6 papers · 1 filter
Emergent Autonomous Drifting for Collision Avoidance in Real-World Winter Driving Scenarios
Elliot Weiss, Michael Thompson, Thomas Lew +1
Real-world collision avoidance is a core motivation for studying the dynamics and control of high sideslip drifting in vehicles, yet the practical benefit of such maneuvers has so…
Cyber Racing Coach: A Haptic Shared Control Framework for Teaching Advanced Driving Skills
Congkai Shen, Siyuan Yu, Yifan Weng +7
This study introduces a haptic shared control framework designed to teach human drivers advanced driving skills. In this context, shared control refers to a driving mode where the…
Spatial Envelope MPC: High Performance Driving without a Reference
Siyuan Yu, Congkai Shen, Yufei Xi +5
This paper presents a novel envelope based model predictive control (MPC) framework designed to enable autonomous vehicles to handle high performance driving across a wide range of…
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…