11 citations · 22 across the 10 of their papers we have counts for
8 papers · 1 filter
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…
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…
HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling
Xin Huang, Guy Rosman, Igor Gilitschenski +4
Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction. Existing approaches explore the discrete nature of human intent before predicti…
Fast Risk Assessment for Autonomous Vehicles Using Learned Models of Agent Futures
Allen Wang, Xin Huang, Ashkan Jasour +1
This paper presents fast non-sampling based methods to assess the risk of trajectories for autonomous vehicles when probabilistic predictions of other agents' futures are generated…
CARPAL: Confidence-Aware Intent Recognition for Parallel Autonomy
Xin Huang, Stephen G. McGill, Jonathan A. DeCastro +4
Predicting driver intentions is a difficult and crucial task for advanced driver assistance systems. Traditional confidence measures on predictions often ignore the way predicted t…