用真实数据生成安全驾驶场景，提升自动驾驶安全性。
SafeAug: Safety-Critical Driving Data Augmentation from Naturalistic Datasets
- 从自然驾驶数据中检测车辆并进行3D变换，模拟危险场景。
- 在KITTI数据集上训练的模型性能优于SMOGN和重要性采样基线。
- 保持图像真实感的同时生成高危驾驶数据，适合安全算法训练。
安全关键驾驶数据对开发安全可信的自动驾驶算法至关重要。由于自然驾驶数据集中此类数据稀缺，现有方法多依赖仿真或人工生成图像，但生成图像与真实数据间仍存在真实性差距。本文提出一种新框架，从自然驾驶数据中增强安全关键驾驶数据以解决该问题。首先使用YOLOv5检测车辆，随后进行深度估计与3D变换，模拟车辆接近及高危驾驶场景，实现对车辆动态数据的精准调整以反映潜在危险情境。相比仿真或人工生成数据，本方法在保持图像真实性的前提下生成更具真实感的安全关键数据。基于KITTI数据集的实验表明，使用该增强数据训练的下游自动驾驶算法性能优于基准方法（包括SMOGN与重要性采样）。
原文摘要 · Abstract (English)
Safety-critical driving data is crucial for developing safe and trustworthy self-driving algorithms. Due to the scarcity of safety-critical data in naturalistic datasets, current approaches primarily utilize simulated or artificially generated images. However, there remains a gap in authenticity between these generated images and naturalistic ones. We propose a novel framework to augment the safety-critical driving data from the naturalistic dataset to address this issue. In this framework, we first detect vehicles using YOLOv5, followed by depth estimation and 3D transformation to simulate vehicle proximity and critical driving scenarios better. This allows for targeted modification of vehicle dynamics data to reflect potentially hazardous situations. Compared to the simulated or artificially generated data, our augmentation methods can generate safety-critical driving data with minimal compromise on image authenticity. Experiments using KITTI datasets demonstrate that a downstream self-driving algorithm trained on this augmented dataset performs superiorly compared to the baselines, which include SMOGN and importance sampling.
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