CN111899509A - Intelligent networking automobile state vector calculation method based on vehicle-road information coupling - Google Patents

Intelligent networking automobile state vector calculation method based on vehicle-road information coupling Download PDF

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CN111899509A
CN111899509A CN202010702827.2A CN202010702827A CN111899509A CN 111899509 A CN111899509 A CN 111899509A CN 202010702827 A CN202010702827 A CN 202010702827A CN 111899509 A CN111899509 A CN 111899509A
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CN111899509B (en
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王庞伟
汪云峰
王力
张名芳
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North China University of Technology
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
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    • 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
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • 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
    • G08G1/042—Detecting movement of traffic to be counted or controlled using inductive or magnetic detectors
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
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Abstract

The invention provides an intelligent networking automobile state vector calculation method based on vehicle-road information coupling, establishes a multi-lane road section scene based on V2X, and designs a vehicle multi-lane space-time trajectory optimization method based on the scene. The method combines the current state information of the vehicle, the environment information near the vehicle, the signal timing information of the downstream of the road section and the like to generate the vehicle space-time trajectory based on V2X. The method can optimize and generate the space-time track of the passing vehicles in the road section in real time according to different road environments and traffic states, increases the mutual cooperation capacity among the vehicles, improves the safety of the passing road section and the vehicle passing efficiency of the intersection, reduces the energy consumption level of the vehicles, and provides a new solution and a theoretical basis for ensuring the road traffic safety and improving the travel efficiency.

Description

Intelligent networking automobile state vector calculation method based on vehicle-road information coupling
Technical Field
The invention belongs to the technical field of vehicle-road cooperation/main line traffic flow control, and particularly relates to an intelligent internet automobile state vector calculation method based on vehicle-road information coupling, which is suitable for any signalized intersection road section in an urban road traffic network.
Background
For an urban traffic network, except for a suburban intersection with small flow rate, a signal-free self-organizing control method is adopted, and at present, an urban traffic system controls intersection traffic flow. The intersection is a main node for connecting all road sections, and the reasonable planning of traffic flow in all road sections is also an important component for improving the traffic efficiency of the intersection. The driving behavior of the vehicle during driving of the road section can be divided into a lane keeping behavior and a lane changing behavior, wherein the course of the overtaking behavior can be disassembled into at least two lane changing behaviors. The lane-changing behavior has more factors to consider than the lane-keeping behavior, so the lane-changing behavior also has higher complexity, and the probability of accidents is greater than that of the lane-keeping behavior. Moreover, the accident site caused by the lane changing behavior occupies multiple lanes, so that the road jam condition is more serious. Apollo establishes a Frelont (Frenet) coordinate for motion decomposition in a trajectory planning based on the current environment information of the vehicle, and improves the traffic efficiency of the vehicle on the premise of ensuring the safety of the vehicle by optimizing the driving trajectory of the vehicle through a segmented jerk algorithm. However, the algorithm only optimizes the track for a single vehicle, and cannot improve the traffic efficiency of the road section and even the vehicles in the road network as a whole. In addition, in order to improve the traffic throughput of road sections in the road network, scholars at home and abroad improve the traffic efficiency of driving into the intersection by optimizing the space-time trajectory of the vehicle driving out of the current lane. However, the common traffic efficiency problem of a multi-lane road section cannot be directly improved by an optimization method of multiple single lanes, and meanwhile, the common traffic efficiency problem has more data calculation amount and higher system complexity.
Aiming at the problems, the invention establishes a multi-lane road section scene based on V2X, and designs a vehicle multi-lane space-time trajectory optimization method based on the scene. The method combines the current state information of the vehicle, the environment information near the vehicle, the signal timing information of the downstream of the road section and the like to generate the vehicle space-time trajectory based on V2X. In addition, the invention designs an online vehicle collaborative space-time trajectory optimization method based on V2X to reduce the influence of the vehicle lane changing process on the space-time trajectory. The method specifies a lane change rule, a lane change safety inspection rule, a collaborative lane change spatiotemporal trajectory updating method and a collaborative lane change spatiotemporal trajectory optimization method based on reinforcement learning of the online collaborative optimization of the vehicles, can effectively solve the collaborative lane change problem of multiple lanes and multiple vehicles, and provides a vehicle spatiotemporal trajectory optimization method in a road section for solving the problem of congestion at a signalized intersection.
Prior Art
1 vehicle road communication technology
With the development of technologies such as wireless communication and new generation internet, the internet of things technology is also applied to road traffic. The Vehicle networking technology of V2X (Vehicle to electric) based on the Vehicle-road communication technology includes a Vehicle-to-Vehicle cooperation technology of V2V (Vehicle to Vehicle), a Vehicle-to-road cooperation technology of V2R (Vehicle to Route), a Vehicle-to-infrastructure cooperation technology of V2I (Vehicle to infrastructure), a Vehicle-to-Cloud and Cloud technology of V2C (Vehicle to Cloud), and the like. The two major standards currently implementing V2X are the LTE-V and DSRC standards, respectively. The omnibearing implementation of the V2X technology can effectively ensure the real-time interaction of the relevant information of the vehicle and fully realize the effective cooperation of the vehicle road system. The road traffic safety is ensured, the vehicle passing efficiency is improved, and a foundation for information intercommunication is laid for forming a safe, interconnected and efficient traffic control system.
2 traffic guidance technology
The traffic guidance technology is a technology based on electronic information, computer technology, network communication and the like, and provides information guidance and other functions for road users according to the starting position and the target position of a traveler. The technology mainly comprises an in-vehicle induction part and an out-vehicle induction part. The in-vehicle guidance is to provide lane distribution information, vehicle speed guidance information and the like for a single vehicle so that the vehicle can reasonably and efficiently avoid emergency situations such as congestion, traffic accidents and the like. And the outside guidance detects the traffic flow group through the traffic flow detector, and performs macroscopic planning and guidance on the traffic flow. The traffic guidance technology improves the controllability of traffic flow, and improves the utilization rate of road traffic.
3 reinforcement learning technique
Reinforcement learning techniques are one type of machine learning techniques, and are also known to encourage or enhance learning. Reinforcement learning techniques are commonly used learning strategies to address the complex situation in which agents interact in an environment. The technique defines rewards to facilitate the model's self-learning function by continually exploring and utilizing relationships between states and actions. The appearance of the reinforcement learning technology provides a strategy mode for realizing artificial intelligence, so that the system can learn new processing capacity when meeting new complex conditions and generalize the new processing capacity to other similar conditions, the computing efficiency of the system is improved, and the processing capacity of the system for dealing with diversified problems is enhanced.
The prior art is not enough
1. At present, the optimization problem of the vehicle space-time trajectory is still limited to macroscopic path optimization or single-lane space-time trajectory, and a multi-lane space-time trajectory optimization algorithm is lacked. The optimization problem of the single-lane space-time trajectory is a problem of continuous time and one-dimensional continuous space; the optimization problem of the multi-lane space-time trajectory is a continuous time and two-dimensional space problem. Compared with the optimization problem of the single-lane space-time trajectory, the optimization problem of the multi-lane space-time trajectory has higher complexity, and the optimization problem of the multi-lane space-time trajectory is difficult to directly solve through the existing lane changing rule.
2. The traditional path method has limited information acquisition capability, often depends on a fixed traffic detector, and lacks real-time, efficient and accurate vehicle running information and traffic state information. The traditional experimental scene lacks robustness facing complex scenes and is difficult to be widely popularized. If the experimental scene can be modularized and the requirements of multi-direction multi-speed vehicle input and multi-direction multi-lane vehicle output are met, preparation for future multi-intersection can be made.
3. The traditional path method mostly does not consider the multi-vehicle cooperative lane changing factor and lacks the consideration of the interference factor of surrounding vehicles. The problem of the multi-lane cooperative track optimization is different from the problem of the single-vehicle track change track optimization, and is a potential multi-vehicle cooperative track change track optimization problem. The mathematical model is established for the existing lane changing rule, so that the multi-vehicle target is difficult to meet simultaneously; for mathematical calculations of the system, it is difficult to process a large amount of data for multiple vehicles simultaneously. And the condition of vehicle lane changing belongs to a common phenomenon, and if a multi-lane vehicle cooperative lane changing rule can be established, the problem of multi-lane and multi-vehicle cooperative lane changing can be orderly and reasonably solved.
Disclosure of Invention
Aiming at the defects of the three related technologies, the invention fully utilizes the advantages of the vehicle-road cooperative theory, establishes a multi-lane road section experimental scene based on V2X, generates a multi-lane space-time track according to the vehicle environment and the information such as target lane phase timing and the like, completes the optimization of the multi-lane vehicle space-time track through the vehicle cooperative lane changing rule, and realizes the optimal strategy of the vehicle passing through the target road section. The following technical scheme is adopted specifically:
the method comprises the following steps:
step (1) determining a state vector of a vehicle
Definition number CmnIs xmn(t)：
xmn(t)＝[xmn(t),ymn(t),vmn(t),vmn'(t)]T(1)
In the formula, CmnThe nth vehicle from the upstream to the downstream is the mth lane from the outside to the inside of the road section of the driving direction of the vehicle; x is the number ofmn(t)，ymn(t) is a vehicle CmnAt the position of the time t, the origin of coordinates is the starting point of the upstream of the vehicle, the positive direction of the x-axis coordinates is the traffic flow driving direction, and the positive direction of the y-axis coordinates is the direction from the outer lane to the inner lane of the traffic flow road section; v. ofmn(t) is a vehicle CmnThe current running speed of the vehicle at the time t, and the positive direction of the coordinate is the traffic flow running direction; v. ofmn' (t) is a vehicle CmnThe current transverse speed of the vehicle at the time t, and the positive direction of the coordinate is the direction from the outer lane to the inner lane of the traffic flow road section;
current vehicle directional acceleration umn(t)：
umn(t)＝[umn(t),umn'(t)]T(2)
In the formula umn(t) is a vehicle CmnThe current longitudinal acceleration of the vehicle at the time t, and the positive direction of the coordinate is the traffic flow driving direction; u. ofmn' (t) is a vehicle CmnThe current lateral acceleration of the vehicle at the time t, and the positive direction of the coordinate is the direction from the outer lane to the inner lane of the traffic flow road section;
calculating vehicle target lane signal timing information and adjacent lane traffic flow information
Figure BDA0002593464020000031
Figure BDA0002593464020000032
Figure BDA0002593464020000041
In the formula (I), the compound is shown in the specification,
Figure BDA0002593464020000042
for vehicle CmnSignal timing information of the present time, Im+1(t) and Im-1(t) is traffic flow information of m +1 and m-1 of adjacent lanes,
Figure BDA0002593464020000043
is composed of
Figure BDA0002593464020000044
Phase remaining time; rmThe red light duration for lane m; gmThe green time duration for lane m;
Figure BDA0002593464020000045
and
Figure BDA0002593464020000046
the average traffic flow speed of the inner measuring lane m +1 and the outer side lane m-1 is obtained; k is a radical ofm+1(t) and km-1(t) is the average traffic flow of the inner measuring lane m +1 and the outer lane m-1Density;
calculating average traffic flow velocity
Figure BDA0002593464020000047
And average traffic flow density km+1(t)
Figure BDA0002593464020000048
Figure BDA0002593464020000049
In the formula, N is a vehicle number;
Figure BDA00025934640200000410
the number is the maximum number in the numbers of the vehicles in the lane m;
Figure BDA00025934640200000411
the minimum serial number is the serial number of the vehicles in the lane m; l is the road segment length;
obtaining a system state equation
Figure BDA00025934640200000412
Step (2) calculating a cost function
Defining a cost function
Figure BDA00025934640200000413
In the formula (I), the compound is shown in the specification,
Figure BDA00025934640200000414
is a vehicle CmnThe moment of departure of the road segment;
Figure BDA00025934640200000415
is a vehicle CmnThe time of driving into the road section;
Figure BDA00025934640200000416
is a fixed cost for the process;
Figure BDA00025934640200000417
is a variable cost of the process;
calculating a fixed cost
Figure BDA00025934640200000418
In the formula (I), the compound is shown in the specification,
Figure BDA00025934640200000419
is a vehicle CmnA target moment of driving out a road segment;
Figure BDA00025934640200000420
is a vehicle CmnA desired speed of the driven road segment;
Figure BDA00025934640200000421
is the vehicle target lane; d is the lane width; w is a1The weight coefficient is the travel time of the road section; w is a2A weight coefficient which is the road section running length; w is a3Is a weight coefficient, w, of the expected speed at the downstream exit of the outgoing section4Is the weight coefficient of the departure from the target lane;
calculating variable costs
h(xmn(t),umn(t))＝w5·(umn(t)2+2umn(t)·vmn(t))·χ(umn(t))+w6·(umn'(t)2+2umn'(t)·vmn'(t)) (11)
In the formula, w5Weight coefficient, w, for the energy change caused by the longitudinal acceleration of the vehicle6A weight coefficient that is a change in energy caused by lateral acceleration of the vehicle; χ (u)mn(t)) is the Heaviside function of the vehicle longitudinal acceleration,
Figure BDA0002593464020000051
calculating vehicle CmnUnlimited free time to exit a road segment
Figure BDA0002593464020000052
Figure BDA0002593464020000053
In the formula (I), the compound is shown in the specification,
Figure BDA0002593464020000054
limiting the speed of the vehicle in the free running area of the road section;
Figure BDA0002593464020000055
limiting the speed of the vehicle at a downstream exit of the m lanes;
Figure BDA0002593464020000056
for vehicle CmnA maximum acceleration;
Figure BDA0002593464020000057
is the vehicle maximum deceleration;
vehicle CmnTarget time of departure of a road section
Figure BDA0002593464020000058
Controlled within a green light signal that can be transmitted
Figure BDA0002593464020000059
In the formula (I), the compound is shown in the specification,
Figure BDA00025934640200000510
for vehicle CmnA candidate moment to drive out a road segment; ximA set of green light periods for lane m; int () is a floor function;
calculating vehicle CmnCandidate time of departure
Figure BDA00025934640200000511
Figure BDA00025934640200000512
In the formula, tm h2hThe time interval of the lowest vehicle heads of two adjacent vehicles of m lanes at the downstream exit of the road section;
calculating the expected speed of the vehicle as the speed limit at the exit of the downstream exit
Figure BDA00025934640200000513
Calculating a cost function
Figure BDA0002593464020000061
Step (3) determining constraints of spatiotemporal trajectories
1) Determination of CmnInitial state of the vehicle
Figure BDA0002593464020000062
2) Determining inter-vehicle distance constraints
xmn(t+τmn)≤xm(n-1)(t)-dmn(19)
In the formula, τmnIndicating vehicle CmnAnd its front vehicle Cm(n-1)Time shift of (a); dmnIndicating vehicle CmnAnd its front vehicle Cm(n-1)Spatial displacement of (a);
3) determining vehicle speed constraints
Figure BDA0002593464020000063
Lateral velocity constraint of vehicle
Figure BDA0002593464020000064
In the formula, alpha-maxIndicating the direction of energy of the front wheels of the vehicleMaximum angle of left turn; α (t) represents a steering angle of a front wheel of the current vehicle; alpha is alphamaxRepresents the maximum angle at which the front wheels of the vehicle can be steered to the right;
4) determining vehicle acceleration constraints
Figure BDA0002593464020000065
In the formula u-maxAn acceleration magnitude representing a maximum longitudinal braking retardation of the vehicle; u. ofmaxAn acceleration magnitude representing a maximum longitudinal acceleration of the vehicle; u. of-max' represents the maximum acceleration of the vehicle laterally to the left; u. ofmax' represents the maximum acceleration of the vehicle laterally to the right;
5) determining vehicle jerk constraints
Figure BDA0002593464020000071
In the formula, j-maxRepresenting the maximum acceleration magnitude of the vehicle in the longitudinal direction and the back direction; j is a function ofmaxRepresenting the magnitude of the maximum acceleration of the vehicle in the longitudinal direction; j is a function of-max' represents the maximum jerk magnitude laterally leftward of the vehicle; j is a function ofmax' represents the magnitude of the maximum jerk laterally rightward of the vehicle;
6) determining signal timing constraints
Figure BDA0002593464020000072
7) Determining wireless communication constraints
Figure BDA0002593464020000073
Plpmn(t)＜15％ (27)
In the formula (I), the compound is shown in the specification,
Figure BDA0002593464020000074
indicating vehicle CmnTime delay condition at time t; plp|mn(t) representsVehicle CmnThe packet loss probability at time t; solving method based on minimum value principle in step (4)
Establishing Hamiltonian
H(x,u,λ,t)＝λT·f(x,u,t)+h(x,u,t) (28)
Where λ is defined as the co-state vector of the vector x, representing a small change from the vector x
Figure BDA0002593464020000077
The additional cost of the resulting change in J;
the essential condition is that
Figure BDA0002593464020000075
Figure BDA0002593464020000076
The Hamilton function of the control problem for the optimization of the vehicle space-time trajectory is
Hmn＝λ1·vmn(t)+λ2·umn(t)+w5·(umn(t)2+2umn(t)·vmn(t))·χ(umn(t)) (31)
Finally, the following functional relationship is determined
Figure BDA0002593464020000081
Wherein C is a undetermined constant;
solving process state vectors
Figure BDA0002593464020000082
Drawings
Fig. 1 is a schematic diagram of a multi-lane road segment scene based on V2X.
FIG. 2 is a schematic diagram of a vehicle multilane spatiotemporal trajectory optimization architecture based on V2X.
FIG. 3 is a schematic diagram of multilane spatiotemporal trajectory optimization.
FIG. 4 is number CmnThe schematic diagram of the lane changing environment of the vehicle.
Fig. 5 is a flow chart of the lane change security check state.
FIG. 6 is a schematic representation of the lane-by-lane vehicle spatiotemporal trajectories through the switch-over process.
Detailed Description
Intelligent networking automobile state vector calculation method based on vehicle-road information coupling
The vehicle multi-lane spatiotemporal trajectory optimization method based on V2X redefines the state vector of the vehicle first. In the state vector of the vehicle, not only the state information of the vehicle itself, such as the conventional position, speed and acceleration, but also the traffic state information, such as the signal timing of the target lane, the traffic density of the adjacent lane and the average traffic speed, are included. The invention firstly introduces the establishment of a multi-lane road section scene and a flow architecture based on V2X; then, state vector definition of the vehicle is described in detail based on the environment and the process; secondly, deducing a cost function and constraint conditions for defining the vehicle track through a formula; and finally, dynamically solving the vehicle track by using a minimum value principle.
The multi-lane road section scene based on V2X takes the upstream of the road section as the scene entrance of the road section, and takes the vehicle driving into the road section and the vehicle state information thereof as system input; taking the downstream of the road section as a scene exit of the road section, and taking the vehicle entering the signalized intersection and the vehicle state information thereof as system output; and establishing a modularized multi-vehicle road section area by taking LTE-V as a communication mode of V2X. The area includes a detection zone, a speed change lane change zone, and a speed change following zone. A multi-lane road segment scenario based on V2X is shown in fig. 1.
The detection area is positioned at the upstream entrance of the road section along the advancing direction of the traffic flow, the width of the detection area is the width of the whole road section, and the length of the detection area is about the length of a car. A separate toroidal coil detector is arranged at each lane entrance. Each detector can send the position information and the time information of the vehicle when the vehicle enters the road section to a system for waiting processing, and informs the current vehicle to join the internet of vehicles of the road section, thereby realizing the intercommunication of the current information and the environmental information of the vehicle.
The speed change lane changing area is located at the middle and middle of the road section along the advancing direction of the traffic flow, the width of the speed change lane changing area is the width of the whole road section, and the length of the speed change lane changing area is a variable area determined by a speed change following area. In the speed change lane change area, the vehicle tracks the track generated by the system to complete the actions of accelerating to the expected speed of the vehicle, driving at a constant speed, changing lanes, decelerating to the target speed and the like, so that the vehicle can ensure the safety and simultaneously improve the maneuverability of the traffic flow of the road section.
The speed change following area is positioned at the downstream outlet of the road section along the advancing direction of the traffic flow, the width of the speed change following area is the width of each lane, and the length of the speed change following area is +30m of the length of the queued vehicles on the current lane. The area is free of a traditional hardware detector, the lane changing behavior of the vehicle is limited in a virtual solid line mode, and the purpose is to ensure that the lane changing frequency of the vehicle is reduced at the downstream of a road section so as to ensure the safety and the crossing traffic efficiency
The vehicle multi-lane space-time trajectory optimization architecture based on V2X is shown in FIG. 2.
Firstly, vehicle environment and vehicle state information collected by road equipment and vehicle-mounted equipment are preprocessed by a system to obtain a target lane and a time interval of a road section; then generating an offline space-time trajectory through various vehicle state constraints on the basis of the above steps; in the process, the lane changing process of the vehicles ensures the passing efficiency of each vehicle through the online vehicle collaborative space-time trajectory optimization; and finally, checking the vehicle lane change environment based on the real-time sensor data of the vehicle and then performing lane change operation so as to complete the optimization of the vehicle multi-lane space-time trajectory based on V2X.
The design of the vehicle offline space-time trajectory generation method based on V2X is based on the target lane and the time period of the vehicle when the vehicle drives out of the road section, which are obtained by the system preprocessing resource dynamic allocation method, and the optimal driving trajectory of the passing road section is planned offline, so that the traffic flow passing efficiency of the road section is improved and the average parking waiting time of the traffic flow is reduced. The optimized spatio-temporal trajectory graph and the original free driving spatio-temporal trajectory graph are shown in fig. 3.
Step 1 vehicle state vector calculation
In order to make the vehicle have corresponding mapping relation in the V2X systemThe vehicle entering the road section is defined as number CmnIn which C ismnAnd the m-th lane represents the driving direction road section of the vehicle from outside to inside, and the n-th vehicle from upstream to downstream. Definition number CmnIs xmn(t) is shown in formula (1).
xmn(t)＝[xmn(t),ymn(t),vmn(t),vmn'(t)]T(1)
In the formula xmn(t)，ymn(t) is a vehicle CmnAt the position (m) of the time t, the origin of coordinates of the position is the upstream starting point of the vehicle, the positive direction of x-axis coordinates is the traffic flow driving direction, and the positive direction of y-axis coordinates is the direction from the outer lane to the inner lane of the traffic flow road section; v. ofmn(t) is a vehicle CmnThe current running speed (m/s) of the vehicle at the time t, and the positive coordinate direction is the traffic flow running direction; v. ofmn' (t) is a vehicle CmnAnd (3) the current transverse speed (m/s) of the vehicle at the time t, wherein the positive coordinate direction is the direction from the outer lane to the inner lane of the traffic road section.
The system control input is the current vehicle directional acceleration umn(t) is shown in equation (2).
umn(t)＝[umn(t),umn'(t)]T(2)
In the formula umn(t) is a vehicle CmnMagnitude of current longitudinal acceleration (m/s) of vehicle at time t2) And the positive direction of the coordinate is the traffic flow driving direction. u. ofmn' (t) is a vehicle CmnMagnitude of current lateral acceleration (m/s) of vehicle at time t2) And the positive direction of the coordinate is the direction from the outer lane to the inner lane of the traffic flow road section.
In order to better define the optimization strategy of the vehicle track under the vehicle-road cooperative environment, the signal timing information of the vehicle target lane
Figure BDA0002593464020000101
And traffic flow information I of m +1 and m-1 of adjacent lanesm+1(t) and Im-1(t) of (d). As shown in equation (3), equation (4) and equation (5).
Figure BDA0002593464020000102
Figure BDA0002593464020000103
Figure BDA0002593464020000104
In the formula (I), the compound is shown in the specification,
Figure BDA0002593464020000105
for vehicle CmnThe current time of day of the target phase state,
Figure BDA0002593464020000106
is composed of
Figure BDA0002593464020000107
Phase remaining time; rmThe red light duration for lane m; gmThe green time duration for lane m;
Figure BDA0002593464020000108
and
Figure BDA0002593464020000109
the average traffic flow speed of the inner measuring lane m +1 and the outer side lane m-1 is obtained; k is a radical ofm+1(t) and km-1(t) is the average traffic flow density of the inner lane m +1 and the outer lane m-1. Average traffic flow velocity
Figure BDA00025934640200001010
And average traffic flow density km+1(t) can be expressed by the formula (6) and the formula (7).
Figure BDA00025934640200001011
Figure BDA00025934640200001012
In the formula, N is a vehicle number;
Figure BDA00025934640200001013
the number is the maximum number in the numbers of the vehicles in the lane m;
Figure BDA00025934640200001014
the minimum serial number is the serial number of the vehicles in the lane m; l is the link length.
The system state equation is shown in equation (8).
Figure BDA0002593464020000111
To facilitate the computational experiments of the method, the vehicles within the road section need to satisfy the following assumptions: (1) the vehicles are all single vehicles with the same length, width and height. (2) No special weather influence exists and the road adhesion coefficient is kept constant. (3) Roads in the road section are straight roads, and vehicles are driven in or out due to the conditions of no ramp, no motor vehicle lane, no parking space and the like. (4) The road section and the road are kept horizontal and have no inclination angle change, namely, the vehicle cannot be in the condition of ascending or descending.
Step 2 cost function calculation
In order to ensure that the vehicle can accurately follow the driving track designed by the system, considering that the vehicle comprises two parts of fixed cost and variable cost in the process of driving from upstream to downstream, a cost function is defined as shown in formula (9).
Figure BDA0002593464020000112
In the formula (I), the compound is shown in the specification,
Figure BDA0002593464020000113
is a vehicle CmnThe moment of departure of the road segment;
Figure BDA0002593464020000114
is a vehicle CmnThe time of driving into the road section;
Figure BDA0002593464020000115
is a fixed cost for the process;
Figure BDA0002593464020000116
is a variable cost of the process. The fixed cost comprises the cost of fixed items such as the running distance is the length of the road section, the downstream exit of the running road section is the content of the expected speed and the running time of the road section are consistent with the expected target time, and the function can be expressed by the formula (10).
Figure BDA0002593464020000117
In the formula (I), the compound is shown in the specification,
Figure BDA0002593464020000118
is a vehicle CmnA target moment of driving out a road segment;
Figure BDA0002593464020000119
is a vehicle CmnA desired speed of the driven road segment;
Figure BDA00025934640200001110
is the vehicle target lane; d is the lane width; w is a1Is a weight coefficient of a travel time of a link, and w1∈R+；w2Is a weight coefficient of the length of travel of the road section, and w2∈R+；w3Is a weight coefficient of a desired speed at a downstream exit of the outgoing section, and w3∈R+。w4Is a weight coefficient of departure from a target lane, and w4∈R+。
The variable cost includes longitudinal acceleration and deceleration, lateral acceleration and deceleration, and the like during the running of the vehicle, and the function can be expressed by formula (11).
h(xmn(t),umn(t))＝w5·(umn(t)2+2umn(t)·vmn(t))·χ(umn(t))+w6·(umn'(t)2+2umn'(t)·vmn'(t)) (11)
In the formula, w5A weight coefficient for an energy change caused by a longitudinal acceleration of the vehicle, and w5∈R+。w6A weight coefficient for an energy change caused by a lateral acceleration of the vehicle, and w6∈R+；χ(umn(t)) is the Heaviside function of the vehicle longitudinal acceleration, whereby the cost of acceleration during longitudinal deceleration, which does not contribute to variable costs, can be separated, and is given by equation (12).
Figure BDA0002593464020000121
Time when vehicle rapidly passes through intersection in low-density traffic flow state, namely vehicle CmnUnlimited free time to exit a road segment
Figure BDA0002593464020000122
Can be calculated from equation (13).
Figure BDA0002593464020000123
In the formula (I), the compound is shown in the specification,
Figure BDA0002593464020000124
limiting the speed of the vehicle in the free running area of the road section;
Figure BDA0002593464020000125
limiting the speed of the vehicle at a downstream exit of the m lanes;
Figure BDA0002593464020000126
for vehicle CmnA maximum acceleration;
Figure BDA0002593464020000127
the maximum deceleration of the vehicle.
In order to improve the passing rate of the networked automobiles at the intersection, the networked automobile CmnTarget time of departure of a road section
Figure BDA0002593464020000128
Control is within the green light signal that can be transmitted, and can be selected by equation (14).
Figure BDA0002593464020000129
In the formula (I), the compound is shown in the specification,
Figure BDA00025934640200001210
for vehicle CmnA candidate moment to drive out a road segment; ximA set of green light periods for lane m; int () is a floor function.
Vehicle CmnCandidate time of departure
Figure BDA00025934640200001211
Can be calculated from equation (15).
Figure BDA00025934640200001212
In the formula, tm h2hThe time interval of the lowest car heads of two adjacent cars in the m lanes at the downstream exit of the road section.
In order to ensure maximum efficiency of vehicle traffic, the desired speed of vehicle egress is defined as the speed limit at the egress downstream exit, as shown in equation (16).
Figure BDA00025934640200001213
In summary, the cost function can be derived as shown in equation (17).
Figure BDA0002593464020000131
Step 3 constraint condition determination of space-time trajectory
In addition to minimizing the cost function value, the process control problem of the internet-connected vehicle running in the road section needs to satisfy the following 5 types of constraint conditions: initial vehicle state, inter-vehicle distance, speed, acceleration, jerk, and other factors that affect safety and comfort.
1) Initial vehicle state
When a vehicle enters a target road section and passes through a geomagnetic coil of a road section detection area, the vehicle is defined as a number CmnThe vehicle of (1). If the longitudinal number n reaches the maximum value n of the longitudinal numbermaxThen the count is restarted from 1. Maximum value n of longitudinal numberingmaxThe number of the vehicles is 2 times larger than that of the vehicles in the current lane oversaturation state, so that the stability of the traffic flow number is guaranteed. Meanwhile, the definition number is CmnThe initial state of the vehicle of (1) is as shown in equation (18).
Figure BDA0002593464020000132
2) Vehicle spacing constraint
The vehicle is inevitably in a following state during traveling, and therefore, for the vehicle C on the m lanesmnAll with the vehicle C in front of itm(n-1)A certain amount of spatial displacement and temporal displacement is guaranteed. The safety constraint formula is shown as formula (19).
xmn(t+τmn)≤xm(n-1)(t)-dmn(19)
In the formula, τmnIndicating vehicle CmnAnd its front vehicle Cm(n-1)Time shift of (a); dmnIndicating vehicle CmnAnd its front vehicle Cm(n-1)Is measured.
3) Vehicle speed constraint
In order to ensure the safety of the vehicles in the road section in the running process, the vehicles in the road section are subjected to speed constraint. For longitudinal speed constraint, namely traffic flow direction constraint, the maximum speed is the speed limit in the road section, and the minimum speed is 0. As shown in equation (20).
Figure BDA0002593464020000133
For the lateral speed constraint, the vehicle yaw angle is mainly constrained according to the vehicle dynamics. As shown in equation (21).
α-max≤α(t)≤αmax(21)
In the formula, alpha-maxRepresents the maximum angle at which the front wheels of the vehicle can be steered to the left; α (t) represents a steering angle of a front wheel of the current vehicle; alpha is alphamaxRepresenting the maximum angle at which the front wheels of the vehicle can be steered to the right.
Therefore, the lateral restraint of the vehicle is as shown in equation (22).
Figure BDA0002593464020000141
4) Vehicle acceleration restraint
In order to ensure that the engine can provide enough power in the acceleration process of the vehicle or the brake pad can provide enough power for the vehicle in the brake process, the acceleration of the vehicle is limited, so that the vehicle can be supported by enough power and the vehicle can have enough brake capacity to avoid traffic accidents. The acceleration constraint is shown in equation (23)
Figure BDA0002593464020000142
In the formula u-maxAn acceleration magnitude representing a maximum longitudinal braking retardation of the vehicle; u. ofmaxAn acceleration magnitude representing a maximum longitudinal acceleration of the vehicle; u. of-max' represents the maximum acceleration of the vehicle laterally to the left; u. ofmax' represents the maximum acceleration of the vehicle laterally to the right.
5) Constraint of jerk
The jerk constraint of a vehicle is the rate of change of vehicle acceleration, which in turn is referred to as a shock or comfort constraint. The purpose of restricting the vehicle jerk is to eliminate the negative effect of too fast acceleration changes on the vehicle running process. Jerk constraint is shown in equation (24).
Figure BDA0002593464020000143
In the formula, j-maxIndicating vehiclesMaximum longitudinal backward jerk; j is a function ofmaxRepresenting the magnitude of the maximum acceleration of the vehicle in the longitudinal direction; j is a function of-max' represents the maximum jerk magnitude laterally leftward of the vehicle; j is a function ofmax' indicates the magnitude of the maximum jerk to the right in the lateral direction of the vehicle.
6) Signal timing constraint
The signal timing constraints of the vehicle can ensure that the vehicle avoids the behavior of crossing the stop line and continuing to run when the red light is turned on to prohibit traffic, against the traffic light indication when passing through the intersection, as shown in the formula (25).
Figure BDA0002593464020000144
7) Wireless communication constraints
The essence of the V2X technology is a wireless communication technology, and the conditions of communication delay and packet loss, etc., which inevitably occur in practical applications, may affect the stability and safety of the vehicle in the system. Therefore, the wireless communication-related parameters are constrained as shown in equation (26) and equation (27).
Figure BDA0002593464020000151
Plp|mn(t)＜15％ (27)
In the formula (I), the compound is shown in the specification,
Figure BDA0002593464020000152
indicating vehicle CmnThe time delay condition at the time t is in seconds; plp|mn(t) represents a vehicle CmnThe probability of packet loss at time t.
Step 4 solving method based on minimum value principle
The control problem of vehicle space-time trajectory optimization based on V2X can be solved and calculated by the principle of minimum value, and a hamilton function of the problem is established, as shown in equation (28).
H(x,u,λ,t)＝λT·f(x,u,t)+h(x,u,t) (28)
Where λ is defined as the co-state vector of the vector x, representing a small change from the vector x
Figure BDA0002593464020000155
Resulting in additional cost for the change in J.
Within the tolerance set U, the minimum optimization input U of the cost function must satisfy the condition that its hamilton function is at the minimum, as shown in formula (29), and its requirements are shown in formula (30).
Figure BDA0002593464020000153
Figure BDA0002593464020000154
Therefore, the Hamilton function of the control problem of the vehicle space-time trajectory optimization based on V2X is shown in equation (31).
Hmn＝λ1·vmn(t)+λ2·umn(t)+w5·(umn(t)2+2umn(t)·vmn(t))·χ(umn(t)) (31)
In the formula, the transverse moving track of the vehicle has no difference change on the change value of the Hamilton function, so that the optimization is carried out in the process of changing the lane of the vehicle in a coordinated mode.
Substituting equation (31) into equation (30) results in the following functional relationship, as shown in equation (32).
Figure BDA0002593464020000161
Wherein C is a undetermined constant.
Solving the process state vector λ should satisfy a fixed cost at the same time
Figure BDA0002593464020000162
The condition (2) is as shown in the formula (33).
Figure BDA0002593464020000163
Second, multilane space-time trajectory optimization method for intelligent networked vehicle
And the cooperative lane changing mechanism of the vehicle redefines the cooperative lane changing rule and sets a lane changing conflict detection method, and after determining lane changing gaps and target vehicles, cooperatively updates the space-time tracks of the vehicles on the target lane and the original lane. The mechanism can solve the problem that the original lane changing mechanism cannot be modeled, and simultaneously solves the problem that the space-time trajectory of vehicles on an original lane and a target lane is changed in a multi-vehicle lane changing process, so that the traffic flow passing efficiency of road sections is improved, and the average parking waiting time of the traffic flow is reduced.
Step 1. rule definition of cooperative lane change
The core rule of the cooperative lane change is that on the premise of ensuring the safety of the vehicle, the timeliness of the lane change of the vehicle is considered preferentially, then the original optimal running track of the vehicle is considered, and the space-time track of the vehicle after the cooperative lane change is updated through online optimization. The method mainly comprises the following four targets: (1) the safety of vehicles in the road section is ensured; (2) minimizing the lane change clearance and the determination time of the target cooperative vehicle; (3) optimizing lane changing tracks of a lane vehicle where the target vehicle is located and the target lane vehicle; (3) the mobility level of the traffic flow entering the intersection is guaranteed.
The invention sets up the process of changing the lane cooperatively based on the above-mentioned goal that sets for, the process mainly includes: (1) checking channel change conflict; (2) determining lane changing gaps and target cooperative vehicles; (3) updating the space-time trajectory of the vehicle; (4) space-time trajectory optimization method based on reinforcement learning
In order to accurately describe the relevant information of the vehicle collaborative lane changing process, a vehicle sending lane changing information is used as a collaborative lane changing main view angle to generate a schematic scene in a road section, as shown in fig. 3. Wherein the number is CmnIs a red vehicle located in the center of the lane m, ready to change lanes from the current lane m to the target lane m + 1. Yellow vehicle is number CmnThe vehicle of (1) a host threat vehicle changing lanes from m to a target lane m + 1; orange vehicle is number CmnFrom m lane to target lanem +1 secondary threat vehicles; blue vehicle number CmnFrom m to the target lane m + 1. Number CmnThe schematic diagram of the lane-changing environment of the vehicle is shown in fig. 4.
Step 2. channel change conflict detection method
In the traffic environment built up in fig. 4, the known number CmnThe position and speed of the lane change initiating vehicle and other vehicle related information can be analyzed by combining the target lane information CmnThe lane change environment. If the target lane is not adjacent to the current lane, the lane changing process of the adjacent lanes is divided into multiple times. Definition CmnThe front vehicle, the rear vehicle and the related vehicles of the adjacent lanes of the target are main threat vehicles and are the targets which are considered preferentially in the lane changing process of the vehicles; defining vehicles possibly influencing the current driving process and the potential lane changing process as secondary threat vehicles; and defining other vehicles which have no influence on the lane changing process as non-threat vehicles.
And loading the offline track map into an online traffic environment, and carrying out potential lane change conflict safety inspection on the potential safety lane change process. The channel-changing conflict examination mainly comprises the following contents: (1) checking whether a main threat vehicle and a secondary threat vehicle (2) exist in the current lane changing environment, and checking whether a vehicle initiating lane changing is in a coordinated lane changing state currently; (3) checking whether the main threat vehicle is in a lane change initiating state or a cooperative lane change state; (4) it is checked whether the secondary threat vehicle is in a lane change initiating state. The channel change security inspection state flow is shown in fig. 5.
Step 3, determining lane changing gap and target cooperative vehicle
The lane change request can be formally initiated to the target lane after the lane change traffic inspection of the previous step, and if the target lane has no main threat vehicle or secondary threat vehicle, the lane change can be directly carried out by combining the vehicle environment information collected by the vehicle-mounted sensor; and if the target lane has the primary threat vehicle or the secondary threat vehicle, determining a lane change gap and the target cooperative vehicle based on the vehicle environment information of the lane change initiating vehicle.
The lane-changing clearance and the target cooperative vehicle are determined based on the main vehicle CmnAnd a target lane (m-1) host threat vehicle C(m+1)n，C(m+1)(n-1)And C(m+1)(n+1)If the traffic flow speed of the target lane is greater than the speed of the main vehicle, selecting the main threat vehicle C with the horizontal position(m+1)nAs a target lane change gap, while the rear vehicle C(m+1)(n+1)As a target cooperative vehicle; if the flow velocity of the target lane is less than or equal to the velocity of the host vehicle, selecting the host threat vehicle C with the horizontal position(m+1)nAs a target lane change gap, while the vehicle C(m+1)nAs a target cooperative vehicle; and if the main threat vehicle at the horizontal position does not exist, selecting the rear vehicle at the horizontal position as a target cooperative vehicle, and taking the front gap as a target gap. In the discussion of the subsequent steps, the target lane flow velocity will be taken as equal to the host vehicle velocity, as an example, as the host threatening vehicle C(m+1)nFor the target to collaborate with the lane change vehicle, the primary threat vehicle C(m+1)nThe forward gap of (2) is set as a target lane change gap.
Step 4. update of space-time trajectory
And obtaining the target lane changing clearance and the target cooperative vehicle through the operation of the previous step. The collaborative lane changing process has the inclusion of meeting different traffic flow saturation degrees, so the traffic flow situation with higher saturation degree is taken as an example for discussion. The cooperative lane change process is as follows: (1) main vehicle CmnTarget cooperative lane changing vehicle C after target lane changing gap(m+1)nInitiating a lane change request; (2) target collaborative lane changing vehicle C(m+1)nAnd a rear vehicle C(m+1)(n+1)Vehicle C with equal speed regulation to initiate lane change requestmnProviding a safe lane change gap; (3) the main vehicle drives into the target lane at a constant speed or a variable speed, and the space-time trajectory of the main vehicle is updated; (4) and updating the space-time trajectories of the vehicles behind the original main lane and the vehicles behind the lane change gap of the target lane. The movement process of the vehicle on the lane where the host vehicle initiating the lane change application is located and the target lane is shown in fig. 6.
In order to improve the efficiency of optimizing the multi-lane space-time trajectory of the vehicle, the invention designs a space-time trajectory optimization algorithm based on a reinforcement learning algorithm, and the optimal trajectory can be quickly matched. The algorithm altogether involves computing the contents of two different inputs: (1) optimizing a space-time trajectory, wherein the current position, the speed and the time period when a target exits a lane are taken as input, and a set of vehicle acceleration is taken as output; (2) and optimizing the multi-lane cooperative lane change by taking the current position and speed of the vehicle and the position and speed of the target lane threat vehicle as input and taking the vehicle acceleration set as output. The method is a process that after a lane change request is initiated by a vehicle, the trajectory of the vehicle in the lane change process can be matched through reinforcement learning, and after the lane change is completed, the space-time trajectory at the moment is matched through reinforcement learning so as to achieve multi-lane trajectory optimization.
The state vector of the vehicle multilane space-time trajectory optimization process satisfies Markov property, i.e. the next state s of the systemt+1Only with the current state stIn this regard, there is no direct association with the preamble state, as shown in equation (34) and equation (35).
Figure BDA0002593464020000195
P[st+1|st]＝P[st+1|s1,s2,s3...st](35)
Where P is the probability matrix for transitions between states.
Defining a quintuple (S, A, P, R, gamma) to describe the multilane space-time trajectory optimization process. Wherein S is a state set, namely the state set comprises the current state of the vehicle and the traffic flow state in the road section; a is a set of executing actions, namely a set of lateral longitudinal accelerations of vehicle control outputs; p is the set of each state transition matrix; r is a reward function of the process and has a negative linear relation with the cost function J; v when gamma is a function of the calculated valueπA discount factor of(s). For a fixed strategy pi, the value function is vπ(s) can be calculated from equation (36).
Figure BDA0002593464020000191
In the formula, EπIs a mathematical expectation for the accumulated return.
For each action a worth, define qπ(s, a) is a function of the action value, and the calculation process is shown in equation (37).
Figure BDA0002593464020000192
The markov property of equation (35) is substituted into equations (36) and (37) to obtain the bellman optimal recursive equation of the optimal state value function v(s) and the optimal action value function q(s), as shown in equations (38) and (39).
Figure BDA0002593464020000193
Figure BDA0002593464020000194
In the formula, s 'and a' are the state and the operation at the next time, respectively.
By means of the above function, maximizing q x (s, a) results in an optimal strategy, as shown in equation (40).
Figure BDA0002593464020000201
Equation (30) can be calculated by the Q-learning algorithm, the algorithm process of which is shown by the pseudo code of Table 1.
TABLE 1Q-learning Algorithm
Figure BDA0002593464020000202
In the table, line 5 sTIs in a termination state; the-greedy strategy used in the 4 th line and the 6 th line is to enhance the diversity exploration capability of the algorithm, and the specific formula is shown as the formula (41); line 7 updates the optimal action function with the subsequent state estimate current value function.
Figure BDA0002593464020000203
Wherein N (a) represents the total number of operations; greedy represents the optimal action of the probability picking algorithm with 1-to pick random actions with a probability to ensure that every action has the possibility of being selected.

Claims (1)

1. An intelligent networking automobile state vector calculation method based on vehicle-road information coupling is characterized by comprising the following steps:
step (1) determining a state vector of a vehicle
Definition number CmnIs xmn(t)：
xmn(t)＝[xmn(t)，ymn(t)，vmn(t)，vmn′(t)]T(1)
In the formula, CmnThe nth vehicle from the upstream to the downstream is the mth lane from the outside to the inside of the road section of the driving direction of the vehicle; x is the number ofmn(t)，ymn(t) is a vehicle CmnAt the position of the time t, the origin of coordinates is the starting point of the upstream of the vehicle, the positive direction of the x-axis coordinates is the traffic flow driving direction, and the positive direction of the y-axis coordinates is the direction from the outer lane to the inner lane of the traffic flow road section; v. ofmn(t) is a vehicle CmnThe current running speed of the vehicle at the time t, and the positive direction of the coordinate is the traffic flow running direction; v. ofmn' (t) is a vehicle CmnThe current transverse speed of the vehicle at the time t, and the positive direction of the coordinate is the direction from the outer lane to the inner lane of the traffic flow road section;
current vehicle directional acceleration umn(t)：
umn(t)＝[umn(t)，umn′(t)]T(2)
In the formula umn(t) is a vehicle CmnThe current longitudinal acceleration of the vehicle at the time t, and the positive direction of the coordinate is the traffic flow driving direction; u. ofmn' (t) is a vehicle CmnThe current lateral acceleration of the vehicle at the time t, and the positive direction of the coordinate is the direction from the outer lane to the inner lane of the traffic flow road section;
calculating vehicle target lane signal timing information and adjacent lane traffic flow information
Figure FDA0002593464010000011
Figure FDA0002593464010000012
Figure FDA0002593464010000013
In the formula (I), the compound is shown in the specification,
Figure FDA0002593464010000014
for vehicle CmnSignal timing information of the present time, Im+1(t) and Im-1(t) is traffic flow information of m +1 and m-1 of adjacent lanes,
Figure FDA0002593464010000015
is composed of
Figure FDA0002593464010000016
Phase remaining time; rmThe red light duration for lane m; gmThe green time duration for lane m;
Figure FDA0002593464010000017
and
Figure FDA0002593464010000018
the average traffic flow speed of the inner measuring lane m +1 and the outer side lane m-1 is obtained; k is a radical ofm+1(t) and km-1(t) is the average traffic flow density of the inner measuring lane m +1 and the outer side lane m-1;
calculating average traffic flow velocity
Figure FDA0002593464010000019
And average traffic flow density km+1(t)
Figure FDA0002593464010000021
Figure FDA0002593464010000022
In the formula, N is a vehicle number;
Figure FDA0002593464010000023
the number is the maximum number in the numbers of the vehicles in the lane m;
Figure FDA0002593464010000024
the minimum serial number is the serial number of the vehicles in the lane m; l is the road segment length;
obtaining a system state equation
Figure FDA0002593464010000025
Step (2) calculating a cost function
Defining a cost function
Figure FDA0002593464010000026
In the formula (I), the compound is shown in the specification,
Figure FDA0002593464010000027
is a vehicle CmnThe moment of departure of the road segment;
Figure FDA0002593464010000028
is a vehicle CmnThe time of driving into the road section;
Figure FDA0002593464010000029
is a fixed cost for the process;
Figure FDA00025934640100000210
is a variable cost of the process;
calculating a fixed cost
Figure FDA00025934640100000211
In the formula (I), the compound is shown in the specification,
Figure FDA00025934640100000212
is a vehicle CmnA target moment of driving out a road segment;
Figure FDA00025934640100000213
is a vehicle CmnA desired speed of the driven road segment;
Figure FDA00025934640100000214
is the vehicle target lane; d is the lane width; w is a1The weight coefficient is the travel time of the road section; w is a2A weight coefficient which is the road section running length; w is a3Is a weight coefficient, w, of the expected speed at the downstream exit of the outgoing section4Is the weight coefficient of the departure from the target lane;
calculating variable costs
h(xmn(t)，umn(t))＝w5·(umn(t)2+2umn(t)·vmn(t))·χ(umn(t))+w6·(umn′(t)2+2umn′(t) (11)
In the formula, w5Weight coefficient, w, for the energy change caused by the longitudinal acceleration of the vehicle6A weight coefficient that is a change in energy caused by lateral acceleration of the vehicle; χ (u)mn(t)) is the Heaviside function of the vehicle longitudinal acceleration,
Figure FDA0002593464010000031
calculating vehicle CmnUnlimited free time to exit a road segment
Figure FDA0002593464010000032
Figure FDA0002593464010000033
In the formula (I), the compound is shown in the specification,
Figure FDA0002593464010000034
limiting the speed of the vehicle in the free running area of the road section;
Figure FDA0002593464010000035
limiting the speed of the vehicle at a downstream exit of the m lanes;
Figure FDA0002593464010000036
for vehicle CmnA maximum acceleration;
Figure FDA0002593464010000037
is the vehicle maximum deceleration;
vehicle CmnTarget time of departure of a road section
Figure FDA0002593464010000038
Controlled within a green light signal that can be transmitted
Figure FDA0002593464010000039
In the formula (I), the compound is shown in the specification,
Figure FDA00025934640100000310
for vehicle CmnA candidate moment to drive out a road segment; ximA set of green light periods for lane m; int () is a floor function;
calculating vehicle CmnCandidate time of departure
Figure FDA00025934640100000311
Figure FDA00025934640100000312
In the formula, tm h2hThe time interval of the lowest vehicle heads of two adjacent vehicles of m lanes at the downstream exit of the road section;
calculating the expected speed of the vehicle as the speed limit at the exit of the downstream exit
Figure FDA00025934640100000313
Calculating a cost function
Figure FDA00025934640100000314
Step (3) determining constraints of spatiotemporal trajectories
1) Determination of CmnInitial state of the vehicle
Figure FDA0002593464010000041
2) Determining inter-vehicle distance constraints
xmn(t+τmn)≤xm(n-1)(t)-dmn(19)
In the formula, τmnIndicating vehicle CmnAnd its front vehicle Cm(n-1)Time shift of (a); dmnIndicating vehicle CmnAnd its front vehicle Cm(n-1)Spatial displacement of (a);
3) determining vehicle speed constraints
Figure FDA0002593464010000042
Lateral velocity constraint of vehicle
Figure FDA0002593464010000043
In the formula, alpha-maxRepresents the maximum angle at which the front wheels of the vehicle can be steered to the left; α (t) represents a steering angle of a front wheel of the current vehicle; alpha is alphamaxRepresents the maximum angle at which the front wheels of the vehicle can be steered to the right;
4) determining vehicle acceleration constraints
Figure FDA0002593464010000044
In the formula u-maxAn acceleration magnitude representing a maximum longitudinal braking retardation of the vehicle; u. ofmaxAn acceleration magnitude representing a maximum longitudinal acceleration of the vehicle; u. of-max' represents the maximum acceleration of the vehicle laterally to the left; u. ofmax' represents the maximum acceleration of the vehicle laterally to the right;
5) determining vehicle jerk constraints
Figure FDA0002593464010000045
In the formula, j-maxRepresenting the maximum acceleration magnitude of the vehicle in the longitudinal direction and the back direction; j is a function ofmaxRepresenting the magnitude of the maximum acceleration of the vehicle in the longitudinal direction; j is a function of-max' represents the maximum jerk magnitude laterally leftward of the vehicle; j is a function ofmax' represents the magnitude of the maximum jerk laterally rightward of the vehicle;
6) determining signal timing constraints
Figure FDA0002593464010000046
7) Determining wireless communication constraints
Figure FDA0002593464010000051
Plp|mn(t)＜15％ (27)
In the formula (I), the compound is shown in the specification,
Figure FDA0002593464010000052
indicating vehicle CmnTime delay condition at time t; plp|mn(t) represents a vehicle CmnThe packet loss probability at time t;
solving method based on minimum value principle in step (4)
Establishing Hamiltonian
H(x，u，λ，t)＝λT·f(x，u，t)+h(x，u，t) (28)
Where λ is defined as the co-state vector of the vector x, representing a small change from the vector x
Figure FDA0002593464010000053
The additional cost of the resulting change in J;
the essential condition is that
Figure FDA0002593464010000054
Figure FDA0002593464010000055
The Hamilton function of the control problem for the optimization of the vehicle space-time trajectory is
Hmn＝λ1·vmn(t)+λ2·umn(t)+w5·(umn(t)2+2umn(t)·vmn(t))·χ(umn(t)) (31)
Finally, the following functional relationship is determined
Figure FDA0002593464010000056
Wherein C is a undetermined constant;
solving process state vectors
Figure FDA0002593464010000061
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112927541A (en) * 2021-01-29 2021-06-08 重庆长安汽车股份有限公司 Traffic flow track generation method, vehicle and transverse control method and system
CN114485702A (en) * 2021-12-30 2022-05-13 国网江苏省电力有限公司连云港供电分公司 Electric vehicle charging path planning method and system
CN115879294A (en) * 2022-11-30 2023-03-31 西部科学城智能网联汽车创新中心(重庆)有限公司 Full-sample traffic flow track generation method and system based on multi-vehicle environment perception

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104167097A (en) * 2014-09-03 2014-11-26 中国科学院合肥物质科学研究院 Dynamic overtaking track generation system based on vehicle-to-vehicle communication and generation method thereof
CN105788252A (en) * 2016-03-22 2016-07-20 连云港杰瑞电子有限公司 Urban trunk road vehicle trajectory reconstruction method based on fixed-point detector and signal timing data fusion
CN107146408A (en) * 2017-05-29 2017-09-08 胡笳 A kind of control method of the environmentally friendly control loop of the road based on car networking
CN107730931A (en) * 2017-09-20 2018-02-23 北方工业大学 Vehicle formation control and signal optimization method under vehicle-road cooperative environment
US20180348770A1 (en) * 2017-06-02 2018-12-06 Honda Motor Co.,Ltd. Running track determining device and automatic driving apparatus
CN110415519A (en) * 2019-07-30 2019-11-05 黄卫 A kind of regional traffic signal method for real-time optimization control based on track of vehicle data
CN111145552A (en) * 2020-01-06 2020-05-12 重庆大学 Planning method for vehicle dynamic lane changing track based on 5G network
WO2020124068A1 (en) * 2018-12-14 2020-06-18 Stc, Inc. Systems and methods to temporarily alter traffic flow
CN111325975A (en) * 2020-02-19 2020-06-23 南京航空航天大学 Centralized optimization coordination method of intelligent networked vehicles in afflux entrance area

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104167097A (en) * 2014-09-03 2014-11-26 中国科学院合肥物质科学研究院 Dynamic overtaking track generation system based on vehicle-to-vehicle communication and generation method thereof
CN105788252A (en) * 2016-03-22 2016-07-20 连云港杰瑞电子有限公司 Urban trunk road vehicle trajectory reconstruction method based on fixed-point detector and signal timing data fusion
CN107146408A (en) * 2017-05-29 2017-09-08 胡笳 A kind of control method of the environmentally friendly control loop of the road based on car networking
US20180348770A1 (en) * 2017-06-02 2018-12-06 Honda Motor Co.,Ltd. Running track determining device and automatic driving apparatus
CN107730931A (en) * 2017-09-20 2018-02-23 北方工业大学 Vehicle formation control and signal optimization method under vehicle-road cooperative environment
WO2020124068A1 (en) * 2018-12-14 2020-06-18 Stc, Inc. Systems and methods to temporarily alter traffic flow
CN110415519A (en) * 2019-07-30 2019-11-05 黄卫 A kind of regional traffic signal method for real-time optimization control based on track of vehicle data
CN111145552A (en) * 2020-01-06 2020-05-12 重庆大学 Planning method for vehicle dynamic lane changing track based on 5G network
CN111325975A (en) * 2020-02-19 2020-06-23 南京航空航天大学 Centralized optimization coordination method of intelligent networked vehicles in afflux entrance area

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
J. F. WANG 等: "Speed guidance model during the green phase based on a connected vehicle", 《SIMULATION》 *
LIN FEN 等: "Integrated Avoid Collision Control of Autonomous Vehicle Based on Trajectory Re-Planning and V2V Information Interaction", 《SENSORS 》 *
谢伯元: "基于信息交互及运动耦合的车辆协同控制方法", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》 *
陈娟: "城市智能汽车周围环境的时空行为预测算法研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112927541A (en) * 2021-01-29 2021-06-08 重庆长安汽车股份有限公司 Traffic flow track generation method, vehicle and transverse control method and system
CN114485702A (en) * 2021-12-30 2022-05-13 国网江苏省电力有限公司连云港供电分公司 Electric vehicle charging path planning method and system
CN115879294A (en) * 2022-11-30 2023-03-31 西部科学城智能网联汽车创新中心(重庆)有限公司 Full-sample traffic flow track generation method and system based on multi-vehicle environment perception
CN115879294B (en) * 2022-11-30 2024-04-26 西部科学城智能网联汽车创新中心(重庆)有限公司 Full-sample vehicle flow track generation method and system based on multi-vehicle environment perception

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