Large Multimodal Models for Embodied Intelligent Driving: Enabling the Next Generation of Autonomous Mobility
Large Multimodal Models for Embodied Intelligent Driving: Enabling the Next Generation of Autonomous Mobility
This research paper introduces a novel semantics and policy dual-driven hybrid decision framework that integrates Large Multimodal Models (LMMs) with deep reinforcement learning to address limitations in modular autonomous driving systems. The framework aims to enable continuous learning and joint decision-making through closed-loop interactions, achieving at least a 19% performance improvement over baselines in lane-change planning simulations. The authors also outline foundational principles of embodied intelligent driving, potential use cases, and key open challenges for future work.
Source: arXiv Robotics — research abstracts · Read original article ↗
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ing 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.
Open source S5
ork 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 plann
Open source S4
Large Multimodal Models for Embodied Intelligent Driving: Enabling the Next Generation of Autonomous Mobility
Open source S1
arXiv:2601.08434v4 Announce Type: replace 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 a
Open source S2
chieve 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 framew
Open source S3
Source：arXiv Robotics — research abstracts · arxiv.org