Resilient Coordinated Movement of Connected Autonomous Vehicles
This addresses the challenge of ensuring safety and reliability in autonomous vehicle networks, which is incremental as it builds on existing consensus and robustness frameworks.
The paper tackles the problem of achieving resilient coordinated movement in a network of connected autonomous vehicles despite the presence of malicious agents, showing that under specific topological constraints, the vehicles can reach consensus in position and velocity using an asynchronous updating strategy with filtering.
In this paper, we consider coordinated movement of a network of vehicles consisting of a bounded number of malicious agents, that is, vehicles must reach consensus in longitudinal position and a common predefined velocity. The motions of vehicles are modeled by double-integrator dynamics and communications over the network are asynchronous with delays. Each normal vehicle updates its states by utilizing the information it receives from vehicles in its vicinity. On the other hand, misbehaving vehicles make updates arbitrarily and might threaten the consensus within the network by intentionally changing their moving direction or broadcasting faulty information in their neighborhood. We propose an asynchronous updating strategy for normal vehicles, based on filtering extreme values received from neighboring vehicles, to save them from being misguided by malicious vehicles. We show that there exist topological constraints on the network in terms of graph robustness under which the vehicles resiliently achieve coordinated movement. Numerical simulations are provided to evaluate the results.