ML-MAS: a Hybrid AI Framework for Self-Driving Vehicles

Hilal Al Shukairi, Rafael C. Cardoso* (Corresponding Author)

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

4 Downloads (Pure)

Abstract

Machine Learning (ML) techniques have been shown to be widely successful in environments that require processing a large amount of perception data, such as in fully autonomous self-driving vehicles. Nevertheless, in such a complex domain, ML-only approaches have several limitations. In this paper, we propose a hybrid Artificial Intelligence (AI) framework for fully autonomous self-driving vehicles that uses rule-based agents from symbolic AI to supplement the ML models in their decision-making. Our framework is evaluated using routes from the CARLA simulation environment, and has been shown to improve the driving score of the ML models.
Original languageEnglish
Title of host publicationAAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems
Place of PublicationLondon
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1191-1199
Number of pages9
ISBN (Electronic)9781450394321
Publication statusPublished - 30 May 2023
Event22nd International Conference on Autonomous Agents and Multiagent Systems - London ExCeL conference centre, London, United Kingdom
Duration: 29 May 20232 Jun 2023
https://aamas2023.soton.ac.uk/

Conference

Conference22nd International Conference on Autonomous Agents and Multiagent Systems
Abbreviated titleAAMAS 2023
Country/TerritoryUnited Kingdom
CityLondon
Period29/05/232/06/23
Internet address

Keywords

  • Hybrid AI
  • BDI
  • deep learning
  • self-driving vehicles
  • CARLA

Fingerprint

Dive into the research topics of 'ML-MAS: a Hybrid AI Framework for Self-Driving Vehicles'. Together they form a unique fingerprint.

Cite this