CN116946089A - Intelligent brake auxiliary system - Google Patents
Intelligent brake auxiliary system Download PDFInfo
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- CN116946089A CN116946089A CN202311211511.3A CN202311211511A CN116946089A CN 116946089 A CN116946089 A CN 116946089A CN 202311211511 A CN202311211511 A CN 202311211511A CN 116946089 A CN116946089 A CN 116946089A
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
- B60T8/00—Arrangements for adjusting wheel-braking force to meet varying vehicular or ground-surface conditions, e.g. limiting or varying distribution of braking force
- B60T8/17—Using electrical or electronic regulation means to control braking
- B60T8/171—Detecting parameters used in the regulation; Measuring values used in the regulation
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
- B60T8/00—Arrangements for adjusting wheel-braking force to meet varying vehicular or ground-surface conditions, e.g. limiting or varying distribution of braking force
- B60T8/17—Using electrical or electronic regulation means to control braking
- B60T8/172—Determining control parameters used in the regulation, e.g. by calculations involving measured or detected parameters
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Abstract
The invention relates to the technical field of vehicle driving control, in particular to an intelligent brake auxiliary system. The system acquires all moving objects and motion information around the automobile, and acquires road condition complexity according to the motion information; analyzing the speed change relevance among moving objects and the motion characteristics of the moving objects for changing direction and speed, acquiring the influence degree and the motion change possibility of the moving objects, and adjusting the collision possibility of a simulation route according to the influence degree and the motion change possibility; the vehicle speed risk degree is estimated by utilizing the motion information, the influence degree and the motion change possibility, and then the safety evaluation index is obtained by combining the road condition complexity and the collision possibility; and adjusting the speed of the automobile by using the safety evaluation index. According to the invention, by analyzing the speed change relativity between the moving objects and the motion characteristics of the moving objects in direction and speed changing, the automobile safety evaluation index is obtained more comprehensively and accurately, and the safety and the adaptability of the intelligent brake auxiliary system are improved.
Description
Technical Field
The invention relates to the technical field of vehicle driving control, in particular to an intelligent brake auxiliary system.
Background
With the improvement of the living standard of people, automobiles become an indispensable part of daily travel. However, the progress of urbanization has increased the complexity of road traffic, and has placed higher demands on safe driving. In the current background of rapid development of automobiles, serious traffic accidents caused by the fact that a driver fails to timely step on a brake occur more frequently, which brings attention to an intelligent brake auxiliary system.
The existing intelligent braking auxiliary system mainly relies on detecting the current road condition to predict the track between the automobile and other moving objects, and estimates the collision risk through the predicted collision distance of the track and the predicted travelling intention of other moving objects, so as to regulate the speed or brake the automobile; however, the existing intelligent brake auxiliary system does not consider the mutual influence relation of speeds among all movements and estimates the travelling intention of other moving objects according to the historical speed change lane change information of other moving objects under the current road conditions, and cannot comprehensively estimate the driving safety risk of the automobile; when complex road conditions or emergency situations occur, the intelligent auxiliary braking system is low in safety and adaptability.
Disclosure of Invention
In order to solve the technical problem that the existing intelligent brake auxiliary system cannot comprehensively estimate the automobile driving safety risk, the invention aims to provide an intelligent brake auxiliary system, which adopts the following technical scheme:
The invention provides an intelligent brake auxiliary system, which comprises:
the road condition information acquisition module is used for acquiring video frame images of road conditions around the automobile at each moment in the running process of the automobile, wherein the video frame images contain all moving objects around the automobile under the road conditions at each moment; taking the motion speed of the moving object and the distance between the moving object and an automobile at real time as real-time motion information;
the road condition information analysis module is used for acquiring the road condition complexity at the corresponding moment according to the real-time motion information of the moving object; obtaining the influence degree corresponding to the moving object according to the difference of the change rate of the moving speed between the moving object and other moving objects; acquiring the motion change possibility corresponding to the moving object according to the direction change frequency and the speed change of the moving object in the motion direction;
the road condition risk assessment module is used for acquiring the simulation routes of the moving object and the automobile, screening out the simulation collision object in the moving object according to the simulation route, and acquiring the simulation collision information of the simulation collision object; acquiring collision possibility according to the simulated collision information by combining the influence degree and the motion change possibility; acquiring a vehicle speed risk degree at a corresponding moment according to the vehicle speed, the influence degree, the motion change possibility and the real-time motion information of the moving object;
The self-adaptive speed regulation module is used for acquiring a safety evaluation index according to the road condition complexity, the collision possibility and the vehicle speed risk; and adjusting the speed of the automobile according to the safety evaluation index.
Further, the obtaining the road condition complexity at the corresponding moment according to the real-time motion information of the moving object includes:
summing the ratio of the motion speed of each moving object to the vehicle distance under the road condition at real time to obtain road condition resistance parameters; multiplying the road condition resistance parameters by the number of the moving objects and normalizing to obtain the road condition complexity at the corresponding moment.
Further, the acquiring the influence degree corresponding to the moving object includes:
acquiring the motion speed change rate of the moving object between each group of adjacent frames;
in each group of adjacent frames, obtaining the difference of the motion speed change rate between the motion object and each other motion object, and carrying out negative correlation mapping and normalization on the difference of the motion speed change rate to obtain a speed similarity index; accumulating the speed similarity indexes between the moving object and all other moving objects to obtain the overall speed similarity under adjacent frames;
And accumulating the overall speed similarity of all adjacent frames corresponding to the moving object to obtain the influence degree corresponding to the moving object.
Further, the acquiring the movement speed change rate includes:
calculating the motion speed difference of the moving object between each group of adjacent frames, and normalizing the motion speed difference between each group of adjacent frames to obtain the motion speed change rate of the corresponding moving object.
Further, the obtaining the motion change probability corresponding to the moving object according to the direction change frequency and the speed change of the moving object comprises:
calculating a frame number ratio of a frame number corresponding to the change of the motion direction of the moving object to a frame number corresponding to the moving object, and taking the frame number ratio as a direction-changing frequency corresponding to the moving object; calculating the speed variance of the moving speed of the moving object in all the video frame images, and normalizing the product of the speed variance and the turning frequency to obtain the movement change possibility of the corresponding moving object.
Further, the screening the simulated collision object according to the simulated route and acquiring the simulated collision information of the simulated collision object includes:
Taking an intersection point of the moving object and the simulated route of the automobile as a simulated collision point, wherein the moving object corresponding to the simulated collision point is a simulated collision body; the simulated collision information comprises simulated collision times and simulated collision time frame number intervals, the number of the simulated collision points is recorded as the simulated collision times, and the time interval of the corresponding moment of the simulated collision points is the simulated collision time interval.
Further, the method for acquiring the collision possibility comprises the following steps:
taking the product of the collision time interval, the influence degree and the motion change possibility of the simulated collision objects as a collision comprehensive evaluation index, summing the reciprocal of the collision comprehensive evaluation index of each simulated collision object, multiplying the reciprocal by the simulated collision times, and normalizing to obtain the collision possibility.
Further, the method for acquiring the vehicle speed risk degree comprises the following steps:
calculating the ratio of the product value of the motion speed, the influence degree and the motion change possibility of each motion object to the vehicle distance, summing the ratios of all the motion objects, multiplying the sum by the vehicle speed and normalizing to obtain the vehicle speed risk degree corresponding to the vehicle speed.
Further, the method for obtaining the security evaluation index comprises the following steps:
and adding the product of the road condition complexity, the collision possibility and the vehicle speed risk degree at each moment with a preset adjustment parameter, carrying out negative correlation mapping, and carrying out normalization processing to obtain a safety evaluation index.
Further, the adjusting the vehicle speed according to the safety evaluation index includes:
when the safety evaluation index is greater than or equal to a preset safety threshold, the current automobile speed is increased according to the safety evaluation index; when the safety evaluation index is larger than or equal to a preset dangerous threshold value and smaller than a preset safety threshold value, the current automobile speed is kept; and when the safety evaluation index is smaller than a preset dangerous threshold value, reducing the current automobile speed according to the preset safety evaluation index.
The invention has the following beneficial effects:
according to the invention, all surrounding moving objects and relevant motion information in the running process of the automobile are obtained through video frame images, and road condition complexity is obtained according to the motion information; considering that the motion of all the moving objects on the road is influenced by other surrounding moving objects in the actual running process, the speed change of a certain moving object is very likely to drive the speed change of other moving objects, and the influence degree of the moving objects by other moving objects is obtained by analyzing the motion speed change rate between adjacent frames; meanwhile, the motion state change of the moving object is considered to be influenced by the motion characteristic of the moving object, and the motion change possibility of the moving object is obtained by analyzing the change frequency and the motion characteristic of the speed change of the moving object in the video frame image, wherein the motion change possibility reflects the tendency of the moving object to adapt to the motion state change of the moving object under the current condition; acquiring relevant collision information by simulating a simulation route of an automobile and a moving object, and adjusting collision probability predicted by historical motion information by combining the influence degree and the motion change probability of the moving object to acquire accurate collision probability; the speed risk degree of the current automobile is estimated through the movement information, the influence degree and the movement change possibility, and the higher the speed risk degree is, the higher the safety risk is, the smaller the speed is; and finally, acquiring a safety evaluation index of the automobile running according to the road condition complexity, the influence degree and the speed risk, and adjusting the current automobile speed according to the safety evaluation index, so that the running speed is improved while the safety risk is reduced. According to the intelligent auxiliary braking system, the association among the road condition information, the simulation route and the moving object is analyzed, so that more comprehensive and accurate safety evaluation indexes are obtained, and the speed of the automobile is adaptively adjusted, so that the safety and the adaptability of the intelligent auxiliary braking system are higher.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of an intelligent brake assist system according to one embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to specific embodiments, structures, features and effects of an intelligent brake auxiliary system according to the present invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the intelligent brake auxiliary system provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a system block diagram of an intelligent brake assist system according to an embodiment of the invention is shown, the system includes: the road condition information acquisition module 101, the road condition information analysis module 102, the road condition risk assessment module 103 and the adaptive speed regulation module 104.
The road condition information acquisition module 101 is used for acquiring video frame images of road conditions around the automobile at each moment in the running process of the automobile, wherein the video frame images contain all moving objects around the automobile under the road conditions at each moment; the motion speed of the moving object and the distance between the moving object and the automobile at real time are used as real-time motion information.
The system in the embodiment of the invention needs to adaptively regulate the speed of the automobile under different road conditions, so that the road condition information around the automobile in the driving process needs to be collected by the road condition information collecting module 101. In one embodiment of the invention, the vehicle-mounted cameras are arranged around the automobile to collect videos of the automobile in real time in the running process, the collected videos are decomposed into a series of video frame images, and then the gray processing is carried out on each frame image by a weighted gray processing method, so that the road condition information can be conveniently obtained and analyzed later. Considering that the traffic road condition of the automobile is mainly influenced by the motion conditions of other surrounding moving objects, detecting all the moving objects in each preprocessed video frame image by adopting an optical flow method, namely all the moving objects around the automobile under the road condition at each moment, including all the moving objects such as motor vehicles, non-motor vehicles and pedestrians and the like and possibly influencing the current running of the automobile; the method is characterized in that the method comprises the steps of obtaining the motion speed of all moving objects and the related information of the distance between the moving objects and the automobile to serve as the motion information of the moving objects around the automobile through an optical flow method, and taking the motion information obtained at real time as real-time motion information, so that the correlation and motion change information among the moving objects can be analyzed conveniently, the current automobile speed can be adjusted in real time, and the safety risk is reduced. It should be noted that, the optical flow method, the weighted graying method and the video frame extraction are well known in the art, and are not described herein.
The road condition information analysis module 102 is configured to obtain the road condition complexity at the corresponding moment according to the real-time motion information of the moving object; according to the difference of the change rate of the moving speed between the moving object and other moving objects, obtaining the influence degree of the corresponding moving object; and acquiring the motion change possibility of the corresponding moving object according to the direction change frequency and the speed change of the moving object.
Under normal conditions, the road condition is mainly influenced by the real-time motion state of all surrounding moving objects in the running process of the automobile, the complexity of the road condition directly reflects the safe driving risk of the current automobile in the running process, and the more complex the current road condition is, the larger the running resistance is and the larger the safe driving risk is borne; therefore, the embodiment of the invention obtains the road condition complexity at the corresponding moment through the real-time motion information of all the moving objects around the automobile under the current road condition.
Preferably, in one embodiment of the present invention, considering that, in the driving process of the automobile, when there are more other surrounding moving objects, the traffic flow is larger, not only the automobile is limited to self-driving, but also the possibility that the surrounding moving objects adjust the self-moving state to adapt to the road condition is greater, and the driving difficulty of the automobile is increased; the smaller the relative distance between the automobile and other surrounding moving objects is, the smaller the reaction time and distance are, and the greater the safety risk is; finally, considering that the greater the speed of other moving objects around the automobile, the automobile will generally keep a relatively high following speed to keep up with the traffic flow speed, so as to avoid rear congestion or rear-end collision, but this not only increases the driving pressure of the driver, but also presents a greater safety risk. Based on the real-time motion information of the moving objects, acquiring the road condition complexity at the corresponding moment comprises summing the ratio of the motion speed of each moving object to the vehicle distance under the road condition at the real-time moment to obtain road condition resistance parameters; multiplying the road condition resistance parameter by the number of moving objects and normalizing to obtain the road condition complexity at the corresponding moment. The calculation formula of the road condition complexity is as follows:
Wherein,,represents the road condition complexity of the current moment, +.>For the standard normalization function, +.>Indicating the current time +.>Motion speed of individual moving object, < >>Indicating the current time +.>The distance between the individual moving object and the car, < >>Indicating the number of all moving objects detected around the car at the current time.
However, in the actual driving process, the safety risk of the automobile driving is not only influenced by the complexity of road conditions, but also influenced by the interaction between moving objects, and the motion state of a certain moving object is changed, so that other moving objects are very likely to adjust the motion state of the moving object to adapt to the road condition change.
The interaction association influence among the moving objects is mainly reflected in the relation of the movement speed change among the moving objects, and when one moving object performs acceleration and deceleration, the adjacent moving object can be influenced to rapidly judge according to the current road condition information and perform corresponding speed adjustment. When a certain moving object accelerates, all adjacent moving objects keep up with the traffic flow speed, the possibility of accelerating is increased, and the safe driving risk is increased; when a certain moving object decelerates, the adjacent vehicle keeps a certain safe following distance and also decelerates, so that the possibility of the following vehicle to decelerate, change lanes or rear-end collision is also higher. Based on the above, the embodiment of the invention obtains the corresponding influence degree of the moving object according to the difference of the change rate of the moving speed between the moving object and other moving objects.
Preferably, in one embodiment of the present invention, acquiring the corresponding influence degree of the moving object includes acquiring a rate of change of a moving speed of the moving object between each group of adjacent frames; in each group of adjacent frames, obtaining the variation rate difference of the motion speed between the motion object and each other motion object, and carrying out negative correlation mapping and normalization on the variation rate difference of the motion speed to obtain a speed similarity index; accumulating the speed similarity indexes between the moving object and all other moving objects to obtain the overall speed similarity under the adjacent frames; and accumulating the overall speed similarity of all adjacent frames corresponding to the moving object to obtain the influence degree of the corresponding moving object. The calculation formula of the influence degree is expressed as:
wherein,,indicating the current time +.>The degree to which the individual moving object is affected by the speed change of the other moving objects,/->Indicating the current time +.>Rate of change of speed between adjacent frames of individual moving object,/->Indicating the current time +.>Rate of change of speed between adjacent frames of individual moving objects,/->For the standard normalization function, +.>Representing the number of all detected moving objects around the vehicle at the current time, < >>And->Respectively represent +.>Individual moving objects and- >Frame number of video frame image corresponding to each moving object, < > for each moving object>Indicate->Individual moving objects and->Number of frames in which the respective moving objects are simultaneously present in the corresponding video frame image,/or->Is +.>Is an exponential function of the base.
In the calculation formula of the influence degree, considering that each moving object may not be simultaneously present in the same frame of video frame image, byScreening out->The moving object and->Video frame images of the moving objects appear simultaneously, and the change rate of the moving speed among the moving objects is calculated between each group of adjacent frames of the corresponding video frame images; />Indicate->The moving object and->The speed change rate difference of each moving object between each group of adjacent frames is subjected to negative correlation mapping and normalization to obtain a speed similarity index; when the variation rate difference of the moving speeds among the moving objects is smaller, the moving speed variation trend is more similar, namely, the speed similarity index is also larger; when->The movement speed of the individual moving bodies suddenly changes, +.>The motion speed variation of the individual moving objects is also more likely to be affected by the degree of similarity;then it indicates the +.>Overall speed similarity between individual moving objects and all other moving objects; obtaining all moving object pairs +. >Individual moving objectsIs a function of the degree of influence of (a).
And calculating the influence degree of each moving object under the change of the moving speed of other moving objects through the influence degree calculation formula, so that the subsequent determination of the safety evaluation index is facilitated, and the safety risk is further reduced.
Preferably, in one embodiment of the present invention, obtaining the motion speed change rate includes calculating a motion speed difference of the moving object between each set of adjacent frames, and normalizing the motion speed difference between each set of adjacent frames to obtain the motion speed change rate of the corresponding moving object. The calculation formula of the motion speed change rate is specifically expressed as follows:
wherein,,indicate->Rate of change of motion velocity between each group of adjacent frames of individual moving object, +.>Represents +.>Person moving object->Motion speed corresponding to frame time, +.>Represents +.>Person moving object->The motion speed corresponding to the frame time.
In the calculation formula of the rate of change of the movement speed,representing the difference in velocity variation of a moving object between adjacent frames by +.>The difference in velocity change of the moving object between adjacent frames is normalized as a denominator. Other normalization methods may be employed in other embodiments of the present invention, and are not limited herein.
In complex traffic roads, some moving objects tend to frequently change lanes in traffic flow to accommodate current road conditions; while other moving objects can more stably run on the same lane, and adapt to road conditions by adjusting the speed of the moving objects. The lane change or speed change shows the motion characteristics of each moving object in complex road conditions, and meanwhile, the lane change or speed change of each moving object influences the motion state change of other moving objects. When a certain moving object performs corresponding speed change or lane change, other adjacent moving objects keep a certain safe driving distance or speed, the greater the possibility of performing corresponding speed change or lane change, even more moving objects may be caused to perform corresponding speed change or lane change, the complexity of traffic road conditions will be further improved, and the safe driving risk will be further improved. Therefore, the possibility of the motion object to change direction or adjust the speed again can be estimated by analyzing the motion characteristics of the motion object, and based on the possibility, the embodiment of the invention obtains the motion change possibility of the corresponding motion object according to the change frequency and the speed change of the motion direction of the motion object.
Preferably, in an embodiment of the present invention, the obtaining the motion change probability of the corresponding moving object according to the direction change frequency and the speed change of the moving object includes calculating a frame number ratio of a frame number corresponding to the motion direction change of the moving object to a frame number corresponding to the moving object, and taking the frame number ratio as the direction change frequency of the corresponding moving object; calculating the speed variance of the moving speed of the moving object in all video frame images, and normalizing the product of the speed variance and the direction-changing frequency to obtain the movement change possibility of the corresponding moving object. The calculation formula of the motion change probability is expressed as:
Wherein,,indicate->Possibility of movement variation of individual moving objects, < >>For the standard normalization function, +.>Representing the +.>The number of frames in which the direction of motion of the individual moving object is changed, < >>Indicate->Frame number of video frame image corresponding to each moving object, < > for each moving object>Indicate->The motion velocity variance of each moving object in the corresponding video frame image.
In the calculation formula of the motion change probability,indicate->The direction change frequency of the motion direction of each moving object in the corresponding video frame image; />Indicate->The motion speed variance of each moving object in the corresponding video frame image, namely the fluctuation degree of the motion speed change; the direction-changing frequency and the speed variance show the motion characteristics of the motion state change of the current moving object for adapting to the current road condition, and the influence of the motion state change among the moving objects is reflected from the side surface; when the direction-changing frequency is larger, the current moving object is more prone to change direction again to adapt to road conditions, namely the movement change possibility is larger, and otherwise, the movement possibility is smaller; the larger the speed variance, the larger the fluctuation degree of the motion speed of the current moving object, the larger the tendency of the speed change again, namely the larger the motion change possibility, and the smaller the reverse.
Thus, the road condition complexity, the influence degree of each moving object and the movement change possibility of each moving object at the corresponding moment are obtained.
The road condition risk assessment module 103 is used for acquiring simulation routes of the moving object and the automobile, screening out simulation collision objects in the moving object according to the simulation routes, and acquiring simulation collision information of the simulation collision objects; according to the simulated collision information, combining the influence degree and the motion change possibility to acquire the collision possibility; and acquiring the vehicle speed risk degree at the corresponding moment according to the vehicle speed, the influence degree, the motion change possibility and the real-time motion information of the moving object.
The simulated route can provide reference bases for evaluating and improving driving parameters and performance in traffic road condition analysis, and potential dangerous areas and moments can be identified by analyzing the simulated route data, so that a driver avoids potential hazards in the driving process, and road safety is improved.
In one embodiment of the invention, real-time motion information of the automobile and all moving objects is analyzed and simulated in SUMO traffic simulation software to obtain simulation routes of the automobile and all moving objects; SUMO is traffic simulation software with powerful functions and wide use, and can accurately simulate the past and future driving routes of the automobile and all moving objects by reasonably setting the motion behavior models and road condition information of the automobile and the moving objects; and then screening out the simulated collision objects which possibly collide with the automobile in the future driving route from all the moving objects according to the simulated route, and acquiring collision information of the simulated collision objects. The SUMO traffic simulation software is currently known simulation software, and will not be described in detail here. It should be noted that the simulated route may be obtained by other software or algorithms well known to those skilled in the art, and is not described and limited herein.
Preferably, in an embodiment of the present invention, screening the simulated collision object according to the simulated route and obtaining the simulated collision information of the simulated collision object includes taking an intersection point of the simulated route of the moving object and the vehicle as a simulated collision point, and the moving object corresponding to the simulated collision point is the simulated collision object; the simulated collision information comprises simulated collision times and simulated collision time intervals, the number of the simulated collision points is recorded as the simulated collision times, and the time interval of the moment corresponding to the simulated collision points is the simulated collision time interval.
However, considering that the simulation route is obtained by simulation based on historical or real-time motion information, and the future actual traveling route of the moving object is also influenced by the influence degree and the motion change possibility of the moving object, when the influence degree among the moving objects is larger or the motion change possibility of the moving object is larger, the simulation accuracy of the traffic simulation software is lower, and the deviation degree of the future actual traveling route and the simulation route is larger, the possibility of the simulation collision is also changed. Therefore, in the embodiment of the invention, in order to obtain accurate future collision probability, the collision probability is obtained according to the simulated collision information and by combining the influence degree and the motion change probability. And accurately estimating collision risk according to the correlation between the moving object and the automobile, further estimating the safe driving evaluation index of the automobile according to the predicted collision risk, and further regulating the speed of the automobile so as to reduce the risk of safety accidents.
Preferably, in one embodiment of the present invention, the method of acquiring the collision probability includes taking a product of a collision time interval, a degree of influence, and a motion change probability of the simulated collision objects as a collision integrated evaluation index, summing up the inverse of the collision integrated evaluation index of each simulated collision object and multiplying the calculated inverse of the collision integrated evaluation index by the number of simulated collisions and normalizing the calculated inverse of the collision integrated evaluation index to obtain the collision probability. The specific calculation formula of the collision probability is expressed as:
wherein,,indicating the collision possibility of all moving objects with the car,/->For the standard normalization function, +.>Representing the number of simulated collisions, +.>Indicate->Degree of influence of individual simulated collision objects, +.>Indicate->Possibility of motion change of individual simulated collision objects, < >>Then indicate +.>And a simulated collision time interval corresponding to each simulated collision object.
In the calculation formula of the possibility of collision,for the comprehensive evaluation of the index of the collision, wherein +.>Reflects the influence of the influence degree of other objects on the simulation collision accuracy of the current simulation collision object, +.>Reflects the influence of the possibility of motion change on the accuracy of the simulated collision of the current simulated collision object, +.>The simulation collision time interval is represented, and the larger the time interval is, the larger the road condition uncertainty of the current simulation collision object is; when- >、/>Or->The larger any one value of the three values is, the greater the possibility of the motion state of the current simulation collision body is changed, the lower the simulation accuracy is, and the lower the collision possibility is; the inverse of the comprehensive collision evaluation index shows the inverse proportion relation of the simulation collision time interval, the influence degree, the motion change possibility and the collision possibility; />The number of simulated collisions is indicated, and the greater the number of simulated collisions is, the greater the collision possibility of the automobile and any moving object is, so that the collision possibility is obtained by summing the reciprocal of the comprehensive collision evaluation index and multiplying the reciprocal by the number of simulated collisions and then normalizing the reciprocal.
Under complex road conditions, too high a vehicle speed has a certain safety risk. The excessive speed of the vehicle not only reduces the perception capability of the driver to the surrounding environment, but also reduces the time and opportunity for dealing with emergency; when in emergency braking or avoiding, the braking distance can be prolonged due to the fact that the vehicle speed is too high, and accident risks are increased. Therefore, in complex road conditions, it is important to maintain a certain driving speed. In order to comprehensively and accurately evaluate the safety risk of the automobile under the current road condition, the embodiment of the invention acquires the automobile speed risk degree at the corresponding moment according to the automobile speed, the influence degree, the motion change possibility and the real-time motion information of the moving object, and further adjusts the current automobile speed by combining the risk degree of the current automobile speed, thereby reducing the safety risk.
Preferably, in one embodiment of the present invention, it is considered that, under a complex road condition, when the moving speed of a moving object around an automobile is higher and the influence degree or the movement change possibility of the moving object by other moving objects is greater, the moving speed of the moving object is greater, if the current automobile speed is greater, the movement speed change of other moving objects around the automobile may cause insufficient reaction time when the automobile faces an emergency or increase safety risk due to a greater braking distance of the automobile; meanwhile, the smaller the vehicle distance between the vehicle and the moving object is, the greater the safety risk of the vehicle is still faced. Based on the above, the method for obtaining the vehicle speed risk comprises the steps of calculating the ratio of the product value of the motion speed, the influence degree and the motion change possibility of each moving object to the vehicle distance, summing the ratios of all the moving objects, multiplying the sum by the vehicle speed, and normalizing to obtain the vehicle speed risk corresponding to the vehicle speed. The calculation formula of the vehicle speed risk degree is expressed as follows:
wherein,,speed risk representing the speed of the vehicle at the current moment, < >>For the standard normalization function, +.>Indicating the number of all moving objects around the car at the present moment,/- >Indicating the current time +.>The corresponding movement speed of the individual moving bodies, +.>Indicating the current time +.>Degree of influence of individual moving objects, +.>Indicating the current time +.>Possibility of movement variation of individual moving objects, < >>Indicating the current time +.>The distance between the moving object and the automobile.
In a calculation formula of the vehicle speed risk degree, the proportional relation between the three and the vehicle speed risk degree is represented by multiplying the motion speed of the moving object, the influence degree of the current moving object by other moving objects and the motion change possibility of the current moving object, and the larger the motion speed of the moving object and the corresponding influence degree are, the larger the influence of the vehicle speed risk at the current moment is; the greater the motion speed and the motion change possibility of the moving object, the greater the influence of the vehicle speed risk at the current moment; then, the inverse proportion relation between the vehicle distance and the vehicle speed risk degree is expressed through division, when the vehicle distance is larger, the influence of the vehicle speed risk at the current moment is smaller, and otherwise, the influence is larger; finally, accumulating the influence of each moving object on the speed risk of the automobile to obtain the speed risk influence of all moving objects on the current automobile, and multiplying the accumulated speed risk influence by the automobile speed at the current moment to represent the speed risk degree at the current moment; the greater the vehicle speed of the vehicle at the current moment, the greater the vehicle speed risk degree; the greater the risk influence of all moving objects on the automobile at the current moment, the greater the vehicle speed risk degree.
The risk of the current road condition information is evaluated, the collision possibility and the vehicle speed risk degree of the vehicle at the current moment are obtained, and further follow-up safe driving evaluation and speed regulation of the vehicle can be carried out according to the current road condition risk, so that the risk of safety accidents is reduced.
The self-adaptive speed regulation module 104 is used for acquiring a safety evaluation index according to the road condition complexity, the collision possibility and the vehicle speed risk; and adjusting the speed of the automobile according to the safety evaluation index.
The safety evaluation of the automobile is a process of comprehensively evaluating the risk and the safety degree in the running process, and the safety evaluation index can be obtained to evaluate the running safety of the automobile, so that an important decision basis is provided, appropriate measures are taken to reduce the risk of traffic accidents, and the safety and smoothness of road traffic are maintained.
The invention considers the road condition complexity including the traffic flow, surrounding moving objects and other factors, reflects the complex condition involved in the driving process, and has higher driving difficulty and safety risk as the road condition complexity is higher and the requirement on the response processing capacity of sudden emergency is higher; the collision probability is influenced by a plurality of factors such as the relevance between road conditions and moving objects, and a higher collision probability means that a larger collision risk exists; the vehicle speed risk degree considers the influence of the vehicle running speed and the running environment on traffic safety, and the probability and the severity of accidents are increased when the vehicle runs at a high speed. Therefore, the road condition complexity, the collision possibility and the vehicle speed risk are all important evaluation parameters for evaluating the safety of the vehicle. Therefore, the embodiment of the invention acquires the safety evaluation index according to the road condition complexity, the collision possibility and the vehicle speed risk, evaluates the safety risk degree of the running of the vehicle according to the safety evaluation index, and carries out self-adaptive speed regulation on the vehicle so as to reduce the risk.
Preferably, in one embodiment of the present invention, the method for obtaining the safety evaluation index includes adding the product of the road condition complexity, the collision probability and the vehicle speed risk at each moment to a preset adjustment parameter, performing negative correlation mapping, and performing normalization processing to obtain the safety evaluation index. The calculation formula of the safety evaluation index is expressed as:
wherein,,safety evaluation index indicating the current time, +.>For the standard normalization function, +.>Represents the road condition complexity of the current moment, +.>Speed risk representing the speed of the vehicle at the current moment, < >>Indicating the collision probability of all moving objects with the car at the present moment,/for the car>To prevent the denominator from being 0 for the preset tuning parameter, in one embodiment of the present invention,/->Taking 0.001, the practitioner can set according to the specific situation.
In a safety evaluation index formula, the product of the road condition complexity, collision probability and vehicle speed risk at each moment is added with a preset adjustment parameter to obtain the reciprocal to represent that the three are in a negative correlation with the safety evaluation index, namely, when the road condition complexity at the current moment is larger, the safety evaluation index is smaller, and the safety risk is larger; when the vehicle speed risk of the vehicle speed at the current moment is larger, the safety evaluation index is smaller, and the safety risk is larger; the greater the collision probability, the smaller the safety evaluation index, and the greater the safety risk. In other embodiments of the present invention, the negative correlation mapping may be performed by other methods, which will not be described in detail.
So far, the safety evaluation index at the current moment is obtained, and the vehicle speed is adaptively adjusted through the safety evaluation index.
Preferably, in one embodiment of the present invention, adjusting the vehicle speed according to the safety evaluation index includes, when the safety evaluation index is equal to or greater than a preset safety threshold, adjusting the current vehicle speed according to the safety evaluation index; when the safety evaluation index is greater than or equal to a preset dangerous threshold value and smaller than the preset safety threshold value, the current automobile speed is maintained; and when the safety evaluation index is smaller than the preset dangerous threshold value, reducing the current automobile speed according to the preset safety index. In one embodiment of the present invention, considering that the range of the safety evaluation index is between 0 and 1, the formula for adjusting the current vehicle speed according to the safety evaluation index is specifically expressed as:
wherein,,indicating the adjusted speed of the vehicle>Represents the speed of the motor vehicle at the current time,/->Safety evaluation index indicating the current time, +.>Representing a preset safety threshold,/->Representing a preset risk threshold. In the embodiment of the invention, a safety threshold value is preset>Is set as0.8, preset risk threshold +.>Set to 0.3, the user can set according to the specific situation. Because the value range of the security evaluation index is between 0 and 1, therefore +. >Can realize the speed adjustment of the vehicle, and is->The speed of the vehicle can be regulated. It should be noted that, in other embodiments of the present invention, other specific vehicle speed adjustment methods may be set according to the features of specific safe driving indexes, which are not described and limited herein.
The smaller the safe driving index is, the more dangerous the automobile is in at the present moment, and the safer the automobile is in the running state. When the safe driving evaluation index Y is larger than or equal to a preset safe threshold value 0.8, the current driving is in a very safe state, and the vehicle speed can be properly improved; when the safe driving index is larger than or equal to the preset dangerous threshold value 0.3 and smaller than the preset safe threshold value 0.8, the current driving state is in a safer state, and the normal running of the current vehicle speed can be kept; when the safe driving index is smaller than the preset dangerous threshold value of 0.3, the current driving state is in a dangerous state, and the vehicle speed needs to be adaptively adjusted, so that the vehicle speed is reduced.
It should be noted that, the adaptive speed regulation module 104 performs adaptive regulation on the vehicle speed by calculating the safety evaluation index, when the road condition information analysis module 102 determines that the complexity of the current road condition is greater than or equal to the preset complexity threshold, the adaptive speed regulation module 104 is started to perform the vehicle speed regulation, and when the complexity of the road condition is less than the preset complexity threshold, the current running is in a safe state, without starting the adaptive speed regulation module, and the driver can drive the vehicle by himself by virtue of the driving experience. In the embodiment of the invention, the preset complexity threshold is 0.6, and the user can set the complexity threshold according to specific conditions.
In summary, the embodiment of the invention acquires the video frame image in the running process of the automobile in real time, and detects and acquires the moving objects around the automobile and related motion information; acquiring the corresponding road condition complexity by analyzing the motion information, acquiring the influence degree of the corresponding moving object on other moving objects according to the speed change among the moving objects, and analyzing the direction changing frequency and the speed change of the moving object to acquire the motion change possibility of the corresponding moving object; acquiring relevant collision information by simulating a simulation route of an automobile and a moving object, and adjusting predicted collision probability by combining the influence degree of the moving object and the motion change probability to acquire accurate collision probability; then judging the current vehicle speed risk according to the related motion information, influence degree and motion change possibility of the moving object and the vehicle speed; and finally, acquiring a safety evaluation index of the automobile running according to the road condition complexity, the influence degree and the speed risk, and adjusting the current automobile speed according to the safety evaluation index, so that the running speed is improved while the safety risk is reduced. According to the invention, through analyzing the relevance among road condition information, the simulation route and the moving object, the more comprehensive and accurate automobile safety evaluation index is obtained, and the automobile speed is adaptively adjusted, so that the safety and the adaptability of the intelligent auxiliary brake system are higher, the safety risk is reduced, and the running speed is improved.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (10)
1. An intelligent brake assist system, the system comprising:
the road condition information acquisition module is used for acquiring video frame images of road conditions around the automobile at each moment in the running process of the automobile, wherein the video frame images contain all moving objects around the automobile under the road conditions at each moment; taking the motion speed of the moving object and the distance between the moving object and an automobile at real time as real-time motion information;
the road condition information analysis module is used for acquiring the road condition complexity at the corresponding moment according to the real-time motion information of the moving object; obtaining the influence degree corresponding to the moving object according to the difference of the change rate of the moving speed between the moving object and other moving objects; acquiring the motion change possibility corresponding to the moving object according to the direction change frequency and the speed change of the moving object in the motion direction;
The road condition risk assessment module is used for acquiring the simulation routes of the moving object and the automobile, screening out the simulation collision object in the moving object according to the simulation route, and acquiring the simulation collision information of the simulation collision object; acquiring collision possibility according to the simulated collision information by combining the influence degree and the motion change possibility; acquiring a vehicle speed risk degree at a corresponding moment according to the vehicle speed, the influence degree, the motion change possibility and the real-time motion information of the moving object;
the self-adaptive speed regulation module is used for acquiring a safety evaluation index according to the road condition complexity, the collision possibility and the vehicle speed risk; and adjusting the speed of the automobile according to the safety evaluation index.
2. The intelligent brake assist system of claim 1, wherein the acquiring the road condition complexity at the corresponding time according to the real-time motion information of the moving object comprises:
summing the ratio of the motion speed of each moving object to the vehicle distance under the road condition at real time to obtain road condition resistance parameters; multiplying the road condition resistance parameters by the number of the moving objects and normalizing to obtain the road condition complexity at the corresponding moment.
3. The intelligent brake assist system of claim 1 wherein said obtaining a degree of influence corresponding to said moving object comprises:
acquiring the motion speed change rate of the moving object between each group of adjacent frames;
in each group of adjacent frames, obtaining the difference of the motion speed change rate between the motion object and each other motion object, and carrying out negative correlation mapping and normalization on the difference of the motion speed change rate to obtain a speed similarity index; accumulating the speed similarity indexes between the moving object and all other moving objects to obtain the overall speed similarity under adjacent frames;
and accumulating the overall speed similarity of all adjacent frames corresponding to the moving object to obtain the influence degree corresponding to the moving object.
4. An intelligent braking assistance system according to claim 3 wherein said obtaining said rate of change of movement speed comprises:
calculating the motion speed difference of the moving object between each group of adjacent frames, and normalizing the motion speed difference between each group of adjacent frames to obtain the motion speed change rate of the corresponding moving object.
5. The intelligent brake assist system of claim 1, wherein the acquiring the motion change probability corresponding to the moving object according to the direction change frequency and the speed change of the moving object comprises:
calculating a frame number ratio of a frame number corresponding to the change of the motion direction of the moving object to a frame number corresponding to the moving object, and taking the frame number ratio as a direction-changing frequency corresponding to the moving object; calculating the speed variance of the moving speed of the moving object in all the video frame images, and normalizing the product of the speed variance and the turning frequency to obtain the movement change possibility of the corresponding moving object.
6. The intelligent brake assist system of claim 1 wherein said screening for simulated collision objects based on said simulated route and obtaining simulated collision information for said simulated collision objects comprises:
taking an intersection point of the moving object and the simulated route of the automobile as a simulated collision point, wherein the moving object corresponding to the simulated collision point is a simulated collision body; the simulated collision information comprises simulated collision times and simulated collision time frame number intervals, the number of the simulated collision points is recorded as the simulated collision times, and the time interval of the corresponding moment of the simulated collision points is the simulated collision time interval.
7. The intelligent brake assist system of claim 6 wherein the method of obtaining a likelihood of a collision comprises:
taking the product of the collision time interval, the influence degree and the motion change possibility of the simulated collision objects as a collision comprehensive evaluation index, summing the reciprocal of the collision comprehensive evaluation index of each simulated collision object, multiplying the reciprocal by the simulated collision times, and normalizing to obtain the collision possibility.
8. The intelligent brake assist system of claim 1 wherein the method of obtaining a vehicle speed risk comprises:
calculating the ratio of the product value of the motion speed, the influence degree and the motion change possibility of each motion object to the vehicle distance, summing the ratios of all the motion objects, multiplying the sum by the vehicle speed and normalizing to obtain the vehicle speed risk degree corresponding to the vehicle speed.
9. The intelligent brake assist system of claim 1 wherein the method of obtaining a safety evaluation index comprises:
and adding the product of the road condition complexity, the collision possibility and the vehicle speed risk degree at each moment with a preset adjustment parameter, carrying out negative correlation mapping, and carrying out normalization processing to obtain a safety evaluation index.
10. The intelligent brake assist system of claim 1 wherein said adjusting said vehicle speed in accordance with said safety assessment indicator comprises:
when the safety evaluation index is greater than or equal to a preset safety threshold, the current automobile speed is increased according to the safety evaluation index; when the safety evaluation index is larger than or equal to a preset dangerous threshold value and smaller than a preset safety threshold value, the current automobile speed is kept; and when the safety evaluation index is smaller than a preset dangerous threshold value, reducing the current automobile speed according to the preset safety evaluation index.
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