Andrea Fabris, L. Parolini, Sebastian Schneider, A. Cenedese
{"title":"Use of probabilistic graphical methods for online map validation","authors":"Andrea Fabris, L. Parolini, Sebastian Schneider, A. Cenedese","doi":"10.1109/ivworkshops54471.2021.9669245","DOIUrl":null,"url":null,"abstract":"In the world of autonomous driving, high resolution maps play a fundamental role. Such maps are highly accurate representations of the environment and are essential for all the algorithms of strategy and path planning operations. Unfortunately, it is not always possible to guarantee the total reliability of these maps and therefore it is necessary to design a procedure for their validation. In this paper we introduce a framework for validating map data at run-time based on probabilistic graphical models. Results from simulations show the capabilities of the proposed approach and highlight the need to find an appropriate balance between model accuracy and complexity.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ivworkshops54471.2021.9669245","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In the world of autonomous driving, high resolution maps play a fundamental role. Such maps are highly accurate representations of the environment and are essential for all the algorithms of strategy and path planning operations. Unfortunately, it is not always possible to guarantee the total reliability of these maps and therefore it is necessary to design a procedure for their validation. In this paper we introduce a framework for validating map data at run-time based on probabilistic graphical models. Results from simulations show the capabilities of the proposed approach and highlight the need to find an appropriate balance between model accuracy and complexity.