发表机构
School of Automation, Nanjing University of Science and Technology（南京理工大学自动化学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对未来感知自动驾驶规划器，提出PDT分析框架，通过分解与验证模块评估未来信息对驾驶决策的支撑性，实验发现多数配置未达鲁棒性要求，可识别决策迁移的失败或不确定场景。
AI 中文摘要
未来感知表示和世界模型正越来越多地用于基于提案的自动驾驶规划器，以改进轨迹选择。然而，代理目标或受限子集的改进常被解读为规划性能提升，却未验证提案排序、选定轨迹、全规模效用及关键驾驶组件。我们提出Proxy-to-Decision Transfer（PDT，代理到决策迁移）框架，这是一种分析框架，用于评估学习到的未来信息何时能支持可靠的驾驶性能提升主张。其决策迁移分解模块通过分数边际、开关条件效用及支持与选择遗憾来定位价值损失；其可靠性约束验证模块要求精确配对、最小有意义效应、规模扩展确认、安全无补偿、序列可比性及家族级鲁棒性。在使用NAVSIM-v1评估的代表性未来感知规划器上，组件BCE从0.705降至0.530，同时保持选定的PDM从0.963降至0.961。另一候选方案在512条记录的前缀上提升了0.00909，其场景自举95%置信区间为[0.000744, 0.0177]，但2048条记录及完整支持区间包含零。提案级重放进一步证实了开关效用分解，但432个筛选配置中无一个通过两半、两种子的鲁棒性门限。因此，PDT可识别在代理、子集、聚合及选择证据中决策迁移失败或仍不确定的情况。
英文摘要
Future-aware representations and world models are increasingly used in proposal-based autonomous-driving planners to improve trajectory selection. However, improvements in proxy objectives or restricted subsets are often interpreted as planning gains without verifying proposal ordering, selected trajectories, full-scale utility, and critical driving components. We propose the Proxy-to-Decision Transfer (PDT) Framework, an analysis framework that evaluates when learned future information supports a reliable driving-performance improvement claim. Its Decision-Transfer Decomposition Module localizes value loss through score margins, switch-conditioned utility, and support-versus-selection regret. Its Reliability-Constrained Validation Module requires exact pairing, a minimum meaningful effect, scale-expanded confirmation, safety non-compensation, sequential comparability, and family-level robustness. On a representative future-aware planner evaluated with NAVSIM-v1, component BCE decreases from 0.705 to 0.530 while held selected PDM decreases from 0.963 to 0.961. A separate candidate improves a 512-record prefix by 0.00909, with a scene-bootstrap 95% interval of [0.000744, 0.0177], but its 2048-record and complete-support intervals include zero. A proposal-level replay further confirms the switch-utility decomposition, yet none of 432 screened configurations passes the two-half, two-seed robustness gate. PDT therefore identifies where decision transfer fails or remains indeterminate across proxy, subset, aggregate, and selection evidence.
Comments25 pages, 8 figures, 7 tables
