SOTIF PCOD

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 Learning (DL) models on the generated dataset using Average Precision (AP) and Recall metrics.

搜集汇总

背景与挑战

背景概述

SOTIF PCOD是一个用于自动驾驶安全评估的LiDAR点云数据集，通过模拟SOTIF相关用例生成，包含21种不同天气条件（如晴朗、多云、雨天）和三个时间段（中午、日落、夜晚），共547帧数据。该数据集旨在评估3D物体检测方法在复杂环境下的适应性和性能，并提供了使用平均精度和召回率等指标进行评估的基准。