11 citations · 19 across the 6 of their papers we have counts for
6 papers
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…
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…
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…