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From Prediction to Planning With Goal Conditioned Lane Graph Traversals

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arxiv 2302.07753 v2 pith:OAFOXORL submitted 2023-02-15 cs.RO

Marcel Hallgarten , Martin Stoll , Andreas Zell This is my paper · ORCID
classification cs.RO
keywords predictionmodelgoalplanningpotentialadditionaladoptedarchitectures
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The field of motion prediction for automated driving has seen tremendous progress recently, bearing ever-more mighty neural network architectures. Leveraging these powerful models bears great potential for the closely related planning task. In this letter we propose a novel goal-conditioning method and show its potential to transform a state-of-the-art prediction model into a goal-directed planner. Our key insight is that conditioning prediction on a navigation goal at the behaviour level outperforms other widely adopted methods, with the additional benefit of increased model interpretability. We train our model on a large open-source dataset and show promising performance in a comprehensive benchmark.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. G2DP: Diffusion Planning with Spatio-Temporal Grid Guidance

    cs.RO 2026-06 unverdicted novelty 6.0 of 10

    G2DP constructs a differentiable spatio-temporal cost volume from occupancy and route maps to guide diffusion denoising for collision-free trajectories, reporting SOTA closed-loop scores on nuPlan.

  2. DeepUrban: Interaction-Aware Trajectory Prediction and Planning for Automated Driving by Aerial Imagery

    cs.CV 2026-01 conditional novelty 6.0 of 10

    Adding the new DeepUrban drone dataset to nuScenes training reduces vehicle ADE/FDE on nuScenes validation by up to 44.1%/44.3%.

  3. Int2Planner: An Intention-based Multi-modal Motion Planner for Integrated Prediction and Planning

    cs.RO 2025-01 conditional novelty 6.0 of 10

    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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