8 papers
ReWorld: Learning Better Representations for World Action Models
Tianze Xia, Lijun Zhou, Kaixin Xiong +9
World Action Models (WAMs) model future environment evolution under action conditioning, offering a scalable paradigm for autonomous driving. However, existing approaches focus lar…
DriveLaW:Unifying Planning and Video Generation in a Latent Driving World
Tianze Xia, Yongkang Li, Lijun Zhou +9
World models have become crucial for autonomous driving, as they learn how scenarios evolve over time to address the long-tail challenges of the real world. However, current approa…
UniDriveVLA: Unifying Understanding, Perception, and Action Planning for Autonomous Driving
Yongkang Li, Lijun Zhou, Sixu Yan +11
Vision-Language-Action (VLA) models have recently emerged in autonomous driving, with the promise of leveraging rich world knowledge to improve the cognitive capabilities of drivin…
Rethinking Driving World Model as Synthetic Data Generator for Perception Tasks
Kai Zeng, Zhanqian Wu, Kaixin Xiong +12
Recent advancements in driving world models enable controllable generation of high-quality RGB videos or multimodal videos. Existing methods primarily focus on metrics related to g…
ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving
Yongkang Li, Kaixin Xiong, Xiangyu Guo +12
Recent studies have explored leveraging the world knowledge and cognitive capabilities of Vision-Language Models (VLMs) to address the long-tail problem in end-to-end autonomous dr…
Genesis: Multimodal Driving Scene Generation with Spatio-Temporal and Cross-Modal Consistency
Xiangyu Guo, Zhanqian Wu, Kaixin Xiong +10
We present Genesis, a unified framework for joint generation of multi-view driving videos and LiDAR sequences with spatio-temporal and cross-modal consistency. Genesis employs a tw…