Trent Weiss
I am a PhD Candidate in Computer Science at the University of Virginia Link Lab, working with Professor Madhur Behl. My undergraduate studies were in Mechinical Engineering with a focus on Mechatronics. Before joining the Link Lab in 2017, I was a robotics engineer with the Research & Technology division of The Boeing Company.
My research interests are in Robotics and Autonomous/Cyber-Physical Systems. Specifically, I am interested in improving autonomous vehicles by taking them to their extreme limit in autonomous racing scenarios.
Email / GitHub / Google Scholar / LinkedIn
Research
The Gauss-Legendre Rectangle (GLR) Algorithm for Collision Risk Estimation
IROS 2025 (Under Review), 2025
code
An approach to collision risk estimation based on Gauss-Legendre Quadrature and Non-homogenous Poisson Processes
DeepRacing: A framework for autonomous racing
Design, Automation & Test in Europe Conference (DATE), 2020
bib / code / paper
DeepRacing is a novel end-to-end framework, and a virtual testbed for training and evaluating algorithms for autonomous racing, implemented in a realistic video game environment.
Patents
Method and apparatus of coordinating independent automated systems
Patent
A method of coordinating automated systems, one with pre-defined operating instructions, and the other determining it’s behavior on-the-fly based on process requirements.
Optimized-coverage selective laser ablation systems and methods
Patent
A method for preparing a complex 3D part surface with laser ablation where manually programming a laser path is either impossible or infeasible.
Teaching
CS 4102: Algorithms
Taught problem-solving skills and algorithm analysis techniques applied to problems such as sorting, searching, and dynamic programming.
CS 2102: Discrete Mathematics
Taught foundational mathematics and logical reasoning necessary for CS Undergraduates, making use of the automated proof assistant Lean.
Honors and Awards
UVA Link Lab Distinguished Research Award
Selected for showcasing the highest quality research in cyber-physical at the UVA Link Lab
2021-2022 ARCS Scholar: Metro Washington Chapter
Selected for a scholarship by the Metro Washington Chapter of the ARCS Foundation.
Best Paper Award for the 2020 IROS Workshop on Perception, Learning, and Control for Autonomous Agile Vehicles
My paper “DeepRacing AI: Agile Trajectory Synthesis for Autonomous Racing” was honored with the Best Paper Award at the IROS workshop where it was presented.
2019 Graduate Teaching Award
Awarded the 2019 Graduate Student Teaching Award at the University of Virginia for outstanding service as a Graduate TA.
Other Work
Ongoing Research
Anywhere Autonomous Vehicles Are Needed :)
My ongoing work uses an extension of the Differential Bayesian Filtering framework to generate overtaking trajectories in racing situations.
Overtaking with the Differential Bayesian Filtering framework
Cavalier Autonomous Racing
Texas Motor Speedway, Indianapolis Motor Speedway, and Lucas Oil Raceway
I am also a member of Cavalier Autonomous Racing, University of Virginia’s entry into the Indy Autonomous Challenge, the world’s premier autonomous racing league. My work there is guided by the words of Famed American racer Mario Andretti: “If everything seems under control, then you are not going fast enough”
Perception and Tracking
I am the co-lead for the perception and vehicle tracking team for Cavalier Autonomous Racing. Vehicle detection is a challenging task. At 150MPH, it's a different game entirely. I develop various techniques for detecting the pose of other vehicles in the vicinity with various sensors and fusing those measurements to produce 15-dimensional state estimate of any opponent vehicles including pose, velocity, and acceleration.
EKF Localization
Racing at high speed requires precise knowledge of the vehicle's pose and velocity. I build sensor fusion stacks capable of estimating the vehicle's current state with sensor data pulled from GPS antennae, several on-board IMUs, as well as the tachometer's wheel speed.