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Found 6 papers, 1 papers with code
Incorporating Explanations into Human-Machine Interfaces for Trust and Situation Awareness in Autonomous Vehicles
In this sense, explainability of real-time decisions is a crucial and natural requirement for building trust in autonomous vehicles.
Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving
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.
Explaining Autonomous Driving Actions with Visual Question Answering
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.
Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions
First, we provide a thorough overview of the state-of-the-art and emerging approaches for XAI-based autonomous driving.
Towards Safe, Explainable, and Regulated Autonomous Driving
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
Analysis of Word Embeddings Using Fuzzy Clustering
In data dominated systems and applications, a concept of representing words in a numerical format has gained a lot of attention.
