US 12,190,722 B2
Method and system for traffic signal control with a learned model
Parth Jaggi, Toronto (CA); Scott Sanner, Scarborough (CA); and Baher Abdulhai, Mississauga (CA)
Assigned to THE GOVERNING COUNCIL OF THE UNIVERSITY OF TORONTO, Toronto (CA)
Filed by THE GOVERNING COUNCIL OF THE UNIVERSITY OF TORONTO, Toronto (CA)
Filed on Jun. 13, 2022, as Appl. No. 17/838,772.
Claims priority of provisional application 63/202,508, filed on Jun. 14, 2021.
Prior Publication US 2022/0398921 A1, Dec. 15, 2022
Int. Cl. G08G 1/08 (2006.01); G06N 5/01 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G08G 1/01 (2006.01); G08G 1/082 (2006.01)
CPC G08G 1/08 (2013.01) [G06N 5/01 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G08G 1/0129 (2013.01); G08G 1/082 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for traffic signal control of a traffic network with a learned model, the traffic network comprising one or more intersections and sensors associated with the intersections to determine vehicle traffic approaching each intersection, the method comprising, for each timestep:
receiving sensor readings from the traffic network, the sensor readings comprising positions and speeds of vehicles approaching each intersection;
using a learned dynamics model that takes the sensor readings as input, predicting a plurality of possibilities for position and velocity of the vehicles approaching each intersection in a future timestep;
determining an action for the one or more intersections by performing a tree search on the plurality of possibilities and selecting the possibility with a highest action value; and
outputting the action to the traffic network for implementation as a traffic control action at the one or more intersections.