Pith Number

pith:G7KKMBNG

pith:2024:G7KKMBNGRFQM7X5VLH3IRVQLAY

not attested not anchored not stored refs resolved

Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation

Jan Kautz, Jose M. Alvarez, Kailin Li, Shihao Wang, Shiyi Lan, Yishen Ji, Yu-Gang Jiang, Zhenxin Li, Zhiding Yu, Zhiqi Li, Ziyue Zhu, Zuxuan Wu

Hydra-MDP trains an end-to-end planner by distilling knowledge from both human demonstrations and rule-based experts into a multi-head decoder that outputs diverse trajectories.

arxiv:2406.06978 v4 · 2024-06-11 · cs.CV

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

This method achieves the 1st place in the Navsim challenge, demonstrating significant improvements in generalization across diverse driving environments and conditions.

C2weakest assumption

The multi-head decoder can simultaneously absorb conflicting signals from human and rule-based teachers without mode collapse or degraded performance on any single metric.

C3one line summary

Hydra-MDP uses multi-teacher distillation and a multi-head decoder to learn diverse, metric-specific trajectories in an end-to-end autonomous-driving planner, winning the Navsim challenge.

References

19 extracted · 19 resolved · 2 Pith anchors

[1] Quad: Query-based in- terpretable neural motion planning for autonomous driving 2024

[3] NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles 2021 · arXiv:2106.11810

[4] VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning · arXiv:2402.13243

[5] Transfuser: Imita- tion with transformer-based sensor fusion for autonomous driving 2022

[6] Navsim: Data-driven non-reactive autonomous vehicle simulation 2024

Formal links

2 machine-checked theorem links

Cited by

41 papers in Pith

Distill to Think, Foresee to Act: Cognitive-Physical Reinforcement Learning for Autonomous Driving

Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving

Towards Physically Consistent 4D Scene Reconstruction for Closed-loop Autonomous Driving Simulation

Grounding Driving VLA via Inverse Kinematics

Distill to Think, Foresee to Act: Cognitive-Physical Reinforcement Learning for Autonomous Driving

Receipt and verification

First computed	2026-05-18T03:50:37.320811Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

37d4a605a68960cfdfb559f688d60b062c21aa89e90533927c8571bc25b2f50c

Aliases

arxiv: 2406.06978 · arxiv_version: 2406.06978v4 · doi: 10.48550/arxiv.2406.06978 · pith_short_12: G7KKMBNGRFQM · pith_short_16: G7KKMBNGRFQM7X5V · pith_short_8: G7KKMBNG

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/G7KKMBNGRFQM7X5VLH3IRVQLAY \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 37d4a605a68960cfdfb559f688d60b062c21aa89e90533927c8571bc25b2f50c

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "96d6a7a8343c519df05069304470aab8a6f9c4b32489e818f8e558a24f651279",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2024-06-11T06:18:26Z",
    "title_canon_sha256": "bfc3f6c68a9e2b362c4e80b0e33a14901356db046042ed1ac0bb4b097b81116e"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2406.06978",
    "kind": "arxiv",
    "version": 4
  }
}