Authors:
Milin Patel
1
and
Rolf Jung
2
Affiliations:
1
Institute for Advanced Driver Assistance Systems and Connected Mobility, Kempten University of Applied Sciences, Benningen, Germany
;
2
Faculty of Computer Science, Kempten University of Applied Sciences, Kempten, Germany
Keyword(s):
Simulation-Based, Performance Evaluation, Deep Learning, 3D Object Detection, LiDAR Point Cloud, SOTIF-related Use Case.
Abstract:
Safety of the Intended Functionality (SOTIF) addresses sensor performance limitations and deep learning-based object detection insufficiencies to ensure the intended functionality of Automated Driving Systems (ADS). This paper presents a methodology examining the adaptability and performance evaluation of the 3D object detection methods on a LiDAR point cloud dataset generated by simulating a SOTIF-related Use Case. The major contributions of this paper include defining and modeling a SOTIF-related Use Case with 21 diverse weather conditions and generating a LiDAR point cloud dataset suitable for application of 3D object detection methods. The dataset consists of 547 frames, encompassing clear, cloudy, rainy weather conditions, corresponding to different times of the day, including noon, sunset, and night. Employing MMDetection3D and OpenPCDET toolkits, the performance of State-of-the-Art (SOTA) 3D object detection methods is evaluated and compared by testing the pre-trained Deep Lea
rning (DL) models on the generated dataset using Average Precision (AP) and Recall metrics.
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