arXiv research

This paper explores formal verification for autonomous systems, identifying limitations and proposing improvements.

Generalizes energy-momentum method for non-autonomous Hamiltonian systems.

Proves rigidity of 3D partially hyperbolic systems via autonomous dynamics.

The study explores autonomous systems and their connections to contact geometry and Frobenius manifolds.

The study improves deep learning models for safer autonomous vehicles.

This work improves safety validation of autonomous vehicles by finding interpretable failures.

Bayesian inference models failure distributions in autonomous systems.

Develops a method to simulate rare dangerous events in autonomous systems.

The paper extends conformal prediction to MDP trajectories for autonomous systems.

This paper presents a novel approach for automatic rule learning applicable to an autonomous driving system using real driving data.

The Surprise index assesses autonomous systems' competency in uncertain environments.

End-to-end autonomous driving perception learns latent features for better performance.

New model enables AI to learn autonomously.

Study learns dynamics of linear systems from multiple short trajectories.

Let $dx_i/dt=f_i(x_1,\cdots,x_n)$, ($i=1,\cdots,n$) be a system of $n$ first order autonomous ordinary differential equations. We use E. Cartan's equivalence method to study the invariants of this system under diffeomorphisms of the form $Φ(t,x_1,\cdots,x_n)=(\varphi_0(t),\varphi_1(x_1),\cdots,\varphi_1(x_1))$.

Studies geometric mechanics for autonomous and nonautonomous systems.

Deep neural networks (DNNs) are found to be vulnerable against adversarial examples, which are carefully crafted inputs with a small magnitude of perturbation aiming to induce arbitrarily incorrect predictions. Recent studies show that adversarial examples can pose a threat to real-world security-critical applications:…

Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye's path planning system. However, a vast majority of work on DRL is focused on toy examples in controlled synthe…

We develop a new method to estimate failure probabilities in complex systems.

Bayesian optimization identifies optimal alloy formulations.

A method uses non-autonomous equations to classify time signals efficiently.

This paper improves parameter estimation for autonomous systems with unmodeled dynamics.

Future autonomous systems need reliable world models and complex action sequences.

Study connects symmetries in dynamical systems to phase plane representations.

Highly Autonomous Driving (HAD) systems rely on deep neural networks for the visual perception of the driving environment. Such networks are trained on large manually annotated databases. In this work, a semi-parametric approach to one-shot learning is proposed, with the aim of bypassing the manual annotation step requ…

Study identifies and validates a method for system identification of Markov jump linear systems.

BagCert efficiently certifies robustness against adversarial patches on image classifiers.

VerifAI toolkit improves neural network-based aircraft taxiing system safety.

Bayesian reflex models AI learning like the autonomic nervous system.

Determining possible failure scenarios is a critical step in the evaluation of autonomous vehicle systems. Real-world vehicle testing is commonly employed for autonomous vehicle validation, but the costs and time requirements are high. Consequently, simulation-driven methods such as Adaptive Stress Testing (AST) have b…

A Lie system is the non-autonomous system of differential equations describing the integral curves of a non-autonomous vector field taking values in a finite-dimensional Lie algebra of vector fields, a so-called Vessiot--Guldberg Lie algebra. This work pioneers the analysis of Lie systems admitting a Vessiot--Guldberg …

While recent developments in autonomous vehicle (AV) technology highlight substantial progress, we lack tools for rigorous and scalable testing. Real-world testing, the $\textit{de facto}$ evaluation environment, places the public in danger, and, due to the rare nature of accidents, will require billions of miles in or…

While machine learning systems show high success rate in many complex tasks, research shows they can also fail in very unexpected situations. Rise of machine learning products in safety-critical industries cause an increase in attention in evaluating model robustness and estimating failure probability in machine learni…

This work develops agents to learn generalizable policies for dynamic network environments.

Reduces multisymplectic Lie systems through symmetry analysis.

Robot science discovers new materials faster.

Proposes a method to improve pWCET estimation for heavy-tailed distributions.

The inverse problem of the calculus of variations consists in determining if the solutions of a given system of second order differential equations correspond with the solutions of the Euler-Lagrange equations for some regular Lagrangian. This problem in the general version remains unsolved. Here, we contribute to it w…

To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the environment. Existing literature in robust reinforcement learning poses the learning problem as a two player game between the autonomous system…

Robots learn new tasks autonomously with minimal human intervention.

RL policy tracks dynamic targets in partially known environments robustly.

The capability to learn and adapt to changes in the driving environment is crucial for developing autonomous driving systems that are scalable beyond geo-fenced operational design domains. Deep Reinforcement Learning (RL) provides a promising and scalable framework for developing adaptive learning based solutions. Deep…

DeepRacing uses neural networks to predict trajectories for autonomous racing in video games.

A machine learning environment for detecting autonomous vehicle corner cases.

CalNF models rare failures with limited data, improving safety in autonomous systems.

Review of automation's role in chemical discovery, emphasizing future challenges.

Nowadays autonomous technologies are a very heavily explored area and particularly computer vision as the main component of vehicle perception. The quality of the whole vision system based on neural networks relies on the dataset it was trained on. It is extremely difficult to find traffic sign datasets from most of th…

The aim of this paper is to give an explicit formula in order to compute the Maslov index of the fundamental solution of a linear autonomous Hamiltonian system, in terms of the Conley-Zehnder index and the time one flow.