학술논문
Semi-Automatic Ground Truth Trajectory Estimation and Smoothing using Roadside Cameras
Document Type
Conference
Source
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) Intelligent Transportation Systems (ITSC), 2023 IEEE 26th International Conference on. :4577-4583 Sep, 2023
Subject
Language
ISSN
2153-0017
Abstract
High precision trajectory data is crucial for the validation and verification of algorithms for automated driving. For instance, ego-vehicle-localization algorithms, traffic prediction, traffic scene assessment and other components profit from highly accurate trajectory data for evaluation and bench-marking. In this work we present a ground truth data generation pipeline that is able to produce trajectories of traffic participants in a semi-automated process from static roadside cameras. Our approach consists of an assisted manual labeling step, homography projection, followed by feature computation, state estimation and trajectory smoothing using Rauch-Tung-Striebel Smoothers (RTS). We evaluate our approach in a field experiment and compare the produced trajectories to a commercial high precision (GNSS/INS) system, where we reach a mean position error of 1.16m and a mean speed error of 0.73m/s over seven driving sequences. For relative object distances below 40m to the camera origin, position errors are below 0.41m and the respective speed error is below 0.30m/s.