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Found 5 papers, 1 papers with code
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.
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.
Benchmarking Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) is widely seen as having the potential to be significantly more sample efficient than model-free RL.
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