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Parting with Misconceptions about Learning-based Vehicle Motion Planning
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The release of nuPlan marks a new era in vehicle motion planning research, offering the first large-scale real-world dataset and evaluation schemes requiring both precise short-term planning and long-horizon ego-forecasting. Existing systems struggle to simultaneously meet both requirements. Indeed, we find that these tasks are fundamentally misaligned and should be addressed independently. We further assess the current state of closed-loop planning in the field, revealing the limitations of learning-based methods in complex real-world scenarios and the value of simple rule-based priors such as centerline selection through lane graph search algorithms. More surprisingly, for the open-loop sub-task, we observe that the best results are achieved when using only this centerline as scene context (i.e., ignoring all information regarding the map and other agents). Combining these insights, we propose an extremely simple and efficient planner which outperforms an extensive set of competitors, winning the nuPlan planning challenge 2023.
Forward citations
Cited by 4 Pith papers
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Scaling Laws of Motion Forecasting and Planning -- Technical Report
Motion forecasting models improve with compute as a power law, with optimal model size growing 1.5x faster than dataset size, and closed-loop driving failures also decreasing with scale.
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Int2Planner: An Intention-based Multi-modal Motion Planner for Integrated Prediction and Planning
Int2Planner samples intention points from the ego vehicle's route path to generate multi-modal planning trajectories, improving integrated prediction and planning on nuPlan and a private dataset.
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Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Model
A reactive closed-loop driving simulator that uses a diffusion renderer with retrieval from real recordings, plus a nuPlan behavioral controller, to generate sensor images in response to an end-to-end driving model's actions.
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Generative AI for Autonomous Driving: A Review
A review of generative models (VAEs, GANs, diffusion, transformers, LLMs) applied to map generation, scenario generation, trajectory prediction, and motion planning for autonomous driving.
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