{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:00:22Z","timestamp":1790240422599,"version":"4.1.0"},"reference-count":42,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cooperative perception can improve the situational awareness of connected and autonomous vehicles by exchanging complementary sensing information among nearby agents. However, systematic multi-vehicle cooperation also increases vehicle-to-everything (V2X) communication demand, processing overhead, and operational resource use. This study introduces EcoCert-V2X, a risk-aware adaptive cooperation framework that selects the level of cooperative feature acquisition according to action-admissibility scores derived from the pre-communication operating context. Rather than transmitting all available peer information systematically, EcoCert-V2X selects among a frozen set of cooperation actions while accounting for perception quality, communication volume, latency, graphics processing unit (GPU) action energy, modeled communication energy, and operational carbon. The framework is evaluated using a leakage-controlled development, calibration, and locked confirmatory protocol on the V2V4Real benchmark, which contains real-world vehicle-to-vehicle light detection and ranging (LiDAR) observations. Across 1846 confirmatory samples from eight scenarios and 30 paired operational seeds, EcoCert-V2X achieved a scenario-balanced task risk of 0.6513 compared with 0.6433 for full cooperation. The resulting risk difference of +0.0080 satisfied the prespecified non-inferiority criterion, with a one-sided 95% upper bound of +0.0188 below the margin of 0.03. Communication volume decreased by 14.94%, while operational carbon decreased by 4.07%. The observed GPU action energy reduction remained statistically inconclusive, and the absolute risk target of 0.5730 was not reached. Under six frozen packet erasure and signal-to-noise ratio (SNR) perturbation conditions, aggregate non-inferiority was preserved, although localized scenario\u2013seed sensitivity remained. These results show that risk-aware adaptive V2X cooperation can reduce communication demand while preserving relative cooperative perception performance in connected-vehicle environments.<\/jats:p>","DOI":"10.3390\/s26196025","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T11:09:08Z","timestamp":1790161748000},"page":"6025","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EcoCert-V2X: Risk-Aware Adaptive Cooperation for Communication-Efficient V2X Cooperative Perception"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8460-9240","authenticated-orcid":false,"given":"Malek","family":"Alrashidi","sequence":"first","affiliation":[{"name":"Department of Computer Science, Applied College, University of Tabuk, Tabuk 71491, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"software","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7278-6175","authenticated-orcid":false,"given":"Wajih","family":"Abdallah","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Applied College, University of Tabuk, Tabuk 71491, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ji, A., Ju, X., Liu, N., Chen, J., and Dai, Z. 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