从学习模式自动驾驶车辆-交通配对到规划器决策：Argoverse 2 上的边际保持研究
From Learned-Mode AV-Traffic Pairing to Planner Decisions: A Marginal-Preserving Study on Argoverse 2
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中文总结 AI 辅助
本研究通过边际保持乘积控制，在Argoverse 2上探究学习模式中AV-交通配对对规划器决策的影响，发现干预改变决策但参与者级指标不变，且无法分离配对效应与集中度变化。
中文摘要 AI 辅助
联合运动预测将每辆自动驾驶车辆（AV）的未来与周围交通配对，但参与者级指标并未显示这种结构对规划器是否重要。我们通过一种边际保持乘积控制来研究这一问题，该控制在学习模式中移除自动驾驶车辆-交通配对，同时保留固定的恒定速度配对，并在规划器条件化之前保持轨迹、参与者级边际、候选、成本项和权重以及回退固定不变。该干预还改变了候选条件化的集中度。在 1,400 个留出的 Argoverse 2 场景上的十二次运行中，该干预在 $\ au=4$ m 时改变了 3.0% 的路线级离线选择。在两种训练规模下，控制减去联合的已记录轨迹遗憾分别为 $-0.026$ 和 $-0.118$；交叉区间和种子-$t$ 区间跨越零。在 $\ au=1$ m 时，相对成本在 87.9% 的路线评估中发生变化，路线级离线选择在 8.1% 中发生变化。在集中度匹配之前，描述性结果估计有利于控制。大部分差距沿近似集中度匹配路径消失；剩余对比为 $+0.112$ 和 $-0.047$，且两个交叉区间均跨越零。参与者级预测指标保持相同。配对强度和温度扫描表明，决策对比随配对移除和更尖锐的条件化而增长。尽管参与者级指标保持不变，该干预仍改变了规划器决策。然而，匹配分析无法将学习模式自动驾驶车辆-交通配对的任何已记录结果效应与伴随的条件化集中度变化分开。
英文摘要
Joint motion forecasts pair each autonomous-vehicle (AV) future with surrounding traffic, but actor-level metrics do not show whether that structure matters to a planner. We study this question with a marginal-preserving product control that removes AV-traffic pairing among the learned modes while retaining the fixed constant-velocity pair and holding trajectories, actor-level marginals before planner conditioning, candidates, the cost terms and weights, and fallback fixed. The intervention also changes candidate-conditioned concentration. Across twelve runs on 1,400 held-out Argoverse 2 scenarios, the intervention changes 3.0% of route-level offline selections at $τ=4$ m. Control-minus-joint recorded-trajectory regret is $-0.026$ and $-0.118$ at the two training sizes; crossed and seed-$t$ intervals span zero. At $τ=1$ m, relative costs change in 87.9% of route evaluations and route-level offline selections in 8.1%. Before concentration matching, descriptive outcome estimates favor the control. Most of this gap disappears along an approximate concentration-matching path; the remaining contrasts are $+0.112$ and $-0.047$, and both crossed intervals span zero. Actor-level forecast metrics remain identical. Pairing-strength and temperature sweeps show that the decision contrast grows with pairing removal and sharper conditioning. The intervention changes planner decisions even though actor-level metrics remain unchanged. The matching analysis, however, cannot separate any recorded-outcome effect of learned-mode AV-traffic pairing from the accompanying change in conditioned concentration.
