A Hierarchical Model-Based Optimization Control Approach for Cooperative Merging by Connected Automated Vehicles

Abstract

Gap selection and dynamic speed profiles of interacting vehicles at on-ramps affect the safety and efficiency of highway merging sections. This paper puts forward a hierarchical control approach for Connected Automated Vehicles (CAVs) to achieve efficient and safe merging operations. A tactical layer controller employs a second-order car-following model with a cooperative merging mode to represent a cooperative merging process and generates an optimal vehicle merging sequence and time instants when on-ramp CAVs start to adapt their speeds and positions to prepare merging into the target gaps respectively. An operational layer controller is designed based on Model Predictive Control (MPC). It uses a third-order vehicle dynamics model and optimizes desired accelerations for CAVs and the time instants when the on-ramp CAVs initiate the lane-changing executions respectively. Both the tactical layer controller and operational layer controller derive their control commands by minimizing an objective function for different time horizons. The objective function penalizes deviations of CAVs' inter-vehicle gaps to their desired values, relative speeds to their direct predecessors, and actual or desired accelerations, subject to constraints on velocities, actual or desired accelerations, and inter-vehicle gaps. The performance of the proposed hierarchical control framework and a benchmark on-ramp merging method using a first-in-firstout rule to determine the merging sequence is demonstrated under 135 scenarios with different initial conditions, desired time gap settings, and numbers of on-ramp vehicles. The experimental results show the superiority of the hierarchical control approach.

optimization of Traffic Efficiency at On-ramps with Connected Automated Vehicles

“…For equilibrium scenarios, the inter-vehicle distances for mainline vehicles are initially desired inter-vehicle distance. The two initial relative positions for the on-ramp vehicle are chosen: 1) the on-ramp vehicle enters into the control zone together with a mainline vehicle, vehicle (2,3) being selected in this paper; 2) the on-ramp vehicle has the same longitudinal distance to vehicle (2,3) and vehicle (2,4) when it enters into the control zone [18]. The initial position for the on-ramp vehicle is a fixed value -62 m. The mainline vehicles' initial positions are calculated by using the on-ramp vehicle's position, the relative position type, and inter-vehicle distances between consecutive mainline vehicles.…”

“…The parameters are set as follows: D1=0. The parameter used for the operational controller is the same as in [18]. The parameters D1, D2, D3, and D4 are manually tuned by making the decisions fit with real optimal decisions for different initial settings.…”

“…One way to optimize traffic operation is by evaluating all different or feasible merging sequences with some selected performance indicators [16,17]. In our previous study, we proposed a hierarchical architecture of the merging control system and found that speed-adaptation time instant when an on-ramp vehicle starting to adapt its speed and position to merge into the target gap should be considered to improve traffic operations [18]. The merging time for an on-ramp vehicle is the time duration for it to pass through the on-ramp and accomplish merging into mainline traffic.…”

“…However, how to achieve a minimal merging time of an on-ramp CAV is not known especially considering its speed-adaptation time instant. This paper aims to minimize the merging time of on-ramp vehicles by utilizing an existing hierarchical architecture of the merging control system proposed in our previous research [18]. The hierarchical control architecture includes a decision-maker and an operational layer controller.…”

A platoon regulation algorithm to improve the traffic performance of highway work zones

“…Chen et al. (2020) proposed a hierarchical control approach to facilitate the merging of CVs on ramps. A tactical layer controller applying a second‐order car‐following model and an operation layer based on MPC are combined to minimize an objective function for different time horizons.…”