CN110503823B - Special lane control system and method for automatic driving vehicle - Google Patents
Special lane control system and method for automatic driving vehicle Download PDFInfo
- Publication number
- CN110503823B CN110503823B CN201910624637.0A CN201910624637A CN110503823B CN 110503823 B CN110503823 B CN 110503823B CN 201910624637 A CN201910624637 A CN 201910624637A CN 110503823 B CN110503823 B CN 110503823B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- information
- road
- automatic driving
- processing center
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
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
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
The invention discloses a lane control system and method special for an automatic driving vehicle, which comprises a vehicle-mounted system, a roadside system, a cloud lane management center and a road information prompt system, wherein the vehicle-mounted system comprises a vehicle-mounted information processing center, an information acquisition device, a vehicle-mounted communication module, a human-computer interaction terminal on a manual driving vehicle and an automatic driving terminal on the automatic driving vehicle, the roadside system comprises a roadside information processing center, a video monitoring module, a roadside unit and a second 5G communication module, the road information prompt system comprises an information distribution center, a variable information prompt board, a broadcast and a third 5G communication module, and the cloud lane management center comprises a data processing center, a traffic information database and a fourth 5G communication module; different traffic congestion degree influence coefficients are given to the manually driven vehicle and the automatically driven vehicle, whether a special lane of the automatically driven vehicle is started or not is determined according to the traffic congestion degree index and the permeability index of the automatically driven vehicle, and traffic passing efficiency is improved.
Description
Technical Field
The invention relates to a special lane control technology, in particular to a control system and a method for setting partial lanes as special lanes for an automatic driving vehicle according to the degree of road congestion and the permeability of the automatic driving vehicle.
Background
The key technology of the current automatic driving is continuously broken through, and the situation that the automatic driving vehicle and the manual driving vehicle are mixed can occur along with the smooth road getting on of the automatic driving vehicle in the future. The driving of the manually driven vehicle is greatly influenced by the behavior of the driver, and generally, the driving behavior is unpredictable due to the influence of psychological factors and environmental factors. Compared with a manual driving vehicle, the automatic driving vehicle is not limited by physiological factors and is not interfered by emotional factors, strictly complies with traffic rules, and simultaneously abandons many bad driving behaviors of human drivers, such as frequent lane changing, forced congestion adding and the like. Autonomous vehicles also have the disadvantage that it is difficult to make the most reasonable driving decisions like human drivers under complex traffic conditions. Just because there is a certain difference between the automatic driving vehicle and the manual driving vehicle when the automatic driving vehicle and the manual driving vehicle are running, the automatic driving vehicle and the manual driving vehicle are necessarily in contradiction in a mixed running environment. Although this contradiction is not obvious when the traffic is smooth, it will be amplified when the traffic is congested. Therefore, in order to alleviate the contradiction between the automatic driving vehicle and the manual driving vehicle during the peak time of the trip, reduce the potential traffic safety hazard and improve the traffic efficiency, it is necessary to provide a lane dedicated for the automatic driving vehicle. However, currently, common vehicle lanes such as a bus lane and a multi-occupant (HOV) lane generally exist in a form of fixed intervals and fixed time lengths, and when the vehicle is in a complex and variable traffic environment, the problems of insufficient utilization of road resources, inhibition of other types of vehicles to pass through and the like often exist.
Disclosure of Invention
The invention aims to solve the problems of common special lanes and provides a special lane control system and method for an automatic driving vehicle.
The invention relates to a special lane control system for an automatic driving vehicle, which adopts the technical scheme that: the system comprises a vehicle-mounted system, a roadside system, a cloud lane management center and a road information prompt system, wherein the vehicle-mounted system comprises a vehicle-mounted information processing center, an information acquisition device, a vehicle-mounted communication module, a human-computer interaction terminal installed on an artificial driving vehicle and an automatic driving terminal installed on an automatic driving vehicle, the information acquisition device comprises a GPS/Beidou navigation positioning device and a vehicle-mounted binocular camera, and the vehicle-mounted communication module comprises a vehicle-mounted unit and a first 5G communication module; the GPS/Beidou navigation positioning device transmits the coordinates C and the real-time speed V of the vehicle to a vehicle-mounted information processing center, a vehicle-mounted binocular camera transmits acquired image data P to the vehicle-mounted information processing center, a man-machine interaction terminal issues navigation information M, and an automatic driving terminal transmits working state information S to the vehicle-mounted information processing center and receives different control instructions I; the vehicle-mounted unit packs the ID of the vehicle and the information A output by the vehicle-mounted information processing center into a signal D and sends the signal D to the road side unit, and the first 5G communication module receives the signal U sent by the fourth 5G communication module, decodes the signal U into information H and sends the information H to the vehicle-mounted information processing center; the roadside system comprises a roadside information processing center, a video monitoring module, a roadside unit and a second 5G communication module, wherein the video monitoring module sends video information R to the roadside information processing center, the roadside unit decodes a signal D into data J and sends the data J to the roadside information processing center, the roadside information processing center analyzes and processes the data J to obtain information N, illegal vehicle information L on a road is collected and obtained, the information N and the information L are sent to the second 5G communication module, the second 5G communication module codes the received information N to obtain a signal F and sends the signal F to a fourth 5G communication module, and the illegal vehicle information L is coded to obtain a signal E and sent to a third 5G communication module; the road information prompting system comprises an information issuing center, a variable information prompting board, a broadcast and a third 5G communication module, wherein the third 5G communication module decodes a signal U and a signal E into road prompting information Q and sends the road prompting information Q to the information issuing center, and the information is issued through the variable information prompting board and the broadcast; the cloud lane management center comprises a data processing center, a traffic information database and a fourth 5G communication module, the fourth 5G communication module decodes the signal F into data W and sends the data W to the data processing center, the data processing center calculates the traffic congestion index omega and the penetration index psi of the automatic driving vehicles of each road to obtain the opening and closing information B of the special lane and the traffic congestion information K, the special lane opening and closing information B and the traffic congestion information K are packaged into a signal U and sent to the first 5G communication module and the third 5G communication module, and the traffic information of each road is stored in the traffic information database.
The control method of the lane control system special for the automatic driving vehicle adopts the technical scheme that the control method comprises the following steps:
step 1): the vehicle-mounted information processing center collects information and sends the ID of the vehicle, the coordinates C of the vehicle and the real-time speed information V to the road side unit through the vehicle-mounted unit;
step 2): the data processing center according to the formula
Calculate each road section at TkMixed-driving leveling average vehicle kilometer number of time interval
Alpha is the influence coefficient of the crowdedness of the manually driven vehicles, beta is the influence coefficient of the crowdedness of the automatically driven vehicles, upsiloniIs the real-time speed of a manually-driven vehicle, upsilonjIs the real-time speed of the autonomous vehicle, m is the man-operated vehicle traffic volume, n is the autonomous vehicle traffic volume, and t is the duration;
step 3): the data processing center according to the formula
Calculating the traffic congestion degree index omega of the section in the time period,
is the velocity upsilon of each section in the free flowfThe average number of kilometers of the traffic flow under the condition, wherein zeta is a weight coefficient of the length of each road section;
step 4), the data processing center grades the congestion degree of the road according to the numerical interval of the traffic congestion degree index omega to obtain road congestion degree information K; calling reference data Z of the road traffic information of the previous week in the traffic information database to obtain a time period when the traffic congestion degree index is in an ascending trend and a time period when the traffic congestion degree index is in a descending trend, and screening out a road section with omega e (1,1.75) as a candidate road section of a special lane of the automatic driving vehicle to be started;
step 5) the data processing center processes the data according to a formula
Calculating the permeability index psi of the automatic driving vehicles of each candidate road section, classifying each candidate road section according to the number of one-way lanes according to the permeability index psi, setting a special lane for the automatic driving vehicles, generating special lane opening and closing state information B, and opening the special lane for the automatic driving vehicles; and packaging the special lane opening and closing state information B and the road congestion degree information K into a signal U, and sending the signal U to the first 5G communication module and the third 5G communication module.
The invention has the advantages that after the technical scheme is adopted:
1. the invention is based on urban roads, is opened in one way in the morning and evening traffic travel peak time period of a working day, can effectively solve the contradiction generated in the traveling process of an automatic driving vehicle and a manual driving vehicle in a dense traffic environment, relieves the congestion condition of mixed traffic flow, ensures the traffic safety of roads and improves the traffic efficiency of the roads.
2. The invention combines the advantages of 5G communication (LTE-V) and dedicated short-range communication technology (DSRC), realizes the multidimensional communication between the vehicle and the roadside system, between the vehicle and the cloud lane management center, between the roadside system and the cloud lane management system, and enhances the rapidity and the accuracy of controlling the automatic driving dedicated lane.
3. The method is different from the traditional method for setting the special lane with the fixed interval or the fixed time length, and the cloud lane management center determines the starting time and the position of the automatic driving special lane through real-time analysis and calculation, so that the method has stronger flexibility and is more targeted for relieving traffic jam.
4. The invention utilizes the traffic information database of the cloud lane management center to regularly store and update the traffic information of each time period of each road every week, and the traffic information is used as a reference index for starting the special lane of the automatic driving vehicle in the next week, thereby having strong timeliness.
5. According to the invention, different traffic congestion degree influence coefficients are given to the two vehicles according to the running characteristics of the manually driven vehicle and the automatically driven vehicle, and whether to start a special lane of the automatically driven vehicle is determined according to the calculated traffic congestion degree index and the permeability index of the automatically driven vehicle, so that the accuracy of the method on road condition analysis and special lane control is embodied.
6. The invention classifies different driving scenes on the road after the lane special for the automatic driving vehicle is opened, adopts different methods to control and manage different scenes, and improves the safety and traffic efficiency of the system.
Drawings
The invention is described in further detail below with reference to the figures and the detailed description.
FIG. 1 is a block diagram of a lane control system for an autonomous vehicle according to the present invention;
FIG. 2 is a flow chart of a control method of the lane control system for an autonomous vehicle according to the present invention;
FIG. 3 is a schematic diagram of a scenario in which the present invention is implemented;
in the figure: 1. a vehicle-mounted information processing center; 2, GPS/Beidou navigation positioning device; 3. a vehicle-mounted binocular camera; 4. a human-computer interaction terminal; 5. an automatic driving terminal; 6. an on-board unit; 7. a first 5G communication module; 8. a roadside information processing center; 9. a video monitoring module; 10. a road side unit; 11. a second 5G communication module; 12. an information issuing center; 13. a variable information prompt panel; 14. broadcasting; 15. a third 5G communication module; 16. a data processing center; 18. a fourth 5G communication module; 19. a traffic information database.
Detailed Description
As shown in fig. 1, the lane control system dedicated for an autonomous vehicle of the present invention includes a vehicle-mounted system, a road side system, a cloud lane management center, and a road information prompt system. The cloud lane management center is used for receiving road information and vehicle information, determining whether to start the special lane of the automatic driving vehicle or not according to traffic jam conditions and the permeability of the automatic vehicle in the traffic peak period, and closing the special lane of the automatic driving vehicle when the traffic peak is finished. The roadside system carries out real-time information interaction with the vehicle on the road, sends the acquired road information and vehicle information on the road to the cloud lane management center, and receives the opening and closing instruction of the lane special for the automatic driving vehicle from the cloud lane management center. The vehicle-mounted system is in butt joint with the cloud lane management center on one hand, uploads navigation information to the cloud lane management center, and transmits a received instruction to the automatic driving operation terminal or a driver so as to control the vehicle to run, and is in butt joint with the road side system on the other hand to receive information of the road side system. The road information prompting system receives a signal from the cloud lane management center, announces lane change information and is used for prompting a manually driven vehicle to change lanes.
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 installed on a manually-driven vehicle and an automatic driving terminal 5 installed on an automatic driving vehicle. And the automatic driving vehicle and the manual driving vehicle are both provided with an information acquisition device and a vehicle-mounted communication module. The information acquisition device, the vehicle-mounted communication module, the man-machine interaction terminal 4 and the automatic driving terminal 5 are all in bidirectional interconnection with the vehicle-mounted information processing center 1.
The information acquisition device comprises a GPS/Beidou navigation positioning device 2 and a vehicle-mounted binocular camera 3. The on-board communication module includes an on-board unit 6 and a first 5G communication module 7.
The GPS/Beidou navigation positioning device 2, the vehicle-mounted binocular camera 3, the human-computer interaction terminal 4 and the automatic driving terminal 5 collect various information in the vehicle. The GPS/Beidou navigation positioning device 2 is used for positioning a running vehicle and transmitting the coordinate C and the real-time speed V of the vehicle to the vehicle-mounted information processing center 1 in real time. The vehicle-mounted binocular camera 3 is used for collecting and recording road conditions, recognizing road surface information such as obstacles, vehicles, lane lines and pedestrians on the road, and transmitting the acquired image data P to the vehicle-mounted information processing center 1. The human-computer interaction terminal 4 is used for issuing vehicle navigation information M to a driver, and the driver can select a destination for navigation through voice or touch instructions O. The automatic driving terminal 5 sends the working state information S of each control system in the vehicle to the vehicle-mounted information processing center 1 in real time, and continuously receives different control instructions I from the vehicle-mounted information processing center 1 to change the driving direction and speed of the automatic driving vehicle.
The navigation information M comprises road congestion degree information K, special lane opening and closing state information B, the current optimal running path, the current optimal running vehicle speed and the like.
The on-board unit 6 and the first 5G communication module 7 acquire vehicle exterior information. The on-board unit 6 packages the vehicle ID information and information a (vehicle coordinates C, real-time vehicle speed V) output from the on-board information processing center 1 into a signal D, and transmits the signal D to the roadside unit 10 of the roadside system by a dedicated short range communication technology (DSRC). The first 5G communication module 7 communicates with a fourth 5G communication module 18 in the cloud-end lane management center through a 5G network, and receives a signal U sent from the fourth 5G communication module 18, where the signal U includes dedicated lane opening/closing state information B and road congestion degree information K, that is, U ═ { B; k, and simultaneously decoding the signal U into information H, and sending the information H to the vehicle-mounted information processing center 1.
The information A output by the vehicle-mounted information processing center 1 comprises vehicle coordinates C and real-time vehicle speed V. The signal D includes the own vehicle ID, the vehicle coordinate C, and the real-time vehicle speed V, i.e., D ═ ID; c; v }.
The vehicle-mounted information processing center 1 is the core of the vehicle-mounted system and is used for transmitting instructions to other modules of the vehicle-mounted system by collecting and fusing information from the inside and the outside of the vehicle.
The roadside system includes: the road side information processing system comprises a road side information processing center 8, a video monitoring module 9 and a road side communication module, wherein the output end of the video monitoring module 9 is connected with the input end of the road side information processing center 8, and the road side information processing center 8 and the road side communication module are interconnected in a two-way mode. The roadside communication module comprises a roadside unit 10 and a second 5G communication module 11.
Video monitoring module 9 monitors in real timeAnd (4) road traffic conditions, and sending the video information R to the roadside information processing center 8 in real time. The roadside unit 10 decodes the signal D obtained by communication with the on-board unit 6 into data J, and transmits the data J to the roadside information processing center 8. The roadside information processing center 8 obtains the information N by analyzing and processing the data J, i.e., counting and classifying the vehicle ID, the vehicle coordinates C, and the real-time vehicle speed information V to be obtained. The information N comprises the traffic volume m of the manually-driven vehicles in each time interval of each road, the traffic volume N of the automatically-driven vehicles, and the real-time vehicle speed upsilon of each manually-driven vehicleiReal-time vehicle speed v of each autonomous vehiclejAll vehicle coordinates C, i.e., N ═ m; n; upsilon isi；υj(ii) a C }. Meanwhile, the roadside information processing center 8 collects the violation 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 18 of the cloud lane management center, and at the same time, encodes the received information L to obtain a signal E, that is, E ═ L, and sends the signal E to the third 5G communication module 15 in the road information prompting system.
The road information prompt system includes: the information distribution center 12, the variable information prompt board 13, the broadcast 14 and the third 5G communication module 15. The output end of the third 5G communication module 15 is connected with the input end of the information distribution center 12, and the output end of the information distribution center 12 is respectively connected with the variable information prompt board 13 and the broadcast 14. The third 5G communication module 15 receives the signal U from the fourth 5G communication module 18 and the signal E from the second 5G communication module 11, and decodes these signals into road prompting information Q, where the road prompting information Q includes road congestion degree information K, exclusive lane open/close state information B, and illegal vehicle information L, that is, Q ═ { K; b; l }. The second 5G communication module 11 transmits the road prompt information Q to the information distribution center 12. The information distribution center 12 announces the lane opening/closing information and the traffic information through the variable information presentation board 13 and the broadcast 14. The variable information display panel 13 receives the data signal X from the information distribution center 12, and notifies the information on the opening and closing of the driveway exclusive for automatic driving and the information on the degree of road congestion at the intersection ahead in a text form, thereby promoting safe driving of the vehicle. The broadcast 14 receives the data signal Y from the information distribution center 12, and announces the information on the automatic driving lane at the intersection ahead and the road congestion degree information in a voice broadcast form, thereby promoting safe driving of the vehicle.
High in clouds lane management center includes: the data processing center 16, the traffic information database 19 and the fourth 5G communication module 18 are all interconnected in two directions, and the data processing center 16, the traffic information database 19 and the fourth 5G communication module 18 are all interconnected in two directions.
The fourth 5G communication module 18 receives the signal F (F ═ m; n; upsilon) transmitted from the second 5G communication module 11i；υj(ii) a C) and decoded into data W, sent to the data processing center 16. The data processing center 16 packages the exclusive lane open/close state information B and the road congestion degree information K into a signal U (U ═ B; K }), and sends the signal U to the first 5G communication module 7 and the third 5G communication module 15. The data processing center 16 integrates the data W, analyzes the real-time road conditions of each road, calculates the traffic congestion degree index ω and the permeability index ψ of the autonomous vehicles of each road in real time to obtain a section conforming to the opening and closing of the dedicated lane of the autonomous vehicles, sends the opening and closing information B of the dedicated lane and the traffic congestion degree information K of the road to the fourth 5G communication module 18, and stores the processed traffic information G of each road into the traffic information database 19. The traffic information database 19 stores and updates the traffic information of each time period of each road every week, calculates the average traffic congestion degree index of each time period of the last week, calculates to obtain the trend graph of the average traffic congestion degree index of each time period of each road in one week, analyzes to obtain the time period in which the traffic congestion degree index is in an ascending trend and the time period in which the traffic congestion degree index is in a descending trend, and sends the reference data Z of the traffic information to the data processing center 16 as the reference index for opening and closing the lane special for the automatic driving vehicle.
With reference to fig. 2, when the lane control system for the autonomous vehicle of the present invention works, first, the cloud lane management center sets a portion of lanes in the urban road where congestion is likely to occur as lanes dedicated to the autonomous vehicle through analysis and calculation. Meanwhile, the lane starting instruction is transmitted to the road information prompting system by the cloud lane management center, the road information prompting system controls the variable lane lamp to be turned on through the information issuing center, and vehicles running on the road are prompted through the variable information prompting board and the broadcast. After the special lane is opened, the cloud lane management center, the road side system and the road information prompting system work cooperatively to transmit lane opening and closing information to the automatic driving vehicle and the manual driving vehicle, and the vehicles on the road run according to the regulations. With the lapse of time, after a traffic trip peak passes, the road traffic condition tends to be stable, and at the moment, the cloud lane management center closes the lane special for the automatic driving vehicle and prompts the vehicle through a road side system and a road information prompting system. At this time, the vehicle can make a free lane change, and the manually driven vehicle and the automatically driven vehicle start to be mixed. The specific working process is as follows:
take the early peak of a certain city working day as an example: the current time period is TkThe city has X road sections, and the length of each road section is recorded as
Where i ∈ (1, X). The man-operated vehicle and the automatic vehicle run on each road section of the city, the road side system of each road section monitors the road section traffic information by taking each 3min as a time section, and then the road side system can be divided into 480 time sections in one day, which are marked as T1→T480。
All vehicles on each road section acquire and fuse various information from the inside and outside of the vehicle through the vehicle-mounted information processing center 1, and the vehicle ID, the vehicle coordinate C and the real-time vehicle speed information V are sent to the road side unit 10 of each road section road side system through the vehicle-mounted unit 6, namely, a signal D is { ID; c; v is sent to the road side units 10 of the road side systems of each road segment. The roadside unit 10 sets the signal D to { ID; c; v is decoded into data J and sent to the roadside information processing center 8.
The roadside information processing center 8 of each road segment roadside system counts and classifies the vehicle ID, the vehicle coordinates C and the real-time vehicle speed information V to be obtained by analyzing and processing the data J from the roadside unit 10And obtaining information N (traffic m of manually driven vehicles, traffic N of automatically driven vehicles, real-time speed upsilon of each manually driven vehiclei(t), real-time vehicle speed upsilon of each autonomous vehiclej(t), all vehicle coordinates C), N ═ m; n; upsilon isi；υj(ii) a C }. Meanwhile, the roadside information processing center 8 of each road section collects and obtains the violation 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 18 of the cloud lane management center. And meanwhile, the information L is coded to obtain a signal E, and the signal E is sent to a third 5G communication module 15 of the road information prompt system.
The data processing center 16 of the cloud lane management center receives the decoded data W (the man-made vehicle traffic m, the automatic driving vehicle traffic n, the real-time vehicle speed upsilon of each man-made vehicle) sent by the second 5G communication module 11iReal-time vehicle speed v of each autonomous vehiclejAll vehicle coordinates C), calculating each road section at T based on the decoded data WkMixed-driving leveling average vehicle kilometer number of time interval
The calculation formula is as follows:
wherein: alpha is the influence coefficient of the crowdedness of the manually driven vehicle, beta is the influence coefficient of the crowdedness of the automatically driven vehicle, and the real-time speed upsilon of the manually driven vehicleiReal-time upsilon of an autonomous vehiclejThe unit of (a) is km/h; the period of time t is 0.05 h.
The data processing center 16 makes each road section in free flow velocity upsilonfAverage number of kilometers of traffic flow under the condition
And TkMixed-driving leveling average vehicle kilometer number of time interval
The corrected result according to the length of the section is used as the traffic congestion degree index omega of the section in the period. Wherein the free flow velocity upsilonfAverage number of kilometers of traffic flow under the condition
Is calculated by the formula
h is the road segment length; the calculation formula of the length weight coefficient zeta of each path section is
A correction factor of
The calculation formula of the traffic congestion degree index omega is
Wherein the content of the first and second substances,
for road section RiIs the total number of the segments, i belongs to (1, X), and the time duration of the time period is 0.05 h.
The data processing center 16 ranks the degree of congestion of the road according to the numerical value section in which the traffic congestion degree index ω is located, and obtains road congestion degree information K. Wherein, the significance of different numerical values of the traffic congestion degree index is shown in the following table:
| value range of omega | Road congestion rating |
| ω＜1 | Clear |
| 1≤ω＜1.75 | Light congestion |
| ω≥1.75 | Moderate and severe congestion |
The data processing center 16 of the cloud lane management center calls the reference data Z about the traffic information of the urban road of the previous week in the traffic information database 19 to obtain a time period when the traffic congestion degree index is in an ascending trend and a time period when the traffic congestion degree index is in a descending trend, which are respectively marked as Tu1→Tu2、Td1→Td2。
The data processing center 16 of the cloud lane management center records the dynamic change of the traffic congestion index omega in real time, and reaches the initial time period T when the traffic congestion index omega is in the rising trend at the momentouAnd then screening out the road section of omega epsilon (1,1.75) as a candidate road section of the special lane of the automatic driving vehicle to be started. Meanwhile, the data processing center 16 calculates the permeability index ψ of the autonomous vehicle for each candidate section in real time based on the decoded data W from each candidate section, which is calculated by the formula:
wherein: beta is an influence coefficient of the degree of congestion of the automatic driving vehicle,
for mixing the driving flow and leveling the vehicle kilometers, automatically driving the vehicle real-time upsilonjThe unit of (1) is km/h, and the time duration of the period is 0.05 h.
The data processing center 16 classifies each candidate road section by the number of one-way lanes according to the calculated permeability index ψ of the autonomous vehicle, and sets a lane dedicated for the autonomous vehicle.
For a road segment of one unidirectional n lanes,
when in use
The data processing center 16 of the cloud lane management center generates the special lane open/close state information B according to the selected road section meeting the open condition of the special lane of the automatic driving vehicle, opens the special lane of the automatic driving vehicle, packs the special lane open/close state information B and the obtained road congestion degree information K into a signal U (U ═ B; K }), and sends the signal U and the signal U to the first 5G communication module 7 of the vehicle-mounted system and the third 5G communication module 15 of the road information prompting system.
After the lane dedicated for the automatic driving vehicle is started, on one hand, after the vehicle-mounted information processing center 1 of the vehicle-mounted system receives the information H which is sent by the second 5G communication module 7 and is obtained by decoding the signal U, the vehicle-mounted system plans the optimal navigation path for the vehicle again according to the destination and the type of the vehicle by using the GPS/Beidou navigation positioning device 2 and the vehicle-mounted binocular camera 3. Wherein, the automatic driving terminal 5 of the automatic driving vehicle will follow the optimal navigation path to drive, 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 continues to monitor the illegal vehicle 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 prompting system. In the third aspect, the information distribution center 12 of the road information presentation system distributes the received exclusive lane open/close state information B and road congestion degree information K on the variable information presentation panel 13, and presents the vehicles along the way by the broadcast 14.
The data processing center 16 of the cloud lane management center divides the last road segment adjacent to the exclusive lane open road segment into a buffer road segment and a mixed road segment. Vehicles on the road run on the buffer road section according to instructions and run freely on the mixed road section. The vehicle-mounted processing centers 1 of the automatic driving vehicle and the manual driving vehicle send different instructions to the human-computer interaction terminal 4 or the automatic driving terminal 5 at different road sections.
As shown in the scene diagram of fig. 3, the cloud lane management center, the roadside system and the vehicle communicate with each other through the multidimensional communication system composed of the 5G communication (LTE-V) and the dedicated short range communication technology (DSRC) shown in fig. 1, and the vehicle may have the following four driving scenes during the driving process of the dedicated lane open road section and the mixed road section, which are respectively:
the first method comprises the following steps: in the open road section of the exclusive lane for the autonomous vehicle, the autonomous terminal 5 of the autonomous vehicle which has not yet entered the exclusive lane starts entering the exclusive lane for the autonomous vehicle by traveling according to the desired vehicle speed and the desired turning angle after receiving the control command from the on-vehicle processing center 1.
And the second method comprises the following steps: in the section of the lane opening dedicated to the automatic driving vehicle, for the manual driving vehicle which is already running on the lane dedicated to the automatic driving vehicle, the human-computer interaction terminal 4 pushes real-time information for the driver through the vehicle-mounted tablet after receiving the information from the vehicle-mounted processing center 1, wherein the information comprises: the special lane open-close state information B prompt, the front road congestion degree information K, the current optimal running path and the current optimal running speed. After receiving the prompt of the special lane opening and closing state information B, the driver must leave the special lane of the automatic driving vehicle in order according to the pushed information.
And the third is that: in the buffer section, the automatic driving terminal 5 of the automatic driving vehicle drives according to the expected vehicle speed and the expected turning angle after receiving the control instruction from the vehicle-mounted processing center 1, changes the lane in advance, and prepares to enter the lane special for the automatic driving vehicle.
And fourthly: in the buffer road section, after the human-computer interaction terminal 4 of the manually-driven vehicle receives the information from the vehicle-mounted processing center 1, the driver starts lane changing according to the prompt pushed by the vehicle-mounted panel and forbids to enter a special lane of the automatically-driven vehicle.
For other vehicles which are already running on a specified lane, the reasonable speed of the vehicles must be kept, the vehicles continue to run on the current lane, and lane change is prohibited.
The data processing center 16 of the cloud lane management center monitors the change of the traffic congestion degree index omega of the open road section of the lane special for the automatic driving vehicle in real time, and the time reaches the initial time period T when the traffic congestion degree index is in a descending trendodWhen the road congestion index omega belongs to (0,1), the automatic driving special lane of the road section is closed, the special lane closing signal is transmitted to the road information prompting system and the vehicle-mounted system of the road section through the fourth 5G communication module 18, and the automatic driving vehicles and the manual driving vehicles of the road section start to mix.
Claims (10)
1. The utility model provides an automatic special lane control system of driving vehicle, includes on-vehicle system, roadside system, high in the clouds lane management center and road information reminder system, characterized by: 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) installed on an artificial driving vehicle and an automatic driving terminal (5) installed on an automatic driving vehicle, wherein the information acquisition device comprises a GPS/Beidou navigation positioning device (2) and a vehicle-mounted binocular camera (3), and the vehicle-mounted communication module comprises a vehicle-mounted unit (6) and a first 5G communication module (7); the GPS/Beidou navigation positioning device (2) transmits the coordinates C and the real-time speed V of the vehicle to the vehicle-mounted information processing center (1), the vehicle-mounted binocular camera (3) transmits the acquired image data P to the vehicle-mounted information processing center (1), the man-machine interaction terminal (4) issues navigation information M, and the automatic driving terminal (5) transmits the working state information S to the vehicle-mounted information processing center (1) and receives different control instructions I; the vehicle-mounted unit (6) packs the ID of the vehicle and the information A output by the vehicle-mounted information processing center (1) into a signal D and sends the signal D to the road side unit (10), and the first 5G communication module (7) receives the signal U sent by the fourth 5G communication module (18), decodes the signal U into information H and sends the information H to the vehicle-mounted information processing center (1);
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), wherein the video monitoring module (9) sends video information R to the roadside information processing center (8), the roadside unit (10) decodes a signal D into data J and sends the data J to the roadside information processing center (8), the roadside information processing center (8) analyzes and processes the data J to obtain information N, and illegal vehicle information L on a road is collected, the information N and the information L are sent to a 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 a fourth 5G communication module (18), coding the violation vehicle information L to obtain a signal E and sending the signal E to a third 5G communication module (15);
the road information prompting system comprises an information issuing center (12), a variable information prompting board (13), a broadcast (14) and a third 5G communication module (15), wherein the third 5G communication module (15) decodes a signal U and a signal E into road prompting information Q and sends the road prompting information Q to the information issuing center (12), and the information issuing center (12) issues the information through the variable information prompting board (13) and the broadcast (14);
the cloud lane management center comprises a data processing center (16), a traffic information database (19) and a fourth 5G communication module (18), the fourth 5G communication module (18) decodes the signal F into data W and sends the data W to the data processing center (16), the data processing center (16) calculates a traffic congestion index omega and an automatic driving vehicle permeability index psi of each road to obtain special lane opening and closing state information B and road congestion information K, the special lane opening and closing state information B and the road congestion information K are packaged into a signal U and sent to a first 5G communication module (7) and a third 5G communication module (15), and traffic information of each road is stored in the traffic information database (19);
the data processing center (16) receives the relevant information and calculates the T of each road sectionkThe method comprises the steps of averaging the traffic kilometers of mixed driving vehicles at a time interval, further calculating to obtain a traffic congestion degree index omega, calling reference data Z of traffic information of roads in a week in a traffic information database (19), obtaining a time interval when the traffic congestion degree index is in an ascending trend and a time interval when the traffic congestion degree index is in a descending trend, recording dynamic changes of the traffic congestion degree index omega in real time, screening a road section with omega (1,1.75) as a candidate road section of a lane special for an automatic driving vehicle to be started when the time reaches an initial time interval when the traffic congestion degree index omega is in the ascending trend, calculating the permeability index psi of the automatic driving vehicle of each candidate road section in real time, and calculating the permeability index psi of the automatic driving vehicle ofThe permeability index psi classifies the candidate road sections according to the number of the one-way lanes, sets the driveway special for the automatic driving vehicle, generates the opening and closing state information B of the driveway special according to the selected road sections meeting the opening conditions of the driveway special for the automatic driving vehicle, opens the driveway special for the automatic driving vehicle, monitors the change of the traffic congestion index omega of the opening road section of the driveway special for the automatic driving vehicle in real time, and closes the driveway special for the road section with the traffic congestion index omega belonging to (0,1) when the time reaches the initial time period when the traffic congestion index omega is in the descending trend.
2. The lane control system for an autonomous vehicle as claimed in claim 1, wherein: the navigation information M comprises road congestion degree information K, special lane opening and closing state information B, the current optimal running path and the current optimal running speed.
3. The lane control system for an autonomous vehicle as claimed in claim 1, wherein: the information A comprises vehicle coordinates C and real-time vehicle speed V.
4. The lane control system for an autonomous vehicle as claimed in claim 1, wherein: the information N comprises the traffic volume m of the manually-driven vehicles in each time period of each road, the traffic volume N of the automatically-driven vehicles, and the real-time vehicle speed upsilon of each manually-driven vehicleiReal-time vehicle speed v of each autonomous vehiclejAnd all vehicle coordinates C.
5. The lane control system for an autonomous vehicle as claimed in claim 1, wherein: the road prompt information Q comprises road congestion degree information K, special lane opening and closing state information B and violation vehicle information L.
6. A control method of the driveway control system for the autonomous vehicle as claimed in claim 1, characterized by comprising the steps of:
step 1): the vehicle-mounted information processing center (1) collects information and sends the ID of the vehicle, the coordinates C of the vehicle and the real-time speed information V to the road side unit (10) through the vehicle-mounted unit (6);
step 2): the data processing center (16) is based on the formula
Calculate each road section at TkMixed-driving leveling average vehicle kilometer number of time interval
Alpha is the influence coefficient of the crowdedness of the manually driven vehicles, beta is the influence coefficient of the crowdedness of the automatically driven vehicles, upsiloniIs the real-time speed of a manually-driven vehicle, upsilonjIs the real-time speed of the autonomous vehicle, m is the man-operated vehicle traffic volume, n is the autonomous vehicle traffic volume, and t is the duration;
step 3): the data processing center (16) is based on the formula
Calculating the traffic congestion degree index omega of the section in the time period,
is the velocity upsilon of each section in the free flowfThe average number of kilometers of the traffic flow under the condition, wherein zeta is a weight coefficient of the length of each road section;
step 4), the data processing center (16) grades the congestion degree of the road according to the numerical interval of the traffic congestion degree index omega to obtain road congestion degree information K; the data processing center (16) calls reference data Z of the traffic information of the road of the previous week in the traffic information database (19) to obtain a time period when the traffic congestion degree index is in an ascending trend and a time period when the traffic congestion degree index is in a descending trend, records the dynamic change of the traffic congestion degree index omega in real time, and screens out a road section with omega (epsilon 1,1.75) as a candidate road section of a lane special for the automatic driving vehicle to be started when the time reaches the initial time period when the traffic congestion degree index omega is in the ascending trend;
number of step 5)According to a formula by a processing center (16)
Calculating the permeability index psi of the autonomous vehicles in each candidate road section, classifying each candidate road section according to the number of one-way lanes according to the permeability index psi, and setting the lane special for the autonomous vehicles
One of the lanes is set as a lane dedicated to the autonomous vehicle when
Two lanes are set as the lanes dedicated to the automatic driving vehicle when
Setting n-1 lanes as lanes special for the automatic driving vehicle, generating opening and closing state information B of the lanes special for the automatic driving vehicle, and opening the lanes special for the automatic driving vehicle; and packaging the special lane opening and closing state information B and the road congestion degree information K into a signal U, and sending the signal U to the first 5G communication module (7) and the third 5G communication module (15).
7. The control method of the driveway control system for the autonomous vehicle as claimed in claim 6, wherein: after the lane special for the automatic driving vehicle in the step 5) is started, after the vehicle-mounted information processing center (1) receives the information H which is sent by the first 5G communication module (7) and decodes the signal U into the information, the vehicle-mounted system plans the optimal navigation path for the vehicle again by using the GPS/Beidou navigation positioning device (2) and the vehicle-mounted binocular camera (3); the roadside information processing center (8) monitors the violation vehicles on the road in real time by analyzing the video information R from the video monitoring module (9), and sends violation vehicle information L to the road information prompting system; the exclusive lane open/close state information B and the road congestion degree information K are distributed on a variable information presentation board (13) and presented by a broadcast (14).
8. The control method of the driveway control system for the autonomous vehicle as claimed in claim 7, wherein: the data processing center (16) divides the previous road section adjacent to the special lane opening road section into a buffer road section and a mixed road section, and the vehicle runs on the buffer road section according to the instruction and runs freely on the mixed road section; the vehicle-mounted information processing center (1) sends different instructions to the human-computer interaction terminal (4) or the automatic driving terminal (5) at different road sections.
9. The control method of the driveway control system for the autonomous vehicle as claimed in claim 8, wherein: in the road section of the special lane of the automatic driving vehicle, for the automatic driving vehicle which does not enter the special lane, the automatic driving terminal (5) of the automatic driving vehicle drives according to the expected speed and the expected turning angle after receiving the control instruction from the vehicle-mounted information processing center (1) and enters the special lane of the automatic driving vehicle; for a manually driven vehicle which runs on a special lane of the automatic driving vehicle, a human-computer interaction terminal (4) of the manually driven vehicle pushes real-time information for a driver through a vehicle-mounted flat plate after receiving the information from a vehicle-mounted information processing center (1); in the buffer road section, the automatic driving terminal (5) drives according to the expected speed and the expected turning angle after receiving the control instruction from the vehicle-mounted information processing center (1), and the human-computer interaction terminal (4) starts lane changing according to the prompt pushed by the vehicle-mounted panel after receiving the information from the vehicle-mounted information processing center (1).
10. The control method of the driveway control system for the autonomous vehicle as claimed in claim 9, wherein: the data processing center (16) monitors the change of the traffic congestion degree index omega of the open road section of the special lane of the automatic driving vehicle in real time, and when the time reaches the initial period of descending trend of the traffic congestion degree index, the automatic driving special lane of the road section with the traffic congestion degree index omega belonging to (0,1) is closed.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910624637.0A CN110503823B (en) | 2019-07-11 | 2019-07-11 | Special lane control system and method for automatic driving vehicle |
| PCT/CN2020/095585 WO2021004222A1 (en) | 2019-07-11 | 2020-06-11 | Control system and method for dedicated lane for autonomous vehicle |
| GB2107328.3A GB2594368B (en) | 2019-07-11 | 2020-06-11 | Dedicated lane control system and method for autonomous vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910624637.0A CN110503823B (en) | 2019-07-11 | 2019-07-11 | Special lane control system and method for automatic driving vehicle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110503823A CN110503823A (en) | 2019-11-26 |
| CN110503823B true CN110503823B (en) | 2021-06-22 |
Family
ID=68585980
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910624637.0A Active CN110503823B (en) | 2019-07-11 | 2019-07-11 | Special lane control system and method for automatic driving vehicle |
Country Status (3)
| Country | Link |
|---|---|
| CN (1) | CN110503823B (en) |
| GB (1) | GB2594368B (en) |
| WO (1) | WO2021004222A1 (en) |
Families Citing this family (34)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110503823B (en) * | 2019-07-11 | 2021-06-22 | 江苏大学 | Special lane control system and method for automatic driving vehicle |
| CN110910669A (en) * | 2019-11-28 | 2020-03-24 | 深圳成谷科技有限公司 | Virtual isolation-based control method and device for automatic driving special lane |
| CN111199650B (en) * | 2020-01-07 | 2021-02-26 | 深圳佰才邦技术有限公司 | Cloud lane control method and system based on 5G network |
| KR20230003143A (en) * | 2020-04-28 | 2023-01-05 | 후아웨이 테크놀러지 컴퍼니 리미티드 | Method and apparatus for passing a vehicle through a boom barrier |
| CN111695418A (en) * | 2020-04-30 | 2020-09-22 | 上汽大众汽车有限公司 | Method and system for safe driving based on road condition detection |
| CN113330497A (en) * | 2020-06-05 | 2021-08-31 | 曹庆恒 | Automatic driving method and device based on intelligent traffic system and intelligent traffic system |
| CN112258849B (en) * | 2020-10-26 | 2022-03-29 | 上海市城市建设设计研究总院（集团）有限公司 | Dynamic automatic driving special lane based on expressway and use method thereof |
| CN112389448B (en) * | 2020-11-23 | 2022-07-01 | 重庆邮电大学 | Abnormal driving behavior identification method based on vehicle state and driver state |
| CN112735185A (en) * | 2020-12-21 | 2021-04-30 | 南京熊猫电子股份有限公司 | Vehicle management system based on 5G and Beidou technology |
| CN113409567B (en) * | 2021-01-04 | 2022-08-05 | 清华大学 | Traffic assessment method and system for mixed traffic lane of public transport and automatic driving vehicle |
| CN112937639B (en) * | 2021-02-07 | 2022-10-14 | 安徽万航轨道交通装备有限公司 | Railway locomotive automatic control system based on Internet of things |
| CN112874526A (en) * | 2021-02-23 | 2021-06-01 | 丁立言 | Automatic driving automobile guiding device based on 5G technology |
| CN113096416B (en) * | 2021-03-29 | 2022-02-15 | 长沙理工大学 | Dynamic cooperative control method for variable speed limit of automatic driving special lane and general lane in confluence area on expressway |
| CN113335293B (en) * | 2021-06-22 | 2022-09-02 | 吉林大学 | Highway road surface detection system of drive-by-wire chassis |
| CN113542087B (en) * | 2021-07-06 | 2022-10-18 | 上海电气集团智能交通科技有限公司 | Vehicle-mounted human-computer interaction system of digital tramcar and communication control method thereof |
| CN113611141B (en) * | 2021-08-06 | 2022-08-23 | 上海振华重工(集团)股份有限公司 | Logistics park traffic control system and method based on automatic and manual driving mixed driving |
| CN113870593A (en) * | 2021-09-09 | 2021-12-31 | 华中科技大学 | Road right dynamic distribution method for automatic driving automobile |
| CN113947896B (en) * | 2021-09-22 | 2022-12-20 | 山东高速建设管理集团有限公司 | Special lane traffic indication method and system |
| CN113628459B (en) * | 2021-09-23 | 2022-05-24 | 西南交通大学 | Bus priority method for reserved intersection facing intermittent bus lane |
| CN114023066B (en) * | 2021-11-08 | 2022-09-20 | 河海大学 | Traffic flow control method for special lane for automatic driving |
| CN114220270A (en) * | 2021-12-17 | 2022-03-22 | 新唐信通(浙江)科技有限公司 | Flexible lane management-oriented full-path information issuing method and system |
| CN114375005B (en) * | 2022-03-21 | 2022-06-28 | 深圳粤讯通信科技有限公司 | Vehicle road communication test system based on 5G antenna |
| CN114613145B (en) * | 2022-05-12 | 2022-08-19 | 中运科技股份有限公司 | Passenger traffic flow perception early warning system and method under big data |
| CN114999148A (en) * | 2022-05-16 | 2022-09-02 | 国汽智图(北京)科技有限公司 | Congestion degree monitoring method and device, computer equipment and storage medium |
| CN114999188A (en) * | 2022-05-20 | 2022-09-02 | 清华大学 | Lane isolation marking line control equipment and system, and lane control method and device |
| CN114820819B (en) * | 2022-05-26 | 2023-03-31 | 广东机电职业技术学院 | Expressway automatic driving method and system |
| CN115116217B (en) * | 2022-05-26 | 2023-09-26 | 东北林业大学 | Dynamic measuring and calculating method and system for saturation flow rate and starting loss time of lane |
| CN114973737B (en) * | 2022-06-07 | 2023-09-26 | 清华大学 | Method for controlling way of automatically driving vehicle to enter and exit special lane for automatic driving |
| CN115223362B (en) * | 2022-07-05 | 2024-02-09 | 上海市城市建设设计研究总院（集团）有限公司 | Special lane for dynamic automatic driving of urban road and use method thereof |
| CN115188189A (en) * | 2022-07-07 | 2022-10-14 | 清华大学 | Special lane dynamic setting method for supporting automatic driving truck vehicle queue operation |
| CN115840404B (en) * | 2022-12-21 | 2023-11-03 | 浙江大学 | Cloud control automatic driving system based on automatic driving special road network and digital twin map |
| CN115662145B (en) * | 2022-12-26 | 2023-03-10 | 广东汇通信息科技股份有限公司 | Emergency shunting method for vehicles on accident road section based on big data |
| CN116206475B (en) * | 2023-04-24 | 2023-07-14 | 成都西谷曙光数字技术有限公司 | LDSW road safety driving system and working method thereof |
| CN116416806A (en) * | 2023-06-12 | 2023-07-11 | 天津市政工程设计研究总院有限公司 | Intelligent network allies oneself with autopilot freight transportation lane control system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105590481A (en) * | 2014-11-10 | 2016-05-18 | 现代摩比斯株式会社 | Autonomous driving vehicle, autonomous driving management apparatus, and method of controlling the same |
| CN105741595A (en) * | 2016-04-27 | 2016-07-06 | 常州加美科技有限公司 | Unmanned vehicle navigation driving method based on cloud database |
| CN106971546A (en) * | 2017-05-18 | 2017-07-21 | 重庆大学 | Section bus permeability method of estimation based on bus GPS data |
| CN107564317A (en) * | 2017-10-27 | 2018-01-09 | 左依宁 | A kind of automatic driving vehicle control system and method |
| CN108399762A (en) * | 2018-05-08 | 2018-08-14 | 北京航空航天大学 | A kind of automatic Pilot and pilot steering vehicle mix the intersection control method under the conditions of row |
| CN108615384A (en) * | 2016-12-12 | 2018-10-02 | 普天信息技术有限公司 | Track distribution method based on cloud platform and the cloud platform for track distribution |
| CN109910894A (en) * | 2019-04-09 | 2019-06-21 | 南京锦和佳鑫信息科技有限公司 | Automatic Pilot dedicated Lanes are automatic, hand pattern switching system and its application |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002157696A (en) * | 2000-11-22 | 2002-05-31 | Natl Inst For Land & Infrastructure Management Mlit | Obstacle avoiding method of ahs with dedicated lane |
| JP6652045B2 (en) * | 2016-12-26 | 2020-02-19 | トヨタ自動車株式会社 | Self-driving vehicle |
| US10710588B2 (en) * | 2017-05-23 | 2020-07-14 | Toyota Motor Engineering & Manufacturing North America, Inc. | Merging and lane change acceleration prediction energy management |
| CN108109415B (en) * | 2017-12-20 | 2021-01-08 | 东风汽车集团有限公司 | Internet automatic driving control system and method with anti-congestion function |
| CN108716173A (en) * | 2018-06-11 | 2018-10-30 | 周建海 | A kind of autonomous driving vehicle accommodation road and its application method |
| CN109903575A (en) * | 2019-04-09 | 2019-06-18 | 南京锦和佳鑫信息科技有限公司 | A kind of into and out method of automatic Pilot special lane inlet and outlet ring road |
| CN110503823B (en) * | 2019-07-11 | 2021-06-22 | 江苏大学 | Special lane control system and method for automatic driving vehicle |
-
2019
- 2019-07-11 CN CN201910624637.0A patent/CN110503823B/en active Active
-
2020
- 2020-06-11 WO PCT/CN2020/095585 patent/WO2021004222A1/en active Application Filing
- 2020-06-11 GB GB2107328.3A patent/GB2594368B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105590481A (en) * | 2014-11-10 | 2016-05-18 | 现代摩比斯株式会社 | Autonomous driving vehicle, autonomous driving management apparatus, and method of controlling the same |
| CN105741595A (en) * | 2016-04-27 | 2016-07-06 | 常州加美科技有限公司 | Unmanned vehicle navigation driving method based on cloud database |
| CN108615384A (en) * | 2016-12-12 | 2018-10-02 | 普天信息技术有限公司 | Track distribution method based on cloud platform and the cloud platform for track distribution |
| CN106971546A (en) * | 2017-05-18 | 2017-07-21 | 重庆大学 | Section bus permeability method of estimation based on bus GPS data |
| CN107564317A (en) * | 2017-10-27 | 2018-01-09 | 左依宁 | A kind of automatic driving vehicle control system and method |
| CN108399762A (en) * | 2018-05-08 | 2018-08-14 | 北京航空航天大学 | A kind of automatic Pilot and pilot steering vehicle mix the intersection control method under the conditions of row |
| CN109910894A (en) * | 2019-04-09 | 2019-06-21 | 南京锦和佳鑫信息科技有限公司 | Automatic Pilot dedicated Lanes are automatic, hand pattern switching system and its application |
Non-Patent Citations (1)
| Title |
|---|
| 基于元胞自动机的自动驾驶技术对交通运行的影响研究;王文 等;《现代交通技术》;20180228;第15卷(第1期);正文全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| GB2594368A (en) | 2021-10-27 |
| GB2594368B (en) | 2022-06-08 |
| WO2021004222A1 (en) | 2021-01-14 |
| GB202107328D0 (en) | 2021-07-07 |
| CN110503823A (en) | 2019-11-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110503823B (en) | Special lane control system and method for automatic driving vehicle | |
| CN108364494B (en) | Intelligent road traffic management method, system and platform | |
| CN110675629B (en) | Big data-based highway congestion prediction and active prevention and control method | |
| CN108806283B (en) | Control method of traffic signal lamp and Internet of vehicles platform | |
| CN108538069B (en) | System and method for controlling vehicle speed in ramp merging area | |
| US6708085B2 (en) | Probe car control method and traffic control system | |
| CN104778834B (en) | Urban road traffic jam judging method based on vehicle GPS data | |
| CN109859479B (en) | Vehicle road guiding evacuation method based on cloud computing | |
| CN110111592B (en) | Method for dynamically matching optimal signal timing scheme based on traffic signal controller | |
| CN108986509B (en) | Urban area path real-time planning method based on vehicle-road cooperation | |
| CN109345031B (en) | Coordinated trunk line planning method and configuration system based on traffic flow data | |
| CN113313936A (en) | Traffic light detection system for a vehicle | |
| CN105513375A (en) | Regional safety traffic control system | |
| CN105489034A (en) | Main line full traffic control system and method | |
| CN105185145A (en) | Intelligent dynamic road condition map navigation system and method | |
| CN107564310A (en) | A kind of bus or train route interacted system and method based on the processing of Traffic Information cloud | |
| CN109118761A (en) | A kind of wisdom traffic solution and its management system based on artificial intelligence | |
| CN105321357A (en) | Setting system and method for same-phase turn-right and pedestrian signals of independent intersection | |
| DE102020210499A1 (en) | DATA CONTROLLED RULES | |
| CN111915885B (en) | Urban road traffic simulation experiment method and system | |
| CN112201033B (en) | Road traffic operation active control strategy selection method | |
| CN105741566A (en) | Traffic information display system controlled based on intelligent traffic management system | |
| CN107564336B (en) | Signalized intersection left turn conflict early warning system and early warning method | |
| CN112767683A (en) | Path induction method based on feedback mechanism | |
| CN111489571A (en) | V2X-based vehicle intersection priority passing method and system |
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 |