Mykel J. Kochenderfer
Stanford University
499 Papers
1.6K Citations
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. Previous affiliations of Mykel J. Kochenderfer include Massachusetts Institute of Technology & University of Edinburgh.
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Papers
•Posted Content
Modeling Human Driving Behavior through Generative Adversarial Imitation Learning.
Raunak P. Bhattacharyya,Blake Wulfe,Derek J. Phillips,Alex Kuefler,Jeremy Morton,Ransalu Senanayake,Mykel J. Kochenderfer +6 more
TL;DR: Experiments show that modifications to GAIL can successfully model highway driving behavior, accurately replicating human demonstrations and generating realistic, emergent behavior in the traffic flow arising from the interaction between driving agents.
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Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic
Maxime Bouton,Alireza Nakhaei,Kikuo Fujimura,Mykel J. Kochenderfer +3 more
- 01 Oct 2019
TL;DR: In this article, a reinforcement learning approach is presented to learn how to interact with drivers with different cooperation levels in a dense merging scenario with less deadlocks than with online planning methods.
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Uncorrelated Encounter Model of the National Airspace System, Version 2.0
Andrew Weinert,E Harkleroad,J D Griffith,Matthew W. M. Edwards,Mykel J. Kochenderfer +4 more
- 19 Aug 2013
TL;DR: The model is based on the use of Bayesian networks to represent relationships between dynamic variables and to construct random aircraft trajectories that are statistically similar to those observed in the radar data and is a framework from which representative intruder trajectories can be generated and used in fast-time Monte-Carlo simulations to provide accurate estimates of collision risk.
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Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace From Position Data
TL;DR: This paper develops a method for learning a probabilistic generative model of aircraft motion in terminal airspace, the controlled airspace surrounding a given airport, and finds that the model generates realistic trajectories, provides accurate predictions, and captures the statistical properties of the aircraft trajectories.
Optimizing the Next Generation Collision Avoidance System for Safe, Suitable, and Acceptable Operational Performance
TL;DR: An iterative tuning process reduced the operational impact on the air traffic system and improved acceptability of alerts, and a 15-month effort that resulted in substantial improvements are summarized.
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