{"title":"预测道路使用者轨迹的自动驾驶汽车自动控制算法","authors":"A. Azarchenkov, M. Lyubimov","doi":"10.51130/graphicon-2020-2-4-53","DOIUrl":null,"url":null,"abstract":"One of the problems faced by developers of artificial intelligence algorithms when creating car control systems is that the actions of other road users are difficult to predict and have a large variability. Even if we assume that all actions comply with traffic rules and participants do not make mistakes, that is, to bring the actual environment closer to the ideal, the task of automating vehicle control still contains many difficulties. This paper describes what difficulties exist in the field of predicting the trajectory of objects, shows concepts that will help in solving this problem, and also describes a particular method of forecasting, which allows you to make a forecast for cars moving along traffic lanes. The main forecasting stages and the results of testing the method collected by using a ready-made data set are given. The results presented in the form of a set of metrics, are compared with another algorithm for predicting trajectories. As a result, the advantages and disadvantages of the created solution were identified.","PeriodicalId":344054,"journal":{"name":"Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2","volume":"132 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Algorithm for Predicting the Trajectory of Road Users to Automate Control of an Autonomous Vehicle\",\"authors\":\"A. Azarchenkov, M. Lyubimov\",\"doi\":\"10.51130/graphicon-2020-2-4-53\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the problems faced by developers of artificial intelligence algorithms when creating car control systems is that the actions of other road users are difficult to predict and have a large variability. Even if we assume that all actions comply with traffic rules and participants do not make mistakes, that is, to bring the actual environment closer to the ideal, the task of automating vehicle control still contains many difficulties. This paper describes what difficulties exist in the field of predicting the trajectory of objects, shows concepts that will help in solving this problem, and also describes a particular method of forecasting, which allows you to make a forecast for cars moving along traffic lanes. The main forecasting stages and the results of testing the method collected by using a ready-made data set are given. The results presented in the form of a set of metrics, are compared with another algorithm for predicting trajectories. As a result, the advantages and disadvantages of the created solution were identified.\",\"PeriodicalId\":344054,\"journal\":{\"name\":\"Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2\",\"volume\":\"132 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.51130/graphicon-2020-2-4-53\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.51130/graphicon-2020-2-4-53","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Algorithm for Predicting the Trajectory of Road Users to Automate Control of an Autonomous Vehicle
One of the problems faced by developers of artificial intelligence algorithms when creating car control systems is that the actions of other road users are difficult to predict and have a large variability. Even if we assume that all actions comply with traffic rules and participants do not make mistakes, that is, to bring the actual environment closer to the ideal, the task of automating vehicle control still contains many difficulties. This paper describes what difficulties exist in the field of predicting the trajectory of objects, shows concepts that will help in solving this problem, and also describes a particular method of forecasting, which allows you to make a forecast for cars moving along traffic lanes. The main forecasting stages and the results of testing the method collected by using a ready-made data set are given. The results presented in the form of a set of metrics, are compared with another algorithm for predicting trajectories. As a result, the advantages and disadvantages of the created solution were identified.