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Urban intersection mixed traffic flow collaborative optimization underlying control method

An intersection and collaborative optimization technology, applied in the field of traffic engineering, can solve the problems of less research on urban unsignalized intersections and unrealistic assumptions

Active Publication Date: 2019-11-19
SOUTHWEST JIAOTONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Existing studies on ICVs mostly only consider a hypothetical 100% CAV penetration (i.e., all vehicles are ICVs), but this assumption is unrealistic, at least in the near future
At the same time, most of the existing collaborative decision-making research considers the scenarios of expressways or urban expressways, while there are few studies on the traffic scenarios of urban unsignalized intersections

Method used

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  • Urban intersection mixed traffic flow collaborative optimization underlying control method
  • Urban intersection mixed traffic flow collaborative optimization underlying control method
  • Urban intersection mixed traffic flow collaborative optimization underlying control method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0113] A bottom-level control method for collaborative optimization of mixed traffic flow at urban intersections includes the following steps:

[0114] S1. Determine a micro car following model, and use the micro car following model to describe the car following state of the vehicle. The car following state of the vehicle includes the speed, acceleration, and position of the vehicle.

[0115] This embodiment uses the Gipps car following model, which is as follows:

[0116] v(t+τ)=min(v e ,V(t)+aτ,v safe ),

[0117]

[0118]

[0119] x(t+τ)=x(t)+v(t)τ+0.5u(t)τ 2 ,

[0120] The above use v(t+τ) to represent the speed of the vehicle at t+τ, where v e Is the desired speed, v(t)+aτ represents the speed obtained by accelerating with a constant acceleration a, v safe Indicates the minimum safe speed. τ is the reaction time of the vehicle driving; b is the constant deceleration, when the vehicle brakes and decelerates, the default is always to perform deceleration at the constant deceleratio...

Embodiment 2

[0188] A bottom-level control method for collaborative optimization of mixed traffic flow at urban intersections includes the following steps:

[0189] S1. Determine a micro car following model, and use the micro car following model to describe the car following state of the vehicle. The car following state of the vehicle includes the speed, acceleration, and position of the vehicle.

[0190] This embodiment uses the Gipps car following model, which is as follows:

[0191] v(t+τ)=min(v e ,v(t)+aτ,v safe ),

[0192]

[0193]

[0194] x(t+τ)=x(t)+v(t)τ+0.5u(t)τ 2 ,

[0195] The above use v(t+τ) to represent the speed of the vehicle at t+τ, where v e Is the desired speed, v(t)+aτ represents the speed obtained by accelerating with a constant acceleration a, v safe Indicates the minimum safe speed. τ is the reaction time of the vehicle driving; b is the constant deceleration, when the vehicle brakes and decelerates, the default is always to perform deceleration at the constant deceleratio...

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Abstract

The invention provides an urban intersection mixed traffic flow collaborative optimization underlying control method, and belongs to the field of traffic engineering. The method comprises the following steps: a microcosmic car following model is determined; an initial trajectory of a car is predicted; a conflict cooperation model is established for the conflict type of an intersection; a cooperative control strategy set is drawn up; whether the car can smoothly pass through a conflict area or not is judged; if the car can smoothly pass through the conflict area, the car continues to run according to the speed of the microcosmic car following model; if the car cannot smoothly pass through the conflict area, the specific situation of the car in the process of passing through the conflict area needs to be judged, and a corresponding cooperative control strategy needs to be made; and the running track of a target car is optimized according to the cooperative control strategy, the optimization problem is summarized into an optimal control problem of discrete time state constraints, the problem is solved by the idea of dynamic programming to obtain a cooperative optimization control strategy about the target car, and the cooperative optimization control strategy is applied to the target car to control the running of the target car.

Description

Technical field [0001] The invention relates to a coordinated optimization bottom-level control method for mixed traffic flow at urban intersections, and belongs to the field of traffic engineering. Background technique [0002] With the continuous advancement of the research and development of intelligent networked technologies, intelligent networked vehicles can now be driven on the road. Coupled with its "smart" and "safe" characteristics, intelligent networked vehicles will be widely popularized in the future, but traditional manual driving vehicles are very It will not be completely replaced in a long period of time. Therefore, the future road traffic situation will definitely be a mixed traffic situation of intelligent networked vehicles and traditional manual driving vehicles. At the same time, with the rapid development of intelligent networked systems, research on intelligent networked vehicles has attracted more and more attention. [0003] Level intersections are the co...

Claims

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Application Information

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IPC IPC(8): G08G1/01G05D1/02
CPCG05D1/0223G08G1/0145
Inventor 孙湛博
Owner SOUTHWEST JIAOTONG UNIV
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