CN117218881B

CN117218881B - Intelligent vehicle collaborative import decision planning method and system in full network environment

Info

Publication number: CN117218881B
Application number: CN202311473566.1A
Authority: CN
Inventors: 陈雪梅; 刘家赫; 吴甲; 肖龙; 薛杨武; 沈晓旭; 赵小萱
Original assignee: Beijing Institute of Technology BIT; Advanced Technology Research Institute of Beijing Institute of Technology
Current assignee: Beijing Institute of Technology BIT; Advanced Technology Research Institute of Beijing Institute of Technology
Priority date: 2023-11-08
Filing date: 2023-11-08
Publication date: 2024-02-13
Anticipated expiration: 2043-11-08
Also published as: CN117218881A

Abstract

The invention provides a method and a system for planning collaborative import decision of an intelligent vehicle in a full network environment, and belongs to the technical field of traffic control systems. The method comprises the following steps: the dynamic arrival time of the ramp vehicles reaching the junction is taken as a variable, so that when the expected speed of the ramp vehicles on the right lane of the main line reaches the junction after the dynamic arrival time, the ramp vehicles respectively have assumed equal distances with the ramp vehicles before entering the gap and after entering the gap, the dynamic arrival time is obtained, and the scene and strategy of the collaborative import are subjected to more definite and comprehensive classified discussion based on the dynamic arrival time; the intelligent vehicle decision planning system can realize cooperative afflux under multiple situations, and improves the adaptability of the intelligent vehicle decision planning system to different ramp afflux situations.

Description

Intelligent vehicle collaborative import decision planning method and system in full network environment

Technical Field

The invention relates to the technical field of traffic control systems, in particular to a method and a system for planning intelligent vehicle collaborative import decision in a full network environment.

Background

The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.

In a Vehicle-road Cloud cooperative environment, a ramp Vehicle can communicate with a main line Vehicle, a road side unit and a Cloud platform through a Vehicle-to-evaluation (V2X) technology, including Vehicle-to-Vehicle (V2V), vehicle-to-Infrastructure (V2I), vehicle-to-Cloud (V2C) and the like. The ramp vehicles can send out a cooperative request to the main line vehicles, and make a cooperative import strategy with the main line vehicles and execute corresponding running instructions; therefore, the main line vehicle can carry out corresponding speed adjustment or track change according to the information of the position, speed, target track and the like of the vehicle on the ramp, and a proper remittance space is created for the ramp vehicle. Therefore, the intelligent vehicle intelligent network system has important significance and value for realizing safe, efficient and comfortable integration by researching the cooperative integration of the intelligent vehicles in the intelligent network environment.

The conventional ramp control method can effectively regulate traffic demand and supply, but cannot microscopically consider complex driving behavior of the vehicle, and thus cannot consider microscopic synergic afflux behavior of the vehicle. Rule-based import-decision methods may consider collaborative importation, i.e., having the main line vehicle and the ramp vehicle follow the same or different rules to achieve collaboration. For example, the main line vehicle can adjust the speed of the main line vehicle according to the position and the speed of the ramp vehicle, and a proper gap is created for the ramp vehicle; the ramp vehicles can select proper entry points according to the position and the speed of the main line vehicles, so that the interference of the main line vehicles is avoided. When the cooperative import is considered, each vehicle is a cooperative individual, and the common interests of the vehicles and other vehicles are considered, so that the coordination is realized through a communication or control technology. This situation can be modeled with a cooperative game, such as pareto optimality. When the cooperative import problem is considered based on the optimization method, a cooperative optimization problem is formed between a self vehicle (namely a vehicle needing decision planning on a ramp, which is hereinafter referred to as a ramp vehicle) and other vehicles, and the vehicle-vehicle coordination is realized through the vehicle networking communication technology by considering common objective functions and constraint conditions of the self vehicle and other vehicles.

However, the current ramp collaborative import technology of the intelligent network-connected vehicle does not carry out relatively clear and comprehensive classification discussion on the scene and strategy of collaborative import, and the collaborative import decision planning strategy cannot realize effective planning decisions aiming at different scenes, so that automatic safe collaborative import of the vehicle in the whole network-connected environment cannot be realized.

Disclosure of Invention

In order to solve the defects of the prior art, the invention provides the intelligent vehicle collaborative import decision planning method and system under the full network environment, which realize the collaborative import of the intelligent network vehicles under different conditions and improve the adaptability of the intelligent vehicle decision planning system to different ramp import situations.

In order to achieve the above purpose, the present invention adopts the following technical scheme:

in a first aspect, the invention provides a collaborative import decision planning method for intelligent vehicles in a full networking environment.

An intelligent vehicle collaborative import decision planning method under a full network environment is applied to a scene of expanding an import gap together at the upstream and downstream of a fleet on the right side of a main line, and comprises the following steps:

the dynamic arrival time of the ramp vehicles reaching the junction is taken as a variable, so that after the dynamic arrival time, when the ramp vehicles reach the junction at the expected speed on the lane on the right side of the main line, the vehicles before the junction gap of the ramp vehicles and the vehicles after the junction gap have assumed equal distances with the ramp vehicles respectively, and the dynamic arrival time is obtained;

According to the obtained dynamic arrival time, the front vehicles and the rear vehicles of the ramp vehicles, which enter the gap, have set safety distances with the ramp vehicles, and the planned positions of the front vehicles and the rear vehicles of the ramp vehicles, which enter the gap, after the dynamic arrival time are obtained;

and carrying out optimal track planning on each vehicle according to the dynamic arrival time, the expected speed and the planning position of each vehicle.

As a further definition of the first aspect of the present invention, determining the before-entry-gap vehicle and the after-entry-gap vehicle from a sequential sequence through the junction, the determining of the sequential sequence includes:

the method comprises the steps of carrying out uniform speed prediction of expected running speed on vehicles on a main line road to obtain the time when the vehicles on the upstream of the main line are expected to reach a merging point;

average calculation is carried out on the current speed of the ramp vehicle and the speed expected to reach the junction point, the assumed average speed of the ramp vehicle is obtained, and the time of the ramp vehicle expected to reach the junction point is obtained according to the assumed average speed;

and sequencing the estimated arrival time of each vehicle from small to large according to the estimated arrival time of the upstream vehicle of the main line and the estimated arrival time of the ramp vehicle at the junction point, so as to obtain a sequence passing through the junction point.

As a further limitation of the first aspect of the present invention, the acceleration of the downstream vehicle of the before-entering-gap vehicle and the acceleration of the before-entering-gap vehicle are controlled to be the same as those of the before-gap vehicle in the dynamic arrival time, and the acceleration of the upstream vehicle of the after-entering-gap vehicle is controlled to be the same as those of the after-entering-gap vehicle in the dynamic arrival time;

according to the dynamic arrival time, the current speed, the expected speed and the position of the sink point of the ramp vehicle, performing optimal track control of the ramp vehicle;

according to the dynamic arrival time, the planning position and the expected speed of the vehicle after entering the gap, the optimal track control of the vehicle after entering the gap is carried out;

and performing optimal track control of the vehicles before entering the gap according to the dynamic arrival time, the planned position and the expected speed of the vehicles before entering the gap.

In a second aspect, the invention provides an intelligent vehicle collaborative import decision-making system in a full networking environment.

An intelligent vehicle collaborative import decision planning system in a full network environment, which is applied to upstream and downstream common expansion import gap of a right side motorcade of a main line, comprises:

a dynamic arrival time calculation module configured to: the dynamic arrival time of the ramp vehicles reaching the junction is taken as a variable, so that after the dynamic arrival time, when the ramp vehicles reach the junction at the expected speed on the lane on the right side of the main line, the vehicles before the junction gap of the ramp vehicles and the vehicles after the junction gap have assumed equal distances with the ramp vehicles respectively, and the dynamic arrival time is obtained;

A planned location generation module configured to: according to the obtained dynamic arrival time, the front vehicles and the rear vehicles of the ramp vehicles, which enter the gap, have set safety distances with the ramp vehicles, and the planned positions of the front vehicles and the rear vehicles of the ramp vehicles, which enter the gap, after the dynamic arrival time are obtained;

an optimal trajectory control module configured to: and carrying out optimal track planning on each vehicle according to the dynamic arrival time, the expected speed and the planning position of each vehicle.

In a third aspect, the invention provides a collaborative import decision planning method for intelligent vehicles in a full networking environment.

The intelligent vehicle collaborative import decision planning method in the full network environment is applied to a scene that only the upstream of a motorcade on the right side of a main line enlarges an import gap, and comprises the following steps:

taking the dynamic arrival time of the ramp vehicles reaching the junction as a variable, enabling the vehicles before converging into the gap to have a set headway from the junction just before the dynamic arrival time reaches, and further obtaining the dynamic arrival time;

when the ramp vehicles reach the junction point at the dynamic arrival time, the planning position of the vehicles after entering the gap and the junction point have a set time interval, so that the planning position of the vehicles after entering the gap is obtained;

As a further definition of the third aspect of the present invention, determining the lead-in-gap vehicle from a sequential sequence through the junction, the determining of the sequential sequence includes:

As a further limitation of the third aspect of the present invention, controlling the acceleration of the upstream vehicle of the after-gap vehicle to be the same as the after-gap vehicle in the dynamic arrival time;

according to the dynamic arrival time, the current speed, the expected speed and the position of the sink point of the ramp vehicle, performing optimal track control of the ramp vehicle; and performing optimal track control on the vehicles after entering the gap according to the dynamic arrival time, the planned position and the expected speed of the vehicles after entering the gap.

In a fourth aspect, the invention provides an intelligent vehicle collaborative import decision planning system in a full networking environment.

An intelligent vehicle collaborative import decision planning system in a full network environment is applied to a scene that only a right side of a main line is in a motorcade upstream to enlarge an import gap, and comprises the following steps:

a dynamic arrival time calculation module configured to: taking the dynamic arrival time of the ramp vehicles reaching the junction as a variable, enabling the vehicles before converging into the gap to have a set headway from the junction just before the dynamic arrival time reaches, and further obtaining the dynamic arrival time;

a main road vehicle planning module configured to: when the ramp vehicles reach the junction point at the dynamic arrival time, the planning position of the vehicles after entering the gap and the junction point have a set time interval, so that the planning position of the vehicles after entering the gap is obtained;

In a fifth aspect, the invention provides a collaborative import decision planning method for intelligent vehicles in a full networking environment.

The intelligent vehicle collaborative convergence decision-making planning method in the full network environment is applied to a scene of left lane change of a vehicle after a main line gap, and comprises the following steps:

Acquiring the expected speed of a lane fleet on the left side of the main line, setting the channel changing time of the vehicle after entering the gap as the dynamic arrival time, and acquiring the dynamic arrival time according to the maximum acceleration of the vehicle after entering the gap, the expected speed of the lane fleet on the left side of the main line, the acceleration coefficient and the initial speed of the vehicle after entering the gap;

obtaining an expected track change end position of the vehicle after the gap according to the dynamic arrival time, the initial position of the vehicle after the gap is converged, the expected speed of a lane fleet on the left side of the main line and the initial speed of the vehicle after the gap is converged; after the dynamic arrival time, taking the longitudinal position of the middle point of the left lane gap of the main line, which is closest to the expected lane change end position of the post-gap vehicle, as the planning position of the post-gap vehicle in the dynamic arrival time;

As a further definition of the fifth aspect of the present invention, determining the vehicles after entering the gap according to a sequential sequence through the junction, the determining of the sequential sequence includes:

As a further limitation of the fifth aspect of the present invention, both the upstream vehicle and the downstream vehicle of the vehicle after the gap maintain the original expected speed to travel at a constant speed;

according to the dynamic arrival time, the current speed of the ramp vehicle, the expectation of arriving at the sink and the position of the sink, performing optimal track control of the ramp vehicle;

and performing optimal track control on the vehicles after entering the gap according to the dynamic arrival time, the planned position of the vehicles after entering the gap and the final expected speed.

In a sixth aspect, the invention provides an intelligent vehicle collaborative import decision planning system in a full networking environment.

An intelligent vehicle collaborative convergence decision-making planning system in a full network environment is applied to a scene of left lane change of a vehicle after a main line gap, and comprises the following steps:

A dynamic arrival time calculation module configured to: acquiring the expected speed of a lane fleet on the left side of the main line, setting the channel changing time of the vehicle after entering the gap as the dynamic arrival time, and acquiring the dynamic arrival time according to the maximum acceleration of the vehicle after entering the gap, the expected speed of the lane fleet on the left side of the main line, the acceleration coefficient and the initial speed of the vehicle after entering the gap;

the clearance rear car planning control module is configured to: obtaining an expected track change end position of the vehicle after the gap according to the dynamic arrival time, the initial position of the vehicle after the gap is converged, the expected speed of a lane fleet on the left side of the main line and the initial speed of the vehicle after the gap is converged; after the dynamic arrival time, taking the longitudinal position of the middle point of the left lane gap of the main line, which is closest to the expected lane change end position of the post-gap vehicle, as the planning position of the post-gap vehicle in the dynamic arrival time;

In a seventh aspect, the invention provides a collaborative import decision planning method for intelligent vehicles in a full networking environment.

An intelligent vehicle collaborative converging decision-making planning method in a full network environment is applied to ramp vehicles and comprises the following steps:

Acquiring traffic flow information on a main line lane, and judging the scene of the current main line road;

under the scene that the upstream and downstream of a motorcade on the right side of the main line jointly expand the import gap, executing the process of the intelligent vehicle collaborative import decision planning method in the full-network environment according to the first aspect of the invention;

executing the process of the intelligent vehicle collaborative import decision planning method in the full-network environment in the third aspect of the invention under the scene that only the upstream of a motorcade on the right side of a main line enlarges the import gap;

and executing the process of the intelligent vehicle collaborative import decision-making planning method in the full-network environment in the fifth aspect of the invention under the scene that the vehicle changes lanes leftwards after the main line gap.

In an eighth aspect, the invention provides an intelligent vehicle collaborative import decision planning system in a full networking environment.

An intelligent vehicle collaborative integration decision-making planning system in a full network environment, which is applied to ramp vehicles, comprises:

a data acquisition module configured to: acquiring traffic flow information on a main line lane, and judging the scene of the current main line road;

a first scenario decision module configured to: under the scene that the upstream and downstream of a motorcade on the right side of the main line jointly expand the import gap, executing the process of the intelligent vehicle collaborative import decision planning method in the full-network environment according to the first aspect of the invention;

A second scenario decision module configured to: executing the process of the intelligent vehicle collaborative import decision planning method in the full-network environment in the third aspect of the invention under the scene that only the upstream of a motorcade on the right side of a main line enlarges the import gap;

a third scenario decision module configured to: and executing the process of the intelligent vehicle collaborative import decision-making planning method in the full-network environment in the fifth aspect of the invention under the scene that the vehicle changes lanes leftwards after the main line gap.

In a ninth aspect, the present invention provides an intelligent vehicle in a full networking environment.

An intelligent vehicle in a full network environment comprises a controller, wherein the controller is used for judging the scene of a current main line road according to acquired traffic road information;

In a tenth aspect, the present invention provides a computer readable storage medium having stored thereon a program which when executed by a processor implements the steps of the intelligent vehicle collaborative import decision-making method in a full network environment according to the first, third, fifth or seventh aspects of the present invention.

In an eleventh aspect, the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, where the processor implements the steps in the intelligent vehicle collaborative import decision-making method in a full network environment according to the first, third, fifth, or seventh aspects of the present invention when the processor executes the program.

Compared with the prior art, the invention has the beneficial effects that:

1. the invention creatively provides a method and a system for planning the collaborative import decision of intelligent vehicles in a full-network environment, which realize the collaborative import of the intelligent network vehicles in different scenes, carry out more clear and comprehensive classified discussion on the scenes and strategies of the collaborative import based on dynamic arrival time, realize the optimal calculation of the variable speed maneuver parameters of each intelligent vehicle, and improve the precision and the safety of the intelligent vehicle decision planning system on the scenes of different ramp imports.

2. The invention creatively provides an intelligent vehicle collaborative import decision planning method and system in a full-network environment, which can be applied to different scenes that an import gap is enlarged together at the upstream and downstream of a right-side motorcade of a main line, and only the upstream of the right-side motorcade of the main line enlarges the import gap or the motorcade of the main line changes lanes to the left after the gap, thereby realizing the import precise control of different scenes and improving the adaptability of the intelligent vehicle decision planning system to the import scenes of different ramp.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.

Fig. 1 is a flow chart of a method for intelligent vehicle collaborative import decision planning in a full networking environment provided in embodiment 1 of the present invention;

FIG. 2 is a schematic diagram of a common expansion of an entry gap between upstream and downstream of a fleet on the right side of a main line according to embodiment 1 of the present invention;

FIG. 3 is a schematic view of the embodiment 3 of the present invention, wherein only the upstream of the right fleet of vehicles is enlarged and converged;

fig. 4 is a schematic diagram of a car left track change after a main line gap provided in embodiment 5 of the present invention.

Detailed Description

The invention will be further described with reference to the drawings and examples.

It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

Embodiments of the invention and features of the embodiments may be combined with each other without conflict.

The invention provides an intelligent vehicle collaborative import decision planning method in a full network environment, which comprises the steps of observing an intelligent vehicle (also called a self vehicle or a ramp vehicle) on a ramp through a sensor of the intelligent vehicle, communicating with a nearby road side unit through a vehicle network, acquiring traffic flow information on a main line lane, judging which traffic situation the current main line belongs to, and selecting a corresponding collaborative import strategy; wherein, the traffic scenario includes: (1) The upstream and downstream of the motorcade on the right side of the main line jointly enlarge the afflux gap; (2) only the right fleet of mainlines upstream enlarges the ingress gap; and (3) changing the track left after the main line is in a gap.

Example 1:

aiming at the situation of the scene (1), the embodiment provides a cooperative import decision planning method for intelligent vehicles in a full-network environment, namely, the upstream and downstream of a fleet on the right side of a main line jointly enlarge an import gap, as shown in fig. 1, and the method comprises the following steps:

S1: the method comprises the steps of carrying out uniform speed prediction on expected running speed of vehicles on a main line road (namely the main line road needing to enter), so as to obtain the time when the vehicles on the upstream of the main line are expected to reach a junction point; the method comprises the steps of obtaining the assumed average speed of the intelligent vehicle on the ramp by carrying out average calculation on the current speed of the intelligent vehicle on the ramp and the speed expected to reach a current point, so as to obtain the expected arrival time of the intelligent vehicle on the ramp;

s2: sequencing the predicted arrival time of each vehicle from small to large according to the predicted arrival time of the self vehicle on the upstream vehicle of the main line and the ramp, so as to obtain a decision result of a converging sequence, namely the sequence passing through the converging points;

s3: the time of reaching the junction point of the self-vehicle planning on the ramp is calculated as variable-dynamic arrival time, so that after the dynamic arrival time, the self-vehicle reaches the junction point at the expected speed on the lane on the right side of the main line, and the front vehicle and the rear vehicle which enter the gap respectively have assumed equal distances with the self-vehicle; thus, the dynamic arrival time can be solved; furthermore, according to the known dynamic arrival time, the front vehicles and the rear vehicles of the entering gap of the self-vehicle and the self-vehicle are required to have proper safety distances, so that the planning positions of the front vehicles and the rear vehicles of the entering gap after the dynamic arrival time are solved; for the downstream vehicles of the front gap vehicle, the downstream vehicles and the front gap vehicle are controlled to synchronously move through the Internet of vehicles, namely the acceleration is the same as the front gap vehicle in the dynamic arrival time; for an upstream vehicle of the post-gap vehicle, controlling the upstream vehicle to synchronously move with the post-gap vehicle through the Internet of vehicles, namely, enabling acceleration to be the same as the post-gap vehicle in dynamic arrival time;

S4: and according to the obtained future planning targets of the vehicles, obtaining the track of the vehicles in the future time by solving the optimal control problem, and further completing the collaborative import process.

More specifically, as shown in FIG. 2, the location of the sink is taken as the origin of coordinatesoTaking the running direction of the main line vehicle asyShaft with ramp side andythe vertical direction of the axis isxA shaft comprising the following process:

the vehicles on the left lane of the main line are denser, the vehicles on the right lane of the main line have no good condition for changing lanes to the left, and the vehicles on the right side are downstream (namely the origin of coordinatesoTo the right of (2) is free-flow, meaning that the downstream vehicles can travel close to the highest speed limit, in which case the downstream vehicles on the right side of the main line are jointly widened in clearance by corresponding deceleration and acceleration actions, to assist the entry actions of the three self-vehicles on the ramp.

The initial time is recorded asThe desired speed of each incoming ramp vehicle at the time of the incoming is set as the travel speed of the right-hand fleet of vehicles on the main line to minimize the impact of the main line vehicles (Mainline Vehicles, MVs) upstream of the junction, and the incoming ramp vehicles are brought to the junction (>) The planning time of (2) is recorded as- >For->I.e. determination of the dynamic arrival time of the ramp into the vehicle based on the dynamic arrival time of the into vehicle +.>And (3) carrying out corresponding decision planning on the motorcade on the right side of the main line, so that space-time conflict is avoided, and a collaborative import strategy can be realized.

Assuming that the course of movement of the merging vehicle from the initial position to the merging point is a uniform acceleration course, the merging vehicle assumes an average acceleration during the courseThe requirements are satisfied:

（1）；

wherein,for acceleration coefficient +.>Is the included angle between the ramp and the main line lane, +.>For maximum acceleration +.>To the initial speed of the merging vehicle.

I.e. initial longitudinal position of merging vehiclesThe requirements are satisfied:

（2）；

assumed average speed of the afferent vehicle:

（3）；

under this assumption, the sink vehicle expects the time to reach the junction:

（4）；

upstream of the point of engagementnThe main line of the vehicle is recorded as from near to farThe Time (TTA) for the main line vehicle upstream of the junction to be expected to reach the junction is calculated as follows:

（5）；

wherein,indicating the desired speed of the fleet (including upstream and downstream vehicles) to the right of the main line.

For a host vehicle upstream of a junction point and a host vehicle to be joined, it is ordered from small to large in predicted arrival time (sort), and a joining Sequence (MS) is obtained:

（6）；

After the Merging sequence is acquired, the preceding vehicles and the following vehicles in the sequence are the Front vehicles (GFV) and the Rear vehicles (GRV) of the Merging Gap (mergence Gap) of the own Vehicle, and the speed of the own Vehicle and the main line Vehicle are required to be the expected speed of the right lane of the main line when Merging. During a time periodIn this case, it is still assumed that the host vehicle is traveling at a constant speed. When the ramp vehicles reach the junction, the distances between the front vehicles and the rear vehicles at the gap and the junction are equal, namely, the vertical coordinates of the front vehicles and the rear vehicles are added to be zero:

（7）；

is the distance between the front vehicle and the current combining point in the gap, < >>Is the distance between the rear vehicle and the current-combining point after the gap, < > is shown as->Is in->Lower longitudinal position->The driver is in->The lower longitudinal position can be solved for the variable +.>The value of (1), i.e. the planned time for the own vehicle to reach the junction, is not large enough to merge into the gap if the front and rear vehicles travel at a constant speed, so it is necessary to determine that both are at +.>Planning position at the time:

（8）；

wherein,representing the planned position of the front car in gap, +.>Represents the planned position of the vehicle after the gap, 0 represents the longitudinal position of the point of engagement (+.>）；/>To ensure the safe gathering of the ramp vehicles, the time interval between the ramp vehicles and the front and rear vehicles is ensured.

To sum up, the longitudinal variable speed maneuver parameters of the oncoming own vehicle on the ramp:

（9）；

wherein the final stateIncluding final speed->And final longitudinal position (+)>）。

The track planning process is as follows: time elapsed from initial state of ramp afflux vehicleReaching the final stateThe method comprises the steps of carrying out a first treatment on the surface of the And solving the optimal control problem corresponding to the longitudinal speed change maneuver, and obtaining the planned track.

Longitudinal variable speed maneuver parameters for gapped front and rear vehicles:

（10）；

wherein,longitudinal variable-speed maneuver parameters representing a gap front truck, < >>Representing the longitudinal speed change maneuver parameters of the vehicle after the gap, the final state of the vehicle before the gap +.>Including final speed->And final longitudinal positionThe method comprises the steps of carrying out a first treatment on the surface of the Final state of the vehicle after clearance->Including final speed->And final longitudinal position->。

The track planning process for the front and rear vehicles in the gap is as follows: time elapsed from initial state of gap front truckReach final state->The method comprises the steps of carrying out a first treatment on the surface of the After the gap the vehicle is in the initial state for a time +.>Reach final state->And solving an optimal control problem corresponding to the longitudinal speed change maneuver, and obtaining a planned track.

For the downstream vehicles of the gap front vehicle, the vehicle is communicatedThe passing vehicle network controls the synchronous movement of the passing vehicle network and the gap front vehicle, namely the acceleration is in a time periodThe inner part is the same as the front vehicle in the gap; for the upstream vehicle of the post-gap vehicle, the synchronous movement of the upstream vehicle and the post-gap vehicle is controlled through the Internet of vehicles, namely the acceleration is in the time period +. >The inner part is the same as the rear vehicle in the gap.

Before the front self-vehicle on the ramp is assembled, other ramp vehicles keep running at a constant speed; when the own vehicle 0 is converged to the right lane of the main line, the following convergence process of the own vehicle 1 and the own vehicle 2 is the same as above.

Example 2:

the embodiment 2 of the invention provides an intelligent vehicle collaborative import decision planning system in a full network environment, which is applied to the upstream and downstream of a fleet on the right side of a main line to jointly enlarge an import gap, and comprises the following steps:

The working method of the system is the same as the intelligent vehicle collaborative import decision planning method in the full network environment provided in embodiment 1, and will not be described here again.

Example 3:

aiming at a scene (2), namely a situation that only the upstream of a motorcade on the right side of a main line expands an import gap, the invention provides an intelligent vehicle collaborative import decision planning method under a full-network environment, as shown in fig. 3, vehicles on a lane on the left side of the main line are denser, and the vehicles on the lane on the right side of the main line do not have good conditions for changing tracks to the left. Downstream of the fleet on the right of the main line is in a non-free flow state, meaning that downstream vehicles cannot accelerate at will. In scenario (2), the vehicles upstream of the right fleet of mainlines expand the gap through deceleration behavior to assist the entry behavior of three self-vehicles on the ramp.

Dynamic arrival time of incoming vehicleThe driver needs to turn the front vehicle in the gap->When the moment is just equal to the moment of the confluence point +.>：

（11）；

Is in->Lower longitudinal position->Indicating the desired speed of the fleet (including upstream and downstream vehicles) to the right of the main line.

Dynamic arrival timeThe method comprises the following steps:

（12）；

when the self-vehicle is on the ramp at momentWhen the current point is reached, the planned position of the vehicle behind the gap is +.>Needs to have a headway with the current point +.>：

（13）；

To sum up, the longitudinal variable speed maneuver parameters of the oncoming own vehicle on the ramp：

（14）；

Wherein the final stateIncluding final speed->(i.e., the desired speed) and the final longitudinal position.

Planning parameters of a post-clearance vehicleThe method comprises the following steps:

（15）；

wherein the final state of the vehicle after the clearanceIncluding final speed->And final longitudinal position->。

The track planning process for the vehicle after the gap is as follows: time elapsed from initial state of vehicle after clearanceReach final state->And solving an optimal control problem corresponding to the longitudinal speed change maneuver, and obtaining a planned track.

For the upstream vehicle of the post-gap vehicle, the synchronous motion of the upstream vehicle and the post-gap vehicle is controlled through the Internet of vehicles, namely the acceleration is controlled in a time periodThe inner part is the same as the rear vehicle in the gap.

Example 4:

the embodiment 4 of the invention provides an intelligent vehicle collaborative import decision planning system in a full network environment, which is applied to a scene that only the upstream of a motorcade on the right side of a main line enlarges an import gap, and comprises the following steps:

The working method of the system is the same as the intelligent vehicle collaborative import decision planning method in the full network environment provided in embodiment 3, and will not be described here again.

Example 5:

aiming at a scene (3), namely, the condition that the entry clearance is enlarged only at the upstream of a vehicle team on the right side of a main line, the invention provides an intelligent vehicle collaborative entry decision planning method under a full-network environment, as shown in fig. 4, vehicles on a lane on the left side of the main line are sparse, and the vehicles on the lane on the right side of the main line have good conditions for changing tracks leftwards. Downstream of the fleet on the right of the main line is in a non-free flow state, meaning that downstream vehicles cannot accelerate at will. In scenario 3, a target afflux gap is selected according to the time when the ramp vehicle is expected to reach the junction, and the afflux gap is enlarged by the left lane exchange behavior of the vehicle after the gap so as to assist the afflux behavior of the ramp vehicle.

In scenario 3, the density of vehicles in the left lane of the main line is low, and the vehicles in the right lane of the main line have good left lane changing conditions. When the post-gap vehicle is left-shifted as an auxiliary vehicle, the number of main line right-hand lane vehicles affected by the cooperative strategy is minimized (only 1). Recording the expected speed of the left lane fleet of the main line asThe lane change time of the vehicle after the gap is also set asThen->The need to additionally satisfy:

（16）；

wherein,for acceleration coefficient +.>For maximum acceleration of the vehicle behind the gap, +.>Indicating the desired speed of the fleet (including upstream and downstream vehicles) to the right of the main line.

Longitudinal position of expected lane change end point of vehicle after clearanceThe method comprises the following steps:

（17）；

the main line left lane vehicle actually maintains the desired speedDriving according to the uniform speed prediction model, and at timeAfterwards, will be->The longitudinal position of the midpoint of the left lane gap of the main line closest thereto is marked +.>ThenIn order to get in the back of the gap->The planned position of the moment.

For a post-gap vehicle, its planning parametersThe method comprises the following steps:

（18）；

final state of vehicle after clearanceIncluding final speed->And final longitudinal position->The track planning process for the vehicle after the gap is as follows: after the gap the vehicle is in the initial state for a time +.>Reach final state->And solving an optimal control problem corresponding to the transverse lane change maneuver to obtain a planned track.

The upstream vehicles and the downstream vehicles of the gap rear vehicles can keep the expected speed to run at a constant speed. Before the front-most self-vehicle on the ramp is completed, other ramp vehicles keep running at a constant speed, and after the self-vehicle 0 is converged into the lane on the right side of the main line, the subsequent converging processes of the self-vehicle 1 and the self-vehicle 2 are the same as above.

Example 6:

the embodiment 6 of the invention provides an intelligent vehicle collaborative convergence decision-making planning system in a full network environment, which is applied to a scene of left track changing of a vehicle after a main line gap, and comprises the following steps:

The working method of the system is the same as the intelligent vehicle collaborative import decision planning method in the full network environment provided in embodiment 5, and will not be described here again.

Example 7:

the embodiment 7 of the invention provides an intelligent vehicle collaborative import decision planning method in a full networking environment, which comprises the following steps:

under the scene that the upstream and downstream of a fleet on the right side of a main line jointly expand an import gap, executing the process of the intelligent vehicle collaborative import decision planning method in the full-network environment in the embodiment 1 of the invention;

executing the process of the intelligent vehicle collaborative import decision planning method in the full-network environment in the embodiment 3 of the invention under the scene that only the upstream of a motorcade on the right side of a main line enlarges the import gap;

in the scenario that the vehicle changes track leftwards after the main line gap, the process of the intelligent vehicle collaborative import decision-making planning method in the full network environment according to the embodiment 5 of the invention is executed.

Example 8:

the embodiment 8 of the invention provides an intelligent vehicle collaborative import decision planning system in a full networking environment, which comprises the following steps:

a first scenario decision module configured to: under the scene that the upstream and downstream of a fleet on the right side of a main line jointly expand an import gap, executing the process of the intelligent vehicle collaborative import decision planning method in the full-network environment in the embodiment 1 of the invention;

a second scenario decision module configured to: executing the process of the intelligent vehicle collaborative import decision planning method in the full-network environment in the embodiment 3 of the invention under the scene that only the upstream of a motorcade on the right side of a main line enlarges the import gap;

a third scenario decision module configured to: in the scenario that the vehicle changes track leftwards after the main line gap, the process of the intelligent vehicle collaborative import decision-making planning method in the full network environment according to the embodiment 5 of the invention is executed.

Example 9:

the embodiment 9 of the invention provides an intelligent vehicle in a full network environment, which comprises a controller, wherein the controller is used for judging the scene of a current main line road according to acquired traffic road information;

Example 10:

an embodiment 10 of the present invention provides a computer readable storage medium, on which a program is stored, where the program, when executed by a processor, implements steps in a method for collaborative import decision planning for an intelligent vehicle in a full network environment according to embodiment 1, embodiment 3, embodiment 5, or embodiment 7 of the present invention.

Example 11:

an embodiment 11 of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and capable of running on the processor, where the processor implements steps in the intelligent vehicle collaborative import decision-making method in the full network environment according to embodiment 1, embodiment 3, embodiment 5, or embodiment 7 of the present invention when executing the program.

It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), or the like.

The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. The intelligent vehicle collaborative import decision planning method in the whole network environment is characterized by being applied to a scene of expanding an import gap together at the upstream and downstream of a motorcade at the right side of a main line, and comprising the following steps of:

the dynamic arrival time of the ramp vehicles reaching the junction is taken as a variable, so that when the ramp vehicles reach the junction at the expected speed on the lane on the right side of the main line after the dynamic arrival time, the vehicles before the junction gap of the ramp vehicles and the vehicles after the junction gap respectively have assumed equal distances with the ramp vehicles, and the dynamic arrival time is obtained specifically as follows: determining a front bus entering a gap and a rear bus entering the gap according to a sequence passing through a junction, wherein the determining of the sequence comprises the following steps:

sequencing the estimated arrival time of each vehicle from small to large according to the estimated arrival time of the upstream vehicle of the main line and the estimated arrival time of the ramp vehicle at the junction point to obtain a sequence passing through the junction point;

The time of reaching the junction point of the self-vehicle planning on the ramp is calculated as variable-dynamic arrival time, so that after the dynamic arrival time, the self-vehicle reaches the junction point at the expected speed on the lane on the right side of the main line, and the front vehicle and the rear vehicle which enter the gap respectively have assumed equal distances with the self-vehicle; thus, the dynamic arrival time can be solved;

the acceleration of the downstream vehicle of the vehicle before entering the gap and the acceleration of the vehicle before entering the gap are controlled to be the same as those of the vehicle before entering the gap in the dynamic arrival time, and the acceleration of the upstream vehicle of the vehicle after entering the gap is controlled to be the same as those of the vehicle after entering the gap in the dynamic arrival time;

2. An intelligent vehicle collaborative import decision planning system in a full networking environment is characterized in that the intelligent vehicle collaborative import decision planning system is applied to upstream and downstream of a motorcade on the right side of a main line to jointly enlarge an import gap, and comprises:

a dynamic arrival time calculation module configured to: the dynamic arrival time of the ramp vehicles reaching the junction is taken as a variable, so that when the ramp vehicles reach the junction at the expected speed on the lane on the right side of the main line after the dynamic arrival time, the vehicles before the junction gap of the ramp vehicles and the vehicles after the junction gap respectively have assumed equal distances with the ramp vehicles, and the dynamic arrival time is obtained specifically as follows: determining a front bus entering a gap and a rear bus entering the gap according to a sequence passing through a junction, wherein the determining of the sequence comprises the following steps:

3. The intelligent vehicle collaborative import decision planning method in the whole network environment is characterized by being applied to a scene that only the upstream of a motorcade on the right side of a main line enlarges an import gap, and comprising the following steps:

the dynamic arrival time of the ramp vehicles reaching the junction is used as a variable, so that the set headway is arranged between the arrival time of the vehicles before converging into the gap and the junction, and the dynamic arrival time is obtained, specifically: determining the front vehicles entering the gap according to the sequence of the passing junction points, wherein the determining of the sequence of the passing junction points comprises the following steps:

the acceleration of an upstream vehicle of the vehicle after entering the gap is controlled to be the same as that of the vehicle after entering the gap in the dynamic arrival time;

4. The intelligent vehicle collaborative import decision planning system in the full network environment is characterized by being applied to a scene of enlarging an import gap only at the upstream of a motorcade at the right side of a main line, and comprising the following steps:

a dynamic arrival time calculation module configured to: the dynamic arrival time of the ramp vehicles reaching the junction is used as a variable, so that the set headway is arranged between the arrival time of the vehicles before converging into the gap and the junction, and the dynamic arrival time is obtained, specifically: determining the front vehicles entering the gap according to the sequence of the passing junction points, wherein the determining of the sequence of the passing junction points comprises the following steps:

5. The intelligent vehicle collaborative import decision planning method in the whole network environment is characterized by being applied to a scene of left track changing of a vehicle after a main line gap, and comprising the following steps of:

The method comprises the steps of obtaining expected speed of a lane fleet on the left side of a main line, setting channel changing time of a vehicle after entering a gap as dynamic arrival time, and obtaining the dynamic arrival time according to maximum acceleration of the vehicle after entering the gap, expected speed of the lane fleet on the left side of the main line, acceleration coefficient and initial speed of the vehicle after entering the gap, wherein the method specifically comprises the following steps: determining the vehicles after entering the gap according to the sequence of the passing junction points, wherein the determining of the sequence of the passing junction points comprises the following steps:

wherein, the upstream vehicle and the downstream vehicle of the vehicle behind the gap both keep the original expected speed and travel at a constant speed;

6. The intelligent vehicle collaborative import decision planning system in the whole network environment is characterized by being applied to a scene of left track changing of a vehicle after a main line gap, and comprising the following steps:

a dynamic arrival time calculation module configured to: the method comprises the steps of obtaining expected speed of a lane fleet on the left side of a main line, setting channel changing time of a vehicle after entering a gap as dynamic arrival time, and obtaining the dynamic arrival time according to maximum acceleration of the vehicle after entering the gap, expected speed of the lane fleet on the left side of the main line, acceleration coefficient and initial speed of the vehicle after entering the gap, wherein the method specifically comprises the following steps: determining the vehicles after entering the gap according to the sequence of the passing junction points, wherein the determining of the sequence of the passing junction points comprises the following steps:

7. The intelligent vehicle collaborative import decision planning method in the whole network environment is characterized by being applied to ramp vehicles and comprising the following steps of:

executing the intelligent vehicle collaborative import decision planning method in the full-network environment according to claim 1 under the scene that the upstream and downstream of the fleet on the right side of the main line jointly enlarge the import gap;

executing the intelligent vehicle collaborative import decision planning method in the full-network environment according to claim 3 under the scene that only the upstream of a motorcade on the right side of a main line enlarges an import gap;

in the scene of changing the track left after the main line gap, executing the process of the intelligent vehicle collaborative import decision planning method in the full network environment according to claim 5.

8. The intelligent vehicle collaborative import decision planning system in the whole network environment is characterized by being applied to ramp vehicles and comprising:

a first scenario decision module configured to: executing the intelligent vehicle collaborative import decision planning method in the full-network environment according to claim 1 under the scene that the upstream and downstream of the fleet on the right side of the main line jointly enlarge the import gap;

a second scenario decision module configured to: executing the intelligent vehicle collaborative import decision planning method in the full-network environment according to claim 3 under the scene that only the upstream of a motorcade on the right side of a main line enlarges an import gap;

a third scenario decision module configured to: in the scene of changing the track left after the main line gap, executing the process of the intelligent vehicle collaborative import decision planning method in the full network environment according to claim 5.

9. An intelligent vehicle in a full network environment is characterized by comprising a controller, wherein the controller is used for judging the scene of a current main line road according to acquired traffic road information;

10. A computer readable storage medium having stored thereon a program, which when executed by a processor, implements the steps of the intelligent vehicle collaborative import decision-making method in a full networked environment according to any of claims 1,3,5, 7.

11. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor, when executing the program, performs the steps in the intelligent vehicle collaborative import decision-making method in a full networking environment as set forth in any one of claims 1,3,5, 7.