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 control in adverse conditions or avoid collisions. We investigate real-time drifting strategies that put the car where needed while bypassing expensive trajectory optimization. To this end, we design a reinforcement learning agent that builds on the concept of tire energy absorption to autonomously drift through changing and complex waypoint configurations while safely staying within track bounds. We achieve zero-shot deployment on the car by training the agent in a simulation environment built on top of a neural stochastic differential equation vehicle model learned from pre-collected driving data. Experiments on a Toyota GR Supra and Lexus LC 500 show that the agent is capable of drifting smoothly through varying waypoint configurations with tracking error as low as 10 cm while stably pushing the vehicles to sideslip angles of up to 63°.

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 control at the limits of handling suffers when the underlying model overestimates the vehicle's performance capabilities. In this work, we propose a risk-averse MPC framework that explicitly accounts for uncertainty over friction limits and tire parameters. Our approach leverages a sample-based approximation of an optimal control problem with a conditional value at risk (CVaR) constraint. This sample-based formulation enables planning with a set of expressive vehicle dynamics models using different tire parameters. Moreover, this formulation enables efficient numerical resolution via sequential quadratic programming and GPU parallelization. Experiments on a Lexus LC 500 show that risk-averse MPC unlocks reliable performance, while a deterministic baseline that plans using a single dynamics model may lose control of the vehicle in adverse road conditions.

Vehicle mobility and control performance on deformable terrains is governed by the complex interaction that occurs at the tire-terrain interface. Unfortunately, on deformable terrains, accurately measuring terrain information is challenging, and discrepancies between assumed and actual parameters can degrade control performance and cause a loss of vehicle mobility. To address these challenges, this paper proposes an online adaptive Model Predictive Control (MPC) framework for autonomous vehicles operating in off-road environments with deformable terrains. First, we develop a physics-informed learning tire model for deformable terrains that is adaptable online and compatible with MPC. A novel Model Predictive Control formulation is presented for autonomous vehicles operating on deformable terrains and the efficacy of the formulation and proposed tire model is evaluated in simulation with Project Chrono. Comparative experiments, with and without online adaptation, highlight improved speed and path tracking performance through online adaptation when a mismatch between assumed and actual terrain parameters is present.

In this paper, a control barrier function (CBF) is constructed for the lane-keeping problem which is applicable to both human-driven and automated vehicles. Based on the resulting CBF, a safety filter is developed that prevents the vehicle from crossing the lane boundaries, while only modifying the nominal steering input when necessary. The effectiveness of the proposed control approach is demonstrated in a series of numerical simulations and real vehicle experiments with a human driver. The experimental results show that the safety filter can successfully keep the vehicle inside the lane boundaries by seamlessly modifying the steering input of the human driver in a minimally invasive manner.