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Found 10 papers, 4 papers with code
Driver2vec: Driver Identification from Automotive Data
In this paper, we develop a deep learning architecture (Driver2vec) to map a short interval of driving data into an embedding space that represents the driver's behavior to assist in driver identification.
MASA: Motif-Aware State Assignment in Noisy Time Series Data
Such data can be interpreted as a sequence of states, where each state represents a prototype of system behavior.
Drive2Vec: Multiscale State-Space Embedding of Vehicular Sensor Data
With automobiles becoming increasingly reliant on sensors to perform various driving tasks, it is important to encode the relevant CAN bus sensor data in a way that captures the general state of the vehicle in a compact form.
Spectral Graph Wavelets for Structural Role Similarity in Networks
Nodes residing in different parts of a graph can have similar structural roles within their local network topology.
Learning Structural Node Embeddings Via Diffusion Wavelets
Nodes residing in different parts of a graph can have similar structural roles within their local network topology.
Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems
The goal of this paper is to present an end-to-end, data-driven framework to control Autonomous Mobility-on-Demand systems (AMoD, i. e. fleets of self-driving vehicles).
Robotics Multiagent Systems Systems and Control Applications
Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data
We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively.
Driver Identification Using Automobile Sensor Data from a Single Turn
In this paper, we propose a method to predict, from sensor data collected at a single turn, the identity of a driver out of a given set of individuals.
Network Inference via the Time-Varying Graphical Lasso
Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements.
Greedy Gaussian Segmentation of Multivariate Time Series
We consider the problem of breaking a multivariate (vector) time series into segments over which the data is well explained as independent samples from a Gaussian distribution.
Optimization and Control
