$AutoDrive\text{-}P^3$: Unified Chain of Perception-Prediction-Planning Thought via Reinforcement Fine-Tuning

$AutoDrive\text{-}P^3$: Unified Chain of Perception-Prediction-Planning Thought via Reinforcement Fine-Tuning | Signal Canvas | ScienceToStartup

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Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-03-31
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 29
Source count: 4
Coverage: 83%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID autodrive-text-p-3-unified-chain-of-perception-prediction-planning-thought-via-reinforcement-fine-tuning | Route /signal-canvas/autodrive-text-p-3-unified-chain-of-perception-prediction-planning-thought-via-reinforcement-fine-tuning

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/autodrive-text-p-3-unified-chain-of-perception-prediction-planning-thought-via-reinforcement-fine-tuning

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source_context

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Evidence Receipt

Route status: building

Claims: 7

References: 29

Proof: Verification pending

Freshness state: computing

Source paper: $AutoDrive\text{-}P^3$: Unified Chain of Perception-Prediction-Planning Thought via Reinforcement Fine-Tuning

PDF: https://arxiv.org/pdf/2603.28116v1

Repository: https://github.com/haha-yuki-haha/AutoDrive-P3

Source count: 4

Coverage: 83%

Last proof check: 2026-03-31T20:30:25.361Z

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Subject: $AutoDrive\text{-}P^3$: Unified Chain of Perception-Prediction-Planning Thought via Reinforcement Fine-Tuning

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Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
Extensive experiments on both open-loop (nuScenes) and closed-loop (NAVSIMv1/v2) benchmarks demonstrate that our approach achieves state-of-the-art performance in planning tasks.
Implicationpartial
Explicitly stated in the abstract and supported by quantitative results in tables comparing against other methods.
Verificationpartial
partial
Evidencepartial
More importantly, experimental results validate that our proposed P3-GRPO algorithm significantly enhances planning performance through its hierarchical and progressive supervision mechanism, which systematically improves perception and prediction capabilities and consequently leads to more reliable and
Implicationpartial
Directly stated in the analysis section with a description of its mechanism, though specific performance improvement numbers are not provided in the excerpt.
Verificationpartial
partial
Evidencepartial
However, current VLM-based approaches suffer from two major limitations: 1) Some VLMs directly output planning results without chain-of-thought (CoT) reasoning, bypassing crucial perception and prediction stages which creates a significant domain gap and compromises decision-making capability; 2) Other VLMs can generate outputs for perception, prediction, and planning tasks but employ a fragmented decision-making approach where these modules operate separately, leading to a significant lack of synergy that undermines true planning performance.
Implicationpartial
Explicitly and clearly stated as a core limitation in the abstract, forming the primary motivation for the work.
Verificationpartial
partial
Evidencepartial
To address these limitations, we propose AutoDrive-P3, a novel framework that seamlessly integrates Perception, Prediction, and Planning through structured reasoning.
Implicationpartial
Directly stated as the core contribution in the abstract and methodology section.
Verificationpartial
partial
Evidencepartial
Additionally, to balance inference efficiency with performance, we introduce dual thinking modes: detailed thinking and fast thinking.
Implicationpartial
Explicitly mentioned in the abstract as a feature, and results are provided for both modes.
Verificationpartial
partial
Evidencepartial
We first cold start the base model using P3-CoT to make up for the gap between VLM and autonomous driving and learn the CoT answer format.
Implicationpartial
Described in the methodology section and Figure 4 caption, though the specific details of the dataset are not in the excerpt.
Verificationpartial
partial
Evidencepartial
AutoDrive-P3 (Ours-Detailed) 0.15 0.30 0.54 0.33 0.00 0.02 0.15 0.06 Qwen2.5-VL-3B
Implicationpartial
Supported by direct numerical comparisons in the provided results table, showing lower error metrics.
Verificationpartial
partial

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$AutoDrive\text{-}P^3$: Unified Chain of Perception-Prediction-Planning Thought via Reinforcement Fine-Tuning

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