MTD-GPT: A Multi-Task Decision-Making GPT Model for Autonomous Driving at Unsignalized Intersections

TL;DR

This paper introduces MTD-GPT, a multi-task decision-making model for autonomous vehicles at unsignalized intersections, combining reinforcement learning and GPT to handle multiple driving tasks effectively.

Contribution

It presents a novel multi-task decision-making framework using GPT trained on both expert and multi-task data, improving autonomous driving at complex intersections.

Findings

MTD-GPT performs comparably or better than state-of-the-art models.

The approach effectively manages multiple driving tasks simultaneously.

The model demonstrates robustness in complex intersection scenarios.

Abstract

Autonomous driving technology is poised to transform transportation systems. However, achieving safe and accurate multi-task decision-making in complex scenarios, such as unsignalized intersections, remains a challenge for autonomous vehicles. This paper presents a novel approach to this issue with the development of a Multi-Task Decision-Making Generative Pre-trained Transformer (MTD-GPT) model. Leveraging the inherent strengths of reinforcement learning (RL) and the sophisticated sequence modeling capabilities of the Generative Pre-trained Transformer (GPT), the MTD-GPT model is designed to simultaneously manage multiple driving tasks, such as left turns, straight-ahead driving, and right turns at unsignalized intersections. We initially train a single-task RL expert model, sample expert data in the environment, and subsequently utilize a mixed multi-task dataset for offline GPT training. This approach abstracts the multi-task decision-making problem in autonomous driving as a sequence modeling task. The MTD-GPT model is trained and evaluated across several decision-making tasks, demonstrating performance that is either superior or comparable to that of state-of-the-art single-task decision-making models.