weather information indicating the current running environment of the vehicle, or the higher the duty ratio of the blurred region in the target picture information, the worse the first weather information indicating the current running environment of the vehicle.
Step S24, determining a target protection strategy level of the current vehicle according to the target weather information;
the protection policy level corresponding to the target weather information in the preset protection policy levels is used as the target protection policy level of the current vehicle.
For example, the preset protection policy Level includes 4 levels, namely Level 0, level 1, level2 and Level3, respectively, where Level 0, level 1, level2 and Level3 correspond to different weather conditions, and when the target weather information is most matched with the weather conditions in Level 0, level 1, level2 and Level3, the preset protection policy Level corresponding to the most matched weather condition is used as the target protection policy Level of the current vehicle.
In some embodiments, the preset protection policy level includes condition information; determining a target protection policy level of the current vehicle according to the target weather information, including: performing similarity calculation on the target weather information and the condition information in the preset protection strategy level to obtain a similarity result; and determining the target protection strategy grade corresponding to the current vehicle according to the similarity result.
When the preset protection policy level is obtained, when the protection policy level is set, and the condition information when the protection policy level is used is set together, so that the preset protection policy level comprises the protection policy level and the condition information corresponding to the protection policy level.
The method includes the steps that similarity calculation is conducted on the target weather information and the condition information in the preset protection strategy grades to obtain a similarity result, and when the similarity result is maximum, the protection strategy grade corresponding to the condition information is determined to be the target protection strategy grade corresponding to the current vehicle.
And step S25, indicating the current vehicle to execute the target protection action corresponding to the target protection strategy level.
After determining the target protection policy level corresponding to the current vehicle, the current vehicle is set as a target protection action corresponding to the target protection policy level, so that the current vehicle continues to travel according to the target protection action.
Step S3: after the preset time, when the current vehicle is detected to meet the preset recovery condition of the target recovery mechanism corresponding to the target protection strategy level, the current vehicle is instructed to execute the target recovery action corresponding to the target recovery mechanism.
As the current vehicle travels, the current vehicle is instructed to execute the target restoration action corresponding to the target restoration mechanism when the current vehicle is detected to meet the preset restoration condition of the target restoration mechanism corresponding to the target protection policy level according to the preset time.
For example, the preset protection policy Level includes 4 levels, namely Level 0, level 1, level2 and Level 3.Level 0, level 1, level2 and Level 3 respectively correspond to a recovery mechanism, the Level 0 cannot exit or disable the intelligent driving assistance function, and no recovery mechanism exists; after the function exits, the Level 1 needs to wait for a time (a calibratable value) of T1, and then the intelligent driving assistance function can be activated again. After the function of Level2 exits and is disabled, for example, after the intelligent driving sensor judges that the environment is good, the intelligent driving assistance function can be re-activated after waiting for the time of T2 (the calibratable value). After the function of Level 3 is withdrawn and disabled, the intelligent driving sensor cannot recover even if the intelligent driving sensor judges that the environment is good, and the intelligent driving auxiliary function can be activated again only after the next ignition period.
Therefore, when the current vehicle meets the target protection strategy Level of Level 1, if the time T1 (the calibratable value) is detected, the intelligent driving auxiliary function can be restarted; if the current vehicle meets the target protection strategy grade of Level 3, the current vehicle needs to wait for the next ignition period to reactivate and use the intelligent driving auxiliary function.
Optionally, the setting of the target recovery mechanism and the preset recovery condition in the target recovery mechanism may be set by itself according to the requirement.
Referring to fig. 4, fig. 4 is a schematic diagram of an intelligent driving assistance protection device 200 provided in an embodiment of the present application, where the intelligent driving assistance protection device 200 includes a determining module 201, a first detecting module 202, and a second detecting module 203, where the determining module 201 is configured to determine a preset protection policy level of intelligent driving assistance of a current vehicle; wherein each preset protection policy level corresponds to a recovery mechanism; a first detection module 202, configured to detect a target protection policy level of the current vehicle based on the preset protection policy level, and instruct the current vehicle to execute a target protection action corresponding to the target protection policy level; and the second detection module 203 is configured to instruct the current vehicle to execute the target recovery action corresponding to the target recovery mechanism when detecting that the current vehicle meets the preset condition of the target recovery mechanism corresponding to the target protection policy level after the preset time.
In some embodiments, the determining module 201 performs, in the determining the preset protection policy level of the current vehicle intelligent driving assistance:
Acquiring test data of the current vehicle adopting intelligent driving assistance in different driving environments;
and dividing the test data according to preset dividing conditions to obtain the preset protection strategy level of the intelligent driving assistance of the current vehicle.
In some embodiments, the first detection module 202 performs, when detecting, based on the preset protection policy level, a target protection policy level of the current vehicle, and indicating that the current vehicle performs a target protection action corresponding to the target protection policy level, the following steps:
acquiring candidate local environment information around the running environment where the current vehicle is located;
preprocessing the candidate local environment information to obtain target local environment information;
determining target weather information according to the target local environment information;
determining a target protection strategy level of the current vehicle according to the target weather information;
and indicating the current vehicle to execute the target protection action corresponding to the target protection strategy level.
In some embodiments, the candidate local environment information includes candidate picture information, and the target local environment information includes target picture information; the first detection module 202 performs, in the process of preprocessing the candidate local environment information to obtain the target local environment information:
Detecting whether a target object in the candidate picture information is clear or not;
screening out candidate picture information with clear target objects, and taking the candidate picture information with clear target objects as target picture information.
In some embodiments, the target local environment information includes target picture information and target sensor information; the first detection module 202 performs, in determining the target weather information according to the target local environment information:
performing feature extraction analysis on the target picture information to obtain first weather information of the running environment of the current vehicle;
obtaining second weather information of the running environment of the current vehicle according to the target sensor information;
and fusing the first weather information and the second weather information based on evidence theory, and determining the target weather information.
In some embodiments, the first detection module 202 performs, in performing feature extraction analysis on the target picture information to obtain first weather information of the current driving environment of the vehicle, the following steps:
performing feature extraction on the target picture information according to a preset feature extraction network to obtain a feature vector corresponding to the target picture information;
And carrying out fuzzy detection on the feature vector to obtain a fuzzy region in the target picture information, and obtaining first weather information of the running environment of the current vehicle according to the fuzzy region.
In some embodiments, the preset protection policy level includes condition information; the first detection module 202 performs, in determining the target protection policy level of the current vehicle according to the target weather information:
performing similarity calculation on the target weather information and the condition information in the preset protection strategy level to obtain a similarity result;
and determining the target protection strategy grade corresponding to the current vehicle according to the similarity result.
It should be noted that, for convenience and brevity of description, specific working processes of the above-described apparatus may refer to corresponding processes in the foregoing embodiments of the intelligent driving assistance protection method, and are not described herein again.
Referring to fig. 5, fig. 5 is a schematic block diagram of an electronic device according to an embodiment of the present application.
As shown in fig. 5, the electronic device 300 includes a processor 301 and a memory 302, the processor 301 and the memory 302 being connected by a bus 303, such as an I2C (Inter-integrated Circuit) bus.
In particular, the processor 301 is used to provide computing and control capabilities, supporting the operation of the entire server. The processor 301 may be a central processing unit (Central Processing Unit, CPU), the processor 301 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Specifically, the memory 302 may be a Flash chip, a Read-only memory (ROM) disk, an optical disk, a U-disk, a removable hard disk, or the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of a portion of the structure related to the embodiments of the present application and is not limiting of the electronic device to which the embodiments of the present application apply, and that a particular electronic device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
The processor 301 is configured to execute a computer program stored in the memory, and implement the intelligent driving assistance protection method provided in any embodiment of the present application when the computer program is executed.
In some embodiments, the processor 301 is configured to run a computer program stored in a memory, apply to an electronic device, and implement the following steps when executing the computer program:
determining a preset protection strategy level of intelligent driving assistance of the current vehicle; wherein each preset protection policy level corresponds to a recovery mechanism;
detecting a target protection strategy level of a current vehicle based on the preset protection strategy level, and indicating the current vehicle to execute a target protection action corresponding to the target protection strategy level;
after the preset time, when the current vehicle is detected to meet the preset recovery condition of the target recovery mechanism corresponding to the target protection strategy level, the current vehicle is instructed to execute the target recovery action corresponding to the target recovery mechanism.
In some embodiments, the processor 301 performs, in the determining the preset protection policy level of the current vehicle intelligent driving assistance:
acquiring test data of the current vehicle adopting intelligent driving assistance in different driving environments;
And dividing the test data according to preset dividing conditions to obtain the preset protection strategy level of the intelligent driving assistance of the current vehicle.
In some embodiments, the processor 301, when detecting the target protection policy level of the current vehicle based on the preset protection policy level, indicates that the current vehicle performs the target protection action corresponding to the target protection policy level, performs:
acquiring candidate local environment information around the running environment where the current vehicle is located;
preprocessing the candidate local environment information to obtain target local environment information;
determining target weather information according to the target local environment information;
determining a target protection strategy level of the current vehicle according to the target weather information;
and indicating the current vehicle to execute the target protection action corresponding to the target protection strategy level.
In some embodiments, the candidate local environment information includes candidate picture information, and the target local environment information includes target picture information; the processor 301 performs, in the process of preprocessing the candidate local environment information to obtain the target local environment information, the following steps:
Detecting whether a target object in the candidate picture information is clear or not;
screening out candidate picture information with clear target objects, and taking the candidate picture information with clear target objects as target picture information.
In some embodiments, the target local environment information includes target picture information and target sensor information; the processor 301 performs, in determining the target weather information according to the target local environment information:
performing feature extraction analysis on the target picture information to obtain first weather information of the running environment of the current vehicle;
obtaining second weather information of the running environment of the current vehicle according to the target sensor information;
and fusing the first weather information and the second weather information based on evidence theory, and determining the target weather information.
In some embodiments, the processor 301 performs, in performing feature extraction analysis on the target picture information to obtain first weather information of the current running environment of the vehicle, performing:
performing feature extraction on the target picture information according to a preset feature extraction network to obtain a feature vector corresponding to the target picture information;
And carrying out fuzzy detection on the feature vector to obtain a fuzzy region in the target picture information, and obtaining first weather information of the running environment of the current vehicle according to the fuzzy region.
In some embodiments, the preset protection policy level includes condition information; the processor 301 performs, in determining the target protection policy level of the current vehicle according to the target weather information:
performing similarity calculation on the target weather information and the condition information in the preset protection strategy level to obtain a similarity result;
and determining the target protection strategy grade corresponding to the current vehicle according to the similarity result.
It should be noted that, for convenience and brevity of description, specific working processes of the electronic device described above may refer to corresponding processes in the foregoing embodiments of the intelligent driving assistance protection method, and are not described herein again.
The embodiments of the present application also provide a storage medium for computer readable storage, where the storage medium stores one or more computer programs, and the one or more computer programs are executable by one or more processors to implement the steps of any of the intelligent driving assistance protection methods as provided in the embodiments of the present application.
The storage medium may be an internal storage unit of the electronic device of the foregoing embodiment, for example, an electronic device memory. The storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device.
Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, functional modules/units in the apparatus disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware embodiment, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
It should be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments. The foregoing is merely illustrative of the embodiments of the present application, but the scope of the present application is not limited thereto, and any equivalent modifications or substitutions will be apparent to those skilled in the art within the scope of the present application, and these modifications or substitutions are intended to be included in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. An intelligent driving assistance protection method, characterized in that the method comprises:
determining a preset protection strategy level of intelligent driving assistance of the current vehicle; wherein each preset protection policy level corresponds to a recovery mechanism;
detecting a target protection strategy level of a current vehicle based on the preset protection strategy level, and indicating the current vehicle to execute a target protection action corresponding to the target protection strategy level;
after the preset time, when the current vehicle is detected to meet the preset recovery condition of the target recovery mechanism corresponding to the target protection strategy level, the current vehicle is instructed to execute the target recovery action corresponding to the target recovery mechanism;
the detecting, based on the preset protection policy level, the target protection policy level of the current vehicle, and indicating the current vehicle to execute the target protection action corresponding to the target protection policy level, includes:
acquiring candidate local environment information around the running environment where the current vehicle is located;
preprocessing the candidate local environment information to obtain target local environment information;
determining target weather information according to the target local environment information;
Determining a target protection strategy level of the current vehicle according to the target weather information;
and indicating the current vehicle to execute the target protection action corresponding to the target protection strategy level.
2. The method of claim 1, wherein determining a preset protection strategy level for intelligent driving assistance for the current vehicle comprises:
acquiring test data of the current vehicle adopting intelligent driving assistance in different driving environments;
and dividing the test data according to preset dividing conditions to obtain the preset protection strategy level of the intelligent driving assistance of the current vehicle.
3. The method of claim 1, wherein the candidate local environment information comprises candidate picture information and the target local environment information comprises target picture information;
preprocessing the candidate local environment information to obtain target local environment information, wherein the method comprises the following steps:
detecting whether a target object in the candidate picture information is clear or not;
screening out candidate picture information with clear target objects, and taking the candidate picture information with clear target objects as target picture information.
4. The method of claim 1, wherein the target local environment information comprises target picture information and target sensor information;
Determining target weather information according to the target local environment information, including:
performing feature extraction analysis on the target picture information to obtain first weather information of the running environment of the current vehicle;
obtaining second weather information of the running environment of the current vehicle according to the target sensor information;
and fusing the first weather information and the second weather information based on evidence theory, and determining the target weather information.
5. The method of claim 4, wherein performing feature extraction analysis on the target picture information to obtain first weather information of the current driving environment of the vehicle, comprises:
performing feature extraction on the target picture information according to a preset feature extraction network to obtain a feature vector corresponding to the target picture information;
and carrying out fuzzy detection on the feature vector to obtain a fuzzy region in the target picture information, and obtaining first weather information of the running environment of the current vehicle according to the fuzzy region.
6. The method of claim 1, wherein the preset protection policy level includes condition information;
Determining a target protection policy level of the current vehicle according to the target weather information, including:
performing similarity calculation on the target weather information and the condition information in the preset protection strategy level to obtain a similarity result;
and determining the target protection strategy grade corresponding to the current vehicle according to the similarity result.
7. An intelligent driving assistance protection device, characterized by comprising:
the determining module is used for determining a preset protection strategy level of intelligent driving assistance of the current vehicle; wherein each preset protection policy level corresponds to a recovery mechanism;
the first detection module is used for detecting the target protection strategy level of the current vehicle based on the preset protection strategy level and indicating the current vehicle to execute the target protection action corresponding to the target protection strategy level;
the second detection module is used for indicating the current vehicle to execute the target recovery action corresponding to the target recovery mechanism when detecting that the current vehicle meets the preset condition of the target recovery mechanism corresponding to the target protection strategy level after the preset time;
the first detection module detects the target protection policy level of the current vehicle based on the preset protection policy level, and indicates that the current vehicle executes a target protection action corresponding to the target protection policy level, and then executes:
Acquiring candidate local environment information around the running environment where the current vehicle is located;
preprocessing the candidate local environment information to obtain target local environment information;
determining target weather information according to the target local environment information;
determining a target protection strategy level of the current vehicle according to the target weather information;
and indicating the current vehicle to execute the target protection action corresponding to the target protection strategy level.
8. An electronic device, characterized in that the electronic device comprises a processor and a memory;
the memory is used for storing a computer program;
the processor is configured to execute the computer program and to implement the intelligent driving assistance protection method according to any one of claims 1 to 6 when the computer program is executed.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when executed by one or more processors, causes the one or more processors to perform the steps of the intelligent driving assistance protection method according to any one of claims 1 to 6.
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| FR3079804B1 (en) * | 2018-04-06 | 2020-09-04 | Renault Sas | METHOD AND SYSTEM FOR ASSISTANCE TO THE DRIVER OF A MOTOR VEHICLE EQUIPPED WITH A DRIVING ASSISTANCE SYSTEM |
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| CN110481554B (en) * | 2019-08-06 | 2021-04-27 | 浙江吉利汽车研究院有限公司 | Intelligent driving auxiliary control method and system |
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| CN115892053A (en) * | 2021-08-06 | 2023-04-04 | 宝马股份公司 | Vehicle-mounted intelligent management method and system for improving automatic driving performance |
| CN115221209A (en) * | 2022-07-07 | 2022-10-21 | 重庆长安汽车股份有限公司 | Method and device for recommending driving assistance function of vehicle, vehicle and storage medium |
| CN115431995B (en) * | 2022-10-18 | 2023-12-22 | 广州万协通信息技术有限公司 | Equipment control method and device based on different-level auxiliary driving |
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