CN115344971A - Optimized layout method for transverse and longitudinal positions of lane special for automatic driving under man-machine hybrid driving scene - Google Patents
Optimized layout method for transverse and longitudinal positions of lane special for automatic driving under man-machine hybrid driving scene Download PDFInfo
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Abstract
The invention relates to an optimal layout method for the transverse and longitudinal positions of an automatic driving special lane in a man-machine mixed driving scene, which comprises the following steps: s1, abstracting a road section and a ramp of an express way into a directed network graph, and considering a basic road section of a lane, a lane change connecting section, an upper ramp and a lower ramp; s2, dividing the motion behaviors of the vehicle on the expressway into a vehicle following behavior and a lane changing behavior, and determining cost functions of the two behaviors; s3, obtaining an OD matrix of the vehicle on a lower ramp of the expressway; s4, establishing a mixed traffic flow distribution model considering the automatic driving special lane; s5, establishing an optimal layout decision model of the automatic driving special lane; and S6, solving to obtain an optimal layout scheme of the transverse and longitudinal positions of the automatic driving special lane. The invention is based on the lane-level express way network modeling, fully inspects the following and lane changing behaviors of vehicles on the express way, can jointly optimize the layout of the transverse and longitudinal positions of the automatic driving special lane, and provides a new and scientific quantitative decision-making method.
Description
Technical Field
The invention relates to the technical field of vehicle-road networking and automatic driving traffic design, in particular to an automatic driving special lane transverse and longitudinal position optimized layout method in a man-machine hybrid driving scene.
Background
Man-machine hybrid drive traffic flows consisting of human-driven vehicles and autonomous vehicles will operate together in existing traffic infrastructure, such as urban expressways, for a long period of time in the future. The automatic driving vehicle special road is taken as an important road weight distribution means in a man-machine hybrid driving scene, is optimally distributed on the expressway, and is a key for further improving the running performance of the urban expressway system in the man-machine hybrid driving scene.
In the existing technology for optimizing layout of the automatic driving special lanes, the number of the automatic driving special lanes is determined by calculating the matching degree of the traffic capacity and the traffic flow, and the conclusion of the number is only given out the relation between the number of the automatic driving special lanes required to be arranged on the basic road section of the express way and the permeability of automatic driving vehicles, but the topological structure of the ramp on and off the express way is not considered, and the influence of OD requirements on the layout of the transverse positions of the automatic driving special lanes is not considered.
Disclosure of Invention
The invention aims to solve the technical problem of providing an automatic driving special lane transverse and longitudinal position optimized layout method in a man-machine mixed driving scene, which can jointly optimize the transverse and longitudinal positions of the automatic driving special lane and provide a new and scientific quantitative decision method for setting the automatic driving special lane for an express way.
The technical scheme adopted by the invention for solving the technical problems is as follows: the method for optimally arranging the transverse and longitudinal positions of the automatic driving special lane in a man-machine mixed driving scene comprises the following steps of:
s1, abstracting a road section and a ramp of an express way into a directed network graph, and considering a basic road section of a lane, a lane change connecting section, an upper ramp and a lower ramp at the same time;
s2, dividing the motion behaviors of the vehicle on the expressway into two types of vehicle following and lane changing, and respectively determining cost functions of the two types of motion behaviors;
s3, obtaining an OD matrix of a vehicle on a ramp-down ramp of the expressway;
s4, establishing a mixed traffic flow distribution model considering the automatic driving special lane;
s5, establishing an optimal layout decision model of the automatic driving special lane;
and S6, solving to obtain an optimal layout scheme of the transverse and longitudinal positions of the automatic driving special lane.
According to the scheme, the step S1 comprises the following steps:
s101, abstracting a basic road section of a lane, an upper ramp and a lower ramp into one-way edges in a network diagram, wherein each lane comprises two attributes of a transverse position l and a longitudinal position a;
s102, abstracting a lane change connecting section between a ramp and a lane, and between lanes as a bidirectional edge in a network diagram, wherein the number of the bidirectional edge is b;
s103, abstracting a starting point and an end point of the express way and an intersection point of an upper ramp, a lower ramp and a basic road section of the lane into nodes in a network graph;
and S104, connecting the nodes and the edges to form a directed network graph, namely completing modeling of the expressway facility.
According to the scheme, the step S2 comprises the following steps:
s201, dividing behaviors of the vehicle on the expressway into two main categories of following and changing lanes: the following behavior is a driving behavior that a vehicle runs along with a preceding vehicle in a lane and keeps a certain safe distance, and the following behavior corresponds to the operation of a driver on an accelerator and a brake; the lane changing behavior is a behavior that a vehicle changes from one lane to another lane by searching for a safety interval existing in an adjacent lane, and the lane changing behavior corresponds to the operation of a steering wheel by a driver;
S202、C a，l，MTF the single-lane traffic capacity of man-machine mixed driving traffic flow on the lanes a and l is represented, and the calculation method is given by the following formula:
C a，l，MTF ＝C HV +(C AV -C HV )(P AV ) 2
in the formula: p AV Permeability for autonomous vehicles within a lane; c HV The vehicle permeability is 100% and the lane traffic capacity is expressed,
h HV a saturated headway for a human to drive a vehicle; c AV Represents P AV Is 100% of the traffic capacity of the lane,
h AV a saturated headway for an autonomous vehicle;
s203, calculating the travel cost of the following vehicle by adopting the following formula:
in the formula: l, a are respectively the serial numbers of the transverse position and the longitudinal position of the lane; t is t a，l Driving cost for lanes a, l; t is t a，l，free Representing travel time of driving on lanes a and l in a free flow scene; x is a radical of a fluorine atom a，l，HV Representing the flow of human-driven vehicles on lanes a, l; x is the number of a，l，AV Representing the flow of autonomous vehicles on lanes a, l; e and f are coefficients to be calibrated;
s204, calculating the lane-changing travel cost by adopting the following formula:
t b ＝K 1 (v out -v in ) 2 +K 2 d in +K 3
in the formula: v. of out The driving speed of the lane before lane changing; v. of in Is the driving speed of the target lane; d in Is the density of the target lane; k 1 ，K 2 ，K 3 Is the coefficient to be calibrated.
According to the scheme, the step S3 comprises the following steps:
s301, recording the serial number of the upper ramp and the serial number of the lower ramp of the vehicle by using the video detectors arranged at the upper ramp and the lower ramp;
s302, counting the situations of the vehicles on the ramp up and down in the morning and evening in the working day and on the weekend holiday respectively to obtain OD matrixes of the express way on the working day and on the weekend holiday, wherein the OD matrixes comprise a starting point, an end point, an upper ramp and a lower ramp of the express way;
s303, splitting the OD matrix into the OD of the human-driven vehicle according to the permeability of the automatic-driven vehicle HV And OD of autonomous vehicle AV The calculation formula is as follows:
OD HV ＝OD total ×(1-P AV )
OD AV ＝OD total ×PA V
according to the scheme, the step S4 comprises the following steps:
s401, establishing a mixed traffic flow distribution model considering the automatic driving special lane, wherein the specific mathematical expression is as follows:
Subject to：
in the formula:
representing human-driven vehicle traffic for the ith path between OD and r-s;
representing the autonomous vehicle flow for the ith path between OD and r-s;
a decision variable is used for indicating whether the lanes a and l are provided with the automatic driving special lane, and when the lane a and l are provided with the automatic driving special lane, 1 is taken, and 0 is not taken; m is a penalty term, and a sufficiently large number is taken.
According to the scheme, the step S5 comprises the following steps:
establishing an optimized layout decision model of the automatic driving special lane, wherein the specific mathematical expression is as follows:
Subject to：
t b ＝K 1 (v out -v in ) 2 +K 2 d in +K 3
in the formula: x is a radical of a fluorine atom a，l，HV ，x a，l，AV The traffic flow distribution model is mixed in the step S401, TTT is the system travel time,
to not set the dedicated lane path count variable,
in order to set a path count variable after a dedicated path, 1 is taken when the path passes through a lane change with the number b, and 0 is not set.
According to the scheme, the step S6 comprises the following steps:
s601, solving an optimal layout decision model of the automatic driving special lane by adopting a heuristic algorithm, wherein a mixed traffic flow distribution model is solved by adopting an MATLAB (matrix laboratory) optimization tool box;
s602, obtaining the optimal
Will be described in
The road sections numbered corresponding to the part 1 are set as the special roads for the automatic driving vehicle, and the rest are mixed lanes.
According to the scheme, the heuristic algorithm is a genetic algorithm or a simulated annealing algorithm.
The method for optimally arranging the transverse and longitudinal positions of the lane special for automatic driving in the man-machine hybrid driving scene has the following beneficial effects:
the invention is based on lane-level express way network modeling, fully inspects the following and lane changing behaviors of vehicles on the express way, avoids the defect that the prior method can only determine the number of the automatic driving lanes on the basic road section of the express way, can jointly optimize the layout of the transverse and longitudinal positions of the automatic driving special lanes, and provides a new and scientific quantitative decision-making method for setting the automatic driving special lanes for the express way.
Drawings
FIG. 1 is a flow chart of an optimal layout method for the longitudinal and transverse positions of an automatic driving lane in a man-machine mixed driving scene according to the present invention;
FIG. 2 is a schematic diagram of a freeway modeling method of the present invention;
fig. 3 is a schematic diagram of the layout of the driveway exclusive for the automatic driving of the expressway in the embodiment of the invention.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
One section of an urban expressway comprises 7 upper ramps and 7 lower ramps, and when the automatic driving vehicles reach a certain proportion, the method provided by the application is adopted, and under the condition of the given permeability of the automatic driving vehicles, the automatic driving special lanes are arranged in the transverse direction and the longitudinal direction to obtain the optimal road network operation performance, as shown in figure 1, the method specifically comprises the following steps:
s1, abstracting a road section and a ramp of an express way into a directed topology network, wherein the process needs to consider a basic road section of a lane, a lane change connecting section, an upper ramp and a lower ramp at the same time;
s101, abstracting basic road sections of lanes, upper ramps and lower ramps into one-way edges in a network diagram, wherein each lane comprises two attributes, namely a transverse position l and a longitudinal position a;
s102, abstracting a lane change connecting section between a ramp and a lane, and between lanes as a bidirectional edge in a network diagram, wherein the number of the bidirectional edge is b;
s103, abstracting a starting point and an end point of the express way and an intersection point of an upper ramp, a lower ramp and a basic road section of the lane into nodes in a network graph;
and S104, connecting the nodes and the edges to form a directed network graph, namely completing modeling of the expressway equipment, as shown in the figure 2.
S2, dividing the behaviors of the vehicle on the expressway into two categories, namely vehicle following and lane changing, and respectively determining cost functions of the two behaviors;
s201, dividing behaviors of the vehicle on the expressway into two categories of vehicle following and lane changing. The following behavior is the driving behavior that the vehicle runs along with the front vehicle in the lane and keeps a certain safe distance, and corresponds to the operation of the accelerator and the brake by the driver. Lane change, that is, the behavior of a vehicle changing from one lane to another lane by finding a safety interval existing in an adjacent lane, wherein the lane change behavior corresponds to the operation of a steering wheel by a driver;
S202、C a，l，MTF the single-lane traffic capacity of man-machine mixed driving traffic flow on the lanes a and l is represented by the following calculation method:
C a，l，MTF ＝C HV +(C AV -C HV )(P AV ) 2
in the formula: p AV Permeability for autonomous vehicles within a lane; c HV Representing the lane capacity of 100% of the permeability of the automobile driven by human,
h HV the saturated headway time of a vehicle is 1s for human driving; c AV Represents P AV Is 100% of the traffic capacity of the lane,
h AV and 2s is taken for the saturated headway of the automatic driving vehicle.
S203, calculating the travel cost of the following vehicle by adopting the following formula:
in the formula: l, a are respectively the serial numbers of the transverse position and the longitudinal position of the lane; t is t a，l As a lane aL driving cost; t is t a，l，free Representing travel time of driving on lanes a and l in a free flow scene; x is a radical of a fluorine atom a，l，HV Representing the flow of human-driven vehicles on lanes a, l; x is the number of a，l，AV Representing the flow of autonomous vehicles on lanes a, l; and e and f are coefficients to be calibrated.
S204, calculating the lane-changing travel cost by adopting the following formula:
t b ＝K 1 (v out -v in ) 2 +K 2 d in +K 3
in the formula: v. of out The driving speed of the lane before lane changing; v. of in Is the driving speed of the target lane; d in Is the density of the target lane; k 1 ，K 2 ，K 3 Is the coefficient to be calibrated.
S3, obtaining an OD matrix of the vehicle on a lower ramp of the expressway;
s301, recording the serial number of the upper ramp and the serial number of the lower ramp of the vehicle by using the video detectors arranged at the upper ramp and the lower ramp;
s302, counting up the up-down ramp conditions of the vehicle in the morning and evening peak periods of the working day (such as 7-8, 17-00. The early peak OD matrix is shown in table 1:
TABLE 1 early peak OD matrix
| |
3 | 5 | 6 | 9 | 11 | 13 | 15 | 16 |
| 1 | 180 | 206 | 95 | 474 | 377 | 380 | 318 | 1108 |
| 2 | 413 | 280 | 280 | 194 | 213 | 186 | 216 | 380 |
| 4 | - | 198 | 110 | 501 | 368 | 313 | 216 | 315 |
| 7 | - | - | - | 526 | 381 | 409 | 425 | 594 |
| 8 | - | - | - | 511 | 376 | 368 | 430 | 568 |
| 10 | - | - | - | - | 632 | 398 | 325 | 500 |
| 12 | - | - | - | - | - | 735 | 545 | 966 |
| 14 | - | - | - | - | - | - | 484 | 741 |
S303, enabling the OD matrix to be based on the permeability of the automatic driving vehicle (P is taken in the embodiment) AV = 0.4), split into ODs for human-driven vehicles HV And OD of autonomous vehicle AV The calculation formula is as follows:
OD HV ＝OD total ×(1-P AV )
OD AV ＝OD total ×P AV
the split early peak OD matrices are shown in tables 2-3:
TABLE 2 early peak OD after splitting HV Matrix of
| |
3 | 5 | 6 | 9 | 11 | 13 | 15 | 16 |
| 1 | 108 | 123.6 | 57 | 284.4 | 226.2 | 228 | 190.8 | 664.8 |
| 2 | 247.8 | 168 | 168 | 116.4 | 127.8 | 111.6 | 129.6 | 228 |
| 4 | - | 118.8 | 66 | 300.6 | 220.8 | 187.8 | 129.6 | 189 |
| 7 | - | - | - | 315.6 | 228.6 | 245.4 | 255 | 356.4 |
| 8 | - | - | - | 306.6 | 225.6 | 220.8 | 258 | 340.8 |
| 10 | - | - | - | - | 379.2 | 238.8 | 195 | 300 |
| 12 | - | - | - | - | - | 441 | 327 | 579.6 |
| 14 | - | - | - | - | - | - | 290.4 | 444.6 |
TABLE 3 early peak OD after splitting AV Matrix array
| |
3 | 5 | 6 | 9 | 11 | 13 | 15 | 16 |
| 1 | 72 | 82.4 | 38 | 189.6 | 150.8 | 152 | 127.2 | 443.2 |
| 2 | 165.2 | 112 | 112 | 77.6 | 85.2 | 74.4 | 86.4 | 152 |
| 4 | - | 79.2 | 44 | 200.4 | 147.2 | 125.2 | 86.4 | 126 |
| 7 | - | - | - | 210.4 | 152.4 | 163.6 | 170 | 237.6 |
| 8 | - | - | - | 204.4 | 150.4 | 147.2 | 172 | 227.2 |
| 10 | - | - | - | - | 252.8 | 159.2 | 130 | 200 |
| 12 | - | - | - | - | - | 294 | 218 | 386.4 |
| 14 | - | - | - | - | - | - | 193.6 | 296.4 |
S304, establishing a mixed traffic flow distribution model considering the arrangement of an automatic driving special lane or a human driving vehicle special lane;
Subject to：
in the formula:
represents the human-driven vehicle traffic of the ith path between OD and r-s;
representing the autonomous vehicle flow for the ith path between OD and r-s;
the decision variable represents whether the lanes a and l are provided with the automatic driving special lane, and when the lanes a and l are provided with the automatic driving special lane, 1 is taken, and 0 is not taken; m is a penalty term, and a sufficiently large number is taken. The other parameters are as defined above.
S4, establishing an automatic driving special lane optimization model;
establishing an optimized layout decision model of the driveway special for automatic driving, wherein the specific mathematical expression is as follows:
Subject to：
t b ＝K 1 (v out -v in ) 2 +K 2 d in +K 3
(x a,l,HV ,x a,l,AV ) Derived from a mixed traffic flow distribution model 51)
In the formula: the TTT is the time of the system trip,
to not set the dedicated lane path count variable,
in order to set a path counting variable after the special path, 1 is taken when the path passes through the path change with the number b, and 0 is not set.
And S5, solving to obtain a transverse and longitudinal position combined optimized layout scheme of the automatic driving special lane.
S501, solving an optimized layout decision model of the driveway special for automatic driving by adopting a heuristic algorithm (such as a genetic algorithm and a simulated annealing algorithm), wherein the mixed traffic flow distribution model is solved by adopting an MATLAB optimization tool box.
S502, obtaining the optimal
Will be provided with
The road sections numbered corresponding to the part 1 are set as the special roads for the automatic driving vehicle, and the rest are mixed lanes.
As shown in fig. 3, the corresponding autopilot lane position.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (8)
1. A method for optimally arranging the transverse and longitudinal positions of an automatic driving special lane in a man-machine mixed driving scene is characterized by comprising the following steps:
s1, abstracting a road section and a ramp of an express way into a directed network graph, and considering a basic road section of a lane, a lane change connecting section, an upper ramp and a lower ramp at the same time;
s2, dividing the movement behaviors of the vehicle on the expressway into two types, namely following the vehicle and changing lanes, and respectively determining cost functions of the two movement behaviors;
s3, obtaining an OD matrix of a vehicle on a ramp-down ramp of the expressway;
s4, establishing a mixed traffic flow distribution model considering the automatic driving special lane;
s5, establishing an optimal layout decision model of the automatic driving special lane;
and S6, solving to obtain an optimal layout scheme of the transverse and longitudinal positions of the automatic driving special lane.
2. The method for optimally arranging the transverse and longitudinal positions of the driveways special for automatic driving under the scene of man-machine mixed driving according to claim 1, wherein the step S1 comprises the following steps:
s101, abstracting basic road sections of lanes, upper ramps and lower ramps into one-way edges in a network diagram, wherein each lane comprises two attributes of a transverse position l and a longitudinal position a;
s102, abstracting a lane change connecting section between a ramp and a lane, and between lanes as a bidirectional edge in a network diagram, wherein the number of the bidirectional edge is b;
s103, abstracting a starting point and an end point of the express way and an intersection point of an upper ramp, a lower ramp and a basic road section of the lane into nodes in a network graph;
and S104, connecting the nodes and the edges to form a directed network graph, namely completing modeling of the expressway facility.
3. The method for optimally arranging the transverse and longitudinal positions of the driveways special for automatic driving under the scene of man-machine mixed driving according to claim 1, wherein the step S2 comprises the following steps:
s201, dividing the behaviors of the vehicle on the expressway into two categories, namely following the vehicle and changing the lane: the following behavior is a driving behavior that the vehicle drives in a lane following a front vehicle and keeps a certain safe distance, and the following behavior corresponds to the operation of a driver on an accelerator and a brake; the lane changing behavior is a behavior that a vehicle changes from one lane to another lane by searching for a safety interval existing in an adjacent lane, and the lane changing behavior corresponds to the operation of a steering wheel by a driver;
S202、C a，l，MTF the single-lane traffic capacity of man-machine mixed driving traffic flow on the lanes a and l is represented, and the calculation method is given by the following formula:
C a，l，MTF ＝C HV +(C AV -C HV )(P AV ) 2
in the formula: p AV Permeability for autonomous vehicles within a lane; c HV The vehicle permeability is 100% and the lane traffic capacity is expressed,
h HV a saturated headway for a human to drive a vehicle; c AV Represents P AV Is 100% of the traffic capacity of the lane,
h AV a saturated headway for an autonomous vehicle;
s203, calculating the travel cost of the following vehicle by adopting the following formula:
in the formula: l, a are respectively the serial numbers of the transverse position and the longitudinal position of the lane; t is t a，l Driving cost for lanes a, l; t is t a，l，free Representing travel time of driving on lanes a and l in a free flow scene; x is the number of a，l，HV Representing the flow of human-driven vehicles on lanes a, l; x is the number of a，l，AV Representing the flow of autonomous vehicles on lanes a, l; e and f are coefficients to be calibrated;
s204, calculating the lane-changing travel cost by adopting the following formula:
t b ＝K 1 (v out -v in ) 2 +K 2 d in +K 3
in the formula: v. of out The driving speed of the lane before lane changing; v. of in Is the driving speed of the target lane; d in Is the density of the target lane; k 1 ，K 2 ，K 3 Is the coefficient to be calibrated.
4. The optimal layout method for the transverse and longitudinal positions of the driveways special for automatic driving under the scene of man-machine mixed driving as claimed in claim 1, wherein the step S3 comprises the following steps:
s301, recording the serial number of the on-ramp and the serial number of the off-ramp of the vehicle by using a video detector arranged at the on-ramp and the off-ramp;
s302, counting the situations of the vehicles on the ramp on the working day and the vehicles on the ramp on the weekend and the weekend on the peak at the morning and at the evening respectively to obtain OD matrixes of the express way on the working day and the weekend on the ramp, wherein the OD matrixes comprise a starting point, a terminal point, an upper ramp and a lower ramp of the express way;
s303, splitting the OD matrix into the OD of the human-driven vehicle according to the permeability of the automatic-driven vehicle HV And OD of autonomous vehicle AV The calculation formula is as follows:
OD HV ＝OD total ×(1-P AV )
OD AV ＝OD total ×P AV 。
5. the optimal layout method for the transverse and longitudinal positions of the driveways special for automatic driving under the scene of man-machine mixed driving as claimed in claim 1, wherein the step S4 comprises the following steps:
s401, establishing a mixed traffic flow distribution model considering the automatic driving special lane, wherein the specific mathematical expression is as follows:
Subject to：
in the formula:
representThe human-driven vehicle flow of the ith path between OD and r-s;
representing the autonomous vehicle traffic for the ith path between OD and r-s;
the decision variable represents whether the lanes a and l are provided with the automatic driving special lane, and 1 is taken when the automatic driving special lane is provided, and 0 is not taken when the automatic driving special lane is provided; m is a penalty term, and a sufficiently large number is taken.
6. The optimal layout method for the transverse and longitudinal positions of the lanes special for automatic driving in the man-machine hybrid driving scene according to claim 5, wherein the step S5 comprises the following steps:
establishing an optimized layout decision model of the driveway special for automatic driving, wherein the specific mathematical expression is as follows:
Subject to：
t b ＝K 1 (v out -v in ) 2 +K 2 d in +K 3
in the formula: x is the number of a，l，HV ，x a，l，AV From aThe step S401 is obtained by mixing a traffic flow distribution model, TTT is system travel time,
to not set the dedicated lane path count variable,
in order to set a path count variable after a dedicated path, 1 is taken when the path passes through a lane change with the number b, and 0 is not set.
7. The optimal layout method for the transverse and longitudinal positions of the driveways special for automatic driving under the scene of man-machine mixed driving as claimed in claim 1, wherein the step S6 comprises the following steps:
s601, solving an optimal layout decision model of the automatic driving special lane by adopting a heuristic algorithm, wherein a mixed traffic flow distribution model is solved by adopting an MATLAB (matrix laboratory) optimization tool box;
s602, obtaining the optimal
Will be described in
The road sections numbered corresponding to the part 1 are set as the special roads for the automatic driving vehicle, and the rest are mixed lanes.
8. The optimal layout method for the transverse and longitudinal positions of the lanes special for automatic driving in the man-machine hybrid driving scene as claimed in claim 7, wherein the heuristic algorithm is a genetic algorithm or a simulated annealing algorithm.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116246466A (en) * | 2023-03-13 | 2023-06-09 | 长安大学 | Hybrid traffic flow management method and system considering autopilot multimode characteristics |
| CN116394969A (en) * | 2022-12-26 | 2023-07-07 | 交通运输部公路科学研究所 | Road vehicle control method and device with automatic driving special lane |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116394969A (en) * | 2022-12-26 | 2023-07-07 | 交通运输部公路科学研究所 | Road vehicle control method and device with automatic driving special lane |
| CN116394969B (en) * | 2022-12-26 | 2023-11-24 | 交通运输部公路科学研究所 | Road vehicle control method and device with automatic driving special lane |
| CN116246466A (en) * | 2023-03-13 | 2023-06-09 | 长安大学 | Hybrid traffic flow management method and system considering autopilot multimode characteristics |
| CN116246466B (en) * | 2023-03-13 | 2024-01-23 | 长安大学 | Hybrid traffic flow management method and system considering autopilot multimode characteristics |
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