CN114566050A - Tunnel robot inspection speed control method for traffic operation safety - Google Patents
Tunnel robot inspection speed control method for traffic operation safety Download PDFInfo
- Publication number
- CN114566050A CN114566050A CN202210224890.9A CN202210224890A CN114566050A CN 114566050 A CN114566050 A CN 114566050A CN 202210224890 A CN202210224890 A CN 202210224890A CN 114566050 A CN114566050 A CN 114566050A
- Authority
- CN
- China
- Prior art keywords
- speed
- lane
- robot
- traffic flow
- calculating
- 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.)
- Granted
Links
- 238000007689 inspection Methods 0.000 title claims abstract description 58
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000012937 correction Methods 0.000 claims description 69
- 229920006395 saturated elastomer Polymers 0.000 claims description 5
- 238000012545 processing Methods 0.000 abstract description 5
- 206010039203 Road traffic accident Diseases 0.000 abstract description 2
- 238000007781 pre-processing Methods 0.000 abstract description 2
- 231100000279 safety data Toxicity 0.000 abstract 1
- 230000002776 aggregation Effects 0.000 description 6
- 238000004220 aggregation Methods 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000000725 suspension Substances 0.000 description 1
Images
Classifications
-
- 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
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C1/00—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people
- G07C1/20—Checking timed patrols, e.g. of watchman
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a tunnel robot inspection speed control method for traffic operation safety, belonging to the field of traffic safety data acquisition, according to the relation between the traffic state and the accident frequency of the traffic accident, the process of ' data acquisition, data preprocessing, data set counting and processing ' and inspection speed adjustment ' is adopted, by collecting the position, lane and speed of the vehicle, the traffic flow state is estimated from the aspects of traffic flow density, speed, flow, collision event, lane speed, lane occupancy and the like, the inspection speed can be adjusted according to the traffic flow state, the inspection speed of the robot is adjusted according to the traffic flow state, a method is provided for controlling the inspection speed of the robot, the robot can run at a high speed when an accident happens, the circulation speed of the robot is shortened, more data are collected, more areas are monitored, and the accident and traffic jam can be found more quickly; and when an accident occurs slowly, the running speed is reduced, and the inspection energy loss of the robot is saved.
Description
Technical Field
The invention relates to the technical field of traffic safety analysis, in particular to a tunnel robot inspection speed control method for traffic operation safety.
Background
The suspension type inspection robot for the long and large tunnel can quickly detect the traffic flow state, predict and study safety risks by a control platform algorithm, interfere the traffic flow, reduce the driving collision probability in advance and improve the driving safety level. The cruising speed of the current inspection robot is single, the speed adjustment according to the traffic flow state cannot be realized, and the road sections with safety risks can be captured in time; on the other hand, the algorithm and the calculation power of the control platform can ensure that the control platform can make a judgment quickly, so that the inspection robot can process information in time according to the requirement of traffic operation safety and make a real-time control strategy. The published documents and patents do not relate to the research of the robot inspection speed control method for traffic operation safety.
Disclosure of Invention
In order to overcome the defects of the prior art, the method is based on the relation between the traffic flow state and the accident occurrence probability, calculates the traffic flow density, speed, flow, lane occupancy difference and lane speed difference on the basis of vehicle position, lane and speed information acquired for many times, analyzes the traffic flow state, adjusts the inspection speed of the robot according to the traffic flow state, provides a method for controlling the inspection speed of the robot, ensures high-speed running when the accident is high, shortens the circulation speed of the robot, acquires more data and finds the accident and traffic flow change more quickly; and when an accident occurs slowly, the running speed is reduced, and the inspection energy loss of the robot is saved.
In order to solve the technical problems, the invention provides a tunnel robot inspection speed control method for traffic operation safety, which is used for performing predictive analysis on traffic conflicts at intersections and comprises the following steps:
step 1: determining the maximum running speed of the robot, the data acquisition time interval and the data set timing interval, and calculating the space range of data acquisition according to the distance between the robot and the road surface and the shooting angle of the robot camera;
step 2: acquiring position, lane and speed information of the vehicle once in each acquisition time interval, numbering the vehicles, and calculating the number of the vehicles, the lane occupancy and the average speed of each lane in the acquisition time interval range;
and 3, step 3: calculating the conflict time among the vehicles and judging the conflict risk according to the acquired position, lane and speed information of the vehicles; repeating the step 2 and the step 3 until the set time interval is reached;
and 4, step 4: counting the annual accident sum of the robot in the current operation interval, calculating the average monthly accident number, and determining an accident correction coefficient;
and 5: counting the traffic flow density, the traffic flow speed and the flow in the set time interval, calculating a traffic flow density correction coefficient, a traffic flow speed correction coefficient and a flow correction coefficient, and calculating a lane occupancy difference coefficient, a lane speed difference coefficient and a risk coefficient;
step 6: and (3) determining the inspection speed of the robot according to the traffic flow density correction coefficient, the traffic flow speed correction coefficient, the flow correction coefficient, the lane occupancy difference coefficient, the lane speed difference coefficient and the risk coefficient, changing the inspection speed of the robot, emptying the vehicle number information, and returning to the step 2.
Further, the spatial range of data acquisition is calculated according to the distance between the robot and the road surface and the shooting angle of the robot camera, and the following formula is adopted:
L＝2H·tanα，
in the formula, L is a spatial range of data acquisition, H is a distance between the robot and a road surface, and α is 1/2 of a shooting angle of a camera equipped with the robot.
Further, the step of collecting the position, lane and speed information of the vehicle once in each collection time interval, numbering the vehicles, and calculating the number of the vehicles, the lane occupancy and the average speed of each lane in the collection range at the moment comprises the following steps:
the robot identifies the vehicles, numbers the identified vehicles, the number of the jth vehicle of the ith lane in the kth acquisition interval is k-i-j, and the total number of lanes is nyAnd collecting the position x of the vehiclek,i,jLane i and speed vk,i,j；
Counting the number of vehicles in the ith lane within the collection time interval range as nk,iCalculating the lane occupancy and the link occupancy of the ith lane according to the following formula:
in the formula, Ok,iIs a lane occupancy of the ith lane, OkThe road section occupancy is shown as l is the saturated vehicle head distance;
preferably, l is 8 m;
calculating the average speed of the lane and the average speed of the road section of the ith lane according to the following formulas:
in the formula, vk,iIs the average speed of the ith lane, vkIs the average speed of the road section.
Further, the step of calculating the collision time between vehicles and judging the collision risk according to the collected position, lane and speed information of the vehicles comprises the following steps:
calculating the time of collision between vehicles according to the following formula, and judging the risk of collision and the time of collision TTC between vehiclesk,i,jAccording to the formula:
in the formula, TTCk,i,jAs time of conflict between vehicles, xk,i,jIs the position of the vehicle, i is the lane, vk,i,jAs the speed of the vehicle, /)cIs a standard vehicle length;
preferably,/cTaking 4.5 m;
the risk of collision between vehicles is judged according to the following model:
furthermore, the operation interval is N.t.V before and after the current position of the robotoWhere N.t is the data set time interval, VoThe current inspection speed of the robot.
Further, the calculating the average number of accidents per month and determining an accident correction coefficient includes:
the monthly average number of accidents was calculated according to the following formula:
in the formula, NmonAverage number of accidents per month, NyearThe number of historical accidents in the last year in the range;
Nmon>6.0 Accident correction factor fsTaking 1.0; 4.0 < NmonLess than or equal to 6.0, and accident correction coefficient fsTaking 0.9;2.0＜Nmonless than or equal to 4.0, and accident correction coefficient fsTaking 0.8; n is a radical ofmonLess than or equal to 2.0, and accident correction coefficient fsTake 0.7.
Further, the calculating a traffic flow density correction coefficient, a traffic flow speed correction coefficient, and a flow correction coefficient, and calculating a lane occupancy difference coefficient, a lane speed difference coefficient, and a risk coefficient includes:
calculating a traffic flow density and a traffic flow density correction coefficient according to the following formula:
wherein K is the traffic flow density, fKFor the correction coefficient of traffic flow density, N is the number of times for repeatedly collecting position, lane and speed information of vehicles to reach the integrated time interval, l is the saturated headway, k is the collection interval number, OkIs road segment occupancy, KjBlocking density for traffic flow;
calculating a traffic flow speed and a traffic flow speed correction factor according to the following formula:
fv＝e-v/v max
wherein v is the traffic flow velocity, fvCorrection factor for traffic flow speed, vmaxLimiting the speed of the road;
calculating the traffic flow and the traffic flow correction coefficient according to the following formula:
Q＝Kv
fQ＝1-Q/C
wherein Q is the traffic flow, fQIs a correction coefficient for the flow rate of the traffic flow,c is the actual traffic capacity of the road;
calculating a lane occupancy difference coefficient according to the following formula:
in the formula (f)O,kFor the difference coefficient of lane occupancy at the time of collecting data for the k-th time, fOIs a coefficient of difference in lane occupancy, nyIs the total number of lanes, OkIs road segment occupancy;
calculating a lane speed difference coefficient according to the following formula:
in the formula (f)cv,kIs the lane speed difference coefficient f at the k-th data acquisitioncvIs a lane speed difference coefficient;
calculating a risk coefficient according to the following formula;
in the formula, frAs risk factor, Nk,lFor the number of low risks in the kth acquisition, Nk,mIs the number of risks in the k-th acquisition, Nk,hFor the number of high risks in the kth acquisition, nk,iThe number of vehicles in the ith lane in the time interval range is collected.
Further, the determining the inspection speed of the robot according to the traffic flow density correction coefficient, the traffic flow speed correction coefficient, the flow correction coefficient, the lane occupancy difference coefficient, the lane speed difference coefficient and the risk coefficient includes:
and calculating the inspection speed of the robot according to the following formula:
in the formula, V is the inspection speed of the robot, fKAs a correction factor for the density of the traffic flow, fvAs a traffic flow speed correction factor, fQAs a flow correction factor, fOIs a lane occupancy difference coefficient, fcvIs a coefficient of lane speed difference, frIs a risk factor, vmaxThe speed limit of the road is obtained.
Compared with the prior art, the tunnel robot inspection speed control method for traffic operation safety has the following technical effects:
1) compared with other robot routing inspection speed control methods for traffic operation safety, the robot routing inspection speed control method has the advantages that the frequency of traffic accidents is higher when the traffic flow state is poor through research, the routing inspection speed can be adjusted according to the traffic flow state, the routing inspection speed is increased when the traffic flow state is poor, the circulation period of the robot is shortened, more traffic data are collected, more areas are monitored, and accidents and traffic jam are found more quickly; when the traffic flow state is good, the inspection speed is reduced, and the energy consumption in operation is reduced.
2) Compared with other robot inspection speed control methods oriented to traffic operation safety, the method adopts the processes of data acquisition, data preprocessing, data aggregation and counting, data aggregation and processing and inspection speed adjustment, and performs aggregation processing on data after multiple times of data acquisition, so that data operation is reduced, calculation and storage cost is saved, the frequency of speed change is reduced, energy loss caused by acceleration and deceleration of the robot is saved, and errors caused by data randomness are avoided through the aggregation processing.
3) Compared with other robot inspection speed control methods for traffic operation safety, the method has the advantages that three traffic flow parameters (traffic flow density, speed and flow) necessary for traffic monitoring are collected, the traffic flow state is identified on the basis of the traffic flow density, the speed and the flow, and the speed of the robot is adjusted according to the traffic flow state.
4) Compared with other robot inspection speed control methods facing traffic operation safety, the method has the advantages that the Time To Collision (TTC) is calculated by using the headway and the speed difference in traffic flow, the risk grade between vehicles is judged, the risk grade is divided into four grades of no risk, low risk, medium risk and high risk, the accident risk coefficient of the traffic flow is estimated according to the quantity of each grade, and finally the inspection speed of the robot is adjusted according to the risk coefficient.
5) Compared with other robot inspection speed control methods oriented to traffic operation safety, the robot inspection speed control method has the advantages that data are acquired and processed in different lanes, the difference among the lanes is considered, the characteristics of each lane are reserved, the average effect in traffic flow density, speed and flow calculation is weakened, the traffic flow state is shown through the difference among the lanes, and the robot inspection speed is adjusted.
Drawings
Fig. 1 is a schematic work flow diagram of a speed control method for a tunnel inspection robot according to an embodiment of the present invention;
fig. 2 is a schematic view of a working principle of the tunnel inspection robot disclosed by the embodiment of the invention.
Detailed Description
In order to better understand the technical content of the present invention, specific embodiments are described below with reference to the accompanying drawings.
As shown in fig. 1, the speed control method for the tunnel inspection robot provided by the invention comprises the following steps:
s1, the robot runs along the track at the top of the tunnel, and the maximum running speed VmaxThe distance between the robot and the road surface is 9m, and the robot is provided with a shooting angle of a camera, wherein the track is parallel to the road surface, the data acquisition time interval t is 10s, the data set timing interval Nt is 5min, the data are collected for 30 times and processed in a set mode, the distance between the robot and the road surface is 9m2 α — 2 × 66 ° -132 °, as shown in fig. 2, the spatial range L of data acquisition is calculated according to the formula:
L＝2H·tanα＝2×9×tan66°＝40m
s2, the robot has performed data acquisition 29 times, and will perform data acquisition 30 (k is 30) th time, and the number of lanes nyAt the 30 th data acquisition, the acquired vehicle number, position, lane and speed are as follows:
TABLE 1 time 30 data acquisition information
The number of vehicles in the 1 st lane in the time acquisition range is n30,1The number of vehicles in the 2 nd lane is n as 430,2The number of vehicles in the 3 rd lane is n as 430,3＝3。
Lane occupancy O of ith lane30,iAnd road segment occupancy rate O30Calculating according to a formula:
O30,2＝0.8，O30,3＝0.6
l is the saturated head space, and the default is 8 m.
Average lane speed v of ith lanek,iAnd the average speed v of the road sectionkCalculating according to a formula:
v30,2＝52km/h，v30,2＝63.67km/h
s3, according to the position x of the vehiclek,i,jLane i and speed vk,i,jCalculating the time to collision TTC between vehiclesk,i,jAnd determining the collision risk and the time to collision TTC between vehiclesk,i,jAccording to the formula:
lcthe default is 4.5m for the standard vehicle length.
Judging the conflict time between vehicles to judge the conflict risk between the vehicles, wherein the judgment condition of the conflict risk is as follows:
because v is30,1,1≥v30,1,2Therefore TTC30,1,1＝∞>2s, no risk; because v is30,1,3<v30,1,4Therefore, it is
1.5s<TTC30,1,3The time is less than or equal to 2.0s, and the risk is low; 1.0s<TTC30,2,31.26s ≤ 1.5s, medium risk; 0.0s<TTC30,1,10.9s is less than or equal to 1.0s, so that the risk is high; rest TTC30,i,jInfinity, no risk.
Thus the number N of low risks in the 30 th acquisition30,l1; number of risks N in 30 th acquisition30,m1; number of high risks N in 30 th acquisition30,h＝1；
Steps S2 and S3 have been repeated 30 times, k is equal to N is equal to 30, the aggregation time interval is reached, and the aggregation processing of the data is started.
S4, the annual accident sum of the running interval of the robot at the current moment, and the current inspection speed V of the robotoThe operation interval is 20km/h, and the current moment of the robot is locatedN.t.V before and after the positiono30 × 10s × 20km/h is 1.67km, which is the range of the number of historical accidents N of the last yearyearAverage number of accidents per month N25monCalculating according to a formula:
Nmon>6.0 Accident correction factor fsTaking 1.0; 4.0 < NmonLess than or equal to 6.0, and accident correction coefficient fsTaking 0.9; 2.0 < NmonLess than or equal to 4.0, and accident correction coefficient fsTaking 0.8; n is a radical ofmon is less than or equal to 2.0, and the accident correction coefficient fsTaking 0.7;
because 2.0 < Nmon2.08 ≤ 4.0, so the accident correction factor fs＝0.8。
S5, counting the traffic flow density, the traffic flow speed and the flow in the time interval of the set, and calculating a traffic flow density correction coefficient, a traffic flow speed correction coefficient and a flow correction coefficient; the data collected at 30 times are shown in the following table:
230 times of collected road data information in table
Density of traffic flow obstructions Kj70p cu/km; correction coefficient f of traffic flow density K and traffic flow densityKCalculating according to a formula:
speed limit v for roadmaxCalculating the traffic flow speed v and the correction coefficient f of the traffic flow speed when the speed is 90km/hvAccording to the formula:
the actual traffic capacity C of the road is 1600pcu/h, the traffic flow Q and the correction coefficient f of the traffic flowQAnd calculating according to a formula:
Q＝Kv＝21.88×52.67＝1152pcu/h
step S5-2, calculating a lane occupancy difference coefficient, a lane speed difference coefficient, and a lane occupancy difference coefficient fOAccording to the formula
Calculating a lane speed difference coefficient fcvAccording to the formula
Number N of low risks from data acquisition 30 times by robotk,lThe number of risks Nk,mAnd a high risk number Nk,hThe amounts are shown in the following table:
TABLE 330 Low, Medium, and high risk statistics from data acquisition
Coefficient of risk frCalculating according to a formula;
s6, determining a robot inspection speed V according to the traffic flow density correction coefficient, the traffic flow speed correction coefficient, the flow correction coefficient, the lane occupancy difference coefficient, the lane speed difference coefficient and the risk coefficient, wherein the robot inspection speed V is calculated according to a formula:
the robot changes the inspection speed, runs at the speed of 16.47km/h, empties the stored vehicle number information in the background, and returns to step S2.
Claims (8)
1. A tunnel robot inspection speed control method for traffic operation safety is characterized by comprising the following steps:
step 1: determining the maximum running speed of the robot, the data acquisition time interval and the data set timing interval, and calculating the space range of data acquisition according to the distance between the robot and the road surface and the shooting angle of the robot camera;
step 2: acquiring position, lane and speed information of the vehicle once in each acquisition time interval, numbering the vehicles, and calculating the number of the vehicles, the lane occupancy and the average speed of each lane in the acquisition time interval range;
and step 3: calculating the conflict time among the vehicles and judging the conflict risk according to the acquired position, lane and speed information of the vehicles; repeating the step 2 and the step 3 until the set time interval is reached;
and 4, step 4: counting the annual accident sum of the robot in the current operation interval, calculating the average monthly accident number, and determining an accident correction coefficient;
and 5: counting the traffic flow density, the traffic flow speed and the flow in the set time interval, calculating a traffic flow density correction coefficient, a traffic flow speed correction coefficient and a flow correction coefficient, and calculating a lane occupancy difference coefficient, a lane speed difference coefficient and a risk coefficient;
step 6: and (3) determining the inspection speed of the robot according to the traffic flow density correction coefficient, the traffic flow speed correction coefficient, the flow correction coefficient, the lane occupancy difference coefficient, the lane speed difference coefficient and the risk coefficient, changing the inspection speed of the robot, emptying the vehicle number information, and returning to the step 2.
2. The method for controlling the inspection speed of the tunnel robot facing traffic operation safety according to claim 1, wherein the spatial range of data acquisition is calculated according to the distance between the robot and the road surface and the shooting angle of the robot camera, and the following formula is adopted:
L＝2H·tanα，
in the formula, L is a spatial range of data acquisition, H is a distance between the robot and a road surface, and α is 1/2 of a shooting angle of a camera equipped with the robot.
3. The method for controlling the inspection speed of the tunnel robot facing traffic operation safety according to claim 1, wherein the method comprises the following steps of collecting the position, lane and speed information of the vehicle once in each collection time interval, numbering the vehicles, and calculating the number of the vehicles, the lane occupancy and the average speed of each lane in the collection range at the moment:
the robot identifies the vehicles, numbers the identified vehicles, the number of the jth vehicle of the ith lane in the kth acquisition interval is k-i-j, and the total number of lanes is nyAnd collecting the position x of the vehiclek,i,jLane i and speed vk,i,j；
Counting the number of vehicles in the ith lane within the collection time interval range as nk,iCalculating the lane occupancy and the link occupancy of the ith lane according to the following formula:
in the formula, Ok,iIs a lane occupancy of the ith lane, OkThe road section occupancy is shown as l is the saturated vehicle head distance;
calculating the average speed of the lane and the average speed of the road section of the ith lane according to the following formula:
in the formula, vk,iIs the average speed of the ith lane, vkIs the average speed of the road section.
4. The tunnel robot inspection speed control method facing traffic operation safety according to claim 1, wherein the method for calculating collision time between vehicles and judging collision risk according to the collected position, lane and speed information of the vehicles comprises the following steps:
calculating the time of collision between vehicles according to the following formula, and judging the risk of collision, the time of collision between vehicles TTCk,i,jAccording to the formula:
in the formula, TTCk,i,jAs time of conflict between vehicles, xk,i,jIs the position of the vehicle, i is the lane, vk,i,jAs the speed of the vehicle,/cIs a standard vehicle length;
the risk of collision between vehicles is judged according to the following model:
5. the method for controlling the inspection speed of the tunnel robot for traffic operation safety according to claim 1, wherein the operation sections are N.t.V before and after the current position of the robotoWhere N.t is the data set time interval, VoThe current inspection speed of the robot.
6. The method for controlling the inspection speed of the tunnel robot facing traffic operation safety according to claim 1, wherein the calculating the average number of accidents per month and determining the accident correction coefficient comprises:
the monthly average number of accidents was calculated according to the following formula:
in the formula, NmonMean number of accidents per month, NyearThe number of historical accidents in the last year in the range;
Nmon>6.0 Accident correction factor fsTaking 1.0; 4.0 < NmonLess than or equal to 6.0, accident correction coefficient fsTaking 0.9; 2.0 < NmonLess than or equal to 4.0, and accident correction coefficient fsTaking 0.8; n is a radical ofmonLess than or equal to 2.0, and accident correction coefficient fsTake 0.7.
7. The method for controlling the inspection speed of the tunnel robot facing the traffic operation safety according to claim 1, wherein the calculating of the traffic flow density correction coefficient, the traffic flow speed correction coefficient and the flow correction coefficient, and the calculating of the lane occupancy difference coefficient, the lane speed difference coefficient and the risk coefficient comprises:
calculating a traffic flow density and a traffic flow density correction coefficient according to the following formula:
wherein K is the traffic flow density, fKFor the correction coefficient of traffic flow density, N is the number of times for repeatedly collecting position, lane and speed information of vehicles to reach the integrated time interval, l is the saturated headway, k is the collection interval number, OkIs road segment occupancy, KjBlocking density for traffic flow;
calculating a traffic flow speed and a traffic flow speed correction factor according to the following formula:
wherein v is the traffic flow velocity, fvCorrection factor for traffic flow speed, vmaxLimiting the speed of the road;
calculating the traffic flow and the traffic flow correction coefficient according to the following formula:
Q＝Kv；
fQ＝1-Q/C；
wherein Q is the traffic flow, fQThe correction coefficient is the traffic flow correction coefficient, and C is the actual traffic capacity of the road;
calculating a lane occupancy difference coefficient according to the following formula:
in the formula (f)O,kFor the difference coefficient of lane occupancy at the time of collecting data for the k-th time, fOAs a lane occupancy difference coefficient, nyIs the total number of lanes, OkIs road segment occupancy;
calculating a lane speed difference coefficient according to the following formula:
in the formula (f)cv,kIs the lane speed difference coefficient f at the k-th data acquisitioncvIs a lane speed difference coefficient;
calculating a risk coefficient according to the following formula;
in the formula (f)rAs risk factor, Nk,lFor the number of low risks in the kth acquisition, Nk,mAs to the number of risks in the k-th acquisition,Nk,hfor the number of high risks in the kth acquisition, nk,iThe number of vehicles in the ith lane in the time interval range is collected.
8. The method for controlling the inspection speed of the tunnel robot facing traffic operation safety according to claim 1, wherein the determining of the inspection speed of the robot according to the traffic flow density correction coefficient, the traffic flow speed correction coefficient, the flow correction coefficient, the lane occupancy difference coefficient, the lane speed difference coefficient and the risk coefficient comprises:
and calculating the inspection speed of the robot according to the following formula:
in the formula, V is the inspection speed of the robot, fKAs a correction factor for the density of the traffic flow, fvAs a traffic flow speed correction factor, fQAs a flow correction factor, fOIs a lane occupancy difference coefficient, fcvIs a coefficient of lane speed difference, frIs a risk factor, vmaxThe speed limit of the road is obtained.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210224890.9A CN114566050B (en) | 2022-03-09 | 2022-03-09 | Tunnel robot inspection speed control method for traffic operation safety |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210224890.9A CN114566050B (en) | 2022-03-09 | 2022-03-09 | Tunnel robot inspection speed control method for traffic operation safety |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114566050A true CN114566050A (en) | 2022-05-31 |
| CN114566050B CN114566050B (en) | 2024-01-26 |
Family
ID=81718206
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210224890.9A Active CN114566050B (en) | 2022-03-09 | 2022-03-09 | Tunnel robot inspection speed control method for traffic operation safety |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114566050B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115762184A (en) * | 2023-01-09 | 2023-03-07 | 江西方兴科技股份有限公司 | Tunnel passing speed control method and device and computer equipment |
| CN117055639A (en) * | 2023-09-04 | 2023-11-14 | 珠海习坎智能科技有限公司 | Self-adaptive speed control method for tunnel inspection robot |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2009106232A (en) * | 2009-02-25 | 2010-08-27 | Юрий Федорович Макаров (RU) | METHOD OF REGULATION OF TRANSPORT FLOWS ON THE HIGHWAY |
| CN103294060A (en) * | 2013-06-05 | 2013-09-11 | 苏州工业园区服务外包职业学院 | Medium-low-speed cable patrol robot control system |
| CN107730937A (en) * | 2017-10-26 | 2018-02-23 | 东南大学 | The tunnel gateway dynamic vehicle speed abductive approach that a kind of street accidents risks minimize |
| CN108280894A (en) * | 2017-12-08 | 2018-07-13 | 浙江国自机器人技术有限公司 | Not parking method for inspecting for power equipment |
| CN109448383A (en) * | 2018-12-24 | 2019-03-08 | 广东创瑜机电工程有限公司 | A kind of tunnel event analysis implementation method based on video flowing |
| KR102021589B1 (en) * | 2019-04-09 | 2019-09-16 | 미성엠프로 주식회사 | Appratus and system for controlling traffic flows using millimeter wave communication facilities and hologram in road tunnel |
| KR102027174B1 (en) * | 2019-06-18 | 2019-11-04 | 주식회사 리트코이엔에스 | A system for controlling entry of a vehicle into tunnel for traffic |
| CN110888423A (en) * | 2018-09-06 | 2020-03-17 | 刘春梅 | Intelligent inspection system for tunnel |
| CN111009122A (en) * | 2019-11-13 | 2020-04-14 | 青岛国信城市信息科技有限公司 | Tunnel traffic operation risk assessment method |
| CN112201057A (en) * | 2020-09-08 | 2021-01-08 | 同济大学 | Expressway vehicle speed and ramp cooperative control method based on accident risk |
| CN112885100A (en) * | 2021-03-11 | 2021-06-01 | 湖南联智科技股份有限公司 | Highway tunnel operation management and control system |
| CN113284345A (en) * | 2021-04-30 | 2021-08-20 | 广州大广高速公路有限公司 | Road tunnel traffic risk assessment system and method |
| CN113504780A (en) * | 2021-08-26 | 2021-10-15 | 上海同岩土木工程科技股份有限公司 | Full-automatic intelligent inspection robot and inspection method for tunnel structure |
| CN113703458A (en) * | 2021-08-31 | 2021-11-26 | 中铁第四勘察设计院集团有限公司 | Obstacle position sensing method and system suitable for tunnel inspection robot |
-
2022
- 2022-03-09 CN CN202210224890.9A patent/CN114566050B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2009106232A (en) * | 2009-02-25 | 2010-08-27 | Юрий Федорович Макаров (RU) | METHOD OF REGULATION OF TRANSPORT FLOWS ON THE HIGHWAY |
| CN103294060A (en) * | 2013-06-05 | 2013-09-11 | 苏州工业园区服务外包职业学院 | Medium-low-speed cable patrol robot control system |
| CN107730937A (en) * | 2017-10-26 | 2018-02-23 | 东南大学 | The tunnel gateway dynamic vehicle speed abductive approach that a kind of street accidents risks minimize |
| CN108280894A (en) * | 2017-12-08 | 2018-07-13 | 浙江国自机器人技术有限公司 | Not parking method for inspecting for power equipment |
| CN110888423A (en) * | 2018-09-06 | 2020-03-17 | 刘春梅 | Intelligent inspection system for tunnel |
| CN109448383A (en) * | 2018-12-24 | 2019-03-08 | 广东创瑜机电工程有限公司 | A kind of tunnel event analysis implementation method based on video flowing |
| KR102021589B1 (en) * | 2019-04-09 | 2019-09-16 | 미성엠프로 주식회사 | Appratus and system for controlling traffic flows using millimeter wave communication facilities and hologram in road tunnel |
| KR102027174B1 (en) * | 2019-06-18 | 2019-11-04 | 주식회사 리트코이엔에스 | A system for controlling entry of a vehicle into tunnel for traffic |
| CN111009122A (en) * | 2019-11-13 | 2020-04-14 | 青岛国信城市信息科技有限公司 | Tunnel traffic operation risk assessment method |
| CN112201057A (en) * | 2020-09-08 | 2021-01-08 | 同济大学 | Expressway vehicle speed and ramp cooperative control method based on accident risk |
| CN112885100A (en) * | 2021-03-11 | 2021-06-01 | 湖南联智科技股份有限公司 | Highway tunnel operation management and control system |
| CN113284345A (en) * | 2021-04-30 | 2021-08-20 | 广州大广高速公路有限公司 | Road tunnel traffic risk assessment system and method |
| CN113504780A (en) * | 2021-08-26 | 2021-10-15 | 上海同岩土木工程科技股份有限公司 | Full-automatic intelligent inspection robot and inspection method for tunnel structure |
| CN113703458A (en) * | 2021-08-31 | 2021-11-26 | 中铁第四勘察设计院集团有限公司 | Obstacle position sensing method and system suitable for tunnel inspection robot |
Non-Patent Citations (3)
| Title |
|---|
| 何璐佳;杨墨;毛杰勇;刘世强;: "隧道机器人巡检系统的关键技术应用", 电子技术与软件工程, no. 02 * |
| 刘雨辰;张恒博;王新科;李铁柱;: "基于毫米波雷达的路侧感知技术在太湖隧道中的应用", 公路交通科技(应用技术版), no. 04 * |
| 吴华;孟伶智;柳长安;杨国田;: "混杂动态系统的电缆隧道巡检机器人防摆控制", 华中科技大学学报(自然科学版), no. 1 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115762184A (en) * | 2023-01-09 | 2023-03-07 | 江西方兴科技股份有限公司 | Tunnel passing speed control method and device and computer equipment |
| CN117055639A (en) * | 2023-09-04 | 2023-11-14 | 珠海习坎智能科技有限公司 | Self-adaptive speed control method for tunnel inspection robot |
| CN117055639B (en) * | 2023-09-04 | 2024-03-29 | 珠海习坎智能科技有限公司 | Self-adaptive speed control method for tunnel inspection robot |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114566050B (en) | 2024-01-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114566050A (en) | Tunnel robot inspection speed control method for traffic operation safety | |
| CN110155046B (en) | Automatic emergency braking hierarchical control method and system | |
| CN104157156B (en) | A kind of highway Dangerous Area speed of a motor vehicle dynamic management method for early warning | |
| CN103895649B (en) | A kind of driver safety driving warning method | |
| CN113487874B (en) | System and method for collecting, identifying and classifying following behavior scene data | |
| CN102419905B (en) | Traffic-wave theory-based traffic influence area determining method of expressway accidents | |
| CN109177982A (en) | Consider the vehicle driving Hazard degree assessment method of driving style | |
| CN110705388A (en) | Target vehicle lane change identification method for auxiliary driving based on prediction feedback | |
| CN103093636B (en) | Expressway turning segment variable velocity-limiting control method | |
| CN110316198A (en) | A kind of safe-guard system and operation method for highway speed-raising | |
| CN111599181A (en) | Typical natural driving scene recognition and extraction method for intelligent driving system test | |
| CN1804928A (en) | Machine vision based estimation method for local geometry of driveway and vehicle location | |
| CN111038502A (en) | Safe vehicle distance pre-estimation, correction, early warning and driving qualification evaluation method and system | |
| EP4169792A1 (en) | Method for recognizing roughness of road on which vehicle is traveling, vehicle and storage medium | |
| CN104442841B (en) | Adhesion control method and system for stably outputting vehicle traction | |
| CN112365706B (en) | Abnormal vehicle distance identification method based on multi-source information | |
| CN105844915A (en) | Method for determining traffic flow fundamental diagram in variable speed limit control state | |
| CN115476861B (en) | Safety evaluation system of intelligent network-connected automobile | |
| CN112530201A (en) | Method and device for selecting right switching lane gap of intelligent vehicle intersection | |
| CN114241754B (en) | Real-time control method based on accident precursor characteristics of highway confluence influence area | |
| CN114038223B (en) | Vehicle crowdsourcing acquisition method and system for road speed limit information | |
| CN109917792B (en) | Autonomous anti-collision system based on unmanned sightseeing electric vehicle | |
| CN106364443A (en) | Collision behavior real-time detection method based on automobile intelligent terminal data | |
| CN114255590B (en) | Traffic operation analysis method based on RFID data | |
| CN114394091B (en) | Vehicle speed control method for traffic vehicles in adaptive cruise system under parallel road scene |
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 |