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.
Improved anomaly detection for launch vehicle propulsion systems using LSTM and statistical relabeling.
Study identifies 1,012 persistent wallet cohorts on Solana pump.fun, showing coordinated buying behavior.
Survival analysis of 832,941 Solana token launches shows a significant decline in graduation rate.
ChatGPT launch boosted AI-related crypto assets by 10.7% to 15.6%.
CDLF predicts product life-cycles in cold-start phases with high accuracy.
Study predicts success of crypto-tokens on Pump.fun platform.
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.
PHBench predicts Series A funding from Product Hunt launch signals with 7.8% accuracy.
Generative model predicts vehicle faults up to 1000 hours in advance.
Backtests of structured strategies lose much of their predictive power in live trading.
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…
This paper reviews Gaussian process-based multi-fidelity techniques for different fidelity relationships.
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…
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…
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.
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…
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…
The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures. However, AVs and Internet of Vehicles (IoV) are vulnerable to diffe…
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…
Modern vehicle fleets, e.g., for ridesharing platforms and taxi companies, can reduce passengers' waiting times by proactively dispatching vehicles to locations where pickup requests are anticipated in the future. Yet it is unclear how to best do this: optimal dispatching requires optimizing over several sources of unc…
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…
This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is prop…
New system assigns vehicles to routes for cost and energy efficiency.
In this paper we propose a novel observer-based method to improve the safety and security of connected and automated vehicle (CAV) transportation. The proposed method combines model-based signal filtering and anomaly detection methods. Specifically, we use adaptive extended Kalman filter (AEKF) to smooth sensor reading…
CitySim dataset captures vehicle trajectories for safety research.
Bayesian model for energy consumption helps electric vehicles navigate efficiently.
A machine learning environment for detecting autonomous vehicle corner cases.
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 method detects vehicles far from tunnel CCTV using AI.
This work improves safety validation of autonomous vehicles by finding interpretable failures.
JAMPR learns to solve complex VRP with time windows.
We experimentally achieve a 19% capacity gain per Watt of electrical supply power in a 12-span link by eliminating gain flattening filters and optimizing launch powers using machine learning by deep neural networks in a massively parallel fiber context.
Study uses vehicle trajectory data to predict traffic incidents on highways.
Automated road infrastructure mapping using connected vehicle data and deep learning.
We propose a method for recognizing moving vehicles, using data from roadside audio sensors. This problem has applications ranging widely, from traffic analysis to surveillance. We extract a frequency signature from the audio signal using a short-time Fourier transform, and treat each time window as an individual data …