6 papers · 1 filter
Unified Map Prior Encoder for Mapping and Planning
Zongzheng Zhang, Sizhe Zou, Guantian Zheng +12
Online mapping and end-to-end (E2E) planning in autonomous driving remain largely sensor-centric, leaving rich map priors, including HD/SD vector maps, rasterized SD maps, and sate…
Scaling Up Occupancy-centric Driving Scene Generation: Dataset and Method
Bohan Li, Xin Jin, Hu Zhu +9
Driving scene generation is a critical domain for autonomous driving, enabling downstream applications, including perception and planning evaluation. Occupancy-centric methods have…
OmniNWM: Omniscient Driving Navigation World Models
Bohan Li, Zhuang Ma, Dalong Du +10
Autonomous driving world models are expected to work effectively across three core dimensions: state, action, and reward. However, existing methods are typically restricted to frag…
Active Learning from Scene Embeddings for End-to-End Autonomous Driving
Wenhao Jiang, Duo Li, Menghan Hu +3
In the field of autonomous driving, end-to-end deep learning models show great potential by learning driving decisions directly from sensor data. However, training these models req…
AVID: Adapting Video Diffusion Models to World Models
Marc Rigter, Tarun Gupta, Agrin Hilmkil +1
Large-scale generative models have achieved remarkable success in a number of domains. However, for sequential decision-making problems, such as robotics, action-labelled data is o…
A Simple Approach to Unifying Diffusion-based Conditional Generation
Xirui Li, Charles Herrmann, Kelvin C. K. Chan +4
Recent progress in image generation has sparked research into controlling these models through condition signals, with various methods addressing specific challenges in conditional…