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A GPT-based Decision Transformer for Multi-Vehicle Coordination at Unsignalized Intersections

Eunjae Lee, Minhee Kang, Yoojin Choi, Heejin Ahn

TL;DR

The results show that the Decision Transformer can outperform the training data in terms of total travel time and can be generalized effectively to various scenarios, including noise-induced velocity variations, continuous interaction environments, and different vehicle numbers and road configurations.

Abstract

In this paper, we explore the application of the Decision Transformer, a decision-making algorithm based on the Generative Pre-trained Transformer (GPT) architecture, to multi-vehicle coordination at unsignalized intersections. We formulate the coordination problem so as to find the optimal trajectories for multiple vehicles at intersections, modeling it as a sequence prediction task to fully leverage the power of GPTs as a sequence model. Through extensive experiments, we compare our approach to a reservation-based intersection management system. Our results show that the Decision Transformer can outperform the training data in terms of total travel time and can be generalized effectively to various scenarios, including noise-induced velocity variations, continuous interaction environments, and different vehicle numbers and road configurations.

A GPT-based Decision Transformer for Multi-Vehicle Coordination at Unsignalized Intersections

TL;DR

The results show that the Decision Transformer can outperform the training data in terms of total travel time and can be generalized effectively to various scenarios, including noise-induced velocity variations, continuous interaction environments, and different vehicle numbers and road configurations.

Abstract

In this paper, we explore the application of the Decision Transformer, a decision-making algorithm based on the Generative Pre-trained Transformer (GPT) architecture, to multi-vehicle coordination at unsignalized intersections. We formulate the coordination problem so as to find the optimal trajectories for multiple vehicles at intersections, modeling it as a sequence prediction task to fully leverage the power of GPTs as a sequence model. Through extensive experiments, we compare our approach to a reservation-based intersection management system. Our results show that the Decision Transformer can outperform the training data in terms of total travel time and can be generalized effectively to various scenarios, including noise-induced velocity variations, continuous interaction environments, and different vehicle numbers and road configurations.
Paper Structure (19 sections, 4 equations, 4 figures, 5 tables, 2 algorithms)

This paper contains 19 sections, 4 equations, 4 figures, 5 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Problem Statement and Solution
  3. Problem Statement
  4. Decision Transformer
  5. States
  6. Actions
  7. Return-To-Gos
  8. Data Acquisition
  9. Collision-free dataset
  10. Collision dataset
  11. Experiments
  12. Model Training
  13. Mixed with Collision Data
  14. Comparison with Optimal Solution
  15. Generalization
  16. ...and 4 more sections

Figures (4)

  • Figure 1: Multi-vehicle coordination with Decision Transformer
  • Figure 2: Decision Transformer model architecture
  • Figure 3: Visualization of the driving trajectories of our model and AIM with vehicle speed trend at 2 cases
  • Figure 4: Environments with noise-free and noise.