GB2594368A - Control system and method for dedicated lane for autonomous vehicle - Google Patents
Control system and method for dedicated lane for autonomous vehicle Download PDFInfo
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- GB2594368A GB2594368A GB2107328.3A GB202107328A GB2594368A GB 2594368 A GB2594368 A GB 2594368A GB 202107328 A GB202107328 A GB 202107328A GB 2594368 A GB2594368 A GB 2594368A
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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
- G08G1/0133—Traffic data processing for classifying traffic situation
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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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- 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
- G08G1/015—Detecting movement of traffic to be counted or controlled with provision for distinguishing between two or more types of vehicles, e.g. between motor-cars and cycles
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- 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
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/80—Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
Abstract
A control system and method for a dedicated lane for an autonomous vehicle. The system comprises a vehicle-mounted system, a roadside system, a cloud lane management center, and a road information prompt system. The vehicle-mounted system comprises a vehicle-mounted information processing center (1), an information acquisition device, a vehicle-mounted communication module, a human-computer interaction terminal (4) provided on a manually driven vehicle, and an automatic driving terminal (5) provided on an autonomous vehicle. The roadside system comprises a roadside information processing center (8), a video monitoring module (9), a roadside unit (10), and a second 5G communication module (11). The road information prompt system comprises an information issue center (12), a variable information prompt board (13), a broadcast (14), and a third 5G communication module (15). The cloud lane management center comprises a data processing center (16), a traffic information database (19), and a fourth 5G communication module (18). Different traffic congestion impact factors are assigned for the manually driven vehicle and the autonomous vehicle. Whether to start an autonomous vehicle dedicated lane is decided based on a traffic congestion index and an autonomous vehicle penetration index, so that the traffic efficiency is improved.
Description
DEDICATED LANE CONTROL SYSTEM AND METHOD FOR
AUTONOMOUS VEHICLE
Technical Field
The present invention relates to a dedicated lane control technology, in particular to a control system and method of setting a part of a lane as a dedicated lane for autonomous vehicles according to the level of road congestion and the penetration rate of autonomous vehicles.
Background
The current key technologies of autonomous driving continue to make breakthroughs. As autonomous vehicles run on the road in the future, there will be a traffic situation where autonomous vehicles and manually driven vehicles are mixed. The motivation of artificially driven vehicles is greatly affected by the driver's behavior. Generally, due to the influence of psychological and traffic environmental factors, driving behavior is unpredictable. Compared with manually driven vehicles, autonomous vehicles are not restricted by physiological factors, nor are they affected by emotional factors. Autonomous vehicles rigidly obey traffic rules and abandon many bad driving behaviors, such as frequent lane changes and forced driving. However, autonomous vehicles are difficult to make the most reasonable driving decisions like human drivers under complex traffic conditions. The two types of vehicle will inevitably have contradictions in a mixed environment. Although this contradiction is not obvious when traffic is stable, it will be magnified when traffic is jammed. Therefore, in order to alleviate the contradiction between autonomous vehicles and manual-driving vehicles during rush hours, reduce traffic safety hazards, and improve traffic efficiency, it is necessary to set up dedicated lanes for autonomous vehicles. However, currently common motor vehicle lanes, such as bus lanes and multi-occupant (HOV) lanes, generally exist in the form of fixed intervals and long time periods. When facing a complex and changeable traffic environment, there is often a lack of road resource utilization.
Summary
The purpose of the present invention is to address the problems of common dedicated lanes, and propose a dedicated lane control system and method for autonomous vehicles. According to the dynamic traffic environment, the dedicated lane opening and closing instructions are given to enable autonomous vehicles and manual driving vehicles.
The technical solution adopted by the dedicated lane control system for the autonomous vehicles of the present invention is as follows. It includes a vehicle-mounted system, a roadside system, a cloud lane management center, and a road information prompt system. The vehicle-mounted system includes a vehicle-mounted information processing center, an information collection device, a vehicle-mounted communication module, a human-computer interaction terminal installed on a manually driven vehicle and an autonomous driving terminal installed on an autonomous vehicle. The information collection device includes a GPS/Beidou navigation and positioning device and a vehicle-mounted binocular camera. The vehicle-mounted communication module includes a vehicle-mounted unit and a first 5G communication module. The GPS/Beidou navigation and positioning device transmits the vehicle's coordinates C and real-time vehicle speed V to the vehicle-mounted information processing center, and the vehicle-mounted binocular camera transmits the acquired image data P to the vehicle-mounted information processing center, human-computer interaction terminal releases navigation information M, and the autonomous driving terminal sends the working status information S to the vehicle-mounted information processing center and receives different control commands I; the vehicle-mounted unit packs its own vehicle ID and the information A output by the vehicle-mounted information processing center into a signal D and sends it to a roadside unit, the first 5G communication module receives a signal U sent by a fourth 5G communication module and decodes it into an information H and sends it to the vehicle-mounted information processing center; the roadside system includes a roadside information processing center, a video monitoring module, a roadside unit and a second 5G communication module, the video monitoring module sends a video information R to the roadside information processing center, the roadside unit decodes a signal D into data J and sends the data.1 to the roadside information processing center, and the roadside information processing center analyzes and processes the data J to obtain an information N, collects an information L of illegal vehicles on the road, and sends the information N and the information L to the second 5G communication module. The second 5G communication module encodes the received information N to obtain a signal F and send it to the fourth 5G communication module, the illegal vehicle information L is encoded to obtain a signal E and sent it to a third 5G communication module; the road information prompt system includes an information release center, a variable information prompt board, a broadcast and the third 5G communication module, the third 5G communication module decodes the signal U and the signal E into a road prompt information Q and sends it to the information release center. The information is released through the variable information prompt board and broadcast announcements; the cloud lane management center includes a processing center, a traffic information database and the fourth 5G communication module. The fourth 5G communication module decodes the signal F into data W and sends the data W to the processing center. The processing center calculates a traffic congestion index co and autonomous vehicles penetration index Iv of each road, and a dedicated lane opening and closing information B and a road congestion information K are obtained, packaged into the signal U and sent to the first 5G communication module and the third 5G communication module, and each road traffic information is stored in the traffic information database.
The technical solution adopted by the control method of the dedicated lane control system for the autonomous vehicles includes the following steps: Step 1): the vehicle-mounted information processing center collects an information, and sends the vehicle ID, vehicle coordinates C, and real-time vehicle speed information V to the roadside unit through the vehicle-mounted unit; Step 2): the processing center calculates the average number of vehicle kilometers in the mixed traffic flow of each road segment during the Tk period according to the formula ck = a MI Tr rf(t) ictt -FR Ett.i fog.°5 12/Writ a is the congestion factor of manually driven vehicles, p is the m=n congestion factor of autonomous vehicles, and m is the manually driven vehicle m is the real-time vehicle speed of the autonomous vehicle, m is the traffic volume of the manually driven vehicle, n is the traffic volume of the autonomous vehicle, and t is the length of the time; Step 3): according to the formula Co = --1 the processing center calculates the traffic ck congestion index co of the road section during the period, which is the average number of vehicle kilometers of each road section under the condition of free flow speed, and is the weight coefficient of each road section length; Step 4): the processing center ranks the congestion degree of the road according to the numerical interval of the traffic congestion index W, and obtains the road congestion degree information K; calls the reference data Z of the road traffic information of the previous week in the traffic information database to get the time periods when the traffic congestion index showed an upward trend and a downward trend. The road sections(u) E (1, 1.75)) were selected as the candidate road sections to be opened for dedicated lanes of autonomous driving vehicles.
Step 5): the processing center calculates the penetration index w of each candidate road E.,Li fo0.0 5 vim at section according to the formula _ According to the penetration index each sk(ri+m) candidate road section was classified according to the number of lanes, and the dedicated lanes for autonomous driving vehicles were set up. Subsequently, the open and close state information B of the dedicated lane is generated and the dedicated lane of the autonomous driving vehicle is opened. The information B of the opening and closing status of the dedicated lane and the information K of road congestion are packaged together into signal U and sent to the first SG communication module and the third SG communication module.
Further, after the dedicated lane for autonomous vehicles is open, the vehicle-mounted information processing center receives the information H sent by the second 5G communication module to decode the signal U. The vehicle-mounted system uses the GPS/Beidou navigation and positioning device and the vehicle-mounted binocular camera re-plans the optimal navigation path for the vehicle. The roadside information processing center monitors the illegal vehicles on the road in real time by analyzing the video information R from the video monitoring module, and sends the illegal vehicle information L to the road information prompt system. The opening and closing state information B and the road congestion degree information K are posted on the variable information prompt board and prompted by broadcasting.
Further, the processing center divides the previous road section adjacent to the opening section of the dedicated lane into a buffer section and a mixed section. Vehicles drive on the buffer section and drive on the mixed section; the vehicle processing center provides human-computer interaction terminals on different sections. Or the autonomous driving terminal sends different instructions.
Further, in the opening section of the dedicated lane for autonomous vehicles, for autonomous vehicles that have not yet entered the dedicated lane, their autonomous driving terminal will drive according to the expected speed and desired corner after receiving the control instructions from the vehicle-mounted information processing center, and then enter the dedicated lanes. For manually driven vehicles that are already driving on the dedicated lanes for autonomous vehicles, the human-computer interaction terminal will push real-time information to the driver through the vehicle-mounted tablet after receiving the information from the vehicle-mounted information processing center; in the buffer section, autonomous driving After receiving the control instructions from the vehicle-mounted information processing center, the terminal drives according to the expected vehicle speed and desired corner. After the human-computer interaction terminal receives the information from the vehicle-mounted information processing center, the driver starts to change lanes according to the prompts pushed by the vehicle-mounted tablet.
Further, the processing center monitors the changes in the traffic congestion index o of the open section of the dedicated lanes for autonomous vehicles in real time. When the time reaches the starting period when the traffic congestion index shows a downward trend, the dedicated lanes which traffic congestion index in the range of o.) E (0, 1) is closed.
Further, it also includes the approximate optimal solution of the coefficient a, /3. Their values are as follows: entering the initial value of the parameter in the processing center 16, calculating the average vehicle delay AVD value this week, introducing an exponential function a = exp (x) for the calibration of the parameter, and start each new cycle, the coefficient is accumulated by 0.05, and update the value of the parameter a according to the formula a = exp (x).The value of /3 updated by the constraint a + fit = 1, and the AVD in a period will be calculated. If the AVD in the period is less than or equal to the AVD in the previous period, the coefficient sum will continue to be updated in the next period, otherwise the value of the coefficient sum will be output. The value of the coefficient sum obtained at this time is the approximate optimal solution of the coefficient sum suitable for the road network.
The advantages of the present invention after adopting the above technical solution are: 1. The present invention is based on urban roads and used in one direction during the morning and evening traffic peak hours on working days. It can effectively solve the contradiction between autonomous vehicles and manual driving vehicles in the course of driving in dense traffic environments, and make the mixed traffic flow more stable. It ensures road traffic safety and improve road traffic efficiency.
2. The present invention combines the advantages of 5G communication (LIE -V) and dedicated short-range communication technology (DSRC) to realize multi-dimensional communication between the vehicle and the roadside system, the vehicle and the cloud lane management center, and the roadside system and the cloud lane management system. It enhanced the speed and accuracy of the dedicated lane control for autonomous driving.
3. The invention is different from the traditional setting method of fixed interval or long timing lane. The cloud lane management center determines the opening time and location of the automatic driving lane through real-time analysis and calculation, which has stronger flexibility and is more targeted for alleviating traffic congestion.
4. The present invention uses the traffic information database of the cloud lane management center to regularly store and update the traffic information of each road in each time period of the week, and is used as a reference index for the opening of a dedicated lane for autonomous vehicles in the next week, which has strong timeliness.
5. The present invention assigns different traffic congestion influence coefficients to these two types of vehicles based on the driving characteristics of manually driven vehicles and autonomous vehicles. Then determines whether to turn on the autonomous vehicles according to the calculated traffic congestion index and the penetration index of autonomous vehicles. The dedicated lane reflects the accuracy of the present invention in road condition analysis and dedicated lane control.
6. The present invention classifies different driving scenarios that appear on the road after the dedicated lanes for autonomous vehicles are opened, and adopts different methods for control and management for different scenarios, thereby improving the safety and traffic efficiency
Brief Description of the Drawings
The present invention will be described in further detail below in conjunction with the drawings and specific embodiments.
FIG. 1 is a structural block diagram of a dedicated lane control system for autonomous vehicles of the present invention; FIG. 2 is a flow chart of a control method of a dedicated lane control system for autonomous vehicles of the present invention; FIG. 3 is a schematic diagram of a scene when the present invention is implemented; FIG. 4 is a flow chart of obtaining parameters and approximate optimal solutions according to the present invention; In the drawings: 1. Vehicle-mounted information processing center; 2. GPS/Beidou navigation and positioning device; 3. Vehicle-mounted binocular camera; 4. Human-computer interaction terminal; 5. Autonomous driving terminal; 6. Vehicle unit; 7. First 5G communication module; 8. Roadside information processing Center; 9. Video Monitoring module; 10. Roadside unit; 11. Second 5G communication module; 12. Information release center; 13. Variable information prompt Board; 14. Broadcast; 15. Third 5G communication module; 16. Processing center; 17. Fourth 5G communication module; 18. Traffic information database.
Detailed Description of the Embodiments
The present invention will be further described below with reference to the drawings and specific embodiments, but the protection scope of the present invention is not limited to this.
As shown in Fig. 1, a dedicated lane control system for autonomous vehicles of the present invention includes a vehicle-mounted system, a roadside system, a cloud lane management center, and a road information prompt system. Among them, the cloud lane management center is used to receive road information and vehicle information, and decide whether to open a dedicated lane for autonomous vehicles during rush hours based on traffic congestion and autonomous vehicles penetration, and close the dedicated lane for autonomous vehicles at the end of the traffic rush. The roadside system conducts real-time information interaction with the road vehicle on which it is located, sends the road information and vehicle information obtained on the road where it is located to the cloud lane management center, and receives the opening and closing instructions of the dedicated lane for autonomous vehicles from the cloud lane management center. On the one hand, the vehicle-mounted system is connected to the cloud lane management center, uploads navigation information to the cloud lane management center, and transmits the received instructions to the autonomous driving operation terminal or the driver to control the operation of the vehicle. On the other hand, it is connected to the roadside system. Receive information from the roadside system. The road information prompt system receives the signal from the cloud lane management center and announces the lane change information to prompt the manual driving vehicle to change lanes.
Among them, the vehicle-mounted system includes a vehicle-mounted information processing center 1, an information collection device, a vehicle-mounted communication module, a human-computer interaction terminal 4 installed on a manually driven vehicle, and an autonomous driving terminal 5 installed on an autonomous vehicle. Information collection devices and vehicle-mounted communication modules are installed on both autonomous and manually driven vehicles. The information collection device, the vehicle-mounted communication module, the human-computer interaction terminal 4 and the autonomous driving terminal 5 are all bidirectionally interconnected with the vehicle-mounted information processing center 1.
The information collection device includes GPS/Beidou navigation and positioning device 2 and vehicle-mounted binocular camera 3. The vehicle-mounted communication module includes the vehicle-mounted unit 6 and the first SG communication module 7.
GPS/Beidou navigation and positioning device 2, vehicle-mounted binocular camera 3, human-computer interaction terminal 4, and autonomous driving terminal 5 collect various information in the car. Among them, the GPS/Beidou navigation and positioning device 2 is used to locate a vehicle in motion, and transmit the vehicle's coordinates C and real-time vehicle speed V to the vehicle-mounted information processing center 1 in real time. The vehicle-mounted binocular camera 3 is used to collect and record road conditions, identify obstacles, vehicles, lane lines, pedestrians and other road information on the road, and transmit the acquired image data P to the vehicle-mounted information processing center 1. The human-computer interaction terminal 4 is used to release vehicle navigation information M to the driver, and the driver can select a destination for navigation through voice or touch commands 0. The autonomous driving terminal 5 sends the working status information S of the various control systems in the vehicle to the onboard information processing center 1 in real time, and continuously receives different control commands I from the onboard information processing center 1 to change the driving direction and speed of the autonomous vehicle.
The navigation information M includes road congestion information K, dedicated lane opening and closing state information B, the current optimal route, and the current optimal vehicle speed.
The vehicle-mounted unit 6 and the first 5G communication module 7 acquire information outside the vehicle. Among them, the vehicle-mounted unit 6 packs its own vehicle ID information and the information A (vehicle coordinates C, real-time vehicle speed V) output from the vehicle-mounted information processing center 1 into a signal D, and sends it to the roadside system through a dedicated short-range communication technology (DSRC). Side unit 10 in. The first 5G communication module 7 communicates with the fourth 5G communication module 17 in the cloud lane management center through the 50 network, and receives the signal U sent from the fourth 5G communication module 17. The signal U contains the dedicated lane opening and closing status information B and the road Congestion degree information K, namely U= (13; K}, and at the same time decode the signal U into information H, and send it to the vehicle-mounted information processing center 1.
The information A output by the vehicle-mounted information processing center 1 includes vehicle coordinates C and real-time vehicle speed V. The signal D contains the vehicle ID, vehicle coordinates C and real-time vehicle speed V, namely D= {1D; C; V}.
The vehicle-mounted information processing center 1 is the core of the vehicle-mounted system, which transmits instructions to other modules of the vehicle-mounted system by collecting and fusing information from inside and outside the vehicle.
The roadside system includes: a roadside information processing center 8, a video monitoring module 9, and a roadside communication module. The output end of the video monitoring module 9 is connected to the input end of the roadside information processing center 8, and the roadside information processing center 8 and roadside communication Two-way interconnection of modules. The roadside communication module includes a roadside unit 10 and a second 5G communication module I I. The video monitoring module 9 monitors road traffic conditions in real time, and sends the video information R to the roadside information processing center 8 in real time. The roadside unit decodes the signal D obtained by communicating with the vehicle-mounted unit 6 into data J, and sends it to the roadside information processing center 8. The roadside information processing center 8 analyzes and processes the data J, the vehicle ID, the vehicle coordinate C, and the real-time vehicle speed information V to be obtained to perform statistics and classification to obtain the information N. The information N includes the traffic volume of manual driving vehicles m, the traffic volume of autonomous vehicles n, the real-time speed ui of each manual driving vehicle, the real-time speed uj of each autonomous vehicle, and the coordinates of all vehicles C, That is, N= Im; n; ui; m; C At the same time, the roadside information processing center 8 collects the illegal vehicle information L on the road by analyzing the video information R, and sends the information N and the information L to the second 5G communication module 11. The second 5G communication module 11 encodes the received information N to obtain a signal F, and sends the signal F to the fourth 5G communication module 17 of the cloud lane management center, and at the same time encodes the received information L to obtain a signal E= {L}, the signal E is sent to the third 5G communication module 15 in the road information prompt system.
The road information prompt system includes: an information release center 12, a variable information prompt board 13, a broadcast 14, and a third 50 communication module 15. The output terminal of the third 5G communication module 15 is connected to the input terminal of the information publishing center 12, and the output terminal of the information publishing center 12 is connected to the variable information prompt board 13 and the broadcast 14 respectively. Among them, the third 50 communication module 15 receives the signal U from the fourth 50 communication module 17 and the signal E from the second 5G communication module II, and decodes these signals into road prompt information Q, which includes road congestion. Information K, dedicated lane opening and closing state information B, and illegal vehicle information L, namely Q= {K; B; LI. The second 5G communication module 11 sends the road prompt information Q to the information release center 12. The information release center 12 announces lane opening and closing information and road condition information through the variable information prompt board 13 and the broadcast 14. The variable information prompt board 13 receives the data signal X from the information release center 12, and announces the opening and closing information of the autonomous driving lane and the road congestion information at the intersection ahead in text form to encourage the safe driving of the vehicle. The broadcaster 14 receives the data signal Y from the information release center 12, and announces the opening and closing information of the autonomous driving lane and the road congestion information at the intersection ahead through voice broadcast, so as to promote safe driving of the vehicle.
The cloud lane management center includes: a processing center 16, a traffic information database 18, and a fourth 5G communication module 17. The processing center 16 and the traffic information database 18 and the fourth 5G communication module 17 all realize two-way interconnection.
The fourth 50 communication module 17 receives the signal F (F= fm; n; Cll sent from the second 50 communication module 11, decodes it into data W, and sends it to the processing center 16. The processing center 16 packages the dedicated lane opening and closing state information B and the road congestion information K into a signal U (U= {B; K)), and sends it to the first 50 communication module 7 and the third 50 communication module 15. The processing center 16 integrates the data W, analyzes the real-time road conditions of each road, and calculates the traffic congestion index co and the autonomous vehicles penetration index ty of each road in real time, and obtains road sections that comply with the opening and closing of the dedicated lanes for autonomous vehicles, and The dedicated lane opening and closing information B and the road congestion information K are sent to the fourth 5G communication module 17, and the processed road traffic information G is stored in the traffic information database 18 at the same time. The traffic information database 18 will store and update the traffic information of each road in each time period of the week, calculate the average traffic congestion index of each time period in the previous week, and obtain statistics of the average traffic congestion index trend graph of each road in each time period of the week. Analyze the time period when the traffic congestion index shows an upward trend and the time period when the traffic congestion index shows a downward trend, and send the reference data Z of this traffic information to the processing center 16 as a reference for the opening and closing of the dedicated lanes for autonomous vehicles index.
With reference to Fig. 2, when a dedicated lane control system for autonomous vehicles of the present invention works, first, the cloud lane management center sets some lanes in congested urban roads as dedicated lanes for autonomous vehicles through analysis and calculation. At the same time, the cloud lane management center communicates the lane opening instruction to the road information prompt system. The road information prompt system controls the variable lane lights to turn on through the information release center, and uses the variable information prompt board and broadcast to control the vehicles driving on the road. prompt. After the dedicated lane is opened, the cloud lane management center, roadside system, and road information prompt system will work together to convey lane opening and closing information to autonomous vehicles and manually driven vehicles, and vehicles on the road will drive in accordance with regulations. As time goes by, after the traffic peaks pass, the road traffic conditions tend to stabilize. At this time, the cloud lane management center closes the dedicated lanes for autonomous vehicles and prompts the vehicles through the roadside system and the road information prompt system. At this point, the vehicle can change lanes freely, and the manually driven vehicle and the autonomous vehicles start to mix. The specific working process is as follows: Take the morning peak of a working day in a city as an example: the current time period is Tk, there are X road segments in the city, and the length of each road segment is recorded as, where iE (1, X). Manually driven vehicles and autonomous vehicles drive on various road sections in the city. The roadside system of each road section monitors the traffic information of the road section every 3 minutes. The day can be divided into 480 time periods, denoted as Ti -> T480.
All vehicles on each road section collect and integrate various information from inside and outside the vehicle through the vehicle-mounted information processing center 1, and send their own vehicle ID, vehicle coordinates C, and real-time vehicle speed information V to the road side of each road section through the vehicle-mounted unit 6. In the roadside unit 10 of the system, the signal D= {113; C; V} is sent to the roadside unit 10 of the roadside system of each road section. The roadside unit 10 decodes the signal D= {ID; C; V} into data J and sends it to the roadside information processing center 8.
The roadside information processing center 8 of the roadside system of each road section analyzes and processes the data J from the roadside unit 10, and performs statistics and classification of the vehicle ID, vehicle coordinates C, and real-time vehicle speed information V to be obtained, thereby obtaining information N (manual The traffic volume of driving vehicles m, the traffic volume of autonomous vehicles n, the real-time speed a(t) of each manually driven vehicle, the real-time speed a(t) of each autonomous vehicles, the coordinates of all vehicles C), N =I'm; n; a; a; CI. At the same time, the roadside information processing center 8 of each road section collects the illegal vehicle information L on the road by analyzing the video information from the video monitoring module 9.
The roadside system of each road section encodes the information N through the second 5G communication module 11 to obtain a signal F, and sends the signal F to the fourth 5G communication module 17 of the cloud lane management center. At the same time, the information L is encoded to obtain the signal E, and the signal E is sent to the third 5G communication module 15 of the road information prompt system.
The processing center 16 of the cloud lane management center receives the decoded data W sent from the second 5G communication module Ii (manually driven vehicle traffic m, autonomous vehicles traffic n, real-time vehicle speed a of each manually driven vehicle, each The real-time vehicle speed uj of an autonomous vehicle, all vehicle coordinates C, calculate the average vehicle-kilometers of the mixed traffic flow of each road segment in the Tk period according to the decoded data W, the calculation formula is as follows: 0.05 0.05 a rin_ fo vi (t)dt + )6' f 0 vi(t)dt ck = Among them: a is the influence coefficient of artificially driven vehicle congestion degree, f3 is the influence coefficient of autonomous vehicles congestion degree, the unit of real-time vehicle speed a of manually-driven vehicle and real-time a of autonomous vehicles is km/h; the duration of this period t is 0.05 hours.
Among them, the initial value of a is 0.5, the initial value of /3 is also 0.5. And according to the current related research, because autonomous driving has better car-following characteristics, compared with manual driving, its contribution to traffic congestion is less, that is a /3. Therefore, in the following content, the continuous value of the parameters and will be calibrated.
The processing center 16 uses the ratio of the average number of vehicle kilometers of each road section under the condition of free flow speed 1)1 to the average number of vehicle kilometers Ck of the mixed traffic flow during the Tk period to be corrected according to the length of the road section as the traffic congestion index of the road section during the period W. Among them, the formula = 0.05h, * for calculating the average number of vehicle m = n kilometers under the condition of free flow speed is, h is the length of the road section; the calculation formula for the weight coefficient of each road section length is, the correction coefficient is, and the calculation formula for the traffic congestion index co is. Among them, is the length of the road section, X is the total number of road sections, i E (1,X), the duration of this period is 0.05 hours.
The processing center 16 ranks the congestion degree of the road according to the numerical range of the traffic congestion degree index co, and obtains road congestion degree information K. Among them, the meaning of different values of the traffic congestion index is shown in the following table: Value range Road congestion rating co < 1 Smooth 1 < oi < 1.75 Mild congestion w > 1.75 Moderate and severe congestion The processing center 16 of cloud Lane Management Center calls the reference data Z of traffic information of urban road in the last week in traffic information database 18, and the time periods when the traffic congestion index is on the rise and the traffic congestion index is on the decline are respectively recorded as T,11 T02 and Td -Vrd2.
The cloud lane management center processing center 16 real-time record the dynamic changes of the traffic congestion index, when the moment arrives at the beginning period T011 where the congestion index shows an increasing trend, select the section(a) c (1,1.75)) as a candidate section to open the dedicated lane for autonomous vehicles. At the same time, the processing center according to decode data from each candidate section 16 W, real-time calculation of each candidate road bits of autonomous vehicles penetration index, the calculation formula is: r7L1 fowls vi (t)dt = Sk(m+ n) Processing center 16 classifies each candidate road section according to the calculated penetration index LP of autonomous vehicles according to the number of one-way lanes, and sets up special lanes for autonomous vehicles. For a one-way road with N lanes, > -n, set up a dedicated lane.
14) > set up two dedicated lanes. n -1
qi > ,setup (n -1) dedicated lanes.
The processing center 16 of the cloud lane management center generates the opening and closing status information B of the dedicated lane for autonomous vehicles according to the selected road sections meeting the opening conditions of the dedicated lane for autonomous vehicles. At the same time, the opening and closing status information B of the special lane and the obtained road congestion information K are packaged into a signal U (U = 03; K1) and sent to the vehicle-mounted system. The first 5G communication module 7 and the third 5G communication module 15 of the road information prompt system.
After the special lane of autonomous vehicles is opened, on the one hand, after receiving the information h sent by the second 50 communication module 7 to decode the signal U into H, the vehicle-mounted system uses GPS/Beidou navigation and positioning device 2 and vehicle-mounted binocular camera 3 to re plan the optimal navigation path for the vehicle according to the destination and vehicle type. Among them, the autonomous driving terminal 5 of the autonomous vehicles will follow the optimal navigation path, and the driver of the manual driving vehicle will receive the prompt from the human-computer interaction terminal 4. On the other hand, the roadside information processing center 8 of the roadside system continuously monitors the illegal vehicles on the road in real time by analyzing the video information r from the video monitoring module 9, and sends the illegal vehicle information 1 to the road information prompt system. Third, the information release center 12 of the road information prompt system publishes the received special lane opening and closing status information B and road congestion information K on the variable information prompt board 13, and prompts the vehicles along the way through broadcast 14.
The processing center 16 of the cloud Lane Management Center divides the last section adjacent to the open section of the dedicated lane into buffer section and mixed traffic section. The vehicles on the road drive according to the instructions in the buffer section and drive freely in the mixed road section. The vehicle-mounted information processing center 1 of autonomous vehicles and manual driving vehicle will send different instructions to human-computer interaction terminal 4 or autonomous driving terminal 5 at different road sections.
As shown in FIG. 3, the cloud Lane Management Center, roadside system and vehicles communicate through the multi -dimensional communication system composed of 50 communication (LTE -V) and dedicated short-range communication technology (DSRC) as shown in FIG. 1. The following four driving scenarios will appear in the driving process of vehicles in the open section of the dedicated lane and the mixed road section, respectively: The first kind: in the opening section of the special lane for autonomous vehicles, for the autonomous vehicles that have not entered the special lane, the autonomous driving terminal 5 will drive according to the expected speed and the desired corner after receiving the control command from the vehicle processing center 1, and start to enter the special lane for autonomous vehicles.
The second kind: in the open section of the dedicated lane for autonomous vehicles, the human-computer interaction terminal 4, after receiving the information from the vehicle-mounted information processing center I, pushes the real-time information for the driver through the vehicle-mounted panel. The information includes: Special Lane opening and closing status information B prompt, road congestion information ahead Information K, the optimal path of current driving, the optimal speed of current driving. After receiving the special lane opening and closing status information B prompt, the driver must orderly leave the special lane of the autonomous vehicles according to the push information.
The third kind: on the buffer section, the autonomous driving terminal 5 of the autonomous vehicles drives according to the expected speed and the expected angle after receiving the control command from the vehicle-mounted information processing center 1, changes lanes in advance and prepares to enter the special lane for autonomous vehicles.
The fourth kind: in the buffer section, after receiving the information from the vehicle-mounted information processing center I, the driver starts to change lanes according to the prompts pushed by the vehicle-mounted panel, and it is forbidden to enter the special lane for autonomous vehicles.
For other vehicles that have been driving in the specified lane, they must maintain a reasonable speed, continue to drive in the current lane, and do not change lanes.
The processing center 16 of the cloud Lane Management Center monitors the change of traffic congestion index o of the open section of the dedicated lane for autonomous vehicles in real time. When the time reaches Tod, when the traffic congestion index shows a downward trend, the processing center 16 of the cloud Lane Management Center starts to close the autonomous driving lane of the road section with the traffic congestion index w 6(0,1), and through the fourth 5G communication module 17 At this time, autonomous vehicles and manual driving vehicles start to mix in these sections.
As shown in FIG. 4, after the dedicated lane is closed, the traffic efficiency of the road network during the opening period of the dedicated lane on that day is analyzed. The traffic efficiency of the road network is represented by the average vehicle delay (AVD). Among them, AVD is the average value of the difference between the actual travel time of all vehicles and the travel time calculated at the free flow rate. The daily AVD calculation formula can be expressed as Er ZE-i(ta-ti)+E7j., 0.-ti formula AVD - . The value of a will affect the change of the traffic El-B:(n+n) congestion index, which will affect the change of the opening timing of the dedicated lane, and ultimately lead to the change of the AVD. For a specific road network in a city, because its traffic flow data shows strong regularity in a continuous time series, and there are obvious similarities in the traffic flow data between weeks, the week number q can be taken as parameter update cycle. Takes the value of the parameter a and /3 as input, and the weekly AVD statistics as the output, with the goal of minimizing AVD. Through the continuous value of the parameter sum, the parameters and approximations suitable for the road network are obtained by analysis Optimal solution; the specific process is: Enter the initial value of the parameter in the processing center 16, a = = 0.5 The exponential function a = exp (x) is introduced for parameter update. Next, calculate the AVD for this week and record the specific value of the AVD. In order to balance the efficiency of parameter update and the accuracy of the optimal solution, for the beginning of each new cycle, the value of x adds 0.05, And get the value of the new parameter a based on the exponential function.
Meanwhile, the value of the new parameter /3 can be calculated by the function a + /3 = 1. Next, The AVD in the period is obtained by calculation. If the AVD in the period is less than or equal to the AVD in the previous period, the parameters will continue to be updated in the next period, otherwise the value of the parameter will be output.
Claims (11)
- Claims What is claimed is: 1. A dedicated lane control system for an autonomous vehicle, comprising a vehicle-mounted system, a roadside system, a cloud lane management center, and a road information prompt system, characterized in that the vehicle-mounted system comprises a vehicle-mounted information processing center (1), an information collection device, a vehicle-mounted communication module, a human-computer interaction terminal (4) installed on a manual driving vehicle, and an autonomous driving terminal (5) installed on an autonomous vehicle, wherein the information collection device comprises a GPS/13eidou navigation and positioning device (2) and a vehicle-mounted binocular camera (3), the vehicle-mounted communication module comprises a vehicle-mounted unit (6) and a first 5G communication module (7); the GPS/Beidou navigation and positioning device (2) transmits the vehicle's coordinates C and real-time vehicle speed V to the vehicle-mounted information processing center (1), the vehicle-mounted binocular camera (3) transmits acquired image data P to the vehicle-mounted information processing center (1), the human-computer interaction terminal (4) releases a navigation information M, and the autonomous driving terminal (5) transmits the working status information S is sent to the vehicle-mounted information processing center ( I) and receives different control commands I; the vehicle unit (6) packs its own vehicle ID and the information A output by the vehicle-mounted information processing center (1) into a signal D and sends it to the roadside unit (10), the first 5G communication module (7) receives the signal U sent by the fourth 5G communication module (17), decodes it into information H, and sends it to the vehicle-mounted information processing center (01 the roadside system comprises a roadside information processing center (8), a video monitoring module (9), a roadside unit (10) and a second 50 communication module (11). The video monitoring module (9) sends video information R to the roadside information processing center (8), the roadside unit (10) decodes the signal D into data J and sends it to the roadside information processing center (8), the roadside information processing center (8) analyzes and processes the data J to obtain information N, collect the information L of the illegal vehicle on the road, and send the information N and the information L to the second 5G communication module (11), and the second 5G communication module (11) encodes the received information N to obtain the signal F and send it to the fourth 5G communication module (17), encode the illegal vehicle information L to obtain a signal E and send it to the third 5G communication module (1 5);the road information prompt system comprises an information release center (12), a variable information prompt board (13), a broadcast (14) and a third 50 communication module (15). The third 50 communication module (15) combines signals U and the signal E is decoded into road prompt information Q and sent to the information release center (12), and the information release center (12) announces through the variable information prompt board (13) and broadcast (14); The cloud lane management center comprises a processing center (16), a traffic information database (18), and a fourth 5G communication module (17). The fourth 5G communication module (17) decodes the signal F into data W and sends it to the data The processing center (16) and the processing center (16) calculate the traffic congestion index co and the autonomous vehicles penetration index p of each road, and obtain the dedicated lane opening and closing information B and road congestion information K, which are packaged into a signal U and sent To the first 50 communication module (7) and the third 5G communication module (15), each road traffic information is stored in the traffic information database (18).
- 2. The dedicated lane control system for the autonomous vehicles according to claim 1, wherein the navigation information M comprises road congestion information K, dedicated lane opening and closing state information B, the current optimal path, and the current optimal vehicle speed.
- 3. The dedicated lane control system for the autonomous vehicles according to claim 1, wherein the information A of the vehicle comprises vehicle coordinates C and real-time vehicle speed V.
- 4. The dedicated lane control system for the autonomous vehicles according to claim 1, characterized in that: the information N comprises manual driving vehicle traffic volume m, autonomous vehicles traffic volume n, each time period of each road, the real-time vehicle speed vi of a manually driven vehicle, the real-time vehicle speed vj of each autonomous vehicle and all the vehicle coordinates C.
- 5. The dedicated lane control system for the autonomous vehicles according to claim I, wherein the road prompt information Q comprises road congestion information K, dedicated lane opening and closing state information B, and illegal vehicle information L.
- 6. A control method of the dedicated lane control system for the autonomous vehicles according to claim 1, characterized in that it comprises the following steps: Step 1): The vehicle-mounted information processing center (1) collects information, and sends the vehicle ID, vehicle coordinates C, and real-time vehicle speed information V to the roadside unit (10) through the vehicle unit (6); Step 2): The processing center (16) calculates the average number of kilometers in the mixed traffic flow of each road segment during the Tk period according to the formula; a is the congestion coefficient of manual driving vehicles, f3 is the congestion coefficient of autonomous vehicles, and vi The real-time speed of a manually driven vehicle, vj is the real-time speed of an autonomous vehicle, m is the traffic volume of the manually driven vehicle, n is the traffic volume of the autonomous vehicle, and t is the duration; Step 3): according to the formula to = 1* -I the processing center calculates the traffic ck congestion index @ of the road section during the period, which is the average number of vehicle kilometers of each road section under the condition of free flow speed, and t; is the weight coefficient of each road section length; Step 4): The processing center (16) ranks the congestion degree of the road according to the numerical range of the traffic congestion index co, and obtains the road congestion degree information K; calls the traffic information database (18) of the previous week' s road traffic information With reference to data Z, the time period when the traffic congestion index is on the rise and the time period when the traffic congestion index is on the downward trend is obtained, and road sections OA e (1, 1.75)) are screened out as candidate road sections to be opened for autonomous vehicles lanes; Step 5): The processing center (16) calculates the penetration index iv of each candidate road section according to the formula, and classifies each candidate road section according to the number of one-way lanes according to the penetration index ii and sets a dedicated lane for autonomous vehicles to generate Dedicated lane opening and closing status information B, open a dedicated lane for autonomous vehicles; the dedicated lane opening and closing status information B and road congestion information K are packaged into a signal U and sent to the first 5G communication module (7) and the third 5G communication module (15).
- 7. The control method for the dedicated lane control system for the autonomous vehicles according to claim 6, characterized in that: after the dedicated lane for autonomous vehicles in step 5) is opened, the vehicle-mounted information processing center (1) receives the second 5G After the communication module (7) decodes the signal U into the information H, the vehicle-mounted system uses the GPS/Beidou navigation and positioning device (2) and the vehicle-mounted binocular camera (3) to re-plan the optimal navigation path for the vehicle; roadside information processing The center (8) monitors the illegal vehicles on the road in real time by analyzing the video information R from the video monitoring module (9), and sends the illegal vehicle information L to the road information prompt system; dedicated lane opening and closing status information B and road congestion information K is posted on the variable information prompt board (13) and prompted by broadcasting (14).
- 8. The control method of the dedicated lane control system for the autonomous vehicles according to claim 7, characterized in that: the processing center (16) divides the previous road section adjacent to the opening section of the dedicated lane into a buffer section and a mixed section. The vehicle drives on the buffer road section according to the instructions and drives on the mixed road section; the vehicle processing center (1) sends different instructions to the human-computer interaction terminal (4) or the autonomous driving terminal (5) on different road sections.
- 9. The control method of the dedicated lane control system for the autonomous vehicles according to claim 8, characterized in that: when the dedicated lanes for autonomous vehicles are opened, for autonomous vehicles that have not yet entered the dedicated lanes, the autonomous driving terminal (5) After receiving the control instruction from the vehicle-mounted information processing center (1), drive according to the expected speed and desired corner, and enter the autonomous vehicles lane: for the manually driven vehicle that is already driving in the autonomous vehicles lane, the human-computer interaction After receiving the information from the vehicle-mounted information processing center (1), the terminal (4) pushes real-time information to the driver through the vehicle-mounted tablet; in the buffer section, the autonomous driving terminal (5) receives the control from the vehicle-mounted information processing center (1) After the instruction, drive according to the desired vehicle speed and desired corner. After the human-computer interaction terminal (4) receives the information from the vehicle-mounted information processing center (1), the driver starts to change lanes according to the prompt pushed by the vehicle-mounted tablet.
- 10, The control method of the dedicated lane control system for the autonomous vehicles according to claim 9, characterized in that: the processing center (16) monitors the change of traffic congestion index w of the open section of the dedicated lane for autonomous vehicles in real time, and when the time When reaching the starting period when the traffic congestion index shows a downward trend, the dedicated autonomous driving lanes of the road section with the traffic congestion index a) E (0, 1) are closed.
- 11. The control method of the dedicated lane control system for the autonomous vehicles according to any one of claims 6-10, characterized in that it further comprises obtaining the approximate optimal solution of the parameters a and fl, specifically: entering the initial value of the parameter in the processing center 16, calculating the average vehicle delay AVD value this week, introducing an exponential function a = exp (x) for the calibration of the parameter, and start each new cycle, the coefficient is accumulated by 0.05, and update the value of the parameter a according to the formula a = exp (x).The value of 13 updated by the constraint a + = 1, and the AVD in a period is calculated. If the AVD in the period is less than or equal to the AVD in the previous period, the coefficient sum will continue to be updated in the next period, otherwise the value of the coefficient sum will be output. The value of the coefficient sum obtained at this time is the approximate optimal solution of the coefficient sum suitable for the road network.
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| PCT/CN2020/095585 WO2021004222A1 (en) | 2019-07-11 | 2020-06-11 | Control system and method for dedicated lane for autonomous vehicle |
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