Probing Multimodal LLMs as World Models for Driving

• URL: http://arxiv.org/abs/2405.05956v2
• Date: Fri, 25 Oct 2024 19:31:37 GMT
• Title: Probing Multimodal LLMs as World Models for Driving
• Authors: Shiva Sreeram, Tsun-Hsuan Wang, Alaa Maalouf, Guy Rosman, Sertac Karaman, Daniela Rus,
• Abstract summary: We look at the application of Multimodal Large Language Models (MLLMs) in autonomous driving. Despite advances in models like GPT-4o, their performance in complex driving environments remains largely unexplored.
• Score: 72.18727651074563
• License:
• Abstract: We provide a sober look at the application of Multimodal Large Language Models (MLLMs) in autonomous driving, challenging common assumptions about their ability to interpret dynamic driving scenarios. Despite advances in models like GPT-4o, their performance in complex driving environments remains largely unexplored. Our experimental study assesses various MLLMs as world models using in-car camera perspectives and reveals that while these models excel at interpreting individual images, they struggle to synthesize coherent narratives across frames, leading to considerable inaccuracies in understanding (i) ego vehicle dynamics, (ii) interactions with other road actors, (iii) trajectory planning, and (iv) open-set scene reasoning. We introduce the Eval-LLM-Drive dataset and DriveSim simulator to enhance our evaluation, highlighting gaps in current MLLM capabilities and the need for improved models in dynamic real-world environments.

Related papers

• Exploring the Interplay Between Video Generation and World Models in Autonomous Driving: A Survey [61.39993881402787]
  World models and video generation are pivotal technologies in the domain of autonomous driving. This paper investigates the relationship between these two technologies. By analyzing the interplay between video generation and world models, this survey identifies critical challenges and future research directions.
  arXiv  Detail & Related papers  (2024-11-05T08:58:35Z)
• Large Language Models for Autonomous Driving (LLM4AD): Concept, Benchmark, Simulation, and Real-Vehicle Experiment [15.52530518623987]
  Large Language Models (LLMs) have the potential to enhance various aspects of autonomous driving systems. This paper introduces novel concepts and approaches to designing LLMs for autonomous driving (LLM4AD)
  arXiv  Detail & Related papers  (2024-10-20T04:36:19Z)
• DrivingDojo Dataset: Advancing Interactive and Knowledge-Enriched Driving World Model [65.43473733967038]
  We introduce DrivingDojo, the first dataset tailor-made for training interactive world models with complex driving dynamics. Our dataset features video clips with a complete set of driving maneuvers, diverse multi-agent interplay, and rich open-world driving knowledge.
  arXiv  Detail & Related papers  (2024-10-14T17:19:23Z)
• OccLLaMA: An Occupancy-Language-Action Generative World Model for Autonomous Driving [12.004183122121042]
  OccLLaMA is an occupancy-language-action generative world model. We build a unified multi-modal vocabulary for vision, language and action. OccLLaMA achieves competitive performance across multiple tasks.
  arXiv  Detail & Related papers  (2024-09-05T06:30:01Z)
• DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models [31.552397390480525]
  We introduce DriveVLM, an autonomous driving system leveraging Vision-Language Models (VLMs) DriveVLM integrates a unique combination of reasoning modules for scene description, scene analysis, and hierarchical planning. We propose DriveVLM-Dual, a hybrid system that synergizes the strengths of DriveVLM with the traditional autonomous driving pipeline.
  arXiv  Detail & Related papers  (2024-02-19T17:04:04Z)
• Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives [56.2139730920855]
  We present a systematic analysis of MM-VUFMs specifically designed for road scenes. Our objective is to provide a comprehensive overview of common practices, referring to task-specific models, unified multi-modal models, unified multi-task models, and foundation model prompting techniques. We provide insights into key challenges and future trends, such as closed-loop driving systems, interpretability, embodied driving agents, and world models.
  arXiv  Detail & Related papers  (2024-02-05T12:47:09Z)
• DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving [69.82743399946371]
  DriveMLM is a framework that can perform close-loop autonomous driving in realistic simulators. We employ a multi-modal LLM (MLLM) to model the behavior planning module of a module AD system. This model can plug-and-play in existing AD systems such as Apollo for close-loop driving.
  arXiv  Detail & Related papers  (2023-12-14T18:59:05Z)
• Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning [54.67880602409801]
  In this paper, we study the problem of pre-training world models with abundant in-the-wild videos for efficient learning of visual control tasks. We introduce Contextualized World Models (ContextWM) that explicitly separate context and dynamics modeling. Our experiments show that in-the-wild video pre-training equipped with ContextWM can significantly improve the sample efficiency of model-based reinforcement learning.
  arXiv  Detail & Related papers  (2023-05-29T14:29:12Z)
• Cycle-Consistent World Models for Domain Independent Latent Imagination [0.0]
  High costs and risks make it hard to train autonomous cars in the real world. We propose a novel model-based reinforcement learning approach called Cycleconsistent World Models.
  arXiv  Detail & Related papers  (2021-10-02T13:55:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.