9 citations · 13 across the 6 of their papers we have counts for
6 papers
Hierarchical Imitation Learning for Stochastic Environments
Maximilian Igl, Punit Shah, Paul Mougin +5
Many applications of imitation learning require the agent to generate the full distribution of behaviour observed in the training data. For example, to evaluate the safety of auton…
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…
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios
Yiren Lu, Justin Fu, George Tucker +9
Imitation learning (IL) is a simple and powerful way to use high-quality human driving data, which can be collected at scale, to produce human-like behavior. However, policies base…
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…
StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving
Jinkyu Kim, Reza Mahjourian, Scott Ettinger +4
We introduce a motion forecasting (behavior prediction) method that meets the latency requirements for autonomous driving in dense urban environments without sacrificing accuracy.…
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…