14 citations · 30 across the 13 of their papers we have counts for
13 papers
Is Your Trajectory Displacement Safe in Long-tail?
Qiao Sun, Weicheng Zheng, Yixin Huang +1
Long-tail scenarios remain a major bottleneck for autonomous driving evaluation, even as datasets grow by orders of magnitude. Existing evaluation pipelines are rarely human-aligne…
DriveMA: Rethinking Language Interfaces in Driving VLAs with One-Step Meta-Actions
Weicheng Zheng, Yixin Huang, Qiao Sun +2
Driving Vision-Language-Action Models (Driving VLAs) commonly introduce natural-language reasoning as an intermediate interface for end-to-end planning, but reasoning-centric inter…
DriveMA: Driving Vision-Language-Action Models with verifiable Meta-Actions
Weicheng Zheng, Yixin Huang, Qiao Sun +2
Driving Vision-Language-Action Models (Driving VLAs) aim to use language to improve end-to-end planning, but the language-action gap limits this promise. We propose DriveMA, a Driv…
Action Images: End-to-End Policy Learning via Multiview Video Generation
Haoyu Zhen, Zixian Gao, Qiao Sun +7
World action models (WAMs) have emerged as a promising direction for robot policy learning, as they can leverage powerful video backbones to model the future states. However, exist…
Generalizing Motion Planners with Mixture of Experts for Autonomous Driving
Qiao Sun, Huimin Wang, Jiahao Zhan +7
Large real-world driving datasets have sparked significant research into various aspects of data-driven motion planners for autonomous driving. These include data augmentation, mod…
Large Trajectory Models are Scalable Motion Predictors and Planners
Qiao Sun, Shiduo Zhang, Danjiao Ma +7
Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding div…
Uncertainty-Aware Decision Transformer for Stochastic Driving Environments
Zenan Li, Fan Nie, Qiao Sun +2
Offline Reinforcement Learning (RL) enables policy learning without active interactions, making it especially appealing for self-driving tasks. Recent successes of Transformers ins…
Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills
Zenan Li, Fan Nie, Qiao Sun +2
Learning-based vehicle planning is receiving increasing attention with the emergence of diverse driving simulators and large-scale driving datasets. While offline reinforcement lea…
P4P: Conflict-Aware Motion Prediction for Planning in Autonomous Driving
Qiao Sun, Xin Huang, Brian C. Williams +1
Motion prediction is crucial in enabling safe motion planning for autonomous vehicles in interactive scenarios. It allows the planner to identify potential conflicts with other tra…
InterSim: Interactive Traffic Simulation via Explicit Relation Modeling
Qiao Sun, Xin Huang, Brian C. Williams +1
Interactive traffic simulation is crucial to autonomous driving systems by enabling testing for planners in a more scalable and safe way compared to real-world road testing. Existi…
VectorFlow: Combining Images and Vectors for Traffic Occupancy and Flow Prediction
Xin Huang, Xiaoyu Tian, Junru Gu +2
Predicting future behaviors of road agents is a key task in autonomous driving. While existing models have demonstrated great success in predicting marginal agent future behaviors,…
M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction
Qiao Sun, Xin Huang, Junru Gu +2
Predicting future motions of road participants is an important task for driving autonomously in urban scenes. Existing models excel at predicting marginal trajectories for single a…
DenseTNT: Waymo Open Dataset Motion Prediction Challenge 1st Place Solution
Junru Gu, Qiao Sun, Hang Zhao
In autonomous driving, goal-based multi-trajectory prediction methods are proved to be effective recently, where they first score goal candidates, then select a final set of goals,…