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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
Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks
Connected automated driving has the potential to significantly improve urban
traffic efficiency, e.g., by alleviating issues due to occlusion. Cooperative
behavior planning can be employed to jointly optimize the motion of multiple
vehicles. Most existing approaches to automatic intersection management,
however, only consider fully automated traffic. In practice, mixed traffic,
i.e., the simultaneous road usage by automated and human-driven vehicles, will
be prevalent. The present work proposes to leverage reinforcement learning and
a graph-based scene representation for cooperative multi-agent planning. We
build upon our previous works that showed the applicability of such machine
learning methods to fully automated traffic. The scene representation is
extended for mixed traffic and considers uncertainty in the human drivers'
intentions. In the simulation-based evaluation, we model measurement
uncertainties through noise processes that are tuned using real-world data. The
paper evaluates the proposed method against an enhanced first in - first out
scheme, our baseline for mixed traffic management. With increasing share of
automated vehicles, the learned planner significantly increases the vehicle
throughput and reduces the delay due to interaction. Non-automated vehicles
benefit virtually alike.Comment: 8 pages, 7 figures, 34th IEEE Intelligent Vehicles Symposium (IV),
updated to accepted versio
Reinforcement Learning Aided Sequential Optimization for Unsignalized Intersection Management of Robot Traffic
We consider the problem of optimal unsignalized intersection management for
continual streams of randomly arriving robots. This problem involves repeatedly
solving different instances of a mixed integer program, for which the
computation time using a naive optimization algorithm scales exponentially with
the number of robots and lanes. Hence, such an approach is not suitable for
real-time implementation. In this paper, we propose a solution framework that
combines learning and sequential optimization. In particular, we propose an
algorithm for learning a shared policy that given the traffic state
information, determines the crossing order of the robots. Then, we optimize the
trajectories of the robots sequentially according to that crossing order. This
approach inherently guarantees safety at all times. We validate the performance
of this approach using extensive simulations. Our approach, on average,
significantly outperforms the heuristics from the literature. We also show
through simulations that the computation time for our approach scales linearly
with the number of robots. We further implement the learnt policies on physical
robots with a few modifications to the solution framework to address real-world
challenges and establish its real-time implementability.Comment: 13 pages, 27 figure
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