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.
Rethinking imitation-based planner for autonomous driving
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
In recent years, imitation-based driving planners have reported considerable success. However, due to the absence of a standardized benchmark, the effectiveness of various designs remains unclear. The newly released nuPlan addresses this issue by offering a large-scale real-world dataset and a standardized closed-loop benchmark for equitable comparisons. Utilizing this platform, we conduct a comprehensive study on two fundamental yet underexplored aspects of imitation-based planners: the essential features for ego planning and the effective data augmentation techniques to reduce compounding errors. Furthermore, we highlight an imitation gap that has been overlooked by current learning systems. Finally, integrating our findings, we propose a strong baseline model-PlanTF. Our results demonstrate that a well-designed, purely imitation-based planner can achieve highly competitive performance compared to state-of-the-art methods involving hand-crafted rules and exhibit superior generalization capabilities in long-tail cases. Our models and benchmarks are publicly available. Project website https://jchengai.github.io/planTF.
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cs.RO 3representative citing papers
A diffusion-based autonomous driving planner that injects gradients from a spatio-temporal occupancy-and-route cost volume into late denoising steps improves closed-loop safety and progress across three benchmarks.
LiloDriver uses LLMs and memory-augmented planning in a four-stage pipeline to outperform rule-based and learning-based methods on both common and rare scenarios in the nuPlan benchmark.
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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G2DP: Diffusion Planning with Spatio-Temporal Grid Guidance
A diffusion-based autonomous driving planner that injects gradients from a spatio-temporal occupancy-and-route cost volume into late denoising steps improves closed-loop safety and progress across three benchmarks.
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LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios
LiloDriver uses LLMs and memory-augmented planning in a four-stage pipeline to outperform rule-based and learning-based methods on both common and rare scenarios in the nuPlan benchmark.