CN111754774B - Safe self-organizing traffic control method for intelligent network-connected automobile at expressway ramp port - Google Patents

Safe self-organizing traffic control method for intelligent network-connected automobile at expressway ramp port Download PDF

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CN111754774B
CN111754774B CN202010632949.9A CN202010632949A CN111754774B CN 111754774 B CN111754774 B CN 111754774B CN 202010632949 A CN202010632949 A CN 202010632949A CN 111754774 B CN111754774 B CN 111754774B
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vehicle
ramp
vehicles
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confluence point
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CN111754774A (en
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黄晋
胡展溢
杨泽宇
孟天闯
杨殿阁
钟志华
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Tsinghua University
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes

Abstract

The invention discloses a safe self-organizing passing control method for an intelligent network-connected automobile at a ramp port of an expressway, which is characterized by comprising the following steps of: step 1, when a ramp vehicle drives into a communication area, a roadside intelligent agent judges the passing sequence of all vehicles in the communication area; step 2, determining respective following objects of all vehicles in the communication area; step 3, realizing ramp confluence by using a nonlinear dynamics control method; and 4, after the ramp vehicles pass through the confluence point, ending the confluence process, and switching each vehicle to the self-adaptive cruise control. The control method based on the nonlinear dynamics of the vehicle can ensure that the vehicle keeps a reasonable vehicle distance before entering a collision area, so that collision avoidance in the collision area is realized.

Description

Safe self-organizing traffic control method for intelligent network-connected automobile at expressway ramp port
Technical Field
The invention relates to an intelligent network-connected automobile road safety control technology, in particular to an intelligent network-connected automobile safety self-organizing traffic control method for a ramp port of an expressway. Therefore, vehicle-vehicle constraint is established, and intelligent networked vehicles on a main road and a ramp of the highway can pass through the ramp intersection safely and efficiently in a certain sequence by using a dynamic control method.
Background
With the increasing demand of people on trips, roads play an important role in solving the problems of trips. As an indispensable part of the highway system, a cooperative control technique related to a ramp junction has been widely studied. Firstly, a ramp port relates to the intersection of ramp vehicles and main highway traffic flow, so that the ramp port is a high-incidence section of an expressway accident; secondly, traffic jam is easily caused at unorganized ramp intersections; finally, because the driving of the conventional vehicle is influenced by the driver and the line of sight of the driver on the ramp section is blocked, dangerous working conditions such as sudden braking and sudden acceleration are easy to occur (Xu L, Lu J, Wang C, et al. cooperative driving control of connected and automatic vehicles on high speeds [ J ]. Journal of south University (English Edition),2019,35(2): 220-. Statistics show that nearly 50% of accidents on expressways occur at ramp mouths.
The intelligent internet automobile is provided with a vehicle-mounted sensing unit and a communication unit, and can acquire real-time motion state information of other vehicles in a vehicle-to-vehicle communication (V2V) mode; the vehicle information is transmitted to the road side unit by vehicle-to-road communication (V2I), and the road side unit transmits the all-area traffic state information to each vehicle by road-to-vehicle communication (I2V). The intelligent networked automobile reasonably adjusts the running state of the automobile by using the information, realizes the regional multi-automobile cooperative running, and further realizes the safe and efficient traffic of the road junction.
At present, the traffic control strategy of the intelligent network automobile at the highway turn road junction is researched by many people, but most of the existing researches are based on a linear kinematics control model, the emphasis is on realizing collision avoidance between automobiles by planning the change of vehicle kinematics parameters, the existing researches are established on the basis of a large amount of communication and high-efficiency calculation, the strong nonlinearity of the actual vehicle longitudinal dynamics is not considered, and the collision avoidance in the confluence process is difficult to be strictly ensured.
Disclosure of Invention
In order to realize that the intelligent network-connected automobile safely and efficiently passes through the ramp junction of the expressway, the invention provides an intelligent network-connected automobile safe self-organizing traffic control method focusing on dynamics control. Once vehicles on a ramp drive into the communication area, the roadside intelligent agent immediately collects the driving information of all vehicles in the communication area and judges the passing sequence according to the driving information, and each vehicle is informed of the vehicles needing to be followed after the passing sequence is determined; and each vehicle starts the vehicle longitudinal motion control based on the nonlinear dynamics, thereby ensuring that the inter-vehicle distance adjustment of the ramp vehicle is completed before the ramp vehicle reaches the conflict area. In addition, the method considers the influence of parameter time-varying uncertainty in the vehicle control process, better conforms to the actual scene, and enables the vehicle control process to be more reliable.
In order to achieve the purpose, the invention adopts the following technical scheme: an intelligent network-connected automobile safety self-organizing passing control method for expressway ramp ports is characterized by comprising the following steps:
step 1, when a ramp vehicle drives into a communication area, a roadside intelligent agent judges the passing sequence of all vehicles in the communication area;
step 2, determining respective following objects of all vehicles in the communication area;
step 3, realizing ramp confluence by using a nonlinear dynamics control method;
and 4, after the ramp vehicles pass through the confluence point, ending the confluence process, and switching each vehicle to the self-adaptive cruise control.
Further, step 1 specifically includes:
step 1.1, the roadside intelligent body detects that a vehicle enters a communication area, and the monitored moment is set as 0 moment;
step 1.2, the roadside intelligent agent collects the driving information of all vehicles on the ramp and all vehicles on the main road at the time 0 in the communication area, and the information comprises the speed and the position;
step 1.3, calculating the time length required by each vehicle to reach the confluence point by the roadside agent;
wherein, step 1.3 specifically includes:
step 1.3.1, calculation method for main road vehicle
Figure GDA0003474826220000021
Wherein the subscript i represents the parameter of vehicle number i, LiThe distance between the vehicle number i and the confluence point is represented; v. ofi(0) Indicates the speed of the vehicle No. i at the time point 0, tiThe estimated time of the number i vehicle on the main road reaching the confluence point is shown;
step 1.3.2, calculation method for ramp vehicle
Figure GDA0003474826220000022
Where the subscript j represents the parameters of the vehicle number j, vlimIndicating ramp speed limit, amax,jRepresents the maximum acceleration, S, of the vehicle # jacc,jIndicates the distance, v, required for the No. j vehicle to accelerate to the ramp speed limitj(0) Represents the speed of the j car at the time 0;
Figure GDA0003474826220000031
wherein t isjPredicting the time length of reaching the confluence point for the number j vehicle on the ramp, vj(0) Indicates the speed of the j car at time 0, LjIndicating the distance of the j car from the confluence point.
And step 1.4, sequencing the predicted arrival time of all vehicles at the confluence point, passing the vehicles with short time, further obtaining a passing sequence, and sending the sequence to each vehicle by the roadside intelligent agent.
Further, step 2 specifically includes:
step 2.1, after the step 1 is finished, each vehicle receives the passing sequence, and for the (k + 1) th vehicle passing through the confluence point, the following vehicle is the kth passing vehicle;
and 2.2, acquiring the running information of the following vehicle of each vehicle by using a vehicle-vehicle communication technology, wherein the information comprises speed, position and acceleration.
Further, step 3 specifically includes:
3.1, establishing a nonlinear longitudinal dynamic model of the vehicle;
Figure GDA0003474826220000032
wherein x isi(t) is the displacement; v. ofi(t) vehicle speed; u. ofiIs vehicle driving force or braking force; miIs the vehicle mass; c. Civi(t)|vi(t) | is an air resistance term; fiFor the rolling resistance and ramp resistance terms, the subscript i represents vehicle number i.
Step 3.2, further considering the perturbation of parameters of the appearance in the actual scene on the basis of the longitudinal dynamics model, and obtaining the longitudinal dynamics model with parameter time-varying uncertainty;
Figure GDA0003474826220000033
wherein σi(t) is the parameter time-varying uncertainty portion;
step 3.3, solving the nonlinear dynamical system control problem in the step 3.2, and giving an explicit expression u of the longitudinal force control law of the vehiclei(t)，
ui(t)＝p1+p2+p3
Wherein p is1Is a control law for a nominal part; p is a radical of2Is a control law for eliminating initial errors; p is a radical of3Is a control law for the uncertainty part.
The invention has the beneficial effects that:
1. the control method based on the vehicle nonlinear dynamics can ensure that the vehicles keep reasonable vehicle distance before entering the collision area, thereby realizing collision avoidance in the collision area;
2. the invention fully considers the driving requirements of different road sections in the expressway ramp entrance area, gives consideration to the safety, economy, comfort and high efficiency of vehicle driving outside the conflict area, and strictly ensures the safety of vehicle driving inside the conflict area;
3. the traffic control method used by the invention focuses on the nonlinear dynamic control of the vehicle, is simple and reliable to implement, and provides a new idea for the intelligent network-connected automobile self-organizing traffic method at the ramp port of the expressway.
Drawings
FIG. 1 is a schematic view of the division of the highway ramp entrance area;
FIG. 2 is a flow chart of a safe self-organizing traffic control method of an intelligent network-connected automobile at a ramp port of an expressway, according to the present invention;
fig. 3 is a schematic view of the vehicle position at the initial time of the expressway section.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflict.
The embodiment provides a safe self-organizing traffic control method for an intelligent network-connected automobile at a ramp port of an expressway, which comprises the following steps:
step 1, when a ramp vehicle drives into a communication area, a roadside intelligent agent judges the passing sequence of all vehicles in the communication area; wherein: the roadside agent is a signal tower established near a junction of a ramp, and has a communication function and a calculation function, wherein the communication function is realized by means of a Vehicle-to-Infrastructure (V2I) technology, and the calculation function is realized by means of a processor platform carrying a memory and an arithmetic unit;
step 1, detecting that a vehicle enters a communication area by a roadside intelligent object, and not setting the monitored moment as 0 moment;
step 1.2, the roadside intelligent agent collects the driving information of all vehicles (all vehicles in the vehicle include all vehicles on a ramp and all vehicles on a main road) in a communication area at the time 0, and the information includes speed and position;
step 1.3, the roadside agent calculates the time length required by each vehicle to reach the confluence point, and the calculation method is as follows
1) Calculation method for main road vehicle
Figure GDA0003474826220000041
Wherein the subscript i represents the parameter of vehicle number i, LiThe distance between the vehicle number i and the confluence point is represented; v. ofi(0) Indicates the speed of the vehicle No. i at the time point 0, tiThe estimated time of the number i vehicle on the main road reaching the confluence point is shown;
2) calculation method for ramp vehicle
Figure GDA0003474826220000051
Where the subscript j represents the parameters of the vehicle number j, vlimIndicating ramp speed limit, amax,jRepresents the maximum acceleration, S, of the vehicle # jacc,jAnd the distance required by the vehicle number j to accelerate to the ramp speed limit is shown.
Figure GDA0003474826220000052
Wherein t isjPredicting the time length of reaching the confluence point for the number j vehicle on the ramp, vj(0) Indicates the speed of the j car at time 0, LjThe distance between the vehicle number j and the confluence point is represented;
and step 1.4, sequencing the predicted arrival time of all vehicles at the confluence point, passing the vehicles with short time, further obtaining a passing sequence, and sending the sequence to each vehicle by the roadside intelligent agent.
And 2, determining respective following objects by each vehicle in the communication area.
Wherein:
step 2.1; after step 1, each vehicle receives the passing sequence, and for the (k + 1) th vehicle passing through the confluence point, the following vehicle is the kth passing vehicle;
step 2.2; with vehicle-to-vehicle communication technology, each vehicle acquires its driving information, including speed, position, and acceleration, of the following vehicle.
Step 3, realizing safe and efficient ramp confluence by using nonlinear dynamics control method
Wherein:
3.1, establishing a nonlinear longitudinal dynamic model of the vehicle;
Figure GDA0003474826220000053
wherein x isi(t) is the displacement; v. ofi(t) vehicle speed; u. ofiIs vehicle driving force (or braking force); miIs the vehicle mass; c. Civi(t)|vi(t) | is an air resistance term; fiFor the rolling resistance and ramp resistance terms, the subscript i represents vehicle number i.
Step 3.2, combining the parameter perturbation existing in the objective under the actual scene on the basis of the longitudinal dynamics model to obtain a longitudinal dynamics model with parameter time-varying uncertainty;
Figure GDA0003474826220000061
wherein σi(t) is the parameter time-varying uncertainty portion;
step 3.3, solving the nonlinear dynamical system control problem in the step 3.2, and giving an explicit expression u of the longitudinal force control law of the vehiclei(t)，
ui(t)＝p1+p2+p3
Wherein p is1Is a control law for a nominal part; p is a radical of2Is a control law for eliminating initial errors; p is a radical of3Is a control law for the uncertainty part. To obtain the display expression, the following definitions are firstly performed:
1) vehicle spacing error ei(t) satisfies
Figure GDA0003474826220000062
Wherein h isiIs a constant greater than zero, ei＝dd-xi-1+xi+li-1Is a vehicle-to-vehicle distance error, wherein ddIs a constant value, representing a desired inter-vehicle distance;
2)Mi、ciand FiBoth consisting of a nominal part and a time-varying part, the nominal part being invariant with time, respectively denoted
Figure GDA0003474826220000063
And
Figure GDA0003474826220000064
the control law displays the expression as follows:
Figure GDA0003474826220000065
II thereiniAs the worst case of uncertainty of the parameter, i.e. maximum deviation
And 4, step 4: and when the ramp vehicles pass through the confluence point and then the confluence process is finished, each vehicle is switched to the self-adaptive cruise control.
Although the present application has been disclosed in detail with reference to the accompanying drawings, it is to be understood that such description is merely illustrative and not restrictive of the application of the present application. The scope of the present application is defined by the appended claims and may include various modifications, adaptations, and equivalents of the invention without departing from the scope and spirit of the application.

Claims (1)

1. An intelligent networking automobile safety self-organizing passing control method for expressway ramp ports is characterized by comprising the following steps:
step 1, when a ramp vehicle drives into a communication area, a roadside intelligent agent judges the passing sequence of all vehicles in the communication area;
the step 1 specifically comprises the following steps:
step 1.1, the roadside intelligent body detects that a vehicle enters a communication area, and the monitored moment is set as 0 moment;
step 1.2, the roadside intelligent agent collects the driving information of all vehicles on the ramp and all vehicles on the main road at the time 0 in the communication area, and the information comprises the speed and the position of the vehicles;
step 1.3, calculating the time length required by each vehicle to reach the confluence point by the roadside agent;
wherein, step 1.3 specifically includes:
step 1.3.1, calculation method for main road vehicle
Figure FDA0003474826210000011
Wherein the subscript i represents the parameter of vehicle number i, LiThe distance between the vehicle number i and the confluence point is represented; v. ofi(0) Indicates the speed of the vehicle No. i at the time point 0, tiThe estimated time of the number i vehicle on the main road reaching the confluence point is shown;
step 1.3.2, calculation method for ramp vehicle
Figure FDA0003474826210000012
Where the subscript j represents the parameters of the vehicle number j, vlimIndicating ramp speed limit, amax，jRepresents the maximum acceleration, S, of the vehicle # jaccRepresenting the distance required for accelerating to the ramp speed limit;
Figure FDA0003474826210000013
wherein t isjPredicting the time length of reaching the confluence point for the number j vehicle on the ramp, vj(0) Indicates the speed of the j car at time 0, LjThe distance between the vehicle number j and the confluence point is represented;
step 1.4, sequencing the predicted arrival time of all vehicles at the confluence point, wherein the time is short and the vehicles pass through the confluence point first, so as to obtain a passing sequence, and sending the roadside agent sequence to each vehicle;
step 2, determining respective following objects of all vehicles in the communication area;
step 2.1, after the step 1 is finished, each vehicle receives the passing sequence, and for the (k + 1) th vehicle passing through the confluence point, the following vehicle is the kth passing vehicle;
2.2, each vehicle acquires the running information of the following vehicle by using a vehicle-vehicle communication technology, wherein the information comprises the speed, the position and the acceleration of the vehicle;
step 3, realizing ramp confluence by using a nonlinear dynamics control method;
3.1, establishing a nonlinear longitudinal dynamic model of the vehicle;
Figure FDA0003474826210000021
wherein x isi(t) is the displacement; v. ofi(t) vehicle speed; u. ofiIs vehicle driving force or braking force; miIs the vehicle mass; c. Civi(t)|vi(t) | is an air resistance term; fiSubscript i represents vehicle number i as rolling resistance and ramp resistance terms;
step 3.2, combining the parameter perturbation existing in the objective under the actual scene on the basis of the nonlinear longitudinal dynamics model to obtain a longitudinal dynamics model with parameter time-varying uncertainty;
Figure FDA0003474826210000022
wherein σi(t) is the parameter time-varying uncertainty portion;
step 3.3, solving the nonlinear dynamical system control problem in the step 3.2, and giving an explicit expression u of the longitudinal force control law of the vehiclei(t)，
ui(t)＝p1+p2+p3
Wherein p is1Is a control law for a nominal part; p is a radical of2Is a control law for eliminating initial errors; p is a radical of3To control laws for uncertainty parts
And 4, after the ramp vehicles pass through the confluence point, ending the confluence process, and switching each vehicle to the self-adaptive cruise control.
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