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.

翻译：自动驾驶技术有望变革交通运输体系。然而，在无信号交叉口等复杂场景中实现安全、精准的多任务决策，仍是自动驾驶汽车面临的一项挑战。本文提出了一种创新方法，即开发多任务决策生成式预训练变换器（MTD-GPT）模型。该模型融合了强化学习（RL）的固有优势与生成式预训练变换器（GPT）的先进序列建模能力，旨在同时管理无信号交叉口处的多项驾驶任务，如左转、直行和右转。我们首先训练一个单任务强化学习专家模型，在环境中采集专家数据，随后利用混合多任务数据集进行离线GPT训练。该方法将自动驾驶中的多任务决策问题抽象为序列建模任务。MTD-GPT模型在多项决策任务上进行了训练与评估，其性能优于或可与最先进的单任务决策模型相媲美。