Search Results for author: Shobhit Gupta

Found 6 papers, 0 papers with code

Video-based assessment of intraoperative surgical skill

no code implementations13 May 2022 Sanchit Hira, Digvijay Singh, Tae Soo Kim, Shobhit Gupta, Gregory Hager, Shameema Sikder, S. Swaroop Vedula

The neural network approach using attention mechanisms also showed high sensitivity and specificity.


Real-time Eco-Driving Control in Electrified Connected and Autonomous Vehicles using Approximate Dynamic Programming

no code implementations5 Aug 2021 Shreshta Rajakumar Deshpande, Shobhit Gupta, Abhishek Gupta, Marcello Canova

This paper presents a hierarchical multi-layer Model Predictive Control (MPC) approach for improving the fuel economy of a 48V mild-hybrid powertrain in a connected vehicle environment.

Autonomous Vehicles

Eco-Driving of Connected and Autonomous Vehicles with Sequence-to-Sequence Prediction of Target Vehicle Velocity

no code implementations31 May 2021 Shobhit Gupta, Marcello Canova

The Eco-Driving control problem seeks to perform fuel efficient speed planning for a Connected and Autonomous Vehicle (CAV) that can exploit information available from advanced mapping, and from Vehicle-to-Everything (V2X) communication.

Autonomous Vehicles

A GPU Implementation of a Look-Ahead Optimal Controller for Eco-Driving Based on Dynamic Programming

no code implementations3 Apr 2021 Zhaoxuan Zhu, Shobhit Gupta, Nicola Pivaro, Shreshta Rajakumar Deshpande, Marcello Canova

Predictive energy management of Connected and Automated Vehicles (CAVs), in particular those with multiple power sources, has the potential to significantly improve energy savings in real-world driving conditions.

energy management Management

A Deep Reinforcement Learning Framework for Eco-driving in Connected and Automated Hybrid Electric Vehicles

no code implementations13 Jan 2021 Zhaoxuan Zhu, Shobhit Gupta, Abhishek Gupta, Marcello Canova

Connected and Automated Vehicles (CAVs), in particular those with multiple power sources, have the potential to significantly reduce fuel consumption and travel time in real-world driving conditions.

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