no code implementations • 10 Apr 2024 • Shahin Atakishiyev, Mohammad Salameh, Randy Goebel
In this sense, explainability of real-time decisions is a crucial and natural requirement for building trust in autonomous vehicles.
no code implementations • 18 Mar 2024 • Shahin Atakishiyev, Mohammad Salameh, Randy Goebel
The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing development of highly autonomous vehicles, largely due to advances in deep learning, the availability of large-scale training datasets, and improvements in integrated sensor devices.
1 code implementation • 19 Jul 2023 • Shahin Atakishiyev, Mohammad Salameh, Housam Babiker, Randy Goebel
The end-to-end learning ability of self-driving vehicles has achieved significant milestones over the last decade owing to rapid advances in deep learning and computer vision algorithms.
no code implementations • 21 Dec 2021 • Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, Randy Goebel
First, we provide a thorough overview of the state-of-the-art and emerging approaches for XAI-based autonomous driving.
no code implementations • 20 Nov 2021 • Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, Randy Goebel
There has been recent and growing interest in the development and deployment of autonomous vehicles, encouraged by the empirical successes of powerful artificial intelligence techniques (AI), especially in the applications of deep learning and reinforcement learning.
Autonomous Driving Explainable Artificial Intelligence (XAI) +1
no code implementations • 17 Jul 2019 • Shahin Atakishiyev, Marek Z. Reformat
In data dominated systems and applications, a concept of representing words in a numerical format has gained a lot of attention.