Pith. sign in

REVIEW 1 cited by

Research on Enhancing C-V2X Communication via Danger-Aware Vehicular Networking

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2410.00012 v2 pith:H46FQ4XL submitted 2024-09-14 cs.NI cs.SYeess.SY

classification cs.NIcs.SYeess.SY
keywords communicationc-v2xenhancingprotocolvehiclesadvancingaimedallowing
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

This paper presents a protocol that optimizes message dissemination in C-V2X technology, crucial for advancing intelligent transportation systems (ITS) aimed at enhancing road safety. As vehicle density and velocity rise, the volume of data requiring communication significantly increases. By considering the risk levels that vehicles encounter and using inter-vehicle proximity as a key indicator of potential hazards, the proposed protocol prioritizes communication, allowing vehicles facing higher risks to transmit their messages first. Our results show that this prioritization effectively reduces the number of concurrent transmissions, leading to improved performance metrics such as packet delivery ratio, throughput, latency, and lower probabilities of channel congestion and collision.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Adaptive Network Selection for Latency-Aware V2X Systems under Varying Network and Vehicle Densities

    cs.NI 2025-08 unverdicted novelty 4.0 of 10

    A heuristic network selection method for V2X achieves near-optimal utility in under 15 milliseconds, outperforming MILP and Q-learning baselines in simulation.

Pith tools