Authors:
Ziad Elmassik
;
Mohamed Sabry
and
Amr El Mougy
Affiliation:
Computer Science Department, German University in Cairo, Cairo, Egypt
Keyword(s):
Object Classification, Classification Performance, Sensor Fusion, Autonomous Vehicles.
Abstract:
Autonomous vehicles rely on a variety of sensors for accurate perception and understanding of the scene. Behind these sensors, complex networks and systems perform the driving tasks. Data from the sensors is constantly perturbed by various noise elements, which compromises the reliability of the vehicle’s perception systems. Sensor fusion may be applied to overcome these challenges, especially when the data from the different sensors lead to contradicting results. Nevertheless, weather conditions such as rain, snow, fog, and direct sunlight have an impact on the quality of sensor data, in different ways. This challenge has not been studied in depth, according to the best knowledge of the authors. Accordingly, this paper presents an extensive study of perception systems under different weather conditions, using real-life datasets (nuScenes and the CADCD). We identify a set of evaluation metrics and study the quality of data from different sensors in different scenarios and conditions.
Our performance analysis produces insight as to the proper sensor mix that should be used in different weather conditions.
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