1 code implementation • 12 Sep 2023 • Enna Sachdeva, Nakul Agarwal, Suhas Chundi, Sean Roelofs, Jiachen Li, Mykel Kochenderfer, Chiho Choi, Behzad Dariush
The widespread adoption of commercial autonomous vehicles (AVs) and advanced driver assistance systems (ADAS) may largely depend on their acceptance by society, for which their perceived trustworthiness and interpretability to riders are crucial.
2 code implementations • 19 Sep 2018 • Yu Yao, Mingze Xu, Chiho Choi, David J. Crandall, Ella M. Atkins, Behzad Dariush
Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving.
no code implementations • ICCV 2019 • Chiho Choi, Behzad Dariush
Inferring relational behavior between road users as well as road users and their surrounding physical space is an important step toward effective modeling and prediction of navigation strategies adopted by participants in road scenes.
no code implementations • 29 May 2019 • Athma Narayanan, Isht Dwivedi, Behzad Dariush
This paper examines the problem of dynamic traffic scene classification under space-time variations in viewpoint that arise from video captured on-board a moving vehicle.
no code implementations • 17 Sep 2019 • Srikanth Malla, Isht Dwivedi, Behzad Dariush, Chiho Choi
In the proposed approach, a predictive distribution of future forecast is jointly modeled with the uncertainty of predictions.
no code implementations • 1 Oct 2019 • Athma Narayanan, Avinash Siravuru, Behzad Dariush
The Tactical Driver Behavior modeling problem requires understanding of driver actions in complicated urban scenarios from a rich multi modal signals including video, LiDAR and CAN bus data streams.
no code implementations • 7 Dec 2019 • Behnoosh Parsa, Athma Narayanan, Behzad Dariush
In this paper, we propose a novel Spatio-Temporal Pyramid Graph Convolutional Network (ST-PGN) for online action recognition for ergonomic risk assessment that enables the use of features from all levels of the skeleton feature hierarchy.
no code implementations • CVPR 2020 • Srikanth Malla, Behzad Dariush, Chiho Choi
In an attempt to address this problem, we introduce TITAN (Trajectory Inference using Targeted Action priors Network), a new model that incorporates prior positions, actions, and context to forecast future trajectory of agents and future ego-motion.
no code implementations • 13 Apr 2020 • Isht Dwivedi, Srikanth Malla, Behzad Dariush, Chiho Choi
Third, the semantic context of the scene are modeled and take into account the environmental constraints that potentially influence the future motion.
no code implementations • 4 May 2020 • Jun Hayakawa, Behzad Dariush
The main contribution of this paper is a new framework and algorithm that integrates these three networks in order to estimate the ego-motion and surrounding vehicle state.
no code implementations • 3 Aug 2020 • Jun Hayakawa, Behzad Dariush
The proposed method outperforms methods using a single-stream temporal relation network based on evaluations using the JAAD public dataset.
no code implementations • 19 Oct 2020 • Nakul Agarwal, Yi-Ting Chen, Behzad Dariush, Ming-Hsuan Yang
Spatio-temporal action localization is an important problem in computer vision that involves detecting where and when activities occur, and therefore requires modeling of both spatial and temporal features.
no code implementations • 10 Nov 2020 • Srikanth Malla, Chiho Choi, Behzad Dariush
This paper considers the problem of multi-modal future trajectory forecast with ranking.
no code implementations • CVPR 2022 • Reza Ghoddoosian, Isht Dwivedi, Nakul Agarwal, Chiho Choi, Behzad Dariush
Experimental results show efficacy of the proposed methods both qualitatively and quantitatively in two domains of cooking and assembly.
no code implementations • ICCV 2023 • Reza Ghoddoosian, Isht Dwivedi, Nakul Agarwal, Behzad Dariush
We present a novel method for weakly-supervised action segmentation and unseen error detection in anomalous instructional videos.