An Introduction to the Evaluation of Perception Algorithms and LiDAR Point Clouds Using a Copula-Based Outlier Detector

• Type: Article in International Scientific Journal
• Date: 2023-09-17

Authors

José Machado da Silva (Author) FEUP; Nuno Reis (Author) FEUP; Correia, M. V. (Author) FEUP

Journal

Title: Remote Sensing

Vol. 31

Pages: 1-4570

ISSN: 2072-4292

Publisher: MDPI

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 2 Citations

COMPENDEX

Scientific classification

CORDIS: Technological sciences > Engineering > Knowledge engineering

Associated Projects

THEIA - Automated Perception Driving

Other information

Authenticus ID: P-00Y-FPZ

DOI: 10.3390/rs15184570

Abstract (EN): The increased demand for and use of autonomous driving and advanced driver assistance systems has highlighted the issue of abnormalities occurring within the perception layers, some of which may result in accidents. Recent publications have noted the lack of standardized independent testing formats and insufficient methods with which to analyze, verify, and qualify LiDAR (Light Detection and Ranging)-acquired data and their subsequent labeling. While camera-based approaches benefit from a significant amount of long-term research, images captured through the visible spectrum can be unreliable in situations with impaired visibility, such as dim lighting, fog, and heavy rain. A redoubled focus upon LiDAR usage would combat these shortcomings; however, research involving the detection of anomalies and the validation of gathered data is few and far between when compared to its counterparts. This paper aims to contribute to expand the knowledge on how to evaluate LiDAR data by introducing a novel method with the ability to detect these patterns and complement other performance evaluators while using a statistical approach. Although it is preliminary, the proposed methodology shows promising results in the evaluation of an algorithm's confidence score, the impact that weather and road conditions may have on data, and fringe cases in which the data may be insufficient or otherwise unusable.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 18