no code implementations • 3 Mar 2022 • Elmira Amirloo, Amir Rasouli, Peter Lakner, Mohsen Rohani, Jun Luo
Multi-agent trajectory prediction is a fundamental problem in autonomous driving.
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.
no code implementations • ICCV 2021 • Ershad Banijamali, Mohsen Rohani, Elmira Amirloo, Jun Luo, Pascal Poupart
In autonomous driving (AD), accurately predicting changes in the environment can effectively improve safety and comfort.
no code implementations • 14 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.
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.
no code implementations • 3 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.
no code implementations • 16 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.
3 code implementations • 19 Oct 2020 • Ming Zhou, Jun Luo, Julian Villella, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, Aurora Chongxi Huang, Ying Wen, Kimia Hassanzadeh, Daniel Graves, Dong Chen, Zhengbang Zhu, Nhat Nguyen, Mohamed Elsayed, Kun Shao, Sanjeevan Ahilan, Baokuan Zhang, Jiannan Wu, Zhengang Fu, Kasra Rezaee, Peyman Yadmellat, Mohsen Rohani, Nicolas Perez Nieves, Yihan Ni, Seyedershad Banijamali, Alexander Cowen Rivers, Zheng Tian, Daniel Palenicek, Haitham Bou Ammar, Hongbo Zhang, Wulong Liu, Jianye Hao, Jun Wang
We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving.
2 code implementations • 19 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.
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.