Research map / 3D vision / Depth and stereo
End-To-End Interpretable Neural Motion Planner
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the learned cost volume and trajectory sampling planner rather than IL | End-to-End Interpretable Neural Motion Planner · Driving test set | Collision Rate at 3.0s (%): 1.72 -> 0.78 | Helped |
| Use the learned cost volume and trajectory sampling planner rather than IL | End-to-End Interpretable Neural Motion Planner · Driving test set | L2 at 3.0s (m): 1.923 -> 2.353 | Hurt |
| Use the learned cost volume and trajectory sampling planner rather than ACC | End-to-End Interpretable Neural Motion Planner · Driving test set | Lane Violation at 3.0s (%): 0.64 -> 2.99 | Hurt |
| Jointly train detection and motion forecasting with planning (ablation) | End-to-End Interpretable Neural Motion Planner · Driving test set | Collision Rate at 3s (%): 1.42 -> 0.78 | Helped |
| Use the longer input sequence (ablation) | End-to-End Interpretable Neural Motion Planner · Driving test set | Collision Rate at 3s (%): 1.40 -> 0.93 | Helped |
| Add the traffic-rule penalty to the planning loss (ablation) | End-to-End Interpretable Neural Motion Planner · Driving test set | Collision Rate at 3s (%): 1.40 -> 0.78 | Helped |
| Add the traffic-rule penalty to the planning loss (ablation) | End-to-End Interpretable Neural Motion Planner · Driving test set | Traffic Violation at 3s (%): 3.26 -> 2.99 | Helped |
Closest papers by meaning
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- SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Driving (CVPR 2026 · similarity 0.86)
- Prioritizing Perception-Guided Self-Supervision: A New Paradigm for Causal Modeling in End-to-End Autonomous Driving (NeurIPS 2025 · similarity 0.86)
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