{"title":"基于神经网络的应急决策支持系统地形可通行性评价方法","authors":"A. Pershutkin, A. Dukhanov, Petr Gladilin","doi":"10.1109/AICT47866.2019.8981782","DOIUrl":null,"url":null,"abstract":"This paper deals with a new method to evaluate the velocity of a transport unit based on static and dynamic data on terrain relief, urban objects, and the values of hydrometeorological parameters. We considered the existing and available approaches to off-road routes based on the parameters mentioned above (considering velocity reduction depending on terrain parameters) and defined the problem statement. Then, we designed the method to evaluate the velocity on a terrain surface using an artificial neural network. Our method considers the static data (e.g., type of surface) and dynamic hydro-meteorological parameters of the terrain. To determine the efficiency of the method, we conducted the experiment using data from the Leningrad region. The experiment shows significantly increased accuracy in the velocity evaluation of the transport unit.","PeriodicalId":329473,"journal":{"name":"2019 IEEE 13th International Conference on Application of Information and Communication Technologies (AICT)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Approach to Terrain Trafficability Evaluation Based on a Neural Network for Emergency Decision-Support Systems\",\"authors\":\"A. Pershutkin, A. Dukhanov, Petr Gladilin\",\"doi\":\"10.1109/AICT47866.2019.8981782\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper deals with a new method to evaluate the velocity of a transport unit based on static and dynamic data on terrain relief, urban objects, and the values of hydrometeorological parameters. We considered the existing and available approaches to off-road routes based on the parameters mentioned above (considering velocity reduction depending on terrain parameters) and defined the problem statement. Then, we designed the method to evaluate the velocity on a terrain surface using an artificial neural network. Our method considers the static data (e.g., type of surface) and dynamic hydro-meteorological parameters of the terrain. To determine the efficiency of the method, we conducted the experiment using data from the Leningrad region. The experiment shows significantly increased accuracy in the velocity evaluation of the transport unit.\",\"PeriodicalId\":329473,\"journal\":{\"name\":\"2019 IEEE 13th International Conference on Application of Information and Communication Technologies (AICT)\",\"volume\":\"77 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 13th International Conference on Application of Information and Communication Technologies (AICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AICT47866.2019.8981782\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 13th International Conference on Application of Information and Communication Technologies (AICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AICT47866.2019.8981782","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Approach to Terrain Trafficability Evaluation Based on a Neural Network for Emergency Decision-Support Systems
This paper deals with a new method to evaluate the velocity of a transport unit based on static and dynamic data on terrain relief, urban objects, and the values of hydrometeorological parameters. We considered the existing and available approaches to off-road routes based on the parameters mentioned above (considering velocity reduction depending on terrain parameters) and defined the problem statement. Then, we designed the method to evaluate the velocity on a terrain surface using an artificial neural network. Our method considers the static data (e.g., type of surface) and dynamic hydro-meteorological parameters of the terrain. To determine the efficiency of the method, we conducted the experiment using data from the Leningrad region. The experiment shows significantly increased accuracy in the velocity evaluation of the transport unit.