Realistic World Model for Autonomous Driving: Integrating Physical Constraints and Multi-agent Interactions
Autonomous Driving;World Model;Trajectory Forecasting;Motion Planning
I have read and agree with the venue's withdrawal policy on behalf of myself and my co-authors.
@misc{
kim2024realistic,
title={Realistic World Model for Autonomous Driving: Integrating Physical Constraints and Multi-agent Interactions},
author={Jeonghwan Kim and Jiongdao Jin and Jay Heo and Jaehong Yoon and Sung Ju Hwang},
year={2024},
url={https://openreview.net/forum?id=r91tAISb88}
}
Ensuring safety in autonomous driving, particularly in complex and dynamic environments, remains a significant challenge. To address this issue, we propose a novel traffic world model. While existing trajectory forecasting methods typically focus on predicting individual agents and may neglect critical factors such as vehicle dimensions, orientation, and physical constraints, our model incorporates these elements comprehensively. Unlike previous methods that often result in unrealistic scenarios such as collisions or off-road driving, our model integrates physical constraints and introduces innovative loss functions—including safe distance loss and road departure loss—to ensure that the generated trajectories are both realistic and feasible. By simultaneously predicting the trajectories of all agents and explicitly modeling interactions across various scenarios, our approach significantly enhances realism and safety. Our world model functions as a generator, simulator, and trajectory forecasting tool, demonstrating substantial improvements over traditional methods and achieving competitive performance in reducing collision and off-road rates.
I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
~Jeonghwan_Kim5 ~Jiongdao_Jin1 ~Jay_Heo1 ~Jaehong_Yoon1 ~Sung_Ju_Hwang1
Jeonghwan Kim Jiongdao Jin Jay Heo Jaehong Yoon Sung Ju Hwang
I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Autonomous Driving World Model Trajectory Forecasting Motion Planning
I certify that there is no acknowledgement section in this submission for double blind review.
kim|realistic_world_model_for_autonomous_driving_integrating_physical_constraints_and_multiagent_interactions
/pdf/ea952e902eb120f6cc588906abc90317686d5119.pdf
learning on time series and dynamical systems
I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.
I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
Realistic World Model for Autonomous Driving: Integrating Physical Constraints and Multi-agent Interactions
ICLR 2025 Conference Withdrawn Submission
ICLR.cc/2025/Conference/Withdrawn_Submission