CN109461322B - Intelligent navigation method, device, equipment and readable storage medium - Google Patents
Intelligent navigation method, device, equipment and readable storage medium Download PDFInfo
- Publication number
- CN109461322B CN109461322B CN201811511619.3A CN201811511619A CN109461322B CN 109461322 B CN109461322 B CN 109461322B CN 201811511619 A CN201811511619 A CN 201811511619A CN 109461322 B CN109461322 B CN 109461322B
- Authority
- CN
- China
- Prior art keywords
- lane
- vehicle
- intersection
- target
- current
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 64
- 238000004590 computer program Methods 0.000 claims description 11
- 206010039203 Road traffic accident Diseases 0.000 abstract description 7
- 230000000694 effects Effects 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 8
- 101100517651 Caenorhabditis elegans num-1 gene Proteins 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
- Navigation (AREA)
Abstract
The invention discloses an intelligent navigation method, which comprises the steps of judging the advancing direction of a vehicle at a target intersection according to the destination of the current vehicle; acquiring the number of vehicles of each lane at a target intersection; obtaining the time delay required by the current vehicle to enter the target road section from the current intersection according to the traffic light condition of the current intersection; and selecting the shortest optimal lane for passing through the target intersection according to the traffic light condition of the target road section and the number of vehicles in each lane. The method can ensure that the straight-going vehicle can not influence the vehicles turning left and right, and simultaneously passes through the target intersection in the shortest time, and the lanes do not need to be switched in the driving process, and the lane selected by the method is the optimal lane, so that the traffic jam problem can be effectively relieved, the probability of traffic accidents is further reduced, and the method is suitable for the smart city advocated at present. In addition, the application also provides an intelligent navigation device, equipment and a computer readable storage medium with the technical effects.
Description
Technical Field
The present invention relates to the field of intelligent traffic navigation technologies, and in particular, to an intelligent navigation method, apparatus, device, and computer-readable storage medium.
Background
In the society at present, especially in big cities, the number of vehicles is increasing day by day, and the congestion of roads is accompanied, so that the life experience is seriously reduced, the probability of traffic accidents is increased, the working efficiency of the society is seriously reduced, and the happiness index of people is reduced.
As a cause, a very important cause of congestion is that the driver cannot select an optimal lane. The following often occur: the first condition is as follows: the right-turn lane at the rightmost side is selected by the vehicle in the straight-going direction only by the vehicle, but when the vehicle reaches the target intersection, the straight-going direction is a red light, and all right-turn vehicles behind the vehicle cannot turn right at the moment; case two: the left-turn lane at the leftmost side is selected by the vehicle in the straight-going direction, when the vehicle reaches the target intersection, the straight-going direction is a red light, the left-turn direction is a green light, and all left-turn vehicles behind the vehicle cannot turn left; case three: vehicles in the straight-going direction can not accurately know the number of vehicles in each straight-going lane and can not estimate the time for reaching the target intersection, so that one lane is selected for driving blindly, but the selected lane is a lane with a large number of vehicles, so that the time for passing through the target intersection is prolonged, the continuous backlog of the vehicles behind is caused, and the vehicle congestion is caused.
The above three situations are very common in the peak hours of going to and from work in a first-line or second-line large city. However, no good measures are provided so far, so that traffic jam is often caused, and particularly, the jam phenomenon is more serious on urban main roads.
Disclosure of Invention
The invention aims to provide an intelligent navigation method, an intelligent navigation device, intelligent navigation equipment and a computer readable storage medium, which aim to solve the problem of traffic jam caused by the fact that an existing navigation system cannot provide a driver with optimal lane selection.
In order to solve the above technical problem, the present invention provides an intelligent navigation method, including:
judging the advancing direction of the vehicle at the target intersection according to the destination of the current vehicle;
acquiring the number of vehicles of each lane at a target intersection;
obtaining the time delay required by the current vehicle to enter the target road section from the current intersection according to the traffic light condition of the current intersection;
selecting the shortest optimal lane for passing through the target intersection according to the traffic light condition of the target road section and the number of vehicles in each lane;
the target road section is a passing road section between the current intersection and the target intersection.
Optionally, the obtaining, according to the traffic light condition at the current intersection, the time delay required for the current vehicle to enter the target road segment from the current intersection includes:
calculating the time delay required by the current vehicle to enter the target road section from the current intersection by adopting t0_ delay ═ t0+ t0_ r _ b;
wherein t0_ delay is the time delay required for the current vehicle to enter the target road section from the current intersection, and t0 is the time taken by the vehicle to pass through the current intersection; t0_ r _ b is the time when the current intersection changes from red to green.
Optionally, when it is determined that the advancing direction of the vehicle at the target intersection is a straight direction, the selecting, according to the traffic light condition of the target road segment and the number of vehicles in each lane, the shortest optimal lane for passing through the target intersection includes:
calculating the shortest time required by the vehicle when the vehicle reaches the target intersection by adopting t _ et-t 0_ delay + NUM _ MIN t1, wherein t _ et is the estimated time of the vehicle passing through the target road section; NUM _ MIN is the minimum value of the number of vehicles in all lanes, and t1 is the time required by each vehicle to pass through the target intersection;
in the case that the straight-ahead direction at the target intersection is red light and the time for the distance to become green light is t1_ r _ b:
if T1_ r _ b + (2n-1) × (T _ et < T _1_ r _ b + (2n) × T, or T _ et < T1_ r _ b, wherein n is a positive integer greater than 1, and T is a complete cycle time for changing the red light to the green light in the straight-ahead direction, selecting the straight-ahead lane with the least number of vehicles as the optimal lane;
if T1_ r _ b + (2n) × T < T _ et < T1_ r _ b + (2n +1) × T, selecting the lane with the least number of vehicles in all the lanes at the leftmost side and the rightmost side as the optimal lane;
in the case that the straight-going direction of the target intersection is green light and the time when the distance becomes red light is t1_ b _ r:
if T1_ b _ r + (2n-1) × T < T _ et < T1_ b _ r + (2n) × T, or T _ et < T1_ b _ r, selecting the lane with the least number of vehicles in all lanes including the leftmost side and the rightmost side as the optimal lane;
and if T1_ b _ r + (2n) × T < T _ et < T1_ b _ r + (2n +1) × T, selecting the straight lane with the least number of vehicles as the optimal lane.
Optionally, after selecting the shortest optimal lane for passing through the target intersection, the method further includes:
and prompting the selected optimal lane in a voice and/or video mode.
The application also provides an intelligent navigation device, including:
the advancing direction judging module is used for judging the advancing direction of the vehicle at the target intersection according to the destination of the current vehicle;
the vehicle number acquisition module is used for acquiring the number of vehicles of each lane at the target intersection;
the time delay acquisition module is used for acquiring the time delay required by the current vehicle to enter the target road section from the current intersection according to the traffic light condition of the current intersection;
the optimal lane selection module is used for selecting the shortest optimal lane when the vehicle passes through the target intersection according to the traffic light condition of the target road section and the number of vehicles in each lane;
the target road section is a passing road section between the current intersection and the target intersection.
Optionally, the time delay obtaining module is configured to:
calculating the time delay required by the current vehicle to enter the target road section from the current intersection by adopting t0_ delay ═ t0+ t0_ r _ b;
wherein t0_ delay is the time delay required for the current vehicle to enter the target road section from the current intersection, and t0 is the time taken by the vehicle to pass through the current intersection; t0_ r _ b is the time when the current intersection changes from red to green.
Optionally, when it is determined that the advancing direction of the vehicle at the target intersection is the straight-ahead direction, the optimal lane selection module is configured to:
calculating the shortest time required by the vehicle when the vehicle reaches the target intersection by adopting t _ et-t 0_ delay + NUM _ MIN t1, wherein t _ et is the estimated time of the vehicle passing through the target road section; NUM _ MIN is the minimum value of the number of vehicles in all lanes, and t1 is the time required by each vehicle to pass through the target intersection;
in the case that the straight-ahead direction at the target intersection is red light and the time for the distance to become green light is t1_ r _ b:
if T1_ r _ b + (2n-1) × T < T _ et < T1_ r _ b + (2n) × T or T _ et < T1_ r _ b, wherein n is a positive integer greater than 1, and T is a complete cycle time of changing red light into green light in the straight-ahead direction, selecting the straight-ahead lane with the least number of vehicles as the optimal lane;
if T1_ r _ b + (2n) × T < T _ et < T1_ r _ b + (2n +1) × T, selecting the lane with the least number of vehicles in all the lanes at the leftmost side and the rightmost side as the optimal lane;
in the case that the straight-going direction of the target intersection is green light and the time when the distance becomes red light is t1_ b _ r:
if T1_ b _ r + (2n-1) × T < T _ et < T1_ b _ r + (2n) × T, or T _ et < T1_ b _ r, selecting the lane with the least number of vehicles in all lanes including the leftmost side and the rightmost side as the optimal lane;
and if T1_ b _ r + (2n) × T < T _ et < T1_ b _ r + (2n +1) × T, selecting the straight lane with the least number of vehicles as the optimal lane.
Optionally, the method further comprises:
and the prompting module is used for prompting the selected optimal lane in a voice and/or video mode after the optimal lane which is shortest in time when the vehicle passes through the target intersection is selected.
The application also provides an intelligent navigation device, including:
a memory for storing a computer program;
and the processor is used for realizing the steps of any one of the intelligent navigation methods when the computer program is executed.
The present application further provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when being executed by a processor, the computer program implements the steps of any one of the intelligent navigation methods.
The intelligent navigation method provided by the invention judges the advancing direction of the vehicle at the target intersection according to the destination of the current vehicle; acquiring the number of vehicles of each lane at a target intersection; obtaining the time delay required by the current vehicle to enter the target road section from the current intersection according to the traffic light condition of the current intersection; and selecting the shortest optimal lane for passing through the target intersection according to the traffic light condition of the target road section and the number of vehicles in each lane. The method can ensure that the straight-going vehicle can not influence the vehicles turning left and right, and simultaneously passes through the target intersection in the shortest time, and the lanes do not need to be switched in the driving process, and the lane selected by the method is the optimal lane, so that the traffic jam problem can be effectively relieved, the probability of traffic accidents is further reduced, and the method is suitable for the smart city advocated at present. In addition, the application also provides an intelligent navigation device, equipment and a computer readable storage medium with the technical effects.
Drawings
In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flow chart of one embodiment of an intelligent navigation method provided herein;
FIG. 2 is a schematic diagram of a current intersection, a target intersection, and a target road segment;
FIG. 3 is an overall system block diagram of the intelligent navigation method provided by the present application;
FIG. 4 is a schematic diagram of an intelligent navigation method provided by the present application;
fig. 5 is a block diagram of an intelligent navigation device according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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 invention.
Fig. 1 shows a flowchart of a specific embodiment of an intelligent navigation method provided by the present application, where the method includes:
step S101: judging the advancing direction of the vehicle at the target intersection according to the destination of the current vehicle;
according to the destination and the current position of the current vehicle, the advancing direction of the vehicle at the target intersection can be judged to be a straight-going direction, a left-turning direction or a right-turning direction from the navigation path.
Step S102: acquiring the number of vehicles of each lane at a target intersection;
the current vehicle number of each lane at the target intersection is obtained, and each lane comprises a left-turn lane, a straight lane and a right-turn lane. Specifically, the camera can be used for collecting images of the target intersection area, and the images are processed and then analyzed to obtain the number of vehicles on each lane. Of course, other obtaining manners may also be possible, and are not limited herein.
Step S103: obtaining the time delay required by the current vehicle to enter the target road section from the current intersection according to the traffic light condition of the current intersection;
and obtaining the time delay required by the current vehicle to enter the target road section from the current intersection according to the traffic light condition of the current intersection where the vehicle is located.
As a specific implementation, the time delay required for the current vehicle to enter the target road segment from the current intersection can be calculated by adopting t0_ delay ═ t0+ t0_ r _ b; wherein t0_ delay is the time delay required for the current vehicle to enter the target road section from the current intersection, and t0 is the time taken by the vehicle to pass through the current intersection; t0_ r _ b is the time when the current intersection changes from red to green.
Step S104: selecting the shortest optimal lane for passing through the target intersection according to the traffic light condition of the target road section and the number of vehicles in each lane;
as shown in fig. 2, the schematic diagram of the current intersection, the target intersection, and the target road segment is a passing road segment between the current intersection and the target intersection. According to vehicle data on different lanes and traffic light conditions of a current intersection and a target intersection, the time of the vehicle reaching the target intersection is estimated, and the optimal lane is intelligently selected, so that the purpose that the vehicle which turns left and right cannot be influenced by straight-going vehicles is ensured, the vehicle passes through the target intersection in the shortest time, and the lane does not need to be switched in the driving process.
The intelligent navigation method provided by the invention judges the advancing direction of the vehicle at the target intersection according to the destination of the current vehicle; acquiring the number of vehicles of each lane at a target intersection; obtaining the time delay required by the current vehicle to enter the target road section from the current intersection according to the traffic light condition of the current intersection; and selecting the shortest optimal lane for passing through the target intersection according to the traffic light condition of the target road section and the number of vehicles in each lane. The method can ensure that the straight-going vehicle can not influence the vehicles turning left and right, and simultaneously passes through the target intersection in the shortest time, and the lanes do not need to be switched in the driving process, and the lane selected by the method is the optimal lane, so that the traffic jam problem can be effectively relieved, the probability of traffic accidents is further reduced, and the method is suitable for the smart city advocated at present.
As a specific implementation manner, when it is determined that the advancing direction of the vehicle at the target intersection is the straight direction, the implementation process of selecting the shortest optimal lane for passing through the target intersection according to the traffic light condition of the target road and the number of vehicles in each lane specifically includes:
calculating the shortest time required by the vehicle when the vehicle reaches the target intersection by adopting t _ et-t 0_ delay + NUM _ MIN t1, wherein t _ et is the estimated time of the vehicle passing through the target road section; NUM _ MIN is the minimum value of the number of vehicles in all lanes, and t1 is the time required by each vehicle to pass through the target intersection;
in the case that the straight-ahead direction at the target intersection is red light and the time for the distance to become green light is t1_ r _ b:
if T1_ r _ b + (2n-1) × T < T _ et < T1_ r _ b + (2n) × T or T _ et < T1_ r _ b, wherein n is a positive integer greater than 1, and T is a complete cycle time of changing red light into green light in the straight-ahead direction, selecting the straight-ahead lane with the least number of vehicles as the optimal lane;
if T1_ r _ b + (2n) × T < T _ et < T1_ r _ b + (2n +1) × T, selecting the lane with the least number of vehicles in all the lanes at the leftmost side and the rightmost side as the optimal lane;
in the case that the straight going direction of the target intersection is green light and the time when the distance becomes red light is t1_ b _ r:
if T1_ b _ r + (2n-1) × T < T _ et < T1_ b _ r + (2n) × T, or T _ et < T1_ b _ r, selecting the lane with the least number of vehicles in all lanes including the leftmost side and the rightmost side as the optimal lane;
and if T1_ b _ r + (2n) × T < T _ et < T1_ b _ r + (2n +1) × T, selecting the straight lane with the least number of vehicles as the optimal lane.
Further, the method further comprises the following steps after selecting the shortest optimal lane for passing through the target intersection: and prompting the selected optimal lane in a voice and/or video mode. By prompting the optimal lane for the driver, the driver can enter the optimal lane in time, and traffic jam at the intersection is avoided.
Referring to fig. 3, an overall system block diagram of the intelligent navigation method provided by the present application and fig. 4, a schematic diagram of the intelligent navigation method provided by the present application are shown, and a specific implementation process of the intelligent navigation method provided by the present application is further described in detail below, where the method specifically includes:
and judging the advancing direction of the vehicle at the target intersection according to the current destination of the vehicle.
The vehicle starts the navigation system, inputs the destination, and then the navigation system learns the destination of the vehicle, so that the advancing direction of the vehicle at the target intersection is learned as follows: left turn, straight travel, or right turn.
Determining the type of the lane selected to enter according to the advancing direction at the target intersection, wherein the lane types are a left-turn lane, a straight lane and a right-turn lane;
when the advancing direction of the vehicle at the target intersection is a left-turning direction, the vehicle directly enters a left-turning lane; and when the advancing direction of the vehicle at the target intersection is the right-turn direction, the vehicle directly enters a right-turn lane.
When the advancing direction of the vehicle at the target intersection is a straight-ahead direction, the following judgment is carried out:
the navigation system can know the number of vehicles on each lane, LEFT _ NUM, MID _0_ NUM, MID _1_ NUM … … MID _ N-1_ NUM and RIGHT _ NUM, wherein LEFT _ NUM is the number of vehicles in a LEFT-turn lane, MID _0_ NUM, MID _1_ NUM … … MID _ N-1_ NUM are the number of vehicles in the middle N straight lanes, and RIGHT _ NUM is the number of vehicles in a RIGHT-turn lane.
Since current navigation systems are capable of determining a specific location of a vehicle, the method is as follows:
when the vehicle enters the leftmost lane of the target road section, LEFT _ NUM +1 is adopted, when the vehicle leaves the target intersection, LEFT _ NUM-1 and LEFT _ NUM are set to be 0 in initial value;
the MID _ M _ NUM +1 is set when the vehicle enters the straight lane M of the target section, and the MID _ M _ NUM +1 is set when the vehicle leaves the target intersection. 0< ═ M < ═ N-1, and the initial value of MID _ M _ NUM is 0;
RIGHT _ NUM +1 when the vehicle enters the RIGHT-most lane of the target road segment, and RIGHT _ NUM-1 when it leaves the target intersection. The initial value of RIGHT _ NUM is 0.
The navigation system starts intelligent calculation when the vehicle reaches the current intersection, and calculates the time delay at the current intersection.
Suppose that the time required for the vehicle to enter the target intersection from the current intersection is T0, the time for the red light of the current intersection to turn green is T0_ r _ b, and the period of one traffic light of the target intersection is T.
If the vehicle is a right-turn entry target link at the current intersection, t0_ r _ b is 0.
If the vehicle is traveling straight at the current intersection into the target link and the straight traveling direction is green, t0_ r _ b is 0, and if the straight traveling direction is red, the time to turn green is t0_ r _ b.
If the vehicle is left-turning into the target road section at the current intersection and the left-turning direction is green, t0_ r _ b is 0, and if the left-turning direction is red, the time to the left-turning green is t0_ r _ b.
It can be known that the time delay of the vehicle entering the target section from the current intersection is t0_ delay — t0+ t0_ r _ b.
And finally, carrying out optimal judgment on the lane by combining the traffic light condition of the target intersection.
Assuming that the time required for the vehicle to leave the target intersection is t1, the minimum value of the number of vehicles in all lanes (including the leftmost LEFT-turn lane and the rightmost RIGHT-turn lane) is NUM _ MIN, i.e., NUM _ MIN is the minimum value of LEFT _ NUM, MID _0_ NUM, MID _1_ NUM … …, MID _ N-1_ NUM, RIGHT _ NUM.
The minimum value of the number of vehicles in the straight-through lane (excluding the leftmost left-turn lane and the rightmost right-turn lane) is MID _ NUM _ MIN, namely the MID _ NUM _ MIN is the minimum value of MID _0_ NUM, MID _1_ NUM … … and MID _ N-1_ NUM.
Assuming that the straight-going direction of the target intersection is red, the time of the distance changing into the green light is T1_ r _ b, and the time of one complete cycle of the red light changing into the green light in the straight-going direction is T, the shortest time of the vehicle reaching the target intersection is estimated to be T _ et which is T0_ delay + NUM _ MIN T1.
T1_ r _ b + (2n-1) × T < T _ et < T1_ r _ b + (2n) × T, or T _ et < T1_ r _ b, wherein n is 1,2,3 … …, namely when the predicted time minimum value of all lanes reaches the target intersection, the straight-going direction of the target intersection is still red, and at the moment, the straight-going lane with the least number of vehicles is selected as the optimal lane, namely the straight-going lane M is selected, wherein MID _ M _ NUM is MID _ NUM _ MIN.
T1_ r _ b + (2n) × T < T _ et < T1_ r _ b + (2n +1) × T, wherein n is 0,1,2,3 … …, namely when the predicted time minimum value of all lanes reaches the target intersection, the straight-going direction of the target intersection is green, and the lane with the least number of vehicles (including the leftmost lane and the rightmost lane) is selected as the optimal lane.
Assume that the straight going direction of the target intersection is green, the time for the distance to turn to red is T1_ b _ r, and the straight going direction is green for a complete period of time T.
T1_ b _ r + (2n-1) × T < T _ et < T1_ b _ r + (2n) × T, or T _ et < T1_ b _ r, where n is 1,2,3 … …, that is, when the predicted time minimum value of all lanes reaches the target intersection, the straight-going direction of the target intersection is green, and then the lane with the least number of vehicles (including the leftmost lane and the rightmost lane) is selected as the optimal lane.
T1_ b _ r + (2n) × T < T _ et < T1_ b _ r + (2n +1) × T, wherein n is 0,1,2,3 … …, namely when the predicted time minimum value of all lanes reaches the target intersection, the straight-going direction of the target intersection is red, and at the moment, the optimal lane of the straight-going lane with the least number of vehicles at the predicted time is selected. That is, the straight lane M is selected, where MID _ M _ NUM is MID _ NUM _ MIN.
The optimal lane is selected based on the navigation system, manual intervention is not needed, the existing navigation system is utilized, the optimal lane is intelligently provided, the vehicle can pass through the target intersection at the fastest speed, the vehicle which turns left and right can not be influenced by the vehicle which goes straight can be guaranteed, and the utilization rate of the lane is improved. And the lane need not be switched during the driving process, the probability of traffic accidents is further reduced, the method is simple, the traffic jam problem is effectively relieved, and the method is suitable for the smart city and the intelligent traffic advocated at present.
In the following, the intelligent navigation device provided by the embodiment of the present invention is introduced, and the intelligent navigation device described below and the intelligent navigation method described above may be referred to correspondingly.
Fig. 5 is a block diagram of an intelligent navigation device according to an embodiment of the present invention, where the intelligent navigation device according to fig. 5 may include:
a forward direction determination module 100, configured to determine a forward direction of the vehicle at the target intersection according to a destination of the current vehicle;
a vehicle number obtaining module 200, configured to obtain the number of vehicles in each lane at the target intersection;
the time delay obtaining module 300 is configured to obtain a time delay required for a current vehicle to enter a target road section from a current intersection according to a traffic light condition of the current intersection;
an optimal lane selection module 400, configured to select an optimal lane that is the shortest when passing through a target intersection according to traffic light conditions of a target road segment and the number of vehicles in each lane;
the target road section is a passing road section between the current intersection and the target intersection.
Optionally, the time delay obtaining module is configured to:
calculating the time delay required by the current vehicle to enter the target road section from the current intersection by adopting t0_ delay (t 0+ t0_ r _ b);
wherein t0_ delay is the time delay required for the current vehicle to enter the target road section from the current intersection, and t0 is the time taken by the vehicle to pass through the current intersection; t0_ r _ b is the time when the current intersection changes from red to green.
Optionally, when it is determined that the advancing direction of the vehicle at the target intersection is the straight-ahead direction, the optimal lane selection module is configured to:
calculating the shortest time required by the vehicle when the vehicle reaches the target intersection by adopting t _ et-t 0_ delay + NUM _ MIN t1, wherein t _ et is the estimated time of the vehicle passing through the target road section; NUM _ MIN is the minimum value of the number of vehicles in all lanes, and t1 is the time required by each vehicle to pass through the target intersection;
in the case that the straight-ahead direction at the target intersection is red light and the time for the distance to become green light is t1_ r _ b:
if T1_ r _ b + (2n-1) × T < T _ et < T1_ r _ b + (2n) × T or T _ et < T1_ r _ b, wherein n is a positive integer greater than 1, and T is a complete cycle time of changing red light into green light in the straight-ahead direction, selecting the straight-ahead lane with the least number of vehicles as the optimal lane;
if T1_ r _ b + (2n) × T < T _ et < T1_ r _ b + (2n +1) × T, selecting the lane with the least number of vehicles in all the lanes at the leftmost side and the rightmost side as the optimal lane;
in the case that the straight-going direction of the target intersection is green light and the time when the distance becomes red light is t1_ b _ r:
if T1_ b _ r + (2n-1) × T < T _ et < T1_ b _ r + (2n) × T, or T _ et < T1_ b _ r, selecting the lane with the least number of vehicles in all lanes including the leftmost side and the rightmost side as the optimal lane;
and if T1_ b _ r + (2n) × T < T _ et < T1_ b _ r + (2n +1) × T, selecting the straight lane with the least number of vehicles as the optimal lane.
Optionally, the method further comprises:
and the prompting module is used for prompting the selected optimal lane in a voice and/or video mode after the optimal lane which is shortest in time when the vehicle passes through the target intersection is selected.
The intelligent navigation device of this embodiment is configured to implement the foregoing intelligent navigation method, and therefore specific embodiments in the intelligent navigation device can be seen in the foregoing embodiments of the intelligent navigation method, for example, the forward direction determining module 100, the vehicle number obtaining module 200, the time delay obtaining module 300, and the optimal lane selecting module 400, which are respectively configured to implement steps S101, S102, S103, and S104 in the foregoing intelligent navigation method, so that the specific embodiments thereof may refer to descriptions of corresponding embodiments of each part, and are not described herein again.
The method can ensure that the straight-going vehicle can not influence the vehicles turning left and right, and simultaneously passes through the target intersection in the shortest time, and the lanes do not need to be switched in the driving process, and the lane selected by the method is the optimal lane, so that the traffic jam problem can be effectively relieved, the probability of traffic accidents is further reduced, and the method is suitable for the smart city advocated at present.
In addition, this application still provides an intelligent navigation equipment, includes:
a memory for storing a computer program;
and the processor is used for realizing the steps of any one of the intelligent navigation methods when the computer program is executed.
The present application further provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when being executed by a processor, the computer program implements the steps of any one of the intelligent navigation methods.
The intelligent navigation device and the computer readable storage medium provided by the application correspond to the intelligent navigation method, and the specific implementation modes thereof can be referred to each other, which is not described herein again.
This application has guaranteed that the through traffic can not influence the vehicle that turns left and turn right, furthest's improvement the utilization ratio in lane. In addition, the optimal lane is provided, vehicles can pass through the target intersection in the shortest time, lane changing is not needed in the driving process, and the probability of traffic accidents is reduced. The traffic jam at crossroad can effectively be alleviated in this application, especially city arterial road is fit for the wisdom city and the intelligent transportation who advocate now, promotes city resident's trip and experiences.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed in the embodiment corresponds to the method disclosed in the embodiment, so that the description is simple, and the relevant points can be referred to the description of the method part.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The intelligent navigation method, the intelligent navigation device, the intelligent navigation equipment and the computer readable storage medium provided by the invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (8)
1. An intelligent navigation method, comprising:
judging the advancing direction of the vehicle at the target intersection according to the destination of the current vehicle;
acquiring the number of vehicles of each lane at a target intersection;
obtaining the time delay required by the current vehicle to enter the target road section from the current intersection according to the traffic light condition of the current intersection;
selecting the shortest optimal lane for passing through the target intersection according to the traffic light condition of the target road section and the number of vehicles in each lane;
the target road section is a passing road section between a current intersection and a target intersection; when the advancing direction of the vehicle at the target road junction is judged to be the straight-going direction, the step of selecting the shortest optimal lane for passing through the target road junction according to the traffic light condition of the target road section and the number of vehicles in each lane comprises the following steps:
calculating the shortest time required by the vehicle when the vehicle reaches the target intersection by adopting t _ et = t0_ delay + NUM _ MIN t1, wherein t _ et is the estimated time of the vehicle passing through the target road section; t0_ delay is the time delay required for the current vehicle to enter the target road section from the current intersection; NUM _ MIN is the minimum value of the number of vehicles in all lanes, and t1 is the time required by each vehicle to pass through the target intersection;
in the case that the straight-ahead direction at the target intersection is red light and the time for the distance to become green light is t1_ r _ b:
if T1_ r _ b + (2n-1) × T < T _ et < T1_ r _ b + (2n) × T or T _ et < T1_ r _ b, wherein n is a positive integer greater than 1, and T is a complete cycle time of changing red light into green light in the straight-ahead direction, selecting the straight-ahead lane with the least number of vehicles as the optimal lane;
if T1_ r _ b + (2n) × T < T _ et < T1_ r _ b + (2n +1) × T, selecting the lane with the least number of vehicles in all the lanes at the leftmost side and the rightmost side as the optimal lane;
in the case that the straight-going direction of the target intersection is green light and the time when the distance becomes red light is t1_ b _ r:
if T1_ b _ r + (2n-1) × T < T _ et < T1_ b _ r + (2n) × T, or T _ et < T1_ b _ r, selecting the lane with the least number of vehicles in all lanes including the leftmost side and the rightmost side as the optimal lane;
and if T1_ b _ r + (2n) × T < T _ et < T1_ b _ r + (2n +1) × T, selecting the straight lane with the least number of vehicles as the optimal lane.
2. The intelligent navigation method of claim 1, wherein the obtaining of the time delay required for the current vehicle to enter the target road segment from the current intersection according to the traffic light condition of the current intersection comprises:
calculating the time delay required by the current vehicle to enter the target road section from the current intersection by adopting t0_ delay = t0+ t0_ r _ b;
wherein t0 is the time taken by the vehicle to pass through the current intersection; t0_ r _ b is the time when the current intersection changes from red to green.
3. The intelligent navigation method according to claim 1 or 2, further comprising, after selecting the shortest optimal lane for use through the target intersection:
and prompting the selected optimal lane in a voice and/or video mode.
4. An intelligent navigation device, comprising:
the advancing direction judging module is used for judging the advancing direction of the vehicle at the target intersection according to the destination of the current vehicle;
the vehicle number acquisition module is used for acquiring the number of vehicles of each lane at the target intersection;
the time delay acquisition module is used for acquiring the time delay required by the current vehicle to enter the target road section from the current intersection according to the traffic light condition of the current intersection;
the optimal lane selection module is used for selecting the shortest optimal lane when the vehicle passes through the target intersection according to the traffic light condition of the target road section and the number of vehicles in each lane;
the target road section is a passing road section between a current intersection and a target intersection; when the advancing direction of the vehicle at the target intersection is determined to be the straight-ahead direction, the optimal lane selection module is configured to:
calculating the shortest time required by the vehicle when the vehicle reaches the target intersection by adopting t _ et = t0_ delay + NUM _ MIN t1, wherein t _ et is the estimated time of the vehicle passing through the target road section; t0_ delay is the time delay required for the current vehicle to enter the target road section from the current intersection; NUM _ MIN is the minimum value of the number of vehicles in all lanes, and t1 is the time required by each vehicle to pass through a target intersection;
in the case that the straight-ahead direction at the target intersection is red light and the time for the distance to become green light is t1_ r _ b:
if T1_ r _ b + (2n-1) × T < T _ et < T1_ r _ b + (2n) × T or T _ et < T1_ r _ b, wherein n is a positive integer greater than 1, and T is a complete cycle time of changing red light into green light in the straight-ahead direction, selecting the straight-ahead lane with the least number of vehicles as the optimal lane;
if T1_ r _ b + (2n) × T < T _ et < T1_ r _ b + (2n +1) × T, selecting the lane with the least number of vehicles in all the lanes at the leftmost side and the rightmost side as the optimal lane;
in the case that the straight-going direction of the target intersection is green light and the time when the distance becomes red light is t1_ b _ r:
if T1_ b _ r + (2n-1) × T < T _ et < T1_ b _ r + (2n) × T, or T _ et < T1_ b _ r, selecting the lane with the least number of vehicles in all lanes including the leftmost side and the rightmost side as the optimal lane;
and if T1_ b _ r + (2n) × T < T _ et < T1_ b _ r + (2n +1) × T, selecting the straight lane with the least number of vehicles as the optimal lane.
5. The intelligent navigation device of claim 4, wherein the time delay acquisition module is to:
calculating the time delay required by the current vehicle to enter the target road section from the current intersection by adopting t0_ delay = t0+ t0_ r _ b;
wherein t0 is the time taken by the vehicle to pass through the current intersection; t0_ r _ b is the time when the current intersection changes from red to green.
6. The intelligent navigation device according to claim 4 or 5, further comprising:
and the prompting module is used for prompting the selected optimal lane in a voice and/or video mode after the optimal lane which is shortest in time when the vehicle passes through the target intersection is selected.
7. An intelligent navigation device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the intelligent navigation method as claimed in any one of claims 1 to 3 when executing the computer program.
8. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the intelligent navigation method according to any one of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811511619.3A CN109461322B (en) | 2018-12-11 | 2018-12-11 | Intelligent navigation method, device, equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811511619.3A CN109461322B (en) | 2018-12-11 | 2018-12-11 | Intelligent navigation method, device, equipment and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109461322A CN109461322A (en) | 2019-03-12 |
| CN109461322B true CN109461322B (en) | 2022-08-12 |
Family
ID=65613027
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811511619.3A Active CN109461322B (en) | 2018-12-11 | 2018-12-11 | Intelligent navigation method, device, equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109461322B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109782776B (en) * | 2019-03-28 | 2022-07-29 | 北京百度网讯科技有限公司 | Lane selection method and device for unmanned vehicle and storage medium |
| CN110085043A (en) * | 2019-05-20 | 2019-08-02 | 广州小鹏汽车科技有限公司 | A kind of the determination method and vehicle control system in optimal lane |
| CN113554885A (en) * | 2021-06-23 | 2021-10-26 | 北京汽车股份有限公司 | Method and system for guiding vehicle steering at congested intersection |
| CN114446054A (en) * | 2022-01-24 | 2022-05-06 | 广东以诺通讯有限公司 | Method, device, equipment and storage medium for relieving traffic congestion |
| CN115083190B (en) * | 2022-06-17 | 2023-04-25 | 东风汽车集团股份有限公司 | Automatic driving system and method for multi-lane traffic intersection |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104318788A (en) * | 2014-10-05 | 2015-01-28 | 哈尔滨工业大学 | Method for improving space resource utilization efficiency of intersection entrance lane |
| CN106408984A (en) * | 2016-11-28 | 2017-02-15 | 东莞职业技术学院 | Urban traffic flow guidance system based on Internet of things |
| CN108492605A (en) * | 2018-05-21 | 2018-09-04 | 苏州纽克斯电源技术股份有限公司 | A kind of road junction roadway air navigation aid and device |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3966170B2 (en) * | 2002-12-05 | 2007-08-29 | 株式会社デンソー | Driving assistance device |
| CN104101353B (en) * | 2013-04-15 | 2017-09-29 | 北京四维图新科技股份有限公司 | A kind of air navigation aid, device and real-time navigation system |
| JP6597033B2 (en) * | 2015-08-03 | 2019-10-30 | アイシン・エィ・ダブリュ株式会社 | Recommended lane determination system, method and program |
| CN106373415A (en) * | 2016-10-20 | 2017-02-01 | 深圳市元征科技股份有限公司 | Lane guiding method and apparatus |
| CN108180919A (en) * | 2017-12-12 | 2018-06-19 | 沈阳美行科技有限公司 | A kind of optimization method and device of programme path |
-
2018
- 2018-12-11 CN CN201811511619.3A patent/CN109461322B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104318788A (en) * | 2014-10-05 | 2015-01-28 | 哈尔滨工业大学 | Method for improving space resource utilization efficiency of intersection entrance lane |
| CN106408984A (en) * | 2016-11-28 | 2017-02-15 | 东莞职业技术学院 | Urban traffic flow guidance system based on Internet of things |
| CN108492605A (en) * | 2018-05-21 | 2018-09-04 | 苏州纽克斯电源技术股份有限公司 | A kind of road junction roadway air navigation aid and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109461322A (en) | 2019-03-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109461322B (en) | Intelligent navigation method, device, equipment and readable storage medium | |
| CN108806283B (en) | Control method of traffic signal lamp and Internet of vehicles platform | |
| JP2020166837A (en) | Lane change method for unmanned vehicle, device, storage medium and program | |
| CN106384519B (en) | Traffic light control method and device | |
| CN109448415B (en) | Lane selection method, system and device and readable storage medium | |
| CN111613070B (en) | Traffic signal lamp control method, traffic signal lamp control device, electronic equipment and computer storage medium | |
| CN108871357B (en) | Method for displaying accident lane of congested road section on electronic map | |
| JP5562691B2 (en) | Congestion time calculation device, departure time calculation device, no traffic time calculation device, and program | |
| JP2009053925A (en) | Behavior prediction device | |
| CN111613071A (en) | Signal lamp adjusting method, device and system | |
| WO2022237634A1 (en) | Information processing method and device, and computer storage medium | |
| CN103185589B (en) | A kind of group vehicle running path planing method, device and system | |
| CN103730005B (en) | Method and system for predicting journey running time | |
| CN111429735B (en) | Motor vehicle signal lamp control method and device and computer readable storage medium | |
| KR20230093275A (en) | Traffic congestion detection method, device, electronic device and storage medium | |
| CN113327419A (en) | Green wave speed determination method and device, electronic equipment and storage medium | |
| CN115593440B (en) | Vehicle control method, device, equipment and computer readable storage medium | |
| CN111583677A (en) | Intersection vehicle induction method based on multi-path scheme under cooperative vehicle and road environment | |
| CN112991784B (en) | Self-adaptive variable lane control method and device in signal period | |
| JP5208016B2 (en) | Merge / Exit Determination Device and Merge / Exit Determination Program | |
| CN111369784A (en) | Method and device for controlling traffic flow of lane | |
| CN111932912A (en) | Secondary releasing method of viaduct lower ramp intersection based on queuing length detection | |
| CN109643447B (en) | Image processing apparatus and image pickup apparatus | |
| CN116052126A (en) | Method and device for detecting drivable area, vehicle and storage medium | |
| CN113470353A (en) | Traffic grade determination method and device, storage medium and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |