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Choe et al., 2018 - Google Patents

Deep Q learning with LSTM for traffic light control

Choe et al., 2018

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Document ID
1806950051029824440
Author
Choe C
Baek S
Woon B
Kong S
Publication year
Publication venue
2018 24th Asia-Pacific Conference on Communications (APCC)

External Links

Snippet

Most Conventional traffic light control (TLC) techniques do not provide enough efficiency to control dynamic traffic situations in real-time. Recently, DQN (Deep Q Network) algorithm is considered for TLC at the intersection because of its optimization technique for complex …
Continue reading at www.academia.edu (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled

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