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This study introduces a decision layer that utilizes a language-guided intent module to anticipate intent divergence in autonomous driving, addressing the critical issue of intent misinterpretation that leads to planning failures. By computing a smoothed intent-geometry divergence score, the system gates planned maneuvers before commitment, effectively maintaining vehicle trajectory within safe corridors during off-road departures and crash scenarios. The results show that this gating mechanism significantly reduces false triggers and enhances detection speed, demonstrating its efficacy in preventing decision failures in real-time driving contexts.
Gating maneuvers before commitment can prevent planning failures in autonomous vehicles, achieving rapid response times that keep trajectories safe in critical scenarios.
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.
