Optimization Method for the Location of the Exclusive Lane for Mixed Human-Machine Driving Autonomous Driving and the Signal Timing at Intersections

Abstract

The present invention relates to a method for jointly optimizing the position of a dedicated lane for autonomous vehicles and the signal timing at intersections during mixed human-machine driving, comprising the following steps: S1. Divide the urban road network into basic road sections, weaving sections, and intersection queue sections, and abstract them into a directed network diagram; S2. Establish travel time calculation formulas for basic road sections, weaving sections, and intersection queue sections; S3. Establish a mixed traffic flow distribution model considering the position of the dedicated lane for autonomous vehicles and the signal timing at intersections; S4. Establish a decision-making model for the position of the dedicated lane for autonomous vehicles and the signal timing at intersections; S5. Solve to obtain an optimization scheme for the position of the dedicated lane for autonomous vehicles and the signal timing at intersections. The present invention is based on lane-level road network modeling. By dividing the basic road sections, weaving sections, and intersection queue sections of the road network, it examines the interaction relationship between the lateral position of the dedicated lane for autonomous vehicles and the signal timing at intersections, providing a new and scientific quantitative decision-making method for dedicated lanes for autonomous driving in urban road networks.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle-road networking and autonomous driving traffic design, and in particular to a method for optimizing the position of a dedicated lane for human-machine mixed driving autonomous vehicles and intersection signal timing. Background Technique

[0002] For a long time in the future, a traffic flow of human-machine mixed driving composed of human-driven vehicles and autonomous driving vehicles will co-run on the existing traffic infrastructure. As important means of road right allocation in the human-machine mixed driving scenario, optimizing the layout of dedicated lanes for autonomous driving vehicles on the road network and simultaneously optimizing the signal control at intersections are the keys to further improving the operation performance of the urban road network in the human-machine mixed driving scenario.

[0003] In the existing technologies, the layout of dedicated lanes for autonomous driving on sections and the signal control at intersections are often considered separately, without considering the influence of the lateral position layout of dedicated lanes for autonomous driving and intersection signal timing. Therefore, the present invention proposes a method for optimizing the position of a dedicated lane for human-machine mixed driving autonomous vehicles and intersection signal timing, which has important theoretical significance and engineering value for guiding the reasonable and economical setting of dedicated lanes for autonomous driving and signal timing. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for optimizing the position of a dedicated lane for human-machine mixed driving autonomous vehicles and intersection signal timing, which fully considers the interaction relationship between the lateral position of the dedicated lane for autonomous driving vehicles and intersection signal timing, and provides a scientific quantitative decision-making method for setting dedicated lanes for autonomous driving in the urban road network.

[0005] The technical solution adopted by the present invention to solve its technical problems is: to provide a method for optimizing the position of a dedicated lane for human-machine mixed driving autonomous vehicles and intersection signal timing, including the following steps:

[0006] S1. Divide the urban road network into basic sections, weaving sections, and intersection queue sections, and abstract them into a directed network diagram;

[0007] S2. Establish travel time calculation formulas for basic sections, weaving sections, and intersection queue sections;

[0008] S3. Establish a mixed traffic flow distribution model considering the position of the dedicated lane for autonomous driving vehicles and intersection signal timing;

[0009] S4. Establish a decision-making model for the position of the dedicated lane for autonomous driving vehicles and intersection signal timing;

[0010] S5. Solve to obtain an optimization scheme for the position of the dedicated lane for autonomous driving vehicles and intersection signal timing.

[0011] According to the above solution, step S1 includes the following steps:

[0012] S101. Abstract the basic lane segments as one-way edges in the network graph, where each lane is numbered a;

[0013] S102. Abstract the lane-changing connection segments between lanes in the lane-changing weaving section as two-way edges in the network graph, numbered b;

[0014] S103. Abstract the intersection points as nodes in the network graph, numbered c;

[0015] S104. Connect the nodes and edges to form a directed network graph, which completes the modeling of the urban road network.

[0016] According to the above solution, step S2 includes the following steps:

[0017] S201. Calculate the travel cost of the basic segment using the following formula:

[0018]

[0019] In the formula: a is the number of the lane respectively; t a is the driving cost of lane a; t a,free represents the travel time of driving on lane a under the free flow scenario; x a,HV represents the traffic flow of human-driven vehicles on lane a,l; x a,AV represents the traffic flow of autonomous vehicles on lane a; e, f are coefficients to be calibrated;

[0020] S202. Calculate the travel cost of the weaving section using the following formula:

[0021]

[0022] In the formula: v free is the free flow speed, x out is the traffic flow of the lane before lane change; v in is the traffic flow of the target lane; K1, K2, K3, K4 are coefficients to be calibrated, calibrated by minimizing the error between the assigned traffic flow and the actual traffic flow obtained from the traffic assignment model based on the above formula.

[0023] S203. Calculate the travel cost of the intersection queuing section using the following formula:

[0024]

[0025] In the formula: c is the signal cycle length, λ n is the green signal ratio of approach n, q n is the traffic flow of approach n, xn is the saturation degree of approach n, s n is the saturated flow rate of approach n.

[0026] According to the above scheme, the specific mathematical expression of the mixed traffic flow distribution model in step S3 is as follows:

[0027]

[0028] Subject to:

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035]

[0036]

[0037] In the formula: represents the traffic flow of human-driven vehicles on the i-th path between OD pair r-s; represents the traffic flow of autonomous vehicles on the i-th path between OD pair r-s; OD HV represents the OD of human-driven vehicles, OD AV represents the OD of autonomous vehicles, represents the penetration rate of autonomous vehicles between OD pair r-s; represents whether a dedicated lane for autonomous vehicles is set up on lane a. Take 1 when it is set up and 0 when it is not. M is a penalty term and takes a sufficiently large number.

[0038] According to the above scheme, the specific mathematical expression of the decision model for the location of the dedicated lane for autonomous vehicles and intersection signal timing in step S4 is as follows:

[0039]

[0040] Subject to:

[0041] C min < C < C max

[0042] 0 < λn <1

[0043] Where: x a , x b , x c Calculated by the mixed traffic flow distribution model in step S3, c min Is the minimum cycle, c max Is the maximum cycle, and TTC is the system travel cost.

[0044] According to the above solution, step S5 of the above includes the following steps:

[0045] S501. Solve the optimization layout decision model of the dedicated lane for autonomous driving using a heuristic algorithm, where the mixed traffic flow distribution model is solved using the MATLAB optimization toolbox;

[0046] S502. Obtain the optimal c, λ n , and set the sections corresponding to the part where Is 1 as the dedicated lane for autonomous driving vehicles, and the rest are mixed traffic lanes. The intersection signal timing cycle is c, and the green signal ratio of the n - approach is λ n .

[0047] According to the above solution, the heuristic algorithm is a genetic algorithm or a simulated annealing algorithm.

[0048] Implementing the method for optimizing the position of the dedicated lane for human - machine mixed - driving autonomous driving and intersection signal timing of the present invention has the following beneficial effects:

[0049] Based on lane - level road network modeling, by dividing the road network into basic sections, weaving sections, and intersection queue sections, the present invention fully examines the interaction relationship between the lateral position of the dedicated lane for autonomous driving vehicles and intersection signal timing, providing a new and scientific quantitative decision - making method for setting dedicated lanes for autonomous driving in urban road networks. Description of the Drawings

[0050] Figure 1 Is the flow chart of the method for jointly optimizing the position of the dedicated lane for autonomous driving vehicles and intersection signal timing in the human - machine mixed - driving scenario of the present invention;

[0051] Figure 2 Is the schematic diagram of the urban road network structure;

[0052] Figure 3 Is the schematic diagram of the urban road network modeling method;

[0053] Figure 4 Is the example of the layout of the dedicated lane for autonomous driving and signal timing optimization. Detailed Embodiment

[0054] For a clearer understanding of the technical features, objectives, and effects of the present invention, the specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0055] As Figures 1-4 shown, a certain urban road network includes a total of one intersection and four road segments connecting the four directions of the intersection. When the proportion of autonomous vehicles reaches a certain level, the method proposed by the present invention is adopted. As Figure 1 shown, under the given autonomous vehicle penetration rate condition, the lateral position of the exclusive lane for autonomous vehicles and the intersection signal timing are optimized to obtain the optimal road network operation performance, including the following steps:

[0056] S1. Divide the urban road network into basic road segments, weaving sections, and intersection queue segments, and abstract them into a directed network diagram:

[0057] S101. Abstract the basic road segments of the lanes into one-way edges in the network diagram, where each lane is numbered a;

[0058] S102. Abstract the lane-changing connection segments between lanes in the lane-changing weaving section into two-way edges in the network diagram, numbered b;

[0059] S103. Abstract the intersection points into nodes in the network diagram, numbered c;

[0060] S104. Connect the above nodes and edges to form a directed network diagram, which completes the modeling of the urban road network, as shown in the appendix Figure 2 shown.

[0061] S2. Establish the travel time calculation formulas for basic road segments, weaving sections, and intersection queue segments:

[0062] S201. Calculate the travel cost of the basic road segment using the following formula:

[0063]

[0064] In the formula: a is the number of the lane respectively; t a is the driving cost of lane a; t a,free represents the travel time of driving on lane a under the free flow scenario; x a,HV represents the traffic flow of human-driven vehicles on lane a,l; x a,AV represents the traffic flow of autonomous vehicles on lane a; e and f are coefficients to be calibrated.

[0065] S202. Calculate the travel cost of the weaving section using the following formula:

[0066]

[0067] In the formula: v freeis the free flow speed, x out is the traffic flow of the lane before lane change; v in is the traffic flow of the target lane; K1, K2, K3, K4 are coefficients to be calibrated, and are calibrated by minimizing the error between the assigned traffic flow obtained from the traffic assignment model based on the above formula and the actual traffic flow.

[0068] S203. Calculate the travel cost of the intersection queue section using the following formula:

[0069]

[0070] In the formula: c is the signal cycle length, λ n is the green signal ratio of approach n, q n is the traffic flow of approach n, x n is the saturation of approach n, s n is the saturation flow rate of approach n. Step 3) Establish a mixed traffic flow assignment model considering the position of the dedicated lane for autonomous vehicles and the intersection signal timing:

[0071] S3. Establish a mixed traffic flow assignment model considering the position of the dedicated lane for autonomous vehicles and the intersection signal timing. The specific mathematical expression is:

[0072]

[0073] Subject to:

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] In the formula: represents the traffic flow of human-driven vehicles on the i-th path between OD pair r-s; represents the traffic flow of autonomous vehicles on the i-th path between OD pair r-s; OD hV represents the OD of human-driven vehicles, OD AVDenote the OD of the autonomous vehicle, denote the penetration rate of autonomous vehicles between OD pair r-s; denote whether a dedicated lane for autonomous vehicles is set up on lane a. Take 1 when it is set up and 0 when it is not. M is a penalty term and takes a sufficiently large number. Other parameters are the same as in the previous formula.

[0083] S4. Establish a decision model for the location of the dedicated lane for autonomous vehicles and intersection signal timing. The specific mathematical expression is:

[0084]

[0085] Subject to:

[0086] c min <C<C max

[0087] 0<λ n <1

[0088] In the formula: x a ,x b ,x c is calculated by the mixed traffic flow distribution model in step S3, c min is the minimum cycle, c max is the maximum cycle, and TTC is the system travel cost. S5. Solve to obtain the optimization scheme for the location of the dedicated lane for autonomous vehicles and intersection signal timing:

[0089] S501. Use a heuristic algorithm (such as genetic algorithm, simulated annealing algorithm) to solve the optimization layout decision model for the dedicated lane for autonomous vehicles, and use the MATLAB optimization toolbox to solve the mixed traffic flow distribution model.

[0090] S502. Obtain the optimal c,λ n , and set the sections corresponding to the part where is 1 as the dedicated lane for autonomous vehicles, and the rest as mixed traffic lanes. The intersection signal timing cycle is c, and the green signal ratio of the n approach lanes is λ n . The results are shown in Table 1-2.

[0091] Table 1 Layout scheme of the dedicated lane for autonomous vehicles

[0092]

[0093] Table 2 Signal timing scheme

[0094]

[0095] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit of the present invention and the scope protected by the claims. All of these are within the protection scope of the present invention.

Claims

1. A method for optimizing the location of a dedicated lane for human-machine mixed driving autonomous driving and the signal timing at intersections, characterized in that, It includes the following steps: S1. Divide the urban road network into basic sections, weaving sections, and intersection queue sections, and abstract them into a directed network graph; S2. Establish travel time calculation formulas for basic sections, weaving sections, and intersection queue sections; The step S2 includes the following steps: S201. Calculate the travel cost of the basic section using the following formula: Wherein: is the lane number; is the lane travel cost; represents the travel time on the lane under free flow scenario ; represents the traffic flow of human-driven vehicles on the lane ; represents the traffic flow of autonomous vehicles on the lane ; is the coefficient to be calibrated; S202. Calculate the travel cost of the weaving section using the following formula: where: v free is the free - flow speed, x out is the traffic flow of the lane before lane - changing; x in is the traffic flow of the target lane; , , , are the coefficients to be calibrated, calibrated by minimizing the error between the assigned traffic flow obtained from the traffic assignment model based on the above formula and the actual traffic flow; S203. Calculate the travel cost of the intersection queue section using the following formula: Wherein: is the signal cycle duration, λ n is the green signal ratio of the approach, q is the traffic flow of the approach, n is the saturation of the approach, and s is the saturation flow rate of the approach; s n is the saturation flow rate of the approach; ​ S3. Establish a mixed traffic flow assignment model considering the position of the autonomous vehicle dedicated lane and intersection signal timing; S4. Establish a decision-making model for the position of the autonomous vehicle dedicated lane and intersection signal timing; S5. Solve to obtain the optimization scheme for the position of the autonomous vehicle dedicated lane and intersection signal timing.

2. The method for optimizing the position of the dedicated lane for human-machine co-driving autonomous driving and the intersection signal timing according to claim 1, wherein, The step S1 includes the following steps: S101. Abstract the basic lane sections into one-way edges in the network graph, where each lane is numbered ; S102. Abstract the lane-changing connection section between lanes in the lane-changing and weaving section as a bidirectional edge in the network graph, numbered ; S103. Abstract the intersection point as a node in the network diagram and number it as ; S104. Connect the nodes and edges to form a directed network graph, which completes the modeling of the urban road network.

3. The method for optimizing the position of the dedicated lane for human-machine mixed driving and intersection signal timing according to claim 1, characterized in that, The mathematical expression of the mixed traffic flow assignment model in the step S3 is: Subject to: In the formula: represents the traffic flow of human-driven vehicles on the r-s th path between OD pairs i ; represents the traffic flow of autonomous vehicles on the r-s th path between OD pairs i ; represents the OD of human-driven vehicles, represents the OD of autonomous vehicles, represents the penetration rate of autonomous vehicles between OD pairs r-s ; represents whether a dedicated lane for autonomous vehicles is set up on lane a , taking 1 when it is set up and 0 when it is not; M is a penalty term, taking a sufficiently large number.

4. The method for optimizing the position of the dedicated lane for human-machine mixed driving and autonomous driving and the intersection signal timing according to claim 3, wherein The specific mathematical expression of the decision-making model for the position of the autonomous vehicle dedicated lane and intersection signal timing in the step S4 is: where: x a , x b , x c is calculated by the mixed traffic flow distribution model in step S3, is the minimum cycle, is the maximum cycle, TTC is the system travel cost.

5. The method for optimizing the position of the dedicated lane for human-machine mixed driving and the signal timing at intersections according to claim 1, wherein The step S5 includes the following steps: S501. Solve the optimization layout decision model of the autonomous vehicle dedicated lane using a heuristic algorithm, where the mixed traffic flow assignment model is solved using the MATLAB optimization toolbox; S502. Obtain the optimal . Set the road sections corresponding to the parts with equal to 1 as dedicated lanes for autonomous vehicles, and the rest as mixed traffic lanes. The signal timing cycle at intersections is , and the green signal ratio of the approach is .

6. The method for optimizing the position of the dedicated lane for human-machine co-driving and autonomous driving and the intersection signal timing according to claim 5, wherein The heuristic algorithm is a genetic algorithm or a simulated annealing algorithm.

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