承诺前的门控：预测意图分歧以防止自动驾驶中交互后的决策失败
Gating Before Commitment: Anticipating Intent Divergence to Prevent Post-Interaction Decision Failures in Autonomous Driving
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中文总结 AI 辅助
该研究提出承诺前的门控决策层，通过语言引导意图模块检测自动驾驶车辆交互中的意图分歧，可修复规划、降低误触发率，其对故障的最快检测和不确定性否决作用获实验支持。
中文摘要 AI 辅助
车辆交互过程中的意图误解会导致反复出现的规划失败。我们研究一种决策层，其中语言引导的意图模块读取结构化描述符，计算平滑后的意图-几何分歧分数，并在承诺前、走廊包络的上游对规划的机动进行门控。在重放的1次越野驶出场景和4次碰撞片段中，采用冻结的已公开实现，门控是唯一能修复规划的层：在主案例中，它在漂移开始后72毫秒触发，但在走廊出口前161毫秒触发，在所有10次重放中都将轨迹保持在走廊内。首次校准在5.9分钟内产生9次误触发，每次都因将不确定性视为一半冲突；预先注册的重新设计将不确定性视为弃权（不执行），将误触发率降至每分钟0.341次。两次 ablation 实验界定了模型的作用：完整分数在部署的合格性下检测5次故障中的4次最快，在未否决规则下检测5次中的3次（000871晚一个周期；000228在不确定路段的前触发，5个片段无法将其归类为信号或巧合；删除置信项会损失2次检测），而在域内轨道上，在相同误报下，几何规则的检测率提高了两倍多。证据支持门控机制；模型的已证明作用是对这些故障的最快检测以及对几何规则的不确定性否决。
英文摘要
Intent misinterpretation during vehicle interactions causes recurring planning failures. We study a decision layer in which a language-guided intent module reads structured descriptors, computes a smoothed intent-geometry divergence score, and gates the planned maneuver before commitment, upstream of a corridor envelope. On a replayed off-road departure and four crash clips under a frozen, disclosed implementation, gating is the only layer that repairs the plan: on the main case it fires 72 ms after the drift onset but 161 ms before the corridor exit, keeping the trajectory in the corridor in all ten replays. The first calibration draws nine false triggers in 5.9 minutes, each from scoring uncertainty as half a conflict; a preregistered redesign treating uncertainty as abstention cuts this to 0.341 per minute. Two ablations bound the model's contribution: the full score detects fastest on four of five failures under the deployed eligibility, three of five against the unvetoed rule (000871 by one cycle; 000228 by a pre-onset fire on an uncertain stretch that five clips cannot classify as signal or coincidence; dropping the confidence term costs two detections), while on in-domain tracks at equal false positives the geometric rule more than triples its detection. The evidence supports the gating mechanism; the model's demonstrated roles are the fastest detection on these failures and an uncertainty veto on the geometric rule.
发表机构
- University of South Florida（南佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。
