Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving

no code implementations • 18 Oct 2022 • Eli Bronstein, Mark Palatucci, Dominik Notz, Brandyn White, Alex Kuefler, Yiren Lu, Supratik Paul, Payam Nikdel, Paul Mougin, Hongge Chen, Justin Fu, Austin Abrams, Punit Shah, Evan Racah, Benjamin Frenkel, Shimon Whiteson, Dragomir Anguelov

We demonstrate the first large-scale application of model-based generative adversarial imitation learning (MGAIL) to the task of dense urban self-driving.

Autonomous Driving Imitation Learning +1

Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation

no code implementations • 6 May 2022 • Maximilian Igl, Daewoo Kim, Alex Kuefler, Paul Mougin, Punit Shah, Kyriacos Shiarlis, Dragomir Anguelov, Mark Palatucci, Brandyn White, Shimon Whiteson

The beam search refines these policies on the fly by pruning branches that are unfavourably evaluated by a discriminator.

Autonomous Driving

Zero-shot Learning with Semantic Output Codes

no code implementations • NeurIPS 2009 • Mark Palatucci, Dean Pomerleau, Geoffrey E. Hinton, Tom M. Mitchell

To achieve this, we define the notion of a semantic output code classifier (SOC) which utilizes a knowledge base of semantic properties of $Y$ to extrapolate to novel classes.

Zero-Shot Learning