Building the Future of Transportation: A Comprehensive Survey on AV Perception, Localization, and Mapping

Autonomous vehicles (AVs) depend on perception, localization, and mapping to interpret their surroundings and navigate safely. This paper reviews existing methodologies and best practices in these domains, focusing on object detection, object tracking, localization techniques, and environmental mapp...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 25; číslo 7; s. 2004
Hlavní autoři: Patil, Ashok Kumar, Punugupati, Bhargav, Gupta, Himanshi, Mayur, Niranjan S., Ramesh, Srivatsa, Honnavalli, Prasad B.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 23.03.2025
MDPI
Témata:
Accuracy
Algorithms
Autonomous vehicles
Classification
Datasets
Deep learning
Driverless cars
Efficiency
Global positioning systems
GPS
Liu, Timothy
Localization
Mapping
Neural networks
object detection
Optical radar
Proposals
Remote sensing
Review
Sensors
SLAM
Surveys
Telematics
tracking algorithms
Transportation
Transportation industry
Germany
localization
autonomous vehicles
mapping
SLAM
perception module
LiDAR
sensor fusion
object detection
HD maps
tracking algorithms
ISSN:1424-8220, 1424-8220
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Abstract Autonomous vehicles (AVs) depend on perception, localization, and mapping to interpret their surroundings and navigate safely. This paper reviews existing methodologies and best practices in these domains, focusing on object detection, object tracking, localization techniques, and environmental mapping strategies. In the perception module, we analyze state-of-the-art object detection frameworks, such as You Only Look Once version 8 (YOLOv8), and object tracking algorithms like ByteTrack and BoT-SORT (Boosted SORT). We assess their real-time performance, robustness to occlusions, and suitability for complex urban environments. We examine different approaches for localization, including Light Detection and Ranging (LiDAR)-based localization, camera-based localization, and sensor fusion techniques. These methods enhance positional accuracy, particularly in scenarios where Global Positioning System (GPS) signals are unreliable or unavailable. The mapping section explores Simultaneous Localization and Mapping (SLAM) techniques and high-definition (HD) maps, discussing their role in creating detailed, real-time environmental representations that enable autonomous navigation. Additionally, we present insights from our testing, evaluating the effectiveness of different perception, localization, and mapping methods in real-world conditions. By summarizing key advancements, challenges, and practical considerations, this paper provides a reference for researchers and developers working on autonomous vehicle perception, localization, and mapping.
AbstractList Autonomous vehicles (AVs) depend on perception, localization, and mapping to interpret their surroundings and navigate safely. This paper reviews existing methodologies and best practices in these domains, focusing on object detection, object tracking, localization techniques, and environmental mapping strategies. In the perception module, we analyze state-of-the-art object detection frameworks, such as You Only Look Once version 8 (YOLOv8), and object tracking algorithms like ByteTrack and BoT-SORT (Boosted SORT). We assess their real-time performance, robustness to occlusions, and suitability for complex urban environments. We examine different approaches for localization, including Light Detection and Ranging (LiDAR)-based localization, camera-based localization, and sensor fusion techniques. These methods enhance positional accuracy, particularly in scenarios where Global Positioning System (GPS) signals are unreliable or unavailable. The mapping section explores Simultaneous Localization and Mapping (SLAM) techniques and high-definition (HD) maps, discussing their role in creating detailed, real-time environmental representations that enable autonomous navigation. Additionally, we present insights from our testing, evaluating the effectiveness of different perception, localization, and mapping methods in real-world conditions. By summarizing key advancements, challenges, and practical considerations, this paper provides a reference for researchers and developers working on autonomous vehicle perception, localization, and mapping.
Autonomous vehicles (AVs) depend on perception, localization, and mapping to interpret their surroundings and navigate safely. This paper reviews existing methodologies and best practices in these domains, focusing on object detection, object tracking, localization techniques, and environmental mapping strategies. In the perception module, we analyze state-of-the-art object detection frameworks, such as You Only Look Once version 8 (YOLOv8), and object tracking algorithms like ByteTrack and BoT-SORT (Boosted SORT). We assess their real-time performance, robustness to occlusions, and suitability for complex urban environments. We examine different approaches for localization, including Light Detection and Ranging (LiDAR)-based localization, camera-based localization, and sensor fusion techniques. These methods enhance positional accuracy, particularly in scenarios where Global Positioning System (GPS) signals are unreliable or unavailable. The mapping section explores Simultaneous Localization and Mapping (SLAM) techniques and high-definition (HD) maps, discussing their role in creating detailed, real-time environmental representations that enable autonomous navigation. Additionally, we present insights from our testing, evaluating the effectiveness of different perception, localization, and mapping methods in real-world conditions. By summarizing key advancements, challenges, and practical considerations, this paper provides a reference for researchers and developers working on autonomous vehicle perception, localization, and mapping.Autonomous vehicles (AVs) depend on perception, localization, and mapping to interpret their surroundings and navigate safely. This paper reviews existing methodologies and best practices in these domains, focusing on object detection, object tracking, localization techniques, and environmental mapping strategies. In the perception module, we analyze state-of-the-art object detection frameworks, such as You Only Look Once version 8 (YOLOv8), and object tracking algorithms like ByteTrack and BoT-SORT (Boosted SORT). We assess their real-time performance, robustness to occlusions, and suitability for complex urban environments. We examine different approaches for localization, including Light Detection and Ranging (LiDAR)-based localization, camera-based localization, and sensor fusion techniques. These methods enhance positional accuracy, particularly in scenarios where Global Positioning System (GPS) signals are unreliable or unavailable. The mapping section explores Simultaneous Localization and Mapping (SLAM) techniques and high-definition (HD) maps, discussing their role in creating detailed, real-time environmental representations that enable autonomous navigation. Additionally, we present insights from our testing, evaluating the effectiveness of different perception, localization, and mapping methods in real-world conditions. By summarizing key advancements, challenges, and practical considerations, this paper provides a reference for researchers and developers working on autonomous vehicle perception, localization, and mapping.
Audience Academic
Author Gupta, Himanshi
Punugupati, Bhargav
Ramesh, Srivatsa
Mayur, Niranjan S.
Honnavalli, Prasad B.
Patil, Ashok Kumar
AuthorAffiliation Department of Computer Science Engineering, PES University, Bangalore 560085, India; bhargav.punugupati@gmail.com (B.P.); himanshig1403@gmail.com (H.G.); niranjanmayur10@gmail.com (N.S.M.); vatsa2402@gmail.com (S.R.); prasadhb@pes.edu (P.B.H.)
AuthorAffiliation_xml – name: Department of Computer Science Engineering, PES University, Bangalore 560085, India; bhargav.punugupati@gmail.com (B.P.); himanshig1403@gmail.com (H.G.); niranjanmayur10@gmail.com (N.S.M.); vatsa2402@gmail.com (S.R.); prasadhb@pes.edu (P.B.H.)
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/40218517$$D View this record in MEDLINE/PubMed
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Keywords localization
autonomous vehicles
mapping
SLAM
perception module
LiDAR
sensor fusion
object detection
HD maps
tracking algorithms
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RelatedPersons Liu, Timothy
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Snippet Autonomous vehicles (AVs) depend on perception, localization, and mapping to interpret their surroundings and navigate safely. This paper reviews existing...
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SubjectTerms Accuracy
Algorithms
Autonomous vehicles
Classification
Datasets
Deep learning
Driverless cars
Efficiency
Global positioning systems
GPS
Liu, Timothy
Localization
Mapping
Neural networks
object detection
Optical radar
Proposals
Remote sensing
Review
Sensors
SLAM
Surveys
Telematics
tracking algorithms
Transportation
Transportation industry
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Title Building the Future of Transportation: A Comprehensive Survey on AV Perception, Localization, and Mapping
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