Cooperative Merging For Mixed Traffic Flow With Connected Automated Vehicles And Human-Driving Vehicles

In order to build a country with strong transportation network,the connected automated vehicle(CAV)has become the strategic direction of the development of China’s automobile industry,but also the commanding height of the international technology and industrial competition.The cooperative decision-making and planning technology is one of several critical problems the CAV is grappling with.The rapid theoretical development and commercialization of CAV has led to the problem of mixed traffic,i.e.,traffic mixed with CAVs and conventional human-operated vehicles(HVs).In the field of transportation research,the impacts of mixed traffic flow have not been fully understood.As human-operated vehicles are usually egoistic,non-cooperative and stochastic,this has added extra difficulties and complexities to the cooperative decision-making and planning technology.Therefore,the research of collaborative decision-making and planning technology in mixed traffic flow environment has important theoretical value and practical significance.In the research field of transportation engineering and management,the efforts mostly are spent on the trajectory design problem of individual vehicles,system-efficient solutions are not guaranteed.This paper is a synthesis of optimal control theory,operations research,transportation planning and management,which aims at solving the cooperative decision-making problem for mixed traffic in ramp merging scenario.In detail,the following is the main research work :(1)Based on an ideal freeway ramp merging section(including one main road and one ramp),the ramp merging decision model is constructed which is appropriate for different deterministic car-following models.This model considers both safety and etiquette to make deterministic merge decisions.A micro traffic flow simulation environment was established by Matlab programming,and the traffic effects under different traffic conditions and different penetration rates of CAVs were analyzed from micro and macro perspectives.(2)Using discrete optimization,a cooperative decision-making for mixed traffic(CDMMT)mechanism was developed to facilitate ramp merging.The CDMMT mechanism can be described as a bi-level optimization program in which state-constrained optimal control-based trajectory design problems are imbedded in a sequencing problem.A bi-level dynamic programming-based solution approach was developed to efficiently solve the problem.The proposed modeling mechanism and solution approach are generic to deterministic decisions and can guarantee system-efficient solutions.A micro-simulation environment is built for model validation and analysis of mixed traffic.(3)To better encapsulate the stochasticity in HVs’ behaviors,a closed-loop optimal control approach was applied to extend the previously developed Deterministic-CDMMT.The upper-level merge sequencing problem was solved using a dynamic-programming-based approach.Considering real-time computational performance,three solution methods,including dynamic programming(DP),dynamic matrix predictive control(DMC),and simplified discrete control,are developed to solve the lower-level trajectory planning problem.(4)To solving the merging trajectory planning problems through computational optimal control methods,the quintic polynomial curve was used for smoothing processing.Based on the CDMMT mechanism,a two-stage cooperative merging trajectory planning method was established,which optimizing the control of CAVs to facilitate merging.Based on the trajectory tracking model of linear time-varying model predictive control,the traceability of the planned trajectory was tested.In this thesis,the above studies provide basic theoretical support for the traffic state analysis,traffic management and control in the mixed traffic flow environment in the future.