Abstract
Connected cooperative and automated mobility
(CCAM) benefits from reliable wireless vehicle-to-everything
(V2X) communication links in safety-critical and time-sensitive
situations. The ego vehicle’s perception, primarily derived from
LIDAR, RADAR, and camera data, is limited by the line-of-sight
(LOS). Sensor information beyond the LOS can be acquired
by reliable V2X communication links from other cooperative
vehicles or infrastructure elements. We identify CCAM use
cases for both real-world applications and test phases, which
stand to gain from understanding spatial reliability regions for
communication links. Frame error rate (FER) classes for these
regions, from the perspective of the ego vehicle, are provided
to aid decision-making for autonomous vehicles. We propose a
testbed architecture for system validation, verification, and test
scenario generation, which integrates FER prediction through a
high-performance open-source computing reference framework
(HOPE). Our study demonstrates that the measured FER within
a city scenario closely aligns with the FER obtained via a
hardware-in-the-loop (HiL) framework and a non-stationary
geometry-based stochastic channel model (GSCM) that utilizes
OpenStreetMap data enriched with event-specific static objects.
We use the GSCM and the HiL framework to overcome the
fundamental limits of estimating the FER in non-stationary
scenarios. As a final demonstration of the HOPE framework,
we achieve an 80 % accuracy in predicting the FER class.
(CCAM) benefits from reliable wireless vehicle-to-everything
(V2X) communication links in safety-critical and time-sensitive
situations. The ego vehicle’s perception, primarily derived from
LIDAR, RADAR, and camera data, is limited by the line-of-sight
(LOS). Sensor information beyond the LOS can be acquired
by reliable V2X communication links from other cooperative
vehicles or infrastructure elements. We identify CCAM use
cases for both real-world applications and test phases, which
stand to gain from understanding spatial reliability regions for
communication links. Frame error rate (FER) classes for these
regions, from the perspective of the ego vehicle, are provided
to aid decision-making for autonomous vehicles. We propose a
testbed architecture for system validation, verification, and test
scenario generation, which integrates FER prediction through a
high-performance open-source computing reference framework
(HOPE). Our study demonstrates that the measured FER within
a city scenario closely aligns with the FER obtained via a
hardware-in-the-loop (HiL) framework and a non-stationary
geometry-based stochastic channel model (GSCM) that utilizes
OpenStreetMap data enriched with event-specific static objects.
We use the GSCM and the HiL framework to overcome the
fundamental limits of estimating the FER in non-stationary
scenarios. As a final demonstration of the HOPE framework,
we achieve an 80 % accuracy in predicting the FER class.
Originalsprache | Englisch |
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Titel | 2024 IEEE Vehicular Networking Conference (VNC) |
Seiten | 9-16 |
Seitenumfang | 8 |
ISBN (elektronisch) | 979-8-3503-6270-1 |
DOIs | |
Publikationsstatus | Veröffentlicht - 1 Mai 2024 |
Veranstaltung | 2024 IEEE Vehicular Networking Conference (VNC) - Kobe, Kobe, Japan Dauer: 29 Mai 2024 → 31 Mai 2024 |
Konferenz
Konferenz | 2024 IEEE Vehicular Networking Conference (VNC) |
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Land/Gebiet | Japan |
Stadt | Kobe |
Zeitraum | 29/05/24 → 31/05/24 |
Research Field
- Enabling Digital Technologies