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This arXiv preprint proposes a hybrid framework that pairs Large Multimodal Models for semantic understanding with deep reinforcement learning for real-time policy optimization in autonomous driving. Its headline result, at least a 19% improvement over baselines on a simulated lane-change task, is a narrow, simulation-only benchmark, so it doesn't yet show real-world readiness. Readers should watch whether the joint LMM-RL design holds up on broader scenarios and physical vehicles.
Large Multimodal Models for Embodied Intelligent Driving: Enabling the Next Generation of Autonomous Mobility
arXiv:2601.08434v4 Announce Type: replace-cross Abstract: The advent of Large Multimodal Models (LMMs) offers a promising technology to tackle the limitations of modular design in autonomous driving, which often falters in open-world scenarios requiring sustained environmental understanding and logical reasoning. Besides, embodied artificial intelligence facilitates policy optimization through closed-loop interactions to achieve the continuous learning capability, thereby advancing autonomous driving toward embodied intelligent (EI) driving. However, such capability will be constrained by relying solely on LMMs to enhance EI driving without joint decision-making. This article proposes a novel semantics and policy dual-driven hybrid decision framework to tackle this challenge, ensuring continuous learning and joint decision. The framework merges LMMs for semantic understanding and cognitive representation, and deep reinforcement learning for real-time policy optimization. This article particularly details the foundational principles of EI driving and LMMs, and also introduces the emerging opportunities this framework enables, encompassing potential benefits and representative use cases. The proposed framework, evaluated through a lane-change planning case study, achieves at least a 19% performance improvement over the baselines, as verified by simulations. Finally, key open challenges and future research directions are identified to guide subsequent work.
Abstract:The advent of Large Multimodal Models (LMMs) offers a promising technology to tackle the limitations of modular design in autonomous driving, which often falters in open-world scenarios requiring sustained environmental understanding and logical reasoning. Besides, embodied artificial intelligence facilitates policy optimization through closed-loop interactions to achieve the continuous learning capability, thereby advancing autonomous driving toward embodied intelligent (EI) driving. However, such capability will be constrained by relying solely on LMMs to enhance EI driving without joint decision-making. This article proposes a novel semantics and policy dual-driven hybrid decision framework to tackle this challenge, ensuring continuous learning and joint decision. The framework merges LMMs for semantic understanding and cognitive representation, and deep reinforcement learning for real-time policy optimization. This article particularly details the foundational principles of EI driving and LMMs, and also introduces the emerging opportunities this framework enables, encompassing potential benefits and representative use cases. The proposed framework, evaluated through a lane-change planning case study, achieves at least a 19% performance improvement over the baselines, as verified by simulations. Finally, key open challenges and future research directions are identified to guide subsequent work.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2601.08434 [cs.RO]
(or arXiv:2601.08434v4 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2601.08434 Focus to learn more arXiv-issued DOI via DataCite
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