103 citations · 137 across the 10 of their papers we have counts for
10 papers
LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction
Elmira Amirloo, Amir Rasouli, Peter Lakner +2
Multi-agent trajectory prediction is a fundamental problem in autonomous driving. The key challenges in prediction are accurately anticipating the behavior of surrounding agents an…
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…
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…
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
Ming Zhou, Jun Luo, Julian Villella +34
Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Despite more than a decade of research and development, the problem of how to competently i…
Understanding and Mitigating the Limitations of Prioritized Experience Replay
Yangchen Pan, Jincheng Mei, Amir-massoud Farahmand +4
Prioritized Experience Replay (ER) has been empirically shown to improve sample efficiency across many domains and attracted great attention; however, there is little theoretical u…
Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural Networks
Nima Mohajerin, Mohsen Rohani
We investigate the multi-step prediction of the drivable space, represented by Occupancy Grid Maps (OGMs), for autonomous vehicles. Our motivation is that accurate multi-step predi…