Experimental study and modeling of the lower-level controller of automated vehicle

TL;DR

This paper presents a new lower-level control model for automated vehicles based on field experiments, demonstrating improved accuracy over existing models and highlighting its importance for traffic flow stability.

Contribution

The study introduces a novel lower-level control model for AVs derived from real-world data, outperforming traditional models in accuracy and stability analysis.

Findings

Proposed model better captures actual acceleration, especially troughs.

Existing models overestimate traffic stability regions.

New model enhances understanding of AV traffic flow dynamics.

Abstract

Accurate modeling of lower-level controller plays an important role in the traffic flow of automated vehicles (AVs). However, there lacks enough attention with this respect. To address this issue, we conduct a field experiment with two vehicles that are equipped with developable autonomous driving system, where one can customize the upper-level control algorithm. Based on the field experimental data, a new lower-level control model is developed and compared with two widely used ones. The comparison results show that the proposed model outperforms the two previous models in capturing the observed actual acceleration, especially the troughs of the acceleration time series. Furthermore, theoretical analysis indicates that comparing with the proposed model, the two previous models significantly overestimate the stability region of the traffic flow of the AVs and the capacity of stable traffic flow. Our study is expected to further shed light on the importance of accurate lower-level control modeling.