{"title":"停放一辆视觉引导汽车","authors":"A. Driss, V. Rodrigues, P. Cohens","doi":"10.1109/CCA.1994.381269","DOIUrl":null,"url":null,"abstract":"This paper presents a neural-network solution to the problem of parking of a vision-guided automobile vehicle. Using training experiments with 3-D parking profiles extracted from a sequence of images, the vehicle learns and generalizes its behavior, when recognizing free parking slots and manoeuvering inside a parking slot. Using a modular set of functions, implemented by neural networks trained in simulated environments, the vehicle accomplishes parking tasks in various parking situations.<<ETX>>","PeriodicalId":173370,"journal":{"name":"1994 Proceedings of IEEE International Conference on Control and Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Parking a vision-guided automobile vehicle\",\"authors\":\"A. Driss, V. Rodrigues, P. Cohens\",\"doi\":\"10.1109/CCA.1994.381269\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a neural-network solution to the problem of parking of a vision-guided automobile vehicle. Using training experiments with 3-D parking profiles extracted from a sequence of images, the vehicle learns and generalizes its behavior, when recognizing free parking slots and manoeuvering inside a parking slot. Using a modular set of functions, implemented by neural networks trained in simulated environments, the vehicle accomplishes parking tasks in various parking situations.<<ETX>>\",\"PeriodicalId\":173370,\"journal\":{\"name\":\"1994 Proceedings of IEEE International Conference on Control and Applications\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1994 Proceedings of IEEE International Conference on Control and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCA.1994.381269\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1994 Proceedings of IEEE International Conference on Control and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCA.1994.381269","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents a neural-network solution to the problem of parking of a vision-guided automobile vehicle. Using training experiments with 3-D parking profiles extracted from a sequence of images, the vehicle learns and generalizes its behavior, when recognizing free parking slots and manoeuvering inside a parking slot. Using a modular set of functions, implemented by neural networks trained in simulated environments, the vehicle accomplishes parking tasks in various parking situations.<>
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