639 research outputs found
Coordination of Cooperative Autonomous Vehicles Toward safer and more efficient road transportation
While intelligent transportation systems come in many shapes and sizes, arguably the most transformational realization will be the autonomous vehicle. As such vehicles become commercially available in the coming years, first on dedicated roads and under specific conditions, and later on all public roads at all times, a phase transition will occur. Once a sufficient number of autonomous vehicles is deployed, the opportunity for explicit coordination appears. This article treats this challenging network control problem, which lies at the intersection of control theory, signal processing, and wireless communication. We provide an overview of the state of the art, while at the same time highlighting key research directions for the coming decades
Optimisation-based coordination of connected, automated vehicles at intersections
In this paper, we analyse the performance of a model predictive controller for coordination of connected, automated vehicles at intersections. The problem has combinatorial complexity, and we propose to solve it approximately by using a two stage procedure where (1) the vehicle crossing order in which the vehicles cross the intersection is found by solving a mixed integer quadratic program and (2) the control commands are subsequently found by solving a nonlinear program. We show that the controller is persistently safe and compare its performance against traffic lights and two simpler optimisation-based coordination schemes. The results show that our approach outperforms the considered alternatives in terms of both energy consumption and travel-time delay, especially for medium to high traffic loads
Game-theoretical approach to decentralized multi-drone conflict resolution and emergent traffic flow operations
This paper introduces decentralized control concepts for drones using
differential game theory. The approach optimizes the behavior of an ego drone,
assuming the anticipated behavior of the opponent drones using a receding
horizon approach. For each control instant, the scheme computes the Nash
equilibrium control signal which is applied for the control period. This
results in a multi-drone conflict resolution scheme that is applied to all
drones considered. The paper discusses the approach and presents the numerical
algorithm, showing several examples that illustrate the performance of the
model. We examine at the behavior of the ego drone, and the resulting
collective drone flow operations. The latter shows that while the approach aims
to optimize the operation cost of the ego drone, the experiments provide
evidence that resulting flow operations are very efficient due to the
self-organization of various flow patterns. The presented work contributes to
the state of the art in providing a generic approach to multi-drone conflict
resolution with good macroscopic flow performance characteristics. The approach
enables relatively straightforward inclusion of error due to sensing and
communication. The approach also allows for including different risk levels
(e.g., for malfunctioning of sensor and communication technology), priority
rules, regulations, and higher-level control signals (e.g., routing, dynamic
speed limits).Comment: Submitted to the TRB Annual Meeting 202
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A Multi-Vehicle Control Framework With Application to Automated Valet Parking
We introduce a distributed control method for coordinating multiple vehicles in the framework of an automated valet parking (AVP) system. The control functionality is distributed between an infrastructure server, called parking area management (PAM) system, and local autonomous vehicle control units. Via a vehicle-to-infrastructure (V2I) communication interface, model predictive control (MPC) decisions of the vehicles are shared with the coordination unit in the PAM. This unit in turn computes a coupling feedback which is shared with the vehicles. The control system is integrated in an automated test-system to cope with the high test requirements and short development cycles of highly automated systems. Evaluations conducted with the test-system show the functionality of the proposed distributed control method for multi-vehicle coordination. Results indicate safe coordination, and an efficiency increase compared to an uncoordinated method in an AVP simulation environment
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