Differentiable Hybrid Traffic Simulation
نویسندگان
چکیده
We introduce a novel differentiable hybrid traffic simulator , which simulates using model of both macroscopic and microscopic models can be directly integrated into neural network for control flow optimization. This is the first that compute gradients states across time steps inhomogeneous lanes. To gradient between two types in framework, we present intermediate conversion component bridges lanes manner as well. also show use analytical to accelerate overall process enhance scalability. Thanks these gradients, our provide more efficient scalable solutions complex learning problems posed engineering than other existing algorithms. Refer https://sites.google.com/umd.edu/diff-hybrid-traffic-sim project.
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ژورنال
عنوان ژورنال: ACM Transactions on Graphics
سال: 2022
ISSN: ['0730-0301', '1557-7368']
DOI: https://doi.org/10.1145/3550454.3555492