Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Malawade, Arnav"'
Autor:
Zhang, Yifan, Malawade, Arnav Vaibhav, Zhang, Xiaofang, Li, Yuhui, Seong, DongHwan, Faruque, Mohammad Abdullah Al, Huang, Sitao
Autonomous systems (AS) are systems that can adapt and change their behavior in response to unanticipated events and include systems such as aerial drones, autonomous vehicles, and ground/aquatic robots. AS require a wide array of sensors, deep-learn
Externí odkaz:
http://arxiv.org/abs/2306.15748
Autor:
Wang, Junyao, Malawade, Arnav Vaibhav, Zhou, Junhong, Yu, Shih-Yuan, Faruque, Mohammad Abdullah Al
Effectively capturing intricate interactions among road users is of critical importance to achieving safe navigation for autonomous vehicles. While graph learning (GL) has emerged as a promising approach to tackle this challenge, existing GL models r
Externí odkaz:
http://arxiv.org/abs/2304.08600
Autonomous vehicles use multiple sensors, large deep-learning models, and powerful hardware platforms to perceive the environment and navigate safely. In many contexts, some sensing modalities negatively impact perception while increasing energy cons
Externí odkaz:
http://arxiv.org/abs/2202.11330
Although autonomous vehicles (AVs) are expected to revolutionize transportation, robust perception across a wide range of driving contexts remains a significant challenge. Techniques to fuse sensor data from camera, radar, and lidar sensors have been
Externí odkaz:
http://arxiv.org/abs/2201.06644
Autor:
Malawade, Arnav V., Yu, Shih-Yuan, Hsu, Brandon, Muthirayan, Deepan, Khargonekar, Pramod P., Faruque, Mohammad A. Al
In autonomous vehicles (AVs), early warning systems rely on collision prediction to ensure occupant safety. However, state-of-the-art methods using deep convolutional networks either fail at modeling collisions or are too expensive/slow, making them
Externí odkaz:
http://arxiv.org/abs/2111.06123
Publikováno v:
TC-CPS Newsletter Volume 05, Issue 01 (Mar. 2020)
Cyber-Physical Additive Manufacturing (AM) constructs a physical 3D object layer-by-layer according to its digital representation and has been vastly applied to fast prototyping and the manufacturing of functional end-products across fields. The comp
Externí odkaz:
http://arxiv.org/abs/2110.02259
Autor:
Malawade, Arnav Vaibhav, Yu, Shih-Yuan, Hsu, Brandon, Kaeley, Harsimrat, Karra, Anurag, Faruque, Mohammad Abdullah Al
Recently, road scene-graph representations used in conjunction with graph learning techniques have been shown to outperform state-of-the-art deep learning techniques in tasks including action classification, risk assessment, and collision prediction.
Externí odkaz:
http://arxiv.org/abs/2109.01183
Autor:
Malawade, Arnav, Odema, Mohanad, Lajeunesse-DeGroot, Sebastien, Faruque, Mohammad Abdullah Al
Autonomous vehicles (AV) are expected to revolutionize transportation and improve road safety significantly. However, these benefits do not come without cost; AVs require large Deep-Learning (DL) models and powerful hardware platforms to operate reli
Externí odkaz:
http://arxiv.org/abs/2107.10895
Autor:
Malawade, Arnav V., Costa, Nathan D., Muthirayan, Deepan, Khargonekar, Pramod P., Faruque, Mohammad A. Al
If machine failures can be detected preemptively, then maintenance and repairs can be performed more efficiently, reducing production costs. Many machine learning techniques for performing early failure detection using vibration data have been propos
Externí odkaz:
http://arxiv.org/abs/2102.11450
Autor:
Yu, Shih-Yuan, Malawade, Arnav V., Muthirayan, Deepan, Khargonekar, Pramod P., Faruque, Mohammad A. Al
Despite impressive advancements in Autonomous Driving Systems (ADS), navigation in complex road conditions remains a challenging problem. There is considerable evidence that evaluating the subjective risk level of various decisions can improve ADS' s
Externí odkaz:
http://arxiv.org/abs/2009.06435