CN115188194A - Highway traffic lane level accurate induction system and method - Google Patents
Highway traffic lane level accurate induction system and method Download PDFInfo
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- CN115188194A CN115188194A CN202210829528.4A CN202210829528A CN115188194A CN 115188194 A CN115188194 A CN 115188194A CN 202210829528 A CN202210829528 A CN 202210829528A CN 115188194 A CN115188194 A CN 115188194A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
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Abstract
The invention discloses a highway traffic lane level accurate induction system and a highway traffic lane level accurate induction method, which relate to the technical field of highway traffic and comprise the following specific steps: data acquisition: acquiring environmental data, position data and driving data; data processing: acquiring lane-level safe vehicle speed or safe vehicle distance according to the environment data, the position data and the driving data; calculating lane-level predicted travel time based on the environmental data, the driving data and lane-level safe vehicle speed or safe vehicle distance; obtaining a guidance planning path: obtaining a guidance planning path according to the environmental data and the lane-level predicted travel time, and displaying the guidance planning path on an electronic map; according to the invention, the safety degree is predicted by combining the road surface data and the weather prediction data, and the safe vehicle speed and the safe vehicle distance are obtained by combining the environment data and the road flow data, so that the occurrence of safety accidents is greatly reduced, and the occurrence of vehicle jam is greatly reduced.
Description
Technical Field
The invention relates to the technical field of highway traffic, in particular to a highway traffic lane level accurate induction system and method.
Background
In recent years, with the continuous development of economic society, the construction of highways enters a rapid development period, road networks are increased, the interoperability is enhanced, and the traffic flow is rapidly increased. However, the traffic safety management problem has attracted people's attention while the highway exerts great social and economic benefits and shortens the space-time distance between cities. Especially, the traffic accidents caused by overspeed driving and severe weather such as mist and snow constitute great threat to the life and property safety of people, and the tragedy of the scene, the serious consequence and the deep influence cannot be compared with the traffic accidents on the common road.
According to statistics, accidents caused by overspeed, fog and snow driving on the expressway account for more than 90 percent of the total number of the accidents.
A Traffic Guidance System (TGS), also called Traffic Flow Guidance System (TFGS), also called Traffic Route Guidance System (TRGS) or Vehicle Navigation System (VNS), is based on modern technologies such as electronics, computers, networks and communications, and provides optimal Route Guidance instructions to road users according to the origin and destination of travelers or helps road users find an optimal Route from the origin to the destination by obtaining real-time Traffic information.
Therefore, how to reduce road traffic accidents by using the traffic guidance system is an urgent problem to be solved by those skilled in the art.
Disclosure of Invention
In view of this, the present invention provides a system and a method for accurately inducing highway traffic lanes, which overcome the above-mentioned drawbacks.
In order to achieve the above purpose, the invention provides the following technical scheme:
a highway traffic lane level accurate induction method comprises the following specific steps:
data acquisition: acquiring environmental data, position data and driving data;
data processing: acquiring lane-level safe vehicle speed or safe vehicle distance according to the environmental data, the position data and the driving data; calculating lane-level predicted travel time based on the environmental data, the driving data and lane-level safe vehicle speed or safe vehicle distance;
obtaining a guidance planning path: and obtaining a guidance planning path according to the environment data and the lane-level predicted travel time, and presenting the guidance planning path on the electronic map.
Optionally, the environment data includes: weather data, weather prediction data, road surface state data of each road section lane level, and traffic flow data of each road section lane level.
Optionally, the travel data includes basic data, real-time travel data, and historical travel data.
Optionally, the data processing includes the specific steps of:
calling environmental data of the current area according to the position data;
acquiring a lane-level pre-driving path according to the position data, the driving data and the estimated driving destination;
calculating to obtain lane-level safe vehicle speed or safe vehicle distance of each road section in the lane-level pre-driving path according to the safe driving model;
and calculating the expected travel time of each road section at the lane level based on the environmental data, the driving data and the lane level safe speed or safe distance of each road section.
Optionally, the obtaining step of the guidance planning path is:
obtaining the safety degree of the lane-level pre-driving path based on the lane-level road surface state data of each road section;
acquiring safety degree change data of the lane-level pre-driving path according to the weather prediction data;
and generating a guidance planning path based on the lane-level predicted travel time and the safety degree change data of the corresponding pre-driving path.
Optionally, the method further comprises accident early warning, and whether early warning is given out is judged according to the relation between the traffic flow data of the lane level of each road section and the traffic flow data of the predicted lane level of each road section.
An accurate induction system at highway traffic lane level comprising:
the environment monitoring device comprises: for collecting environmental data;
the video monitoring device comprises: for obtaining video data;
and (3) inducing the terminal: the system is used for acquiring the driving data and the position data, displaying the guidance planning path and carrying out alarm reminding;
a data processing device: the system is used for calculating the lane-level traffic flow data of each road section according to the environmental data or the video data; acquiring lane-level predicted travel time, lane-level safe vehicle speed and safe vehicle distance according to the environment data, the position data and the driving data; and predicting the travel time according to the environment data and the lane level to obtain a guidance planning path.
Optionally, the environment monitoring device includes: the system comprises a rain and snow coverage identification module, a weather prediction module and a traffic flow counting module; the rain and snow coverage identification module, the weather prediction module and the traffic flow counting module are all connected with the data processing device.
Optionally, the guidance terminal includes: the system comprises a positioning module, a terminal data module, a map module, a display module and an odometer charging module; the positioning module, the terminal data module, the map module, the display module and the mileage charging module are all connected with the data processing device.
Optionally, the system further comprises an accident early warning module, configured to compare the traffic flow data of the lane level of each road segment with the traffic flow data of the predicted lane level of each road segment.
According to the technical scheme, compared with the prior art, the invention discloses an expressway traffic lane level accurate induction system and method, safety degree prediction is carried out by combining road surface data and weather prediction data, safe vehicle speed and safe vehicle distance are obtained by environment data and road flow data, and the predicted running time of each lane of a preset road section in different time stages can be obtained by calculating the lane level predicted travel time, so that accidents are greatly reduced, and a multi-level and multi-level lane level induction scheme can be generated at the downstream under the condition that the accidents or the blockage occur at the upstream so as to reduce the upstream blocking pressure.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of the system of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The embodiment of the invention discloses an accurate induction method for highway traffic lane levels, which comprises the following specific steps as shown in figure 1:
step 1, acquiring environmental data, position data and driving data;
wherein the environmental data includes: weather data, weather prediction data, road surface state data of each road section and lane level, traffic flow data of each road section and lane level and the like;
the position data comprises longitude, latitude, acquisition time and the like;
the travel data includes basic data, real-time travel data, historical travel data, and the like;
further, the basic data includes ID data of the vehicle, the model of the vehicle, the driver's driving age, the driving skill level, and the like;
the real-time driving data includes a current speed, a current distance, a current lane, and the like of the vehicle.
Step 2, acquiring lane-level safe vehicle speed or safe vehicle distance according to the environmental data, the position data and the driving data; calculating lane-level predicted travel time based on the environmental data, the driving data and lane-level safe vehicle speed or safe vehicle distance;
the method comprises the following specific steps:
step 21, determining a driveway capable of being driven by the vehicle according to the model of the vehicle;
step 22, obtaining current position data of the vehicle, and calling weather data of a current road section and lane grade road surface state data of the current road section according to the current position data;
step 23, acquiring a lane-level pre-driving route according to the position data, the driveable lane of the vehicle and the predicted driving destination;
24, calculating to obtain safe vehicle speeds and safe vehicle distances of different lane-level pre-driving paths according to the safe driving model;
and 25, obtaining the lane-level predicted travel time based on the driving data, the environment data and the lane-level safe vehicle speed or safe vehicle distance.
The calculation formula of the safe vehicle speed is as follows:
wherein f is the adhesion coefficient of the road surface; i is the road slope; and L is the distance between the vehicle and the obstacle.
The more detailed steps of step 24 are:
241, acquiring road gradients of different lanes in the pre-driving path, wherein the road gradients can be acquired through a road parameter database recorded by a line design construction drawing;
step 242, obtaining road surface states of different lanes, and obtaining road surface adhesion coefficients of different lanes in the pre-driving path according to the road surface states of the different lanes;
step 243, obtaining the average driving speed and flow of vehicles in different lanes on the road section in the pre-driving path;
step 244, calculating the maximum safe vehicle speed and the minimum safe vehicle distance of different lanes by adopting a safe driving model;
and step 245, sending the calculated maximum safe vehicle speed and minimum safe vehicle distance of different lanes to a driver through the guidance terminal.
Wherein, the division of the road section is as follows: the intersection of the expressway is taken as a node, roads of the two nodes are divided into various road sections according to the preset length, and the road sections are divided on the basis of hundred meters in the embodiment.
The specific steps of step 25 are:
251, calculating lane-level travel time according to the traffic flow data of the lane-level road section, the real-time driving data and the lane-level safe vehicle speed;
step 252, lane level predicted travel times for each time interval are calculated based on the historical travel data and the lane level travel times.
And if the time period is a holiday, the driving data of the same time for many years is taken as the historical driving data.
Step 3, obtaining a guidance planning path according to the environmental data and the lane-level predicted travel time, and displaying the guidance planning path on an electronic map;
the detailed steps of the step 3 are as follows:
step 31, obtaining the safety degree of each road section in the lane-level pre-driving path based on the road surface state data of each road section;
step 32, acquiring safety degree change data of each road section in the lane-level pre-driving path according to the weather prediction data;
and step 33, generating a guidance planning path based on the lane-level predicted travel time and the safety degree change data of the corresponding pre-driving path.
The travel time of the lane-level pre-travel path between the current position and the destination can be obtained according to the lane-level predicted travel time, and the guidance planning path is generated according to the safety degree change data of the corresponding pre-travel path and the technical grade of the driver.
In another embodiment, the safety degree change data and the predicted travel time of the lane-level pre-travel path corresponding to the lane-level pre-travel path may be generated into a list and sent to the driver, and the driver may select the list.
In another embodiment, the lane-level pre-travel paths in the list are sorted from top to bottom according to the recommended values according to the above-mentioned features.
In a further embodiment, the lane-level pre-travel paths in the list are sorted from short to high according to the above-mentioned characteristics.
Wherein the degree of safety may also be referred to as the degree of availability; the road surface of each lane is divided into six grades of 0-5 according to the accumulated snow, accumulated water and thickness of accumulated ice, the water discharge of the road section and obstacles on the road section; the higher the value, the safer 0 represents no traffic.
During the driving process, the system also comprises overspeed reminding, reverse driving reminding, too small distance reminding, line crossing reminding and path error reminding.
The reminding mode comprises voice reminding, display reminding and indicator light reminding.
In another embodiment, the method also comprises accident early warning, and whether the early warning is given out is judged according to the relation between the traffic flow data of the lane level of each road section and the traffic flow data of the predicted lane level of each road section;
the method comprises the following specific steps:
according to traffic flow data, weather data and predicted weather data of lane-level road sections among any road sections, predicting the traffic flow data of each lane at the entrance of the next road section by using a length memory model;
comparing and analyzing the actual traffic flow data of each lane at the entrance of the next road section with the predicted traffic flow data of each lane, and judging the traffic state;
judging whether each lane in any road section has abnormal conditions according to the analysis result of the traffic flow data mutation of each lane, and carrying out early warning in time aiming at the possible traffic events;
if the abnormal condition occurs, sending the early warning information to the downstream vehicle of the lane in any road section;
after receiving the early warning information, the downstream vehicle acquires the lane-level pre-driving route again, updates the predicted driving route, and displays the updated driving route on the electronic map, so that a driver can visually and clearly see the driving route and the detailed conditions of the driving route, such as the congestion condition, the road surface condition, the conditions of adjacent vehicles and the like.
In another embodiment, the guidance and issuance device is configured according to a preset condition, and the guidance and issuance device displays data of each lane in the form of an image and plays the whole situation of the high-speed road section in the form of scrolling characters under the image, for example, the whole situation of the front road section is closed due to low visibility, and the like.
The preset condition may be: selecting a proper spacing distance according to historical data; an intersection of a road; downstream locations of road segments where traffic events are likely to occur, etc.;
the data for displaying each lane in the form of an image is specifically: displaying the congestion condition and the safety degree on each lane in different colors, and displaying traffic events on each lane in different icons, such as: green is used for indicating that no congestion occurs, red is used for indicating that congestion occurs, the safety degree of the lane is indicated according to preset shapes such as black, triangle, circle and the like, the accident types are respectively displayed at corresponding positions by different icons according to the accident conditions, and the text conditions are displayed at the lower end of the guidance and distribution device.
The traffic events include traffic jam, traffic accident, and the like.
In another embodiment, traffic data may also be obtained from the video data.
An accurate induction system at highway traffic lane level, see fig. 2, comprising:
the environment monitoring device comprises: for collecting environmental data;
the video monitoring device comprises: for acquiring video data;
and (3) inducing the terminal: the system is used for acquiring the driving data and the position data, displaying the guidance planning path and carrying out alarm reminding;
a data processing device: the system is used for calculating the lane-level traffic flow data of each road section according to the environmental data or the video data; acquiring lane-level predicted travel time, lane-level safe vehicle speed and safe vehicle distance according to the environment data, the position data and the driving data; and predicting the travel time according to the environment data and the lane level to obtain a guidance planning path.
The monitoring devices on all lanes are the same, and the present embodiment is described in detail by taking any one lane as an example, wherein the environment monitoring device includes: the system comprises a rain and snow coverage identification module, a weather prediction module and a traffic flow statistical module; the rain and snow coverage identification module, the weather prediction module and the traffic flow counting module are all connected with the data processing device.
The rain and snow coverage identification module comprises a millimeter wave radar detection unit, an ice and snow depth radar monitoring unit, an infrared sensor detection unit, a visibility detection unit and an obstacle camera unit and is used for detection.
Wherein, the induction terminal includes: the system comprises a positioning module, a terminal data module, a map module, a display module, a voice module and an odometer charging module; the positioning module, the terminal data module, the map module, the display module and the mileage charging module are all connected with the data processing device.
The system further comprises an accident early warning module used for comparing the traffic flow data of the lane level of each road section with the traffic flow data of the predicted lane level of each road section.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A highway traffic lane level accurate induction method is characterized by comprising the following specific steps:
data acquisition: acquiring environmental data, position data and driving data;
data processing: acquiring lane-level safe vehicle speed or safe vehicle distance according to the environmental data, the position data and the driving data; calculating lane-level predicted travel time based on the environmental data, the driving data and lane-level safe vehicle speed or safe vehicle distance;
obtaining a guidance planning path: and obtaining a guidance planning path according to the environment data and the lane-level predicted travel time, and presenting the guidance planning path on an electronic map.
2. The method for accurately inducing the highway traffic lane according to claim 1, wherein the environmental data comprises: weather data, weather prediction data, road surface state data of each road section lane level, and traffic flow data of each road section lane level.
3. The method for accurately inducing the highway traffic lane level according to claim 1, wherein the driving data comprises basic data, real-time driving data and historical driving data.
4. The method for accurately inducing the highway traffic lane according to claim 1, wherein the data processing comprises the following specific steps:
calling environmental data of the current area according to the position data;
acquiring a lane-level pre-driving path according to the position data, the driving data and the estimated driving destination;
calculating to obtain lane-level safe vehicle speed or safe vehicle distance of each road section in the lane-level pre-driving path according to the safe driving model;
and calculating the expected travel time of each road section at the lane level based on the environment data, the driving data and the lane level safe vehicle speed or safe vehicle distance of each road section.
5. The method for accurately inducing the highway traffic lane according to claim 2, wherein the step of obtaining the induced planned path comprises the following steps:
obtaining the safety degree of the lane-level pre-driving path based on the lane-level road surface state data of each road section;
acquiring safety degree change data of the lane-level pre-driving path according to the weather prediction data;
and generating a guidance planning path based on the lane-level predicted travel time and the safety degree change data of the corresponding pre-driving path.
6. The method as claimed in claim 1, further comprising accident pre-warning, wherein whether pre-warning is given is judged according to the relationship between the traffic flow data of each road section lane and the traffic flow data of each road section predicted lane.
7. The utility model provides an accurate induction system of highway traffic lane level which characterized in that includes:
the environment monitoring device comprises: for collecting environmental data;
the video monitoring device comprises: for acquiring video data;
and (3) inducing the terminal: the system is used for acquiring the driving data and the position data, displaying the guidance planning path and carrying out alarm reminding;
a data processing device: the system is used for calculating the lane-level traffic flow data of each road section according to the environmental data or the video data; acquiring lane-level predicted travel time, lane-level safe vehicle speed and safe vehicle distance according to the environment data, the position data and the driving data; and predicting the travel time according to the environment data and the lane level to obtain a guidance planning path.
8. The system of claim 7, wherein the environmental monitoring device comprises: the system comprises a rain and snow coverage identification module, a weather prediction module and a traffic flow counting module; the rain and snow coverage identification module, the weather prediction module and the traffic flow counting module are all connected with the data processing device.
9. The system of claim 7, wherein the guidance terminal comprises: the system comprises a positioning module, a terminal data module, a map module, a display module and an odometer charging module; the positioning module, the terminal data module, the map module, the display module and the mileage charging module are all connected with the data processing device.
10. The system of claim 7, further comprising an accident early warning module for comparing the traffic data at the lane level of each road segment with the traffic data at the expected lane level of each road segment.
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