Offline Reconstruction of Missing Vehicle Trajectory Data from 3D LIDAR

被引:0
|
作者
Sazara, Cem [1 ]
Nezafat, Reza Vatani [1 ]
Cetin, Mecit [1 ]
机构
[1] Old Dominion Univ, Transportat Res Inst, Norfolk, VA 23529 USA
来源
2017 28TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV 2017) | 2017年
关键词
CAR-FOLLOWING MODEL;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:792 / 797
页数:6
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