A Meta-Tracking Approach for Predicting the Driver or Passenger Intent
Bashar I. Ahmad, Patrick Langdon, Simon Godsill · 2018
This paper introduces a Bayesian framework for estimating the probability of a driver or passenger(s) returning to the vehicle, from the available partial (noisy) track of his/her location. The latter can be provided by a smartphone navigational service and/or other dedicated user to vehicle positioning solution, for instance RF-based. The proposed approach treats the addressed intent prediction problem, i.e. not tracking the object's state (e.g. the driver/passenger position, velocity, etc.) or predicting its next few values, within an object tracking formulation, leading to a Kalman-filter-based implementation of the inference routine. Hence, it is dubbed meta-tracker in lieu of a conventional “sensor-level” tracking algorithm and relies on utilising bridging distributions to encapsulate the long term dependencies in the trajectory followed by the driver or passenger as dictated by the intended endpoint, if any. Two example trajectories are shown to demonstrate the effectiveness of this flexible framework.