Search Results for author: Mohsen Rohani

Found 10 papers, 2 papers with code

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

no code implementations CVPR 2021 Elmira Amirloo, Mohsen Rohani, Ershad Banijamali, Jun Luo, Pascal Poupart

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.

Autonomous Driving Motion Planning

PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D

no code implementations14 Dec 2020 Amir Rasouli, Tiffany Yau, Peter Lakner, Saber Malekmohammadi, Mohsen Rohani, Jun Luo

To this end, we propose a new pedestrian action prediction dataset created by adding per-frame 2D/3D bounding box and behavioral annotations to the popular autonomous driving dataset, nuScenes.

Autonomous Driving Motion Planning +1

Bifold and Semantic Reasoning for Pedestrian Behavior Prediction

no code implementations ICCV 2021 Amir Rasouli, Mohsen Rohani, Jun Luo

Our method benefits from 1) a bifold encoding approach where different data modalities are processed independently allowing them to develop their own representations, and jointly to produce a representation for all modalities using shared parameters; 2) a novel interaction modeling technique that relies on categorical semantic parsing of the scenes to capture interactions between target pedestrians and their surroundings; and 3) a bifold prediction mechanism that uses both independent and shared decoding of multimodal representations.

Semantic Parsing

Graph-SIM: A Graph-based Spatiotemporal Interaction Modelling for Pedestrian Action Prediction

no code implementations3 Dec 2020 Tiffany Yau, Saber Malekmohammadi, Amir Rasouli, Peter Lakner, Mohsen Rohani, Jun Luo

2) We introduce a new dataset that provides 3D bounding box and pedestrian behavioural annotations for the existing nuScenes dataset.

Autonomous Vehicles Clustering

Multi-Modal Hybrid Architecture for Pedestrian Action Prediction

no code implementations16 Nov 2020 Amir Rasouli, Tiffany Yau, Mohsen Rohani, Jun Luo

Pedestrian behavior prediction is one of the major challenges for intelligent driving systems in urban environments.

Understanding and Mitigating the Limitations of Prioritized Experience Replay

2 code implementations19 Jul 2020 Yangchen Pan, Jincheng Mei, Amir-Massoud Farahmand, Martha White, Hengshuai Yao, Mohsen Rohani, Jun Luo

Prioritized Experience Replay (ER) has been empirically shown to improve sample efficiency across many domains and attracted great attention; however, there is little theoretical understanding of why such prioritized sampling helps and its limitations.

Autonomous Driving Continuous Control +1

Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural Networks

no code implementations CVPR 2019 Nima Mohajerin, Mohsen Rohani

Although in the transformed sequences the KITTI dataset is heavily biased toward static objects, by learning the difference between subsequent OGMs, our proposed method provides accurate prediction over both the static and moving objects.

Autonomous Driving Prediction Of Occupancy Grid Maps

Cannot find the paper you are looking for? You can Submit a new open access paper.