Version 1
: Received: 6 June 2023 / Approved: 7 June 2023 / Online: 7 June 2023 (07:08:34 CEST)
How to cite:
Reis, N.; Machado da Silva, J.; Correia, M.V. An Introduction to the Evaluation of Perception Algorithms & LiDAR Point Clouds using a Copula-based Outlier Detector. Preprints2023, 2023060499. https://doi.org/10.20944/preprints202306.0499.v1
Reis, N.; Machado da Silva, J.; Correia, M.V. An Introduction to the Evaluation of Perception Algorithms & LiDAR Point Clouds using a Copula-based Outlier Detector. Preprints 2023, 2023060499. https://doi.org/10.20944/preprints202306.0499.v1
Reis, N.; Machado da Silva, J.; Correia, M.V. An Introduction to the Evaluation of Perception Algorithms & LiDAR Point Clouds using a Copula-based Outlier Detector. Preprints2023, 2023060499. https://doi.org/10.20944/preprints202306.0499.v1
APA Style
Reis, N., Machado da Silva, J., & Correia, M.V. (2023). An Introduction to the Evaluation of Perception Algorithms & LiDAR Point Clouds using a Copula-based Outlier Detector. Preprints. https://doi.org/10.20944/preprints202306.0499.v1
Chicago/Turabian Style
Reis, N., José Machado da Silva and Miguel Velhote Correia. 2023 "An Introduction to the Evaluation of Perception Algorithms & LiDAR Point Clouds using a Copula-based Outlier Detector" Preprints. https://doi.org/10.20944/preprints202306.0499.v1
Abstract
The increased demand and use of autonomous vehicles and advanced driver-assistance systems has been constrained by an incidence of accidents involving errors with the perception layer’s functionality. In tandem, recent papers have noted the lack of standardized, independent testing formats and insufficient methods with which to analyze, verify and qualify LiDAR-based data and categorization. While camera-based approaches benefit from an ample amount of research, camera images can be unreliable in situations with impaired visibility such as dim lighting and fog. This paper aims to introduce a novel method based entirely on LiDAR data with the capability to detect anomalous patterns as well as complementing other performance evaluators using a Copula-based approach. With a promising set of preliminary results, this methodology may be used to evaluate an algorithm’s confidence score, the impact conditions may have on LiDAR data and detect cases in which LiDAR data may be insufficient or otherwise unusable.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
