CN115107786B - Driving behavior correction system and method for intelligent automobile - Google Patents
Driving behavior correction system and method for intelligent automobile Download PDFInfo
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- CN115107786B CN115107786B CN202210780955.8A CN202210780955A CN115107786B CN 115107786 B CN115107786 B CN 115107786B CN 202210780955 A CN202210780955 A CN 202210780955A CN 115107786 B CN115107786 B CN 115107786B
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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
- 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
- B60W40/09—Driving style or behaviour
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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
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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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
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
- B60W2050/0029—Mathematical model of the driver
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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
- B60W2556/00—Input parameters relating to data
- B60W2556/10—Historical data
Abstract
The invention provides a driving behavior correction system and a driving behavior correction method for an intelligent automobile, wherein the method comprises the steps of obtaining historical driving event data and generating a driving habit model according to the historical driving event data; acquiring current driver data, current running vehicle data, current running road data and current time data; obtaining current driving habit models of other drivers from the driving habit models; judging whether a preset standard driving behavior model is safe or not according to the current driving habit model; when the safety is determined, modifying the standard driving behavior model to obtain a current standard driving behavior model; judging whether the driving behavior of the current driver is safe or not according to the current standard driving behavior model, the current driver data and the current driving vehicle data; when the driving behavior correction alarm information is not safe, the driving behavior correction alarm information is sent. According to the scheme, the driving behavior of the driver can be corrected so as to effectively avoid adverse effects of other vehicles on the current vehicle.
Description
Technical Field
The invention relates to the technical field of intelligent automobile control, in particular to a driving behavior correction system and method for an intelligent automobile.
Background
With the rapid development of economy and the improvement of living standard of people, various types of motor vehicles put into use are increasing, the pressure of road traffic is increasing, and the frequency of traffic accidents is also increasing. Traffic accidents frequently occur frequently, and great economic loss is caused to daily life of people. Poor driving behavior is one of the main causes of traffic accidents, and in order to prevent traffic accidents, it is important to develop good driving habits for drivers in addition to reinforcing safety education for the drivers. However, the effect obtained by the current safety education is not ideal enough, and often happens after traffic accidents or illegal driving of drivers, the timeliness is not good and the intelligence is not enough.
Therefore, a scheme for correcting the driving behavior of the driver in real time is needed for the vehicle.
Disclosure of Invention
The invention provides a driving behavior correction system and method for an intelligent automobile based on the problems, and by implementing the scheme of the invention, the driving habits of other vehicles on a road where the vehicle runs can be predicted in advance, and the standard driving behavior model of the driver of the current vehicle is modified so as to effectively avoid adverse effects of the other vehicles on the current vehicle.
In view of this, an aspect of the present invention proposes a driving behavior correction system for an intelligent automobile, comprising: the system comprises an acquisition module, a driving habit model construction module and a control processing module;
the acquisition module is used for acquiring historical driving event data;
the driving habit model construction module is used for generating a driving habit model according to the historical driving event data according to time dimension or road dimension or vehicle model dimension or driver dimension;
the acquisition module is also used for acquiring current driver data, current running vehicle data, current running road data and current time data;
the control processing module is used for:
obtaining current driving habit models of other drivers from the driving habit models according to the current driving road data and/or the current time data;
judging whether a preset standard driving behavior model is safe or not according to the current driving habit model;
when the standard driving behavior model is determined to be unsafe, modifying the standard driving behavior model according to the current driving habit model to obtain a current standard driving behavior model;
judging whether the driving behavior of the current driver is safe or not according to the current standard driving behavior model, the current driver data and the current driving vehicle data;
When the driving behavior of the current driver is not safe, sending driving behavior correction alarm information;
the historical driving event data at least comprises driver data, driving vehicle data, driving area data, driving time data and driving road data.
Optionally, the driver data includes driver eye rotation data, eyelid state data and head motion information, and stimulus-response time data of the driver, physiological data of the driver, and driver operation behavior data; wherein the driver operation behavior data at least includes, for example, inter-vehicle distance maintenance data, accelerator pedal depression data, brake pedal depression data, turn signal lamp usage data, and steering wheel rotation data;
the driving vehicle data comprise vehicle position data, vehicle speed data, oil consumption/electricity consumption data, steering data and vehicle machine data;
the driving road data includes road address data, lane number data, lane driving identification data, road surface flatness data, camber data, and gradient data.
Optionally, the acquiring module is further configured to acquire first image data of the steering wheel in a return state;
the control processing module is further used for carrying out character recognition on the first image data, and determining the central axis of the steering wheel according to the character group in the horizontal direction;
The acquisition module is also used for acquiring current image data of the steering wheel of the vehicle in a running state;
the control processing module is further used for obtaining the deflection angle of the central axis according to the current image data and the first image data and obtaining the rotation angle of the steering wheel;
the acquisition module is also used for acquiring the rotation time of the steering wheel;
and the control processing module is also used for calculating and obtaining the rotation speed according to the rotation angle and the rotation time.
Optionally, the acquisition module is further used for acquiring pavement image data of the driving road;
the control processing module is also used for identifying a water accumulation area, a damaged area, an obstacle and a sharp object from the image data;
the acquisition module is further used for respectively acquiring three-dimensional data of the water accumulation area, the damaged area, the obstacle and the sharp object;
the control processing module is further used for respectively calculating ponding area data, damaged area data, barrier data and sharp object data according to the three-dimensional data; the damage area data at least comprise damage area positions and damage degrees, the barrier data at least comprise barrier positions and barrier volumes, and the sharp object data at least comprise sharp object distribution positions and sharp degrees;
The control processing module is further used for obtaining the road surface flatness data according to the ponding area data, the damaged area data, the obstacle data and the sharp object data.
Optionally, the acquiring module is further configured to acquire first face data and first head pose data of the current driver;
the control processing module is further configured to:
determining an eye region according to the first face data to obtain a first eye pattern;
extracting first eye movement data, and processing the first eye movement data to obtain first eye movement characteristic data;
and inputting a preset head recognition model according to the first head gesture data and the first eye movement characteristic data so as to judge whether the current driver has unsafe driving behaviors.
Another aspect of the present invention provides a driving behavior correcting method for an intelligent automobile, the driving behavior correcting method comprising:
acquiring historical driving event data;
generating a driving habit model according to the historical driving event data according to the time dimension or the road dimension or the vehicle model dimension or the driver dimension;
acquiring current driver data, current running vehicle data, current running road data and current time data;
Obtaining current driving habit models of other drivers from the driving habit models according to the current driving road data and/or the current time data;
judging whether a preset standard driving behavior model is safe or not according to the current driving habit model;
when the standard driving behavior model is determined to be unsafe, modifying the standard driving behavior model according to the current driving habit model to obtain a current standard driving behavior model;
judging whether the driving behavior of the current driver is safe or not according to the current standard driving behavior model, the current driver data and the current driving vehicle data;
when the driving behavior of the current driver is not safe, sending driving behavior correction alarm information;
the historical driving event data at least comprises driver data, driving vehicle data, driving area data, driving time data and driving road data.
Optionally, the driver data includes driver eye rotation data, eyelid state data and head motion information, and stimulus-response time data of the driver, physiological data of the driver, and driver operation behavior data; wherein the driver operation behavior data at least includes, for example, inter-vehicle distance maintenance data, accelerator pedal depression data, brake pedal depression data, turn signal lamp usage data, and steering wheel rotation data;
The driving vehicle data comprise vehicle position data, vehicle speed data, oil consumption/electricity consumption data, steering data and vehicle machine data;
the driving road data includes road address data, lane number data, lane driving identification data, road surface flatness data, camber data, and gradient data.
Optionally, the method for acquiring the steering wheel rotation data includes:
acquiring first image data of the steering wheel in a return state;
performing character recognition on the first image data, and determining the central axis of the steering wheel according to the character group in the horizontal direction;
acquiring current image data of a steering wheel of a vehicle in a running state;
obtaining a deflection angle of the central axis according to the current image data and the first image data, and obtaining a rotation angle of the steering wheel;
acquiring the rotation time of the steering wheel;
and calculating according to the rotation angle and the rotation time to obtain the rotation speed.
Optionally, the method for acquiring the road surface flatness data comprises the following steps:
collecting pavement image data of a driving road, and identifying a water accumulation area, a damaged area, an obstacle and a sharp object from the image data;
Respectively acquiring three-dimensional data of the water accumulation area, the damaged area, the barrier and the sharp object;
respectively calculating ponding region data, damaged region data, barrier data and sharp object data according to the three-dimensional data; the damage area data at least comprise damage area positions and damage degrees, the barrier data at least comprise barrier positions and barrier volumes, and the sharp object data at least comprise sharp object distribution positions and sharp degrees;
and obtaining the road surface flatness data according to the ponding area data, the damaged area data, the obstacle data and the sharp object data.
Optionally, the driving behavior correction method further includes:
collecting first face data and first head pose data of the current driver;
determining an eye region according to the first face data to obtain a first eye pattern;
extracting first eye movement data, and processing the first eye movement data to obtain first eye movement characteristic data;
and inputting a preset head recognition model according to the first head gesture data and the first eye movement characteristic data so as to judge whether the current driver has unsafe driving behaviors.
By adopting the technical scheme of the invention, the driving behavior correction system comprises an acquisition module, a driving habit model construction module and a control processing module; the acquisition module is used for acquiring historical driving event data; the driving habit model building module is used for generating a driving habit model according to the historical driving event data according to time dimension or road dimension or vehicle model dimension or driver dimension; the acquisition module is also used for acquiring current driver data, current running vehicle data, current running road data and current time data; the control processing module is used for: obtaining current driving habit models of other drivers from the driving habit models according to the current driving road data and/or the current time data; judging whether a preset standard driving behavior model is safe or not according to the current driving habit model; when the standard driving behavior model is determined to be unsafe, modifying the standard driving behavior model according to the current driving habit model to obtain a current standard driving behavior model; judging whether the driving behavior of the current driver is safe or not according to the current standard driving behavior model, the current driver data and the current driving vehicle data; and when the driving behavior of the current driver is not safe, sending out driving behavior correction alarm information. Through implementation of the scheme, the driving habits of other vehicles on the road where the vehicle runs can be predicted in advance, and the standard driving behavior model of the driver of the current vehicle is modified, so that adverse effects of the other vehicles on the current vehicle are effectively avoided.
Drawings
FIG. 1 is a schematic block diagram of a driving behavior correction system for a smart car provided in one embodiment of the present invention;
fig. 2 is a flowchart of a driving behavior correction method for a smart car according to another embodiment of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced otherwise than as described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
The terms first, second and the like in the description and in the claims of the present application and in the above-described figures, are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
A driving behavior correcting system and method for an intelligent automobile according to some embodiments of the present invention will be described with reference to fig. 1 to 2.
As shown in fig. 1, one embodiment of the present invention provides a driving behavior correction system for a smart car, including: the system comprises an acquisition module, a driving habit model construction module and a control processing module;
the acquisition module is used for acquiring historical driving event data;
the driving habit model construction module is used for generating a driving habit model according to the historical driving event data according to time dimension or road dimension or vehicle model dimension or driver dimension;
the acquisition module is also used for acquiring current driver data, current running vehicle data, current running road data and current time data;
The control processing module is used for:
obtaining current driving habit models of other drivers from the driving habit models according to the current driving road data and/or the current time data;
judging whether a preset standard driving behavior model is safe or not according to the current driving habit model;
when the standard driving behavior model is determined to be unsafe, modifying the standard driving behavior model according to the current driving habit model to obtain a current standard driving behavior model;
judging whether the driving behavior of the current driver is safe or not according to the current standard driving behavior model, the current driver data and the current driving vehicle data;
when the driving behavior of the current driver is not safe, sending driving behavior correction alarm information;
the historical driving event data at least comprises driver data, driving vehicle data, driving area data, driving time data and driving road data.
It will be appreciated that a standard driving behavior model for the driver may be established based on road traffic related regulatory requirements and road traffic safety test data.
In the actual running process of the vehicle, the road traffic condition is changed instantaneously due to the influence of factors such as abrupt weather, road surface condition change, unpredictable running conditions of other drivers and the like, and the driving behaviors of the drivers are sometimes required to be standardized by a standard stricter than a standard driving behavior model, so that the traffic accidents can be effectively avoided.
In an embodiment of the present invention, the historical driving event data includes at least driver data, driving vehicle data, driving region data, driving time data, driving road data; the driving habit model building module is used for generating driving habit models according to the historical driving event data by utilizing a neural network trained in advance according to time dimension or road dimension or vehicle model dimension or driver dimension, and it can be understood that a plurality of driving habit models can be generated according to time dimension, road dimension, vehicle model dimension and driver dimension respectively. It should be noted that the driving habit model is constructed based on historical driving event data, and can characterize the driving habits of other vehicles passing through the road, such as "on XX road, 9:00 to 19:00, 30 to 40 large trucks pass through each hour, and the speed of the large trucks exceeds the speed limit by more than 10 percent, the situation that the auxiliary road vehicle is suddenly blocked to the main road traffic flow without lighting at the position of entering the main road on the YY road is 70 percent of probability, the situation that the traffic flow is large at the TT period and the ZZ road is 80 percent of probability, and the like.
For the driver of the current vehicle implemented by the driving behavior correction system provided by the embodiment of the present invention, first, the current driving habit model of other drivers may be obtained from the driving habit model according to the current driving road data and/or the current time data, so as to determine the style of driving vehicles of other drivers on the current passing road. And judging whether a preset standard driving behavior model is safe or not according to the current driving habit model, namely judging whether the driving behavior is required according to the standard driving behavior model, and judging whether the risk brought by other vehicles driving with the current driving habit model can be prevented/actively avoided, such as whether the safe distance can be kept to avoid sudden braking of the front vehicle, collision caused by sudden lane change of other vehicles or the like under the condition of driving with the standard driving habit model. When the standard driving behavior model is determined to be unsafe, the standard driving behavior model is modified according to the current driving habit model to obtain the current standard driving behavior model, such as lowering the highest speed limit of the standard driving behavior model, increasing the standard requirement of the distance between the standard driving behavior model and a preceding vehicle, and the like. And finally, judging whether the driving behavior of the current driver is safe or not according to the current standard driving behavior model, the current driver data and the current driving vehicle data, and sending driving behavior correction alarm information when the driving behavior of the current driver is not safe so as to prompt the driver to correct the driving behavior according to the requirement.
By adopting the technical scheme of the embodiment, the driving behavior correction system comprises an acquisition module, a driving habit model construction module and a control processing module; the acquisition module is used for acquiring historical driving event data; the driving habit model building module is used for generating a driving habit model according to the historical driving event data according to time dimension or road dimension or vehicle model dimension or driver dimension; the acquisition module is also used for acquiring current driver data, current running vehicle data, current running road data and current time data; the control processing module is used for: obtaining current driving habit models of other drivers from the driving habit models according to the current driving road data and/or the current time data; judging whether a preset standard driving behavior model is safe or not according to the current driving habit model; when the standard driving behavior model is determined to be unsafe, modifying the standard driving behavior model according to the current driving habit model to obtain a current standard driving behavior model; judging whether the driving behavior of the current driver is safe or not according to the current standard driving behavior model, the current driver data and the current driving vehicle data; and when the driving behavior of the current driver is not safe, sending out driving behavior correction alarm information. Through implementation of the scheme, the driving habits of other vehicles on the road where the vehicle runs can be predicted in advance, and the standard driving behavior model of the driver of the current vehicle is modified, so that adverse effects of the other vehicles on the current vehicle are effectively avoided.
It should be noted that the block diagram of the driving behavior correction system for intelligent vehicles shown in fig. 1 is only illustrative, and the number of the illustrated modules does not limit the scope of the present invention.
In some possible embodiments of the invention, the driver data includes driver eye rotation data, eyelid state data and head motion information, and driver stimulus-response time data, driver physiological data, and driver operational behavior data; wherein the driver operation behavior data at least includes, for example, inter-vehicle distance maintenance data, accelerator pedal depression data, brake pedal depression data, turn signal lamp usage data, and steering wheel rotation data;
the driving vehicle data comprise vehicle position data, vehicle speed data, oil consumption/electricity consumption data, steering data and vehicle machine data (such as navigation and music use states);
the driving road data includes road address data, lane number data, lane driving identification data, road surface flatness data, camber data, and gradient data.
It will be appreciated that in the embodiment of the present invention, the eyeball rotation data, eyelid state data, head motion information, etc. of the driver may be obtained through image acquisition and recognition techniques, while the stimulus-response time data of the driver, the physiological data of the driver, etc. may be obtained through sensor acquisition of related data (such as heartbeat, body temperature, neural response data, blood sugar content, blood oxygen content, respiratory rate, etc.), and then calculated using the acquired data, and the mental state of the driver, the determination of whether or not there is unsafe driving, etc. may be accurately analyzed using the eyeball rotation data, eyelid state data, head motion information, and the stimulus-response time data of the driver, the physiological data of the driver, etc.
The driver operation behavior data at least comprise vehicle distance maintenance data, accelerator pedal stepping data, brake pedal stepping data, steering lamp use data and steering wheel rotation data, wherein the data can be obtained by acquiring original data through a camera, a pressure sensor and the like in a vehicle and analyzing and processing the original data, the data are the most direct manifestation of the driving behavior of the driver, and accurate data support can be provided for determining whether the driving behavior of the driver is safe or not.
The road condition is one of factors which have great influence on the safe running of the vehicle, and the running road data can be acquired, processed and analyzed through acquisition equipment such as cameras, three-dimensional measuring devices, positioning devices and the like and a control processing module. The driving road data at least comprises road address data, lane number data, lane driving identification data, road surface evenness data, bending data, gradient data and the like. By collecting the driving road data, a more accurate driving habit model can be determined according to the random strain of the special road conditions.
In some possible embodiments of the present invention, the acquiring module is further configured to acquire first image data of the steering wheel in a return state;
The control processing module is further used for carrying out character recognition on the first image data, and determining the central axis of the steering wheel according to the character group in the horizontal direction;
the acquisition module is also used for acquiring current image data of the steering wheel of the vehicle in a running state;
the control processing module is further used for obtaining the deflection angle of the central axis according to the current image data and the first image data and obtaining the rotation angle of the steering wheel;
the acquisition module is also used for acquiring the rotation time of the steering wheel;
and the control processing module is also used for calculating and obtaining the rotation speed according to the rotation angle and the rotation time.
It will be appreciated that the steering wheel is the core control component of the vehicle during travel, and in embodiments of the present invention, it may be determined whether the driver is safe to control the steering wheel by collecting and analyzing moving image data of the steering wheel. Specifically, first image data of the steering wheel in a return state is obtained, character recognition is carried out on the first image data, a horizontal line is determined according to a character group (such as a vehicle trademark character or a graph, an identification character of a control key and the like) in a horizontal direction, and then a straight line which is perpendicular to the horizontal line and equally divides the character group (or the whole steering wheel) is determined according to symmetry of the character group (or the whole steering wheel), namely, a central axis of the steering wheel. Acquiring current image data of a steering wheel of a vehicle in a running state in real time during running of the vehicle; obtaining a deflection angle of the central axis according to the current image data and the first image data (for example, using a rotation center of a steering wheel as an origin, using the central axis as a Y axis and using a vertical line of the central axis as an X axis, establishing a coordinate system, and obtaining the deflection angle by measuring the length of an intersecting line segment and combining coordinate calculation), thereby obtaining the rotation angle of the steering wheel; meanwhile, the rotation time of the steering wheel (such as a specially designed timer for timing) can be obtained, and finally, the rotation speed is calculated according to the rotation angle and the rotation time. The rotation angle and the rotation speed of the steering wheel can show whether the driver controls the steering wheel safely or not.
It can be understood that, for the case of rotation exceeding one revolution, the rotation number can be recorded by the camera, and the rotation number is 360 degrees and the deflection angle is added to obtain the rotation angle.
In some possible embodiments of the present invention, the acquiring module is further configured to acquire road surface image data of a driving road;
the control processing module is also used for identifying a water accumulation area, a damaged area, an obstacle and a sharp object from the image data;
the acquisition module is further used for respectively acquiring three-dimensional data of the water accumulation area, the damaged area, the obstacle and the sharp object;
the control processing module is further used for respectively calculating ponding area data, damaged area data, barrier data and sharp object data according to the three-dimensional data; the damage area data at least comprise damage area positions and damage degrees, the barrier data at least comprise barrier positions and barrier volumes, and the sharp object data at least comprise sharp object distribution positions and sharp degrees;
the control processing module is further used for obtaining the road surface flatness data according to the ponding area data, the damaged area data, the obstacle data and the sharp object data.
It may be appreciated that, in order to acquire more detailed road surface information to establish a more accurate driving habit model, in an embodiment of the present invention, by acquiring road surface image data of a driving road, identifying a water accumulation area, a damaged area, an obstacle and a sharp object from the image data according to pre-stored model data of the water accumulation area, the damaged area, the obstacle and the sharp object, further acquiring three-dimensional data of the water accumulation area, the damaged area, the obstacle and the sharp object (such as acquiring three-dimensional data by a laser scanning device, an ultrasonic scanning device, a multi-eye camera device, a structured light image acquisition device, etc.), and calculating at least a water accumulation position and a water accumulation depth of the water accumulation area, a position and a damage degree of the damaged area, a position and a volume of the obstacle, a distribution position and a sharp degree of the sharp object, etc. according to a preset measurement algorithm or a measurement model; and obtaining the road surface flatness data according to the ponding area data, the damaged area data, the obstacle data and the sharp object data.
In some possible embodiments of the present invention, the acquiring module is further configured to acquire first face data and first head pose data of the current driver;
The control processing module is further configured to:
determining an eye region according to the first face data to obtain a first eye pattern;
extracting first eye movement data, and processing the first eye movement data to obtain first eye movement characteristic data;
and inputting a preset head recognition model according to the first head gesture data and the first eye movement characteristic data so as to judge whether the current driver has unsafe driving behaviors.
It can be understood that, during driving, the driver should look at the road surface information with the largest viewing angle, if the driver has a drift and negligence of vision, a lack of concentration, an incorrect head posture, etc., accidents may occur, in the embodiment of the present invention, the first eye area is determined by collecting the first face data and the first head posture data of the current driver, and then the first eye pattern is obtained by performing eye recognition on the first face data, and then the first eye pattern is extracted and processed to obtain the first eye feature data; and finally, inputting a preset head recognition model according to the first head gesture data and the first eye movement characteristic data so as to judge whether the current driver has unsafe driving behaviors. By means of the scheme of the embodiment, whether unsafe driving behaviors exist in the current driver can be accurately identified through states of human eyes and the head.
Referring to fig. 2, another embodiment of the present invention provides a driving behavior correction method for a smart car, the driving behavior correction method including:
acquiring historical driving event data;
generating a driving habit model according to the historical driving event data according to the time dimension or the road dimension or the vehicle model dimension or the driver dimension;
acquiring current driver data, current running vehicle data, current running road data and current time data;
obtaining current driving habit models of other drivers from the driving habit models according to the current driving road data and/or the current time data;
judging whether a preset standard driving behavior model is safe or not according to the current driving habit model;
when the standard driving behavior model is determined to be unsafe, modifying the standard driving behavior model according to the current driving habit model to obtain a current standard driving behavior model;
judging whether the driving behavior of the current driver is safe or not according to the current standard driving behavior model, the current driver data and the current driving vehicle data;
when the driving behavior of the current driver is not safe, sending driving behavior correction alarm information;
The historical driving event data at least comprises driver data, driving vehicle data, driving area data, driving time data and driving road data.
It will be appreciated that a standard driving behavior model for the driver may be established based on road traffic related regulatory requirements and road traffic safety test data.
In the actual running process of the vehicle, the road traffic condition is changed instantaneously due to the influence of factors such as abrupt weather, road surface condition change, unpredictable running conditions of other drivers and the like, and the driving behaviors of the drivers are sometimes required to be standardized by a standard stricter than a standard driving behavior model, so that the traffic accidents can be effectively avoided.
In an embodiment of the present invention, the historical driving event data includes at least driver data, driving vehicle data, driving region data, driving time data, driving road data; the driving habit model building module is used for generating driving habit models according to the historical driving event data by utilizing a neural network trained in advance according to time dimension or road dimension or vehicle model dimension or driver dimension, and it can be understood that a plurality of driving habit models can be generated according to time dimension, road dimension, vehicle model dimension and driver dimension respectively. It should be noted that the driving habit model is constructed based on historical driving event data, and can characterize the driving habits of other vehicles passing through the road, such as "on XX road, 9:00 to 19:00, 30 to 40 large trucks pass through each hour, and the speed of the large trucks exceeds the speed limit by more than 10 percent, the situation that the auxiliary road vehicle is suddenly blocked to the main road traffic flow without lighting at the position of entering the main road on the YY road is 70 percent of probability, the situation that the traffic flow is large at the TT period and the ZZ road is 80 percent of probability, and the like.
For the driver of the current vehicle implemented by the driving behavior correction system provided by the embodiment of the present invention, first, the current driving habit model of other drivers may be obtained from the driving habit model according to the current driving road data and/or the current time data, so as to determine the style of driving vehicles of other drivers on the current passing road. And judging whether a preset standard driving behavior model is safe or not according to the current driving habit model, namely judging whether the driving behavior is required according to the standard driving behavior model, and judging whether the risk brought by other vehicles driving with the current driving habit model can be prevented/actively avoided, such as whether the safe distance can be kept to avoid sudden braking of the front vehicle, collision caused by sudden lane change of other vehicles or the like under the condition of driving with the standard driving habit model. When the standard driving behavior model is determined to be unsafe, the standard driving behavior model is modified according to the current driving habit model to obtain the current standard driving behavior model, such as lowering the highest speed limit of the standard driving behavior model, increasing the standard requirement of the distance between the standard driving behavior model and a preceding vehicle, and the like. And finally, judging whether the driving behavior of the current driver is safe or not according to the current standard driving behavior model, the current driver data and the current driving vehicle data, and sending driving behavior correction alarm information when the driving behavior of the current driver is not safe so as to prompt the driver to correct the driving behavior according to the requirement.
By adopting the technical scheme of the embodiment, the driving habit model is generated according to the historical driving event data by acquiring the historical driving event data; acquiring current driver data, current running vehicle data, current running road data and current time data; obtaining current driving habit models of other drivers from the driving habit models according to the current driving road data and/or the current time data; judging whether a preset standard driving behavior model is safe or not according to the current driving habit model; when the standard driving behavior model is determined to be unsafe, modifying the standard driving behavior model according to the current driving habit model to obtain a current standard driving behavior model; judging whether the driving behavior of the current driver is safe or not according to the current standard driving behavior model, the current driver data and the current driving vehicle data; and when the driving behavior of the current driver is not safe, sending out driving behavior correction alarm information. Through implementation of the scheme, the driving habits of other vehicles on the road where the vehicle runs can be predicted in advance, and the standard driving behavior model of the driver of the current vehicle is modified, so that adverse effects of the other vehicles on the current vehicle are effectively avoided.
In some possible embodiments of the invention, the driver data includes driver eye rotation data, eyelid state data and head motion information, and driver stimulus-response time data, driver physiological data, and driver operational behavior data; wherein the driver operation behavior data at least includes, for example, inter-vehicle distance maintenance data, accelerator pedal depression data, brake pedal depression data, turn signal lamp usage data, and steering wheel rotation data;
the driving vehicle data comprise vehicle position data, vehicle speed data, oil consumption/electricity consumption data, steering data and vehicle machine data (such as navigation and music use states);
the driving road data includes road address data, lane number data, lane driving identification data, road surface flatness data, camber data, and gradient data.
It will be appreciated that in the embodiment of the present invention, the eyeball rotation data, eyelid state data, head motion information, etc. of the driver may be obtained through image acquisition and recognition techniques, while the stimulus-response time data of the driver, the physiological data of the driver, etc. may be obtained through sensor acquisition of related data (such as heartbeat, body temperature, neural response data, blood sugar content, blood oxygen content, respiratory rate, etc.), and then calculated using the acquired data, and the mental state of the driver, the determination of whether or not there is unsafe driving, etc. may be accurately analyzed using the eyeball rotation data, eyelid state data, head motion information, and the stimulus-response time data of the driver, the physiological data of the driver, etc.
The driver operation behavior data at least comprise vehicle distance maintenance data, accelerator pedal stepping data, brake pedal stepping data, steering lamp use data and steering wheel rotation data, wherein the data can be obtained by acquiring original data through a camera, a pressure sensor and the like in a vehicle and analyzing and processing the original data, the data are the most direct manifestation of the driving behavior of the driver, and accurate data support can be provided for determining whether the driving behavior of the driver is safe or not.
The road condition is one of factors which have great influence on the safe running of the vehicle, and the running road data can be acquired, processed and analyzed through acquisition equipment such as cameras, three-dimensional measuring devices, positioning devices and the like and a control processing module. The driving road data at least comprises road address data, lane number data, lane driving identification data, road surface evenness data, bending data, gradient data and the like. By collecting the driving road data, a more accurate driving habit model can be determined according to the random strain of the special road conditions.
In some possible embodiments of the present invention, the method for acquiring steering wheel rotation data includes:
acquiring first image data of the steering wheel in a return state;
Performing character recognition on the first image data, and determining the central axis of the steering wheel according to the character group in the horizontal direction;
acquiring current image data of a steering wheel of a vehicle in a running state;
obtaining a deflection angle of the central axis according to the current image data and the first image data, and obtaining a rotation angle of the steering wheel;
acquiring the rotation time of the steering wheel;
and calculating according to the rotation angle and the rotation time to obtain the rotation speed.
It will be appreciated that the steering wheel is the core control component of the vehicle during travel, and in embodiments of the present invention, it may be determined whether the driver is safe to control the steering wheel by collecting and analyzing moving image data of the steering wheel. Specifically, first image data of the steering wheel in a return state is obtained, character recognition is carried out on the first image data, a horizontal line is determined according to a character group (such as a vehicle trademark character or a graph, an identification character of a control key and the like) in a horizontal direction, and then a straight line which is perpendicular to the horizontal line and equally divides the character group (or the whole steering wheel) is determined according to symmetry of the character group (or the whole steering wheel), namely, a central axis of the steering wheel. Acquiring current image data of a steering wheel of a vehicle in a running state in real time during running of the vehicle; obtaining a deflection angle of the central axis according to the current image data and the first image data (for example, using a rotation center of a steering wheel as an origin, using the central axis as a Y axis and using a vertical line of the central axis as an X axis, establishing a coordinate system, and obtaining the deflection angle by measuring the length of an intersecting line segment and combining coordinate calculation), thereby obtaining the rotation angle of the steering wheel; meanwhile, the rotation time of the steering wheel (such as a specially designed timer for timing) can be obtained, and finally, the rotation speed is calculated according to the rotation angle and the rotation time. The rotation angle and the rotation speed of the steering wheel can show whether the driver controls the steering wheel safely or not.
It can be understood that, for the case of rotation exceeding one revolution, the rotation number can be recorded by the camera, and the rotation number is 360 degrees and the deflection angle is added to obtain the rotation angle.
In some possible embodiments of the present invention, the method for obtaining road surface flatness data includes:
collecting pavement image data of a driving road, and identifying a water accumulation area, a damaged area, an obstacle and a sharp object from the image data;
respectively acquiring three-dimensional data of the water accumulation area, the damaged area, the barrier and the sharp object;
respectively calculating ponding region data, damaged region data, barrier data and sharp object data according to the three-dimensional data; the damage area data at least comprise damage area positions and damage degrees, the barrier data at least comprise barrier positions and barrier volumes, and the sharp object data at least comprise sharp object distribution positions and sharp degrees;
and obtaining the road surface flatness data according to the ponding area data, the damaged area data, the obstacle data and the sharp object data.
It may be appreciated that, in order to acquire more detailed road surface information to establish a more accurate driving habit model, in an embodiment of the present invention, by acquiring road surface image data of a driving road, identifying a water accumulation area, a damaged area, an obstacle and a sharp object from the image data according to pre-stored model data of the water accumulation area, the damaged area, the obstacle and the sharp object, further acquiring three-dimensional data of the water accumulation area, the damaged area, the obstacle and the sharp object (such as acquiring three-dimensional data by a laser scanning device, an ultrasonic scanning device, a multi-eye camera device, a structured light image acquisition device, etc.), and calculating at least a water accumulation position and a water accumulation depth of the water accumulation area, a position and a damage degree of the damaged area, a position and a volume of the obstacle, a distribution position and a sharp degree of the sharp object, etc. according to a preset measurement algorithm or a measurement model; and obtaining the road surface flatness data according to the ponding area data, the damaged area data, the obstacle data and the sharp object data.
In some possible embodiments of the present invention, the driving behavior correction method further includes:
collecting first face data and first head pose data of the current driver;
determining an eye region according to the first face data to obtain a first eye pattern;
extracting first eye movement data, and processing the first eye movement data to obtain first eye movement characteristic data;
and inputting a preset head recognition model according to the first head gesture data and the first eye movement characteristic data so as to judge whether the current driver has unsafe driving behaviors.
It can be understood that, during driving, the driver should look at the road surface information with the largest viewing angle, if the driver has a drift and negligence of vision, a lack of concentration, an incorrect head posture, etc., accidents may occur, in the embodiment of the present invention, the first eye area is determined by collecting the first face data and the first head posture data of the current driver, and then the first eye pattern is obtained by performing eye recognition on the first face data, and then the first eye pattern is extracted and processed to obtain the first eye feature data; and finally, inputting a preset head recognition model according to the first head gesture data and the first eye movement characteristic data so as to judge whether the current driver has unsafe driving behaviors. By means of the scheme of the embodiment, whether unsafe driving behaviors exist in the current driver can be accurately identified through states of human eyes and the head.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, such as the above-described division of units, merely a division of logic functions, and there may be additional manners of dividing in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the above-mentioned method of the various embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.
The foregoing has outlined rather broadly the more detailed description of embodiments of the present application, wherein specific examples are provided herein to illustrate the principles and embodiments of the present application, the above examples being provided solely to assist in the understanding of the methods of the present application and the core ideas thereof; meanwhile, as those skilled in the art will have modifications in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Although the present invention is disclosed above, the present invention is not limited thereto. Variations and modifications, including combinations of the different functions and implementation steps, as well as embodiments of the software and hardware, may be readily apparent to those skilled in the art without departing from the spirit and scope of the invention.
Claims (6)
1. A driving behavior correction system for an intelligent vehicle, comprising: the system comprises an acquisition module, a driving habit model construction module and a control processing module;
the acquisition module is used for acquiring historical driving event data;
the driving habit model construction module is used for generating a driving habit model according to the historical driving event data according to time dimension or road dimension or vehicle model dimension or driver dimension;
the acquisition module is also used for acquiring current driver data, current running vehicle data, current running road data and current time data;
the control processing module is used for:
obtaining current driving habit models of other drivers from the driving habit models according to the current driving road data and/or the current time data;
judging whether a preset standard driving behavior model is safe or not according to the current driving habit model;
when the standard driving behavior model is determined to be unsafe, modifying the standard driving behavior model according to the current driving habit model to obtain a current standard driving behavior model;
judging whether the driving behavior of the current driver is safe or not according to the current standard driving behavior model, the current driver data and the current driving vehicle data;
When the driving behavior of the current driver is not safe, sending driving behavior correction alarm information;
wherein the historical driving event data at least comprises driver data, driving vehicle data, driving area data, driving time data and driving road data;
the driver data includes driver eye rotation data, eyelid state data and head motion information, and stimulus-response time data of the driver, physiological data of the driver, and driver operation behavior data; the driver operation behavior data at least comprise vehicle distance maintenance data, accelerator pedal stepping data, brake pedal stepping data, steering lamp use data and steering wheel rotation data;
the driving vehicle data comprise vehicle position data, vehicle speed data, oil consumption/electricity consumption data, steering data and vehicle machine data;
the driving road data comprises road address data, lane number data, lane driving identification data, road surface flatness data, bending data and gradient data;
the acquisition module is also used for acquiring first image data of the steering wheel in a return state;
the control processing module is further used for carrying out character recognition on the first image data, and determining the central axis of the steering wheel according to the character group in the horizontal direction;
The acquisition module is also used for acquiring current image data of the steering wheel of the vehicle in a running state;
the control processing module is further used for obtaining the deflection angle of the central axis according to the current image data and the first image data and obtaining the rotation angle of the steering wheel;
the acquisition module is also used for acquiring the rotation time of the steering wheel;
and the control processing module is also used for calculating and obtaining the rotation speed according to the rotation angle and the rotation time.
2. The driving behavior correction system for an intelligent automobile according to claim 1, wherein the acquisition module is further configured to acquire road surface image data of a traveling road;
the control processing module is also used for identifying a water accumulation area, a damaged area, an obstacle and a sharp object from the pavement image data;
the acquisition module is further used for respectively acquiring three-dimensional data of the water accumulation area, the damaged area, the obstacle and the sharp object;
the control processing module is further used for respectively calculating ponding area data, damaged area data, barrier data and sharp object data according to the three-dimensional data; the damage area data at least comprise damage area positions and damage degrees, the barrier data at least comprise barrier positions and barrier volumes, and the sharp object data at least comprise sharp object distribution positions and sharp degrees;
The control processing module is further used for obtaining the road surface flatness data according to the ponding area data, the damaged area data, the obstacle data and the sharp object data.
3. The driving behavior correction system for a smart car of claim 2, wherein the acquisition module is further configured to acquire first face data and first head pose data of the current driver;
the control processing module is further configured to:
determining an eye region according to the first face data to obtain a first eye pattern;
extracting first eye movement data, and processing the first eye movement data to obtain first eye movement characteristic data;
and inputting a preset head recognition model according to the first head gesture data and the first eye movement characteristic data so as to judge whether the current driver has unsafe driving behaviors.
4. A driving behavior correction method for an intelligent automobile, characterized by comprising:
acquiring historical driving event data;
generating a driving habit model according to the historical driving event data according to the time dimension or the road dimension or the vehicle model dimension or the driver dimension;
Acquiring current driver data, current running vehicle data, current running road data and current time data;
obtaining current driving habit models of other drivers from the driving habit models according to the current driving road data and/or the current time data;
judging whether a preset standard driving behavior model is safe or not according to the current driving habit model;
when the standard driving behavior model is determined to be unsafe, modifying the standard driving behavior model according to the current driving habit model to obtain a current standard driving behavior model;
judging whether the driving behavior of the current driver is safe or not according to the current standard driving behavior model, the current driver data and the current driving vehicle data;
when the driving behavior of the current driver is not safe, sending driving behavior correction alarm information;
wherein the historical driving event data at least comprises driver data, driving vehicle data, driving area data, driving time data and driving road data;
the driver data includes driver eye rotation data, eyelid state data and head motion information, and stimulus-response time data of the driver, physiological data of the driver, and driver operation behavior data; the driver operation behavior data at least comprise vehicle distance maintenance data, accelerator pedal stepping data, brake pedal stepping data, steering lamp use data and steering wheel rotation data;
The driving vehicle data comprise vehicle position data, vehicle speed data, oil consumption/electricity consumption data, steering data and vehicle machine data;
the driving road data comprises road address data, lane number data, lane driving identification data, road surface flatness data, bending data and gradient data;
the method for acquiring the steering wheel rotation data comprises the following steps:
acquiring first image data of the steering wheel in a return state;
performing character recognition on the first image data, and determining the central axis of the steering wheel according to the character group in the horizontal direction;
acquiring current image data of a steering wheel of a vehicle in a running state;
obtaining a deflection angle of the central axis according to the current image data and the first image data, and obtaining a rotation angle of the steering wheel;
acquiring the rotation time of the steering wheel;
and calculating according to the rotation angle and the rotation time to obtain the rotation speed.
5. The driving behavior correction method for an intelligent automobile according to claim 4, wherein the road surface flatness data acquisition method comprises:
collecting pavement image data of a running road, and identifying a water accumulation area, a damaged area, an obstacle and a sharp object from the pavement image data;
Respectively acquiring three-dimensional data of the water accumulation area, the damaged area, the barrier and the sharp object;
respectively calculating ponding region data, damaged region data, barrier data and sharp object data according to the three-dimensional data; the damage area data at least comprise damage area positions and damage degrees, the barrier data at least comprise barrier positions and barrier volumes, and the sharp object data at least comprise sharp object distribution positions and sharp degrees;
and obtaining the road surface flatness data according to the ponding area data, the damaged area data, the obstacle data and the sharp object data.
6. The driving behavior correction method for an intelligent automobile according to claim 5, characterized in that the driving behavior correction method further comprises:
collecting first face data and first head pose data of the current driver;
determining an eye region according to the first face data to obtain a first eye pattern;
extracting first eye movement data, and processing the first eye movement data to obtain first eye movement characteristic data;
And inputting a preset head recognition model according to the first head gesture data and the first eye movement characteristic data so as to judge whether the current driver has unsafe driving behaviors.
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