MIND: Multi-modal Intelligent Deep Traffic Signal Control

MIND is an intelligent traffic signal controller (software) that comprises three major parts:

  1. Convolutional Neural Networks for interpreting high dimensional sensory data;
  2. Fully Connected Neural Networks as function approximator for managing continuous features of the traffic network; and
  3. Reinforcement Learning as the brain of the controller for learning how to optimize the travel time for users of the traffic network.

Each of these parts are trained together to achieve a single goal, optimizing the traffic signal, with consideration for efficiently moving the most people.

IP: PCT in prosecution

ID:

2039

Keywords:

Artificial Intelligence (AI) , Automotive , Deep Learning , Machine Learning , Neural Networks , Sensors & Instrumentation , Software , Traffic

VPRI Contact

Kurtis Scissons

Director, University Ventures
Innovations & Partnerships Office (IPO)
(416) 978-3557

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