On the Redundant Distributed Observability of Mixed Traffic Transportation Systems

TL;DR

This paper develops a distributed state estimation framework for mixed traffic systems with human-driven and autonomous vehicles, ensuring observability even with sensor faults through network redundancy and connectivity conditions.

Contribution

It introduces a novel distributed observable state-space model and establishes conditions for network topology and redundancy to maintain observability despite sensor faults.

Findings

Strong network connectivity ensures distributed observability.

Redundant information-sharing allows fault isolation.

Simulation confirms effectiveness of the proposed method.

Abstract

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.