Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Kuefler, Alex"'
Autor:
Montali, Nico, Lambert, John, Mougin, Paul, Kuefler, Alex, Rhinehart, Nick, Li, Michelle, Gulino, Cole, Emrich, Tristan, Yang, Zoey, Whiteson, Shimon, White, Brandyn, Anguelov, Dragomir
Simulation with realistic, interactive agents represents a key task for autonomous vehicle software development. In this work, we introduce the Waymo Open Sim Agents Challenge (WOSAC). WOSAC is the first public challenge to tackle this task and propo
Externí odkaz:
http://arxiv.org/abs/2305.12032
Autor:
Bronstein, Eli, Palatucci, Mark, Notz, Dominik, White, Brandyn, Kuefler, Alex, Lu, Yiren, Paul, Supratik, Nikdel, Payam, Mougin, Paul, Chen, Hongge, Fu, Justin, Abrams, Austin, Shah, Punit, Racah, Evan, Frenkel, Benjamin, Whiteson, Shimon, Anguelov, Dragomir
Publikováno v:
IEEE/RSJ international conference on intelligent robots and systems (IROS) 2022, pages 8652-8659
We demonstrate the first large-scale application of model-based generative adversarial imitation learning (MGAIL) to the task of dense urban self-driving. We augment standard MGAIL using a hierarchical model to enable generalization to arbitrary goal
Externí odkaz:
http://arxiv.org/abs/2210.09539
Autor:
Igl, Maximilian, Kim, Daewoo, Kuefler, Alex, Mougin, Paul, Shah, Punit, Shiarlis, Kyriacos, Anguelov, Dragomir, Palatucci, Mark, White, Brandyn, Whiteson, Shimon
Simulation is a crucial tool for accelerating the development of autonomous vehicles. Making simulation realistic requires models of the human road users who interact with such cars. Such models can be obtained by applying learning from demonstration
Externí odkaz:
http://arxiv.org/abs/2205.03195
Accurate depth estimation remains an open problem for robotic manipulation; even state of the art techniques including structured light and LiDAR sensors fail on reflective or transparent surfaces. We address this problem by training a neural network
Externí odkaz:
http://arxiv.org/abs/2006.08903
Autor:
Bhattacharyya, Raunak, Wulfe, Blake, Phillips, Derek, Kuefler, Alex, Morton, Jeremy, Senanayake, Ransalu, Kochenderfer, Mykel
An open problem in autonomous vehicle safety validation is building reliable models of human driving behavior in simulation. This work presents an approach to learn neural driving policies from real world driving demonstration data. We model human dr
Externí odkaz:
http://arxiv.org/abs/2006.06412
Autor:
Bhattacharyya, Raunak P., Phillips, Derek J., Wulfe, Blake, Morton, Jeremy, Kuefler, Alex, Kochenderfer, Mykel J.
Simulation is an appealing option for validating the safety of autonomous vehicles. Generative Adversarial Imitation Learning (GAIL) has recently been shown to learn representative human driver models. These human driver models were learned through t
Externí odkaz:
http://arxiv.org/abs/1803.01044
Autor:
Kuefler, Alex, Kochenderfer, Mykel J.
Recent work on imitation learning has generated policies that reproduce expert behavior from multi-modal data. However, past approaches have focused only on recreating a small number of distinct, expert maneuvers, or have relied on supervised learnin
Externí odkaz:
http://arxiv.org/abs/1710.05090
The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems. Traditional modeling methods have employed simple parametric models and behavioral cloning. This paper adopts
Externí odkaz:
http://arxiv.org/abs/1701.06699
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Bhattacharyya, Raunak, Wulfe, Blake, Phillips, Derek J., Kuefler, Alex, Morton, Jeremy, Senanayake, Ransalu, Kochenderfer, Mykel J.
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems; 2023, Vol. 24 Issue: 3 p2874-2887, 14p