Autonomous Vehicles (AVs) have the potential to solve many traffic problems, such as
accidents, congestion and pollution. However, there are still challenges to overcome, for …

Over the past decades Machine Learning and Deep Learning algorithm played a vital part in
the development of Autonomous Vehicle. It is indeed for the perception system to examine …

This paper proposes an acceleration technique to minimise the unnecessary operations on
a state-of-the-art machine learning model and thus to improve the processing speed while …

Recent breakthroughs in artificial intelligence promote the development of deep neural
networks (DNNs)-based intelligent applications in the Internet of Things (IoT). However …

The computer vision systems that are responsible for driving Autonomous Vehicles (AV) are
evaluated based on their capacity to recognize obstacles and objects located close to the …

Accurate and reliable detection is one of the main tasks of Autonomous Driving Systems
(ADS). While detecting the obstacles on the road during various environmental …

Efficient task processing and data storage are still among the most important challenges in
Autonomous Driving (AD). In-board processing units struggle to deal with the workload of …

With advances in sensing, computing and communication technologies, Connected and
Automated Vehicles (CAVs) are becoming feasible. The advent of CAVs presents new …

Automated Driving Systems (ADS) and Advanced Driving Assistance Systems (ADAS) are
widely investigated for developing safe and intelligent transportation systems. A common …

Future autonomous driving (AD) will require more data processing and storage capacities,
exceeding the in-board capacities, especially with AI application requirements. The cloud or …