Explainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions S Atakishiyev, M Salameh, H Yao, R Goebel arXiv preprint arXiv:2112.11561, 2021 | 111 | 2021 |
A Multi-Component Framework for the Analysis and Design of Explainable Artificial Intelligence MY Kim, S Atakishiyev, HKB Babiker, N Farruque, R Goebel, OR Zaïane, ... Machine Learning and Knowledge Extraction 3 (4), 900-921, 2021 | 37 | 2021 |
A multi-component framework for the analysis and design of explainable artificial intelligence S Atakishiyev, H Babiker, N Farruque, R Goebel, MY Kima, MH Motallebi, ... arXiv preprint arXiv:2005.01908, 2020 | 17 | 2020 |
Towards Safe, Explainable, and Regulated Autonomous Driving S Atakishiyev, M Salameh, H Yao, R Goebel Explainable Artificial Intelligence for Intelligent Transportation Systems …, 2023 | 9 | 2023 |
Explaining Autonomous Driving Actions with Visual Question Answering S Atakishiyev, M Salameh, H Babiker, R Goebel 2023 IEEE 26th International Conference on Intelligent Transportation …, 2023 | 7 | 2023 |
Analysis of Word Embeddings Using Fuzzy Clustering S Atakishiyev, MZ Reformat Recent Developments and the New Direction in Soft-Computing Foundations and …, 2021 | 3 | 2021 |
Incorporating Explanations into Human-Machine Interfaces for Trust and Situation Awareness in Autonomous Vehicles S Atakishiyev, M Salameh, R Goebel arXiv preprint arXiv:2404.07383, 2024 | | 2024 |
Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving S Atakishiyev, M Salameh, R Goebel arXiv preprint arXiv:2403.12176, 2024 | | 2024 |
Evaluation of High-Dimensional Word Embeddings using Cluster and Semantic Similarity Analysis S Atakishiyev | | 2018 |