A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
In this paper, we first give the regular point reduction and the two types of Hamilton-Jacobi equation for a regular controlled Hamiltonian (RCH) system with symmetry and momentum map on the generalization of a semidirect product Lie group. Next, as an application of the theoretical results, we consider the underwater …
MC-pix2pix generates high-quality synthetic sonar data for ATR systems.
Noncritical soft-faults and model deviations are a challenge for Fault Detection and Diagnosis (FDD) of resident Autonomous Underwater Vehicles (AUVs). Such systems may have a faster performance degradation due to the permanent exposure to the marine environment, and constant monitoring of component conditions is requi…
Optimizes UUV hull design with a two-orders-of-magnitude speedup.
Study uses AUVs and RL to map river plumes over multiple days.
Paper presents a new port-Hamiltonian model for vehicle manipulators.
This paper investigates trajectory tracking problem for a class of underactuated autonomous underwater vehicles (AUVs) with unknown dynamics and constrained inputs. Different from existing policy gradient methods which employ single actor-critic but cannot realize satisfactory tracking control accuracy and stable learn…
New mortgage contracts reduce underwater default by adjusting loan balances, but must balance prepayment incentives.
The paper tackles data-driven optimal control of unknown nonlinear systems using RKHS.
Underwater gas reservoirs are used in many situations. In particular, Carbon Capture and Storage (CCS) facilities that are currently being developed intend to store greenhouse gases inside geological formations in the deep sea. In these formations, however, the gas might percolate, leaking back to the water and eventua…
Deep learning detects schools of herring from echograms.
Current auto loans converge to super-prime credit despite remaining underwater.
The paper develops sampling methods for ocean phenomena based on temperature and salinity measurements.
We present a method for Temporal Difference (TD) learning that addresses several challenges faced by robots learning to navigate in a marine environment. For improved data efficiency, our method reduces TD updates to Gaussian Process regression. To make predictions amenable to online settings, we introduce a sparse app…
How to self-localize large teams of underwater nodes using only noisy range measurements? How to do it in a distributed way, and incorporating dynamics into the problem? How to reject outliers and produce trustworthy position estimates? The stringent acoustic communication channel and the accuracy needs of our geophysi…
Framework improves marine mammal monitoring in noisy underwater environments.
A Deep Zero-Inflated Model for Detecting North Atlantic Right Whale Presence
Rapid growth in delivery and freight transportation is increasing in urban areas; as a result the use of delivery trucks and light commercial vehicles is evolving. Major cities can use traffic counting as a tool to monitor the presence of delivery vehicles in order to implement intelligent city planning measures. Class…
Adaptive framework generates challenging adversarial scenarios for autonomous vehicles.
VTrackIt creates a synthetic dataset with infrastructure and vehicle info for AVs.
In order to maximize detection precision rate as well as the recall rate, this paper proposes an in-vehicle multi-source fusion scheme in Keyword Spotting (KWS) System for vehicle applications. Vehicle information, as a new source for the original system, is collected by an in-vehicle data acquisition platform while th…
Proposes a privacy-preserving system for federated learning of road networks.
Generative model predicts vehicle faults up to 1000 hours in advance.
Generative model learns vehicle trajectory distributions for better data generalization.
Proposes CTSDG model for better vehicle intention prediction across domains.
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…
The paper reviews machine learning safety techniques for autonomous vehicles.
New approach uses dynamic programming to efficiently discover failures in autonomous vehicle simulations.
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…
Proposes a learned Bayesian Cramér-Rao bound for unknown measurement models.
This paper explores the capability of deep neural networks to capture key characteristics of vehicle dynamics, and their ability to perform coupled longitudinal and lateral control of a vehicle. To this extent, two different artificial neural networks are trained to compute vehicle controls corresponding to a reference…
This paper presents a method for testing the decision making systems of autonomous vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment until the vehicle is involved in a collision. Instead of applying direct Monte Carlo sampling to find collision scenarios, we formulate the probl…
This paper concerns automated vehicles negotiating with other vehicles, typically human driven, in crossings with the goal to find a decision algorithm by learning typical behaviors of other vehicles. The vehicle observes distance and speed of vehicles on the intersecting road and use a policy that adapts its speed alo…
A novel observer-based method detects and recovers anomalies in CAV sensor readings.
Predict real-time crash risks during hurricane evacuations using connected vehicle data.
This paper compares machine learning methods for recognizing lane change intentions from vehicle trajectories.
A tree-based IDS detects cyber-attacks in AV networks.
An active area of research is to increase the safety of self-driving vehicles. Although safety cannot be guarenteed completely, the capability of a vehicle to predict the future trajectories of its surrounding vehicles could help ensure this notion of safety to a greater deal. We cast the trajectory forecast problem in…
Deep learning predicts vehicle behavior for safer autonomous driving.
A DRL-based strategy improves vehicle tracking accuracy while saving energy.
Deep learning improves vehicle control performance and generalizes well.
Neural LNS improves vehicle routing performance.
Deep Q-learning optimizes same-day delivery with vehicles and drones.
Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles. The majority of current researches fix the number of driving intentions by considering only a specific scenario. However, distinct driving environments usually contain various possible driving…
New approach estimates vehicle and component prices without teardowns.
Autonomous vehicles are expected to navigate in complex traffic scenarios with multiple surrounding vehicles. The correlations between road users vary over time, the degree of which, in theory, could be infinitely large, thus posing a great challenge in modeling and predicting the driving environment. In this paper, we…
New machine learning pipeline solves dynamic vehicle routing problems efficiently.
Vehicle recognition and classification have broad applications, ranging from traffic flow management to military target identification. We demonstrate an unsupervised method for automated identification of moving vehicles from roadside audio sensors. Using a short-time Fourier transform to decompose audio signals, we t…