11 papers
Vec-QMDP: Vectorized POMDP Planning on CPUs for Real-Time Autonomous Driving
Xuanjin Jin, Yanxin Dong, Bin Sun +4
Planning under uncertainty for real-world robotics tasks, such as autonomous driving, requires reasoning in enormous high-dimensional belief spaces, rendering the problem computati…
Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation
Jingyu Li, Zhe Liu, Dongnan Hu +10
World action models~(WAMs) have shown great promise for autonomous driving and urban navigation. Built upon Vision-Language-Action models or video generation models, existing appro…
SparseWorld-TC: Trajectory-Conditioned Sparse Occupancy World Model
Jiayuan Du, Yiming Zhao, Zhenglong Guo +5
This paper introduces a novel architecture for trajectory-conditioned forecasting of future 3D scene occupancy. In contrast to methods that rely on variational autoencoders (VAEs)…
DriveVGGT: Calibration-Constrained Visual Geometry Transformers for Multi-Camera Autonomous Driving
Xiaosong Jia, Yanhao Liu, Yu Hong +5
Feed-forward reconstruction has been progressed rapidly, with the Visual Geometry Grounded Transformer (VGGT) being a notable baseline. However, directly applying VGGT to autonomou…
Uni-World VLA: Interleaved World Modeling and Planning for Autonomous Driving
Qiqi Liu, Huan Xu, Jingyu Li +5
Autonomous driving requires reasoning about how the environment evolves and planning actions accordingly. Existing world-model-based approaches typically predict future scenes firs…
FLARE: Learning Future-Aware Latent Representations from Vision-Language Models for Autonomous Driving
Chengen Xie, Chonghao Sima, Tianyu Li +4
While Vision-Language Models (VLMs) offer rich world knowledge for end-to-end autonomous driving, current approaches heavily rely on labor-intensive language annotations (e.g., VQA…