1 citations · 2 across the 13 of their papers we have counts for
16 papers
Refine Connections, Close the Gap: A Reliable Enhancement Framework for Driving Scene Topology
Xiaoqi Wang, Dingyi Zhaung, Dingyi Zhuang +7
In autonomous driving, understanding scene topology - the connectivity between lanes and traffic elements - is critical for safe path planning and motion control. While current met…
Scaling Curriculum Learning For Autonomous Driving
Cevahir Koprulu, David Paz, Feng Tao +4
Batched simulators for autonomous driving have recently enabled training reinforcement learning (RL) agents at scale, encompassing thousands of traffic scenarios and billions of in…
Adapting Reinforcement Learning for Path Planning in Constrained Parking Scenarios
Feng Tao, Luca Paparusso, Chenyi Gu +6
Real-time path planning in constrained environments remains a fundamental challenge for autonomous systems. Traditional classical planners, while effective under perfect perception…
Dino-Diffusion Modular Designs Bridge the Cross-Domain Gap in Autonomous Parking
Zixuan Wu, Hengyuan Zhang, Ting-Hsuan Chen +4
Parking is a critical pillar of driving safety. While recent end-to-end (E2E) approaches have achieved promising in-domain results, robustness under domain shifts (e.g., weather an…
SMART: Advancing Scalable Map Priors for Driving Topology Reasoning
Junjie Ye, David Paz, Hengyuan Zhang +5
Topology reasoning is crucial for autonomous driving as it enables comprehensive understanding of connectivity and relationships between lanes and traffic elements. While recent ap…
MapGS: Generalizable Pretraining and Data Augmentation for Online Mapping via Novel View Synthesis
Hengyuan Zhang, David Paz, Yuliang Guo +3
Online mapping reduces the reliance of autonomous vehicles on high-definition (HD) maps, significantly enhancing scalability. However, recent advancements often overlook cross-sens…