If and When a Driver or Passenger is Returning to Vehicle: Framework to Infer Intent and Arrival Time

Abstract

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.