Simulating realistic interactions among traffic agents is crucial for
efficiently validating the safety of autonomous driving systems. Existing
leading simulators primarily use an encoder-decoder structure to encode the
historical trajectories for future simulation. However, such a paradigm
complicates the model architecture, and the manual separation of history and
future trajectories leads to low data utilization. To address these challenges,
we propose Behavior Generative Pre-trained Transformers (BehaviorGPT), a
decoder-only, autoregressive architecture designed to simulate the sequential
motion of multiple agents. Crucially, our approach discards the traditional
separation between "history" and "future," treating each time step as the
"current" one, resulting in a simpler, more parameter- and data-efficient
design that scales seamlessly with data and computation. Additionally, we
introduce the Next-Patch Prediction Paradigm (NP3), which enables models to
reason at the patch level of trajectories and capture long-range
spatial-temporal interactions. BehaviorGPT ranks first across several metrics
on the Waymo Sim Agents Benchmark, demonstrating its exceptional performance in
multi-agent and agent-map interactions. We outperformed state-of-the-art models
with a realism score of 0.741 and improved the minADE metric to 1.540, with an
approximately 91.6% reduction in model parameters.

提出了 BehaviorGPT，一种基于解码器的自回归架构，用于模拟多智能体的顺序运动，并通过 Next-Patch Prediction Paradigm (NP3) 在 Waymo Sim Agents Benchmark 的多个指标排名第一。