11 citations · 44 across the 17 of their papers we have counts for
17 papers
Phase-Aware Guidance Injection for Recurrent MAPPO in Assembly-Line Disruption Recovery
Xin Huang, Yongcai Wang, Fengyi Zhang +3
Disruption recovery in industrial assembly lines requires timely decisions under machine faults, worker absence, and emergency orders. Existing methods either rely on rigid handcra…
NashFormer: Leveraging Local Nash Equilibria for Semantically Diverse Trajectory Prediction
Justin Lidard, Oswin So, Yanxia Zhang +8
Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents. Because existing diversity-aware predictor…
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…
Multi-Agent Chance-Constrained Stochastic Shortest Path with Application to Risk-Aware Intelligent Intersection
Majid Khonji, Rashid Alyassi, Wolfgang Merkt +5
In transportation networks, where traffic lights have traditionally been used for vehicle coordination, intersections act as natural bottlenecks. A formidable challenge for existin…
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…
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…
Trajectory Prediction with Linguistic Representations
Yen-Ling Kuo, Xin Huang, Andrei Barbu +4
Language allows humans to build mental models that interpret what is happening around them resulting in more accurate long-term predictions. We present a novel trajectory predictio…
TIP: Task-Informed Motion Prediction for Intelligent Vehicles
Xin Huang, Guy Rosman, Ashkan Jasour +3
When predicting trajectories of road agents, motion predictors usually approximate the future distribution by a limited number of samples. This constraint requires the predictors t…
Fast nonlinear risk assessment for autonomous vehicles using learned conditional probabilistic models of agent futures
Ashkan Jasour, Xin Huang, Allen Wang +1
This paper presents fast non-sampling based methods to assess the risk for trajectories of autonomous vehicles when probabilistic predictions of other agents' futures are generated…
Risk Conditioned Neural Motion Planning
Xin Huang, Meng Feng, Ashkan Jasour +2
Risk-bounded motion planning is an important yet difficult problem for safety-critical tasks. While existing mathematical programming methods offer theoretical guarantees in the co…
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…
DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling
Xin Huang, Stephen G. McGill, Jonathan A. DeCastro +4
Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant systems. While existing approaches may sample from a predicted distribution of vehicle…
Online Risk-Bounded Motion Planning for Autonomous Vehicles in Dynamic Environments
Xin Huang, Sungkweon Hong, Andreas Hofmann +1
A crucial challenge to efficient and robust motion planning for autonomous vehicles is understanding the intentions of the surrounding agents. Ignoring the intentions of the other…
Uncertainty-Aware Driver Trajectory Prediction at Urban Intersections
Xin Huang, Stephen McGill, Brian C. Williams +2
Predicting the motion of a driver's vehicle is crucial for advanced driving systems, enabling detection of potential risks towards shared control between the driver and automation…