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@inproceedings{sadigh-rss16,
author = {Dorsa Sadigh and Shankar Sastry and Sanjit A. Seshia and Anca D. Dragan},
title = {Planning for Autonomous Cars that Leverages Effects on Human Actions},
booktitle = {Proceedings of the Robotics: Science and Systems Conference (RSS)},
month = "June",
year = {2016},
OPTpages = {66--73},
abstract = {Traditionally, autonomous cars make predictions
about other drivers' future trajectories, and plan to
stay out of their way. This tends to result in defensive and
opaque behaviors. Our key insight is that an autonomous
car's actions will actually affect what other cars will do in
response, whether the car is aware of it or not. Our thesis is
that we can leverage these responses to plan more efficient
and communicative behaviors. We model the interaction
between an autonomous car and a human driver as a dynamical
system, in which the robot's actions have immediate
consequences on the state of the car, but also on human
actions. We model these consequences by approximating the
human as an optimal planner, with a reward function that
we acquire through Inverse Reinforcement Learning. When
the robot plans with this reward function in this dynamical
system, it comes up with actions that purposefully change
human state: it merges in front of a human to get them to
slow down or to reach its own goal faster; it blocks two
lanes to get them to switch to a third lane; or it backs up
slightly at an intersection to get them to proceed first. Such
behaviors arise from the optimization, without relying on
hand-coded signaling strategies and without ever explicitly
modeling communication. Our user study results suggest that
the robot is indeed capable of eliciting desired changes in
human state by planning using this dynamical system.},
}