CN108562301A - A kind of method and device for planning of driving path - Google Patents

Abstract

The present invention provides a method and device for planning a travel path, the method comprising: using a preset algorithm to plan a travel path of a target vehicle from a starting position to a destination position to obtain a planned route, wherein the planned route includes a plurality of road sections; In the case that the target vehicle is traveling according to the planned route, query the traffic service level grade of the target road section to be entered by the target vehicle; if the traffic service level grade of the target road section is a preset level, then adopt the decision The tree pruning algorithm calculates the driving path between the starting position and the ending position of the target road section to obtain a local optimal path; replaces the target road section with the local optimal path to update the planned path. In this way, when the target vehicle travels according to the planned route, the planned route can be updated according to the traffic service level of the target section to be driven, thereby improving the accuracy of the planned route.

Description

A method and device for planning a driving route

technical field

The invention relates to the field of traffic technology, in particular to a planning method and device for a driving path.

Background technique

Hazardous chemicals, that is, hazardous chemicals, refer to highly toxic chemicals and other chemicals that are poisonous, corrosive, explosive, flammable, and combustion-supporting, and are harmful to the human body, facilities, and the environment. It can be seen that due to the dangerous nature of hazardous chemicals, various activities targeting hazardous chemicals must be properly carried out. For example, for the transportation of hazardous chemicals, with the development of the economy in recent years, the transportation of hazardous chemicals has become more and more frequent. Therefore, how to plan a suitable driving route for vehicles transporting hazardous chemicals to improve the transportation efficiency of hazardous chemicals , has become the top priority of hazardous chemical transportation services. Usually, the industry mostly uses search algorithms to calculate the transportation route of hazardous chemicals. However, because the actual road conditions are changing anytime and anywhere, it is easy to cause the planned driving route to not match the actual road conditions; One of the road sections is not congested during planning, but in the actual driving process of the target vehicle, the road section becomes congested, which makes it difficult for the target vehicle to pass. Therefore, the accuracy of the route planned by the current driving route planning method is low.

Contents of the invention

Embodiments of the present invention provide a driving route planning method and device to solve the problem of low accuracy of the existing driving route planning method.

In order to solve the problems of the technologies described above, the present invention is achieved in that:

In a first aspect, an embodiment of the present invention provides a method for planning a driving route, including:

Using a preset algorithm to plan the driving path of the target vehicle from the starting position to the destination position to obtain a planned path, wherein the planned path includes a plurality of road sections;

In the case that the target vehicle is traveling according to the planned route, inquiring about the traffic service level grade of the target section to be entered by the target vehicle;

If the traffic service level grade of the target road section is a preset level, a decision tree pruning algorithm is used to calculate the driving path between the starting position and the ending position of the target road section to obtain a local optimal path;

The target road section is replaced by the local optimized route, so as to update the planned route.

Optionally, the preset algorithm is the Dijkstra algorithm.

Optionally, when the target vehicle is traveling according to the planned route, before inquiring about the traffic service level grade of the target section to be entered by the target vehicle, the method further includes:

Obtain the traffic capacity and traffic volume of the target road section;

calculating the road saturation of the target section according to the traffic capacity and the traffic volume;

According to the preset correspondence between road saturation and traffic service level, the traffic service level corresponding to the road saturation of the target section is determined.

Optionally, the step of using the decision tree pruning algorithm to calculate the driving path between the starting position and the ending position of the target road section includes:

If the traffic service level grade of the target road section is the first preset level, then increase the weight value of the length of the target road section, and based on the increased weight value of the length of the target road section, use decision tree pruning A branch algorithm, calculating the shortest transportation path between the start node and the end node of the target road section, to obtain a local optimal path; and/or

If the traffic service level of the target road section is the second preset level, a decision tree pruning algorithm is used to calculate the shortest transportation path between the start node and the end node of the target road section to obtain a local optimization The route, wherein, the calculation parameters when using the decision tree pruning algorithm to calculate the driving route do not include the length of the target road section.

Optionally, the traffic service level grades are divided into grades 1, 2, 3 and 4, wherein, the section with 0≤road saturation≤0.6 is the first level, and the section with 0.6<road saturation≤0.8 The road section with 0.8<road saturation≤1 is the third grade, and the road section with 1<road saturation is the fourth grade.

In the second aspect, the embodiment of the present invention also provides a driving route planning device, including:

The planning module is used to plan the driving path of the target vehicle from the starting position to the destination position by using a preset algorithm to obtain the planned path, wherein the planned path includes a plurality of road sections;

An inquiry module, configured to inquire about the traffic service level grade of the target road segment to be entered by the target vehicle when the target vehicle is traveling according to the planned route;

A calculation module, configured to use a decision tree pruning algorithm to calculate the driving path between the starting position and the ending position of the target road section if the traffic service level of the target road section is a preset level, and obtain local optimization path;

An update module, configured to replace the target road section with the local optimized route, so as to update the planned route.

In the third aspect, the embodiment of the present invention also provides a driving route planning device, including a processor, a memory, and a computer program stored in the memory and operable on the processor, the computer program being controlled by the The processor executes the steps to realize the above-mentioned driving route planning method.

In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned driving route planning method are implemented.

In the embodiment of the present invention, a preset algorithm is used to plan the travel path of the target vehicle from the starting position to the destination position to obtain a planned route, wherein the planned route includes a plurality of road sections; In the case of driving, query the traffic service level grade of the target road section to be entered by the target vehicle; if the traffic service level grade of the target road section is a preset level, then use a decision tree pruning algorithm to The driving path between the starting position and the ending position of the road section is calculated to obtain a local optimal path; the target road section is replaced by the local optimal path to update the planned path. In this way, when the target vehicle travels according to the planned route, the planned route can be updated according to the traffic service level of the target section to be driven, thereby improving the accuracy of the planned route.

Description of drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings that need to be used in the description of the embodiments of the present invention. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those skilled in the art, other drawings can also be obtained based on these drawings without any creative effort.

FIG. 1 is a flow chart of a planning method for a driving route provided by an embodiment of the present invention;

Fig. 2 is a schematic diagram of path planning provided by an embodiment of the present invention;

Fig. 3 is a schematic diagram of an example provided by an embodiment of the present invention

FIG. 4 is a schematic diagram of another path planning provided by an embodiment of the present invention;

Fig. 5 is another schematic diagram of path planning provided by the embodiment of the invention;

Fig. 6 is another schematic diagram of path planning provided by the embodiment of the invention;

Fig. 7 is a structural diagram of a device for planning a driving route provided by an embodiment of the present invention;

Fig. 8 is a structural diagram of another driving route planning device provided by an embodiment of the present invention.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

Referring to FIG. 1, FIG. 1 is a flowchart of a planning method for a driving route provided by an embodiment of the present invention. As shown in FIG. 1, it includes the following steps:

Step 101. Using a preset algorithm to plan a driving route of a target vehicle from a starting position to a destination position to obtain a planned route, wherein the planned route includes a plurality of road sections.

Wherein, the preset algorithm may be various search algorithms, including but not limited to breadth-first search algorithm, depth-first algorithm, Dijkstra algorithm, Floyd algorithm and Bellman-Ford algorithm. Preferably, the target vehicle may be a vehicle for transporting hazardous chemicals; of course, it may also be other vehicles, which are not limited. In addition, both the starting location and the destination location may refer to any positioning location, for example, the starting location may be a certain petroleum company, and the destination location may be a certain gas station.

The planned path includes a plurality of road sections, which may mean that the planned path is composed of a plurality of location nodes and road sections, and the plurality of location nodes include the starting location and the destination location; wherein, every two location nodes are connected There is a section. For example, the starting location is node A, the destination location is node B, and the planned paths are specifically A, B, C, D, wherein there is a road section connected between every two nodes.

Optionally, the preset algorithm is the Dijkstra algorithm.

In this embodiment, the Dijkstra algorithm is the Dijkstra algorithm, which is a single-source shortest path algorithm; it is mainly used to calculate the shortest path from one node to all other nodes. The main feature is that the starting point is the center and expands outward layer by layer until it reaches the end point. For example, a weighted directed graph G=(V, E) can be given, and the Dijkstra algorithm is used to search for the shortest path between the destination location V _n and the starting location V ₀ , the queue of the shortest path at this time _Be Path, and store the shortest path queue and _queue length (l _i , Path _i ), l _i represents the length of the shortest path from node V _i to terminal Vn obtained during path planning, Path _i represents the shortest path queue from node V _i to terminal Vn obtained during path planning, and the participants in path planning The set of all nodes is U _d , which contains _the starting position V ₀ .

In this way, searching for the shortest path through Dijkstra's algorithm has a high success rate.

Step 102 , in the case that the target vehicle is traveling according to the planned route, inquire about the traffic service level grade of the target section that the target vehicle is to drive into.

Wherein, the traffic service level grade mainly refers to the road service level grade, which is mainly used to indicate the traffic status of vehicles or pedestrians on the road. For example, the United States divides the traffic service level into six levels: A, B, C, D, E and F, as follows:

Class A: The traffic flow is free flow, and the driver can maintain his desired speed;

Level B: The traffic flow is a steady flow, the driver is affected to a certain extent, but can still freely choose the driving speed and lane;

Class C: The traffic flow is a steady flow, but due to the large traffic volume, the driver cannot choose the driving speed and change the lane independently;

Class D: The traffic flow is similar to a steady flow, the operation of the vehicle is greatly affected, and the driver basically has no right to freely choose his own driving speed;

Class E: The traffic flow is an unsteady flow. At this time, the speed of vehicles on the road is low, and parking will occur. The road is close to or even equal to the traffic capacity of the road. For the driver, it will feel very uncomfortable;

Class F: The traffic flow is a bound flow, and there will be long-term parking or congestion at this time.

At present, we can usually divide the traffic service level into four levels: 1st, 2nd, 3rd and 4th, and correspond to the six-level grading standard in the United States, where A level is equivalent to level 1, B level is equivalent to level 2, and C level is equivalent to level 2. , D level is equivalent to three levels, E, F level is equivalent to four levels.

The target road section to be entered by the target vehicle may refer to the next road section adjacent to the road section where the target vehicle is currently located. For example, when the target vehicle is traveling according to the planned path from A, B, C, D, and is currently in the AB road section, that is, the road section between A and B, then the target road section to be driven is the BC road section, namely The section between B and C. In addition, the traffic service level grade of the target road section to be driven by the target vehicle can be queried in the database of the Internet or the server; The traffic volume, and then the saturation obtained through the traffic volume is converted into the corresponding traffic service level grade.

Optionally, before the step of inquiring about the traffic service level of the target section to be entered by the target vehicle when the target vehicle is traveling according to the planned route, the method further includes:

Obtain the traffic capacity and traffic volume of the target road section;

calculating the road saturation of the target section according to the traffic capacity and the traffic volume;

According to the preset correspondence between road saturation and traffic service level, the traffic service level corresponding to the road saturation of the target section is determined.

In this embodiment, the calculation of the road saturation of the target section according to the traffic capacity and the traffic volume may be calculated according to the formula V/C, where V is used to represent the traffic volume, C is used to represent capacity. The traffic capacity can refer to the maximum number of vehicles passing through a certain section in a certain section of the road or a certain intersection per unit time under certain road and traffic conditions; of course, it can also be calculated according to V/C+K , V is used to represent the traffic volume, C is used to represent the traffic capacity, and K may represent a weight; the method for calculating the road saturation of the target section according to the traffic capacity and the traffic volume is not limited. Usually capacity is usually fixed. The traffic volume can refer to the transport workload passing on the observed road or the number of traffic participants (such as motor vehicles, pedestrians, bicycles, etc.) passing through a certain section on the road per unit time. The monitoring method mainly uses the number of traffic participants passing through a certain cross-section per unit time.

Among them, the measurement of traffic volume is the number of traffic participants passing through a certain cross-section of the road per unit time. Traffic participants are divided into two types of motor vehicles and non-motor vehicles in terms of vehicles. Motor vehicles also have Cars, buses, buses, trucks, etc., non-motorized vehicles include bicycles, manpower tricycles, etc. Due to the different types of vehicles, although the number of observations is the same, the chaotic levels of road vehicles are completely different.

For this reason, we use cars as the standard for calculating the traffic volume, and other vehicles are converted into the traffic volume of cars for calculation, as shown in Table 1.

Table 1 Conversion Factors Based on Cars in China

For the traffic capacity of the road, it is divided into theoretical traffic capacity and actual traffic capacity. For theoretical traffic capacity, it is realized under ideal conditions, and the formula for calculating traffic capacity is:

In the formula, C represents the theoretical traffic capacity, and T represents the headway under theoretical conditions.

For the calculation of the actual traffic capacity, it is mainly the correction of the theoretical traffic capacity, and different correction factors are used for different types of roads. Taking my country's correction method as an example, the calculation methods of the actual traffic capacity of different road types are different.

(1) Actual traffic capacity of expressway sections

For highway sections, according to the formula:

C _real = C × f _HV × f _p × f _N

In the formula, C _actually represents the actual traffic capacity of the lane of the expressway section;

C represents the ideal traffic capacity of the lane of the expressway section;

f _p represents the overall characteristic coefficient of the driver;

f _N represents the lane correction coefficient of expressways with more than 6 lanes;

f _HV represents the traffic composition correction coefficient;

P _i represents the percentage of the traffic volume of large vehicles, medium-sized vehicles, and trailers (i) in the total traffic volume;

E _i represents the vehicle conversion factor for large vehicles, medium-sized vehicles, and trailers (i).

(2) The actual traffic capacity of the first-class road section

For the first-class road section, according to the formula:

C _real = C × f _HV × f _p × f _N × f _f

In the formula, C _actually represents the actual traffic capacity of the lane of the first-class highway section;

C represents the ideal traffic capacity of the lane of the first-class road section;

f _p represents the overall characteristic coefficient of the driver;

f _N represents the lane correction coefficient;

f _HV represents the traffic composition correction coefficient;

f _f represents the roadside interference correction factor.

(3) The actual traffic capacity of the secondary road section

For secondary road sections, according to the formula:

C _real = C × f _HV + f _w × f _d × f _f

In the formula, C _actually represents the actual traffic capacity of the lane of the first-class highway section;

C represents the ideal traffic capacity of the lane of the first-class road section;

f _HV represents the traffic composition correction coefficient;

f _f represents the roadside interference correction coefficient;

f _d represents the direction correction coefficient;

f _w represents the correction factor of lane width and shoulder width.

Through the above monitoring of traffic volume and calculation of actual road capacity, it is relatively easy to calculate the road saturation of the road where the vehicle is located. From this, we can determine the traffic service level to which the target road section belongs according to the corresponding relationship between road saturation and traffic service level.

In this way, by determining the traffic service level corresponding to the road saturation of the target road segment according to the preset correspondence between road saturation and traffic service level, the calculation is relatively simple, and the traffic service to which the target road segment belongs can be determined relatively quickly. level grade.

Optionally, the traffic service level grades are divided into grades 1, 2, 3 and 4, wherein, the section with 0≤road saturation≤0.6 is the first level, and the section with 0.6<road saturation≤0.8 The road section with 0.8<road saturation≤1 is the third grade, and the road section with 1<road saturation is the fourth grade.

In this embodiment, the above-mentioned division method of the traffic service level is an optimal division method obtained after a large number of tests. Of course, other ways can also be used to divide the traffic service level. For example, the level of traffic service level can be divided into three grades: A, B and C, where grade A corresponds to a saturation between 0 and 0.3, grade B corresponds to a saturation between 0.3 and 0.6, and grade C corresponds to a saturation greater than 0.6 saturation; there is no limit to this.

In this way, by dividing the traffic service level grades into first, second, third and fourth grades, the traffic service level grades can be more consistent with the actual traffic conditions of the road, thereby facilitating route planning.

Step 103: If the traffic service level of the target road section is a preset level, then use the decision tree pruning algorithm to calculate the driving path between the starting position and the ending position of the target road section to obtain a local optimization path.

Wherein, the preset grade can be any grade, for example, in the case that the traffic service level grade is divided into grade 1, grade 2 and grade 3, the preset grade can be grade 1 or grade 2 Or three levels, there is no limit to this, it can be set according to the needs of users. In addition, the local optimal path may include multiple road sections, or one road section, depending on the result obtained through the algorithm.

The decision tree (Decision Tree) pruning algorithm is based on the known probability of occurrence of various situations, by forming a decision tree to obtain the probability that the expected value of the net present value is greater than or equal to zero, evaluate the project risk, and judge its feasibility The decision analysis method is a graphical method that intuitively uses probability analysis. Because this decision-making branch is drawn in a graph that resembles the branches of a tree, it is called a decision tree. Decision tree is an inverted tree structure, which infers classification rules expressed in tree form from a set of unordered and irregular cases. It adopts a top-down recursive method, selects attributes as internal nodes of the decision tree according to certain standards, and constructs different branches according to different values of the attributes, and draws conclusions at the leaf nodes of the tree, so from the root node to the leaf node Each path of a node corresponds to a rule. Decision tree is a top-down inductive process and a greedy algorithm.

The starting position of the target road section may refer to the starting position of the target road section in the direction of travel, and the destination position of the target road section may refer to the end position of the target road section in the direction of travel . For example, point A and point B shown in FIG. 2 , A is the starting position of the target road segment, and B is the end position of the target road segment.

The use of the decision tree pruning algorithm to calculate the driving path between the starting position and the ending position of the target road section to obtain a local optimal path may refer to using the starting position of the target road section as a decision tree The decision point of the target road section is taken as the result node, the length of the road section is taken as the expected income, and the pruning algorithm operation is performed, and the obtained shortest path is used as the local optimization path.

In this way, the decision tree pruning algorithm only needs to calculate the driving path between the starting position and the ending position of the target road section, so as to obtain a local optimal route, which greatly saves calculation time.

Optionally, the step of using the decision tree pruning algorithm to calculate the driving path between the starting position and the ending position of the target road section includes:

If the traffic service level grade of the target road section is the first preset level, then increase the weight value of the length of the target road section, and based on the increased weight value of the length of the target road section, use decision tree pruning A branch algorithm, calculating the shortest transportation path between the start node and the end node of the target road section, to obtain a local optimal path; and/or

If the traffic service level of the target road section is the second preset level, a decision tree pruning algorithm is used to calculate the shortest transportation path between the start node and the end node of the target road section to obtain a local optimization The route, wherein, the calculation parameters when using the decision tree pruning algorithm to calculate the driving route do not include the length of the target road section.

In this embodiment, the first preset level and the second preset level are different traffic service level levels. It should be noted that, in the road section of the first preset level, the driver cannot choose the driving speed and change the lane relatively independently, that is, the traffic flow is in a state between an approximate steady flow and a steady flow, It can be understood that the smoothness of road sections of the first preset level is relatively low. And be in the road section of described second preset level, vehicle traveling speed is low, and parking phenomenon will occur; Stopping or jamming, that is, the traffic flow is an unsteady flow or a bound flow, can be understood as that the road section of the second preset level cannot be driven at all.

In addition, using the decision tree pruning algorithm to calculate the driving route does not include the length of the target road section in the calculation parameters, which may refer to finding the shortest path for nodes that do not include the length of the target road section.

In order to better illustrate this embodiment, an application example will be used below:

Let G=(V,E) be a weighted directed graph. Firstly, through the Dijkstra algorithm, search for the shortest path between the end point v _n and the initial point v ₀ , and use the shortest path as the planning path. Among them, the queue of the shortest path is Path, and store the shortest path queue and queue length (l _i ,Path _i ) from the end point to the intermediate node during the search process from the end point v _n to the initial point v ₀ by Dijkstra algorithm (wherein l _i represents the initial The length of the shortest path from node v _i to terminal v _n obtained during path planning, Path _i represents the shortest path queue from node v _i to terminal v _n obtained during initial path planning), and all nodes participating in the initial planning The collection of is set to U _d (U _d includes the initial point v ₀ and the end point v _n ). Assume that when the point v _m is about to arrive, it is judged that the road conditions from point v _m to point v _m+1 (denoted as v _m v _m+1 ) have changed, and local optimization is triggered. The specific update steps are as follows;

Step A: Updating the real-time route information of the relevant road segment.

Step B: Set the known shortest distance array DT between v _m and all points in U _d (if v _m has an edge with a certain vertex v _i , DT[i]=length(v, v _i ); if v _i is not v _m , DT[i]=∞, DT[0]=0); judging according to the path status, if it is a third-level service level, then jump to Step3; if it is a fourth-level service level, then jump to Step D.

Step C: Calculate min(l _j +DT[j]) of nodes connected to node v _m , assuming the shortest path node v _j at this time, obtain the minimum node planning queue Path _j , and go to step G.

Step D: Judging whether the shortest queues of the child nodes of node M contain the road segment v _m v _m+1 , if they all contain, then go to step E; otherwise, go to step F;

Step E: take the child nodes of node M as node M according to the size of DT[i]=length(v,v _i ), and turn to step D;

Step F: Calculate min(l _i +DT[i]) for the node whose shortest queue does not contain v _m v _m+1 , assuming that it is node v _i at this time, obtain the minimum node planning queue Path _i , and go to step G.

Step G: Update Path.

Among them, the specific example of the above process can be seen in Figure 3. Let the node v _m to node v _m+1 be the initial planned path, and when the node v _m is about to arrive, activate the local optimization, update the relevant path information, and determine the occurrence of road services Level:

1. Judging that there is a third-level service level, find min((1.6l _m+1 +DT[m+1]), (l _m+2 +DT[m+2]), (l _m+3 +DT[m +3]), (l _m+4 +DT[m+4])), assuming the shortest path node v _j at this time, get the minimum node planning queue Path _j , the algorithm ends, update the shortest path queue.

2. Judging the emergence of four levels of service. At this time, the shortest queue of node v _m+2 contains road section v _m v _m+1 , and the shortest queue of nodes v _m+3 and v _m+4 does not contain v _m v _m+1 , find min((l _m+3 +DT[m+3]), (l _m+4 +DT[m+4])), assuming that it is node v _i at this time, the minimum node planning queue Path _i is obtained, the algorithm ends, and the queue is updated.

In this way, the time complexity of the calculation is a constant order, which greatly saves time.

Step 104: Replace the target road section with the local optimized route, so as to update the planned route.

It should be noted that the planned path may be consistent with the original path after being updated, because the local optimal path obtained through the decision tree pruning algorithm may be consistent with the target road section. Let's take a specific example to illustrate:

When the vehicle is traveling according to the planned path shown in Figure 2, when it is about to enter the three sections of path c ₁ , c ₂ , and c ₃ that have not yet entered, the information obtained through real-time monitoring and calculation shows that the three sections The traffic service level of the section path changes, the traffic service level of the road section c ₁ is at the fourth-level traffic service level, and the road sections c ₂ and c ₃ are at the third-level traffic service level. The planning diagram of road section c ₁ is shown in Fig. 4, and the planning diagrams of road sections c ₂ and c ₃ are shown in Fig. 5 and Fig. 6 respectively. It can be seen that the road section c ₁ is in the fourth-level traffic service level, and the local optimal path obtained by the decision tree pruning algorithm must be different from the original road section, that is, the road section shown by the dotted line in Figure 4; while the road section c ₂ is in the The three-level traffic service level indicates that the road section can still be in the traffic state, but the length of the road section needs to be weighted, and after the weighted processing, the decision tree pruning algorithm is used for path planning to obtain a local optimal path , but the algorithm still thinks that the weighted road section is still the shortest path, so there is no change after replacing it with a local optimal path. Assume that in the actual route planning, only the route information of c ₁ , c ₂ , and c ₃ in the planned untraveled route that is about to enter has a large change, and there is a third-level or fourth-level road service level road condition , so until the vehicle reaches the end point, as shown in Figure 6, the final path plan is formed after updating.

In the embodiment of the present invention, the above-mentioned device may be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), a personal digital assistant (personal digital assistant, PDA for short), a mobile Internet device (Mobile Internet Device, MID ) or wearable devices (Wearable Device), etc.

In the embodiment of the present invention, a preset algorithm is used to plan the travel path of the target vehicle from the starting position to the destination position to obtain a planned route, wherein the planned route includes a plurality of road sections; when the target vehicle travels according to the planned route case, query the traffic service level grade of the target road section to be entered by the target vehicle; if the traffic service level grade of the target road section is a preset level, then use the decision tree pruning algorithm to The driving route between the starting position and the ending position is calculated to obtain a local optimal route; the target road section is replaced by the local optimal route to update the planned route. In this way, when the target vehicle travels according to the planned route, the planned route can be updated according to the traffic service level of the target section to be driven, thereby improving the accuracy of the planned route.

Referring to FIG. 7, FIG. 7 is a structural diagram of a planning device for a driving route provided by an embodiment of the present invention. As shown in FIG. 704; where:

The planning module is used to plan the driving path of the target vehicle from the starting position to the destination position by using a preset algorithm to obtain the planned path, wherein the planned path includes a plurality of road sections;

An inquiry module, configured to inquire about the traffic service level grade of the target road segment to be entered by the target vehicle when the target vehicle is traveling according to the planned route;

A calculation module, configured to use a decision tree pruning algorithm to calculate the driving path between the starting position and the ending position of the target road section if the traffic service level of the target road section is a preset level, and obtain local optimization path;

An update module, configured to replace the target road section with the local optimized route, so as to update the planned route.

The driving route planning device 700 can realize various processes and beneficial effects realized by the devices in the above method embodiments, and to avoid repetition, details are not repeated here.

Referring to FIG. 8, FIG. 8 is a schematic diagram of the hardware structure of a planning device for a driving route implementing various embodiments of the present invention.

The device 800 includes but not limited to: a radio frequency unit 801, a network module 802, an audio output unit 803, an input unit 804, a sensor 805, a display unit 806, a user input unit 807, an interface unit 808, a memory 809, a processor 810, and a power supply 811 and other components. Those skilled in the art can understand that the structure of the device shown in FIG. 8 does not constitute a limitation to the device, and the device may include more or fewer components than shown in the figure, or combine some components, or arrange different components. In the embodiment of the present invention, the device includes, but is not limited to, a mobile phone, a tablet computer, a desktop computer, a notebook computer, a palmtop computer, a vehicle terminal, and the like.

Wherein, the processor 810 is configured to use a preset algorithm to plan the traveling path of the target vehicle from the starting position to the destination position to obtain the planned path, wherein the planned path includes a plurality of road sections;

In the case that the target vehicle is traveling according to the planned route, inquiring about the traffic service level grade of the target section to be entered by the target vehicle;

If the traffic service level grade of the target road section is a preset level, a decision tree pruning algorithm is used to calculate the driving path between the starting position and the ending position of the target road section to obtain a local optimal path;

The target road section is replaced by the local optimized route, so as to update the planned route.

Optionally, the preset algorithm is the Dijkstra algorithm.

Optionally, the processor 810 is also configured to acquire the capacity and traffic volume of the target road section;

calculating the road saturation of the target section according to the traffic capacity and the traffic volume;

According to the preset correspondence between road saturation and traffic service level, the traffic service level corresponding to the road saturation of the target section is determined.

Optionally, the decision tree pruning algorithm executed by the processor 810 is used to calculate the driving path between the starting position and the ending position of the target road section, including:

If the traffic service level grade of the target road section is the first preset level, then increase the weight value of the length of the target road section, and based on the increased weight value of the length of the target road section, use decision tree pruning A branch algorithm, calculating the shortest transportation path between the start node and the end node of the target road section, to obtain a local optimal path; and/or

If the traffic service level of the target road section is the second preset level, a decision tree pruning algorithm is used to calculate the shortest transportation path between the start node and the end node of the target road section to obtain a local optimization The route, wherein, the calculation parameters when using the decision tree pruning algorithm to calculate the driving route do not include the length of the target road section.

Optionally, the traffic service level grades are divided into grades 1, 2, 3 and 4, wherein, the section with 0≤road saturation≤0.6 is the first level, and the section with 0.6<road saturation≤0.8 The road section with 0.8<road saturation≤1 is the third grade, and the road section with 1<road saturation is the fourth grade.

The device 800 can realize various processes and beneficial effects realized by the devices in the foregoing embodiments, and to avoid repetition, details are not repeated here.

It should be understood that, in the embodiment of the present invention, the radio frequency unit 801 can be used for receiving and sending signals during sending and receiving information or during a call. Specifically, after receiving the downlink data from the base station, the processor 810 processes it; Uplink data is sent to the base station. Generally, the radio frequency unit 801 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency unit 801 can also communicate with the network and other devices through a wireless communication system.

The device provides users with wireless broadband Internet access through the network module 802, such as helping users to send and receive emails, browse web pages, and access streaming media.

The audio output unit 803 may convert audio data received by the radio frequency unit 801 or the network module 802 or stored in the memory 809 into an audio signal and output as sound. Also, the audio output unit 803 may also provide audio output related to a specific function performed by the device 800 (for example, a call signal reception sound, a message reception sound, etc.). The audio output unit 803 includes a speaker, a buzzer, a receiver, and the like.

The input unit 804 is used for receiving audio or video signals. The input unit 804 may include a graphics processing unit (Graphics Processing Unit, GPU) 8041 and a microphone 8042, and the graphics processing unit 8041 is used for still pictures or video images obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The data is processed. The processed image frames may be displayed on the display unit 806 . The image frames processed by the graphics processor 8041 may be stored in the memory 809 (or other storage media) or sent via the radio frequency unit 801 or the network module 802 . The microphone 8042 can receive sound, and can process such sound into audio data. The processed audio data can be converted into a format that can be sent to a mobile communication base station via the radio frequency unit 801 for output in the case of a phone call mode.

Apparatus 800 also includes at least one sensor 805, such as a light sensor, motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 8061 according to the brightness of the ambient light, and the proximity sensor can turn off the display panel 8061 and/or when the device 800 is moved to the ear. or backlight. As a kind of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in various directions (generally three axes), and can detect the magnitude and direction of gravity when it is stationary, and can be used to identify the posture of the device (such as horizontal and vertical screen switching, related games, Magnetometer posture calibration), vibration recognition related functions (such as pedometer, knocking), etc.; sensor 805 can also include fingerprint sensor, pressure sensor, iris sensor, molecular sensor, gyroscope, barometer, hygrometer, thermometer, infrared ray Sensors, etc., will not be described in detail here.

The display unit 806 is used to display information input by the user or information provided to the user. The display unit 806 may include a display panel 8061, and the display panel 8061 may be configured in the form of a liquid crystal display (Liquid Crystal Display, LCD), an organic light-emitting diode (Organic Light-Emitting Diode, OLED), or the like.

The user input unit 807 can be used to receive input number or character information, and generate key signal input related to user settings and function control of the device. Specifically, the user input unit 807 includes a touch panel 8071 and other input devices 8072 . The touch panel 8071, also referred to as a touch screen, can collect touch operations of the user on or near it (for example, the user uses any suitable object or accessory such as a finger or a stylus on the touch panel 8071 or near the touch panel 8071). operate). The touch panel 8071 may include two parts, a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, and detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into contact coordinates, and sends it to the For the processor 810, receive the command sent by the processor 810 and execute it. In addition, the touch panel 8071 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch panel 8071 , the user input unit 807 may also include other input devices 8072 . Specifically, other input devices 8072 may include, but are not limited to, physical keyboards, function keys (such as volume control keys, switch keys, etc.), trackballs, mice, and joysticks, which will not be repeated here.

Furthermore, the touch panel 8071 can be covered on the display panel 8061, and when the touch panel 8071 detects a touch operation on or near it, it will be sent to the processor 810 to determine the type of the touch event, and then the processor 810 will The type of event provides a corresponding visual output on the display panel 8061. Although in FIG. 8, the touch panel 8071 and the display panel 8061 are used as two independent components to realize the input and output functions of the device, in some embodiments, the touch panel 8071 and the display panel 8061 can be integrated to form a Realize the input and output functions of the device, which is not limited here.

The interface unit 808 is an interface for connecting an external device to the device 800 . For example, an external device may include a wired or wireless headset port, an external power (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device with an identification module, audio input/output (I/O) ports, video I/O ports, headphone ports, and more. Interface unit 808 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to one or more elements within device 800 or may be used to transfer data between.

The memory 809 can be used to store software programs as well as various data. The memory 809 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playback function, an image playback function, etc.); Data created by the use of mobile phones (such as audio data, phonebook, etc.), etc. In addition, the memory 809 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

The processor 810 is the control center of the device. It uses various interfaces and lines to connect various parts of the entire device. By running or executing software programs and/or modules stored in the memory 809, and calling data stored in the memory 809, execution Various functions and processing data of the device, so as to monitor the device as a whole. The processor 810 may include one or more processing units; preferably, the processor 810 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface and application programs, etc., and the modem The processor mainly handles wireless communication. It can be understood that the foregoing modem processor may not be integrated into the processor 810 .

The device 800 may also include a power supply 811 (such as a battery) for supplying power to various components. Preferably, the power supply 811 may be logically connected to the processor 810 through a power management system, so as to manage charging, discharging, and power consumption management through the power management system. Function.

In addition, the device 800 includes some unshown functional modules, which will not be repeated here.

Preferably, the embodiment of the present invention also provides a driving route planning device, including a processor 810, a memory 809, and a computer program stored in the memory 809 and operable on the processor 810, the computer program being programmed by the processor When executing 810, each process of the above embodiment of the driving route planning method can be realized, and the same technical effect can be achieved. In order to avoid repetition, details are not repeated here.

The embodiment of the present invention also provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, each process of the above-mentioned embodiment of the method for planning a driving route can be realized, and the same can be achieved. To avoid repetition, the technical effects will not be repeated here. Wherein, the computer-readable storage medium is, for example, a read-only memory (Read-Only Memory, ROM for short), a random access memory (Random Access Memory, RAM for short), a magnetic disk or an optical disk, and the like.

It should be noted that, in this document, the term "comprising", "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element.

Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product in essence or the part that contributes to the prior art, and the computer software product is stored in a storage medium (such as ROM/RAM, disk, CD) contains several instructions to make a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in various embodiments of the present invention.

Embodiments of the present invention have been described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementations, and the above-mentioned specific implementations are only illustrative, rather than restrictive. Those of ordinary skill in the art will Under the enlightenment of the present invention, many forms can also be made without departing from the gist of the present invention and the protection scope of the claims, all of which belong to the protection of the present invention.

Claims (8)

1. a kind of planing method of driving path, which is characterized in that including：

Driving path using preset algorithm object of planning vehicle from initial position to destination locations, obtains planning path, wherein The planning path includes multiple sections；

In the case where the target vehicle is travelled according to the planning path, target vehicle target road to be driven into is inquired The level of service grade of section；

If the level of service grade of the target road section be predetermined level, use decision tree pruning algorithms, pair with it is described Driving path between the initial position and final position of target road section is calculated, and local optimum path is obtained；

By target road section described in the local optimum path replacement, to update the planning path.

2. according to the method described in claim 1, it is characterized in that, the preset algorithm is Dijkstra's algorithm.

3. according to the method described in claim 1, it is characterized in that, it is described in the target vehicle according to the planning path row It is described before the step of inquiring the level of service grade of target vehicle target road section to be driven into the case of sailing Method further includes：

Obtain the traffic capacity and the volume of traffic of the target road section；

According to the traffic capacity and the volume of traffic, the road saturation degree of the target road section is calculated；

According to the correspondence of preset road saturation degree and level of service grade, determine that the road of the target road section is full The corresponding level of service grade with degree.

4. according to the method described in claim 3, it is characterized in that, it is described use decision tree pruning algorithms, pair with the target The step of driving path between the initial position and final position in section is calculated, including：

If the level of service grade of the target road section is the first predetermined level, increase the length of the target road section Weighted value, and the weighted value of the length based on the target road section after increase, using decision tree pruning algorithms, pair with the mesh The most short transportation route marked between the start node and terminal node in section is calculated, and local optimum path is obtained；And/or

If the level of service grade of the target road section be the second predetermined level, use decision tree pruning algorithms, pair with Most short transportation route between the start node and terminal node of the target road section is calculated, and local optimum path is obtained, Wherein, the length of the target road section is not included in calculating parameter when calculating driving path using the decision tree pruning algorithms Degree.

5. according to the method described in claim 3, it is characterized in that, the level of service grade is divided into level-one, two level, three Grade and level Four, wherein the section of 0≤road saturation degree≤0.6 be the level-one, 0.6<The section of road saturation degree≤0.8 is The two level, 0.8<The section of road saturation degree≤1 be the three-level, 1<The section of road saturation degree is the level Four.

6. a kind of device for planning of driving path, which is characterized in that including：

Planning module is obtained for using driving path of the preset algorithm object of planning vehicle from initial position to destination locations Planning path, wherein the planning path includes multiple sections；

Enquiry module, in the case where the target vehicle is travelled according to the planning path, inquiring the target vehicle The level of service grade of target road section to be driven into；

Computing module uses decision tree beta pruning if the level of service grade for the target road section is predetermined level Algorithm, the driving path pair between the initial position and final position of the target road section calculate, and obtain local optimum Path；

Update module is used for by target road section described in the local optimum path replacement, to update the planning path.

7. a kind of device for planning of driving path, which is characterized in that including：Memory, processor and it is stored in the memory Computer program that is upper and can running on the processor, the processor realize such as right when executing the computer program It is required that the step in the planing method of driving path described in any one of 1-5.

8. a kind of computer readable storage medium, which is characterized in that be stored with computer on the computer readable storage medium Program realizes the rule of the driving path as described in any one of claim 1-5 when the computer program is executed by processor Step in the method for drawing.