CN112669642A - Dynamic path planning algorithm and system based on passing time and vehicle speed prediction - Google Patents
Dynamic path planning algorithm and system based on passing time and vehicle speed prediction Download PDFInfo
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Abstract
The algorithm comprises a lower-layer algorithm OptSub and an upper-layer algorithm DyPath, and the vehicle running path is dynamically planned in real time according to the predicted running time data of the future path in the running process of the vehicle, so that the running time of the vehicle in the complex path is shortest, and intelligent traffic is realized. The invention combines the real-time average speed of each road section with the predicted future average speed in the driving process, calculates the real-time passing time of the road section, predicts the passing time in a period of time in the future and dynamically adjusts the path. The existing traditional prediction algorithm optimizes the path selection by using real-time road conditions, but cannot deal with the traffic condition change of the future road section; the method has the advantages of correctness, effectiveness and applicability, improves the road traffic capacity of vehicles in the environment with frequent road state change, and brings convenience to urban traffic.
Description
Technical Field
The application relates to the technical field of intelligent traffic, in particular to a dynamic path planning algorithm and a system based on passing time and vehicle speed prediction.
Background
With the slow city service construction and the improvement of the living standard of people, the current public service of cities can not meet the daily requirements of people, and a batch of smart cities with distinctive features are to be built for guaranteeing and improving the civil service, innovating social management and maintaining network security.
Therefore, smart cities become a mainstream trend of future development, and intelligent transportation is an extremely important component of the smart cities and also occupies a strategic position. The dynamic route planning algorithm capable of effectively relieving road pressure, reducing accident rate and reducing special vehicle running time is always the key of intelligent traffic. In real life, the road state can be changed, natural factors such as rain, snow, strong wind, strong fog and the like, and human factors such as commuting peaks and traffic accidents can all affect the road state, so that the passing condition of vehicles is further affected. When the path planning is performed, the passing states of different time and different road sections are calculated according to the dynamic road state, so that the method for performing the path planning is called as a dynamic path planning algorithm.
With the rise of the V2I communication network, the real-time road data acquisition becomes more convenient, and a large number of optimization methods based on real-time road condition data appear.
Liu et al improved the heuristic function of the A-star algorithm, changed the road weight from distance to transit duration, but did not explicitly provide a solution to the calculation function of the estimated value from the current node to the end point after the road weight was changed; wu proposes an accumulated weight value updating method and an improved Dijkstra algorithm according to road information in the past time period, but the weight value accumulation of Wu only concerns the congestion condition of a local single road section and does not consider the whole road condition from the position of a vehicle to a destination; the wang et al provide a dynamic evacuation path planning algorithm introducing real-time road conditions for road congestion conditions, and compared with the traditional planning algorithm, the dynamic evacuation path planning algorithm can quickly evacuate road congestion under the condition of road congestion, but is not suitable for normal traffic conditions and cannot plan a fastest driving route for vehicles.
The chiffon and the like use a clustering algorithm to divide customers (vehicles) with relatively close space-time distances, and add the customers (vehicles) to the same driving path as much as possible, so that the path planning efficiency is improved, the search range of the algorithm is reduced, but the efficiency is increased, and the optimal path cannot be planned.
In order to better reflect the real-time condition of the road, the Chumiel et al uses a road section passing time interval to replace average passing time, so that more information quantity is provided, but an optimal path is difficult to plan by using the interval data; kanoh proposes a dynamic route planning algorithm based on a genetic algorithm aiming at a dynamic traffic environment, the effect of the algorithm is superior to that of a Dijkstra algorithm under the dynamic environment, but the algorithm takes a quasi-optimal route as a planning target and cannot provide an optimal route;
the algorithm D is a widely known dynamic path planning algorithm proposed by Stentz, and is also a road-finding algorithm adopted by a mars detector, but is more suitable for finding roads in an unknown environment compared with a city in a known traffic environment.
Although the method uses real-time road conditions to optimize the route selection, the method still cannot well cope with the change of the passing condition of the future road section.
Disclosure of Invention
The invention aims to provide a dynamic path planning algorithm and a system based on transit time and vehicle speed prediction. The algorithm predicts the passing time data according to the future path in the driving process of the automobile and dynamically plans the driving path of the automobile in real time so as to lead the passing time of the automobile in the complex path to be the shortest, thereby realizing intelligent traffic. The invention is suitable for a traffic road network with constantly changing road surface information, and combines real-time data and predicted data to plan a vehicle driving route with shortest time consumption while driving so as to reach a destination in shortest time. The traffic jam is reduced to a certain extent and the influence caused by the traffic jam is relieved.
The invention provides a dynamic path planning algorithm based on the prediction of transit time and vehicle speed, which comprises a lower-layer algorithm OptSub and an upper-layer algorithm DyPath,
wherein:
the lower algorithm OptSub: the system is used for optimizing and generating the current best traffic intersection based on the real-time path predicted by the average speed of the road section, so that the upper-layer algorithm DyPath can update the path and plan the fastest traffic path in real time in the driving process;
the upper-layer algorithm DyPath: the method is used for obtaining the road state which changes constantly by updating the traffic network path passing time in real time in the driving process.
Preferably, the lower algorithm OptSub is used for generating a current optimal traffic intersection for the upper algorithm DyPath to update the path and plan a fastest traffic path in real time during driving, and specifically includes the following steps:
the method comprises the following steps: acquiring a departure place, a destination and a traffic road network map between the departure place and the destination;
step two: acquiring the predicted road passing time data at the current moment; and a global optimal passing path at the current moment is planned according to the real-time road predicted passing time data;
step three: using the real-time global optimal passing path obtained in the step two to guide the vehicle to run on the next road section;
step four: and repeating the second step and the third step until the vehicle reaches the destination.
Preferably, the upper-layer algorithm DyPath updates the traffic network path transit time in real time to obtain a constantly changing road state during the driving process, and specifically includes the following steps:
the method comprises the following steps: presetting a vehicle response distance according to an urban speed limit standard;
step two: updating the next algorithm calling time according to the size relation between the current road section length l (m, n) and the next sub-road section length l (n, n') and the response distance:
if l (m, n) >50, l (n, n') >50, the algorithm calls for the next time: when the distance is n' 50 meters away from the next sub-road section;
if l (m, n) <50, l (n, n') >50, the algorithm calls for the next time: when the distance is n' 50 meters away from the next sub-road section;
if l (m, n) >50, l (n, n') <50, the algorithm next call time: when reaching the intersection n;
if l (m, n) <50, l (n, n') <50, the algorithm calculates the next call time: when reaching the intersection n;
wherein:
m is the initial intersection of the road section where the current vehicle is located; n is the ending crossing of the road section where the current vehicle is located and is also the starting crossing of the next road section; n' is the next section ending intersection predicted by the algorithm; l (m, n) represents the length of the link (m, n), and l (n, n ') represents the length of the link (n, n').
The invention provides a dynamic path planning system based on the prediction of the transit time and the vehicle speed, which comprises a lower-layer algorithm OptSub subsystem and an upper-layer algorithm DyPath subsystem, wherein:
the lower algorithm OptSub subsystem: the system is used for optimizing and generating the current best traffic intersection based on the real-time path predicted by the average speed of the road section, so that the upper-layer algorithm DyPath subsystem can update the path and plan the fastest traffic path in real time in the driving process;
the upper-layer algorithm DyPath subsystem: the method is used for obtaining the road state which changes constantly by updating the traffic network path passing time in real time in the driving process.
Preferably, the lower algorithm OptSub-system comprises a first storage and a first actuator which are connected in communication, and the first storage stores the lower algorithm OptSub which can be executed by the first actuator.
Preferably, the upper-layer algorithm DyPath subsystem includes a second storage and a second actuator, which are in communication connection, and the second storage stores an upper-layer algorithm DyPath that can be executed by the second actuator.
Compared with the prior art, this application can bring following technological effect:
1. the algorithm provided by the invention predicts the passing time data according to the future path in the automobile driving process, and dynamically plans the vehicle driving path in real time so as to ensure that the passing time of the vehicle in the complex path is shortest, thereby realizing intelligent traffic;
2. the invention is suitable for a traffic road network with constantly changing road surface information, combines real-time data and predicted data, and plans a vehicle running route with shortest time consumption while running so as to reach a destination in shortest time, thereby reducing the occurrence of traffic jam and relieving the influence caused by the traffic jam to a certain extent;
3. the invention combines the real-time average speed of each road section with the predicted future average speed in the driving process, calculates the real-time passing time of the road section, predicts the passing time in a period of time in the future and dynamically adjusts the path. The existing traditional prediction algorithm optimizes the path selection by using real-time road conditions, but cannot deal with the traffic condition change of the future road section;
4. the method has the advantages of correctness, effectiveness and applicability, improves the road traffic capacity of vehicles in the environment with frequent road state change, and brings convenience to urban traffic.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
FIG. 1 is a schematic diagram of the structure of the dynamic path planning algorithm of the present invention;
FIG. 2 is a flow chart of the lower algorithm OptSub of the present invention;
FIG. 3 is a flowchart of the upper algorithm DyPath of the present invention;
FIG. 4 is a schematic diagram of a dynamic path planning system according to the present invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, algorithm, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, algorithm, article, or apparatus.
In this application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings. These terms are used primarily to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to a particular orientation or to be constructed and operated in a particular orientation.
Moreover, some of the above terms may be used to indicate other meanings besides the orientation or positional relationship, for example, the term "on" may also be used to indicate some kind of attachment or connection relationship in some cases. The specific meaning of these terms in this application will be understood by those of ordinary skill in the art as appropriate.
In addition, the term "plurality" shall mean two as well as more than two.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
The real-time dynamic path planning algorithm dynamically plans the vehicle driving path in real time based on the future path prediction passing time data in the driving process of the vehicle, thereby realizing intelligent traffic. As shown in fig. 1, the algorithm is specifically divided into two layers: a lower algorithm OptSub and an upper algorithm DyPath.
As shown in fig. 2, is an algorithm flow chart of the lower algorithm OptSub of the present invention:
and after updating is finished, the fastest passing path at the current time t is obtained by backtracking the direction of the father node, wherein the direct successor node of the node n is the optimal direct successor n' to be solved.
In addition, because the traffic condition is not abrupt, and the travel time required for reserving the safe distance is short, the algorithm takes the current time t as the starting time for planning the path from the node n, takes the corresponding real-time data as the real-time data when the node n starts, and combines the predicted data of other road sections to plan the fastest passing path of the current time.
As shown in fig. 3, it is an algorithm flow chart of the upper-layer algorithm DyPath of the present invention:
the upper-layer algorithm DyPath is responsible for generating a traffic network path updating time t, and the predicted passing time of each road section in the traffic network is obtained at the updating time t, so that a real-time weight matrix and other related parameters of the traffic network are obtained and transmitted to the lower-layer algorithm.
The algorithm comprises the following specific steps: in the actual running process of the vehicle, the subsequent crossing of the current crossing is selected in real time, the path planning with the fastest time is achieved, and a driver can determine which lane is selected by the current crossing in time, namely, the left-turn, the right-turn or the straight-going lane. The algorithm is prepared in advance and gives a selection, and the selection depends on the selection of the subsequent intersection (node) of the current intersection (node), so that an appropriate response mechanism is set.
In combination with the above-described flow charts for the lower algorithm OptSub and the upper algorithm DyPath, the present invention provides two embodiments, example 1 and example 2.
The invention meets the requirement of the response mechanism by reserving a certain distance from the current intersection, and the distance is set to be 50 meters according to the speed limit standard of the sea city.
Example 1
The invention provides a dynamic path planning algorithm based on the prediction of transit time and vehicle speed, which comprises a lower-layer algorithm OptSub and an upper-layer algorithm DyPath,
wherein:
the lower algorithm OptSub: the system is used for optimizing and generating the current best traffic intersection based on the real-time path predicted by the average speed of the road section, so that the upper-layer algorithm DyPath can update the path and plan the fastest traffic path in real time in the driving process;
the lower-layer algorithm OptSub generates the current optimal passing sub-road section according to the real-time traffic data of the traffic network in the driving process until the end point is reached, the path does not need to be updated, and the algorithm is ended.
The upper-layer algorithm DyPath: the system is used for updating the traffic network path passing time in real time to obtain a constantly changing road state in the driving process;
the upper-layer algorithm DyPath obtains the road state which changes constantly by updating the traffic network path passing time in real time in the driving process.
Preferably, the lower algorithm OptSub is used for generating a current optimal traffic intersection for the upper algorithm DyPath to update the path and plan a fastest traffic path in real time during driving, and specifically includes the following steps:
the method comprises the following steps: acquiring a departure place, a destination and a traffic road network map between the departure place and the destination;
step two: acquiring the predicted road passing time data at the current moment; and a global optimal passing path at the current moment is planned according to the real-time road predicted passing time data;
step three: using the real-time global optimal passing path obtained in the step two to guide the vehicle to run on the next road section;
step four: and repeating the second step and the third step until the vehicle reaches the destination.
Preferably, the upper-layer algorithm DyPath updates the traffic network path transit time in real time to obtain a constantly changing road state during the driving process, and specifically includes the following steps:
the method comprises the following steps: presetting a vehicle response distance according to an urban speed limit standard;
according to the speed limit standard of Shanghai city, setting the vehicle response distance of the real-time path optimization algorithm to be 50 meters;
step two: updating the next algorithm calling time according to the size relation between the current road section length l (m, n) and the next sub-road section length l (n, n') and the response distance:
if l (m, n) >50, l (n, n') >50, the algorithm calls for the next time: when the distance is n' 50 meters away from the next sub-road section;
if l (m, n) <50, l (n, n') >50, the algorithm calls for the next time: when the distance is n' 50 meters away from the next sub-road section;
if l (m, n) >50, l (n, n') <50, the algorithm next call time: when reaching the intersection n;
if l (m, n) <50, l (n, n') <50, the algorithm calculates the next call time: when reaching the intersection n;
wherein:
m is the initial intersection of the road section where the current vehicle is located; n is the ending crossing of the road section where the current vehicle is located and is also the starting crossing of the next road section; n' is the next section ending intersection predicted by the algorithm; l (m, n) represents the length of the link (m, n), and l (n, n ') represents the length of the link (n, n').
Based on the dynamic path planning algorithm based on the travel time and the vehicle speed prediction, as shown in fig. 4, the invention correspondingly provides an algorithm system for executing the algorithm,
the invention provides a dynamic path planning system based on the prediction of the transit time and the vehicle speed, which comprises a lower-layer algorithm OptSub subsystem and an upper-layer algorithm DyPath subsystem, wherein:
the lower algorithm OptSub subsystem: the system is used for optimizing and generating the current best traffic intersection based on the real-time path predicted by the average speed of the road section, so that the upper-layer algorithm DyPath subsystem can update the path and plan the fastest traffic path in real time in the driving process;
the upper-layer algorithm DyPath subsystem: the method is used for obtaining the road state which changes constantly by updating the traffic network path passing time in real time in the driving process.
Preferably, the lower algorithm OptSub-system comprises a first storage and a first actuator which are connected in communication, and the first storage stores the lower algorithm OptSub which can be executed by the first actuator.
Preferably, the upper-layer algorithm DyPath subsystem includes a second storage and a second actuator, which are in communication connection, and the second storage stores an upper-layer algorithm DyPath that can be executed by the second actuator.
Example 2
A dynamic path planning algorithm based on travel time and vehicle speed prediction comprises a lower-layer algorithm OptSub and an upper-layer algorithm DyPath,
wherein:
the lower algorithm OptSub: the system is used for optimizing and generating the current best traffic intersection based on the real-time path predicted by the average speed of the road section, so that the upper-layer algorithm DyPath can update the path and plan the fastest traffic path in real time in the driving process;
the upper-layer algorithm DyPath: the method is used for obtaining the road state which changes constantly by updating the traffic network path passing time in real time in the driving process.
Preferably, the lower-layer algorithm OptSub is further configured to combine the real-time data and the predicted data to plan a passing path from a traffic intersection to a destination, continuously update an optimal path and a fastest arrival time in a dynamic planning manner, and obtain a fastest passing path at the current time after the update is finished, and includes:
the method comprises the following steps: acquiring a traffic network diagram between a departure place and a destination and corresponding real-time road state prediction data;
step two: planning a global optimal passing path at the current moment according to the real-time road state prediction data;
step three: using the real-time global optimal passing path obtained in the step two to guide the vehicle to run on the next road section;
step four: and repeating the second step and the third step until the vehicle reaches the destination.
Preferably, the upper-layer algorithm DyPath updates the traffic network path transit time in real time to obtain a constantly changing road state during the driving process, and specifically includes the following steps:
the method comprises the following steps: presetting a vehicle response distance according to an urban speed limit standard;
step two: updating the next algorithm calling time according to the magnitude relation between the current road section length l (m, n) and the next sub-road section length l (n, n') and the vehicle response distance:
if the length of the current road segment and the length of the next road segment are both greater than 50 meters (i.e. l (m, n) >50, l (n, n ') >50), the next time the algorithm calls for time t ' as — when the distance from the next sub-road segment to n ' 50 meters, the specific calculation formula is as follows:
if the length of the current road segment is less than 50 meters, and the length of the next road segment is greater than 50 meters (i.e. l (m, n) <50, l (n, n ') >50), the next time the algorithm calls for time t ' as — when the distance from the next sub-road segment is n ' 50 meters, the specific calculation formula is as follows:
if the length of the current road section is greater than 50 meters, and the length of the next road section is less than 50 meters (i.e. l (m, n) >50, l (n, n ') <50), the specific calculation formula is as follows when the next invocation time t' of the algorithm is-the intersection n is reached:
if the length of the current road section and the length of the next road section are both less than 50 meters (l (m, n) <50, l (n, n ') <50), the next calling time t' of the algorithm is-when the intersection n is reached, the specific calculation formula is as follows:
wherein:
m is the initial intersection of the road section where the current vehicle is located; n is the ending crossing of the road section where the current vehicle is located and is also the starting crossing of the next road section; n' is the next section ending intersection predicted by the algorithm; t is the current time; t is tpThe last call time; t' is the next path updating time; l (m, n) represents the length of the link (m, n), and l (n, n ') represents the length of the link (n, n'); v (m, n, t) represents the predicted average speed of travel of the vehicle through the current road segment (m, n) at time t; v (m, n, t)p) Indicates that the vehicle is at tpA predicted average speed of travel through the current road section (m, n) at the moment; v (n, n ', t) represents the predicted average travel speed of the vehicle through the current link (n, n') at time t.
Based on the dynamic path planning algorithm based on the travel time and the vehicle speed prediction, as shown in fig. 4, the invention correspondingly provides an algorithm system for executing the algorithm,
the invention provides a dynamic path planning system based on the prediction of the transit time and the vehicle speed, which comprises a lower-layer algorithm OptSub subsystem and an upper-layer algorithm DyPath subsystem, wherein:
the lower algorithm OptSub subsystem: the system is used for optimizing and generating the current best traffic intersection based on the real-time path predicted by the average speed of the road section, so that the upper-layer algorithm DyPath subsystem can update the path and plan the fastest traffic path in real time in the driving process;
the upper-layer algorithm DyPath subsystem: the method is used for obtaining the road state which changes constantly by updating the traffic network path passing time in real time in the driving process.
Preferably, the lower algorithm OptSub-system comprises a first storage and a first actuator which are connected in communication, and the first storage stores the lower algorithm OptSub which can be executed by the first actuator.
Preferably, the upper-layer algorithm DyPath subsystem includes a second storage and a second actuator, which are in communication connection, and the second storage stores an upper-layer algorithm DyPath that can be executed by the second actuator.
Through the above description of the embodiments, those skilled in the art can clearly understand that the algorithms of the above embodiments can be implemented by software plus a necessary general hardware platform, and the memory and the actuator included in the subsystem can be directly applied to implement the above programs, algorithms, steps, and the like; of course, hardware is also possible, but the former is a more preferred embodiment in many cases. Based on such understanding, the technical solution of the present invention may be substantially or partially embodied in the form of a software product, which is stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (e.g. a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the algorithm according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A dynamic path planning algorithm based on travel time and vehicle speed prediction is characterized by comprising a lower-layer algorithm OptSub and an upper-layer algorithm DyPath,
wherein:
the lower algorithm OptSub: the system is used for optimizing and generating the current best traffic intersection based on the real-time path predicted by the average speed of the road section, so that the upper-layer algorithm DyPath can update the path and plan the fastest traffic path in real time in the driving process;
the upper-layer algorithm DyPath: the method is used for obtaining the road state which changes constantly by updating the traffic network path passing time in real time in the driving process.
2. The dynamic path planning algorithm based on the prediction of the transit time and the vehicle speed as claimed in claim 1, wherein the lower layer algorithm OptSub is used for generating the current best transit intersection for the upper layer algorithm DyPath to update the path and plan the fastest transit path in real time during the driving process, and comprises the following steps:
the method comprises the following steps: acquiring a departure place, a destination and a traffic road network map between the departure place and the destination;
step two: acquiring the predicted road passing time data at the current moment; and a global optimal passing path at the current moment is planned according to the real-time road predicted passing time data;
step three: using the real-time global optimal passing path obtained in the step two to guide the vehicle to run on the next road section;
step four: and repeating the second step and the third step until the vehicle reaches the destination.
3. The dynamic path planning algorithm based on the prediction of the transit time and the vehicle speed as claimed in claim 2, wherein the upper-layer algorithm DyPath obtains the constantly changing road state by updating the transit time of the traffic network path in real time during the driving process, and specifically comprises the following steps:
the method comprises the following steps: presetting a vehicle response distance according to an urban speed limit standard;
step two: updating the next algorithm calling time according to the size relation between the current road section length l (m, n) and the next sub-road section length l (n, n') and the response distance:
if l (m, n) >50, l (n, n') >50, the algorithm calls for the next time: when the distance is n' 50 meters away from the next sub-road section;
if l (m, n) <50, l (n, n') >50, the algorithm calls for the next time: when the distance is n' 50 meters away from the next sub-road section;
if l (m, n) >50, l (n, n') <50, the algorithm next call time: when reaching the intersection n;
if l (m, n) <50, l (n, n') <50, the algorithm calculates the next call time: when reaching the intersection n;
wherein:
m is the initial intersection of the road section where the current vehicle is located; n is the ending crossing of the road section where the current vehicle is located and is also the starting crossing of the next road section; n' is the next section ending intersection predicted by the algorithm; l (m, n) represents the length of the link (m, n), and l (n, n ') represents the length of the link (n, n').
4. The dynamic path planning algorithm based on the prediction of the transit time and the vehicle speed as claimed in claim 1, wherein the lower layer algorithm OptSub is further configured to combine the real-time data and the predicted data to plan a transit path from a traffic intersection to a destination, continuously update the optimal path and the fastest arrival time in a dynamic planning manner, and obtain the fastest transit path at the current moment after the update is finished, and comprises:
the method comprises the following steps: acquiring a traffic network diagram between a departure place and a destination and corresponding real-time road state prediction data;
step two: planning a global optimal passing path at the current moment according to the real-time road state prediction data;
step three: using the real-time global optimal passing path obtained in the step two to guide the vehicle to run on the next road section;
step four: and repeating the second step and the third step until the vehicle reaches the destination.
5. The dynamic path planning algorithm based on the prediction of the transit time and the vehicle speed as claimed in claim 4, wherein the upper-layer algorithm DyPath obtains the constantly changing road status by updating the transit time of the traffic network path in real time during the driving process, and specifically comprises the following steps:
the method comprises the following steps: presetting a vehicle response distance according to an urban speed limit standard;
step two: updating the next algorithm calling time according to the magnitude relation between the current road section length l (m, n) and the next sub-road section length l (n, n') and the vehicle response distance:
if the length of the current road segment and the length of the next road segment are both greater than 50 meters (i.e. l (m, n) >50, l (n, n ') >50), the next time the algorithm calls for time t ' as — when the distance from the next sub-road segment to n ' 50 meters, the specific calculation formula is as follows:
if the length of the current road segment is less than 50 meters, and the length of the next road segment is greater than 50 meters (i.e. l (m, n) <50, l (n, n ') >50), the next time the algorithm calls for time t ' as — when the distance from the next sub-road segment is n ' 50 meters, the specific calculation formula is as follows:
if the length of the current road section is greater than 50 meters, and the length of the next road section is less than 50 meters (i.e. l (m, n) >50, l (n, n ') <50), the specific calculation formula is as follows when the next invocation time t' of the algorithm is-the intersection n is reached:
if the length of the current road section and the length of the next road section are both less than 50 meters (l (m, n) <50, l (n, n ') <50), the next calling time t' of the algorithm is-when the intersection n is reached, the specific calculation formula is as follows:
wherein:
m is the initial intersection of the road section where the current vehicle is located; n is the current vehicleThe ending intersection of the road section is also the starting intersection of the next road section; n' is the next section ending intersection predicted by the algorithm; t is the current time; t is tpThe last call time; t' is the next path updating time; l (m, n) represents the length of the link (m, n), and l (n, n ') represents the length of the link (n, n'); v (m, n, t) represents the predicted average speed of travel of the vehicle through the current road segment (m, n) at time t; v (m, n, t)p) Indicates that the vehicle is at tpA predicted average speed of travel through the current road section (m, n) at the moment; v (n, n ', t) represents the predicted average travel speed of the vehicle through the current link (n, n') at time t.
6. A dynamic path planning system based on travel time and vehicle speed prediction is characterized by comprising a lower-layer algorithm OptSub subsystem and an upper-layer algorithm DyPath subsystem, wherein:
the lower algorithm OptSub subsystem: the system is used for optimizing and generating the current best traffic intersection based on the real-time path predicted by the average speed of the road section, so that the upper-layer algorithm DyPath subsystem can update the path and plan the fastest traffic path in real time in the driving process;
the upper-layer algorithm DyPath subsystem: the method is used for obtaining the road state which changes constantly by updating the traffic network path passing time in real time in the driving process.
7. The dynamic route planning system based on travel time and vehicle speed prediction of claim 6, wherein the lower algorithm OptSub-system comprises a first memory and a first actuator in communication with the first memory, the first memory storing the lower algorithm OptSub of claim 2 for execution by the first actuator.
8. The dynamic path planning system based on travel time and vehicle speed prediction as claimed in claim 7, wherein the upper-level algorithm DyPath subsystem comprises a second storage and a second actuator, which are communicatively connected, and the second storage stores the upper-level algorithm DyPath according to claim 3, which is executable by the second actuator.
9. The dynamic route planning system based on travel time and vehicle speed prediction of claim 6, wherein the lower algorithm OptSub-system comprises a first memory and a first actuator in communication with the first memory, the first memory storing the lower algorithm OptSub of claim 4 for execution by the first actuator.
10. The dynamic path planning system based on travel time and vehicle speed prediction as claimed in claim 9, wherein the upper-level algorithm DyPath subsystem comprises a second storage and a second actuator, which are communicatively connected, and the second storage stores the upper-level algorithm DyPath according to claim 5, which is executable by the second actuator.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113159436A (en) * | 2021-04-29 | 2021-07-23 | 中国移动通信集团陕西有限公司 | Path recommendation method, device and equipment and readable storage medium |
| CN113780808A (en) * | 2021-09-10 | 2021-12-10 | 西南交通大学 | Vehicle service attribute decision optimization method based on flexible bus connection system line |
| CN114023093A (en) * | 2021-08-29 | 2022-02-08 | 北京工业大学 | Dynamic evacuation method for multiple vehicles smoothly driven in short distance |
| CN115273457A (en) * | 2022-06-16 | 2022-11-01 | 重庆长安汽车股份有限公司 | Global optimal path planning method considering dynamic change of travel time of urban road network |
| CN115662114A (en) * | 2022-10-08 | 2023-01-31 | 广州玩鑫信息科技有限公司 | Intelligent traffic system for relieving congestion based on big data and operation method thereof |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101256083A (en) * | 2008-04-09 | 2008-09-03 | 山东大学 | Method for selecting urban traffic network path based on dynamic information |
| CN101739817A (en) * | 2009-11-26 | 2010-06-16 | 西北工业大学 | Shortest path planning method for dynamic origins |
| CN105551244A (en) * | 2015-12-08 | 2016-05-04 | 重庆云途交通科技有限公司 | Dynamic path planning method |
| CN105810001A (en) * | 2016-05-19 | 2016-07-27 | 东华大学 | Real-time dynamic path planning method based on vehicle-mounted ad hoc network |
| CN109959388A (en) * | 2019-04-09 | 2019-07-02 | 南京大学 | A kind of intelligent transportation fining paths planning method based on grid extended model |
| CN111397622A (en) * | 2020-03-26 | 2020-07-10 | 江苏大学 | Intelligent automobile local path planning method based on improved A-algorithm and Morphin algorithm |
-
2020
- 2020-12-22 CN CN202011523115.0A patent/CN112669642A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101256083A (en) * | 2008-04-09 | 2008-09-03 | 山东大学 | Method for selecting urban traffic network path based on dynamic information |
| CN101739817A (en) * | 2009-11-26 | 2010-06-16 | 西北工业大学 | Shortest path planning method for dynamic origins |
| CN105551244A (en) * | 2015-12-08 | 2016-05-04 | 重庆云途交通科技有限公司 | Dynamic path planning method |
| CN105810001A (en) * | 2016-05-19 | 2016-07-27 | 东华大学 | Real-time dynamic path planning method based on vehicle-mounted ad hoc network |
| CN109959388A (en) * | 2019-04-09 | 2019-07-02 | 南京大学 | A kind of intelligent transportation fining paths planning method based on grid extended model |
| CN111397622A (en) * | 2020-03-26 | 2020-07-10 | 江苏大学 | Intelligent automobile local path planning method based on improved A-algorithm and Morphin algorithm |
Non-Patent Citations (3)
| Title |
|---|
| KISEOK SUNG 等: "Shortest paths in a network with time-dependent flow speeds", 《EUROPEAN JOURNAL OF OPERATIONAL RESEARCH》 * |
| 刘张雷 等: "一种基于路网变化的动态路径规划策略", 《交通运输系统工程与信息》 * |
| 刘张雷 等: "城市动态时间最短路径诱导系统实现研究", 《控制工程》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113159436A (en) * | 2021-04-29 | 2021-07-23 | 中国移动通信集团陕西有限公司 | Path recommendation method, device and equipment and readable storage medium |
| CN114023093A (en) * | 2021-08-29 | 2022-02-08 | 北京工业大学 | Dynamic evacuation method for multiple vehicles smoothly driven in short distance |
| CN114023093B (en) * | 2021-08-29 | 2024-03-15 | 北京工业大学 | Multi-vehicle dynamic evacuation method driven smoothly in short distance |
| CN113780808A (en) * | 2021-09-10 | 2021-12-10 | 西南交通大学 | Vehicle service attribute decision optimization method based on flexible bus connection system line |
| CN113780808B (en) * | 2021-09-10 | 2023-04-07 | 西南交通大学 | Vehicle service attribute decision optimization method based on flexible bus connection system line |
| CN115273457A (en) * | 2022-06-16 | 2022-11-01 | 重庆长安汽车股份有限公司 | Global optimal path planning method considering dynamic change of travel time of urban road network |
| CN115662114A (en) * | 2022-10-08 | 2023-01-31 | 广州玩鑫信息科技有限公司 | Intelligent traffic system for relieving congestion based on big data and operation method thereof |
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