103 citations · 155 across the 18 of their papers we have counts for
7 papers · 1 filter
LISPR: An Options Framework for Policy Reuse with Reinforcement Learning
Daniel Graves, Jun Jin, Jun Luo
We propose a framework for transferring any existing policy from a potentially unknown source MDP to a target MDP. This framework (1) enables reuse in the target domain of any form…
Prediction by Anticipation: An Action-Conditional Prediction Method based on Interaction Learning
Ershad Banijamali, Mohsen Rohani, Elmira Amirloo +2
In autonomous driving (AD), accurately predicting changes in the environment can effectively improve safety and comfort. Due to complex interactions among traffic participants, how…
PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D
Amir Rasouli, Tiffany Yau, Peter Lakner +3
Predicting the behavior of road users, particularly pedestrians, is vital for safe motion planning in the context of autonomous driving systems. Traditionally, pedestrian behavior…
Bifold and Semantic Reasoning for Pedestrian Behavior Prediction
Amir Rasouli, Mohsen Rohani, Jun Luo
Pedestrian behavior prediction is one of the major challenges for intelligent driving systems. Pedestrians often exhibit complex behaviors influenced by various contextual elements…
Graph-SIM: A Graph-based Spatiotemporal Interaction Modelling for Pedestrian Action Prediction
Tiffany Yau, Saber Malekmohammadi, Amir Rasouli +3
One of the most crucial yet challenging tasks for autonomous vehicles in urban environments is predicting the future behaviour of nearby pedestrians, especially at points of crossi…
Multi-Modal Hybrid Architecture for Pedestrian Action Prediction
Amir Rasouli, Tiffany Yau, Mohsen Rohani +1
Pedestrian behavior prediction is one of the major challenges for intelligent driving systems in urban environments. Pedestrians often exhibit a wide range of behaviors and adequat…