12 citations · 19 across the 13 of their papers we have counts for
11 papers · 1 filter
Residual Koopman Model Predictive Control for Enhanced Vehicle Dynamics with Small On-Track Data Input
Yonghao Fu, Cheng Hu, Haokun Xiong +6
In vehicle trajectory tracking tasks, the simplest approach is the Pure Pursuit (PP) Control. However, this single-point preview tracking strategy fails to consider vehicle model c…
M-Predictive Spliner: Enabling Spatiotemporal Multi-Opponent Overtaking for Autonomous Racing
Nadine Imholz, Maurice Brunner, Nicolas Baumann +2
Unrestricted multi-agent racing presents a significant research challenge, requiring decision-making at the limits of a robot's operational capabilities. While previous approaches…
R-CARLA: High-Fidelity Sensor Simulations with Interchangeable Dynamics for Autonomous Racing
Maurice Brunner, Edoardo Ghignone, Nicolas Baumann +1
Autonomous racing has emerged as a crucial testbed for autonomous driving algorithms, necessitating a simulation environment for both vehicle dynamics and sensor behavior. Striking…
DTR: Delaunay Triangulation-based Racing for Scaled Autonomous Racing
Luca Tognoni, Neil Reichlin, Edoardo Ghignone +4
Reactive controllers for autonomous racing avoid the computational overhead of full ee-Think-Act autonomy stacks by directly mapping sensor input to control actions, eliminating th…
Planar Velocity Estimation for Fast-Moving Mobile Robots Using Event-Based Optical Flow
Liam Boyle, Jonas Kühne, Nicolas Baumann +2
Accurate velocity estimation is critical in mobile robotics, particularly for driver assistance systems and autonomous driving. Wheel odometry fused with Inertial Measurement Unit…
Drive Fast, Learn Faster: On-Board RL for High Performance Autonomous Racing
Benedict Hildisch, Edoardo Ghignone, Nicolas Baumann +3
Autonomous racing presents unique challenges due to its non-linear dynamics, the high speed involved, and the critical need for real-time decision-making under dynamic and unpredic…
RobotxR1: Enabling Embodied Robotic Intelligence on Large Language Models through Closed-Loop Reinforcement Learning
Liam Boyle, Nicolas Baumann, Paviththiren Sivasothilingam +2
Future robotic systems operating in real-world environments will require on-board embodied intelligence without continuous cloud connection, balancing capabilities with constraints…
Enhancing Autonomous Driving Systems with On-Board Deployed Large Language Models
Nicolas Baumann, Cheng Hu, Paviththiren Sivasothilingam +4
Neural Networks (NNs) trained through supervised learning struggle with managing edge-case scenarios common in real-world driving due to the intractability of exhaustive datasets c…
TinyCenterSpeed: Efficient Center-Based Object Detection for Autonomous Racing
Neil Reichlin, Nicolas Baumann, Edoardo Ghignone +1
Perception within autonomous driving is nearly synonymous with Neural Networks (NNs). Yet, the domain of autonomous racing is often characterized by scaled, computationally limited…
GP-enhanced Autonomous Drifting Framework using ADMM-based iLQR
Yangyang Xie, Cheng Hu, Nicolas Baumann +3
Autonomous drifting is a complex challenge due to the highly nonlinear dynamics and the need for precise real-time control, especially in uncertain environments. To address these l…
RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms
Edoardo Ghignone, Nicolas Baumann, Cheng Hu +4
Autonomous racing presents a complex environment requiring robust controllers capable of making rapid decisions under dynamic conditions. While traditional controllers based on tir…