GatedVLA with Camera-Based Emergency Braking: AlpaSim E2E Closed Loop Challenge Technical Report
Overview
This technical report introduces GatedVLA, a vision-language-action policy designed for the Physical AI AV track. The system pairs a driving-pretrained Qwen-Drive-1.0-4B model with a flow-matching action expert enhanced by a soft visual mask. In local closed-loop evaluations across 150 scenes, the masked policy improved the mean scene score from 0.479 to 0.517. When augmented with a rule-based automatic emergency braking module, the mean score further increased to 0.599, and the mean number of scenes with at-fault collisions decreased from 34.0 to 11.0.
Generated from attributed reports · Updated 3 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · scene score: 0.479 other · Basis not reported
“cross-attention. Compared to an identically trained baseline, the masked policy achieves comparable overall open-loop error while reducing error on left turns. In local closed-loop evaluation across the same 150 scenes (two rollouts per scene), the visual mask increases the mean scene score from 0.479 to 0.517 (paired difference +0.038, 95% bootstrap CI [-0.018, +0.093], wins/losses 37/26, sign test p = 0.21). Incorp”
Exact source · revision 1Source owner not reported
Reported quantity · mean scene score increase: 0.038 other · Basis not reported
“cross-attention. Compared to an identically trained baseline, the masked policy achieves comparable overall open-loop error while reducing error on left turns. In local closed-loop evaluation across the same 150 scenes (two rollouts per scene), the visual mask increases the mean scene score from 0.479 to 0.517 (paired difference +0.038, 95% bootstrap CI [-0.018, +0.093], wins/losses 37/26, sign test p = 0.21). Incorp”
Exact source · revision 1Source owner not reported
Reported quantity · scene score with AEB: 0.599 other · Basis not reported
“orating a rule-based automatic emergency braking (AEB) module at deployment further increases the mean score to 0.599 (+0.082, CI [+0.023, +0.143]) and reduces the mean number of scenes with at-fault collisions from 34.0 to 11.0.”
Exact source · revision 1Source owner not reported
Report timeline
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- arXiv Robotics — research abstractsSignalGatedVLA with Camera-Based Emergency Braking: AlpaSim E2E Closed Loop Challenge Technical Report
This technical report introduces GatedVLA, a vision-language-action policy designed for the Physical AI AV track. The system pairs a driving-pretrained Qwen-Drive-1.0-4B model with a flow-matching action expert enhanced by a soft visual mask. In local closed-loop evaluations across 150 scenes, the masked policy improved the mean scene score from 0.479 to 0.517. When augmented with a rule-based automatic emergency braking module, the mean score further increased to 0.599, and the mean number of scenes with at-fault collisions decreased from 34.0 to 11.0.
Event coverage history
Current attention 3·Peak within the comparable range 4（2026-11-04 16:00 UTC）·Change within the comparable range over 24 hours –
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