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Physically Feasible Vehicle Trajectory Prediction
Predicting the future motion of actors in a traffic scene is a crucial part of any autonomous driving system.
Interaction-Based Trajectory Prediction Over a Hybrid Traffic Graph
Behavior prediction of traffic actors is an essential component of any real-world self-driving system.
Map-Adaptive Goal-Based Trajectory Prediction
We present a new method for multi-modal, long-term vehicle trajectory prediction.
Goal-Directed Occupancy Prediction for Lane-Following Actors
Predicting the possible future behaviors of vehicles that drive on shared roads is a crucial task for safe autonomous driving.
Joint Interaction and Trajectory Prediction for Autonomous Driving using Graph Neural Networks
In this work, we aim to predict the future motion of vehicles in a traffic scene by explicitly modeling their pairwise interactions.
Hybrid Subspace Learning for High-Dimensional Data
One way to achieve this goal is to perform subspace learning to estimate a small set of latent features that capture the majority of the variance in the original data.
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