INTERACT：基于锚定条件预测与信任域优化的自动驾驶交互式规划

中文摘要 AI 辅助

在密集城市交通中驾驶是交互式的：无论是并线还是无保护转弯能否成功，都取决于周围智能体如何响应自车。传统规划器先预测后规划，因此无法考虑这种依赖性。集成预测与规划的方法要么联合训练两者，这会引入任务干扰；要么保持两者分离，但受限于预定义的建议集。我们提出INTERACT：基于锚定条件预测与信任域优化的自动驾驶交互式规划。我们的关键洞察是，周围智能体响应的是轨迹所表达的意图，而非其精确实现，因此单一的反应性预测在整个规划族中保持有效。因此，INTERACT将交互式规划分解为跨驾驶意图的预测和每个意图内的优化。我们从地图几何中推导出一小组多样化的意图，称之为锚点，为每个锚点查询一次专用的自车条件预测模型，并使用交叉熵方法在信任域惩罚下优化每个锚点，该惩罚使优化后的规划保持足够接近其锚点，以使条件化反应仍然适用。因此，预测保持为独立模型，避免了任务干扰，而对锚点的条件化保留了依赖性。由于每个锚点被连续优化，最终规划不限于锚点集，但INTERACT每个锚点仅需一次预测器查询，而非每个候选规划一次，且所有锚点并行处理。在nuPlan和interPlan闭环基准上，INTERACT取得了新的最先进成果，最大的提升恰恰出现在激发该方法的交互式场景中。代码将在论文被接收后发布。

英文摘要

Driving in dense urban traffic is interactive: whether a merge or an unprotected turn succeeds depends on how surrounding agents respond to the ego vehicle. Conventional planners predict first and plan second and, therefore, cannot account for this dependency. Methods that integrate prediction and planning either train both jointly, which introduces task interference, or keep them separate and are restricted to a predefined set of proposals. We present INTERACT: Interactive Planning for Autonomous Driving via Anchor-Conditioned Prediction and Trust-Region Refinement. Our key insight is that surrounding agents react to the intent a trajectory expresses rather than to its exact realization, so a single reactive prediction stays valid across an entire family of plans. INTERACT therefore decomposes interactive planning into prediction across driving intents and optimization within each intent. We derive a small set of diverse intents, which we call anchors, from map geometry, query a dedicated ego-conditioned prediction model once per anchor, and refine every anchor with the Cross-Entropy Method under a trust-region penalty that keeps the refined plan close enough to its anchor for the conditioned reaction to still apply. Prediction thus remains a separate model, avoiding task interference, while conditioning on anchors preserves the dependency. Because each anchor is refined continuously, the final plan is not restricted to the anchor set, yet INTERACT requires only one predictor query per anchor rather than one per candidate plan, with all anchors processed in parallel. On the nuPlan and interPlan closed-loop benchmarks, INTERACT sets a new state of the art, with the largest gains precisely in the interactive scenarios that motivate the method. The code will be released upon acceptance.

发表机构

• Robert Bosch GmbH（罗伯特·博世有限公司）
• University of Freiburg（弗莱堡大学）
• Coburg University（科堡大学）

机构由 AI 辅助整理，请以论文原文为准。