  Smooth handling of pedestrian interactions is a key requirement for
Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS). Such
systems call for early and accurate prediction of a pedestrian's
crossing/not-crossing behaviour in front of the vehicle. Existing approaches to
pedestrian behaviour prediction make use of pedestrian motion, his/her location
in a scene and static context variables such as traffic lights, zebra crossings
etc. We stress on the necessity of early prediction for smooth operation of
such systems. We introduce the influence of vehicle interactions on pedestrian
intention for this purpose. In this paper, we show a discernible advance in
prediction time aided by the inclusion of such vehicle interaction context. We
apply our methods to two different datasets, one in-house collected - NTU
dataset and another public real-life benchmark - JAAD dataset. We also propose
a generic graphical model Factored Latent-Dynamic Conditional Random Fields
(FLDCRF) for single and multi-label sequence prediction as well as joint
interaction modeling tasks. FLDCRF outperforms Long Short-Term Memory (LSTM)
networks across the datasets ($\sim$100 sequences per dataset) over identical
time-series features. While the existing best system predicts pedestrian
stopping behaviour with 70\% accuracy 0.38 seconds before the actual events,
our system achieves such accuracy at least 0.9 seconds on an average before the
actual events across datasets.
