PRECOG: PREdiction Conditioned on Goals in Visual Multi Agent Settings
Abstract
For autonomous vehicles (AVs) to behave appropriately on roads populated by human-driven vehicles, they must be able to reason about the uncertain intentions and decisions of other drivers from rich perceptual information. Towards these capabilities, we present a probabilistic forecasting model of future interactions between a variable number of agents. We perform both standard forecasting and the novel task of conditional forecasting, which reasons about how all agents will likely respond to the goal of a controlled agent (here, the AV). We train models on real and simulated data to forecast vehicle trajectories given past positions and LI-DAR. Our evaluation shows that our model is substantially more accurate in multi-agent driving scenarios compared to existing state-of-the-art. Beyond its general ability to perform conditional forecasting queries, we show that our models predictions of all agents improve when conditioned on knowledge of the AVs goal, further illustrating its capability to model agent interactions.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 27, 2019
- Accession Number
- AD1151905
Entities
People
- Kris Kitani
- Nicholas Rhinehart
- Rowan McAllister
- Sergey Levine
Organizations
- Carnegie Mellon University
- University of California, Berkeley