OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning

• URL: http://arxiv.org/abs/2405.01533v2
• Date: Wed, 16 Apr 2025 15:12:21 GMT
• Title: OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning
• Authors: Shihao Wang, Zhiding Yu, Xiaohui Jiang, Shiyi Lan, Min Shi, Nadine Chang, Jan Kautz, Ying Li, Jose M. Alvarez,
• Abstract summary: We propose a holistic vision-language dataset that aligns agent models with 3D driving tasks through counterfactual reasoning.<n>Our approach enhances decision-making by evaluating potential scenarios and their outcomes, similar to human drivers considering alternative actions.
• Score: 68.45848423501927
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advances in vision-language models (VLMs) have led to a growing interest in autonomous driving to leverage their strong reasoning capabilities. However, extending these capabilities from 2D to full 3D understanding is crucial for real-world applications. To address this challenge, we propose OmniDrive, a holistic vision-language dataset that aligns agent models with 3D driving tasks through counterfactual reasoning. This approach enhances decision-making by evaluating potential scenarios and their outcomes, similar to human drivers considering alternative actions. Our counterfactual-based synthetic data annotation process generates large-scale, high-quality datasets, providing denser supervision signals that bridge planning trajectories and language-based reasoning. Futher, we explore two advanced OmniDrive-Agent frameworks, namely Omni-L and Omni-Q, to assess the importance of vision-language alignment versus 3D perception, revealing critical insights into designing effective LLM-agents. Significant improvements on the DriveLM Q\&A benchmark and nuScenes open-loop planning demonstrate the effectiveness of our dataset and methods.

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