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.

Machine learning models play a key role in safety-critical applications, such as autonomous vehicles and advanced driver assistance systems, where their robustness during inference is essential to ensure reliable operation. Sensor faults, however, can corrupt input signals, potentially leading to severe model failures that compromise reliability. In this context, pretraining emerges as a powerful approach for learning expressive representations applicable to various downstream tasks. Among existing techniques, masking represents a promising direction for learning representations that are robust to corrupted input data. In this work, we extend this concept by specifically targeting robustness to sensor outages during pretraining. We propose a self-supervised masking scheme that simulates common sensor failures and explicitly trains the model to recover the original signal. We demonstrate that the resulting representations significantly improve the robustness of predictions to seen and unseen sensor failures on a vehicle dynamics dataset, maintaining strong downstream performance under both nominal and various fault conditions. As a practical application, we deploy the method on a modified Lexus LC 500 and show that the pretrained model successfully operates as a substitute for a physical sensor in a closed-loop control system. In this autonomous racing application, a supervised baseline trained without sensor failures may cause the vehicle to leave the track. In contrast, a model trained using the proposed masking scheme enables reliable racing performance in the presence of sensor failures.

This paper offers a practical method for certifiably safe operations of an unmanned aerial vehicle (UAV) with limited power and computation, useful for real-time operations where the UAV is exposed to significant disturbances in non-convex free space. We propose a motion planning method based on the Explicit Reference Governor (ERG) framework to ensure the safety of a flying quadrotor UAV. From a small set of experiment data and assumptions on modeling errors, a Lyapunov function is synthesized by which an ERG is constructed to modify the UAV set-points. The method can handle polyhedral obstacles and constraints imposed on the maximum thrust of the UAV and its maximum tilt. We demonstrate the approach with extensive simulations and experiments using a Crazyflie 2.1.

We propose a method for joint estimation of a host vehicle state and a map of the road based on global navigation satellite system (GNSS) and camera measurements. We model the road using a spline representation described by a parameter vector having a Gaussian prior representing the uncertainty of the prior map. Both GNSS and camera measurements, such as lane-mark measurements, have noise characteristics that vary in time. To adapt to the changing noise levels and hence improve positioning performance, we combine the sensor information in an interacting multiple-model (IMM) setting to choose the best combination of the estimators with the vehicle state and the parameter vector of the map as the state vector. In a simulation study, we compare vehicle models with varying complexity, and on a real road segment we show that the proposed method can accurately adjust to changing noise conditions and correct for errors in the prior map.

Novel methods for the inference of radiation intensity functions defined over known surfaces are proposed, intended for use in surveying applications with mobile spectrometers. Previous approaches, based on the maximum likelihood expectation maximization (ML-EM) framework with Poisson likelihoods, are extended to better handle spatially continuous intensity statistics using ideas from Gaussian filtering. The resulting algorithm is evaluated against a classical ML-EM method, and a recently proposed sparse additive point source localization (APSL) algorithm in a Monte-Carlo simulation study. The new generalized ASPL (GASPL) is shown to compare favorably in terms of estimation accuracy when the true intensity is not well described by a set of point sources. Finally, the GASPL is used in an experiment where a detector is mounted to an unmanned aerial vehicle to estimate the intensity and location of radioactive sources placed in a meadow.

We consider the problem of feasible coordination control for multiple homogeneous or heterogeneous mobile vehicles subject to various constraints (nonholonomic motion constraints, holonomic coordination constraints, equality/inequality constraints etc). We develop a general framework involving differential-algebraic equations and viability theory to describe and determine coordination feasibility for a coordinated motion control under heterogeneous vehicle dynamics and various constraints. A heuristic algorithm is proposed for generating feasible trajectories for each individual vehicle. We show several application examples and simulation experiments on multi-vehicle coordination under various constraints to validate the theory and the effectiveness of the proposed algorithm and control schemes.