Uncovering Interpretable Internal States of Merging Tasks at Highway On-Ramps for Autonomous Driving Decision-Making

TL;DR

This paper introduces a probabilistic modeling approach to uncover interpretable internal states during highway on-ramp merging, aiding autonomous vehicle decision-making in complex, interactive scenarios.

Contribution

It presents a novel HMM-GMR model with an EM algorithm to identify and interpret internal states in multi-vehicle merging interactions.

Findings

Three internal states effectively describe merging behavior

Model verified on real-world data

Provides a foundation for autonomous vehicle decision algorithms

Abstract

Humans make daily routine decisions based on their internal states in intricate interaction scenarios. This paper presents a probabilistically reconstructive learning approach to identify the internal states of multi-vehicle sequential interactions when merging at highway on-ramps. We treated the merging task's sequential decision as a dynamic, stochastic process and then integrated the internal states into an HMM-GMR model, a probabilistic combination of an extended Gaussian mixture regression (GMR) and hidden Markov models (HMM). We also developed a variant expectation-maximum (EM) algorithm to estimate the model parameters and verified it based on a real-world data set. Experiment results reveal that three interpretable internal states can semantically describe the interactive merge procedure at highway on-ramps. This finding provides a basis to develop an efficient model-based decision-making algorithm for autonomous vehicles (AVs) in a partially observable environment.