Search Results for author: Mykel Kochenderfer

Found 24 papers, 5 papers with code

Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networks

no code implementations17 Oct 2021 Shushman Choudhury, Kiril Solovey, Mykel Kochenderfer, Marco Pavone

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.

Multi-Agent Path Finding

A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering

no code implementations29 Aug 2021 Raunak Bhattacharyya, Soyeon Jung, Liam Kruse, Ransalu Senanayake, Mykel Kochenderfer

While the rules are governed by interpretable parameters of the driver model, these parameters are learned online from driving demonstration data using particle filtering.

Autonomous Vehicles

Portfolio Construction as Linearly Constrained Separable Optimization

2 code implementations9 Mar 2021 Nicholas Moehle, Jack Gindi, Stephen Boyd, Mykel Kochenderfer

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

no code implementations24 Dec 2020 Geoffrey Pettet, Ayan Mukhopadhyay, Mykel Kochenderfer, Abhishek Dubey

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

no code implementations3 Dec 2020 Kyle Hatch, John Mern, Mykel Kochenderfer

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

no code implementations NeurIPS 2020 Jiaxuan You, Xiaobai Ma, Daisy Yi Ding, Mykel Kochenderfer, Jure Leskovec

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.

Graph Representation Learning Imputation

Designing Emergency Response Pipelines : Lessons and Challenges

no code implementations15 Oct 2020 Ayan Mukhopadhyay, Geoffrey Pettet, Mykel Kochenderfer, Abhishek Dubey

Emergency response to incidents such as accidents, crimes, and fires is a major problem faced by communities.

Uncertainty Aware Wildfire Management

no code implementations15 Oct 2020 Tina Diao, Samriddhi Singla, Ayan Mukhopadhyay, Ahmed Eldawy, Ross Shachter, Mykel Kochenderfer

A major problem in using data-driven models to combat wildfires is the lack of comprehensive data sources that relate fires with relevant covariates.

A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management

no code implementations7 Jun 2020 Ayan Mukhopadhyay, Geoffrey Pettet, Sayyed Vazirizade, Di Lu, Said El Said, Alex Jaimes, Hiba Baroud, Yevgeniy Vorobeychik, Mykel Kochenderfer, Abhishek Dubey

In the last fifty years, researchers have developed statistical, data-driven, analytical, and algorithmic approaches for designing and improving emergency response management (ERM) systems.

Decision Making Decision Making Under Uncertainty

Improving Automated Driving through Planning with Human Internal States

no code implementations28 May 2020 Zachary Sunberg, Mykel Kochenderfer

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

no code implementations6 May 2020 Raunak Bhattacharyya, Ransalu Senanayake, Kyle Brown, Mykel Kochenderfer

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.

Autonomous Vehicles

Learning Near Optimal Policies with Low Inherent Bellman Error

no code implementations ICML 2020 Andrea Zanette, Alessandro Lazaric, Mykel Kochenderfer, Emma Brunskill

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

no code implementations21 Jan 2020 Geoffrey Pettet, Ayan Mukhopadhyay, Mykel Kochenderfer, Yevgeniy Vorobeychik, Abhishek Dubey

This is not a trivial planning problem --- a major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem.

Decision Making Decision Making Under Uncertainty

Optimizing Collision Avoidance in Dense Airspace using Deep Reinforcement Learning

no code implementations20 Dec 2019 Sheng Li, Maxim Egorov, Mykel Kochenderfer

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

no code implementations16 Jul 2019 Mark Koren, Mykel Kochenderfer

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

no code implementations7 May 2019 John Mern, Dorsa Sadigh, Mykel Kochenderfer

Although deep reinforcement learning has advanced significantly over the past several years, sample efficiency remains a major challenge.

Policy Gradient Methods

Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace from Position Data

1 code implementation22 Oct 2018 Shane Barratt, Mykel Kochenderfer, Stephen Boyd

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

no code implementations20 Feb 2018 John Mern, Jayesh K. Gupta, Mykel Kochenderfer

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

no code implementations18 Jan 2018 Lindsey Kuper, Guy Katz, Justin Gottschlich, Kyle Julian, Clark Barrett, Mykel Kochenderfer

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.

Autonomous Driving

Online algorithms for POMDPs with continuous state, action, and observation spaces

5 code implementations18 Sep 2017 Zachary Sunberg, Mykel Kochenderfer

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

5 code implementations3 Feb 2017 Guy Katz, Clark Barrett, David Dill, Kyle Julian, Mykel Kochenderfer

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

no code implementations2 Feb 2017 Zachary Sunberg, Christopher Ho, Mykel Kochenderfer

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.

Autonomous Vehicles

Imitating Driver Behavior with Generative Adversarial Networks

1 code implementation24 Jan 2017 Alex Kuefler, Jeremy Morton, Tim Wheeler, Mykel Kochenderfer

The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems.

Imitation Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.