Modeling Cascading Driver Interventions in Partially Automated Traffic: A Semi-Markov Chain Approach

TL;DR

This paper develops a semi-Markov chain model to analyze cascading driver interventions in partially automated traffic, capturing mode-switching behaviors and their propagation effects to improve understanding of traffic dynamics.

Contribution

It introduces an innovative semi-Markov chain framework with lockout constraints and leader-dependent switching probabilities for modeling complex traffic systems.

Findings

The model captures cascading impacts of driver interventions.

Numerical experiments validate theoretical stability and propagation effects.

Model parameters significantly influence traffic throughput.

Abstract

This paper presents an analytical modeling framework for partially automated traffic, incorporating cascading driver intervention behaviors. In this framework, drivers of partially automated vehicles have the flexibility to switch driving modes (either AV or HDV) under lockout constraints. The cascading impact is captured by making the switching probability leader-dependent, highlighting the influence of the leading vehicle on mode choice and the potential propagation of mode changes throughout traffic. Due to the complexity of this system, traditional Markov-based methods are insufficient. To address this, the paper introduces an innovative semi-Markov chain framework with lockout constraints, ideally suited for modeling the system dynamics. This framework reformulates the system as a nonlinear model whose solution can be efficiently approximated using numerical methods from control theory, such as the Runge-Kutta algorithm. Moreover, the system is proven to be a piecewise affine bilinear system, with the existence of solutions and both local and global stability established via Brouwer's Fixed Point Theorem and the 1D Uncertainty Polytopes Theorem. Numerical experiments corroborate these theoretical findings, confirming the presence of cascading impacts and elucidating the influence of modeling parameters on traffic throughput, thereby deepening our understanding of the system's properties.