Special Issue on “Trust through eXplAInability (XAI), Robustness and Verification of Autonomous Systems”

July 10th, 2024 Daniela Lopez de Luise
We are pleased to announce an extension to the call for papers for the Special Issue entitled “Trust through eXplAInability (XAI), Robustness and Verification of Autonomous Systems” of the IET Radar, Sonar & Navigation journal (IF 1.4, CS 4.1).

Submission deadline: Thursday, 1 August 2024
Expected Publication Month: March 2025
The large success of AI based solutions for autonomous systems applications has strongly impacted people’s minds to get prepared and ready for the use of these systems in our daily life.
Indeed, the booming development of machine learning techniques and their embeddings in suitable, small size computation hardware has made some unrealistic applications of autonomous systems just few years back the reality of today. To effectively adopt those intelligent autonomous systems solutions, the end users need to feel safe in engaging with those machines through the level of trust, under different conditions of operations, they have in the outputs of those solutions.
Interpreting the autonomous systems, and more specifically, autonomous vehicles’ decisions through the explainability of the machine learning techniques composing their solutions is key. The other way to secure the adoption of those techniques onboard autonomous vehicles is to go through rigorous model verification and robustness processes including dealing with anomalies and adversarial attacks.
The focus of this Special Issue is on modern XAI, Robustness and Verification of AI based autonomous vehicles’ decision making in terms of planning, navigation and guidance. Quantum software stacks and libraries facilitate the link from a high-level description of algorithms to a low-level implementation with quantum gates, for solving concrete problems and applications expected to demonstrate quantum advantage.
Topics for this call for papers include but not restricted to:
  • XAI for autonomous vehicle planning and guidance
  • Validation and Verification (V&V) of AI based solution for autonomous vehicle navigation
  • Pruning of deep learning solutions for autonomous vehicles application
  • Embedding of safe deep learning solutions for autonomous vehicles
  • Robustness of the AI based autonomous vehicle’s navigation and guidance through anomaly detection
  • Robustness of the AI based autonomous vehicle’s navigation and guidance through adversarial attacks
Guest Editors:
Nabil Aouf
University of London
United Kingdom
Xiaowei Huang
University of Liverpool
United Kingdom
Abdelhafid Zenati
University of London
United Kingdom
Daniele De Martini
University of Oxford
United Kingdom

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