Leandro Parada

Leandro Parada

Doctoral Researcher

Imperial College London

Leandro Parada has been a PhD student at the Centre for Transport Studies (CTS) since 2020 and is a member of the Transport Systems and Logistics Laboratory. His research interests lie in developing advanced algorithms for autonomous vehicles to operate safely and efficiently in complex urban environments, with a focus on promoting collaboration and coordination between agents using novel methods that combine Multi-Agent Reinforcement Learning (MARL) and Vehicle-to-Vehicle (V2V) networks.

Leandro holds a Mechanical Engineering degree and an MSc in Industrial Engineering, both from Universidad de Concepción. After completing his studies, he worked as a Management Consultant for four years, developing and implementing Decision Support Systems for industries such as Logistics, Healthcare, and Energy.

Interests
  • Collaborative Autonomous Driving
  • Multi-Agent Reinforcement Learning
  • Robotics
Education

  • MSc in Indrustial Engineering, 2016

    Universidad de Concepción

  • Dipl in Mechanical Engineering, 2015

    Universidad de Concepción

Journal Papers

  1. (2023). Risk-aware Controller for Autonomous Vehicles using Model-based Collision Prediction and Reinforcement Learning. Artificial Intelligence. Cite

Preprints

  1. Safe and Efficient Manoeuvring for Emergency Vehicles in Autonomous Traffic using Multi-Agent Reinforcement Learning. In review. Preprint. Cite

Conference Papers

  1. (2022). Transferring Multi-Agent Reinforcement Learning Policies for Autonomous Driving using Sim-to-Real. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022). PDF Cite DOI
  2. (2021). A Decentralised Multi-Agent Reinforcement Learning Approach for the Same-Day Delivery Problem. Transportation Research Board 101st Annual Meeting (TRBAM 2022). Cite