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1. WO2019200477 - METHOD AND SYSTEM FOR MULTIMODAL DEEP TRAFFIC SIGNAL CONTROL

Publication Number WO/2019/200477
Publication Date 24.10.2019
International Application No. PCT/CA2019/050477
International Filing Date 17.04.2019
IPC
G08G 1/07 2006.1
GPHYSICS
08SIGNALLING
GTRAFFIC CONTROL SYSTEMS
1Traffic control systems for road vehicles
07Controlling traffic signals
G06N 20/00 2019.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
G06N 3/08 2006.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
G08G 1/052 2006.1
GPHYSICS
08SIGNALLING
GTRAFFIC CONTROL SYSTEMS
1Traffic control systems for road vehicles
01Detecting movement of traffic to be counted or controlled
052with provision for determining speed or overspeed
G08G 1/08 2006.1
GPHYSICS
08SIGNALLING
GTRAFFIC CONTROL SYSTEMS
1Traffic control systems for road vehicles
07Controlling traffic signals
08according to detected number or speed of vehicles
G08G 1/095 2006.1
GPHYSICS
08SIGNALLING
GTRAFFIC CONTROL SYSTEMS
1Traffic control systems for road vehicles
09Arrangements for giving variable traffic instructions
095Traffic lights
CPC
G06N 3/006
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
004Artificial life, i.e. computers simulating life
006based on simulated virtual individual or collective life forms, e.g. single "avatar", social simulations, virtual worlds or particle swarm optimisation
G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
G06N 3/0454
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0454using a combination of multiple neural nets
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
G08G 1/0112
GPHYSICS
08SIGNALLING
GTRAFFIC CONTROL SYSTEMS
1Traffic control systems for road vehicles
01Detecting movement of traffic to be counted or controlled
0104Measuring and analyzing of parameters relative to traffic conditions
0108based on the source of data
0112from the vehicle, e.g. floating car data [FCD]
G08G 1/0116
GPHYSICS
08SIGNALLING
GTRAFFIC CONTROL SYSTEMS
1Traffic control systems for road vehicles
01Detecting movement of traffic to be counted or controlled
0104Measuring and analyzing of parameters relative to traffic conditions
0108based on the source of data
0116from roadside infrastructure, e.g. beacons
Applicants
  • THE GOVERNING COUNCIL OF THE UNIVERSITY OF TORONTO [CA]/[CA]
Inventors
  • ABDULHAI, Baher
  • SHABESTARY, Soheil
Agents
  • BHOLE IP LAW
Priority Data
62/660,30720.04.2018US
Publication Language English (en)
Filing Language English (EN)
Designated States
Title
(EN) METHOD AND SYSTEM FOR MULTIMODAL DEEP TRAFFIC SIGNAL CONTROL
(FR) PROCÉDÉ ET SYSTÈME DE COMMANDE DE FEU DE SIGNALISATION APPROFONDIE MULTIMODALE
Abstract
(EN) There is provided a system and method for traffic signal control for an intersection of a traffic network. The method includes: receiving sensor readings including a plurality of physical characteristics associated with vehicles approaching the intersection; discretizing the sensor readings based on a grid of cells; associating a value representing the physical characteristic for each of the cells; generating a matrix associated with the physical characteristic; combining each matrix associated with each of the plurality of physical characteristics as separate layers in a multi-layered matrix; determining, using a machine learning model trained with a traffic control training set, one or more traffic actions with the multi-layered matrix as input, the traffic control training set including previously determined multi-layered matrices for a plurality of traffic scenarios at the intersection; and communicating the one or more actions to the traffic network.
(FR) L'invention concerne un système et un procédé de commande de feu de signalisation pour une intersection d'un réseau de circulation. Le procédé consiste : à recevoir des lectures de capteur comprenant une pluralité de caractéristiques physiques associées à des véhicules s'approchant de l'intersection ; à discrétiser les lectures de capteur sur la base d'une grille de cellules ; à associer une valeur représentant la caractéristique physique pour chacune des cellules ; à générer une matrice associée à la caractéristique physique ; à combiner chaque matrice associée à chaque caractéristique de la pluralité de caractéristiques physiques comme couches séparées dans une matrice multicouche ; à déterminer, à l'aide d'un modèle d'apprentissage automatique entraîné avec un ensemble d'apprentissage de commande de circulation, une ou plusieurs actions de circulation avec la matrice multicouche comme entrée, l'ensemble d'apprentissage de commande de circulation comprenant des matrices multicouches préalablement déterminées pour une pluralité de scénarios de circulation au niveau de l'intersection ; et à communiquer lesdites actions au réseau de circulation.
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