Minimizing human intervention in engines, such as traffic lights, through automatic
applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) …

Focusing on the task of point-to-point navigation for an autonomous driving vehicle, we
propose a novel deep learning model trained with end-to-end and multi-task learning …

With the advent of Internet of Things (IoT), The realization of smart city seems to be very
imminent. One of the key parts of a cyber physical system of urban life is transportation. This …

Perception is the fundamental task of any autonomous driving system, which gathers all the
necessary information about the surrounding environment of the moving vehicle. The …

Traffic object detection and recognition systems play an essential role in Advanced Driver
Assistance Systems (ADAS) and Autonomous Vehicles (AV). In this research, we focus on …

This study aims to improve the performance and generalization capability of end-to-end
autonomous driving with scene understanding leveraging deep learning and multimodal …

The technological issues and legal considerations associated with fully automated vehicles
have prompted the automotive industry to focus more on supervised systems and advanced …

For safe autonomous driving, deep neural network (DNN)-based perception systems play
essential roles, where a vast amount of driving images should be manually collected and …

Detecting and classifying vehicles as objects from images and videos is challenging in
appearance-based representation, yet plays a significant role in the substantial real-time …

The automotive industry and researchers have recently shown an interest in autonomous
vehicles. In an autonomous vehicle, different technologies may be used. Radio Detection …