Scene-Adaptive Readout and Decoding in Diffusion Driving VLAs
Overview
This research paper introduces a scene-adaptive readout and decoding framework for diffusion driving vision-language-action models (VLAs). By analyzing trajectory readout across network depth, the authors identify that late-layer representations support useful predictions after a single forward sweep while early layers benefit from continued decoding. The proposed method uses a scene-conditioned readout Mixture of Experts (MoE) to adaptively combine predictions and applies additional refinement only when necessary. Evaluated on Waymo E2E and nuScenes benchmarks, the framework improves validation Relative Frequency of Success (RFS) by up to 0.56, achieves an end-to-end speedup of up to 6.0x on Waymo E2E, and consistently reduces trajectory error on nuScenes. The code is available at https://github.com/sjyu001/SARD.
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Event evidence and corrections
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Reported quantity · speedup factor: 6 other · Basis not reported
“by up to 0.56 while also improving test performance, and achieves an end-to-end speedup of up to $6.0\times$ on Waymo E2E, while consistently reducing trajectory error on nuScenes. These results demonstrate that scene-level readout and decoding allocation improves planning quality while avoiding unnecessary decoding. Our code is available at https://github.com/sjyu001/SARD”
Exact source · revision 1Source owner not reported
Reported quantity · RFS improvement: 0.56 other · Basis not reported
“by up to 0.56 while also improving test performance, and achieves an end-to-end speedup of up to $6.0\times$ on Waymo E2E, while consistently reducing trajectory error on nuScenes. These results demonstrate that scene-level readout and decoding allocation improves planning quality while avoiding unnecessary decoding. Our code is available at https://github.com/sjyu001/SARD”
Exact source · revision 1Source owner not reported
Artifact availability · code: available
“by up to 0.56 while also improving test performance, and achieves an end-to-end speedup of up to $6.0\times$ on Waymo E2E, while consistently reducing trajectory error on nuScenes. These results demonstrate that scene-level readout and decoding allocation improves planning quality while avoiding unnecessary decoding. Our code is available at https://github.com/sjyu001/SARD”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalScene-Adaptive Readout and Decoding in Diffusion Driving VLAs
This research paper introduces a scene-adaptive readout and decoding framework for diffusion driving vision-language-action models (VLAs). By analyzing trajectory readout across network depth, the authors identify that late-layer representations support useful predictions after a single forward sweep while early layers benefit from continued decoding. The proposed method uses a scene-conditioned readout Mixture of Experts (MoE) to adaptively combine predictions and applies additional refinement only when necessary. Evaluated on Waymo E2E and nuScenes benchmarks, the framework improves validation Relative Frequency of Success (RFS) by up to 0.56, achieves an end-to-end speedup of up to 6.0x on Waymo E2E, and consistently reduces trajectory error on nuScenes. The code is available at https://github.com/sjyu001/SARD.
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