Benchmarking LiDAR Sensors for Development and Evaluation of Automotive Perception

Environment perception and representation are some of the most critical tasks in automated driving. To meet the stringent needs of safety standards such as ISO 26262 there is a need for efficient quantitative evaluation of the perceived information. However, to use typical methods of evaluation, such as comparing using annotated data, is not scalable due to the manual effort involved. There is thus a need to automate the process of data annotation. This paper focuses on the LiDAR sensor and aims to identify the limitations of the sensor and provides a methodology to generate annotated data of a measurable quality. The limitations with the sensor are analysed in a Systematic Literature Review on available academic texts and refined by unstructured interviews with experts. The main contributions are 1) the SLR with related interviews to identify LiDAR sensor limitations and 2) the associated methodology which allows us to generate world representations

Identification of Weather Conditions Related to Roadside LiDAR Data

Traffic data collection is essential for traffic safety and operations studies and has been recognized as a fundamental component in the development of intelligent transportation systems. In recent years, growing interest is shown by both industrial and academic communities in high-resolution data that can portray traffic operations for all transportation participants such as connected or conventional vehicles, transit buses, and pedestrians. Roadside Light Detection and Ranging (LiDAR) sensors can be deployed to collect such high-resolution traffic data sets. However, LiDAR sensing could be negatively affected in the context of rain, snow, and wind conditions as the collected 3D point clouds of surrounding objects may drift. Weather-caused impacts can lead to difficulties in data processing and even accuracy compromise. Consequently, solutions are desired and sought, focused on the issue that the affected data have been identified through a labor-intensive and time-consuming process. In this research, a methodology is proposed for developing an automatic identification of the LiDAR data sets that are affected by rain, snow, and wind conditions. First, the impacts of rain, snow, and wind are characterized using statistical measures. Detection distance offset (DDO) and Detection distance offset for wind (DDOW) are calculated and investigated, and it shows that rain or snow conditions can be differentiated according to the standard deviation of the DDOs. Snow conditions can be additionally identified using the sum of the DDOs. Unlike rain and snow, wind conditions can be recognized by the differences between the upper and lower boundaries of DDOs, and therefore, a separate analysis is developed. Based upon the multiple analyses developed in the research, an automatic identification process is designed. The thresholds for identifying rain, snow, and wind conditions are set up, respectively. The process is validated using realistic roadside LiDAR data collected at the intersection of McCarran Blvd and Evans Ave in Reno, Nevada. The validation demonstrated that the proposed identification could precisely detect affected data sets in the context of rain, snow, and wind conditions

Bathymetric Survey of the St. Anthony Channel (Croatia) Using Multibeam Echosounders (MBES)—A New Methodological Semi-Automatic Approach of Point Cloud Post-Processing

Multibeam echosounders (MBES) have become a valuable tool for underwater floor mapping. However, MBES data are often loaded with different measurement errors. This study presents a new user-friendly and methodological semi-automatic approach of point cloud post-processing error removal. The St. Anthony Channel (Croatia) was selected as the research area because it is regarded as one of the most demanding sea or river passages in the world and it is protected as a significant landscape by the Šibenik-Knin County. The two main objectives of this study, conducted within the Interreg Italy–Croatia PEPSEA project, were to: (a) propose a methodological framework that would enable the easier and user-friendly identification and removal of the errors in MBES data; (b) create a high-resolution integral model (MBES and UAV data) of the St. Anthony Channel for maritime safety and tourism promotion purposes. A hydrographic survey of the channel was carried out using WASSP S3 MBES while UAV photogrammetry was performed using Matrice 210 RTK V2. The proposed semi-automatic post-processing of the MBES acquired point cloud was completed in the Open Source CloudCompare software following five steps in which various point filtering methods were used. The reduction percentage in points after the denoising process was 14.11%. Our results provided: (a) a new user-friendly methodological framework for MBES point filtering; (b) a detailed bathymetric map of the St. Anthony Channel with a spatial resolution of 50 cm; and (c) the first integral (MBES and UAV) high-resolution model of the St. Anthony Channel. The generated models can primarily be used for maritime safety and tourism promotion purposes. In future research, ground-truthing methods (e.g., ROVs) will be used to validate the generated models