This study aims to explore the effects of drivers' visual environment on speeding crashes by
using different machine learning techniques. To obtain the data of drivers' visual …

Abstract Using Machine Learning (ML) models for safety-critical perception tasks in
Autonomous Driving (AD) or other domains requires a thorough evaluation of the model …

With the growing use of Deep Neural Networks (DNNs) in various safety-critical applications
comes an increasing need for Verification and Validation (V&V) of these DNNs. Unlike …

Synthetic, ie, computer generated-imagery (CGI) data is a key component for training and
validating deep-learning-based perceptive functions due to its ability to simulate rare cases …

With the advancement of DNNs into safety-critical applications, testing approaches for such
models have gained more attention. A current direction is the search for and identification of …

Validation of AI based perception functions is a key cornerstone of safe automated driving.
Building on the use of richly annotated synthetic data, a novel pedestrian detector validation …

The VALERIE tool pipeline is a synthetic data generator [14] developed with the goal to
contribute to the understanding of domain-specific factors that influence perception …

The progress in deep learning methods has bolstered the development of automated
vehicles during the last decade. However, the deployment of deep learning methods in …

Synthetic, ie, computer-generated imagery (CGI) data is a key component for training and
validating deep-learning-based perceptive functions due to its ability to simulate rare cases …

This chapter introduces a novel data synthesis framework for validation of perception
functions based on machine learning to ensure the safety and functionality of these systems …