Explainable Trust-aware Selection of Autonomous Vehicles Using LIME for One-Shot Federated Learning

Autonomous driving has been gaining a lot of attention in the field of transportation technology in recent years. The use of autonomous vehicles has the potential to reduce the number of road accidents caused by human error, improve traffic flow, increase fuel efficiency and save time for travelers. In federated learning systems, selecting trustworthy autonomous vehicles (AVs) to participate in training is critical for ensuring system performance and reliability. In this work, we propose a trust-aware approach to AV selection that incorporates the performance of each AV using the Local Interpretable Model-Agnostic Explanations (LIME) method and One-Shot Federated Learning. We modify the XAI LIME Deep Q-learning-based AV selection model to include the trust metric, resulting in the Trust-Aware XAI LIME Deep Q-learning-based AV selection model. Our experiments show that the trust-aware approach outperforms the standard approach in terms of both accuracy and reliability, demonstrating the effectiveness of incorporating trust metrics in AV selection.