Simultaneous State and Cyber-attack Estimation for Autonomous Vehicular Flow Models via Boundary Observers

With the rapid development of autonomous driving techniques, the cyber-attack issue has emerged as a serious concern due to the cyber vulnerability of autonomous vehicles. In this paper, we consider the cyber security for the autonomous vehicular flow which suffers from the cyber-attack signals on speed dynamics. To address this problem, the Aw-Rascle-Zhang model is extended by considering the unknown cyber-attack to the autonomous vehicles. A novel simultaneous state and input estimation algorithm is developed for the cyber-attack ARZ model by using the boundary observer. The exponential stability of the simultaneous estimation algorithm is given with a set of inequality conditions. Finally, numerical simulation illustrates that the proposed algorithm can estimate the traffic flow state and unknown cyber-attack simultaneously.