发表机构
NODE lab, University of Alberta; MRT Institute, Karlsruhe Institute of Technology（节点实验室，阿尔伯塔大学; MRT 研究所，卡尔斯鲁厄理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究自动驾驶中高级决策问题，提出DecisionPerceiver将动态代理特征投影到固定大小潜在空间，通过潜在查询数调节特征粒度，还细化动作集，经多场景评估有性能提升、泛化能力及可扩展性。
AI 中文摘要
在自动驾驶中，基于可靠学习的高级决策制定必须适应因场景交通流变化而动态调整大小的输入。DeepSet及其变体在共享编码器方法中代表了当前的技术水平，但它们忽略了明确的交通交互建模，限制了在交叉路口等协商密集型场景中的性能。基于注意力的方法可以捕捉静态和动态代理之间的交互，但会产生二次内存和计算复杂性，并且对表示粒度的控制有限。受基于注意力的架构Perceiver IO的启发，提出了DecisionPerceiver，将动态代理特征投影到固定大小的潜在空间中，通过潜在查询的数量调节特征粒度，提高了大型网络的可扩展性。进一步提出了动作集的更精细离散化，以增加因交互感知而带来的性能提升。在三个需要不同程度交互感知的驾驶场景中进行的广泛评估表明，在各种导航目标上都有一致的性能提升和泛化能力。此外，在车辆数量增加的场景中评估了所提出的架构，以证明其可扩展性。
英文摘要
Reliable learning-based high-level decision making for lane changes and speed control in automated driving must accommodate dynamically sized inputs due to varying scene traffic flow. DeepSet and its variants represent the state of the art among shared-encoder approaches; however, they neglect explicit traffic interaction modeling, limiting performance in negotiation-intensive scenarios such as intersections. Attention-based methods capture interactions among static and dynamic agents, but incur quadratic memory and computational complexity and provide limited control over representation granularity. Inspired by Perceiver IO, an attention-based architecture, DecisionPerceiver, is proposed to project dynamic agent features into a fixed-size latent space, where feature granularity is regulated by the number of latent queries, improving scalability for larger networks. A finer discretization of the action set is further proposed to increase the performance gain due to interaction awareness. Extensive evaluations across three driving scenarios that require different levels of interaction awareness demonstrate consistent performance gains and generalization across various navigation objectives. In addition, the proposed architecture is assessed in scenarios with an increasing number of vehicles to demonstrate scalability.
Comments6 pages, 5 figures, 3 tables, submitted to 2026 IEEE Intelligent Transportation Systems Conference (ITSC)
