Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Mahjoub, Hossein Nourkhiz"'
Autor:
Aryan, FNU, Stepputtis, Simon, Bhagat, Sarthak, Campbell, Joseph, Lee, Kwonjoon, Mahjoub, Hossein Nourkhiz, Sycara, Katia
Scene understanding is a fundamental capability needed in many domains, ranging from question-answering to robotics. Unlike recent end-to-end approaches that must explicitly learn varying compositions of the same scene, our method reasons over their
Externí odkaz:
http://arxiv.org/abs/2410.22626
Autor:
Li, Huao, Mahjoub, Hossein Nourkhiz, Chalaki, Behdad, Tadiparthi, Vaishnav, Lee, Kwonjoon, Moradi-Pari, Ehsan, Lewis, Charles Michael, Sycara, Katia P
Multi-Agent Reinforcement Learning (MARL) methods have shown promise in enabling agents to learn a shared communication protocol from scratch and accomplish challenging team tasks. However, the learned language is usually not interpretable to humans
Externí odkaz:
http://arxiv.org/abs/2409.17348
Autor:
Holley, Dustin, Dsa, Jovin, Mahjoub, Hossein Nourkhiz, Ali, Gibran, Naes, Tyler, Moradi-Pari, Ehsan, Kallepalli, Pawan Sai
Publikováno v:
2024 IEEE Intelligent Vehicles Symposium (IV)
Enhancing simulation environments to replicate real-world driver behavior is essential for developing Autonomous Vehicle technology. While some previous works have studied the yielding reaction of lag vehicles in response to a merging car at highway
Externí odkaz:
http://arxiv.org/abs/2404.09851
Autor:
Knaup, Jacob, D'sa, Jovin, Chalaki, Behdad, Naes, Tyler, Mahjoub, Hossein Nourkhiz, Moradi-Pari, Ehsan, Tsiotras, Panagiotis
Merging into dense highway traffic for an autonomous vehicle is a complex decision-making task, wherein the vehicle must identify a potential gap and coordinate with surrounding human drivers, each of whom may exhibit diverse driving behaviors. Many
Externí odkaz:
http://arxiv.org/abs/2310.07840
Autor:
Le, Viet-Anh, Tadiparthi, Vaishnav, Chalaki, Behdad, Mahjoub, Hossein Nourkhiz, D'sa, Jovin, Moradi-Pari, Ehsan, Malikopoulos, Andreas A.
In this paper, we develop a control framework for the coordination of multiple robots as they navigate through crowded environments. Our framework comprises of a local model predictive control (MPC) for each robot and a social long short-term memory
Externí odkaz:
http://arxiv.org/abs/2310.06964
Autor:
Le, Viet-Anh, Chalaki, Behdad, Tadiparthi, Vaishnav, Mahjoub, Hossein Nourkhiz, D'sa, Jovin, Moradi-Pari, Ehsan
Crowd navigation has received increasing attention from researchers over the last few decades, resulting in the emergence of numerous approaches aimed at addressing this problem to date. Our proposed approach couples agent motion prediction and plann
Externí odkaz:
http://arxiv.org/abs/2309.16838
Autor:
Li, Po-han, Ankireddy, Sravan Kumar, Zhao, Ruihan, Mahjoub, Hossein Nourkhiz, Moradi-Pari, Ehsan, Topcu, Ufuk, Chinchali, Sandeep, Kim, Hyeji
Publikováno v:
NeurIPS 2023
Efficient compression of correlated data is essential to minimize communication overload in multi-sensor networks. In such networks, each sensor independently compresses the data and transmits them to a central node due to limited communication bandw
Externí odkaz:
http://arxiv.org/abs/2305.15523
Autor:
Holley, Dustin, D'sa, Jovin, Mahjoub, Hossein Nourkhiz, Ali, Gibran, Chalaki, Behdad, Moradi-Pari, Ehsan
This paper discusses the limitations of existing microscopic traffic models in accounting for the potential impacts of on-ramp vehicles on the car-following behavior of main-lane vehicles on highways. We first surveyed U.S. on-ramps to choose a repre
Externí odkaz:
http://arxiv.org/abs/2305.12014
Autor:
Chalaki, Behdad, Tadiparthi, Vaishnav, Mahjoub, Hossein Nourkhiz, D'sa, Jovin, Moradi-Pari, Ehsan, Armijos, Andres S. Chavez, Li, Anni, Cassandras, Christos G.
Publikováno v:
IEEE Control Systems Letters, vol. 7, pp. 1766-1771, 2023
A lane-change maneuver on a congested highway could be severely disruptive or even infeasible without the cooperation of neighboring cars. However, cooperation with other vehicles does not guarantee that the performed maneuver will not have a negativ
Externí odkaz:
http://arxiv.org/abs/2303.05991
Autor:
Valiente, Rodolfo, Raftari, Arash, Mahjoub, Hossein Nourkhiz, Razzaghpour, Mahdi, Mahmud, Syed K., Fallah, Yaser P.
Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safet
Externí odkaz:
http://arxiv.org/abs/2212.12819