{"title":"微型车辆实时车道线跟踪算法","authors":"J. Suto","doi":"10.2478/ttj-2021-0036","DOIUrl":null,"url":null,"abstract":"Abstract Autonomous navigation is important not only in autonomous cars but also in other transportation systems. In many applications, an autonomous vehicle has to follow the curvature of a real or artificial road or in other words lane lines. In those application, the key is the lane detection. In this paper, we present a real-time lane line tracking algorithm mainly designed to mini vehicles with relatively low computation capacity and single camera sensor. The proposed algorithm exploits computer vision techniques in combination with digital filtering. To demonstrate the performance of the method, experiments are conducted in an indoor, self-made test track where the effect of several external influencing factors can be observed. Experimental results show that the proposed algorithm works well independently of shadows, bends, reflection and lighting changes.","PeriodicalId":44110,"journal":{"name":"Transport and Telecommunication Journal","volume":"22 1","pages":"461 - 470"},"PeriodicalIF":1.1000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Real-Time Lane Line Tracking Algorithm to Mini Vehicles\",\"authors\":\"J. Suto\",\"doi\":\"10.2478/ttj-2021-0036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Autonomous navigation is important not only in autonomous cars but also in other transportation systems. In many applications, an autonomous vehicle has to follow the curvature of a real or artificial road or in other words lane lines. In those application, the key is the lane detection. In this paper, we present a real-time lane line tracking algorithm mainly designed to mini vehicles with relatively low computation capacity and single camera sensor. The proposed algorithm exploits computer vision techniques in combination with digital filtering. To demonstrate the performance of the method, experiments are conducted in an indoor, self-made test track where the effect of several external influencing factors can be observed. Experimental results show that the proposed algorithm works well independently of shadows, bends, reflection and lighting changes.\",\"PeriodicalId\":44110,\"journal\":{\"name\":\"Transport and Telecommunication Journal\",\"volume\":\"22 1\",\"pages\":\"461 - 470\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2021-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transport and Telecommunication Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/ttj-2021-0036\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"TRANSPORTATION SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transport and Telecommunication Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/ttj-2021-0036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
Real-Time Lane Line Tracking Algorithm to Mini Vehicles
Abstract Autonomous navigation is important not only in autonomous cars but also in other transportation systems. In many applications, an autonomous vehicle has to follow the curvature of a real or artificial road or in other words lane lines. In those application, the key is the lane detection. In this paper, we present a real-time lane line tracking algorithm mainly designed to mini vehicles with relatively low computation capacity and single camera sensor. The proposed algorithm exploits computer vision techniques in combination with digital filtering. To demonstrate the performance of the method, experiments are conducted in an indoor, self-made test track where the effect of several external influencing factors can be observed. Experimental results show that the proposed algorithm works well independently of shadows, bends, reflection and lighting changes.