activity
20192021
most citedSMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving

103 citations · 118 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV2021

Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map

Elmira Amirloo, Mohsen Rohani, Ershad Banijamali +2

While supervised learning is widely used for perception modules in conventional autonomous driving solutions, scalability is hindered by the huge amount of data labeling needed. In…

cs.LG2020

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…

cs.CV20208 cited

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…

cs.CV2020

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…

cs.CV20204 cited

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…

cs.CV20203 cited

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…