Eduardo Candela

Eduardo Candela

Doctoral Researcher

Imperial College London

Eduardo Candela has been a PhD student at the Centre for Transport Studies (CTS) since 2019. He is a member of both the Transport Systems and Logistics Laboratory and the Personal Robotics Laboratory. His research focuses on developing efficient algorithms for identifying safety edge-cases in Autonomous Vehicle Control Systems. He is particularly interested in applying Artificial Intelligence and Optimization to complex engineering settings, such as Autonomous Vehicles.

Eduardo holds an MSc in Operations Research from the Massachusetts Institute of Technology (MIT), a BSc in Industrial Engineering from Instituto Tecnologico Autonomo de Mexico (ITAM), and a BSc in Mechatronics (Robotics) Engineering, also from ITAM.

After obtaining his MSc, Eduardo worked for two years in Silicon Valley as a Data Scientist at Tesla and the Bosch Centre for Artificial Intelligence. He also held a part-time position as a research assistant at Harvard University.

Interests
  • Autonomous Vehicles
  • Reinforcement Learning
Education

  • M.Sc. in Operations Research, 2017

    Massachusetts Institute of Technology (MIT)

  • B.Sc. in Industrial Engineering, 2015

    Instituto Tecnologico Autonomo de Mexico (ITAM)

  • B.Sc. in Mechatronics Engineering, 2015

    Instituto Tecnologico Autonomo de Mexico (ITAM)

Journal Papers

  1. (2023). Risk-aware Controller for Autonomous Vehicles using Model-based Collision Prediction and Reinforcement Learning. Artificial Intelligence. Cite
  2. (2022). Spatial-Temporal Flows-Adaptive Street Layout Control Using Reinforcement Learning. Sustainability. Cite DOI

Preprints

  1. Risk-Aware Stochastic Vehicle Trajectory Prediction with Spatial-Temporal Interaction Modelling. In review. Preprint. Cite
  2. Safe and Efficient Manoeuvring for Emergency Vehicles in Autonomous Traffic using Multi-Agent Reinforcement Learning. In review. Preprint. Cite
  3. Quantitative Risk Indices for Autonomous Vehicle Training Systems. arXiv. Preprint. PDF Cite DOI

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). Fast Collision Prediction for Autonomous Vehicles using a Stochastic Dynamics Model. 2021 IEEE 24th International Conference on Intelligent Transportation Systems (ITSC 2021). Cite
  3. (2020). Intelligent Management of On-street Parking Provision for the Autonomous Vehicles Era. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC 2020). Cite DOI