Abstract
This paper proposes enhanced prediction and control design methods for improving traffic flow with human-driven and automated vehicles. To achieve accurate prediction for the entire time horizon, data-driven and model-based prediction methods were integrated. The goal of the integration was to accurately predict the outflow of the traffic network, which was selected as the highway section in this paper. The proposed novel prediction method was used in the optimal design for calculating controlled inflows on highway ramps. The goal of the design was to reach the maximum outflow of the traffic network, even against disturbances on uncontrolled inflows of the network. The control design leads to an optimization problem based on the min–max principle, i.e., the traffic outflow is considered to be maximized by controlled inflows and to be minimized by uncontrolled inflows. The effectiveness of the prediction and the control methods through simulation examples are illustrated, i.e., traffic outflow can be maximized by the control system under various uncontrolled inflow values.
Highlights
Introduction and MotivationProviding control strategies for automated vehicles has become a subject of significant focus in research and development centers of vehicle industry
Existing control solutions result in speed profiles for automated vehicles, which differ from the speed selection strategy of human drivers
This paper proposed an effective method for the prediction and maximization of traffic outflow on a highway
Summary
Providing control strategies for automated vehicles has become a subject of significant focus in research and development centers of vehicle industry. An important task is to design a velocity profile for vehicles in order to guarantee effective, comfortable, safe and economical traffic by exploiting vehicle dynamics and environmental circumstances, e.g., certain characteristics of fuel consumption, delivered cargo, road inclinations, speed limits, traffic flow and traffic forecast. Existing control solutions result in speed profiles for automated vehicles, which differ from the speed selection strategy of human drivers. The reason for this is that the control systems of automated vehicles can obtain information on the road ahead, e.g., the usage of the road capacity or the upcoming downhill terrain characteristics. It can be shown that the speed selection of automated vehicles and human-driven vehicles are not independent from each other. The ratio of automated vehicles (κ) in the entire traffic network influences the characteristics of the traffic flow through the modification of traffic speed
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