Tru Hoang

Papers

Total Citations

H-Index

About

Tru Hoang is a leading researcher in autonomous vehicle motion planning and decision-making, with a focus on probabilistic and real-time methods. His most-cited work, "Motion Planning of Autonomous Road Vehicles by Particle Filtering" (2019, 52 citations), introduces a novel approach that leverages the structured nature of road networks to simplify complex planning problems. By framing motion planning as a particle filtering problem, Hoang enables vehicles to efficiently generate safe, feasible trajectories while satisfying predefined driving constraints. This contribution has been influential in bridging the gap between theoretical planning algorithms and practical, real-world autonomous driving systems. Beyond this flagship paper, Hoang's research continues to advance the fields of robotics and intelligent transportation, addressing challenges in uncertainty handling and computational efficiency. His work is widely cited by both academic researchers and industry practitioners developing next-generation autonomous vehicles. Hoang's innovative use of probabilistic filtering for motion planning has established him as a key voice in the ongoing effort to make autonomous driving safer and more reliable.