Jam-absorption driving with data assimilation

TL;DR

This paper presents a data assimilation framework using the extended Kalman filter to improve jam-absorption driving by reducing control failures and adapting to changing traffic conditions.

Contribution

It introduces a novel DA framework that enhances JAD performance by mitigating parameter estimation errors and adapting to traffic variability.

Findings

DA effectively reduces control failures in JAD.

Framework adapts to changes in traffic conditions.

Improves robustness of jam-absorption driving.

Abstract

This paper introduces a data assimilation (DA) framework based on the extended Kalman filter-cell transmission model, designed to assist jam-absorption driving (JAD) operation to alleviate sag traffic congestion. To ascertain and demonstrate the effectiveness of the DA framework for JAD operation, in this paper, we initially investigated its impact on the motion and control performance of a single absorbing vehicle. Numerical results show that the DA framework effectively mitigated underestimated or overestimated control failures of JAD caused by misestimation of key parameters (e.g., free flow speed and critical density) of the traffic flow fundamental diagram. The findings suggest that the proposed DA framework can reduce control failures and prevent significant declines and deteriorations in JAD performance caused by changes in traffic characteristics, e.g., weather conditions or traffic composition.