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Differentiable Hybrid Traffic Simulation by Sanghyun Son; Yi-Ling Qiao; Jason Sewall; Ming C. Lin is a Computer Science article available to read on EtoBox.

What is Differentiable Hybrid Traffic Simulation about?

We introduce a novel differentiable hybrid traffic simulator , which simulates traffic using a hybrid model of both macroscopic and microscopic models and can be directly integrated into a neural network for traffic control and flow optimization. This is the first differentiable traffic simulator for macroscopic and hybrid models that can compute gradients for traffic states across time steps and inhomogeneous lanes. To compute the gradient flow between two types of traffic models in a hybrid framework, we present a novel intermediate conversion component that bridges the lanes in a differentiable manner as well. We also show that we can use analytical gradients to accelerate the overall process and enhance scalability. Thanks to these gradients, our simulator can provide more efficient and scalable solutions for complex learning and control problems posed in traffic engineering than other existing algorithms. Refer to https://sites.google.com/umd.edu/diff-hybrid-traffic-sim for our project.

Who reads Differentiable Hybrid Traffic Simulation?

Author

Sanghyun Son; Yi-Ling Qiao; Jason Sewall; Ming C. Lin

Publisher

ACM

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)