Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS
This work addresses the time-consuming manual annotation process for vulnerable road users in automotive radar data, offering an incremental improvement for automated driving systems.
The paper tackles the difficult problem of annotating automotive radar data by proposing an automated labeling method using a portable GNSS system, achieving significant time savings without relevant loss in labeling accuracy.
Annotating automotive radar data is a difficult task. This article presents an automated way of acquiring data labels which uses a highly accurate and portable global navigation satellite system (GNSS). The proposed system is discussed besides a revision of other label acquisitions techniques and a problem description of manual data annotation. The article concludes with a systematic comparison of conventional hand labeling and automatic data acquisition. The results show clear advantages of the proposed method without a relevant loss in labeling accuracy. Minor changes can be observed in the measured radar data, but the so introduced bias of the GNSS reference is clearly outweighed by the indisputable time savings. Beside data annotation, the proposed system can also provide a ground truth for validating object tracking or other automated driving system applications.