CUED Publications database

A Meta-Tracking Approach for Predicting the Driver or Passenger Intent

Ahmad, BI and Langdon, PM and Godsill, SJ (2018) A Meta-Tracking Approach for Predicting the Driver or Passenger Intent. In: UNSPECIFIED pp. 617-621..

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Abstract

© 2018 ISIF 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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div C > Engineering Design
Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 16 Oct 2018 02:13
Last Modified: 07 Mar 2019 13:03
DOI: doi:10.23919/ICIF.2018.8455768