3 papers
cs.RO2026
A Hybrid Sampling-Based Trajectory Planner with Game-Theoretic Guidance for Autonomous Racing
Alexander Langmann, Frederico Pita de Araujo, Mattia Piccinini +1
Autonomous racing demands planning algorithms that balance vehicle dynamics at the limits of handling with strategic decision-making in competitive multi-agent scenarios. Game theo…
cs.RO2026
Benchmarking Empirical and Learning-Based Approaches for Feedforward Steering Control in Autonomous Racing
Georg Jank, Mattia Piccinini, Sebastian Wenk +3
Feedforward steering control is a key component of hierarchical control architectures for autonomous racing. The goal is to reduce steering corrections from the feedback controller…
cs.RO2024
Open-Loop and Feedback Nash Trajectories for Competitive Racing with iLQGames
Matthias Rowold, Alexander Langmann, Boris Lohmann +1
Interaction-aware trajectory planning is crucial for closing the gap between autonomous racing cars and human racing drivers. Prior work has applied game theory as it provides equi…