清华PrismAD，让端到端自动驾驶规划专家各司其职。
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TL;DR - PrismAD is an end-to-end autonomous-driving framework that assigns interaction, road geometry, and navigation intent to separate planning experts, then dynamically fuses their trajectories. This semantic decoupling improves safety and interpretability across multiple driving benchmarks.
- Each expert is a complete prediction-and-planning branch rather than a conventional MoE feed-forward layer.
- A semantic router predicts separate expert weights for surrounding-object prediction and ego-vehicle planning, with sparse Top-K activation during inference.
- Applied to DiffusionDrive, PrismAD reduced the average nuScenes collision rate from 0.08% to 0.04%.
- Geometry and intent experts offered limited L2-error gains but consistently reduced collisions, especially in turning scenarios.
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清华PrismAD，让端到端自动驾驶规划专家各司其职。
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Semantic Scholar citations 0 · Semantic Scholar influential citations 0
TL;DR - PrismAD is an end-to-end autonomous-driving framework that assigns interaction, road geometry, and navigation intent to separate planning experts, then dynamically fuses their trajectories. This semantic decoupling improves safety and interpretability across multiple driving benchmarks.
- Each expert is a complete prediction-and-planning branch rather than a conventional MoE feed-forward layer.
- A semantic router predicts separate expert weights for surrounding-object prediction and ego-vehicle planning, with sparse Top-K activation during inference.
- Applied to DiffusionDrive, PrismAD reduced the average nuScenes collision rate from 0.08% to 0.04%.
- Geometry and intent experts offered limited L2-error gains but consistently reduced collisions, especially in turning scenarios.