5 papers
ReWAM: Reciprocal World Action Models for Interactive Autonomous Driving
Benshan Ma, Pei Liu, Ruiguo Zhong +4
In interactive scenarios, an autonomous driving system is required to generate ego actions under the influence of other agents' behaviors. Existing World Action Models (WAMs) typic…
DA-WAM: Decision-Aligned Future Latents for Driving World Models
Ruiguo Zhong, Benshan Ma, Xiaolong Chen +5
World models for autonomous driving should anticipate the consequences that matter for driving decisions rather than reconstruct every detail of future scenes. However, predictive…
LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments
Pei Liu, Nan Zheng, Lang Zhang +8
World Action Models (WAMs) have emerged as a powerful paradigm for embodied intelligence, yet the prevailing reliance on pixel-level video generation creates a fundamental bottlene…
LiSTAR: Ray-Centric World Models for 4D LiDAR Sequences in Autonomous Driving
Pei Liu, Songtao Wang, Lang Zhang +9
Synthesizing high-fidelity and controllable 4D LiDAR data is crucial for creating scalable simulation environments for autonomous driving. This task is inherently challenging due t…
MagicRoad: Semantic-Aware 3D Road Surface Reconstruction via Obstacle Inpainting
Xingyue Peng, Yuandong Lyu, Lang Zhang +8
Road surface reconstruction is essential for autonomous driving, supporting centimeter-accurate lane perception and high-definition mapping in complex urban environments.While rece…