VaN3Twin integrates ray-tracing into a full-stack V2X simulator to enable accurate multi-technology coexistence modeling and reports 50-70% better agreement with field measurements than prior tools.
Available: https://elib.dlr.de/124092/
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
Temporal Transfer Learning selects source tasks for zero-shot transfer of RL policies to solve a range of coarse-grained advisory autonomy hold durations in traffic optimization more reliably than baselines.
Presents a new ns-3-based open-source simulator for C-V2X Mode 4 and reports that it meets LTE Rel. 14 V2X requirements for up to 250 vehicles in a 100m x 100m worst-case area, with better results in a Manhattan grid.
citing papers explorer
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VaN3Twin: the Multi-Technology V2X Digital Twin with Ray-Tracing in the Loop
VaN3Twin integrates ray-tracing into a full-stack V2X simulator to enable accurate multi-technology coexistence modeling and reports 50-70% better agreement with field measurements than prior tools.
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Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy
Temporal Transfer Learning selects source tasks for zero-shot transfer of RL policies to solve a range of coarse-grained advisory autonomy hold durations in traffic optimization more reliably than baselines.
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Performance Analysis of C-V2X Mode 4 Communication Introducing an Open-Source C-V2X Simulator
Presents a new ns-3-based open-source simulator for C-V2X Mode 4 and reports that it meets LTE Rel. 14 V2X requirements for up to 250 vehicles in a 100m x 100m worst-case area, with better results in a Manhattan grid.