Explainable AI-based Federated Deep Reinforcement Learning for Trusted Autonomous Driving

Gaith Rjoub, Jamal Bentahar, Omar Abdel Wahab

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

Recently, the concept of autonomous driving became prevalent in the domain of intelligent transportation due to the promises of increased safety, traffic efficiency, fuel economy and reduced travel time. Numerous studies have been conducted in this area to help newcomer vehicles plan their trajectory and velocity. However, most of these proposals only consider trajectory planning using conjunction with a limited data set (i.e., metropolis areas, highways, and residential areas) or assume fully connected and automated vehicle environment. Moreover, these approaches are not explainable and lack trust regarding the contributions of the participating vehicles. To tackle these problems, we design an Explainable Artificial Intelligence (XAI) Federated Deep Reinforcement Learning model to improve the effectiveness and trustworthiness of the trajectory decisions for newcomer Autonomous Vehicles (AVs). When a newcomer AV seeks help for trajectory planning, the edge server launches a federated learning process to train the trajectory and velocity prediction model in a distributed collaborative fashion among participating AVs. One essential challenge in this approach is AVs selection, i.e., how to select the appropriate AVs that should participate in the federated learning process. For this purpose, XAI is first used to compute the contribution of each feature contributed by each vehicle to the overall solution. This helps us compute the trust value for each AV in the model. Then, a trust-based deep reinforcement learning model is put forward to make the selection decisions. Experiments using a real-life dataset show that our solution achieves better performance than benchmark solutions (i.e., Deep Q-Network (DQN), and Random Selection (RS)).

Original languageBritish English
Title of host publication2022 International Wireless Communications and Mobile Computing, IWCMC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages318-323
Number of pages6
ISBN (Electronic)9781665467490
DOIs
StatePublished - 2022
Event18th IEEE International Wireless Communications and Mobile Computing, IWCMC 2022 - Dubrovnik, Croatia
Duration: 30 May 20223 Jun 2022

Publication series

Name2022 International Wireless Communications and Mobile Computing, IWCMC 2022

Conference

Conference18th IEEE International Wireless Communications and Mobile Computing, IWCMC 2022
Country/TerritoryCroatia
CityDubrovnik
Period30/05/223/06/22

Keywords

  • Autonomous Vehicles Selection
  • Deep Reinforcement Learning
  • Edge Computing
  • Explainable Artificial Intelligence
  • Federated Learning
  • Trajectory Planning
  • Trust

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