11 research outputs found
An Optimal Coordination Framework for Connected and Automated Vehicles in two Interconnected Intersections
In this paper, we provide a decentralized optimal control framework for
coordinating connected and automated vehicles (CAVs) in two interconnected
intersections. We formulate a control problem and provide a solution that can
be implemented in real time. The solution yields the optimal
acceleration/deceleration of each CAV under the safety constraint at "conflict
zones," where there is a chance of potential collision. Our objective is to
minimize travel time for each CAV. If no such solution exists, then each CAV
solves an energy-optimal control problem. We evaluate the effectiveness of the
efficiency of the proposed framework through simulation.Comment: 8 pages, 5 figures, IEEE CONFERENCE ON CONTROL TECHNOLOGY AND
APPLICATIONS 201
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
Zero-Shot Autonomous Vehicle Policy Transfer: From Simulation to Real-World via Adversarial Learning
In this article, we demonstrate a zero-shot transfer of an autonomous driving
policy from simulation to University of Delaware's scaled smart city with
adversarial multi-agent reinforcement learning, in which an adversary attempts
to decrease the net reward by perturbing both the inputs and outputs of the
autonomous vehicles during training. We train the autonomous vehicles to
coordinate with each other while crossing a roundabout in the presence of an
adversary in simulation. The adversarial policy successfully reproduces the
simulated behavior and incidentally outperforms, in terms of travel time, both
a human-driving baseline and adversary-free trained policies. Finally, we
demonstrate that the addition of adversarial training considerably improves the
performance \eat{stability and robustness} of the policies after transfer to
the real world compared to Gaussian noise injection.Comment: 6 pages, 4 figure
Minimally Disruptive Cooperative Lane-change Maneuvers
A lane-change maneuver on a congested highway could be severely disruptive or
even infeasible without the cooperation of neighboring cars. However,
cooperation with other vehicles does not guarantee that the performed maneuver
will not have a negative impact on traffic flow unless it is explicitly
considered in the cooperative controller design. In this letter, we present a
socially compliant framework for cooperative lane-change maneuvers for an
arbitrary number of CAVs on highways that aims to interrupt traffic flow as
minimally as possible. Moreover, we explicitly impose feasibility constraints
in the optimization formulation by using reachability set theory, leading to a
unified design that removes the need for an iterative procedure used in prior
work. We quantitatively evaluate the effectiveness of our framework and compare
it against previously offered approaches in terms of maneuver time and incurred
throughput disruption.Comment: 6 pages, 2 figure
Combined Optimal Routing and Coordination of Connected and Automated Vehicles
In this letter, we consider a transportation network with a 100\% penetration
rate of connected and automated vehicles (CAVs) and present an optimal routing
approach that takes into account the efficiency achieved in the network by
coordinating the CAVs at specific traffic scenarios, e.g., intersections,
merging roadways, and roundabouts. To derive the optimal route of a travel
request, we use the information of the CAVs that have already received a
routing solution. This enables each CAV to consider the traffic conditions on
the roads. The solution of any new travel request determines the optimal travel
time at each traffic scenario while satisfying all state, control, and safety
constraints. We validate the performance of our framework through numerical
simulations. To the best of our knowledge, this is the first attempt to
consider the coordination of CAVs in a routing problem.Comment: 6 pages, 5 figure