CN114399908A - Method for studying and judging lane-level queuing length of road intersection by utilizing vehicle-mounted ADAS - Google Patents
Method for studying and judging lane-level queuing length of road intersection by utilizing vehicle-mounted ADAS Download PDFInfo
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Abstract
The invention relates to a method for studying and judging lane-level queuing length of a road intersection by utilizing vehicle-mounted ADAS, which comprises the following steps of: continuously collecting ADAS vehicle information; collecting characteristic information of a road intersection in a circle with the ADAS vehicle as the center of the circle and the ADAS vehicle coverage distance as the radius; continuously collecting information of front adjacent vehicles; packaging the information of the front adjacent vehicle and the ADAS vehicle information into a data frame; forming the data frames into continuous data frames; calculating absolute position change data and absolute velocity data in each data frame; if the absolute position change data is less than the absolute position change threshold or the absolute speed data is less than the absolute speed threshold, determining that the front adjacent vehicle is in a queuing state; calculating the queuing length of each lane; and packing the queuing lengths into a road intersection lane-level queuing length set. The invention breaks through various limits of installing fixed detection equipment from avoiding the terrain limit of road intersections; the whole road network is covered; the cost is greatly reduced.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a method for studying and judging lane-level queuing length of a road intersection by utilizing vehicle-mounted ADAS.
Background
With the increasing number of motor vehicles and the increasing travel demand of people, urban road traffic bears more and more burden, and although the urban road traffic network is improved continuously, the problem of road traffic jam also occurs frequently. Particularly, urban road plane intersections serve as important nodes of an urban traffic network to converge traffic flows from different directions, the traffic conditions directly influence the congestion state of a regional road network, and if the traffic control measures are unreasonable, the phenomenon of overlong queue at the intersections can be caused. The queuing length of the signalized intersection is an important factor influencing travel time, is also an important basis for evaluating intersection efficiency and optimizing signal timing, provides necessary data support for urban traffic control, and is beneficial to improving a traffic management method and improving traffic service level.
The current strategy method for the vehicle queuing length at the road intersection is to adopt fixed devices for measurement, and the devices comprise but are not limited to coil detectors, manual information acquisition and video detection devices; in the prior art, a large number of coils, radars and camera information acquisition equipment are required to be arranged on the road side;
the defects of the prior art are as follows:
1. because a large amount of fixed detection equipment needs to be installed on the road side, the cost of installation, purchase and maintenance of the fixed detection equipment is very high, and the installation, use and operation cost of the prior art is high;
2. due to the terrain limitation of the road intersection, including the limitation of trees and buildings on the installation mode of the fixed detection equipment, the coverage range of the fixed detection equipment installed on the roadside has certain limitation, particularly under the condition that the intersection is heavily congested, queued vehicles may exceed a detection area to form a long queue, the queuing length is difficult to estimate by using a detector or a camera with a fixed position, and the technical feasibility is further reduced.
Disclosure of Invention
Aiming at the problems, the invention provides a method for studying and judging the lane-level queuing length of a road intersection by utilizing vehicle-mounted ADAS, which aims to fundamentally avoid the terrain limitation of the road intersection and can flexibly collect the queuing length of each lane of each road intersection; the whole road network is covered; the cost is greatly reduced.
In order to solve the problems, the technical scheme provided by the invention is as follows:
the method for studying and judging the lane-level queuing length of the road intersection by utilizing the vehicle-mounted ADAS comprises the following steps of:
s100, continuously acquiring the information of the ADAS vehicle by using ADAS equipment installed on the ADAS vehicle according to an artificially preset acquisition frequency; the ADAS vehicle information comprises the ADAS vehicle longitude data, the ADAS vehicle latitude data, the ADAS vehicle speed data and the ADAS vehicle course angle data;
s200, collecting characteristic information of all road intersections in a circle with the ADAS vehicle as a circle center and an artificially preset ADAS vehicle coverage distance as a radius; the characteristic information comprises longitude data of a central point of a road intersection, latitude data of the central point of the road intersection, longitude data of a central point of a stop line of a branch road into which the ADAS vehicle drives and latitude data of the central point of the stop line of the branch road into which the ADAS vehicle drives;
s300, judging a target road intersection into which the ADAS vehicle intends to drive according to the ADAS vehicle information and the characteristic information of the road intersection;
s400, judging whether the ADAS vehicle enters a manually preset road intersection queuing research and judgment range of the target road intersection, and according to a judgment result, performing the following operations:
if the ADAS vehicle enters the queuing research and judgment range of the road intersection, S500 is executed; otherwise, executing S200 again;
s500, continuously acquiring the information of front adjacent vehicles of the front adjacent vehicles in the branch roads of the target road intersection; the front neighboring vehicle information includes relative position data and relative speed data between the front neighboring vehicle and the ADAS vehicle; the relative position data includes a relative longitudinal distance and a relative lateral distance;
then, packaging the information of the front adjacent vehicles and the information of the ADAS vehicle, which are acquired by the ADAS vehicle at the same time, into a data frame; then, sequentially arranging each data frame according to the sequence of acquisition time to form continuous data frames of the ADAS vehicle;
s600, calculating in sequence according to the sequence of the acquisition time to obtain absolute position change data and absolute speed data of the front adjacent vehicle in each data frame; then, based on the absolute position change data and the absolute speed data of the preceding nearby vehicle, the following operations are made:
if the absolute position change data of the front adjacent vehicle is smaller than an artificially preset absolute position change threshold value, or the absolute speed data of the front adjacent vehicle is smaller than an artificially preset absolute speed threshold value, determining that the front adjacent vehicle is in a queuing state; then, S700 is executed;
s700, calculating the queuing lengths of all lanes of the branch road into which the ADAS vehicle enters one by one according to the characteristic information, the ADAS vehicle information, and the absolute position change data and the absolute speed data of the front adjacent vehicle; then, packing the queuing length of each lane to form a set of lane-level queuing lengths of the road intersection; and the set of the lane-level queuing lengths of the road intersections is the final result obtained by the method.
Preferably, the ADAS device comprises a camera and a radar sensor.
Preferably, in S300, the method for determining a target intersection where the ADAS vehicle intends to enter according to the ADAS vehicle information and the feature information of the intersection includes the following steps:
s310, calculating the distances from the ADAS vehicle to the intersection central points of all road intersections in a circle with the ADAS vehicle as the center of the circle and the manually preset ADAS vehicle coverage distance as the radius one by one;
s320, calculating azimuth angles from the ADAS vehicle to the intersection center points of all road intersections in a circle with the ADAS vehicle as the center of the circle and the manually preset ADAS vehicle coverage distance as the radius one by one; then calculating the difference value between the ADAS vehicle course angle data of the ADAS vehicle and the azimuth angle of the central point of each road intersection one by one;
s330, judging that the distance between the ADAS vehicle and the center point of the intersection in a circle with the ADAS vehicle as the circle center and the manually preset ADAS vehicle coverage distance as the radius is shortest, and judging that the intersection with the smallest difference between the azimuth angle of the ADAS vehicle and the center point of the intersection and the heading angle data of the ADAS vehicle is the target intersection.
Preferably, the absolute position change data of the preceding neighboring vehicle within each data frame in S600 is expressed as follows:
sj＝s+lfollowing-lleading
wherein: sjAbsolute position change data for a preceding neighboring vehicle; s is the driving distance from the previous data frame to the current data frame of the ADAS vehicle; lfollowingDistance between the ADAS vehicle and the adjacent vehicle in front in the previous data frame; lleadingThe distance between the ADAS vehicle and the preceding neighboring vehicle in the current data frame.
Preferably, the absolute speed data of the preceding neighboring vehicle in S600 is calculated as follows:
vi＝v+Δv
wherein: v. ofiAbsolute speed data for a preceding neighboring vehicle; v is ADAS vehicle speed data; Δ v is the relative speed data between the front neighboring vehicle and the ADAS vehicle.
Preferably, the queuing length is calculated according to the following formula:
L＝dis{O(L0,B0)，H(L1,B1)}-Δl-m
wherein: o (L)0,B0) Coordinates of a road intersection center point of the target road intersection; l is0Longitude data of a road intersection center point which is a road intersection center point of the target road intersection; b is0The latitude data of the road intersection center point of the target road intersection; h (L)1,B1) Coordinates of the ADAS vehicle; l is1ADAS vehicle longitude data for the ADAS vehicle; b is1ADAS vehicle latitude data for ADAS vehicles; dis { O (L)0,B0)，H(L1,B1) The distance between the central point of the road intersection of the target road intersection and the ADAS vehicle is set; delta l is the distance from the intersection center point of the target road intersection to the stop line center point of the branch road into which the ADAS vehicle enters; m is the relative longitudinal distance.
Compared with the prior art, the invention has the following advantages:
1. because the ADAS is adopted to collect the vehicle queue length of each lane at the intersection, the terrain limitation of the intersection is avoided fundamentally, various limitations of installing fixed detection equipment are broken, and the queue length of each lane at each intersection can be collected very flexibly;
2. because the ADAS equipment is arranged on the ADAS vehicle and moves along with the vehicle, when the ADAS vehicle reaches a certain permeability in the total quantity of vehicles in the road network, the vehicle queuing information of the whole road network can be acquired, thereby realizing the covering of the whole road network;
3. as the purchase price, the installation cost and the maintenance cost of the vehicle-mounted ADAS device are far lower than those of the fixed detection device, compared with the prior art, the method disclosed by the invention has the advantage that the cost is greatly reduced.
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FIG. 1 is a schematic flow chart of an embodiment of the present invention;
fig. 2 is a schematic diagram of a pathway intersection in accordance with an embodiment of the present invention.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be purely exemplary and are not intended to limit the scope of the invention, as various equivalent modifications of the invention will occur to those skilled in the art upon reading the present disclosure and fall within the scope of the appended claims.
As shown in fig. 1, the method for studying and judging the lane-level queuing length of a road intersection by using vehicle-mounted ADAS comprises the following steps:
s100, continuously acquiring the information of the ADAS vehicle by using ADAS equipment installed on the ADAS vehicle according to an artificially preset acquisition frequency; the ADAS vehicle information includes ADAS vehicle longitude data, ADAS vehicle latitude data, ADAS vehicle speed data, and ADAS vehicle heading angle data.
S200, collecting characteristic information of all road intersections in a circle with the ADAS vehicle as a circle center and an artificially preset ADAS vehicle coverage distance as a radius; the characteristic information includes intersection center point longitude data, intersection center point latitude data, stop line center point longitude data of a branch road into which the ADAS vehicle is driven, and stop line center point latitude data of a branch road into which the ADAS vehicle is driven.
In this embodiment, the feature information of each intersection is manually measured one by one and then sent to the central processing unit in the background.
It should be noted that the intersection center point of a road intersection is the intersection point of straight lines where the center lines of all the branch roads on the road intersection are located; this is because, according to the construction specifications of traffic roads, the straight lines of the center lines of all the branch roads at each intersection passing through the regular construction design process necessarily intersect at one point.
It should be further explained that, due to the reasons of old urban areas and irregular road design, the intersection center point of the intersection may not be obtained by the above method; in this case, the worker designates a point in the intersection as the intersection center point of the intersection, and the designated point may be, but is not limited to, the geometric center of the intersection in general.
As shown in fig. 2, hasIn particular, for a signal-controlled road intersection with N branches, longitude and latitude information L at a road intersection central point O of the road intersection is collected0、B0(ii) a Midpoint of a branch road AiLatitude and longitude information Li、Bi(ii) a Wherein i represents the number of the branch road, and i is more than or equal to 1 and less than or equal to N; each branch road is uniquely corresponding to one stop line, so that the number of the stop lines is the number of the branch roads; the distance delta l between the two points can be calculated by a geometric method by utilizing the coordinates of the two points.
And S300, judging the target road intersection into which the ADAS vehicle intends to drive according to the ADAS vehicle information and the characteristic information of the road intersection.
In this embodiment, S300 specifically includes the following steps:
and S310, calculating the distances from the ADAS vehicle to the intersection central points of all the road intersections in a circle with the ADAS vehicle as the center of the circle and the manually preset ADAS vehicle coverage distance as the radius one by one.
S320, calculating azimuth angles from the ADAS vehicle to the intersection center points of all road intersections in a circle with the ADAS vehicle as the center of the circle and the manually preset ADAS vehicle coverage distance as the radius one by one; and then calculating the difference value of the ADAS vehicle course angle data of the ADAS vehicle and the azimuth angle of the central point of each road intersection one by one.
S330, judging that the road intersection which takes the ADAS vehicle as the center of a circle and takes the manually preset ADAS vehicle coverage distance as the radius and has the shortest distance between the ADAS vehicle and the center point of the road intersection and the road intersection with the smallest difference between the azimuth angle of the ADAS vehicle and the center point of the road intersection and the heading angle data of the ADAS vehicle is the target road intersection.
S400, judging whether the ADAS vehicle enters a manually preset road intersection queuing research and judgment range of the target road intersection, and according to a judgment result, performing the following operations:
if the ADAS vehicle enters the queuing research and judgment range of the road intersection, S500 is executed; otherwise, S200 is performed again.
S500, continuously acquiring the information of front adjacent vehicles in the branch road of the target road intersection; the front nearby vehicle information includes relative position data and relative speed data between the front nearby vehicle and the ADAS vehicle; the relative position data includes a relative longitudinal distance and a relative lateral distance.
Then, packaging the information of the front adjacent vehicles collected by the ADAS vehicle at the same time and the information of the ADAS vehicle into a data frame; and then, arranging each data frame in sequence according to the sequence of the acquisition time to form continuous data frames of the ADAS vehicle.
In this embodiment, the ADAS device includes a camera and a radar sensor. The ADAS device senses the information of the front adjacent vehicles and the information of the ADAS vehicle in real time and uploads the information to the central processing unit; and the central processing unit completes various calculation and judgment works by utilizing the collected information of the adjacent vehicles in front, the ADAS vehicle information and the characteristic information of the road intersection.
S600, calculating in sequence according to the sequence of the acquisition time to obtain absolute position change data and absolute speed data of the front adjacent vehicle in each data frame; then, based on the absolute position change data and the absolute speed data of the preceding nearby vehicle, the following operations are made:
if the absolute position change data of the front adjacent vehicle is smaller than an artificially preset absolute position change threshold, or the absolute speed data of the front adjacent vehicle is smaller than an artificially preset absolute speed threshold, determining that the front adjacent vehicle is in a queuing state; then S700 is performed.
In the present embodiment, the absolute position change data of the preceding neighboring vehicle in each data frame is expressed by equation (1):
sj＝s+lfollowing-lleading (1)
wherein: sjAbsolute position change data for a preceding neighboring vehicle; s is the driving distance from the previous data frame to the current data frame of the ADAS vehicle; lfollowingDistance between the ADAS vehicle and the adjacent vehicle in front in the previous data frame; lleadingThe distance between the ADAS vehicle and the preceding neighboring vehicle in the current data frame.
In the present embodiment, the absolute speed data of the preceding neighboring vehicle is calculated by equation (2):
vk＝v+Δv (2)
wherein: v. ofkAbsolute speed data for a preceding neighboring vehicle; v is ADAS vehicle speed data; Δ v is the relative speed data between the front neighboring vehicle and the ADAS vehicle.
And S700, calculating the queuing lengths of all lanes of the branch road into which the ADAS vehicle enters one by one according to the characteristic information, the ADAS vehicle information, and the absolute position change data and the absolute speed data of the adjacent vehicles in front.
In this embodiment, the queuing length is calculated according to equation (3):
L＝dis{O(L0,B0)，H(L1,B1)}-Δl-m (3)
wherein: o (L)0,B0) Coordinates of a road intersection center point of the target road intersection; l is0Road intersection center point longitude data which is a road intersection center point of a target road intersection; b is0Road intersection center point latitude data which is a road intersection center point of a target road intersection; h (L)1,B1) Coordinates of the ADAS vehicle; l is1ADAS vehicle longitude data for the ADAS vehicle; b is1ADAS vehicle latitude data for ADAS vehicles; dis { O (L)0,B0)，H(L1,B1) The distance between the central point of the road intersection of the target road intersection and the ADAS vehicle is set; delta l is the distance from the intersection center point of the target road intersection to the stop line center point of the branch road into which the ADAS vehicle enters; m is the relative longitudinal distance.
It should be noted that, with the relative lateral distance n, the queuing length of each lane can be calculated one by using a method, but not limited to, a trigonometric function.
Then, packing the queuing length of each lane to form a set of lane-level queuing lengths of the road intersection; the set of the lane-level queuing lengths of the road intersections is the final result obtained by the method.
And uploading all the road intersection lane-level queuing length sets to a database in real time.
It should be noted that, when the method of the present invention is adopted, the number of ADAS vehicles is more, and the more the ADAS vehicles are, the better the ADAS vehicles are, because each ADAS vehicle feeds back the queuing condition of the road intersection where the ADAS vehicle enters in real time; from the perspective of probability, each ADAS vehicle does disordered movement, and on the premise of giving enough time, the ADAS vehicles can be considered to be uniformly distributed in the road network; therefore, when the number of ADAS vehicles is enough, the queuing information of the whole road network can be obtained by integrating the queuing conditions of the road intersections fed back by all the ADAS vehicles; on the other hand, the more ADAS vehicles are, the more accurately the fed-back queuing situation at the intersection is close to the actual queuing situation at the intersection.
To verify the effectiveness of the present invention, the inventor performed a field test near an intersection between a friendship major road and a railway crossing in wuhan city, northhui, about 2019, 10, 17 pm, 17:00, and performed an actual road test using 4 ADAS vehicles, and the obtained test data are shown in table 1:
TABLE 1. friendship road and railway road measured data
At the moment, the control of the friendship major road direction signal is in a red light state, the phenomenon of vehicle queuing occurs, the railway is normally passed, and the phenomenon of queuing does not occur.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. To those skilled in the art; various modifications to these embodiments will be readily apparent, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the scope of the present invention.
Finally, it should be noted that the above embodiments are merely representative examples of the present invention. It is obvious that the invention is not limited to the above-described embodiments, but that many variations are possible. Any simple modification, equivalent change and modification made to the above embodiments in accordance with the technical spirit of the present invention should be considered to be within the scope of the present invention.
Here, it should be noted that the description of the above technical solutions is exemplary, the present specification may be embodied in different forms, and should not be construed as being limited to the technical solutions set forth herein. Rather, these descriptions are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. Furthermore, the technical solution of the present invention is limited only by the scope of the claims.
The shapes, sizes, ratios, angles, and numbers disclosed to describe aspects of the specification and claims are examples only, and thus, the specification and claims are not limited to the details shown. In the following description, when a detailed description of related known functions or configurations is determined to unnecessarily obscure the focus of the present specification and claims, the detailed description will be omitted.
Where the terms "comprising", "having" and "including" are used in this specification, there may be another part or parts unless otherwise stated, and the terms used may generally be in the singular but may also be in the plural.
It should be noted that although the terms "first," "second," "top," "bottom," "side," "other," "end," "other end," and the like may be used and used in this specification to describe various components, these components and parts should not be limited by these terms. These terms are only used to distinguish one element or section from another element or section. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, with the top and bottom elements being interchangeable or switchable with one another, where appropriate, without departing from the scope of the present description; the components at one end and the other end may be of the same or different properties to each other.
Further, in constituting the component, although it is not explicitly described, it is understood that a certain error region is necessarily included.
In describing positional relationships, for example, when positional sequences are described as being "on.. above", "over.. below", "below", and "next", unless such words or terms are used as "exactly" or "directly", they may include cases where there is no contact or contact therebetween. If a first element is referred to as being "on" a second element, that does not mean that the first element must be above the second element in the figures. The upper and lower portions of the member will change depending on the angle of view and the change in orientation. Thus, in the drawings or in actual construction, if a first element is referred to as being "on" a second element, it can be said that the first element is "under" the second element and the first element is "over" the second element. In describing temporal relationships, unless "exactly" or "directly" is used, the description of "after", "subsequently", and "before" may include instances where there is no discontinuity between steps. The features of the various embodiments of the present invention may be partially or fully combined or spliced with each other and performed in a variety of different configurations as would be well understood by those skilled in the art. Embodiments of the invention may be performed independently of each other or may be performed together in an interdependent relationship.
Claims (6)
1. A method for studying and judging lane-level queuing length of a road intersection by utilizing vehicle-mounted ADAS is characterized by comprising the following steps: comprises the following steps:
s100, continuously acquiring the information of the ADAS vehicle by using ADAS equipment installed on the ADAS vehicle according to an artificially preset acquisition frequency; the ADAS vehicle information comprises the ADAS vehicle longitude data, the ADAS vehicle latitude data, the ADAS vehicle speed data and the ADAS vehicle course angle data;
s200, collecting characteristic information of all road intersections in a circle with the ADAS vehicle as a circle center and an artificially preset ADAS vehicle coverage distance as a radius; the characteristic information comprises longitude data of a central point of a road intersection, latitude data of the central point of the road intersection, longitude data of a central point of a stop line of a branch road into which the ADAS vehicle drives and latitude data of the central point of the stop line of the branch road into which the ADAS vehicle drives;
s300, judging a target road intersection into which the ADAS vehicle intends to drive according to the ADAS vehicle information and the characteristic information of the road intersection;
s400, judging whether the ADAS vehicle enters a manually preset road intersection queuing research and judgment range of the target road intersection, and according to a judgment result, performing the following operations:
if the ADAS vehicle enters the queuing research and judgment range of the road intersection, S500 is executed; otherwise, executing S200 again;
s500, continuously acquiring the information of front adjacent vehicles of the front adjacent vehicles in the branch roads of the target road intersection; the front neighboring vehicle information includes relative position data and relative speed data between the front neighboring vehicle and the ADAS vehicle; the relative position data includes a relative longitudinal distance and a relative lateral distance;
then, packaging the information of the front adjacent vehicles and the information of the ADAS vehicle, which are acquired by the ADAS vehicle at the same time, into a data frame; then, sequentially arranging each data frame according to the sequence of acquisition time to form continuous data frames of the ADAS vehicle;
s600, calculating in sequence according to the sequence of the acquisition time to obtain absolute position change data and absolute speed data of the front adjacent vehicle in each data frame; then, based on the absolute position change data and the absolute speed data of the preceding nearby vehicle, the following operations are made:
if the absolute position change data of the front adjacent vehicle is smaller than an artificially preset absolute position change threshold value, or the absolute speed data of the front adjacent vehicle is smaller than an artificially preset absolute speed threshold value, determining that the front adjacent vehicle is in a queuing state; then, S700 is executed;
s700, calculating the queuing lengths of all lanes of the branch road into which the ADAS vehicle enters one by one according to the characteristic information, the ADAS vehicle information, and the absolute position change data and the absolute speed data of the front adjacent vehicle; then, packing the queuing length of each lane to form a set of lane-level queuing lengths of the road intersection; and the set of the lane-level queuing lengths of the road intersections is the final result obtained by the method.
2. The method for studying and judging the lane-level queuing length at a road intersection by using the vehicle-mounted ADAS as claimed in claim 1, wherein: the ADAS device comprises a camera and a radar sensor.
3. The method for studying and judging the lane-level queuing length at a road intersection by using the vehicle-mounted ADAS as claimed in claim 2, wherein: in S300, the method for determining a target road intersection to which an ADAS vehicle intends to enter according to the ADAS vehicle information and the feature information of the road intersection includes the following steps:
s310, calculating the distances from the ADAS vehicle to the intersection central points of all road intersections in a circle with the ADAS vehicle as the center of the circle and the manually preset ADAS vehicle coverage distance as the radius one by one;
s320, calculating azimuth angles from the ADAS vehicle to the intersection center points of all road intersections in a circle with the ADAS vehicle as the center of the circle and the manually preset ADAS vehicle coverage distance as the radius one by one; then calculating the difference value between the ADAS vehicle course angle data of the ADAS vehicle and the azimuth angle of the central point of each road intersection one by one;
s330, judging that the distance between the ADAS vehicle and the center point of the intersection in a circle with the ADAS vehicle as the circle center and the manually preset ADAS vehicle coverage distance as the radius is shortest, and judging that the intersection with the smallest difference between the azimuth angle of the ADAS vehicle and the center point of the intersection and the heading angle data of the ADAS vehicle is the target intersection.
4. The method for studying and judging the lane-level queuing length at a road intersection by using the vehicle-mounted ADAS as claimed in claim 2, wherein: the absolute position change data of the preceding neighboring vehicle within each data frame in S600 is expressed as follows:
sj＝s+lfollowing-lleading
wherein: sjFor absolute change of position of preceding adjacent vehicleData; s is the driving distance from the previous data frame to the current data frame of the ADAS vehicle; lfollowingDistance between the ADAS vehicle and the adjacent vehicle in front in the previous data frame; lleadingThe distance between the ADAS vehicle and the preceding neighboring vehicle in the current data frame.
5. The method for studying and judging the lane-level queuing length at a road intersection by using the vehicle-mounted ADAS as claimed in claim 2, wherein: the absolute speed data of the preceding neighboring vehicle in S600 is calculated as follows:
vi＝v+Δv
wherein: v. ofiAbsolute speed data for a preceding neighboring vehicle; v is ADAS vehicle speed data; Δ v is the relative speed data between the front neighboring vehicle and the ADAS vehicle.
6. The method for studying and judging the lane-level queuing length of a road intersection by using the vehicle-mounted ADAS according to any one of claims 1 to 5, is characterized in that: the queuing length is calculated according to the following formula:
L＝dis{O(L0,B0)，H(L1,B1)}-Δl-m
wherein: o (L)0,B0) Coordinates of a road intersection center point of the target road intersection; l is0Longitude data of a road intersection center point which is a road intersection center point of the target road intersection; b is0The latitude data of the road intersection center point of the target road intersection; h (L)1,B1) Coordinates of the ADAS vehicle; l is1ADAS vehicle longitude data for the ADAS vehicle; b is1ADAS vehicle latitude data for ADAS vehicles; dis { O (L)0,B0)，H(L1,B1) The distance between the central point of the road intersection of the target road intersection and the ADAS vehicle is set; delta l is the distance from the intersection center point of the target road intersection to the stop line center point of the branch road into which the ADAS vehicle enters; m is the relative longitudinal distance.
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