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.
SafeAug: Safety-Critical Driving Data Augmentation from Naturalistic Datasets
24.09.2024
982067 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Screening Naturalistic Driving Study Data for Safety-Critical Events
Transportation Research Record | 2013
|Screening Naturalistic Driving Study Data for Safety-Critical Events
Online Contents | 2013
|Cooperative Safety Based on Naturalistic Driving Data
ASCE | 2022
|Superhighway Driving Safety Analysis Based on Naturalistic Driving Data
Springer Verlag | 2024
|