4 papers
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…
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…
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…