DriveFuture achieves SOTA results on NAVSIM by conditioning latent world model states on future predictions to directly inform trajectory planning.
Bridging past and future: End-to-end autonomous driving with historical prediction and planning
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EponaV2 advances perception-free driving world models by forecasting comprehensive future 3D geometry and semantic representations, achieving SOTA planning performance on NAVSIM benchmarks.
Attribution statistics derived from multi-view inputs in end-to-end planners can predict planning risks, with reported Spearman correlation of 0.30 with trajectory error and AUROC of 0.77 for collision detection.
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Can Attribution Predict Risk? From Multi-View Attribution to Planning Risk Signals in End-to-End Autonomous Driving
Attribution statistics derived from multi-view inputs in end-to-end planners can predict planning risks, with reported Spearman correlation of 0.30 with trajectory error and AUROC of 0.77 for collision detection.