A Survey of World Models for Autonomous Driving

• URL: http://arxiv.org/abs/2501.11260v1
• Date: Mon, 20 Jan 2025 04:00:02 GMT
• Title: A Survey of World Models for Autonomous Driving
• Authors: Tuo Feng, Wenguan Wang, Yi Yang,
• Abstract summary: Recent breakthroughs in autonomous driving have revolutionized the way vehicles perceive and interact with their surroundings.<n>World models offer high-fidelity representations of the driving environment that integrate multi-sensor data, semantic cues, and temporal dynamics.<n>These world models pave the way for more robust, reliable, and adaptable autonomous driving solutions.
• Score: 63.33363128964687
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent breakthroughs in autonomous driving have revolutionized the way vehicles perceive and interact with their surroundings. In particular, world models have emerged as a linchpin technology, offering high-fidelity representations of the driving environment that integrate multi-sensor data, semantic cues, and temporal dynamics. Such models unify perception, prediction, and planning, thereby enabling autonomous systems to make rapid, informed decisions under complex and often unpredictable conditions. Research trends span diverse areas, including 4D occupancy prediction and generative data synthesis, all of which bolster scene understanding and trajectory forecasting. Notably, recent works exploit large-scale pretraining and advanced self-supervised learning to scale up models' capacity for rare-event simulation and real-time interaction. In addressing key challenges -- ranging from domain adaptation and long-tail anomaly detection to multimodal fusion -- these world models pave the way for more robust, reliable, and adaptable autonomous driving solutions. This survey systematically reviews the state of the art, categorizing techniques by their focus on future prediction, behavior planning, and the interaction between the two. We also identify potential directions for future research, emphasizing holistic integration, improved computational efficiency, and advanced simulation. Our comprehensive analysis underscores the transformative role of world models in driving next-generation autonomous systems toward safer and more equitable mobility.

Related papers

• Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics [50.191655141020505]
  This work advances model-based reinforcement learning by addressing the challenges of long-horizon prediction, error accumulation, and sim-to-real transfer. By providing a scalable and robust framework, the introduced methods pave the way for adaptive and efficient robotic systems in real-world applications.
  arXiv  Detail & Related papers  (2025-01-17T10:39:09Z)
• DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformers [61.92571851411509]
  We introduce a multimodal driving language based on interleaved image and action tokens, and develop DrivingGPT to learn joint world modeling and planning. Our DrivingGPT demonstrates strong performance in both action-conditioned video generation and end-to-end planning, outperforming strong baselines on large-scale nuPlan and NAVSIM benchmarks.
  arXiv  Detail & Related papers  (2024-12-24T18:59:37Z)
• Characterized Diffusion Networks for Enhanced Autonomous Driving Trajectory Prediction [0.6202955567445396]
  We present a novel trajectory prediction model for autonomous driving. Our model enhances the accuracy and reliability of trajectory predictions by incorporating uncertainty estimation and complex agent interactions. The proposed model showcases strong potential for application in real-world autonomous driving systems.
  arXiv  Detail & Related papers  (2024-11-25T15:03:44Z)
• 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)
• Planning-Aware Diffusion Networks for Enhanced Motion Forecasting in Autonomous Driving [0.0]
  Planning-Integrated Forecasting Model (PIFM) is a novel framework inspired by neural mechanisms governing decision-making and multi-agent coordination in the brain. PIFM is able to forecast future trajectories of all agents within a scenario. This architecture enhances model transparency, as it parallels the brain's method of dynamically adjusting predictions based on external stimuli and other agents'behaviors.
  arXiv  Detail & Related papers  (2024-10-25T15:44:51Z)
• 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)
• Probing Multimodal LLMs as World Models for Driving [72.18727651074563]
  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.
  arXiv  Detail & Related papers  (2024-05-09T17:52:42Z)
• World Models for Autonomous Driving: An Initial Survey [16.448614804069674]
  The capability to accurately predict future events and assess their implications is paramount for both safety and efficiency. World models have emerged as a transformative approach, enabling autonomous driving systems to synthesize and interpret vast amounts of sensor data. This paper provides an initial review of the current state and prospective advancements of world models in autonomous driving.
  arXiv  Detail & Related papers  (2024-03-05T03:23:55Z)
• Beyond One Model Fits All: Ensemble Deep Learning for Autonomous Vehicles [16.398646583844286]
  This study introduces three distinct neural network models corresponding to Mediated Perception, Behavior Reflex, and Direct Perception approaches. Our architecture fuses information from the base, future latent vector prediction, and auxiliary task networks, using global routing commands to select appropriate action sub-networks.
  arXiv  Detail & Related papers  (2023-12-10T04:40:02Z)
• The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review [43.30610493968783]
  We review state-of-the-art deep learning-based planning systems, and focus on how they integrate prediction. We discuss the implications, strengths, and limitations of different integration principles.
  arXiv  Detail & Related papers  (2023-08-10T17:53:03Z)
• Predictive World Models from Real-World Partial Observations [66.80340484148931]
  We present a framework for learning a probabilistic predictive world model for real-world road environments. While prior methods require complete states as ground truth for learning, we present a novel sequential training method to allow HVAEs to learn to predict complete states from partially observed states only.
  arXiv  Detail & Related papers  (2023-01-12T02:07:26Z)
• Isolating and Leveraging Controllable and Noncontrollable Visual Dynamics in World Models [65.97707691164558]
  We present Iso-Dream, which improves the Dream-to-Control framework in two aspects. First, by optimizing inverse dynamics, we encourage world model to learn controllable and noncontrollable sources. Second, we optimize the behavior of the agent on the decoupled latent imaginations of the world model.
  arXiv  Detail & Related papers  (2022-05-27T08:07:39Z)

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.