针对自动驾驶安全功能，构建了21种天气的激光雷达数据集并评估模型表现。
Simulation-Based Performance Evaluation of 3D Object Detection Methods with Deep Learning for a LiDAR Point Cloud Dataset in a SOTIF-related Use Case
- 基于SOTIF场景模拟21种天气，生成547帧激光雷达数据
- 在不同光照与天气下，平均精度（AP）和召回率均下降明显
- 适合自动驾驶感知算法测试与安全验证的研究者使用
功能安全（SOTIF）关注传感器性能局限与深度学习检测不足，以确保自动驾驶系统预期功能的安全。本文提出一种方法，通过模拟与SOTIF相关的用车场景，对3D目标检测方法在激光雷达点云数据上的适应性与性能进行评估。主要贡献包括：定义并建模包含21种不同天气条件的SOTIF相关用车场景，并生成适用于3D目标检测方法的数据集。该数据集共包含547帧，涵盖晴天、多云、雨天等天气，对应正午、日落、夜间等不同时段。利用MMDetection3D和OpenPCDET工具包，对预训练的SOTA深度学习模型在该数据集上进行测试，采用平均精度（AP）与召回率进行性能评估。
原文摘要 · Abstract (English)
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 modelling 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.
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