Saksham Bhutani

Bangalore, Karnataka

India

I am a Pre-Doctoral Fellow at the Indian Institute of Science (IISc) working with Dr. Vaibhav Katewa. I am an electronics engineer from Vellore Institute of Technology (VIT) in Vellore, India. My research journey started with Dr. Shunmuga Perumal at the Automotive Research Center at VIT working on perception models for Autonomous Vehicles in challenging environment with poor road infrastructure. I also interned at ETH in Zurich, where I worked on using Computer Vision to extract PPG data from facial videos under the expert guidance of Dr. Moe Elgendi and Dr. Carlo Menon.

I have also had the privilege of leading the Avionics and Software departments at Assailing Falcons, a team that achieved 3rd place Worldwide and 1st place in Asia in the SAE Aero Design Competition in 2021-2022. I also founded the VIT Amateur Radio Club (VARC) under the guidance of Dr. Harish Kittur.

In my free time, I love building and flying drones, capturing breathtaking footage that you can find here. I’m also a licenced amateur radio operator (Callsign: VU3DTU) and an avid traveler, having visited 7 countries (CH, AT, DE, FR, LI, AE & NP) and almost all the states in India. Per aspera ad astra 💫

news

Nov 01, 2023	I have received Pre-doctoral Fellowship from the Kotak-IISc AI Center (KIAC).

selected publications

LaneScanNET: A deep-learning approach for simultaneous detection of obstacle-lane states for autonomous driving systems

P Shunmuga Perumal^†, Yong Wang, M Sujasree , and 4 more authors

Expert Systems with Applications, 2023

Abs Bib HTML

This paper proposes a deep-learning-based novel decision-making network named LaneScanNET to assist the ADS in lane-changing or lane-keeping decision-making. The proposed system assists ADS in detecting obstacles, localization of the Ego Vehicle (EGV) on roads, and estimating lane status in its Field of View (FOV). The proposed LaneScanNET uses a parallel pipeline with an Obstacle Detection Network (ODN) and a Lane Detection Network (LDN) to simultaneously process the incoming image frames for detecting obstacles and segmenting lane lines, respectively. Further, the Obstacle-Lane Fusion Network (OLFN) fuses these results to predict the status of the obstacle lane in the FOV of autonomous vehicles. Vellore Institute of Technology’s (VIT’s) real-time driving dataset on indigenous roads has been collected to train the proposed LaneScanNET with 2464 obstacle-lane images obtained by driving over 60 km. The dataset generated for obstacle detection and its corresponding lane detection has also been made publicly available to promote research work for these developing countries. The proposed system outperforms all existing networks on Indian roads with an accuracy of 75.28% in obstacle detection and 91.36% in lane detection. Additionally, the proposed system can analyze the lane status with an accuracy of 92.54%. The proposed network performs exceptionally well in unforeseen circumstances like shadows, fog, dust, and occlusions making LaneScanNET a robust network that can be an add-on for ADS to make lane-keeping or lane-changing decisions. The LaneScanNET can be integrated into real-time vehicles to assist drivers or ADS.
@article{perumal2023lanescannet, title = {LaneScanNET: A deep-learning approach for simultaneous detection of obstacle-lane states for autonomous driving systems}, author = {Perumal, P Shunmuga and Wang, Yong and Sujasree, M and Tulshain, Shobhit and Bhutani, Saksham and Suriyah, M Kiruthik and Raju, V Umesh Kumar}, journal = {Expert Systems with Applications}, volume = {233}, pages = {120970}, year = {2023}, publisher = {Elsevier}, doi = {https://doi.org/10.1016/j.eswa.2023.120970}, dimensions = {true}, }