4 papers
First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling
Alexander Davydov, Franck Djeumou, Marcus Greiff +4
Combining data-driven models that adapt online and model predictive control (MPC) has enabled effective control of nonlinear systems. However, when deployed on unstable systems, on…
Control Barrier Functions for Shared Control and Vehicle Safety
James Dallas, John Talbot, Makoto Suminaka +4
This manuscript presents a control barrier function based approach to shared control for preventing a vehicle from entering the part of the state space where it is unrecoverable. T…
Reference-Free Formula Drift with Reinforcement Learning: From Driving Data to Tire Energy-Inspired, Real-World Policies
Franck Djeumou, Michael Thompson, Makoto Suminaka +1
The skill to drift a car--i.e., operate in a state of controlled oversteer like professional drivers--could give future autonomous cars maximum flexibility when they need to retain…
Risk-Averse Model Predictive Control for Racing in Adverse Conditions
Thomas Lew, Marcus Greiff, Franck Djeumou +3
Model predictive control (MPC) algorithms can be sensitive to model mismatch when used in challenging nonlinear control tasks. In particular, the performance of MPC for vehicle con…