arXiv research

A new framework uses deep reinforcement learning to improve aircraft separation in busy airspace.

Robot science discovers new materials faster.

In this paper we look at ergodic BSDEs in the case where the forward dynamics are given by the solution to a non-autonomous (time-periodic coefficients) Ornstein-Uhlenbeck SDE with Lévy noise, taking values in a separable Hilbert space. We establish the existence of a unique bounded solution to an infinite horizon disc…

The study improves deep learning models for safer autonomous vehicles.

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

A reliable controller is critical and essential for the execution of safe and smooth maneuvers of an autonomous vehicle.The controller must be robust to external disturbances, such as road surface, weather, and wind conditions, and so on.It also needs to deal with the internal parametric variations of vehicle sub-syste…

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

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

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

We enhance autonomous materials research with problem-aware models.

The paper explores new risk models for autonomous driving.

This paper investigates the autonomous control of massive unmanned aerial vehicles (UAVs) for mission-critical applications (e.g., dispatching many UAVs from a source to a destination for firefighting). Achieving their fast travel and low motion energy without inter-UAV collision under wind perturbation is a daunting c…

We prove that the autonomous norm on the group of Hamiltonian diffeomorphisms of the two-dimensional torus is unbounded. We provide explicit examples of Hamiltonian diffeomorphisms with arbitrarily large autonomous norm. For the proofs we construct quasimorphisms on $Ham(T^2)$ and some of them are Calabi.

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

Adaptive framework generates challenging adversarial scenarios for autonomous vehicles.

Autonomous Vehicles(AV) are one of the brightest promises of the future which would help cut down fatalities and improve travel time while working in harmony. Autonomous vehicles will face with challenging situations and experiences not seen before. These experiences should be converted to knowledge and help the vehicl…

This is a continuation of the work initiated in a previous paper on so-called driven cofactor systems, which are partially decoupling second-order differential equations of a special kind. The main purpose in that paper was to obtain an intrinsic, geometrical characterization of such systems, and to explain the basic u…

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

ApolloRL offers a platform for RL research in autonomous driving.

Autonomous vehicles rely on machine learning to solve challenging tasks in perception and motion planning. However, automotive software safety standards have not fully evolved to address the challenges of machine learning safety such as interpretability, verification, and performance limitations. In this paper, we revi…

No policy can simultaneously be fully autonomous, optimally calibrated, and helpful, proving a trilemma.

A machine learning environment for detecting autonomous vehicle corner cases.

New approach uses dynamic programming to efficiently discover failures in autonomous vehicle simulations.

Air traffic control is a real-time safety-critical decision making process in highly dynamic and stochastic environments. In today's aviation practice, a human air traffic controller monitors and directs many aircraft flying through its designated airspace sector. With the fast growing air traffic complexity in traditi…

Real-time semantic segmentation for autonomous vehicles on FPGA reduces latency and power consumption.

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

Gemini uses inexpensive measurements to correct biases in expensive property evaluations.

Unmanned Aerial Vehicles (UAVs), autonomously-guided aircraft, are widely used for tasks involving surveillance and reconnaissance. A version of the pursuit-evasion problems centered around UAVs and its variants has been extensively studied in recent years due to numerous breakthroughs in AI. We present an approach to …

Safe autonomous decisions made with machine learning predictions using Conformal Decision Theory.

This work improves autonomous racing by creating diverse opponents and adapting risk.

AI mirrors modern math's autonomous development, raising interpretive challenges.

Accurately tracking and predicting behaviors of surrounding objects are key prerequisites for intelligent systems such as autonomous vehicles to achieve safe and high-quality decision making and motion planning. However, there still remain challenges for multi-target tracking due to object number fluctuation and occlus…

OBSER framework infers sub-environments from objects, outperforming scene-based methods.

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…

The paper contains a geometrization of the autonomous multi-time Lagrangian function of electrodynamics. We point out that this multi-time Lagrangian function comes from electrodynamics and the theory of bosonic strings.

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

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

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…

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…

In a mixed-traffic scenario where both autonomous vehicles and human-driving vehicles exist, a timely prediction of driving intentions of nearby human-driving vehicles is essential for the safe and efficient driving of an autonomous vehicle. In this paper, a driving intention prediction method based on Hidden Markov Mo…

LLMs can help explain credit risk models but not autonomously.

Let $D^2$ be the open unit disc in the Euclidean plane and let $G:= Diff(D2; area)$ be the group of smooth compactly supported area-preserving diffeomorphisms of $D^2$. We investigate the properties of G endowed with the autonomous metric. In particular, we construct a bi-Lipschitz homomorphism $Z^k \rightarrow G$ of a…

Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes. Despite its perceived utility, it has not yet been successfully applied in automotive applications. Motivated by the successful demonstrations of…

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

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…

SECRM-2D improves RL-based autonomous driving with safety guarantees.

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…

New model enables AI to learn autonomously.