A dual-track zero-shot benchmark (DeepPlan semantic shift + actuation-noise drift) reveals imitation planners collapse under novel urban density and correlated noise while an RL planner remains more robust.
When planners meet reality: How learned, reactive traffic agents shift nuplan benchmarks,
2 Pith papers cite this work. Polarity classification is still indexing.
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Dash2Sim recovers metric geo-referenced 4D scenes from in-the-wild monocular dashcam videos to enable the ROADWork4D benchmark, revealing that current closed-loop planners fail on work zone lane changes.
citing papers explorer
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Shift & Drift: A Zero-Shot Benchmark for Generalizable and Robust Autonomous Driving Motion Planning
A dual-track zero-shot benchmark (DeepPlan semantic shift + actuation-noise drift) reveals imitation planners collapse under novel urban density and correlated noise while an RL planner remains more robust.
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Dash2Sim: Closed-Loop Driving Simulation from in-the-wild Dashcam Videos
Dash2Sim recovers metric geo-referenced 4D scenes from in-the-wild monocular dashcam videos to enable the ROADWork4D benchmark, revealing that current closed-loop planners fail on work zone lane changes.