VeriDrive: Verifiable Counterfactual Supervision for Cost-Efficient Vision-Language Planning

2026-06-05 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

VeriDrive is a novel framework designed for cost-efficient vision-language planning, addressing the expense and unstructured nature of current driving rationales. It constructs planning-oriented, verifiable counterfactual supervision by converting driving reasoning into a structured Perception-Evaluation-Revision chain. This chain grounds key objects in future motion, evaluates alternative ego trajectories using rule-checkable evidence, revises risky intent towards expert behavior, and generates final planning targets. To scale data construction, VeriDrive employs local generation combined with validator-guided selective correction, only escalating invalid or difficult samples. The framework, built on the nuScenes dataset and trained under the Omni-Q protocol, demonstrates improved L2, Collision, and Intersection metrics compared to OmniDrive. Furthermore, it significantly reduces logged token usage, generation time, and actual paid LLM/VLM costs, proving that auditable intermediate fields and structured revision targets enhance vision-language planning supervision within realistic annotation budgets.

Key takeaway

For Machine Learning Engineers developing vision-language driving models, you should consider adopting structured, verifiable counterfactual supervision. This approach, exemplified by VeriDrive's Perception-Evaluation-Revision chain, can improve planning metrics like L2 and Collision while substantially reducing your LLM/VLM token usage and generation costs. Implement auditable intermediate fields and validator-guided selective correction to enhance supervision quality and scale data generation efficiently.

Key insights

Structured, verifiable counterfactual supervision significantly improves vision-language planning efficiency and performance.

Principles

• Structured reasoning chains enhance VLM planning.
• Auditable intermediate fields improve supervision.
• Validator-guided correction scales data generation.

Method

VeriDrive converts driving reasoning into a Perception-Evaluation-Revision chain, grounding objects, evaluating trajectories with rule-checkable evidence, and revising intent. It scales data via local generation and validator-guided selective correction.

In practice

• Implement Perception-Evaluation-Revision chains.
• Use validator-guided selective correction.
• Integrate auditable intermediate fields.

Topics

• VeriDrive
• Vision-Language Planning
• Counterfactual Supervision
• Autonomous Driving
• LLM/VLM Cost Efficiency
• nuScenes Dataset

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.