Autonomous Vehicles

Deep Imitative Models for Flexible Inference, Planning, and Control

Imitation Learning (IL) is an appealing approach to learn desirable autonomous behavior. However, directing IL to achieve arbitrary goals is difficult. In contrast, planning-based algorithms use dynamics models and reward functions to achieve goals. …

PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings

Forecasting the motion of multiple interacting vehicles. When one is autonmous, conditioning on its goals helps better-predict the motions of other vehicles.

Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning

Autonomous vehicle (AV) software is typically composed of a pipeline of individual components, linking sensor inputs to motor outputs. Erroneous component outputs propagate downstream, hence safe AV software must consider the ultimate effect of each …