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TZID:Asia/Seoul
X-LIC-LOCATION:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
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DTSTART:18871231T000000
DTSTART:19881009T020000
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BEGIN:VEVENT
DTSTAMP:20230103T035312Z
LOCATION:Room 325-AB\, Level 3\, West Wing
DTSTART;TZID=Asia/Seoul:20221209T090000
DTEND;TZID=Asia/Seoul:20221209T103000
UID:siggraphasia_SIGGRAPH Asia 2022_sess176_papers_372@linklings.com
SUMMARY:Differentiable Hybrid Traffic Simulation
DESCRIPTION:Technical Communications, Technical Papers, TOG\n\nDifferentia
 ble Hybrid Traffic Simulation\n\nSon, Qiao, Sewall, Lin\n\nWe introduce a 
 novel differentiable hybrid traffic simulator, which simulates traffic usi
 ng a hybrid model of both macroscopic and microscopic models and can be di
 rectly integrated into a neural network for traffic control and flow optim
 ization. This is the first differentiable traffic simulator for macroscopi
 c and hybrid models that can compute gradients for traffic states across t
 ime steps and inhomogeneous lanes. To compute the gradient flow between tw
 o types of traffic models in a hybrid framework, we present a novel interm
 ediate conversion component that bridges the lanes in a differentiable man
 ner as well. We also show that we can use analytical gradients to accelera
 te the overall process and enhance scalability. Thanks to these gradients,
  our simulator can provide more efficient and scalable solutions for compl
 ex learning and control problems posed in the traffic engineering than oth
 er existing algorithms.\n\nRegistration Category: FULL ACCESS, ON-DEMAND A
 CCESS\n\nLanguage: ENGLISH\n\nFormat: IN-PERSON, ON-DEMAND
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_372&sess=sess17
 6
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