用局部坐标系提升多车仿真效率与鲁棒性
Toward Efficient and Robust Behavior Models for Multi-Agent Driving Simulation
- 以个体为中心的局部坐标表示，实现视角无关编码
- 训练与推理时间显著降低，位置精度优于基线模型
- 适合需要高并发仿真的自动驾驶系统研发
可扩展的多智能体驾驶仿真需要既真实又计算高效的交通行为模型。本文通过优化单个交通参与者的行为模型来实现这一目标。为提升效率，采用以实例为中心的场景表示，每个交通参与者和地图元素在各自的局部坐标系中建模。该设计实现高效、视角无关的场景编码，并允许静态地图标记在仿真步骤间复用。为建模交互，采用查询中心的对称上下文编码器，并引入局部坐标系间的相对位置编码。利用对抗逆强化学习训练行为模型，并提出自适应奖励变换，在训练中自动平衡鲁棒性与真实性。实验表明，本方法在令牌数量增加时仍保持高效，显著减少训练和推理时间，同时在位置精度和鲁棒性上优于多个以代理为中心的基线模型。
原文摘要 · Abstract (English)
Scalable multi-agent driving simulation requires behavior models that are both realistic and computationally efficient. We address this by optimizing the behavior model that controls individual traffic participants. To improve efficiency, we adopt an instance-centric scene representation, where each traffic participant and map element is modeled in its own local coordinate frame. This design enables efficient, viewpoint-invariant scene encoding and allows static map tokens to be reused across simulation steps. To model interactions, we employ a query-centric symmetric context encoder with relative positional encodings between local frames. We use Adversarial Inverse Reinforcement Learning to learn the behavior model and propose an adaptive reward transformation that automatically balances robustness and realism during training. Experiments demonstrate that our approach scales efficiently with the number of tokens, significantly reducing training and inference times, while outperforming several agent-centric baselines in terms of positional accuracy and robustness.
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