M
Mykel J. Kochenderfer
Researcher at Stanford University
Publications - 449
Citations - 12534
Mykel J. Kochenderfer is an academic researcher from Stanford University. The author has contributed to research in topics: Computer science & Markov decision process. The author has an hindex of 41, co-authored 388 publications receiving 8215 citations. Previous affiliations of Mykel J. Kochenderfer include Massachusetts Institute of Technology & University of Edinburgh.
Papers
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Proceedings ArticleDOI
Infrastructure-Enabled Autonomy: An Attention Mechanism for Occlusion Handling
TL;DR: This work proposes a framework that integrates infrastructure-to-vehicle communication in autonomous vehicle decision making, improving operational safety and mobility in challenging environments and demonstrates the value of infrastructure communication through a series of experiments.
Posted Content
Verification of Image-based Neural Network Controllers Using Generative Models
TL;DR: In this article, the authors propose to use GANs to map states to plausible input images to provide safety guarantees for an image-based neural network controller for an autonomous aircraft taxi problem.
Posted Content
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM
Kunal Menda,Jean de Becdelièvre,Jayesh K. Gupta,Ilan Kroo,Mykel J. Kochenderfer,Zachary Manchester +5 more
TL;DR: In this article, the authors formulate certainty-equivalent expectation-maximization as block coordinate-ascent, and provide an efficient implementation for offline identification of partially observed nonlinear systems.
Journal ArticleDOI
Collision Risk and Operational Impact of Speed Change Advisories as Aircraft Collision Avoidance Maneuvers
Sydney M. Katz,Luis E. Alvarez,Michael P. Owen,Samuel Wu,Marc Brittain,Anshuman Das,Mykel J. Kochenderfer +6 more
TL;DR: In this article , the authors investigate the effect of speed change advisories on the safety and operational capabilities of collision avoidance systems and develop an MDP-based collision avoidance logic that issues speed advisories and compare its performance to that of horizontal and vertical logics through Monte Carlo simulation on existing airspace encounter models.
Proceedings Article
Robust Spatial-Temporal Incident Prediction
Ayan Mukhopadhyay,Kai Wang,Andrew Perrault,Mykel J. Kochenderfer,Milind Tambe,Yevgeniy Vorobeychik +5 more
TL;DR: A general approach for incident forecasting that is robust to spatial shifts, and proposes two techniques for solving the resulting robust optimization problem: first, a constraint generation method guaranteed to yield an optimal solution, and second, a more scalable gradientbased approach.