138 citations · 153 across the 6 of their papers we have counts for
8 papers
The Waymo Open Sim Agents Challenge
Nico Montali, John Lambert, Paul Mougin +9
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 (WOS…
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving
Eli Bronstein, Mark Palatucci, Dominik Notz +14
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…
Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation
Maximilian Igl, Daewoo Kim, Alex Kuefler +7
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 car…
Depth by Poking: Learning to Estimate Depth from Self-Supervised Grasping
Ben Goodrich, Alex Kuefler, William D. Richards
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 tran…
Modeling Human Driving Behavior through Generative Adversarial Imitation Learning
Raunak Bhattacharyya, Blake Wulfe, Derek Phillips +4
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…
Multi-Agent Imitation Learning for Driving Simulation
Raunak P. Bhattacharyya, Derek J. Phillips, Blake Wulfe +3
Simulation is an appealing option for validating the safety of autonomous vehicles. Generative Adversarial Imitation Learning (GAIL) has recently been shown to learn representative…