CN113119984A - Advanced driving assistance system and advanced driving assistance method - Google Patents
Advanced driving assistance system and advanced driving assistance method Download PDFInfo
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- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
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Abstract
The present application relates to an advanced driving assistance system including: the first acquisition module acquires and outputs a signal of a driving environment of the vehicle; the second acquisition module is activated when the driver operates the bicycle and is used for acquiring and outputting a signal of the driving behavior of the driver; the training data generation module is used for acquiring the output driving environment and the output driving behavior of the driver and generating training data; the information processing module is used for training and storing a driving style model for a driver, wherein the driving style model describes the corresponding relation between a driving environment and the driving behavior of a single driver, and the information processing center trains and/or updates the driving style model under the condition of obtaining training data; and the decision-making module downloads a driving style model corresponding to the driver of the current own vehicle from the information processing center and outputs driving behaviors corresponding to the driving environment through the driving style model according to the driving environment output by the first acquisition module.
Description
Technical Field
The present application relates to the field of automatic driving of vehicles, and more particularly, to an advanced driving assistance system and an advanced driving assistance method.
Background
The current trend in the automotive industry is to continually develop and refine more and more Advanced Driving Assistance Systems (ADAS). In the meantime, the comfort and personalization of automobile driving are increasingly important for high-end vehicle models, and a driving assistance system tailored to the driver is a research focus at present.
Disclosure of Invention
The object of the present application is to achieve or at least to promote the technical hotspots proposed in the prior art.
According to a first aspect of the present application, an advanced driving assistance system is provided, comprising:
the first acquisition module acquires and outputs a signal of a driving environment of the vehicle;
a second acquisition module which is activated when a driver operates the bicycle, and is used for acquiring and outputting a signal of driving behavior of the driver;
a training data generation module that acquires the driving environment output by the first acquisition module and the driving behavior of the driver output by the second acquisition module, and generates training data;
an information processing module that trains and stores a driving style model for a driver, the driving style model describing a correspondence between a driving environment and a driving behavior of a single driver, wherein the information processing module trains and/or updates the driving style model when the training data is obtained; and
and the decision module downloads a driving style model corresponding to the driver of the current vehicle from the information processing module and outputs driving behaviors corresponding to the driving environment through the driving style model according to the driving environment output by the first acquisition module.
According to a second aspect of the present application, there is provided an advanced driving assistance method including:
acquiring and outputting the driving environment of the vehicle;
acquiring and outputting a driving behavior of a driver while the driver manipulates the bicycle;
acquiring the driving environment and the driving behavior of the driver, and generating training data;
training and storing a driving style model for a driver, the driving style model describing a correspondence between a driving environment and a driving behavior of a single driver, the driving style model being trained and/or updated on the condition that the training data is obtained; and
the driving style model corresponding to the driver of the current own vehicle is downloaded, and the driving behavior corresponding to the driving environment is output through the driving style model according to the driving environment.
According to a third aspect of the present application, a vehicle is proposed comprising an advanced driving assistance system as disclosed in any of the embodiments of the present application.
According to a fourth aspect of the present application, a computer-readable storage medium is proposed, in which a computer program is stored, wherein the computer program is capable of executing the steps of the advanced driving assistance method described according to any of the embodiments of the present application by a processor.
According to a fourth aspect of the present application, a computer device is presented, comprising a memory, a processor and a computer program stored in the memory and executable by the processor, wherein the processor is capable of performing the steps of the advanced driving assistance method as described in any of the embodiments of the present application.
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The disclosure of the present invention will become more readily understood with reference to the accompanying drawings. As is readily understood by those skilled in the art: these drawings are for illustrative purposes only and are not intended to constitute a limitation on the scope of the present invention. Moreover, in the drawings, like numerals are used to indicate like parts, and in which:
fig. 1 schematically shows a block diagram of an advanced driving assistance system according to the present disclosure; and
fig. 2 schematically shows a flow chart of steps of an advanced driving assistance method according to the present disclosure.
Detailed Description
The advanced driving assistance system 10 disclosed herein is capable of "customizing" the automated driving behavior of a vehicle according to the driving style to which the driver driving the vehicle is accustomed. Referring to fig. 1, it can be seen that the advanced driving assistance system 10 disclosed in the present application includes a first acquisition module 1, a second acquisition module 2, a training data generation module 3, an information processing module 4, and a decision module 5. Specifically, the advanced driving assistance system 10 uses the driving style model according to the current driver trained by the information processing module 4 to decide the driving behavior according to the driver's expectation corresponding to the current driving environment. In the context of the present application, a driving style model is understood to be: by means of which the driving behavior of a driver in the driving environment can be mapped from different driving environments in each case. That is, the driving style model describes a one-to-one correspondence between the driving environment and the driving behavior of one driver.
In the scope of the present application, it should be noted that "own vehicle" refers to a vehicle currently under study, that is, an own vehicle.
Here, the driving environment of the current vehicle is obtained by the first obtaining module 1, and specifically, the first obtaining module 1 may obtain information including environmental conditions, that is, current weather, visibility, a type of a driving road surface, curvature, height, a relative distance between the vehicle and surrounding obstacles and a target, a relative speed, and the like, detected by a sensor fusion technique of the vehicle, such as a radar, a camera, a laser, a high-precision map, and an IMU, so that the driving environment information of the vehicle in what road surface condition the vehicle is currently driving under can be obtained.
The second detection module 2 of the advanced driving assistance system 10 is activated only when the driver operates the vehicle, i.e. the second detection module 2 does not detect the driving behavior of the vehicle if the vehicle is not driven by the driver. In the context of the present application, driving behavior may be understood to include some information relating to acceleration and acceleration rate of change of the own vehicle, and the like. It should be noted that the acceleration and the jerk refer to the acceleration and the jerk in a vector, respectively, that is, the acceleration includes not only the acceleration along the longitudinal direction, the lateral direction, and the vertical direction of the vehicle coordinate system, but also the angular acceleration around the longitudinal axis, the lateral axis, and the vertical axis of the vehicle coordinate system.
In a case where the second acquisition module 2 is activated to be able to acquire the driving behavior of the driver, the training data generation module 3 receives the driving environment of the own vehicle acquired by the first acquisition module 1 and the driving behavior of the driver, which is acquired by the second acquisition module 2 and is generated by the driver operating the own vehicle in the above driving environment, and generates training data based on the above driving environment and driving behavior information, and outputs the training data for training out the driving style model. That is, the training data includes driving environment information and driving behavior information of drivers corresponding to different driving environments, respectively.
The training data is thereby transmitted to the information processing module 4. Within the scope of the present application, the information processing module 4 needs to train, store and update the driving style model with training data, that is to say to perform a large amount of data processing and machine learning work. Thus, the information processing module 4 may be generally configured as a server and/or a processor in the cloud, so that a large amount of training data can be processed quickly.
In some embodiments of the present application, a part of the training data, for example 70%, is used for inputting into a training model (machine learning model for training a driving style model) to train the driving style model, and another part, for example 30%, is used for verifying and adjusting the trained driving style model, so that the driving style model has higher reliability to adapt to the driving style of the driver more safely and comfortably.
In some embodiments of the present application, the information processing module 4 further comprises a training data classification unit that classifies training data based on characteristics of different driving environments in the training data. That is, for example, the training data classification unit can classify the training data into a plurality of categories based on different driving environment types, for example, a driving environment type for a curve, a driving environment type for a congested road section. For example, the training data classification unit can classify the training data into a class in which the driving environment type is a curve road surface according to the driving environment type of the road surface on which the curve is traveling, that is, for example, the size of a variable relating to the curve in the driving environment, or the like. And optionally within the scope of the present application, a driving style model corresponding to a different driving environment type can be trained for that driving environment type by a different machine learning training model using the above-classified training data corresponding thereto. For example, the information processing module 4 uses the driving environment and the driving behavior in the training data in which the driving environment type is a curve as input data of the machine learning training model, and for the training data of the curve type, a specific machine learning training model, for example, a Long Short Term Memory (Long Short Term Memory) in a curve scene can be used to train the driving style model. The mode of adopting the appropriate machine learning training model according to the parameters of the driving behaviors concerned by different driving environment types, such as acceleration, speed of the vehicle and the like, can further improve the fitting degree and reliability of the driving style model to the simulation of the real driving behaviors of the driver. For example, in a driving environment where a curve is driving, the parameter of the driving behavior of interest may be the continuity of the speed in the time domain, and therefore a suitable training model, such as a long-short term memory model, may be employed based on this characteristic.
Whereas the decision module 5 of the advanced driving assistance system 10 according to the present application is capable of downloading the driving style model corresponding to the driver of the current own vehicle from the information processing module 4 and outputting the driving behavior corresponding to the driving environment through the driving style model according to the driving environment output by the first acquisition module 1. In the context of the present application, the decision module 5 is provided in a controller of the vehicle, and the decision module 5 outputs the desired driving behavior based on the current driving environment only from the driving style model downloaded from the information processing module 4. That is, the decision module 5 does not train and update the driving style model itself, which enables the advanced driving assistance system 10 of the present application to make a driving behavior response conforming to the style of driver driving more quickly for different driving environments. Furthermore, it can be provided that the decision module 5 can send training data to the information processing module 4 for training and/or updating the driving style model.
In some embodiments of the present application, it can be considered that, in the case that the type of driving environment to which the current driving environment belongs is not covered in the driving style model of the current driver downloaded by the decision module 5, that is, in the case that the driver has not previously had driving behavior in the above-described type of driving environment (the driver has not previously operated the vehicle in the type of driving environment to which the current driving environment belongs) or training data in the current type of driving environment is less and insufficient to form a driving style model for the driver of the current type of driving environment, the information processing module compares each stored driving style model with the driving style model of the driver before the current driving environment, selects the driving style model closest to the previous driving style model as the recommended driving style model to be provided to the decision module 5 for downloading, the decision-making module 5 outputs the desired driving behavior in the driving environment according to the recommended driving style model. Here, that is, the recommended driving style model selected by the information processing module 4 satisfies the following condition: 1. covering a driving environment type to which a current driving environment belongs; and 2. the driving style model is closest to the driving style model of the driver before the current driving environment.
In some embodiments of the application, it can also be provided that, in the case of a driver operating the own vehicle himself, the decision module 5 no longer outputs a driving behavior corresponding to the driving environment, that is to say the decision module 5 is put into the "inactive" state. This can ensure that the driver has the highest level of handling of the vehicle.
As can be seen with reference to fig. 2, the present application also discloses an advanced driver assistance method, comprising:
acquiring and outputting a signal S1 of the driving environment of the own vehicle;
acquiring and outputting a signal S2 of a driving behavior of the driver when the driver manipulates the vehicle;
acquiring the driving environment and the driving behavior of the driver, and generating training data S3;
training and storing a driving style model for the driver, the driving style model describing a correspondence between a driving environment and a driving behavior of a single driver, wherein the driving style model is trained and/or updated with the training data obtained S4; and
a driving style model corresponding to a driver of the current own vehicle is downloaded, and driving behavior corresponding to the driving environment is output through the driving style model according to the driving environment S5.
In the context of the present application, a driving style model is understood to be: by means of which the driving behavior of a driver in the driving environment can be mapped from different driving environments in each case. That is, the driving style model describes a one-to-one correspondence between the driving environment and the driving behavior of one driver.
Here, the driving environment of the current own vehicle is derived by using sensors, such as radar, camera, laser, high-precision map, IMU, and the like, for example, through sensor fusion technology, and includes information on environmental conditions, i.e., current weather, visibility, type of driving road surface, curvature, height, relative distance between the own vehicle and surrounding obstacle and target information, relative speed, and the like. That is, the driving environment can indicate what road surface condition the own vehicle is currently traveling under.
In contrast, in the range in which the driving behavior of the driver is acquired and output when the driver manipulates the own vehicle in step S2, it can be understood that the signal of the driving behavior of the own vehicle is not acquired in the case where the own vehicle is not driven by the driver. In the context of this application, driving behavior may be understood to include some information relating to the speed, acceleration, rate of change of acceleration, etc. of the host vehicle. It should be noted that the velocity, acceleration, and acceleration rate are referred to as a velocity, acceleration, and acceleration rate in a vector, respectively, that is, the velocity, acceleration, and acceleration rate include not only a velocity and acceleration along the longitudinal direction, the lateral direction, and the vertical direction of the vehicle coordinate system, but also an angular velocity, an angular acceleration, and the like around the vertical axis, the lateral axis, and the vertical axis of the vehicle coordinate system.
In a case where the driving behavior of the driver can be obtained at step S2, the obtained driving environment of the own vehicle and the obtained driving behavior resulting from the driver manipulating the own vehicle under the above driving environment are received, and training data are generated based on the above driving environment and driving behavior information, and output for training out the driving style model at step S3. That is, the training data includes driving environment information and driving behavior information of drivers corresponding to different driving environments, respectively.
In the scope of the present application, in step S4, the driving style model needs to be trained, stored, and updated using training data, that is, a large amount of data processing and machine learning work needs to be performed. Therefore, this step S4 can be generally specified to be performed in a server and/or a processor in the cloud, thereby ensuring that a large amount of training data can be processed quickly.
In some embodiments of the present application, a part of the training data, for example 70%, is used for inputting into a training model (machine learning model for training a driving style model) to train the driving style model, and another part, for example 30%, is used for verifying and adjusting the trained driving style model, so that the driving style model has higher reliability to adapt to the driving style of the driver more safely and comfortably.
In some embodiments of the present application, step S4 further includes: the training data is classified based on characteristics of different driving environments in the training data. That is, the training data can be classified into a plurality of categories based on different driving environment types, for example, a driving environment type for a curve, a driving environment type for a congested road section. For example, the training data can be classified into a class in which the driving environment type is a curve road surface according to the characteristics of the driving environment type of the curve road surface, that is, for example, the size of a variable relating to the curve in the driving environment. And optionally, within the scope of the present application, a driving style model corresponding to the driving environment type may be trained by different machine learning training models using corresponding training data for the different driving environment types classified above. For example, the driving environment and the driving behavior in the training data in the curve driving environment type are used as input data of a machine learning training model, and a driving style model can be trained for the training data in the curve type by using a specific machine learning training model, for example, a Long Short Term Memory model (Long Short Term Memory) in a curve scene. The mode of adopting the appropriate machine learning training model according to the parameters of the driving behaviors concerned by different driving environment types, such as acceleration, speed of the vehicle and the like, can further improve the fitting degree and reliability of the driving style model to the simulation of the real driving behaviors of the driver. For example, in a driving environment where a curve is driving, the parameter of the driving behavior of interest may be the continuity of the speed in the time domain, and therefore a suitable training model, such as a long-short term memory model, may be employed based on this characteristic.
In step S5, a driving style model corresponding to the driver of the current own vehicle can be downloaded, and driving behavior corresponding to the driving environment is output by the driving style model according to the obtained driving environment. In the context of the present application, it is conceivable that step S5 is implemented in a controller of the vehicle and that the desired driving behavior is output based on the current driving environment only in accordance with the downloaded driving style model. That is, step S5 does not train and update the driving style model itself, which enables the vehicle to make a driving behavior response that conforms to the style of driver driving more quickly for different driving environments.
In some embodiments of the present application, it can be considered that, in the case where the type of driving environment to which the current driving environment belongs is not covered in the driving style model for the driver, that is, in the case where the driver has not previously driven the behavior for the type of driving environment to which the current driving environment belongs (the driver has not previously operated the vehicle in the current type of driving environment) or the training data in the current type of driving environment is small and insufficient to form the driving style model for the driver of the current driving environment, step S4 compares each stored driving style model with the driving style model of the driver before the current driving environment, selects the driving style model closest to the previous driving style model as the recommended driving style model for downloading, and outputting the desired driving behavior in the driving environment according to the recommended driving style model. In this case, the selected recommended driving style model satisfies the following conditions: 1. covering a driving environment type to which a current driving environment belongs; and 2. the driving style model is closest to the driving style model of the driver before the current driving environment.
In some embodiments of the present application, it can also be provided that, in the case where the driver manipulates the own vehicle by himself, step S5 no longer outputs driving behavior corresponding to the driving environment. This can ensure that the driver has the highest level of handling of the vehicle.
Furthermore, the present application relates to a computer-readable storage medium and/or a computer device, wherein the computer program comprises thereon the steps of the advanced driving assistance method according to any of the embodiments of the present application, which are executable by a processor.
Claims (21)
1. An advanced driving assistance system (10), characterized by comprising:
a first acquisition module (1), wherein the first acquisition module (1) acquires and outputs a signal of a driving environment of a vehicle;
a second acquisition module (2), the second acquisition module (2) being activated when the driver operates the bicycle, for acquiring and outputting a signal of the driving behavior of the driver;
a training data generation module (3), wherein the training data generation module (3) acquires the driving environment output by the first acquisition module (1) and the driving behavior of the driver output by the second acquisition module (2), and generates training data;
an information processing module (4), the information processing module (4) training and storing a driving style model for a driver, the driving style model describing a correspondence between a driving environment and a driving behavior of a single driver, wherein the information processing module (4) trains and/or updates the driving style model upon obtaining the training data; and
a decision module (5), wherein the decision module (5) downloads a driving style model corresponding to the driver of the current vehicle from the information processing module (4), and outputs driving behaviors corresponding to the driving environment through the driving style model according to the driving environment output by the first acquisition module (1).
2. The advanced driving assistance system (10) according to claim 1, characterized in that for the driving style model, the driving style model is trained using the training data and the trained driving style model is then verified and/or adapted by means of the training data.
3. The advanced driving assistance system (10) according to claim 1, characterized in that the information processing module (4) further comprises a training data classification unit, wherein the training data classification unit classifies training data based on characteristics of different driving environment types, thereby classifying training data for different driving environment types.
4. The advanced driving assistance system (10) according to claim 3, characterized in that driving style models respectively corresponding to different driving environment types are trained for the different driving environment types by different training models using corresponding training data.
5. The advanced driving assistance system (10) according to claim 1, characterized in that the decision module (5) does not output the driving behavior corresponding to the driving environment in a case where a driver manipulates the own vehicle.
6. The advanced driving assistance system (10) according to claim 4, characterized in that in case the driving style model does not cover the type of driving environment to which the current driving environment belongs, the information processing module (4) selects a recommended driving style model from the stored driving style models, which is correspondingly closest to the driving style model of the driver before the current driving environment, and transmits the recommended driving style model to the decision module (5).
7. The advanced driving assistance system (10) according to claim 1, characterized in that the information processing module (4) is provided in the cloud.
8. The advanced driving assistance system (10) according to claim 1, characterized in that the driving environment includes environmental conditions, type of driving surface, curvature, altitude, relative distance of the own vehicle from surrounding obstacles, relative speed.
9. The advanced driving assistance system (10) according to claim 1, characterized in that the driving behavior includes information relating to a speed, an acceleration, and an acceleration rate of change of the own vehicle.
10. An advanced driving assistance method characterized by comprising:
acquiring and outputting a driving environment of the own vehicle (S1);
acquiring and outputting a driving behavior of a driver while the driver manipulates the bicycle (S2);
acquiring the driving environment and the driving behavior of the driver, and generating training data (S3);
training and storing a driving style model for a driver, the driving style model describing a correspondence between a driving environment and a driving behavior of a single driver, the driving style model being trained and/or updated with the training data obtained (S4); and
a driving style model corresponding to a driver of a current own vehicle is downloaded, and driving behavior corresponding to the driving environment is output through the driving style model according to the driving environment (S5).
11. The advanced driving assistance method according to claim 10, characterized in that for the driving style model, a part of the training data is used to train the driving style model, and the trained driving style model is verified and/or adjusted by another part of the training data.
12. The advanced driving assistance method according to claim 10, wherein in the step of training and storing a driving style model for the driver, further comprising: the training data is classified based on characteristics of different driving environment types, thereby classifying the training data for the different driving environment types.
13. The advanced driving assistance method according to claim 12, wherein driving style models respectively corresponding to different driving environment types are trained through different training models using corresponding training data for the different driving environment types.
14. The advanced driving assistance method according to claim 10, wherein the driving behavior corresponding to the driving environment is not output in a case where a driver manipulates the own vehicle.
15. The advanced driving assistance method according to claim 13, wherein in the step of training and storing driving style models for the driver (S4), in a case where the driving style models do not cover the type of driving environment to which the current driving environment belongs, a recommended driving style model, which is correspondingly closest to the driving style model of the driver before the current driving environment, is selected from among the stored driving style models, and the recommended driving style model is provided for download.
16. The advanced driving assistance method according to claim 15, wherein the step of training and storing a driving style model for the driver (S4) is performed in the cloud.
17. The advanced driving assistance method according to claim 10, wherein the driving environment includes environmental conditions, type of running surface, curvature, altitude, relative distance between the own vehicle and surrounding obstacles, and relative speed.
18. The advanced driving assistance method according to claim 10, wherein the driving behavior includes information relating to a speed, an acceleration, and an acceleration rate of change of the own vehicle.
19. A vehicle comprising an advanced driving assistance system (10) according to any one of claims 1 to 9.
20. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is capable of executing the steps of the advanced driving assistance method according to any one of claims 10 to 18 by a processor.
21. A computer device comprising a memory, a processor and a computer program stored in the memory and executable by the processor, wherein the processor when executing the program implements the steps of the advanced driving assistance method according to any one of claims 10 to 18.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113859244A (en) * | 2021-10-31 | 2021-12-31 | 东风商用车有限公司 | Engine mode control method, device, equipment and storage medium |
| CN113895464A (en) * | 2021-12-07 | 2022-01-07 | 武汉理工大学 | Intelligent vehicle driving map generation method and system fusing personalized driving style |
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2019
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113859244A (en) * | 2021-10-31 | 2021-12-31 | 东风商用车有限公司 | Engine mode control method, device, equipment and storage medium |
| CN113859244B (en) * | 2021-10-31 | 2023-09-01 | 东风商用车有限公司 | Engine mode control method, device, equipment and storage medium |
| CN113895464A (en) * | 2021-12-07 | 2022-01-07 | 武汉理工大学 | Intelligent vehicle driving map generation method and system fusing personalized driving style |
| CN113895464B (en) * | 2021-12-07 | 2022-04-08 | 武汉理工大学 | Intelligent vehicle driving map generation method and system fusing personalized driving style |
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