Abstract
Automated vehicles are designed to free drivers from driving tasks and are expected to improve traffic safety and efficiency when connected via vehicle-to-vehicle communication, that is, connected automated vehicles (CAVs). The time delays and model uncertainties in vehicle control systems pose challenges for automated driving in real world. Ignoring them may render the performance of cooperative driving systems unsatisfactory or even unstable. This paper aims to design a robust and flexible platooning control strategy for CAVs. A centralized control method is presented, where the leader of a CAV platoon collects information from followers, computes the desired accelerations of all controlled vehicles, and broadcasts the desired accelerations to followers. The robust platooning is formulated as a Min-Max Model Predictive Control (MM-MPC) problem, where optimal accelerations are generated to minimize the cost function under the worst case, where the worst case is taken over the possible models. The proposed method is flexible in such a way that it can be applied to both homogeneous platoon and heterogeneous platoon with mixed human-driven and automated controlled vehicles. A third-order linear vehicle model with fixed feedback delay and stochastic actuator lag is used to predict the platoon behavior. Actuator lag is assumed to vary randomly with unknown distributions but a known upper bound. The controller regulates platoon accelerations over a time horizon to minimize a cost function representing driving safety, efficiency, and ride comfort, subject to speed limits, plausible acceleration range, and minimal net spacing. The designed strategy is tested by simulating homogeneous and heterogeneous platoons in a number of typical and extreme scenarios to assess the system stability and performance. The test results demonstrate that the designed control strategy for CAV can ensure the robustness of stability and performance against model uncertainties and feedback delay and outperforms the deterministic MPC based platooning control.
Highlights
Today’s traffic systems are facing serious congestion [1]
The robust heterogeneous platooning controller is designed under the following additional assumptions compared to homogeneous controller design: (i) The locations and speeds of the human-driven vehicles can be detected by the on-board sensors equipped on the connected automated vehicles (CAVs)
The heterogeneous platooning controller is formulated as a MinMax Model Predictive Control problem as shown in (18), where the superscript H is used to represent the notations for heterogeneous platooning control
Summary
Automated vehicles using advanced sensing, communication, and control technologies have the potential to increase road capacity and improve traffic operations [2,3,4]. Adaptive cruise control (ACC) systems, one of the earliest automated vehicle systems, has already entered the market [5,6,7]. It uses its on-board sensors to detect the ambient environment and regulate the speeds of the vehicle to increase ride comfort. CAVs have more potential to improve traffic performance compared to individual automation, since they can share information and coordinate their behavior to ensure shorter intervehicle distances safely [2, 10, 11] as demonstrated by field tests [12, 13]. With V2I communication between a road side device and electric vehicles, the traffic stability can be improved [14]
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