Page 804 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

the response back to the leader vehicle, which then communicates the decision to the other
vehicles in the cluster, ensuring efficient resource management across the system. If the QoS
is not met, then the leader vehicle executes the task locally and makes decisions, which it then
communicates the decision to other vehicles in the cluster.

This process ultimately facilitates low-cost decision-making and reduces the load on the on-
device resources.

5 References

[1] Hernangomez, Rodrigo, Philipp Geuer, Alexandros Palaios, Daniel Schäufele, Cara
Watermann, Khawla Taleb-Bouhemadi, Mohammad Parvini, Anton Krause, Sanket Partani,
Christian Vielhaus, Martin Kasparick, Daniel F. Külzer, Friedrich Burmeister, Frank H. P.
Fitzek, Hans D. Schotten, Gerhard Fettweis, and Slawomir Stanczak. “Berlin V2X.” IEEE
Dataport, December 8, 2022. https:// doi .org/ 10 .21227/ 8cj7 -q373.
[2] Annu, Phaneendra Pydimarri, Pachamuthu Rajalakshmi, Sunnam Venkata Srikanth,
and Munjagala Prasad. “TiHAN-V2X: A Comprehensive Dataset for Dynamic C-V2X
Communication within the Indian Context.” IEEE Dataport, October 8, 2024. https:// doi
.org/ 10 .21227/ f2kd -9g03.
[3] Kulzer, Daniel F., et al. “AI4Mobile: Use Cases and Challenges of AI-based QoS Prediction
for High-Mobility Scenarios.” April 2021. https:// doi .org/ 10 .1109/ vtc2021 -spring51267
.2021 .9449059.
[4] Palaios, Alexandros, et al. “Machine Learning for QoS Prediction in Vehicular
Communication: Challenges and Solution Approaches.” IEEE Access 11 (January 2023):
92459–92477. https:// doi .org/ 10 .1109/ access .2023 .3303528.
[5] International Telecommunication Union (ITU). “AI for Road Safety Global Initiative.” 2025.
https:// www .itu .int/ en/ ITU -T/ ITS/ AIRoadSafety/ Pages/ default .aspx.
[6] Ain, N. U., Rodrigo Hernangómez, Alexandros Palaios, Martin Kasparick, and Slawomir
Stańczak. “QoS Prediction in Radio Vehicular Environments via Prior User Information.”
arXiv, February 2024. https:// doi .org/ 10 .48550/ arxiv .2402 .17689.
[7] Adadi, Amina, and Mohammed Berrada. “Peeking Inside the Black-Box: A Survey on
Explainable Artificial Intelligence (XAI).” IEEE Access 6 (October 2018): 52138–52160.
https:// doi .org/ 10 .1109/ access .2018 .2870052.
[8] Annu, and P. Rajalakshmi. “Towards 6G V2X Sidelink: Survey of Resource Allocation—
Mathematical Formulations, Challenges, and Proposed Solutions.” IEEE Open Journal of
Vehicular Technology 5 (2024): 344–383. https:// doi .org/ 10 .1109/ OJVT .2024 .3368240.
[9] Fraunhofer Heinrich Hertz Institute. BerlinV2X. GitHub. Accessed June 19, 2025. https://
github .com/ fraunhoferhhi/ BerlinV2X.
[10] Takalani95. QoS Prediction Challenge by ITU AI/ML in 5G Challenge. GitHub. Accessed
June 19, 2025. https:// github .com/ takalani95/ QoS -Prediction -Challenge -by -ITU -AI -ML
-in -5G -Challenge.

768