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A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles
Experimental Validation of a Real-Time Optimal Controller for Coordination of CAVs in a Multi-Lane Roundabout
Roundabouts in conjunction with other traffic scenarios, e.g., intersections,
merging roadways, speed reduction zones, can induce congestion in a
transportation network due to driver responses to various disturbances.
Research efforts have shown that smoothing traffic flow and eliminating
stop-and-go driving can both improve fuel efficiency of the vehicles and the
throughput of a roundabout. In this paper, we validate an optimal control
framework developed earlier in a multi-lane roundabout scenario using the
University of Delaware's scaled smart city (UDSSC). We first provide conditions
where the solution is optimal. Then, we demonstrate the feasibility of the
solution using experiments at UDSSC, and show that the optimal solution
completely eliminates stop-and-go driving while preserving safety.Comment: 6 Pages, 4 Figures, 1 tabl
Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning
Recent advances in combining deep learning and Reinforcement Learning have
shown a promising path for designing new control agents that can learn optimal
policies for challenging control tasks. These new methods address the main
limitations of conventional Reinforcement Learning methods such as customized
feature engineering and small action/state space dimension requirements. In
this paper, we leverage one of the state-of-the-art Reinforcement Learning
methods, known as Trust Region Policy Optimization, to tackle intersection
management for autonomous vehicles. We show that using this method, we can
perform fine-grained acceleration control of autonomous vehicles in a grid
street plan to achieve a global design objective.Comment: Accepted in IEEE Smart World Congress 201
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