Jerrick Hoang
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View article: Physically Feasible Vehicle Trajectory Prediction
Physically Feasible Vehicle Trajectory Prediction Open
Predicting the future motion of actors in a traffic scene is a crucial part of any autonomous driving system. Recent research in this area has focused on trajectory prediction approaches that optimize standard trajectory error metrics. In …
View article: Interaction-Based Trajectory Prediction Over a Hybrid Traffic Graph
Interaction-Based Trajectory Prediction Over a Hybrid Traffic Graph Open
Behavior prediction of traffic actors is an essential component of any real-world self-driving system. Actors' long-term behaviors tend to be governed by their interactions with other actors or traffic elements (traffic lights, stop signs)…
View article: Map-Adaptive Goal-Based Trajectory Prediction
Map-Adaptive Goal-Based Trajectory Prediction Open
We present a new method for multi-modal, long-term vehicle trajectory prediction. Our approach relies on using lane centerlines captured in rich maps of the environment to generate a set of proposed goal paths for each vehicle. Using these…
View article: Joint Interaction and Trajectory Prediction for Autonomous Driving using Graph Neural Networks
Joint Interaction and Trajectory Prediction for Autonomous Driving using Graph Neural Networks Open
In this work, we aim to predict the future motion of vehicles in a traffic scene by explicitly modeling their pairwise interactions. Specifically, we propose a graph neural network that jointly predicts the discrete interaction modes and 5…
View article: Benchmarking Model-Based Reinforcement Learning
Benchmarking Model-Based Reinforcement Learning Open
Model-based reinforcement learning (MBRL) is widely seen as having the potential to be significantly more sample efficient than model-free RL. However, research in model-based RL has not been very standardized. It is fairly common for auth…