CN115240393A - Collision early warning method and device based on driver driving experience and automobile - Google Patents
Collision early warning method and device based on driver driving experience and automobile Download PDFInfo
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Abstract
The invention provides a collision early warning method, a collision early warning device and an automobile based on driver driving experience, wherein the collision early warning method comprises the following steps of S1, acquiring ID information of a driver, and determining a driver experience level corresponding to the ID information of the driver according to a pre-trained driver experience level classification model; s2, acquiring the reaction time and the braking coordination time of the driver according to the experience level of the driver corresponding to the ID information of the driver; s3, acquiring the running information of the vehicle and the running information of the remote vehicle; calculating the minimum safe distance between the vehicle and the remote vehicle according to the reaction time of the driver, the braking coordination time, the driving information of the vehicle and the driving information of the remote vehicle; and S4, acquiring the current actual distance between the vehicle and the remote vehicle, comparing the actual distance with the minimum safe distance, and determining whether the two vehicles perform collision early warning. According to the invention, the reaction time and the braking coordination time of the driver are adjusted according to the driving experience information of the driver, and the satisfaction and the accuracy of the driver on the collision early warning experience are improved.
Description
Technical Field
The invention relates to the technical field of automobiles, in particular to a collision early warning method and device based on driver driving experience and an automobile.
Background
With the rapid development of the car networking technology C-V2X, the ability of the car to sense the outside world based on the C-V2X technology is stronger and stronger. The C-V2X has the characteristics of high reliability and low time delay, and the active safety of the automobile based on the C-V2X is more and more emphasized. The application layer of the vehicular communication system of the cooperative intelligent transportation system and the application data interaction standard (referred to as V2X application layer national standard) are constructed for early warning of different scenes, wherein the collision early warning function comprises forward collision early warning, intersection collision early warning, blind area early warning, reverse overtaking early warning and the like, and the application layer is extremely concerned.
The current minimum safe distance model parameters (including driver reaction time, brake coordination time and the like) are all fixed values, drivers with different experiences are not considered, and the required driver reaction time and brake coordination time are different. If the same safe distance model parameter values are used, this is not suitable for drivers of various experience levels.
Disclosure of Invention
The invention aims to provide a collision early warning method and device based on the driving experience of a driver and an automobile, which can automatically adjust the reaction time and the braking coordination time of different drivers according to the driving experience information of different drivers, and improve the satisfaction degree and the accuracy of the driver on the collision early warning experience.
In one aspect, a collision warning method based on driver driving experience is provided, which includes the following steps:
the method comprises the following steps of S1, obtaining ID information of a driver, and determining a driver experience level corresponding to the ID information of the driver according to a pre-trained driver experience level classification model;
s2, acquiring corresponding driver reaction time and brake coordination time according to the driver experience level corresponding to the ID information of the driver;
s3, acquiring the driving information of the vehicle and the driving information of a remote vehicle; calculating the minimum safe distance between the vehicle and the remote vehicle according to the reaction time of the driver, the braking coordination time, the driving information of the vehicle and the driving information of the remote vehicle;
and S4, acquiring the current actual distance between the vehicle and the remote vehicle, comparing the actual distance with the minimum safe distance, and determining whether the two vehicles perform collision early warning according to the comparison result.
Preferably, in step S1, the training process of the driver experience level classification model specifically includes:
collecting a plurality of driver driving experience sample data;
preprocessing the driver driving experience sample data according to a preset preprocessing rule, and normalizing the data;
marking the experience level of the driver on the driving experience sample data of the driver; the driver experience level at least comprises a novice driver level, an experienced driving skill level and an experienced violation multi-level;
selecting part of driver driving experience sample data as training samples, using the rest of driver driving experience sample data as test samples, and inputting a preset SVM (support vector machine) model for training;
and outputting the trained support vector machine model as a driver experience level classification model, and storing the model in a V-BOX of the vehicle.
Preferably, in step S1, the training process of the driver experience level classification model further includes:
acquiring the reaction time and the brake coordination time of drivers with different experience levels of the drivers, and calculating the average reaction time and the average brake coordination time of the drivers at all levels;
carrying out appropriate correction or adjustment on the average reaction time and the average braking coordination time of each level of driver according to a preset correction rule to obtain the preset reaction time and the preset braking coordination time of each level of driver;
and storing the preset reaction time and the preset brake coordination time corresponding to each level of driver in the V-BOX of the vehicle.
Preferably, in step S1, the determining the experience level of the driver corresponding to the ID information of the driver specifically includes:
acquiring driver driving experience information according to the driver ID information, and preprocessing according to a preset preprocessing rule;
and inputting the preprocessed driver driving experience information into a driver experience level classification model, and determining the driver experience level corresponding to the driver ID information.
Preferably, in step S2, the obtaining of the corresponding driver reaction time and the corresponding brake coordination time specifically includes:
and inquiring the preset reaction time and the preset brake coordination time corresponding to each level of driver according to the experience level of the driver corresponding to the driver ID information, and acquiring the reaction time and the brake coordination time of the driver corresponding to the driver ID information.
Preferably, in step S3, the minimum safe distance between the host vehicle and the distant vehicle is calculated according to the following formula:
wherein v is s The vehicle speed is the vehicle speed; v. of f The remote vehicle speed; a is s Meaning the longitudinal acceleration of the host vehicle; t is the reaction time of the driver; t is t 1 Coordinating time for braking; t is t 2 Increase time for deceleration; d is a radical of 0 Is a safe distance at rest.
Preferably, the step S4 includes: and comparing the current actual distance between the vehicle and the remote vehicle with the minimum safe distance, if the current actual distance is less than or equal to the minimum safe distance, judging that the vehicle is in collision danger, outputting collision early warning information, and early warning the driver.
Preferably, the step S4 further comprises: if the current actual distance is larger than the minimum safe distance, judging that no collision danger exists, and not needing early warning.
On the other hand, the collision early warning device based on the driving experience of the driver is also provided, and the vehicle is early warned by the collision early warning method based on the driving experience of the driver.
On the other hand, the automobile is also provided, and the collision early warning is carried out through the collision early warning device based on the driving experience of the driver.
In summary, the embodiment of the invention has the following beneficial effects:
according to the collision early warning method and device based on the driver driving experience and the automobile, the driver driving experience information is obtained from a traffic safety cloud platform or other platforms, and then the driver driving experience levels are classified through an algorithm of an SVM (support vector machine); looking up a table to obtain different driver reaction time and brake coordination time according to different driving experience levels; according to the GPS positioning, the speed, the steering wheel turning angle and other information of the vehicle and the distant vehicle, and the reaction time and the braking time of the driver of the vehicle, whether the vehicle and the distant vehicle have the possibility of collision or not is calculated, and whether collision early warning is carried out or not is judged. The driver can provide different early warning opportunities according to different driving experiences, and the satisfaction degree of the driver on the collision early warning effect 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 embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive labor.
Fig. 1 is a main flow diagram of a collision warning method based on driver driving experience in an embodiment of the present invention.
FIG. 2 is a schematic diagram illustrating training of a driver experience level classification model according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a logic for calculating the preset reaction time and the preset brake coordination time corresponding to each level of driver in the embodiment of the present invention.
FIG. 4 is a schematic illustration of vehicle braking in an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of an embodiment of a collision warning method based on driver driving experience according to the present invention. In this embodiment, the method comprises the steps of:
the method comprises the following steps of S1, obtaining ID information of a driver, and determining a driver experience level corresponding to the ID information of the driver according to a pre-trained driver experience level classification model; it can be understood that a driver logs in a vehicle through an account number or a face recognition mode and the like, the vehicle records identity information such as an ID (identity) of the driver, and the ID information can uniquely identify the driver and can correspond to a traffic safety cloud platform or other similar platforms; and inquiring the driving experience information of the driver from a traffic safety cloud platform or other similar platforms according to the driver ID information, determining the experience level of the driver according to the driving experience information, further determining the subsequent reaction time and braking coordination time, and performing early warning judgment.
In a specific embodiment, as shown in fig. 2, the training process of the driver experience level classification model specifically includes:
collecting a plurality of driver driving experience sample data; specifically, the driving experience sample data includes: obtaining information such as driving license obtaining time, driver age, driver gender, overspeed frequency, red light running frequency, rear-end collision frequency, drunk driving frequency, accumulated mileage and the like; the specific information type can be adjusted according to the actually acquired information;
preprocessing the driver driving experience sample data according to a preset preprocessing rule, wherein the preprocessing includes abnormal data removal, repeated data removal, missing value supplement, non-numerical value type conversion into numerical values, and normalization processing is carried out on the data;
the driver experience level marking is carried out on the driver experience sample data, marking can be carried out manually or through a clustering algorithm, the marking mode can be selected according to specific actual conditions, and the driver can be marked with the level of a novice driver, the level of the experienced driving technology, the level of the experienced violation, and the like according to requirements;
selecting part of driver driving experience sample data as training samples, using the rest of driver driving experience sample data as test samples, and inputting a preset SVM (support vector machine) model for training;
and outputting the trained support vector machine model as a driver experience level classification model, and storing the model in a V-BOX of the vehicle. The process of collecting experience sample data and training the model can be executed on a cloud or a server outside other vehicles, the training operation of the model is executed according to the actually required frequency, and the support vector machine model stored in the vehicle is periodically updated through OTA or other technologies.
Specifically, as shown in fig. 3, after the model training is completed, the corresponding tables of the reaction time and the braking coordination time corresponding to different experience levels of the driver need to be set, and the specific process is as follows:
obtaining the driver reaction time and the brake coordination time of different driver experience levels, and calculating the average reaction time T of the drivers of all levels * Average brake coordination time t 1 * ；
According to a preset correction rule (according to actual test or experimental calibration), the average reaction time and the average braking coordination time of each level of driver are corrected or adjusted properly to obtain the preset reaction time T and the preset braking coordination time T of each level of driver 1 ；
Coordinating preset reaction time T and preset brake corresponding to each level of driverTime t 1 Stored in the V-BOX of the vehicle. For example, the following table may be used for storage, driver experience level a: the corresponding reaction time a1 is 2s, the brake coordination time b1 is 1s; driver experience level B: the corresponding reaction time a2 is 0.8 and the brake coordination time b2 is 0.5s; driver experience level C: the corresponding reaction time a3 is 1.3s, the brake coordination time b3 is 0.7s,
| driver experience level | Reaction time T | Brake coordination time t 1 |
| A | a1 | b1 |
| B | a2 | b2 |
| C | a3 | b3 |
Specifically, the trained model and the corresponding time setting are carried out, so that the driving experience information of the driver can be obtained according to the ID information of the driver, and the preprocessing is carried out according to the preset preprocessing rule; and inputting the preprocessed driver experience information into a driver experience level classification model, and determining the driver experience level corresponding to the driver ID information. It can be understood that, each time the vehicle is started, the driving experience information of the driver is acquired from the traffic safety cloud platform or other platforms according to the driver ID information, and the information includes information such as driving license acquisition time, driver age, driver gender, overspeed frequency, red light running frequency, rear-end collision frequency, drunk driving frequency, accumulated mileage and the like. The above information is only an example, and can be adjusted according to the actually obtained information, but the information needs to be consistent with the data items required by the support vector machine model; preprocessing driving experience information corresponding to the driver ID; inputting the preprocessed driving experience information into a support vector machine model in the V-BOX, and predicting/estimating the experience level of a driver corresponding to the driver ID through the support vector machine model, such as the level of a novice driver, the experienced driving with good technology, the experienced violation and the like; it need only be performed once each time the vehicle is started.
S2, acquiring corresponding driver reaction time and brake coordination time according to the driver experience level corresponding to the ID information of the driver; it can be understood that, according to the experience level of the driver corresponding to the driver ID, the reaction time T (preset reaction time) and the brake coordination time T of the corresponding experience level of the driver are inquired/obtained 1 (preset brake coordination time), this operation need only be performed once each time the vehicle is started.
S3, acquiring the running information of the vehicle and the running information of the remote vehicle; calculating the minimum safe distance between the vehicle and the remote vehicle according to the reaction time of the driver, the braking coordination time, the driving information of the vehicle and the driving information of the remote vehicle; it can be understood that the driver reaction time T and the brake coordination time T are corresponding to the experience level of the driver 1 And information such as the speed, the acceleration, the steering wheel angle, the positioning data and the like of the remote vehicle and the vehicle are calculated.
In a specific embodiment, the method for calculating the minimum safety distance may refer to the description in appendix C of application layer and application data interaction standard of cooperative intelligent transportation system for vehicles (TCSAE 53-2017): the vehicle braking process is illustrated in fig. 4 by using the minimum safe distance between vehicles model.
According to the braking dynamics of the automobile, taking the minimum safe distance model of the forward collision algorithm as an example:
wherein v is s The vehicle speed is the vehicle speed; v. of f The remote vehicle speed; a is s Meaning the longitudinal acceleration of the host vehicle; t is the reaction time of the driver; t is t 1 Coordinating time for braking; t is t 2 Increase time for deceleration; d 0 Is a safe distance at rest.
And S4, acquiring the current actual distance between the vehicle and the remote vehicle, comparing the actual distance with the minimum safe distance, and determining whether the two vehicles perform collision early warning according to the comparison result. It can be understood that whether the two vehicles are in collision danger or not is calculated according to the minimum safe distance and the current actual distance between the vehicle and the far vehicle, and whether collision early warning is needed or not is calculated. If the current actual distance between the vehicle and the distant vehicle is less than or equal to the minimum safe distance, collision danger exists, and collision early warning information is displayed on an HMI screen or voice prompt is made.
In a specific embodiment, the current actual distance between the vehicle and the distant vehicle is compared with the minimum safe distance, if the current actual distance is less than or equal to the minimum safe distance, it is determined that there is a collision risk, collision early warning information is output, and early warning is performed on a driver. And if the current actual distance is greater than the minimum safe distance, judging that no collision danger exists, and not needing to perform early warning.
The embodiment of the invention also provides a collision early warning device based on the driving experience of the driver, which is used for early warning the vehicle by the collision early warning method based on the driving experience of the driver, and the specific implementation process refers to the process of the collision early warning method based on the driving experience of the driver, and is not repeated herein.
In the embodiment of the invention, the vehicle is early warned by the collision early warning device based on the driving experience of the driver, and the specific implementation process refers to the process of the collision early warning method/device based on the driving experience of the driver, which is not described herein again.
In summary, the embodiment of the invention has the following beneficial effects:
the invention provides a collision early warning method and device based on the driving experience of a driver and an automobile.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (10)
1. A collision early warning method based on driver driving experience is characterized by comprising the following steps:
the method comprises the following steps of S1, obtaining ID information of a driver, and determining a driver experience level corresponding to the ID information of the driver according to a pre-trained driver experience level classification model;
s2, acquiring corresponding driver reaction time and brake coordination time according to the driver experience level corresponding to the ID information of the driver;
s3, acquiring the running information of the vehicle and the running information of the remote vehicle; calculating the minimum safe distance between the vehicle and the distant vehicle according to the reaction time of the driver, the braking coordination time, the driving information of the vehicle and the driving information of the distant vehicle;
and S4, acquiring the current actual distance between the vehicle and the remote vehicle, comparing the actual distance with the minimum safe distance, and determining whether to carry out collision early warning according to the comparison result.
2. The method according to claim 1, wherein in step S1, the training process of the driver experience level classification model specifically comprises:
collecting a plurality of driver driving experience sample data;
preprocessing the driver driving experience sample data according to a preset preprocessing rule, and normalizing the data;
marking the experience level of the driver on the driving experience sample data of the driver; the experience level of the driver at least comprises a novice driver level, an experienced driving skill good level and an experienced violation multi-level;
selecting part of driver driving experience sample data as a training sample, taking the rest driver driving experience sample data as a test sample, and inputting the test sample into a preset SVM (support vector machine) model for training;
and outputting the trained support vector machine model as a driver experience level classification model, and storing the model in a V-BOX of the vehicle.
3. The method according to claim 2, wherein in step S1, the training process of the driver experience level classification model further comprises:
acquiring the reaction time and the braking coordination time of drivers with different experience levels of the drivers, and calculating the average reaction time and the average braking coordination time of the drivers with different levels;
carrying out appropriate correction or adjustment on the average reaction time and the average braking coordination time of each level of driver according to a preset correction rule to obtain the preset reaction time and the preset braking coordination time of each level of driver;
and storing the preset reaction time and the preset brake coordination time corresponding to each level of driver in the V-BOX of the vehicle.
4. The method according to claim 3, characterized in that in step S1, said determining a driver experience level corresponding to said driver' S ID information comprises in particular:
acquiring driver driving experience information according to the driver ID information, and preprocessing according to a preset preprocessing rule;
and inputting the preprocessed driver experience information into a driver experience level classification model, and determining the driver experience level corresponding to the driver ID information.
5. The method according to claim 4, wherein in step S2, the obtaining of the corresponding driver reaction time and brake coordination time specifically comprises:
and inquiring the preset reaction time and the preset brake coordination time corresponding to each level of driver according to the experience level of the driver corresponding to the driver ID information, and acquiring the reaction time and the brake coordination time of the driver corresponding to the driver ID information.
6. The method of claim 5, wherein in step S3, the minimum safe distance of the host vehicle from the distant vehicle is calculated according to the following formula:
wherein v is s The vehicle speed is the vehicle speed; v. of f The remote vehicle speed; a is s Meaning the longitudinal acceleration of the host vehicle; t is the reaction time of the driver; t is t 1 Coordinating time for braking; t is t 2 Increase time for deceleration; d is a radical of 0 Is a safe distance at rest.
7. The method of claim 6, wherein the step S4 comprises:
and comparing the current actual distance between the vehicle and the remote vehicle with the minimum safe distance, if the current actual distance is less than or equal to the minimum safe distance, judging that the vehicle is in collision danger, outputting collision early warning information, and early warning a driver.
8. The method of claim 7, wherein the step S4 further comprises:
if the current actual distance is larger than the minimum safe distance, judging that no collision danger exists, and not needing early warning.
9. A collision warning apparatus based on driver's driving experience, characterized in that a vehicle is warned by the collision warning method based on driver's driving experience according to any one of claims 1 to 8.
10. An automobile characterized in that collision warning is performed by the collision warning apparatus based on driver's driving experience according to claim 9.
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