Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Katare, Dewant"'
Autor:
Katare, Dewant, Noguero, David Solans, Park, Souneil, Kourtellis, Nicolas, Janssen, Marijn, Ding, Aaron Yi
The accuracy and fairness of perception systems in autonomous driving are essential, especially for vulnerable road users such as cyclists, pedestrians, and motorcyclists who face significant risks in urban driving environments. While mainstream rese
Externí odkaz:
http://arxiv.org/abs/2401.10397
Autor:
Katare, Dewant, Perino, Diego, Nurmi, Jari, Warnier, Martijn, Janssen, Marijn, Ding, Aaron Yi
Autonomous driving services rely heavily on sensors such as cameras, LiDAR, radar, and communication modules. A common practice of processing the sensed data is using a high-performance computing unit placed inside the vehicle, which deploys AI model
Externí odkaz:
http://arxiv.org/abs/2304.14271
Autor:
Katare, Dewant
Indiana University-Purdue University Indianapolis (IUPUI)
An Autonomous vehicle depends on the combination of latest technology or the ADAS safety features such as Adaptive cruise control (ACC), Autonomous Emergency Braking (AEB), Automatic Park
An Autonomous vehicle depends on the combination of latest technology or the ADAS safety features such as Adaptive cruise control (ACC), Autonomous Emergency Braking (AEB), Automatic Park
Externí odkaz:
https://hdl.handle.net/1805/21462
Autor:
Damsgaard, Hans Jakob, Grenier, Antoine, Katare, Dewant, Taufique, Zain, Shakibhamedan, Salar, Troccoli, Tiago, Chatzitsompanis, Georgios, Kanduri, Anil, Ometov, Aleksandr, Ding, Aaron Yi, Taherinejad, Nima, Karakonstantis, Georgios, Woods, Roger, Nurmi, Jari
Publikováno v:
In Journal of Systems Architecture May 2024 150
Autor:
Katare, Dewant, Ding, Aaron Yi
Connected vehicular services depend heavily on communication as they frequently transmit data and AI models/weights within the vehicular ecosystem. Energy efficiency in vehicles is crucial to keep up with the fast-growing demand for vehicular data pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c5964850a5bc4caf4e3242e6b2fd687
https://zenodo.org/record/7981282
https://zenodo.org/record/7981282
Autor:
Katare, Dewant, Kourtellis, Nicolas, Park, Souneil, Perino, Diego, Janssen, Marijn, Ding, Aaron Yi
A machine learning model can often produce biased outputs for a familiar group or similar sets of classes during inference over an unknown dataset. The generalization of neural networks have been studied to resolve biases, which has also shown improv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a912da3c8fe5f650717338c85880415f
https://doi.org/10.1109/sec54971.2022.00050
https://doi.org/10.1109/sec54971.2022.00050