Effects of Elevation Changes and Multilevel Structures on Vehicular Signal Propagation

Reliable Vehicle-to-Everything (V2X) evaluation requires propagation models that represent terrain and multilevel infrastructure. Most integrated vehicular simulators still rely on planar models, but the magnitude of the resulting bias is unclear. This study quantitatively compares flattened two-dimensional (2D) and terrain-aware three-dimensional (3D) variants of three scenarios using identical Sionna RT settings. The pipeline combines OpenStreetMap and digital elevation data, Blender/Blosm scene construction, and SUMO mobility traces. The scenarios represent a 4 km mountain pass, an overpass bridge, and an underground tunnel. Path gain is computed at 5.9 GHz; received signal strength and link outage are then derived using a 23 dBm transmit power and a −94 dBm receiver sensitivity. The outage rate increases from 21% to 30% for the mountain pass, from 20% to 33% for the bridge, and from 0% to 80% for the tunnel with 3D geometry. Mean signal-loss discrepancies reach approximately 9 dB for the mountain pass and 10 dB for the bridge. GPU acceleration reduces per-frame processing time by approximately one order of magnitude in a controlled bridge benchmark. The results show that 3D-aware propagation modeling is necessary for credible assessment of V2X applications in environments with elevation changes.

Authors

Institutions

Publication Details

Journal
Future Transportation
Published
2026-10-05
DOI
https://doi.org/10.3390/futuretransp6050225
Primary Topic
Vehicular Ad Hoc Networks (VANETs)
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Effects of Elevation Changes and Multilevel Structures on Vehicular Signal Propagation

Vitaly G. Stepanyants, Aleksandr A. Amerikanov, A. Yu. Romanov, Artem R. Chibirov et al.
Future Transportation
Vehicular Ad Hoc Networks (VANETs)
article

Effects of Elevation Changes and Multilevel Structures on Vehicular Signal Propagation

Vitaly G. Stepanyants, Aleksandr A. Amerikanov, A. Yu. Romanov, Artem R. Chibirov, Andrey V. Fizulin
article en

Abstract

Reliable Vehicle-to-Everything (V2X) evaluation requires propagation models that represent terrain and multilevel infrastructure. Most integrated vehicular simulators still rely on planar models, but the magnitude of the resulting bias is unclear. This study quantitatively compares flattened two-dimensional (2D) and terrain-aware three-dimensional (3D) variants of three scenarios using identical Sionna RT settings. The pipeline combines OpenStreetMap and digital elevation data, Blender/Blosm scene construction, and SUMO mobility traces. The scenarios represent a 4 km mountain pass, an overpass bridge, and an underground tunnel. Path gain is computed at 5.9 GHz; received signal strength and link outage are then derived using a 23 dBm transmit power and a −94 dBm receiver sensitivity. The outage rate increases from 21% to 30% for the mountain pass, from 20% to 33% for the bridge, and from 0% to 80% for the tunnel with 3D geometry. Mean signal-loss discrepancies reach approximately 9 dB for the mountain pass and 10 dB for the bridge. GPU acceleration reduces per-frame processing time by approximately one order of magnitude in a controlled bridge benchmark. The results show that 3D-aware propagation modeling is necessary for credible assessment of V2X applications in environments with elevation changes.

Future TransportationVol. 6(5)
National Research University Higher School of Economics (RU)
Openalex Percentile: Top 21%
Vehicular Ad Hoc Networks (VANETs)
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.