Pith Number

pith:AKLOOSBJ

pith:2023:AKLOOSBJVAHH4UUP3J3MB543UM

not attested not anchored not stored refs resolved

GPT-Driver: Learning to Drive with GPT

Hang Zhao, Jiageng Mao, Junjie Ye, Yue Wang, YuXi Qian

Reformulating motion planning as language modeling lets GPT-3.5 generate precise driving trajectories from scene descriptions.

arxiv:2310.01415 v3 · 2023-10-02 · cs.CV · cs.AI · cs.CL · cs.RO

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\pithnumber{AKLOOSBJVAHH4UUP3J3MB543UM}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

We present a simple yet effective approach that can transform the OpenAI GPT-3.5 model into a reliable motion planner for autonomous vehicles... the fundamental insight of our approach is the reformulation of motion planning as a language modeling problem.

C2weakest assumption

That an LLM prompted and fine-tuned on language descriptions of coordinates will produce numerically precise, collision-free trajectories in safety-critical, out-of-distribution driving scenes without systematic hallucination or unsafe outputs.

C3one line summary

GPT-3.5 is turned into an autonomous-vehicle motion planner by representing driving scenes and trajectories as language tokens and applying a prompting-reasoning-finetuning pipeline, with results shown on nuScenes.

References

17 extracted · 17 resolved · 7 Pith anchors

[1] Do As I Can, Not As I Say: Grounding Language in Robotic Affordances · arXiv:2204.01691

[2] End to End Learning for Self-Driving Cars · arXiv:1604.07316

[3] RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control · arXiv:2307.15818

[4] Language models are few-shot learners 1901

[5] Cheng, B., Girshick, R., Dollar, P., Berg, A

Formal links

2 machine-checked theorem links

Cited by

31 papers in Pith

VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning

Visual Adversarial Attack on Vision-Language Models for Autonomous Driving

Steins;Gate Drive: Semantic Safety Arbitration over Structured Futures for Latency-Decoupled LLM Planning

Self-Supervised Bootstrapping of Action-Predictive Embodied Reasoning

MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving

Receipt and verification

First computed	2026-05-17T23:38:52.291019Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

0296e74829a80e7e528fda76c0f79ba30073cd162829e8088e6a43cbe42a3405

Aliases

arxiv: 2310.01415 · arxiv_version: 2310.01415v3 · doi: 10.48550/arxiv.2310.01415 · pith_short_12: AKLOOSBJVAHH · pith_short_16: AKLOOSBJVAHH4UUP · pith_short_8: AKLOOSBJ

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/AKLOOSBJVAHH4UUP3J3MB543UM \
  | 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: 0296e74829a80e7e528fda76c0f79ba30073cd162829e8088e6a43cbe42a3405

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "40efd13105f5f13dc523cb36e44e23762eadac92986e69c1b3f54d0df8f43431",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.CL",
      "cs.RO"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2023-10-02T17:59:57Z",
    "title_canon_sha256": "bd8b88211642ffee7a5b88d0a713aeba0091526cf84ee70b61015af5407a6e96"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2310.01415",
    "kind": "arxiv",
    "version": 3
  }
}