查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news originating from Karadeniz Technical Un iversity by NewsRx editors, the research stated, “Vehicle-to-vehicle (V2V) commu nication, which plays an important role in intelligent transportation systems, h as been statistically proven to improve traffic efficiency and reduce the probab ility of accidents.” Our news journalists obtained a quote from the research from Karadeniz Technical University: “In real-world applications, it is critical to accurately estimate the path loss parameter in communication channels due to the variable and comple x propagation environments often encountered in inter-vehicle communication scen arios. This paper presents a study on various machine learning methods to improv e path loss estimation in V2V communication using a dataset (192,000 observation s) obtained from field measurements of highway environments in the Trabzon and G umushane provinces in Turkiye. For this purpose, path loss estimation was carrie d out with different machine learning algorithms such as Artificial Neural Netwo rks, Random Forest, Linear Regression, Gradient Boosting, Support Vector Regress ion, and AdaBoost by using various environmental and system features. Then, perf ormance comparisons were conducted between machine learning methods and traditio nal empirical approaches such as logdistance, two-ray, and log-ray. Examining t he outputs reveals that machine learning methods outperform traditional methods and yield results quickly. As a result, the Random Forest and Gradient Boosting methods demonstrated the highest prediction performances, with R2 values of 0.97 and 0.96, MAE values of 0.0557 and 0.0701, and RMSE values of 0.0774 and 0.0964 , respectively, outperforming both empirical methods, other machine learning tec hniques, and the existing studies based on V2V.”
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