CN111276008A - Device and method for conducting personalized guidance on vehicle speed in acceleration lane - Google Patents
Device and method for conducting personalized guidance on vehicle speed in acceleration lane Download PDFInfo
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Abstract
The invention discloses a device for individually guiding vehicle speed in an acceleration lane, which comprises a visual camera, a digital image processor, a millimeter wave radar, a data processor and a display screen. The method also discloses a guiding method, wherein a visual camera acquires vehicle information entering an acceleration lane, and a digital image processor processes the vehicle information to obtain vehicle type information; the millimeter wave radar acquires the speed of the incoming vehicle, the distance between the millimeter wave radar and the incoming vehicle, the speed of a rear vehicle on a traffic lane and the distance between the rear vehicle and the millimeter wave radar; the data processor processes the information acquired by the millimeter wave radar to obtain the remaining distance of the vehicle and the relative distance between the vehicle and the rear vehicle; the data processor also receives the vehicle type information sent by the digital image processor, compares the vehicle type information with the optimal speed curve in the database to determine the difference grade, and outputs a reminding signal through the display screen according to the difference grade. The invention can guide the vehicle to be imported individually, thereby reducing the import risk.
Description
Technical Field
The invention relates to the technical field of traffic safety facilities, in particular to a device and a method for individually guiding vehicle speed in an acceleration lane.
Background
With the development of national economy, the automobile holding capacity is rapidly increased. While road traffic safety is valued, the degree of individual demands of drivers on vehicles and roads is rapidly rising. Different drivers have different driving habits and driving styles, so that the traffic safety facilities cannot meet the requirements of different drivers sometimes, and the driving rhythm of the drivers is influenced. The driver can require the road traffic safety reminding device to meet the safety requirement and meet the driving habits and styles of different drivers when guiding the driver. With the continuous development of intelligent traffic systems, the safety reminding device for road traffic in the future is more and more intelligent and personalized.
The vehicles enter a confluence area of a main lane from an acceleration lane all the time, the confluence area is a key area influencing traffic passing efficiency, the vehicles on the acceleration lane influence the passing of the vehicles on the main lane in the process of converging, and especially the traffic flow on the main lane is often invalid when the vehicles are forcibly converged, so that congestion is caused, and even traffic risks may occur. In the traditional environment, an experienced driver needs to observe the vehicle on the main lane through a rearview mirror, acquire traffic information on the main lane, judge the merging risk based on the current traffic environment, and determine that the vehicle starts to merge after safety. This process is often difficult for novice drivers. In addition, at present, the quality of drivers in China is uneven, some drivers can carry out forced convergence without accelerating and observing during convergence, and the driving safety of vehicles on a main line lane can be endangered.
Disclosure of Invention
The invention aims to provide a device and a method for individually guiding a vehicle speed in an acceleration lane, which are used for providing intelligent and individual guidance for vehicles entering the acceleration lane and improving the safety and traffic efficiency of an influx area.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme.
Technical scheme one
An apparatus for personalized guidance of vehicle speed in an acceleration lane, comprising: the system comprises a visual camera, a digital image processor, a millimeter wave radar, a data processor and a display screen;
the output end of the visual camera is connected with the input end of the digital image processor, the output end of the digital image processor and the output end of the millimeter wave radar are respectively connected with the input end of the data processor, and the output end of the data processor is connected with the input end of the display screen;
the visual camera is used for acquiring the image information of the vehicle entering the acceleration lane and transmitting the acquired image information of the vehicle to the digital image processor;
the digital image processor is used for processing the received image information of the vehicle to obtain the vehicle type information of the vehicle;
the millimeter wave radar is used for acquiring motion parameters of vehicles in an acceleration lane and a traffic lane and distance parameters between the vehicles;
the data processor is used for receiving information transmitted by the digital image processor and the millimeter wave radar, and determining the difference grade by comparing the information with a speed curve of a corresponding vehicle model in the database when the risk of the vehicle model is the lowest;
the display screen is used for receiving the information transmitted by the data processor and outputting a reminding signal.
The first technical scheme of the invention has the characteristics and further improvement that:
the vision camera is installed at an entrance of the acceleration lane.
The millimeter wave radar is installed at the tail end of the acceleration lane.
Technical scheme two
A method for individually guiding vehicle speed in an acceleration lane is based on a device for individually guiding vehicle speed in the acceleration lane, and comprises the following steps:
step 1, a visual camera collects image information of vehicles converged into an acceleration lane;
step 2, the digital image processor processes the image information of the imported vehicle collected by the visual camera to obtain the vehicle type information of the imported vehicle;
step 3, the millimeter wave radar collects the speed v of the vehicle converged on the acceleration lane0Distance L between the incoming vehicle and the millimeter wave radar0A rear vehicle speed v on the traffic lane and behind the merging vehicle1And the distance L between the rear vehicle and the millimeter wave radar1；
Step 4, the data processor calculates the relative distance d between the afflux vehicle and the rear vehicle according to the information collected by the millimeter wave radar in the step 31And the remaining length Dr of the merging vehicle on the acceleration lane;
step 5, the data processor calculates the relative distance d obtained in step 4 according to the information collected in step 31And determining a difference grade by combining the optimal speed curve of the corresponding vehicle type in the database, and finally outputting a reminding signal according to the difference grade.
The second technical scheme of the invention is characterized by further improvement:
in the step 2, a three-branch convolutional neural network based on AlexNET is adopted as the method for obtaining the vehicle type information of the imported vehicle.
Further, the method for obtaining the vehicle type information of the imported vehicle specifically comprises the following steps:
firstly, carrying out size normalization on collected image information of an imported vehicle, then cutting, converting into a tensor data structure, carrying out regularization treatment, and finally obtaining an image classification score through a forward traditional path formed by a convolution layer, a pooling layer and a full-connection layer, and judging the image type according to the image classification score.
In step 4, the specific method of calculation is as follows: the data processor establishes a rectangular coordinate system, the abscissa axis of the rectangular coordinate system is parallel to the main road, and the ordinate axis and the abscissa axis of the rectangular coordinate system are in the same horizontal plane and perpendicular to the main roadThe angle between the millimeter wave radar and the rear vehicle is α0、α1Respectively, distance is L0、L1According to the cosine theorem, the relative distance d between the incoming vehicle and the rear vehicle is calculated1And the remaining length Dr of the merging vehicle on the acceleration lane.
In step 5, the optimal speed curve is specifically that each time of the different vehicle types which are merged into the acceleration lane are collected and recorded in advance, the risk level of the merging action is judged, and the speed curve of the merged vehicle when the corresponding risk level is minimum is the optimal speed curve.
The risk level of the import action is judged as follows:
where round is a rounding function.
In step 5, the difference grade specifically includes:
the data processor queries a database according to the vehicle type information of the imported vehicle, the relative speed and the relative distance between the imported vehicle and the rear vehicle, and matches an optimal speed curve; and comparing the speed of the current vehicle, and dividing the difference grade into N grades according to the speed difference at the same time point.
Compared with the prior art, the invention has the beneficial effects that:
the device for individually guiding the vehicle speed in the acceleration lane processes the vehicle information acquired by the visual camera through the digital image processor to acquire the vehicle type information; the data processor processes the information sent by the vision image processor and the millimeter wave radar, obtains the running environment of the imported vehicle, determines the difference grade by combining the optimal speed curve of the corresponding vehicle type in the database, outputs a reminding signal and conducts personalized guidance on the imported vehicle.
The method for individually guiding the vehicle speed on the acceleration lane can be used for individually guiding different vehicle types, in the guiding process, only the imported vehicle is observed through the visual camera, the vehicle type of the imported vehicle is identified through a deep learning algorithm, an optimal speed curve which is stored in a database and is obtained according to the risk level of the import action of the different vehicle types is combined, the vehicle speed of the imported vehicle is compared to obtain the difference level, the reminding information is sent to the display screen, and the display screen reminds the imported vehicle to adjust the vehicle speed through the picture image so as to achieve the purpose of individually guiding the different vehicle types under different environments, so that the import risk is reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic structural diagram of an embodiment of an apparatus for guiding a vehicle speed individually in an acceleration lane according to the present invention;
FIG. 2 is a flowchart illustrating an embodiment of a method for guiding a vehicle speed individually in an acceleration lane according to the present invention;
FIG. 3 is a schematic view of a scene for guiding a vehicle speed individually in an acceleration lane according to the present invention; in the figure, 0 denotes an incoming vehicle, and 1 denotes a rear vehicle.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides an apparatus for guiding a vehicle speed individually in an acceleration lane, including: the device comprises a visual camera, a digital image processor, a millimeter wave radar, a data processor and a display screen. The output end of the visual camera is connected with the input end of the digital image processor, the output end of the digital image processor and the output end of the millimeter wave radar are respectively connected with the input end of the data processor, and the output end of the data processor is connected with the input end of the display screen.
Referring to fig. 3, the vision camera is installed at an entrance of the acceleration lane, and collects image information of a vehicle entering the acceleration lane, and transmits the collected image information of the vehicle to the digital image processor.
The digital image processor is used for processing the received image information of the vehicle to obtain the vehicle type information of the vehicle.
Referring to fig. 3, a millimeter wave radar is installed at an end position of the acceleration lane for collecting motion parameters of vehicles in the acceleration lane and the traffic lane and distance parameters of the vehicles from each other. Wherein the motion parameters include speed, and the distance parameters include distance and angle.
And the data processor is used for receiving the information transmitted by the digital image processor and the millimeter wave radar, comparing the information with a speed curve of a corresponding vehicle type in the database when the risk of the vehicle type is minimum, determining a difference grade, and outputting a reminding signal according to the difference grade.
The display screen is used for receiving the reminding signal transmitted by the data processor and outputting reminding information.
Referring to fig. 2 and 3, the invention further provides a method for guiding vehicle speed in an acceleration lane, and based on the device for guiding vehicle speed in an acceleration lane, the method comprises the following steps:
step 1, a visual camera collects image information of vehicles converged into an acceleration lane.
And 2, processing the image information of the imported vehicle acquired by the visual camera by the digital image processor to obtain the vehicle type information of the imported vehicle.
The method for acquiring the vehicle type information of the imported vehicle adopts a deep learning method, and specifically is a three-branch convolutional neural network based on AlexNET.
Specifically, firstly, the collected image information of the imported vehicle is subjected to size normalization, then is cut, is converted into a tensor data structure, is subjected to regularization treatment, and is subjected to a forward traditional path formed by a convolution layer, a pooling layer and a full-connection layer to finally obtain an image classification score, and the image type is judged according to the image classification score.
Specifically, the convolution kernel is
Wherein K (m, n) is the value of each point of the convolution kernel, A (x, y) is the value of each point of the input image, and f (i, j) is the value of each point of the input image (i, j); adopting a maximum pooling mode, and calculating the mode that f (i, j) is max0≤n,n<k{ A (i × s + m, i × s + n) }, where s is the step size of the pooling window movement, m, n are the width and height of the pooling window, respectively, A (x, y) is the value of each point of the input image, and f (i, j) is the value of the point of the input image (i, j).
Specifically, in the three-branch convolutional neural network based on the AlexNET, a three-branch structure is embodied in the first 5 convolutional layers, each branch contains 5 convolutional layers, the 6 th layer is a feature fusion layer, the 7 th layer and the 8 th layer are full connection layers, and 4096 neurons are respectively provided by referring to the AlexNET. The number of neurons in the last full junction layer is the same as the number of classification categories.
Specifically, the activation function in the convolutional neural network is ReLU, and the loss function is a cross entropy function.
Specifically, the convolutional neural network back propagation updates the weight by using a random gradient descent method.
Step 3, the millimeter wave radar collects the speed v of the vehicle converged on the acceleration lane0Distance L between the incoming vehicle and the millimeter wave radar0A rear vehicle speed v on the traffic lane and behind the merging vehicle1And the distance L between the rear vehicle and the millimeter wave radar1。
As shown in fig. 3, the millimeter wave radar is installed at the end of the acceleration lane, and scans and monitors the running vehicles on the traffic lane and the acceleration lane.
Step 4, the data processor calculates the relative distance d between the afflux vehicle and the rear vehicle according to the information collected by the millimeter wave radar in the step 31And the remaining length Dr of the merging vehicle on the acceleration lane.
The calculation method specifically comprises the steps that the data processor establishes a rectangular coordinate system, the abscissa axis of the rectangular coordinate system is parallel to the main road, the ordinate axis and the abscissa axis of the rectangular coordinate system are in the same horizontal plane and perpendicular to the abscissa axis, and the angles between the millimeter wave radar and the rear vehicle converged into the vehicle and converged behind the vehicle are α0、α1Respectively, distance is L0、L1According to the cosine theorem, the relative distance d between the incoming vehicle and the rear vehicle is calculated1And the remaining length Dr of the merging vehicle on the acceleration lane.
Step 5, the data processor calculates the relative distance d obtained in step 4 according to the information collected in step 31And determining a difference grade by combining the optimal speed curve of the corresponding vehicle type in the database, and finally outputting a reminding signal according to the difference grade.
In this embodiment, the optimal speed curve is: and collecting and recording each importing action of different vehicle types imported into the acceleration lane in the last three-month period in advance, judging the risk level of the importing action, and when the corresponding risk level is minimum, setting the speed curve of the imported vehicle as the optimal speed curve.
Specifically, the import risk level determination rule is:
firstly, after an afflux vehicle enters an acceleration lane and the vehicle enters the acceleration lane, a data processor determines an afflux Risk level Risk according to the current environmentlevel(ii) a Risk class RisklevelThe system consists of two parts, wherein the first part is related to the remaining length of an acceleration lane, the second part is related to the Time To Collision (TTC) of an incoming vehicle and a rear vehicle on a main lane, and the calculation formula is as follows:
wherein,
dr is the remaining length of the acceleration lane; round is a rounding function; namely, the remaining length of the acceleration lane and the collision time are inversely proportional to the influx risk; in the calculation process of the calculation formula, the related parameters are all numerical values obtained by the sensors, and the influence of units is not considered.
When the vehicle acceleration system works, the visual camera acquires vehicle information entering an acceleration lane, and the digital image processor processes the acquired vehicle information to obtain vehicle type information; vehicle speed v collected by millimeter wave radar and imported into vehicle0Distance L between millimeter wave radar and oncoming vehicle0Rear speed v on the carriageway1Distance L between rear vehicle and millimeter wave radar1And an angle α of the millimeter wave radar to the oncoming vehicle and the trailing vehicle behind the oncoming vehicle0、α1(ii) a The data processor processes the information acquired by the millimeter wave radar to obtain the residual distance Dr of the incoming vehicle and the relative distance d between the incoming vehicle and the rear vehicle1(ii) a Meanwhile, the data processor also receives the vehicle type information sent by the digital image processor, and the vehicle type information is compared with the optimal speed curve in the database, so that the difference grade is divided into 3 grades; when the speed difference at the same time point is less than 3km/h, the difference grade is 1 grade; when the difference is more than or equal to 3km/h and less than or equal to 5km/h, the difference grade is 2 grade; and when the difference is more than 5km/h, the difference grade is 3. And when the difference grade reaches 3 grades, the data processor judges that the speed of the current imported vehicle does not meet the optimal speed curve, and sends prompt information to the display screen. When the difference between the speed of the incoming vehicle and the speed of the rear vehicle is a negative value, the display screen displays an acceleration word; when the difference between the speed of the imported vehicle and the speed of the rear vehicle is a positive value, displaying a deceleration character on a display screen; therefore, the speed of the vehicle which is imported into the vehicle is guided, and the import risk is reduced.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. An apparatus for personalized guidance of vehicle speed in an acceleration lane, comprising: the system comprises a visual camera, a digital image processor, a millimeter wave radar, a data processor and a display screen;
the output end of the visual camera is connected with the input end of the digital image processor, the output end of the digital image processor and the output end of the millimeter wave radar are respectively connected with the input end of the data processor, and the output end of the data processor is connected with the input end of the display screen;
the visual camera is used for acquiring the image information of the vehicle entering the acceleration lane and transmitting the acquired image information of the vehicle to the digital image processor;
the digital image processor is used for processing the received image information of the vehicle to obtain the vehicle type information of the vehicle;
the millimeter wave radar is used for acquiring motion parameters of vehicles in an acceleration lane and a traffic lane and distance parameters between the vehicles;
the data processor is used for receiving information transmitted by the digital image processor and the millimeter wave radar, comparing the information with a speed curve of a corresponding vehicle type in the database when the risk of the vehicle type is the lowest, determining a difference grade and outputting a reminding signal according to the difference grade;
the display screen is used for receiving the reminding signal transmitted by the data processor and outputting reminding information.
2. The device for personalized guidance of vehicle speed in an acceleration lane according to claim 1, characterized in that the vision camera is mounted at the entrance of the acceleration lane.
3. The apparatus for guiding vehicle speed individually for an acceleration lane according to claim 1, wherein said millimeter wave radar is installed at the end position of the acceleration lane.
4. A method for personalized guidance of vehicle speed in an acceleration lane, based on the device for personalized guidance of vehicle speed in an acceleration lane of claim 1, characterized by comprising the following steps:
step 1, a visual camera collects image information of vehicles converged into an acceleration lane;
step 2, the digital image processor processes the image information of the imported vehicle collected by the visual camera to obtain the vehicle type information of the imported vehicle;
step 3, the millimeter wave radar collects the speed v of the vehicle converged on the acceleration lane0Distance L between the incoming vehicle and the millimeter wave radar0A rear vehicle speed v on the traffic lane and behind the merging vehicle1And the distance L between the rear vehicle and the millimeter wave radar1；
Step 4, the data processor calculates the relative distance d between the afflux vehicle and the rear vehicle according to the information collected by the millimeter wave radar in the step 31And the remaining length Dr of the merging vehicle on the acceleration lane;
step 5, the data processor calculates the relative distance d obtained in step 4 according to the information collected in step 31And determining a difference grade by combining the optimal speed curve of the corresponding vehicle type in the database, and finally outputting a reminding signal according to the difference grade.
5. The method for personalized guidance of vehicle speed on an acceleration lane according to claim 4, characterized in that, in step 2, the method for obtaining the vehicle type information of the imported vehicle adopts a three-branch convolutional neural network based on AlexNET.
6. The method for personalized guidance of vehicle speed in an acceleration lane according to claim 5, wherein the method for obtaining the vehicle type information of the imported vehicle is specifically:
firstly, carrying out size normalization on collected image information of an imported vehicle, then cutting, converting into a tensor data structure, carrying out regularization treatment, and finally obtaining an image classification score through a forward traditional path formed by a convolution layer, a pooling layer and a full-connection layer, and judging the image type according to the image classification score.
7. The method for personalized guidance of vehicle speed on an acceleration lane according to claim 4, wherein in step 4, the specific calculation method is that the data processor establishes a rectangular coordinate system, the abscissa axis of the rectangular coordinate system is parallel to the main lane, the ordinate axis of the rectangular coordinate system and the abscissa axis are in the same horizontal plane and perpendicular to the abscissa axis, and the angles of the millimeter wave radar and the rear vehicle converged into the vehicle and converged behind the vehicle are α respectively0、α1Respectively, distance is L0、L1According to the cosine theorem, the relative distance d between the incoming vehicle and the rear vehicle is calculated1And the remaining length Dr of the merging vehicle on the acceleration lane.
8. The method as claimed in claim 4, wherein in step 5, the optimal speed curve is obtained by collecting and recording each importing action of different vehicle types to the acceleration lane in advance, and determining a risk level of the importing action, and the speed curve of the vehicle when the corresponding risk level is minimum is the optimal speed curve.
9. The method for personalized guidance of vehicle speed in an acceleration lane according to claim 8, wherein the determining risk level of the influx maneuver is:
where round is a rounding function.
10. The method for personalized guidance of vehicle speed in an acceleration lane according to claim 9, characterized in that in step 5, the difference level is specifically:
the data processor queries a database according to the vehicle type information of the imported vehicle, the relative speed and the relative distance between the imported vehicle and the rear vehicle, and matches an optimal speed curve; and comparing the speed of the current vehicle, and dividing the difference grade into N grades according to the speed difference at the same time point.
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