Conditions for State and Control Constraint Activation in Coordination of Connected and Automated Vehicles

Connected and automated vehicles (CAVs) provide the most intriguing opportunity to reduce pollution, energy consumption, and travel delays. In earlier work, we addressed the optimal coordination of CAVs using Hamiltonian analysis. In this paper, we investigate the nature of the unconstrained problem and provide conditions under which the state and control constraints become active. We derive a closed-form analytical solution of the constrained optimization problem and evaluate the solution using numerical simulation

Energy-Optimal Coordination of Connected and Automated Vehicles at Multiple Intersections

Urban intersections, merging roadways, roundabouts, and speed reduction zones along with the driver responses to various disturbances are the primary sources of bottlenecks in corridors that contribute to traffic congestion. The implementation of connected and automated technologies can enable a novel computational framework for real-time control aimed at optimizing energy consumption and travel time. In this paper, we propose a decentralized energy-efficient optimal control framework for two adjacent intersections. We derive a closed-form analytical solution that includes interior boundary conditions and evaluate the effectiveness of the solution through simulation. Fuel consumption and travel time are significantly reduced compared to the baseline scenario designed with conventional fixed time signalized intersections

A Real-Time Optimal Eco-driving for Autonomous Vehicles Crossing Multiple Signalized Intersections

This paper develops an optimal acceleration/speed profile for a single autonomous vehicle crossing multiple signalized intersections without stopping in free flow mode. The design objective is to produce both time and energy efficient acceleration profiles of autonomous vehicles based on vehicle to infrastructure communication. Our design approach differs from most existing approaches based on numerical calculations: it begins with identifying the structure of the optimal acceleration profile and then showing that it is characterized by several parameters, which are used for design optimization. Therefore, the infinite dimensional optimal control problem is transformed into a finite dimensional parametric optimization problem, which enables a real-time online analytical solution. The simulation results show quantitatively the advantages of considering multiple intersections jointly rather than dealing with them individually. Based on mild assumptions, the optimal eco-driving algorithm is readily extended to include interfering traffic

Control of a Mixed Autonomy Signalised Urban Intersection: An Action-Delayed Reinforcement Learning Approach

We consider a mixed autonomy scenario where the traffic intersection controller decides whether the traffic light will be green or red at each lane for multiple traffic-light blocks. The objective of the traffic intersection controller is to minimize the queue length at each lane and maximize the outflow of vehicles over each block. We consider that the traffic intersection controller informs the autonomous vehicle (AV) whether the traffic light will be green or red for the future traffic-light block. Thus, the AV can adapt its dynamics by solving an optimal control problem. We model the decision process of the traffic intersection controller as a deterministic delay Markov decision process owing to the delayed action by the traffic controller. We propose Reinforcement-learning based algorithm to obtain the optimal policy. We show - empirically - that our algorithm converges and reduces the energy costs of AVs drastically as the traffic controller communicates with the AVs.Comment: Accepted for Publication at 24th IEEE International Conference on Intelligent Transportation (ITSC'2021