14 citations · 19 across the 10 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…