Trent Weiss
Papers
1
Total Citations
8
H-Index
1
About
Trent Weiss is a researcher at the forefront of autonomous systems, with a primary focus on high-speed robotics and control in racing environments. His most impactful work, "DeepRacing: Parameterized Trajectories for Autonomous Racing" (2020), introduces a novel end-to-end framework and a realistic virtual testbed built on the F1 series, enabling the training and evaluation of algorithms for autonomous racing at extreme speeds. This contribution addresses the challenging problem of controlling vehicles at the limits of traction, blending trajectory optimization with deep learning. With 8 citations, the paper has established Weiss as a key figure in the niche of autonomous racing, providing a benchmark for researchers tackling real-time decision-making under dynamic constraints. His work not only advances autonomous vehicle control but also offers a scalable simulation platform for testing safety-critical maneuvers. Weiss’s research is instrumental for students and engineers interested in the intersection of reinforcement learning, control theory, and motorsport, demonstrating how virtual environments can accelerate the development of robust, high-performance autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1DeepRacing: Parameterized Trajectories for Autonomous Racing8 citations · 2020