  LIDAR has become an important part of many autonomous vehicles with its
advantages on distance measurement and obstacle detection. LIDAR produces point
clouds which have important information about surrounding environment. In this
paper, we collected trajectory data on a two lane urban road using a Velodyne
VLP-16 Lidar. Due to dynamic nature of data collection and limited range of the
sensor, some of these trajectories have missing points or gaps. In this paper,
we propose a novel method for recovery of missing vehicle trajectory data
points using microscopic traffic flow models. While short gaps (less than 5
seconds) can be recovered with simple linear regression, and longer gaps are
recovered with the proposed method that makes use of car following models
calibrated by assigning weights to known points based on proximity to the gaps.
Newell's, Pipes, IDM and Gipps' car following models are calibrated and tested
with the ground truth trajectory data from LIDAR and NGSIM I-80 dataset. Gipps'
calibrated model yielded the best result.
