Resilient Coordinated Movement of Connected Autonomous Vehicles

TL;DR

This paper proposes a resilient control strategy for connected autonomous vehicles to achieve consensus despite malicious agents, using filtering and network robustness conditions, with simulations validating the approach.

Contribution

It introduces an asynchronous updating method with filtering to ensure resilient consensus in vehicle networks with malicious agents, under specific topological constraints.

Findings

Resilient consensus achieved under certain network robustness conditions.

Filtering extreme values prevents malicious influence on vehicle coordination.

Numerical simulations confirm the effectiveness of the proposed method.

Abstract

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.