Rohitash Chandra

Papers

Total Citations

H-Index

About

Rohitash Chandra is a leading researcher at the intersection of artificial intelligence, deep learning, and autonomous systems, with a primary focus on pedestrian trajectory prediction. His work addresses critical challenges in the safe deployment of autonomous driving and robotics by developing more explainable and physically grounded motion forecasting models. Chandra’s major contributions include the creation of goal-driven and dynamics-based deep learning frameworks that incorporate explicit physical constraints, moving beyond purely data-driven approaches that lack interpretability. His most-cited paper, "Pedestrian trajectory prediction using goal-driven and dynamics-based deep learning framework" (2025), has already garnered 17 citations, demonstrating its immediate impact on the field. A related foundational study, "Pedestrian Trajectory Prediction Using Dynamics-based Deep Learning" (2024), further established his approach of integrating a priori assumptions about human movement to enhance model transparency and reliability. By prioritizing explainability alongside accuracy, Chandra’s work is helping to build safer, more trustworthy AI systems for real-world navigation, making him a notable contributor to the advancement of intelligent transportation and human-robot interaction.