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Autonomous driving technology is poised to transform transportation systems.However, achieving safe and accurate multi-task decision-making in complexscenarios, such as unsignalized intersections, remains a challenge forautonomous vehicles. This paper presents a novel approach to this issue withthe development of a Multi-Task Decision-Making Generative Pre-trainedTransformer (MTD-GPT) model. Leveraging the inherent strengths of reinforcementlearning (RL) and the sophisticated sequence modeling capabilities of theGenerative Pre-trained Transformer (GPT), the MTD-GPT model is designed tosimultaneously manage multiple driving tasks, such as left turns,straight-ahead driving, and right turns at unsignalized intersections. Weinitially train a single-task RL expert model, sample expert data in theenvironment, and subsequently utilize a mixed multi-task dataset for offlineGPT training. This approach abstracts the multi-task decision-making problem inautonomous driving as a sequence modeling task. The MTD-GPT model is trainedand evaluated across several decision-making tasks, demonstrating performancethat is either superior or comparable to that of state-of-the-art single-taskdecision-making models.

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