Artificial intelligence (AI) is one of the emerging technologies. In recent decades, artificial
intelligence (AI) has gained widespread acceptance in a variety of fields, including virtual …

This survey reviews explainability methods for vision-based self-driving systems trained with
behavior cloning. The concept of explainability has several facets and the need for …

Autonomous vehicles (AV) are game-changing innovations that promise a safer, more
convenient, and environmentally friendly mode of transportation than traditional vehicles …

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …

Connected and Automated Vehicles (CAVs) represent a rapidly growing technology in the
automotive domain sector, offering promising solutions to address challenges such as traffic …

Autonomous driving in urban environments requires intelligent systems that are able to deal
with complex and unpredictable scenarios. Traditional modular approaches focus on …

Accurately detecting and predicting Lane Change (LC) processes of human-driven vehicles
can help autonomous vehicles better understand their surrounding environment, recognize …

Abstract In Internet of Things (IoT) applications, federated learning is commonly used for
distributedly training models in a privacy-preserving manner. Recently, federated learning is …

The combination of steering angle prediction and control of autonomous vehicles (AVs) is a
challenging task. To improve the real-time steering angle prediction accuracy and the …

Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor
savings. Over the past few years, the number of simulators for autonomous driving has …