3D LiDARによる欠損車両軌跡データのオフライン再構成
Offline reconstruction of missing vehicle trajectory data from 3D LIDAR
LiDARで収集した車両軌跡の欠損区間を、交通流モデル（車間追従モデル）をギャップ近傍の既知点で重み付け校正して補間する手法を提案し、Gippsモデルが最良の精度を示した。
著者: Cem Sazara, Reza Vatani Nezafat, Mecit Cetin
分類: cs.RO
原文アブストラクト
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.