Localization and Mapping for Self-Driving Vehicles: A Survey
The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex task...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/12/2/118 |
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| author | Anas Charroud Karim El Moutaouakil Vasile Palade Ali Yahyaouy Uche Onyekpe Eyo U. Eyo |
| author_facet | Anas Charroud Karim El Moutaouakil Vasile Palade Ali Yahyaouy Uche Onyekpe Eyo U. Eyo |
| author_sort | Anas Charroud |
| collection | DOAJ |
| description | The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicle localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains. |
| first_indexed | 2024-03-07T22:23:27Z |
| format | Article |
| id | doaj.art-14c9463661114d7499db1a2e796a9911 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-07T22:23:27Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-14c9463661114d7499db1a2e796a99112024-02-23T15:25:03ZengMDPI AGMachines2075-17022024-02-0112211810.3390/machines12020118Localization and Mapping for Self-Driving Vehicles: A SurveyAnas Charroud0Karim El Moutaouakil1Vasile Palade2Ali Yahyaouy3Uche Onyekpe4Eyo U. Eyo5Technical Sciences Faculty, Sidi Mohamed Ben Abdellah University, Fès-Atlas 30000, MoroccoLaboratory of Engineering Sciences, Multidisciplinary Faculty of Taza, Sidi Mohamed Ben Abdellah University, Taza 35000, MoroccoCentre for Computational Science and Mathematical Modelling, Coventry University, Priory Road, Coventry CV1 5FB, UKComputer Science, Signals, Automatics and Cognitivism Laboratory, Sciences Faculty of Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fès-Atlas 30000, MoroccoCentre for Computational Science and Mathematical Modelling, Coventry University, Priory Road, Coventry CV1 5FB, UKSchool of Engineering, College of Arts, Technology and Environment, University of the West of England, Bristol BS16 1QY, UKThe upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicle localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains.https://www.mdpi.com/2075-1702/12/2/118autonomous drivingfeature extractionmappinglocalizationautomotive securitySLAM |
| spellingShingle | Anas Charroud Karim El Moutaouakil Vasile Palade Ali Yahyaouy Uche Onyekpe Eyo U. Eyo Localization and Mapping for Self-Driving Vehicles: A Survey Machines autonomous driving feature extraction mapping localization automotive security SLAM |
| title | Localization and Mapping for Self-Driving Vehicles: A Survey |
| title_full | Localization and Mapping for Self-Driving Vehicles: A Survey |
| title_fullStr | Localization and Mapping for Self-Driving Vehicles: A Survey |
| title_full_unstemmed | Localization and Mapping for Self-Driving Vehicles: A Survey |
| title_short | Localization and Mapping for Self-Driving Vehicles: A Survey |
| title_sort | localization and mapping for self driving vehicles a survey |
| topic | autonomous driving feature extraction mapping localization automotive security SLAM |
| url | https://www.mdpi.com/2075-1702/12/2/118 |
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