{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-survey-of-world-models-for-autonomous","title":"A Survey of World Models for Autonomous Driving","arxiv_id":"2501.11260","date":"2025-01-20","proceeding":null,"authors":["Tuo Feng","Wenguan Wang","Yi Yang"],"abstract":"Recent breakthroughs in autonomous driving have been propelled by advances in robust world modeling, fundamentally transforming how vehicles interpret dynamic scenes and execute safe decision-making. 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. This paper systematically reviews recent advances in world models for autonomous driving, proposing a three-tiered taxonomy: (i) Generation of Future Physical World, covering Image-, BEV-, OG-, and PC-based generation methods that enhance scene evolution modeling through diffusion models and 4D occupancy forecasting; (ii) Behavior Planning for Intelligent Agents, combining rule-driven and learning-based paradigms with cost map optimization and reinforcement learning for trajectory generation in complex traffic conditions; (ii) Interaction between Prediction and Planning, achieving multi-agent collaborative decision-making through latent space diffusion and memory-augmented architectures. The study further analyzes training paradigms, including self-supervised learning, multimodal pretraining, and generative data augmentation, while evaluating world models' performance in scene understanding and motion prediction tasks. Future research must address key challenges in self-supervised representation learning, long-tail scenario generation, and multimodal fusion to advance the practical deployment of world models in complex urban environments. Overall, the comprehensive analysis provides a technical roadmap for harnessing the transformative potential of world models in advancing safe and reliable autonomous driving solutions.","url_abs":"https://arxiv.org/abs/2501.11260v3","url_pdf":"https://arxiv.org/pdf/2501.11260v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-survey-of-world-models-for-autonomous","repo_url":"https://github.com/fengzicai/awesomewmad","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"a-survey-of-world-models-for-autonomous","repo_url":"https://github.com/fengzicai/wmad-benchmarks","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"future-prediction","task_name":"Future prediction"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"scene-understanding","task_name":"Scene Understanding"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"},{"task_slug":"survey","task_name":"Survey"},{"task_slug":"trajectory-forecasting","task_name":"Trajectory Forecasting"},{"task_slug":"motion-prediction","task_name":"motion prediction"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2501.11260","atlas_url":"https://app.syntology.ai/?focus=2501.11260","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}