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ARXIV:2604.02190 · AI FOR AUTONOMOUS VEHICLES · SUBMITTED 03 APR · 20:30 UTC · FRESHNESS STALE
ARXIV:2604.02190AI FOR AUTONOMOUS VEHICLESSUBMITTED 03 APR · 20:30 UTCFRESHNESS STALEYongkang Li · Lijun Zhou · Sixu Yan · Bencheng Liao · Tianyi Yan · Kaixin Xiong · +8 at arXiv
A unified vision-language-action system that enhances autonomous driving by decoupling spatial perception and semantic reasoning.
Opportunity summary
Pain A unified vision-language-action system that enhances autonomous driving by decoupling spatial perception and semantic reasoning.
Evidence 0 refs | 0 sources | 67% coverage
Blocker Evidence unverified
A unified vision-language-action system that enhances autonomous driving by decoupling spatial perception and semantic reasoning. However, adapting such models for driving tasks currently faces a critical dilemma between spatial perception and semantic reasoning.
Vision-Language-Action (VLA) models have recently emerged in autonomous driving, with the promise of leveraging rich world knowledge to improve the cognitive capabilities of driving systems. However, adapting such models for driving tasks currently faces…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Vision-Language-Action (VLA) models have recently emerged in autonomous driving, with the promise of leveraging rich world knowledge to improve the cognitive capabilities of driving…
AI for Autonomous Vehicles moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A unified vision-language-action system that enhances autonomous driving by decoupling spatial perception and semantic reasoning.
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10.48550/arXiv.2604.02190A unified vision-language-action system that enhances autonomous driving by decoupling spatial perception and semantic reasoning.
Abstract
Vision-Language-Action (VLA) models have recently emerged in autonomous driving, with the promise of leveraging rich world knowledge to improve the cognitive capabilities of driving systems. However, adapting such models for driving tasks currently faces a critical dilemma between spatial perception and semantic reasoning. Consequently, existing VLA systems are forced into suboptimal compromises: directly adopting 2D Vision-Language Models yields limited spatial perception, whereas enhancing them with 3D spatial representations often impairs the native reasoning capacity of VLMs. We argue that this dilemma largely stems from the coupled optimization of spatial perception and semantic reasoning within shared model parameters. To overcome this, we propose UniDriveVLA, a Unified Driving Vision-Language-Action model based on Mixture-of-Transformers that addresses the perception-reasoning conflict via expert decoupling. Specifically, it comprises three experts for driving understanding, scene perception, and action planning, which are coordinated through masked joint attention. In addition, we combine a sparse perception paradigm with a three-stage progressive training strategy to improve spatial perception while maintaining semantic reasoning capability. Extensive experiments show that UniDriveVLA achieves state-of-the-art performance in open-loop evaluation on nuScenes and closed-loop evaluation on Bench2Drive. Moreover, it demonstrates strong performance across a broad range of perception, prediction, and understanding tasks, including 3D detection, online mapping, motion forecasting, and driving-oriented VQA, highlighting its broad applicability as a unified model for autonomous driving. Code and model have been released at https://github.com/xiaomi-research/unidrivevla
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PROBLEM
A unified vision-language-action system that enhances autonomous driving by decoupling spatial perception and semantic reasoning. However, adapting such models for driving tasks currently faces a critical dilemma between spatial perception and semantic reasoning.
METHOD
Vision-Language-Action (VLA) models have recently emerged in autonomous driving, with the promise of leveraging rich world knowledge to improve the cognitive capabilities of driving systems. However, adapting such models for driving tasks currently faces a critical dilemma betwe...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Vision-Language-Action (VLA) models have recently emerged in autonomous driving, with the promise of leveraging rich world knowledge to improve the cognitive capabilities of driving systems. A public repo...
WHY NOW
AI for Autonomous Vehicles moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Extensive experiments show that UniDriveVLA achieves state-of-the-art performance in open-loop evaluation on nuScenes
Explicitly stated in the abstract with reference to extensive experiments
partial
and closed-loop evaluation on Bench2Drive
Explicitly stated in the abstract with reference to extensive experiments
partial
it demonstrates strong performance across a broad range of perception, prediction, and understanding tasks, including 3D detection, online mapping, motion forecasting, and driving-oriented VQA
Directly stated in abstract with specific task enumeration
partial
we propose UniDriveVLA, a Unified Driving Vision-Language-Action model based on Mixture-of-Transformers that addresses the perception-reasoning conflict via expert decoupling
Explicitly described in abstract as the core methodological contribution
partial
adapting such models for driving tasks currently faces a critical dilemma between spatial perception and semantic reasoning
Directly stated in abstract as motivation for the work
partial
we combine a sparse perception paradigm with a three-stage progressive training strategy to improve spatial perception while maintaining semantic reasoning capability
Described in abstract as a key methodological component
partial
The system might face challenges in real-world deployment due to variations in road conditions and external factors not captured in simulations or benchmarks
Stated in analysis excerpt as a caveat, but not directly in the abstract
partial
highlighting its broad applicability as a unified model for autonomous driving
Directly stated in abstract as a conclusion
partial
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A unified vision-language-action system that enhances autonomous driving by decoupling spatial perception and semantic reasoning.
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AI for Autonomous Vehicles
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