# Large Multimodal Models for Embodied Intelligent Driving: Enabling the Next Generation of Autonomous Mobility

DevFeed: [Large Multimodal Models for Embodied Intelligent Driving: Enabling the Next Generation of Autonomous Mobility](<https://devfeed.tech/articles/large-multimodal-models-for-embodied-intelligent-driving-enabling-the-next-generation-of-autonomous-mobility-117196.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2601.08434>)

Author: Long Zhang, Yuchen Xia, Bingqing Wei, Zhen Liu, Shiwen Mao, Zhu Han, Mohsen Guizani

Published: 2026-11-04T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>)

Topics: [AI agents in autonomous vehicles](<https://devfeed.tech/topics/ai-agents-in-autonomous-vehicles.md>), [autonomous vehicles](<https://devfeed.tech/topics/autonomous-vehicles.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Machine Intelligence](<https://devfeed.tech/topics/machine-intelligence.md>), [Machine Perception](<https://devfeed.tech/topics/machine-perception.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [autonomous-driving](<https://devfeed.tech/tags/autonomous-driving.md>), [challenges](<https://devfeed.tech/tags/challenges.md>), [continuous](<https://devfeed.tech/tags/continuous.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-ro](<https://devfeed.tech/tags/cs-ro.md>), [decision-making](<https://devfeed.tech/tags/decision-making.md>), [deep-reinforcement-learning](<https://devfeed.tech/tags/deep-reinforcement-learning.md>), [framework](<https://devfeed.tech/tags/framework.md>), [multimodal-models](<https://devfeed.tech/tags/multimodal-models.md>), [planning](<https://devfeed.tech/tags/planning.md>), [simulations](<https://devfeed.tech/tags/simulations.md>)

## AI overview

The article proposes a hybrid decision framework for embodied intelligent driving that combines Large Multimodal Models (LMMs), which provide semantic understanding and cognitive representation, with deep reinforcement learning for real-time policy optimization. The authors argue that LMMs alone do not provide the joint decision-making needed for continuous learning through closed-loop interaction. In a simulated lane-change planning case study, the framework improves performance by at least 19% over the baselines. The abstract also identifies open challenges and future research directions, but does not specify them.

## Source excerpt

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.