In this paper, the problem of distributed state estimation of human-driven vehicles (HDVs) by connected autonomous vehicles (CAVs) is investigated in mixed traffic transportation systems. Toward this, a distributed observable state-space model is derived, which paves the way for estimation and observability analysis of HDVs in mixed traffic scenarios. In this direction, first, we obtain the condition on the network topology to satisfy the distributed observability, i.e., the condition such that each HDV state is observable to every CAV via information-exchange over the network. It is shown that strong connectivity of the network, along with the proper design of the observer gain, is sufficient for this. A distributed observer is then designed by locally sharing estimates/observations of each CAV with its neighborhood. Second, in case there exist faulty sensors or unreliable observation data, we derive the condition for redundant distributed observability as a $q$-node/link-connected network design. This redundancy is achieved by extra information-sharing over the network and implies that a certain number of faulty sensors and unreliable links can be isolated/removed without losing the observability. Simulation results are provided to illustrate the effectiveness of the proposed approach.

翻译：本文研究了混合交通系统中联网自动驾驶车辆（CAVs）对人类驾驶车辆（HDVs）的分布式状态估计问题。为此，推导了一种分布式可观测状态空间模型，为混合交通场景下HDVs的估计与可观测性分析奠定了基础。在此方向上，首先，我们获得了满足分布式可观测性的网络拓扑条件，即每个HDV状态可通过网络信息交换被所有CAV观测的条件。研究表明，网络的强连通性结合观测器增益的合理设计足以实现该目标。随后，通过局部共享各CAV与其邻域间的估计/观测数据，设计了一种分布式观测器。其次，针对存在故障传感器或不可靠观测数据的情况，我们推导了冗余分布式可观测性的条件，即$q$节点/链路连通的网络设计。这种冗余性通过额外的网络信息共享实现，意味着在隔离/移除一定数量的故障传感器和不可靠链路后仍能保持可观测性。仿真结果验证了所提方法的有效性。