To operate reliably in real-world traffic, an autonomous car must evaluate the consequences
of its potential actions by anticipating the uncertain intentions of other traffic participants. This
paper presents an integrated behavioral inference and decision-making approach that
models vehicle behavior for both our vehicle and nearby vehicles as a discrete set of
closedloop policies that react to the actions of other agents. Each policy captures a distinct
high-level behavior and intention, such as driving along a lane or turning at an intersection …

This paper reports on an integrated inference and decision-making approach for
autonomous driving that models vehicle behavior for both our vehicle and nearby vehicles
as a discrete set of closed-loop policies. Each policy captures a distinct high-level behavior
and intention, such as driving along a lane or turning at an intersection. We first employ
Bayesian changepoint detection on the observed history of nearby cars to estimate the
distribution over potential policies that each nearby car might be executing. We then sample …