F. Rundo, R. Leotta, V. Piuri, A. Genovese, F. Scotti, S. Battiato
{"title":"Intelligent Road Surface Deep Embedded Classifier for an Efficient Physio-Based Car Driver Assistance","authors":"F. Rundo, R. Leotta, V. Piuri, A. Genovese, F. Scotti, S. Battiato","doi":"10.1109/ICAS49788.2021.9551124","DOIUrl":null,"url":null,"abstract":"Car driving safety represents one of the major targets of the ADAS (Advanced Driver Assistance Systems) technologies deeply investigated by the scientific community and car makers. From intelligent suspension control systems to adaptive braking systems, the ADAS solutions allows to significantly improve both driving comfort and safety. The aim of this contribution is to propose a driving safety assessment system based on deep networks equipped with self-attention Criss-Cross mechanism to classify the driving road surface combined with a physio-based drowsiness monitoring of the driver. The retrieved driving safety assessment performance confirmed the effectiveness of the proposed pipeline.","PeriodicalId":287105,"journal":{"name":"2021 IEEE International Conference on Autonomous Systems (ICAS)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Autonomous Systems (ICAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAS49788.2021.9551124","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Car driving safety represents one of the major targets of the ADAS (Advanced Driver Assistance Systems) technologies deeply investigated by the scientific community and car makers. From intelligent suspension control systems to adaptive braking systems, the ADAS solutions allows to significantly improve both driving comfort and safety. The aim of this contribution is to propose a driving safety assessment system based on deep networks equipped with self-attention Criss-Cross mechanism to classify the driving road surface combined with a physio-based drowsiness monitoring of the driver. The retrieved driving safety assessment performance confirmed the effectiveness of the proposed pipeline.
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