activity
20192021
most citedConcurrent Meta Reinforcement Learning

14 citations · 15 across the 2 of their papers we have counts for

collaborators

5 papers

cs.RO2021

Physically Feasible Vehicle Trajectory Prediction

Harshayu Girase, Jerrick Hoang, Sai Yalamanchi +1

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 appr…

cs.CV20201 cited

Ellipse Loss for Scene-Compliant Motion Prediction

Henggang Cui, Hoda Shajari, Sai Yalamanchi +1

Motion prediction is a critical part of self-driving technology, responsible for inferring future behavior of traffic actors in autonomous vehicle's surroundings. In order to ensur…

cs.LG2020

Improving Movement Predictions of Traffic Actors in Bird's-Eye View Models using GANs and Differentiable Trajectory Rasterization

Eason Wang, Henggang Cui, Sai Yalamanchi +3

One of the most critical pieces of the self-driving puzzle is the task of predicting future movement of surrounding traffic actors, which allows the autonomous vehicle to safely an…

cs.RO2020

Long-term Prediction of Vehicle Behavior using Short-term Uncertainty-aware Trajectories and High-definition Maps

Sai Yalamanchi, Tzu-Kuo Huang, Galen Clark Haynes +1

Motion prediction of surrounding vehicles is one of the most important tasks handled by a self-driving vehicle, and represents a critical step in the autonomous system necessary to…

cs.AI201914 cited

Concurrent Meta Reinforcement Learning

Emilio Parisotto, Soham Ghosh, Sai Bhargav Yalamanchi +3

State-of-the-art meta reinforcement learning algorithms typically assume the setting of a single agent interacting with its environment in a sequential manner. A negative side-effe…