ForeseeDrive: Future-Token Conditioning for Anticipatory Generative Driving
Overview
This research paper presents ForeseeDrive, a method that converts future scene evolution into an interface for shaping generative driving candidates. By deriving query-indexed tokens from logged motion and occupancy data to condition trajectory geometry before candidate generation, the approach aims to recover maneuvers absent in standard sampling menus. The system includes mechanisms like zero-token-gated residual paths and TTC-margin guards to limit drift and ensure safety. Benchmark results show an 89.2 PDMS score on NAVSIM navtest, 0.41 m average L2 error, and a 0.05% collision rate on nuScenes validation, alongside a 76.50 driving score on Bench2Drive. Diagnostics reveal mixed oracle-best quality and localized failures near safe-to-unsafe boundaries, supporting the utility of future-conditioned generation as a practical anticipation interface.
Generated from attributed reports · Updated 2 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 · average L2 error: 0.41 other · Basis not reported
“s. Under their respective benchmark protocols, ForeseeDrive reaches 89.2 PDMS on NAVSIM navtest, 0.41\,m average L2 and 0.05\% collision rate on nuScenes validation, and a 76.50 driving score on Bench2Drive. Controlled DiffusionDrive/NAVSIM diagnostics show an exact zero-token no-op, a small aligned-over-shuffled selected-PDM advantage under preservation, mixed oracle-best quality, and localized TTC-boundary failures”
Exact source · revision 1Source owner not reported
Reported quantity · driving score: 76.5 other · reported trials
“s. Under their respective benchmark protocols, ForeseeDrive reaches 89.2 PDMS on NAVSIM navtest, 0.41\,m average L2 and 0.05\% collision rate on nuScenes validation, and a 76.50 driving score on Bench2Drive. Controlled DiffusionDrive/NAVSIM diagnostics show an exact zero-token no-op, a small aligned-over-shuffled selected-PDM advantage under preservation, mixed oracle-best quality, and localized TTC-boundary failures”
Exact source · revision 1Source owner not reported
Reported quantity · PDMS score: 89.2 other · reported trials
“s. Under their respective benchmark protocols, ForeseeDrive reaches 89.2 PDMS on NAVSIM navtest, 0.41\,m average L2 and 0.05\% collision rate on nuScenes validation, and a 76.50 driving score on Bench2Drive. Controlled DiffusionDrive/NAVSIM diagnostics show an exact zero-token no-op, a small aligned-over-shuffled selected-PDM advantage under preservation, mixed oracle-best quality, and localized TTC-boundary failures”
Exact source · revision 1Source owner not reported
Artifact availability · code: reported pending
“. Together, the benchmark results and bounded mechanism tests support future-conditioned generation as a practical interface for anticipation. Code is included in the supplementary materials.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalForeseeDrive: Future-Token Conditioning for Anticipatory Generative Driving
This research paper presents ForeseeDrive, a method that converts future scene evolution into an interface for shaping generative driving candidates. By deriving query-indexed tokens from logged motion and occupancy data to condition trajectory geometry before candidate generation, the approach aims to recover maneuvers absent in standard sampling menus. The system includes mechanisms like zero-token-gated residual paths and TTC-margin guards to limit drift and ensure safety. Benchmark results show an 89.2 PDMS score on NAVSIM navtest, 0.41 m average L2 error, and a 0.05% collision rate on nuScenes validation, alongside a 76.50 driving score on Bench2Drive. Diagnostics reveal mixed oracle-best quality and localized failures near safe-to-unsafe boundaries, supporting the utility of future-conditioned generation as a practical anticipation interface.
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