  This paper proposes a probabilistic framework for the sequential estimation
of the likelihood of a driver or passenger(s) returning to the vehicle and time
of arrival, from the available partial track of the user location. The latter
can be provided by a smartphone navigational service and/or other dedicated
(e.g. RF based) user-to-vehicle positioning solution. The introduced novel
approach treats the tackled problem as an intent prediction task within a
Bayesian formulation, leading to an efficient implementation of the inference
routine with notably low training requirements. It effectively captures the
long term dependencies in the trajectory followed by the driver/passenger to
the vehicle, as dictated by intent, via a bridging distribution. Two examples
are shown to demonstrate the efficacy of this flexible low-complexity
technique.
