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Found 29 papers, 8 papers with code
Hierarchical Framework for Optimizing Wildfire Surveillance and Suppression using Human-Autonomous Teaming
Initial attack surveillance and suppression models have linked action spaces and objectives, making their optimization computationally challenging.
Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning
The widespread adoption of commercial autonomous vehicles (AVs) and advanced driver assistance systems (ADAS) may largely depend on their acceptance by society, for which their perceived trustworthiness and interpretability to riders are crucial.
Interaction Modeling with Multiplex Attention
Modeling multi-agent systems requires understanding how agents interact.
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning
Characterizing aleatoric and epistemic uncertainty can be used to speed up learning in a training environment, improve generalization to similar testing environments, and flag unfamiliar behavior in anomalous testing environments.
A Light-Weight Multi-Objective Asynchronous Hyper-Parameter Optimizer
We describe a light-weight yet performant system for hyper-parameter optimization that approximately minimizes an overall scalar cost function that is obtained by combining multiple performance objectives using a target-priority-limit scalarizer.
Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networks
The second stage solves only for drones, by routing them over a composite of the road network and the transit network defined by truck paths from the first stage.
A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering
While the rules are governed by interpretable parameters of the driver model, these parameters are learned online from driving demonstration data using particle filtering.
Portfolio Construction as Linearly Constrained Separable Optimization
Mean-variance portfolio optimization problems often involve separable nonconvex terms, including penalties on capital gains, integer share constraints, and minimum position and trade sizes.
Portfolio Optimization
Optimization and Control
Portfolio Management
Hierarchical Planning for Resource Allocation in Emergency Response Systems
We use the emergency response as a case study and show how a large resource allocation problem can be split into smaller problems.
Obstacle Avoidance Using a Monocular Camera
In this work, we present an obstacle avoidance system for small UAVs that uses a monocular camera with a hybrid neural network and path planner controller.
Handling Missing Data with Graph Representation Learning
GRAPE tackles the missing data problem using a graph representation, where the observations and features are viewed as two types of nodes in a bipartite graph, and the observed feature values as edges.
Uncertainty Aware Wildfire Management
A major problem in using data-driven models to combat wildfires is the lack of comprehensive data sources that relate fires with relevant covariates.
Designing Emergency Response Pipelines : Lessons and Challenges
Emergency response to incidents such as accidents, crimes, and fires is a major problem faced by communities.
Modeling Human Driving Behavior through Generative Adversarial Imitation Learning
Imitation learning is an approach for generating intelligent behavior when the cost function is unknown or difficult to specify.
A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management
In the last fifty years, researchers have developed statistical, data-driven, analytical, and algorithmic approaches for designing and improving emergency response management (ERM) systems.
Improving Automated Driving through POMDP Planning with Human Internal States
This work examines the hypothesis that partially observable Markov decision process (POMDP) planning with human driver internal states can significantly improve both safety and efficiency in autonomous freeway driving.
Online Parameter Estimation for Human Driver Behavior Prediction
In this article, we show that online parameter estimation applied to the Intelligent Driver Model captures nuanced individual driving behavior while providing collision free trajectories.
Learning Near Optimal Policies with Low Inherent Bellman Error
This has two important consequences: 1) it shows that exploration is possible using only \emph{batch assumptions} with an algorithm that achieves the optimal statistical rate for the setting we consider, which is more general than prior work on low-rank MDPs 2) the lack of closedness (measured by the inherent Bellman error) is only amplified by $\sqrt{d_t}$ despite working in the online setting.
On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities
This is not a trivial planning problem --- a major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem.
Optimizing Collision Avoidance in Dense Airspace using Deep Reinforcement Learning
New methodologies will be needed to ensure the airspace remains safe and efficient as traffic densities rise to accommodate new unmanned operations.
Efficient Autonomy Validation in Simulation with Adaptive Stress Testing
During the development of autonomous systems such as driverless cars, it is important to characterize the scenarios that are most likely to result in failure.
Object Exchangeability in Reinforcement Learning: Extended Abstract
Although deep reinforcement learning has advanced significantly over the past several years, sample efficiency remains a major challenge.
Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace from Position Data
Models for predicting aircraft motion are an important component of modern aeronautical systems.
Layer-wise synapse optimization for implementing neural networks on general neuromorphic architectures
An optimal set of synapse weights may then be found for a given choice of ANN activation function and SNN neuron.
Toward Scalable Verification for Safety-Critical Deep Networks
The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability.
Online algorithms for POMDPs with continuous state, action, and observation spaces
Online solvers for partially observable Markov decision processes have been applied to problems with large discrete state spaces, but continuous state, action, and observation spaces remain a challenge.
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems.
The Value of Inferring the Internal State of Traffic Participants for Autonomous Freeway Driving
This research uses a simple model for human behavior with unknown parameters that make up the internal states of the traffic participants and presents a method for quantifying the value of estimating these states and planning with their uncertainty explicitly modeled.
Imitating Driver Behavior with Generative Adversarial Networks
The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems.
