CN114724377B - Unmanned vehicle guiding method and system based on vehicle-road cooperation technology - Google Patents
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Abstract
The application provides a method and a system for guiding an unmanned vehicle based on a vehicle-road cooperation technology, wherein the method comprises the following steps: s1, obtaining road network abstraction according to structured map data; s2, acquiring the passing time of each lane; s3, acquiring connection time between adjacent lanes on the same road section; s4, constructing a directed graph with lane numbers as nodes, passage time as side weight and connection time as inter-node connection side weight; s5, acquiring longitude and latitude information of a starting point and longitude and latitude information of a target end point of the vehicle, and acquiring a starting node and a target node of vehicle path planning; s6, obtaining an optimal path of the vehicle; and S7, controlling the unmanned vehicle to execute a vehicle speed and path guiding strategy of the optimal path. According to the unmanned vehicle guiding method based on the vehicle-road cooperation technology, a guiding method for efficient driving on the premise of ensuring safety is provided for the unmanned vehicle through ' road ' control ' of the vehicle.
Description
Technical Field
The invention relates to the technical field of intelligent driving, in particular to a method and a system for guiding an unmanned vehicle based on a vehicle-road cooperation technology.
Background
With the continuous development of the unmanned technology, the unmanned vehicle can realize highly automatic driving, that is, the unmanned system can continuously execute all dynamic driving tasks and automatically execute a minimum risk strategy under the designed operating conditions, however, the driving track of the unmanned vehicle needs to be collected in advance and cannot be autonomously planned. When the running tracks of a plurality of unmanned vehicles conflict, the vehicles cannot acquire running information of each other, the self cannot judge the running intentions of other vehicles, and the unmanned vehicles stop at positions with safe distance from each other for running safety, so that a locking phenomenon is generated. Therefore, it is necessary to construct a vehicle-road cooperative system for guiding the unmanned vehicle.
At present, the construction of urban smart intersections and smart roads serving intelligent networked automobiles is also gradually advanced from a test field to an open test road. Aiming at an intelligent transportation system, a complete multi-dimensional target and traffic situation perception system is formed by means of distributed edge computing, a V2X vehicle networking technology and a data fusion technology. With the continuous development of the vehicle-road cooperative technology, a cloud-road-vehicle cooperative intelligent system is finally formed, so that a technical route of completely automatic driving is realized, which is clear. However, a prerequisite for driving is the need to provide the vehicle with a complete path connecting the start and end points. And the provided complete path point is required to be a continuous longitude and latitude coordinate value of a lane level, so that the unmanned vehicle can be guided to run.
The research on the navigation technology based on vehicle positioning is mature, and a relatively mature vehicle navigation product is formed. However, the current applications for navigation all rely on road-level navigation of electronic maps, and can only be used for manual navigation, but cannot meet the navigation of unmanned vehicles. Since the unmanned vehicle requires longitude and latitude points specific to the lane, not just an approximate driving path. In recent years, lane-level dynamic path planning for vehicle path guidance has a certain result, most researches are based on a vehicle-mounted positioning technology and a vehicle-mounted navigation system, different models are designed according to different optimal criteria by using a shortest path algorithm, and an optimal path is planned, so that manual driving is assisted, the manual driving only stays in 'cloud-vehicle' interaction, how to guide unmanned vehicles to run by using the planned path is not specifically pointed out, and the 'cloud-vehicle' interaction has time delay and cannot meet the performance requirement of real-time safe and accurate guidance. Moreover, after the path planning of the unmanned vehicle is completed, the vehicles have a situation of track conflict, and at the moment, the track or the speed of the vehicles needs to be adjusted, so that the unmanned vehicle can efficiently run on the premise of ensuring safety.
Disclosure of Invention
The invention aims to overcome the defects of the background technology, provides an unmanned vehicle guiding method and system based on a vehicle-road cooperative technology, forms a cloud-road-vehicle intelligent integrated vehicle-road cooperative system based on a distributed edge computing technology, a vehicle high-precision positioning technology and an intelligent networking technology, provides a lane-level global path planning method based on time weight and an unmanned vehicle track conflict local cooperative control method, and guides an unmanned vehicle to efficiently run on the premise of ensuring safety by controlling the vehicle through a road.
In a first aspect, the present application provides a method for guiding an unmanned vehicle based on a vehicle-road coordination technology, comprising the following steps:
s1, acquiring road network abstraction according to structured map data;
s2, acquiring the passing time of each lane;
s3, acquiring connection time between adjacent lanes on the same road section;
s4, constructing a directed graph with lane numbers as nodes, passage time as side weight and connection time as inter-node connection side weight according to the obtained road network abstraction, the passage time weight of each lane and the connection time weight between adjacent lanes in the same road section;
s5, acquiring longitude and latitude information of a starting point and longitude and latitude information of a target end point of the vehicle, and acquiring a path planning starting node and a target node corresponding to the vehicle;
s6, acquiring an optimal path of the vehicle according to the constructed directed graph and the acquired initial node and target node;
and S7, controlling the unmanned vehicle to execute the vehicle speed and the route guidance strategy of the optimal route according to the obtained optimal route.
According to the first aspect, in a first possible implementation manner of the first aspect, the step S2 specifically includes the following steps:
s21, acquiring travel time;
s22, acquiring average crossing passing time;
s23, obtaining average delay time of the intersection;
and S24, acquiring the passing time of each lane according to the acquired travel time, the average crossing passing time and the average crossing delay time.
According to the first aspect, in a second possible implementation manner of the first aspect, the step S3 specifically includes the following steps:
s31, acquiring lane change travel time generated by the connection distance between adjacent lanes on the same road section;
s32, obtaining lane change delay time generated by lane change acceleration and deceleration between adjacent lanes on the same road section;
and step S33, acquiring the connection time between adjacent lanes on the same road section according to the acquired lane change travel time and the acquired lane change delay time.
According to the first aspect, in a third possible implementation manner of the first aspect, the step S5 specifically includes the following steps:
s51, acquiring longitude and latitude information of a starting point and longitude and latitude information of a target end point of a vehicle in a real road network;
step S52, converting the longitude and latitude information of the starting point and the longitude and latitude information of the target end point into the plane coordinate information of the starting point and the plane coordinate information of the end point;
and S53, acquiring a start point most adjacent lane and an end point most adjacent lane according to the start point plane coordinate information and the end point plane coordinate information, and taking the start point most adjacent lane and the end point most adjacent lane as a path planning start node and a target node corresponding to the vehicle.
According to the third possible implementation manner of the first aspect, in a fourth possible implementation manner of the first aspect, the step S53 specifically includes the following steps:
and drawing circles respectively taking the initial plane coordinate information and the terminal plane coordinate information of the vehicle as circle centers, gradually increasing the radius of the drawn circles until lanes are screened in the circles, determining lanes closest to the initial plane coordinate information or the terminal plane coordinate information in the lanes in the circles, and taking the lanes closest to the initial point and the terminal point as path planning initial nodes and target nodes corresponding to the vehicle.
According to the first aspect, in a fifth possible implementation manner of the first aspect, the directed graph
Set of nodes, lane numbering
=
Set of nodes
Set of nodes
Wherein, in the step (A),
is a starting node of the network, and is a starting node,
as a target node, the step S6 specifically includes the following steps:
step S61, obtaining an optimal path according to the formula I:
in the formula (I), the compound is shown in the specification,
as a driven lane in a directed graph
To the lane
The optimal path with the shortest travel time,
is a driveway
Direct to the roadway
The time of flight of (a) is,
indicating a lane
Passing lane
Rear to lane
The travel time of (a);
step S62, slave node set
Is selected so that
Minimum lane numbering
And numbering lanes
Put into node set
In the collection
In the middle willLane numbering
Deleting;
step S63, update and
set of directly connected inter-lane path information values, from node
Is selected such that
Minimum lane numbering
To repeat step S62;
step S64: repeating S63 circularly until the target lane number is found
And put the lane number into the node set
And in node sets
Number lanes
Deleting the final requested lane from the initial lane
To the end lane
The optimal path with the shortest travel time is as follows:
。
according to the first aspect, in a sixth possible implementation manner of the first aspect, the step S7 specifically includes the following steps:
step S71, acquiring a real-time running state and real-time lane information of the vehicle;
and S72, controlling and executing different vehicle speeds and path guiding strategies according to the acquired optimal path, the vehicle real-time running state and the real-time lane information.
According to a sixth possible implementation manner of the first aspect, in a seventh possible implementation manner of the first aspect, the S72 specifically includes the following steps:
step S721, when the real-time lane information is a traffic accident or a construction condition, controlling to execute a re-planning optimal path;
and step S722, when the real-time running state of the vehicle is that the vehicle with the track conflict exists, controlling and executing the optimal path of the vehicle with the track conflict.
According to the seventh possible implementation manner of the first aspect, in an eighth possible implementation manner of the first aspect, the step S722 specifically includes the following steps:
the conflict point of two vehicles in the planned optimal path is obtained, the conflict point is used as the circle center, and a circle is drawn by a preset radius to form a conflict area;
acquiring the speed of two vehicles and the distance between the two vehicles and a conflict point, and acquiring the arrival sequence of the two vehicles to the conflict point;
and controlling the vehicles arriving in the two vehicles in advance to normally run through the conflict area according to the planned path, and controlling the vehicles arriving in the conflict area in the two vehicles to decelerate firstly and then accelerate to the target speed after passing through the conflict area.
In a second aspect, the present application provides an unmanned vehicle guidance system based on vehicle-road coordination technology, comprising:
the road network abstraction obtaining module is used for obtaining road network abstractions according to the structured map data;
the passing time acquisition module is used for acquiring the passing time of each lane;
the connection time acquisition module is used for acquiring the connection time between adjacent lanes in the same road section;
the directed graph acquisition module is in communication connection with the road network abstraction acquisition module, the traffic time acquisition module and the connection time acquisition module and is used for constructing a directed graph which takes the lane number as a node, the traffic time as a side weight and the connection time as a connection side weight between the nodes according to the acquired road network abstraction, the traffic time weight of each lane and the connection time weight between adjacent lanes in the same road section;
the vehicle starting and target node acquisition module is used for acquiring longitude and latitude information of a starting point and longitude and latitude information of a target end point of a vehicle and acquiring a route planning starting node and a target node corresponding to the vehicle;
the optimal path acquisition module is in communication connection with the directed graph acquisition module and the starting and target node acquisition module and is used for acquiring an optimal path of the vehicle according to the constructed directed graph and the acquired starting node and target node;
and the vehicle guiding module is in communication connection with the optimal path acquiring module and is used for controlling the unmanned vehicle to execute the vehicle speed and the path guiding strategy of the optimal path according to the acquired optimal path.
Compared with the prior art, the invention has the following advantages:
according to the unmanned vehicle guiding method based on the vehicle-road cooperation technology, the virtual vehicle passing directed graph is constructed by acquiring the passing time of each lane, the connection time between adjacent lanes in the same road section and road network abstraction, the optimal path of the vehicle is acquired based on the directed graph and the vehicle positioning information, and the unmanned vehicle is guided to efficiently run on the premise of ensuring safety by controlling the vehicle through the road.
Drawings
FIG. 1 is a flowchart of a method for guiding an unmanned vehicle based on a vehicle-road coordination technology according to an embodiment of the present invention;
fig. 2 (a) is a link structured data relationship diagram in structured map data provided by an embodiment of the present application;
fig. 2 (b) is a structural data relationship diagram of an intersection in the structural map data provided by the embodiment of the present application;
FIG. 3 is a flowchart of another method of an unmanned vehicle guidance method based on vehicle-road coordination technology according to an embodiment of the present application;
FIG. 4 is a directed graph of lane numbers and travel times provided by an embodiment of the present application;
fig. 5 is a functional block diagram of an unmanned vehicle guidance system based on vehicle road system technology according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the present embodiments of the invention, examples of which are illustrated in the accompanying drawings. While the invention will be described in conjunction with the specific embodiments, it will be understood that they are not intended to limit the invention to the embodiments described. On the contrary, it is intended to cover alternatives, modifications, and equivalents as may be included within the spirit and scope of the invention as defined by the appended claims. It should be noted that the method steps described herein may be implemented by any functional block or functional arrangement and that any functional block or functional arrangement may be implemented as a physical entity or a logical entity, or a combination of both.
In order that those skilled in the art will better understand the present invention, the following detailed description of the invention is provided in conjunction with the accompanying drawings and the detailed description of the invention.
Note that: the example to be described next is only a specific example, and does not limit the embodiments of the present invention necessarily to the following specific steps, values, conditions, data, orders, and the like. Those skilled in the art can, upon reading this specification, utilize the concepts of the present invention to construct more embodiments than those specifically described herein.
With the rapid development of the unmanned technology, the unmanned vehicles can achieve highly automatic driving, but the driving tracks of the unmanned vehicles need to be collected in advance and cannot be planned autonomously, the problem of driving conflict can be caused when a plurality of unmanned vehicles run simultaneously, the driving information among the vehicles cannot be obtained, and the driving intentions of other vehicles cannot be judged.
In view of this, the present application provides a method for guiding an unmanned vehicle based on a vehicle-road cooperation technique, which can guide the unmanned vehicle to safely and efficiently travel.
Referring to fig. 1, in a first aspect, the present application provides a method for guiding an unmanned vehicle based on vehicle-road coordination technology, including the following steps:
s1, acquiring road network abstraction according to structured map data;
s2, acquiring the passing time of each lane;
s3, acquiring connection time between adjacent lanes on the same road section;
s4, constructing a directed graph with lane numbers as nodes, passage time as side weight and connection time as inter-node connection side weight according to the obtained road network abstraction, the passage time weight of each lane and the connection time weight between adjacent lanes in the same road section;
s5, acquiring longitude and latitude information of a starting point and longitude and latitude information of a target end point of the vehicle, and acquiring a path planning starting node and a target node corresponding to the vehicle;
s6, acquiring an optimal path of the vehicle according to the constructed directed graph and the acquired initial node and target node;
and S7, controlling the unmanned vehicle to execute the vehicle speed and the route guidance strategy of the optimal route according to the obtained optimal route.
According to the method and the system, the shortest passing time is calculated by constructing the directed graph with the lane number as the node and the passing time and the connection time as the weight, the optimal path of the vehicle is obtained, and the unmanned vehicle is guided to safely and efficiently run by global planning of the optimal path.
In an embodiment, the step S1 is specifically implemented as:
acquiring structured map data, wherein the structured map data can accurately describe road section information and intersection information, the road section description information comprises road section numbers, upstream intersection numbers and downstream intersection numbers, and the road section information also comprises lane information, wherein the lane information is described as a lane number, a number of a road section where the lane information is located, a lane center line point set, a lane boundary line point set and whether the lane information is a virtual lane or not; the intersection description information is intersection number and virtual lane information, the intersection information comprises entry intersection relation information and exit intersection relation information, the road section intersection relation information comprises entry road section number and exit road section number, the road section intersection relation comprises lane intersection relation, the lane intersection relation comprises entry road section lane number and exit road section lane number, the lane intersection relation is related to the virtual lane information, and the virtual lane information is described as virtual lane number and virtual lane center line point set. The structured map data relationship diagram is shown in fig. 2 (a) and 2 (b).
In an embodiment, referring to fig. 3, the passing time of each lane and the connection time between adjacent lanes in the same road segment are selected as weights to perform static global path planning, where the step S2 specifically includes the following steps:
s21, acquiring travel time;
s22, obtaining average crossing time;
s23, acquiring average delay time of the intersection;
and S24, acquiring the passing time of each lane according to the acquired travel time, the average crossing passing time and the average crossing delay time.
In an embodiment, the main factors influencing the travel time of the vehicle on the lane are the length of the lane and the average vehicle speed on the lane, and the average vehicle speed is related to the maximum speed limit of the lane and the average density of the lane, the step S21 specifically includes the following steps:
and acquiring the passing time of the vehicle on the lane according to the following formula:
the above formula is a relational expression of the BPR function proposed by the U.S. highway administration, in which,
for the traffic flow on road segment i lane j is
The time of flight of the time of day,
for free-flow travel time on road segment i lane j,
for the actual flow on lane j of road segment i,
for the capacity on lane j of road section i,
and
is constant, usually taken
，
。
Wherein, the first and the second end of the pipe are connected with each other,
the calculation method of (2) is as follows:
wherein the content of the first and second substances,
the length of the section i, which is generally obtained from map data,
the maximum speed limit of the current road section i is the free flow speed of the lane. In general, for the same link i, all the lane lengths belonging to the link are generally equal, and the speed limit is also for the link, except for the special case, so the free-flow travel time on the link is
Free flow of travel time to road section
Are equal, i.e.
。
In one embodiment, the travel time of the lane is also related to the geographical position of the lane, i.e. under the same conditions, the travel time of the middle lane is shorter than that of the side lanes, and there is a certain time loss mainly because the side lanes are affected by pedestrians and non-motor vehicles, and therefore, the updated travel time obtained in step S21
Comprises the following steps:
in the formula (I), the compound is shown in the specification,
taking 5 percent as a boundary loss factor,
is composed of
The lane is a side road, and the lane is a side road,
is composed of
And if the lane is not a side road, performing time loss updating calculation on the passing time based on the geographical position of the lane to obtain the updated travel time so as to improve the accuracy of the travel time of the vehicle planned to run on the lane.
In an embodiment, since the vehicle generally decelerates to the target vehicle speed when passing through the intersection and then travels through the intersection at the target vehicle speed while accelerating to enter the next lane, the average time of the vehicle passing through the intersection is composed of three parts, namely acceleration delay, deceleration delay and virtual lane passing, and the step S22 specifically includes the following steps:
s221, acquiring deceleration loss time of the vehicle when the vehicle decelerates to enter the intersection;
step S222, obtaining intersection passing time of the vehicle passing through the virtual lane length of the intersection;
step S223, obtaining the acceleration loss time of the vehicle entering the next lane from the intersection;
and S224, acquiring the average crossing passing time according to the acquired deceleration loss time, crossing passing time and acceleration loss time.
In a specific embodiment, the step S22 is specifically implemented as:
step S221, acquiring deceleration loss time of the vehicle entering the intersection through the following formula
：
In the formula (I), the compound is shown in the specification,
is the average vehicle speed of the entrance lane,
is the speed of the intersection when passing,
is the vehicle acceleration.
Step S222, obtaining the intersection passing time of the vehicle passing the virtual lane length of the intersection through the following formula
Comprises the following steps:
in the formula (I), the compound is shown in the specification,
is the length of the virtual lane within the intersection.
Step S223, obtaining the acceleration loss time of the vehicle entering the next lane from the intersection through the following formula
Comprises the following steps:
in the formula (I), the compound is shown in the specification,
is the average vehicle speed on the exit lane.
Step S224, obtaining the average crossing passing time of the obtained deceleration loss time, the obtained crossing passing time and the obtained acceleration loss time according to the following formula
Comprises the following steps:
in an embodiment, the intersections are mainly divided into intersections controlled by signal lamps and intersections controlled by no signal lamps, and in step S23, different intersection average delay time calculation methods are executed according to the specific situation of the intersections with or without signal lamps.
For intersections with large traffic flow, traffic signal lamp control is usually arranged, the intersection delay mainly loses the vehicle travel time lost due to traffic flow interruption caused by intersection signal control, and the vehicle travel time is mainly related to signal period, timing, traffic volume, random factors and the like, in one embodiment, an HCM signal intersection delay calculation model of 1985 is adopted to estimate and calculate the average delay time of the intersection, and the expression is as follows:
in the formula (I), the compound is shown in the specification,
indicating the delay time of the signalized intersection;
indicating the period duration of the signal lamp;
indicating green ratio, i.e. effective green time
And signal period
A ratio of (d);
indicating degree of saturation, i.e. observed flow of lane
Traffic saturation flow
The ratio of (a) to (b).
For intersections with small traffic flows, traffic signal lamp control is generally not set, and the driving guide of intelligent unmanned vehicles is aimed at in the application, so that the passing of the vehicles can be coordinated at the signalless intersections, and the vehicles are considered not to be delayed due to long-time parking waiting at the signalless intersections, and therefore, the average delay time of the intersections can not be generated
。
In summary, the average delay at the intersection is expressed by the following relation:
in the formula (I), the compound is shown in the specification,
the signal lamp is controlled by the representative intersection,
the intersection is not controlled by signal lamps, whether the intersection is controlled by traffic lights or not can be obtained when map data are acquired, and therefore when the weight of the travel time of the lane is calculated, the lane travel time can be easily determined
The value of (a).
In summary, the traffic time of a certain lane is expressed by the following relation:
in an embodiment, the adjacent lanes belonging to the same road segment may be connected by lane change, but in the lane change process, the speeds of the two lanes may be different due to different traffic flows, so that in the lane change process, not only the time caused by the extra driving distance but also the delay time caused in the acceleration and deceleration process need to be considered, and the step S3 specifically includes the following steps:
s31, acquiring lane change travel time generated by the connection distance between adjacent lanes on the same road section;
s32, obtaining lane change delay time generated by lane change acceleration and deceleration between adjacent lanes on the same road section;
and step S33, acquiring the connection time between adjacent lanes on the same road section according to the acquired lane change travel time and the acquired lane change delay time.
In a more specific embodiment, the connection time between adjacent lanes on the same road segment, i.e., the profile time resulting from a lane change, is represented by the following relationship:
in the formula (I), the compound is shown in the specification,
the travel time generated by lane change reflects the weight of the connection time between two adjacent lanes belonging to the same road section;
the lane width is represented, and because lane changing is that the lane is driven from the center line of one lane to the center line of the other lane, the lane widths are generally equal, the lane changing extra driving distance is the lane width;
is the vehicle acceleration;
for road sections
Lane
The average traffic speed of the vehicle is higher,
for road sections
Lane
Average traffic speed of above, wherein, lane
And the lane
For road sections
Upper adjacent lane.
In an embodiment, the step S4 specifically includes the following steps:
the serial numbers of different lanes of different road sections in the road network are different, and the serial numbers of all the lanes are unique and non-repeated; taking the lane number of each lane as a node, the travel time of the lane between the accessible lanes of different road sections
By the time of travel between adjacent lanes of the same road section by lane change
The edges form a directed graph. In directed graphs
In the step (1), the first step,
is a set formed by lane numbers, is a node set,
the time that the vehicle takes to reach other lanes is the set of side weights. For the composed directed graph, each node has a predecessor node and a successor node. The partial schematic diagram of the composed directed graph is shown in fig. 4.
In an embodiment, the step S5 specifically includes the following steps:
s51, acquiring longitude and latitude information of a starting point and longitude and latitude information of a target end point of a vehicle in a real road network;
s52, converting the longitude and latitude information of the starting point and the longitude and latitude information of the target end point into the plane coordinate information of the starting point and the plane coordinate information of the end point;
and S53, acquiring a start point most adjacent lane and an end point most adjacent lane according to the start point plane coordinate information and the end point plane coordinate information, and taking the start point most adjacent lane and the end point most adjacent lane as a path planning start node and a target node corresponding to the vehicle.
In an embodiment, the step S53 specifically includes the following steps:
respectively drawing circles with the start plane coordinate information and the end plane coordinate information of the vehicle as circle centers, gradually increasing the radius of the drawn circles until lanes are screened out in the circles, determining lanes closest to the start plane coordinate information or the end plane coordinate information in the lanes in the circles, and taking the lanes closest to the start point and the end point as a path planning start node and a path planning target node corresponding to the vehicle.
In a more specific embodiment, the step S5 specifically includes the following steps:
the system acquires longitude and latitude information of a starting point and longitude and latitude information of a target end point of the vehicle, and then converts the longitude and latitude information into rectangular coordinates under a plane coordinate system. The calculation is too complex considering that all lanes are matched. Therefore, the area where the start point coordinate information is located is first determined according to the start point coordinate information. The method for determining the area in which the mobile terminal is located comprises the following steps: and respectively taking the coordinates of the starting point and the ending point of the vehicle as the circle centers, then drawing a circle by taking r as the radius, and screening out all lanes belonging to the range. If the lane exists in the range, finding the closest lane; if no lane exists in the range, gradually enlarging the range of r, continuing to search until a lane exists in the range, and searching for the closest lane.
For the lane in the starting point range, the longitude and latitude coordinates of two end points of the lane are converted into plane coordinates under an x-axis coordinate system relative to the starting point, wherein the north direction is a y-axis, and the east direction is an x-axis coordinate system, and then the lane line segment in the starting point range can be expressed as follows:
wherein
，
，
The coefficients, representing the linear equations, are solved by the following equation:
in the formula (I), the compound is shown in the specification,
and
the relative coordinates of two end points of the lane line segment are shown. Calculating the distance from the starting point (0, 0) to the lane line segment in the range by an Euclidean distance formula as follows:
if the vertical lines are respectively drawn from the starting point to all lanes in the range, when the vertical lines fall into the lane segments, the distance is calculated by adopting the formula, the lane corresponding to the minimum distance value is taken out and is the closest lane to the starting point, and the lane is the starting node of the path planning
。
For the lane in the end point range, the closest lane to the end point can be found out by adopting the same method, and the lane is the target node of the path planning
。
In one embodiment, directed graphs
Node set composed of lane numbers
=
Set of nodes
Set of nodes
Wherein, in the process,
is used as a starting node and is used as a starting node,
as a target node, the step S6 specifically includes the following steps:
step S61, obtaining an optimal path according to the formula I:
In the formula (I), the compound is shown in the specification,
as a driven lane in a directed graph
To the lane
The optimal path with the shortest travel time,
is a driveway
Directly to the roadway
The time of flight of (a) is,
indicating a lane
Passing lane
Rear to lane
The travel time of (a);
calculating from the initial node by using Dijkstra algorithm
To the target node
Step S62, step S63, and step S64 of the route having the shortest time therebetween:
step S62, slave node set
Is selected such that
Minimum lane numbering
And numbering lanes
Put into node set
In the collection
Number lanes
Deleting;
step S63, update and
set of path information values between directly connected lanes, slave nodes
Is selected so that
Minimum lane numbering
To repeat step S62;
step S64: repeating S63 circularly until findingNumber to target lane
And put the lane number into the node set
And in node sets
Number lanes
Deleting the lane from the initial lane
To the end lane
The optimal path with the shortest travel time is as follows:
。
in an embodiment, the optimal path obtained in step S6 is a set of lane numbers, but for a vehicle, a set of path longitude and latitude points is required, and therefore, after step S6, the method further includes the following steps:
and restoring the obtained calculation result of the optimal path into a longitude and latitude point set in a real road network, wherein the distance between two adjacent longitude and latitude points in the longitude and latitude point set is 1 meter. For the vehicles with the starting points and the ending points which are not on the starting lane and the target lane, an optimal route from the starting position to the starting lane and an optimal lane from the target lane to the ending point position of the vehicles are planned. And finally, sending the obtained longitude and latitude point set to the intelligent networked unmanned vehicle through a vehicle-road cooperative system so as to guide the intelligent networked unmanned vehicle to run.
In an embodiment, after obtaining the optimal path, the driving speed of the vehicle is further planned, so as to achieve the purpose of guiding the vehicle to drive, and after the step S6, the method further includes the following steps:
and (3) calculating the speed of the vehicle to be taken when the vehicle runs to the corresponding road section according to the lane travel time of each road section obtained in the step (2), namely:
in the formula (I), the compound is shown in the specification,
representing the average vehicle speed of the vehicle on lane j of road segment i,
indicating the length of lane j of road segment i,
the travel time of the section i lane j obtained in step 2.
For the initial stage, the vehicle accelerates from the stop state to the average speed on the initial lane, since the distance between two adjacent points in the longitude and latitude point set given to the vehicle in step 7 is 1m, the corresponding speed from the starting point is:
in the formula (I), the compound is shown in the specification,
indicates the first in the initial stage
The speed of the respective latitude and longitude points,
denotes the first in the initial stage
The speed of the respective latitude and longitude points,
indicates a comfortable acceleration, if
Then get
。
For the stop stage, the vehicle is decelerated from the lane movement state to the stop state, and the total deceleration length is as follows:
in the formula (I), the compound is shown in the specification,
indicates the first in the initial stage
The speed of the respective latitude and longitude points,
indicates the first in the initial stage
The speed of the respective latitude and longitude points,
indicating a comfortable deceleration. Wherein the content of the first and second substances,
the longitude and latitude points of the terminal point are shown, and when the distance between the last two points is less than 1m, the deceleration of the vehicle is adjusted to ensure that the speed is just zero when the vehicle reaches the stopping point.
In an embodiment, based on the global planning of the optimal path, a local coordination technical means needs to be further adopted to guide the unmanned vehicle to travel, and therefore, the step S7 specifically includes the following steps:
step S71, acquiring a real-time running state and real-time lane information of the vehicle;
and S72, controlling and executing different vehicle speeds and path guiding strategies according to the acquired optimal path, the vehicle real-time running state and the real-time lane information.
In an embodiment, the step S72 specifically includes the following steps:
step S721, when the real-time lane information is a traffic accident or a construction working condition, controlling to execute the replanning of the optimal path;
and step S722, when the real-time running state of the vehicle is that the vehicle with the track conflict exists, controlling and executing the optimal path of the vehicle with the track conflict.
In an embodiment, the step S72 is implemented as: the intelligent network connection unmanned vehicle and vehicle-road cooperative system carries out real-time information interaction, the vehicle-road cooperative system dynamically adjusts the speed of the vehicle by monitoring the running state information of the vehicle in real time, and when special conditions such as traffic accidents, construction and the like occur on the planned path of the vehicle, the system carries out optimal path planning again by taking the current lane number as a starting point according to the method and the steps and guides the vehicle to carry out path change.
In an embodiment, the step S722 specifically includes the following steps:
step S7221, obtaining conflict points of two vehicles in the planned optimal path, and drawing a circle with a preset radius by taking the conflict points as the circle center to form a conflict area;
step S7222, acquiring the speed of the two vehicles and the distance between the two vehicles and the conflict point, and acquiring the arrival sequence of the two vehicles to the conflict point;
and S7223, controlling the vehicles arriving in advance in the two vehicles to normally run through the conflict area according to the planned path, and controlling the vehicles arriving in the conflict area after the two vehicles to decelerate before passing through the conflict area and then accelerate to the target speed.
In an embodiment, the step S722 is implemented as:
by monitoring the running state of the vehicles in real time, when the tracks of the intelligent networked vehicles conflict with each other, conflict points of the two vehicles are found out through the planned path, and a conflict area is formed by taking the conflict points as the center and taking a certain length as the radius. And determining the time of the conflict vehicle reaching the conflict point according to the distance between the vehicle and the conflict point and the speed of the vehicle, and then determining the sequence of the conflict vehicle passing through the conflict area according to the reaching time. And for the vehicles which arrive at the conflict area firstly, the vehicles are driven at a preset speed, and for the vehicles which arrive at the conflict area later, the speed of the vehicles is adjusted, and the vehicles which arrive at the conflict area firstly pass through the conflict area and then accelerate to pass through the conflict area, so that the passing safety is guaranteed. Deceleration of a vehicle traveling at a later collision zone
The following relationship should be satisfied,
in the formula (I), the compound is shown in the specification,
is the speed of the vehicle traveling in the current lane,
is the distance of the vehicle to the boundary line of the conflict area,
is a safe distance threshold. I.e. vehicle arriving at the collision zone after the vehicle has accelerated
The vehicle which arrives at the conflict area before passes through the conflict area is monitored while the vehicle which arrives at the conflict area before passes through the conflict area, the vehicle which arrives at the conflict area after passes through the conflict area is accelerated to the target vehicle speed at comfortable acceleration, and if the vehicle exists in the conflict area, the vehicle which arrives at the conflict area after passes through the conflict area, the vehicle which arrives at the conflict area is accelerated to the target vehicle speed at a safe distance threshold value from the conflict area
And stopping and waiting until the vehicle in the conflict area is accelerated to the target speed after leaving the conflict area, and then continuing to run.
The embodiment of the invention provides an unmanned vehicle guiding method and system based on a vehicle-road cooperative technology, wherein a cloud-road-vehicle intelligent integrated cooperative control system is formed based on the construction of a vehicle-road cooperative system, an edge computing technology, a high-precision positioning technology, a data fusion technology and a V2X information interaction technology are combined, a global planning and local coordination technical means is adopted, an optimal driving path longitude and latitude point of an unmanned vehicle is globally planned by taking time as weight, and the optimal driving path longitude and latitude point is sent to the unmanned vehicle in stages to guide the unmanned vehicle to drive. Meanwhile, the driving speed of the unmanned vehicle is locally adjusted in consideration of the fact that the vehicle tracks conflict, the unmanned vehicle is guaranteed not to be locked due to the track conflict, and the unmanned vehicle can run in a coordinated mode, so that the purpose that the unmanned vehicle efficiently runs on the premise of guaranteeing safety is achieved.
Based on the same application concept, please refer to fig. 5, the present application provides an unmanned vehicle guidance system based on vehicle-road coordination technology, comprising:
a road network abstraction obtaining module 100, configured to obtain a road network abstraction according to the structured map data;
a passing time obtaining module 200, configured to obtain a passing time of each lane;
a connection time acquisition module 300, configured to acquire connection time between adjacent lanes in the same road segment;
a directed graph obtaining module 400, communicatively connected to the road network abstraction obtaining module 100, the transit time obtaining module 200 and the connection time obtaining module 300, for constructing a directed graph using the lane numbers as nodes, the transit times as side weights and the connection times as connection side weights between the nodes according to the obtained road network abstraction, the transit time weight of each lane and the connection time weight between adjacent lanes in the same road segment;
a vehicle start and target node obtaining module 500, configured to obtain start point longitude and latitude information of a vehicle and longitude and latitude information of a target end point, and obtain a route planning start node and a target node corresponding to the vehicle;
an optimal path obtaining module 600, communicatively connected to the directed graph obtaining module 400 and the start and target node obtaining module 500, for obtaining an optimal path of the vehicle according to the constructed directed graph and the obtained start node and target node;
and the vehicle guiding module 700 is in communication connection with the optimal path acquiring module 600 and is used for controlling the unmanned vehicle to execute the vehicle speed and the path guiding strategy of the optimal path according to the acquired optimal path.
Based on the same application concept, the embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, all or part of the method steps of the method are implemented.
The present application may implement all or part of the processes of the above methods, and may also be implemented by a computer program instructing related hardware, where the computer program may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signal, telecommunications signal, software distribution medium, etc. It should be noted that the computer-readable medium may contain suitable additions or subtractions depending on the requirements of legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer-readable media may not include electrical carrier signals or telecommunication signals in accordance with legislation and patent practice.
Based on the same application concept, embodiments of the present application further provide an electronic device, which includes a memory and a processor, where the memory stores a computer program running on the processor, and the processor executes the computer program to implement all or part of the method steps in the method.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being the control center of the computer device and various interfaces and lines connecting the various parts of the overall computer device.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the computer device by executing or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (e.g., a sound playing function, an image playing function, etc.); the storage data area may store data (e.g., audio data, video data, etc.) created according to the use of the cellular phone. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
As will be appreciated by one of skill in the art, embodiments of the present application may be provided as a method, system, server, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), servers and computer program products according to embodiments of the application. 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.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (8)
1. An unmanned vehicle guiding method based on a vehicle-road cooperation technology is characterized by comprising the following steps:
s1, acquiring road network abstraction according to structured map data;
s2, acquiring the passing time of each lane;
s3, acquiring connection time between adjacent lanes on the same road section;
s4, constructing a directed graph with lane numbers as nodes, passage time as side weight and connection time as inter-node connection side weight according to the obtained road network abstraction, the passage time weight of each lane and the connection time weight between adjacent lanes in the same road section;
s5, acquiring longitude and latitude information of a starting point and longitude and latitude information of a target end point of the vehicle, and acquiring a route planning starting node and a target node corresponding to the vehicle;
s6, acquiring an optimal path of the vehicle according to the constructed directed graph and the acquired initial node and target node;
s7, controlling the unmanned vehicle to execute a vehicle speed and a path guiding strategy of the optimal path according to the obtained optimal path;
the step S2 specifically includes the following steps:
s21, acquiring travel time;
s22, obtaining average crossing time;
s23, acquiring average delay time of the intersection;
s24, acquiring the passing time of each lane according to the acquired travel time, the average crossing passing time and the average crossing delay time;
the step S3 specifically includes the following steps:
s31, acquiring lane change travel time generated by the connection distance between adjacent lanes on the same road section;
step S32, obtaining lane change delay time generated by lane change acceleration and deceleration between adjacent lanes on the same road section;
and step S33, acquiring the connection time between adjacent lanes on the same road section according to the acquired lane change travel time and the acquired lane change delay time.
2. The method for guiding an unmanned vehicle based on vehicle-road coordination technology according to claim 1, wherein the step S5 specifically comprises the following steps:
s51, acquiring longitude and latitude information of a starting point and longitude and latitude information of a target end point of a vehicle in a real road network;
step S52, converting the longitude and latitude information of the starting point and the longitude and latitude information of the target end point into the plane coordinate information of the starting point and the plane coordinate information of the end point;
and S53, acquiring a start point most adjacent lane and an end point most adjacent lane according to the start point plane coordinate information and the end point plane coordinate information, and taking the start point most adjacent lane and the end point most adjacent lane as a path planning start node and a target node corresponding to the vehicle.
3. The unmanned vehicle guidance method based on vehicle-road cooperation technology according to claim 2, wherein the step S53 specifically comprises the steps of:
and drawing circles respectively taking the initial plane coordinate information and the terminal plane coordinate information of the vehicle as circle centers, gradually increasing the radius of the drawn circles until lanes are screened in the circles, determining lanes closest to the initial plane coordinate information or the terminal plane coordinate information in the lanes in the circles, and taking the lanes closest to the initial point and the terminal point as path planning initial nodes and target nodes corresponding to the vehicle.
4. The unmanned vehicle guidance method based on vehicle-road coordination technology according to claim 1, the method is characterized in that a directed graph G = (V, W), and a node set V = { V } composed of lane numbers 0 ，v 1 ，v 2 ，...，v n }, set of nodes U 1 ＝{v start }, node set U 2 ＝{v 0 ，v 1 ，...，v start-1 ，v start+1 ，...，v end ，...，v n In which v start Is a starting node, v end The step S6 is a target node, and specifically includes the following steps:
step S61, obtaining an optimal path according to the formula I:
P[v i ，v j ]＝min{T[v i ，v j ]，T[v i ，v k ]+T[v k ，v j ]the method is represented by the formula one,
in the formula, P [ v ] i ，v j ]For the driven lane v in the directed graph i To the lane v j Optimal path with shortest travel time, tv i ，v j ]Is a lane v i Directly to the lane v j Time of flight of, tv i ，v k ]+T[v k ，v j ]Indicating the lane v i Passing through lane v k Rear to lane v j The travel time of (c);
step S62, slave node set U 2 Is selected such that P [ v ] start ，v i ]Minimum lane number v i And numbering lanes v i Put into node set U 1 In the set U 2 Middle position lane number v i Deleting;
step S63, update and v i Set of directly connected inter-lane path information values, slave node U 2 Is selected such that P [ v ] i ，v j ]Minimum lane number v j To repeat step S62;
step S64: repeating S63 circularly until the target lane number v is found end And put the lane number into the node set U 1 And in node set U 2 Middle position lane number v end Deleting, the final requested starting lane v start To the destination lane v end The optimal path with the shortest travel time is as follows: u shape 1 ＝{v start ，v i ，v j ，...，v end }。
5. The method for guiding an unmanned vehicle based on vehicle-road coordination technology as claimed in claim 1, wherein the step S7 specifically comprises the steps of:
step S71, acquiring real-time running state and real-time lane information of the vehicle;
and S72, controlling and executing different vehicle speeds and path guiding strategies according to the acquired optimal path, the vehicle real-time running state and the real-time lane information.
6. The method for guiding an unmanned vehicle based on vehicle-road coordination technology according to claim 5, wherein said step S72 specifically comprises the steps of:
step S721, when the real-time lane information is a traffic accident or a construction working condition, controlling to execute the replanning of the optimal path;
and step S722, when the real-time running state of the vehicle is that the vehicle with the track conflict exists, controlling and executing the optimal path of the vehicle with the track conflict.
7. The method for guiding an unmanned vehicle based on vehicle-road coordination technology according to claim 6, wherein said step S722 specifically comprises the following steps:
obtaining conflict points of two vehicles in the planned optimal path, and drawing a circle with a preset radius by taking the conflict points as the circle center to form a conflict area;
acquiring the speed of two vehicles and the distance between the two vehicles and a conflict point, and acquiring the arrival sequence of the two vehicles to the conflict point;
and controlling the vehicles arriving in the two vehicles in advance to normally run through the conflict area according to the planned route, and controlling the vehicles arriving in the two vehicles to decelerate before the vehicles arriving in the conflict area pass through the conflict area and then accelerate to the target speed.
8. An unmanned vehicle guidance system based on vehicle-road coordination technology, comprising:
the road network abstraction obtaining module is used for obtaining road network abstractions according to the structured map data;
the passing time acquisition module is used for acquiring the passing time of each lane;
the connection time acquisition module is used for acquiring the connection time between adjacent lanes in the same road section;
the directed graph acquisition module is in communication connection with the road network abstraction acquisition module, the traffic time acquisition module and the connection time acquisition module and is used for constructing a directed graph which takes the lane number as a node, the traffic time as a side weight and the connection time as the connection side weight between the nodes according to the acquired road network abstraction, the traffic time weight of each lane and the connection time weight between adjacent lanes of the same road segment;
the vehicle starting and target node acquisition module is used for acquiring longitude and latitude information of a starting point and longitude and latitude information of a target end point of a vehicle and acquiring a route planning starting node and a target node corresponding to the vehicle;
the optimal path acquisition module is in communication connection with the directed graph acquisition module and the starting and target node acquisition module and is used for acquiring an optimal path of the vehicle according to the constructed directed graph and the acquired starting node and target node;
the vehicle guiding module is in communication connection with the optimal path acquiring module and is used for controlling the unmanned vehicle to execute a vehicle speed and a path guiding strategy of the optimal path according to the acquired optimal path;
the step of obtaining the passing time of each lane specifically comprises the following steps:
s21, acquiring travel time;
s22, obtaining average crossing time;
s23, acquiring average delay time of the intersection;
s24, acquiring the passing time of each lane according to the acquired travel time, the average crossing passing time and the average crossing delay time;
the step of obtaining the connection time between the adjacent lanes on the same road section specifically comprises the following steps:
s31, acquiring lane change travel time generated by the connection distance between adjacent lanes on the same road section;
step S32, obtaining lane change delay time generated by lane change acceleration and deceleration between adjacent lanes on the same road section;
and step S33, acquiring the connection time between adjacent lanes on the same road section according to the acquired lane change travel time and the acquired lane change delay time.
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