Multi-sensor fusion and segmentation for autonomous vehicle multi-object tracking using deep Q networks
Autonomous vehicles, often known as self-driving cars, have emerged as a disruptive technology with the promise of safer, more efficient, and convenient transportation. The existing works provide achievable results but lack effective solutions, as accumulation on roads can obscure lane markings and...
Uloženo v:
| Vydáno v: | Scientific reports Ročník 14; číslo 1; s. 31130 - 32 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
Nature Publishing Group UK
28.12.2024
Nature Publishing Group Nature Portfolio |
| Témata: | |
| ISSN: | 2045-2322, 2045-2322 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Autonomous vehicles, often known as self-driving cars, have emerged as a disruptive technology with the promise of safer, more efficient, and convenient transportation. The existing works provide achievable results but lack effective solutions, as accumulation on roads can obscure lane markings and traffic signs, making it difficult for the self-driving car to navigate safely. Heavy rain, snow, fog, or dust storms can severely limit the car’s sensors’ ability to detect obstacles, pedestrians, and other vehicles, which pose potential safety risks. So, we have presented a multi-sensor fusion and segmentation for multi-object tracking using DQN in self-driving cars. Our proposed scheme incorporates the handling of pipelines for camera and LiDAR data and the development of an autonomous solution for object detection by handling sensor images. An Improved Adaptive Extended Kalman Filter (IAEKF) was used for noise reduction. The Contrast enhancement was done using a Normalised Gamma Transformation based CLAHE (NGT-CLAHE), and the adaptive thresholding was implemented using an Improved Adaptive Weighted Mean Filter (IAWMF) which was used for preprocessing. The multi-segmentation based on orientation employs various segmentation techniques and degrees. The dense net-based multi-image fusion gives more efficiency and a high memory in terms of fast processing time. The Energy Valley Optimizer (EVO) approach is used to select grid map-based paths and lanes. This strategy solves complicated tasks in a simple manner, which leads to ease of flexibility, resilience, and scalability. In addition, the YOLO V7 model is used for detection and categorization. The proposed work is evaluated using metrics such as velocity, accuracy rate, success rate, success ratio, mean squared error, loss rate, and accumulated reward. |
|---|---|
| AbstractList | Autonomous vehicles, often known as self-driving cars, have emerged as a disruptive technology with the promise of safer, more efficient, and convenient transportation. The existing works provide achievable results but lack effective solutions, as accumulation on roads can obscure lane markings and traffic signs, making it difficult for the self-driving car to navigate safely. Heavy rain, snow, fog, or dust storms can severely limit the car’s sensors’ ability to detect obstacles, pedestrians, and other vehicles, which pose potential safety risks. So, we have presented a multi-sensor fusion and segmentation for multi-object tracking using DQN in self-driving cars. Our proposed scheme incorporates the handling of pipelines for camera and LiDAR data and the development of an autonomous solution for object detection by handling sensor images. An Improved Adaptive Extended Kalman Filter (IAEKF) was used for noise reduction. The Contrast enhancement was done using a Normalised Gamma Transformation based CLAHE (NGT-CLAHE), and the adaptive thresholding was implemented using an Improved Adaptive Weighted Mean Filter (IAWMF) which was used for preprocessing. The multi-segmentation based on orientation employs various segmentation techniques and degrees. The dense net-based multi-image fusion gives more efficiency and a high memory in terms of fast processing time. The Energy Valley Optimizer (EVO) approach is used to select grid map-based paths and lanes. This strategy solves complicated tasks in a simple manner, which leads to ease of flexibility, resilience, and scalability. In addition, the YOLO V7 model is used for detection and categorization. The proposed work is evaluated using metrics such as velocity, accuracy rate, success rate, success ratio, mean squared error, loss rate, and accumulated reward. Abstract Autonomous vehicles, often known as self-driving cars, have emerged as a disruptive technology with the promise of safer, more efficient, and convenient transportation. The existing works provide achievable results but lack effective solutions, as accumulation on roads can obscure lane markings and traffic signs, making it difficult for the self-driving car to navigate safely. Heavy rain, snow, fog, or dust storms can severely limit the car’s sensors’ ability to detect obstacles, pedestrians, and other vehicles, which pose potential safety risks. So, we have presented a multi-sensor fusion and segmentation for multi-object tracking using DQN in self-driving cars. Our proposed scheme incorporates the handling of pipelines for camera and LiDAR data and the development of an autonomous solution for object detection by handling sensor images. An Improved Adaptive Extended Kalman Filter (IAEKF) was used for noise reduction. The Contrast enhancement was done using a Normalised Gamma Transformation based CLAHE (NGT-CLAHE), and the adaptive thresholding was implemented using an Improved Adaptive Weighted Mean Filter (IAWMF) which was used for preprocessing. The multi-segmentation based on orientation employs various segmentation techniques and degrees. The dense net-based multi-image fusion gives more efficiency and a high memory in terms of fast processing time. The Energy Valley Optimizer (EVO) approach is used to select grid map-based paths and lanes. This strategy solves complicated tasks in a simple manner, which leads to ease of flexibility, resilience, and scalability. In addition, the YOLO V7 model is used for detection and categorization. The proposed work is evaluated using metrics such as velocity, accuracy rate, success rate, success ratio, mean squared error, loss rate, and accumulated reward. Autonomous vehicles, often known as self-driving cars, have emerged as a disruptive technology with the promise of safer, more efficient, and convenient transportation. The existing works provide achievable results but lack effective solutions, as accumulation on roads can obscure lane markings and traffic signs, making it difficult for the self-driving car to navigate safely. Heavy rain, snow, fog, or dust storms can severely limit the car's sensors' ability to detect obstacles, pedestrians, and other vehicles, which pose potential safety risks. So, we have presented a multi-sensor fusion and segmentation for multi-object tracking using DQN in self-driving cars. Our proposed scheme incorporates the handling of pipelines for camera and LiDAR data and the development of an autonomous solution for object detection by handling sensor images. An Improved Adaptive Extended Kalman Filter (IAEKF) was used for noise reduction. The Contrast enhancement was done using a Normalised Gamma Transformation based CLAHE (NGT-CLAHE), and the adaptive thresholding was implemented using an Improved Adaptive Weighted Mean Filter (IAWMF) which was used for preprocessing. The multi-segmentation based on orientation employs various segmentation techniques and degrees. The dense net-based multi-image fusion gives more efficiency and a high memory in terms of fast processing time. The Energy Valley Optimizer (EVO) approach is used to select grid map-based paths and lanes. This strategy solves complicated tasks in a simple manner, which leads to ease of flexibility, resilience, and scalability. In addition, the YOLO V7 model is used for detection and categorization. The proposed work is evaluated using metrics such as velocity, accuracy rate, success rate, success ratio, mean squared error, loss rate, and accumulated reward.Autonomous vehicles, often known as self-driving cars, have emerged as a disruptive technology with the promise of safer, more efficient, and convenient transportation. The existing works provide achievable results but lack effective solutions, as accumulation on roads can obscure lane markings and traffic signs, making it difficult for the self-driving car to navigate safely. Heavy rain, snow, fog, or dust storms can severely limit the car's sensors' ability to detect obstacles, pedestrians, and other vehicles, which pose potential safety risks. So, we have presented a multi-sensor fusion and segmentation for multi-object tracking using DQN in self-driving cars. Our proposed scheme incorporates the handling of pipelines for camera and LiDAR data and the development of an autonomous solution for object detection by handling sensor images. An Improved Adaptive Extended Kalman Filter (IAEKF) was used for noise reduction. The Contrast enhancement was done using a Normalised Gamma Transformation based CLAHE (NGT-CLAHE), and the adaptive thresholding was implemented using an Improved Adaptive Weighted Mean Filter (IAWMF) which was used for preprocessing. The multi-segmentation based on orientation employs various segmentation techniques and degrees. The dense net-based multi-image fusion gives more efficiency and a high memory in terms of fast processing time. The Energy Valley Optimizer (EVO) approach is used to select grid map-based paths and lanes. This strategy solves complicated tasks in a simple manner, which leads to ease of flexibility, resilience, and scalability. In addition, the YOLO V7 model is used for detection and categorization. The proposed work is evaluated using metrics such as velocity, accuracy rate, success rate, success ratio, mean squared error, loss rate, and accumulated reward. |
| ArticleNumber | 31130 |
| Author | Sasikumar, P. Vinoth, K. |
| Author_xml | – sequence: 1 givenname: K. surname: Vinoth fullname: Vinoth, K. organization: School of Electronics Engineering, Vellore Institute of Technology – sequence: 2 givenname: P. surname: Sasikumar fullname: Sasikumar, P. email: sasikumar.p@vit.ac.in organization: School of Electronics Engineering, Vellore Institute of Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39732930$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9Uk1v1TAQjFARLaV_gAOKxIVLwJ-JfUKoolCpCCHB2do46zSvif2wkyL-ff1eWmh7qA-2tTszGu3Oy-LAB49F8ZqS95Rw9SEJKrWqCBOVYlzWFXlWHDEiZMU4Ywf3_ofFSUobko9kWlD9ojjkuuFMc3JU9N-WcR6qhD6FWLolDcGX4LsyYT-hn2HeFVzuwTIHH6awpPIaLwc7YjntuaHdoJ3LOYK9GnxfZo18d4jb8kfpcf4T4lV6VTx3MCY8uX2Pi19nn3-efq0uvn85P_10UVkp6FxB4wCEloBco1AdSFq7GrrWukYRDZ3smGaWa7CcudbWVLkahXROWUJlw4-L81W3C7Ax2zhMEP-aAIPZF0LsDcR55940hDQoWMuJ0IJAraQTipFWQNOo1uqs9XHV2i7thJ3N44gwPhB92PHDpenDtaG0ViyvJyu8u1WI4feCaTbTkCyOI3jMgzScCq0UZTXP0LePoJuwRJ9ntUc1WkmlMurNfUv_vNwtNAPUCrAxpBTRGTusS8wOh9FQYnbxMWt8TI6P2cfH7KjsEfVO_UkSX0kpg32P8b_tJ1g3pXHYrw |
| CitedBy_id | crossref_primary_10_12677_csa_2025_155123 crossref_primary_10_3390_s25113411 crossref_primary_10_1109_MVT_2025_3552887 crossref_primary_10_3390_su17167284 crossref_primary_10_1038_s41597_025_04636_2 |
| Cites_doi | 10.3390/app122010221 10.3390/s23083794 10.1007/s11760-022-02222-2 10.3390/s22062252 10.1109/LRA.2020.2972865 10.1155/2021/9218137 10.1016/j.robot.2018.11.002 10.1109/IVS.2018.8500511 10.1186/s10033-022-00745-w 10.1016/j.cviu.2020.102907 10.1109/TITS.2019.2909066 10.3390/s22239108 10.1109/CVPR.2012.6248074 10.3390/electronics11192993 10.3390/app11010035 10.1109/WACV48630.2021.00157 10.1109/JAS.2020.1003033 10.1016/j.patcog.2022.108956 10.1109/SDF.2019.8916629 10.1109/SP40001.2021.00076 10.3390/app11073018 10.1109/IVS.2019.8813778 10.3390/app13042094 10.3390/s19204357 10.1016/j.rineng.2024.102510 10.1109/IVS.2018.8500464 10.3390/s23031613 10.3390/jimaging8110306 10.1109/TITS.2021.3059674 10.1109/TIP.2019.2913079 10.1007/s11042-021-11437-3 10.3390/app11041514 10.3390/s23063335 10.3390/s23115110 10.3390/rs15051210 10.1109/TITS.2022.3231259 10.3390/su15032628 10.1016/j.image.2022.116667 10.1007/978-3-031-61066-0_14 10.3390/s22135061 10.3390/s23052845 10.3390/s22165946 10.1109/WACV48630.2021.00232 10.1109/TIV.2023.3235007 10.3390/s21227461 10.3390/app11010029 10.1016/j.oceaneng.2024.119368 10.1007/s11042-023-17456-6 10.1016/j.inffus.2021.07.004 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2024 2024. The Author(s). Copyright Nature Publishing Group 2024 The Author(s) 2024 2024 |
| Copyright_xml | – notice: The Author(s) 2024 – notice: 2024. The Author(s). – notice: Copyright Nature Publishing Group 2024 – notice: The Author(s) 2024 2024 |
| DBID | C6C AAYXX CITATION NPM 3V. 7X7 7XB 88A 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
| DOI | 10.1038/s41598-024-82356-0 |
| DatabaseName | Springer Nature OA Free Journals CrossRef PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central ProQuest One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials - QC Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Biological Science Collection ProQuest Health & Medical Collection Medical Database Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic ProQuest Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | CrossRef Publicly Available Content Database MEDLINE - Academic PubMed |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 2045-2322 |
| EndPage | 32 |
| ExternalDocumentID | oai_doaj_org_article_7007e42b304940a685f4820b4a778bc9 PMC11682159 39732930 10_1038_s41598_024_82356_0 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Vellore Institute of Technology, Vellore |
| GroupedDBID | 0R~ 3V. 4.4 53G 5VS 7X7 88A 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD ABDBF ABUWG ACGFS ACSMW ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AJTQC ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE KQ8 LK8 M0L M1P M2P M48 M7P M~E NAO OK1 PIMPY PQQKQ PROAC PSQYO RNT RNTTT RPM SNYQT UKHRP AASML AAYXX AFFHD AFPKN CITATION PHGZM PHGZT PJZUB PPXIY PQGLB NPM 7XB 8FK K9. PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c541t-a7faa495ae39e48da516f6adbcf7809ad5d292c39ac32fbc618f6e45ff8c01573 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 10 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001385898400048&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2045-2322 |
| IngestDate | Fri Oct 03 12:43:24 EDT 2025 Tue Nov 04 02:03:31 EST 2025 Sun Nov 09 13:11:40 EST 2025 Tue Oct 07 07:43:40 EDT 2025 Wed Feb 19 02:01:34 EST 2025 Sat Nov 29 03:22:02 EST 2025 Tue Nov 18 21:53:57 EST 2025 Fri Feb 21 02:36:07 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Multi-sensor fusion Segmentation Self-driving vehicles Energy Valley Optimizer (EVO) Dense net (D net) YOLO V7 model |
| Language | English |
| License | 2024. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c541t-a7faa495ae39e48da516f6adbcf7809ad5d292c39ac32fbc618f6e45ff8c01573 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://doaj.org/article/7007e42b304940a685f4820b4a778bc9 |
| PMID | 39732930 |
| PQID | 3149798588 |
| PQPubID | 2041939 |
| PageCount | 32 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_7007e42b304940a685f4820b4a778bc9 pubmedcentral_primary_oai_pubmedcentral_nih_gov_11682159 proquest_miscellaneous_3149881263 proquest_journals_3149798588 pubmed_primary_39732930 crossref_citationtrail_10_1038_s41598_024_82356_0 crossref_primary_10_1038_s41598_024_82356_0 springer_journals_10_1038_s41598_024_82356_0 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-12-28 |
| PublicationDateYYYYMMDD | 2024-12-28 |
| PublicationDate_xml | – month: 12 year: 2024 text: 2024-12-28 day: 28 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Scientific reports |
| PublicationTitleAbbrev | Sci Rep |
| PublicationTitleAlternate | Sci Rep |
| PublicationYear | 2024 |
| Publisher | Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio |
| References | cr-split#-82356_CR45.1 cr-split#-82356_CR45.2 W Zhou (82356_CR4) 2019; 21 82356_CR40 H Shi (82356_CR39) 2023; 15 82356_CR41 X Chen (82356_CR24) 2024; 83 Z Lv (82356_CR23) 2022; 132 C Sun (82356_CR5) 2020; 7 J Kim (82356_CR2) 2021; 21 B Shahian Jahromi (82356_CR44) 2019; 19 Y Ji (82356_CR48) 2023; 24 QD Tran (82356_CR10) 2021; 11 M Abdou (82356_CR34) 2022; 22 cr-split#-82356_CR15.2 cr-split#-82356_CR13.2 R Heinzler (82356_CR16) 2020; 5 cr-split#-82356_CR15.1 R Wang (82356_CR3) 2021; 2021 DH Lee (82356_CR49) 2023; 17 82356_CR8 C Wang (82356_CR47) 2022; 35 S Wu (82356_CR38) 2023; 23 K Vinoth (82356_CR17) 2024; 23 82356_CR31 82356_CR32 MH Le (82356_CR30) 2022; 11 Z Ouyang (82356_CR43) 2022; 77 MA Butt (82356_CR18) 2022; 104 Y Zhang (82356_CR26) 2023; 2023 N Lopac (82356_CR6) 2022; 22 X Chen (82356_CR25) 2024; 313 M Hasanujjaman (82356_CR37) 2023; 23 S Masood (82356_CR27) 2022; 2022 A Tampuu (82356_CR36) 2023; 23 Ó Pérez-Gil (82356_CR50) 2022; 81 KC Hung (82356_CR28) 2022; 12 L Caltagirone (82356_CR46) 2019; 111 S Riedmaier (82356_CR12) 2020; 11 82356_CR29 SL Lin (82356_CR9) 2021; 11 W Hou (82356_CR7) 2023; 23 X Ma (82356_CR11) 2020; 11 Z Li (82356_CR33) 2023; 15 82356_CR14 cr-split#-82356_CR13.1 N Defauw (82356_CR35) 2023; 23 82356_CR52 L Wen (82356_CR1) 2020; 193 82356_CR19 L Chen (82356_CR42) 2019; 28 Z Liu (82356_CR20) 2021; 23 G Chen (82356_CR51) 2023; 8 H Florea (82356_CR21) 2022; 22 M Ivanovs (82356_CR22) 2022; 22 |
| References_xml | – ident: 82356_CR14 – volume: 12 start-page: 10221 issue: 20 year: 2022 ident: 82356_CR28 publication-title: Appl. Sci. doi: 10.3390/app122010221 – volume: 23 start-page: 3794 issue: 8 year: 2023 ident: 82356_CR38 publication-title: Sensors doi: 10.3390/s23083794 – volume: 17 start-page: 199 issue: 1 year: 2023 ident: 82356_CR49 publication-title: Signal. Image Video Process. doi: 10.1007/s11760-022-02222-2 – volume: 22 start-page: 2252 issue: 6 year: 2022 ident: 82356_CR22 publication-title: Sensors doi: 10.3390/s22062252 – volume: 5 start-page: 2514 issue: 2 year: 2020 ident: 82356_CR16 publication-title: IEEE Rob. Autom. Lett. doi: 10.1109/LRA.2020.2972865 – volume: 2021 start-page: 9218137 issue: 1 year: 2021 ident: 82356_CR3 publication-title: Comput. Intell. Neurosci. doi: 10.1155/2021/9218137 – volume: 111 start-page: 125 year: 2019 ident: 82356_CR46 publication-title: Robot. Auton. Syst. doi: 10.1016/j.robot.2018.11.002 – ident: #cr-split#-82356_CR13.1 doi: 10.1109/IVS.2018.8500511 – volume: 35 start-page: 54 issue: 1 year: 2022 ident: 82356_CR47 publication-title: Chin. J. Mech. Eng. doi: 10.1186/s10033-022-00745-w – ident: #cr-split#-82356_CR13.2 – volume: 193 start-page: 102907 year: 2020 ident: 82356_CR1 publication-title: Comput. Vis. Image Underst. doi: 10.1016/j.cviu.2020.102907 – volume: 21 start-page: 1951 issue: 5 year: 2019 ident: 82356_CR4 publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2019.2909066 – volume: 22 start-page: 9108 issue: 23 year: 2022 ident: 82356_CR34 publication-title: Sensors doi: 10.3390/s22239108 – ident: 82356_CR52 doi: 10.1109/CVPR.2012.6248074 – volume: 11 start-page: 2993 issue: 19 year: 2022 ident: 82356_CR30 publication-title: Electronics doi: 10.3390/electronics11192993 – volume: 11 start-page: 35 issue: 1 year: 2020 ident: 82356_CR12 publication-title: Appl. Sci. doi: 10.3390/app11010035 – ident: 82356_CR40 doi: 10.1109/WACV48630.2021.00157 – volume: 7 start-page: 395 issue: 2 year: 2020 ident: 82356_CR5 publication-title: IEEE/CAA J. Automatica Sinica doi: 10.1109/JAS.2020.1003033 – volume: 132 start-page: 108956 year: 2022 ident: 82356_CR23 publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2022.108956 – volume: 2022 start-page: 8684138 issue: 1 year: 2022 ident: 82356_CR27 publication-title: Wirel. Commun. Mob. Comput. – ident: 82356_CR41 doi: 10.1109/SDF.2019.8916629 – ident: 82356_CR31 doi: 10.1109/SP40001.2021.00076 – volume: 11 start-page: 3018 issue: 7 year: 2021 ident: 82356_CR9 publication-title: Appl. Sci. doi: 10.3390/app11073018 – ident: #cr-split#-82356_CR45.1 doi: 10.1109/IVS.2019.8813778 – ident: 82356_CR29 doi: 10.3390/app13042094 – volume: 19 start-page: 4357 issue: 20 year: 2019 ident: 82356_CR44 publication-title: Sensors doi: 10.3390/s19204357 – volume: 23 start-page: 102510 year: 2024 ident: 82356_CR17 publication-title: Results Eng. doi: 10.1016/j.rineng.2024.102510 – ident: #cr-split#-82356_CR15.1 doi: 10.1109/IVS.2018.8500464 – volume: 23 start-page: 1613 issue: 3 year: 2023 ident: 82356_CR35 publication-title: Sensors doi: 10.3390/s23031613 – ident: 82356_CR32 doi: 10.3390/jimaging8110306 – volume: 23 start-page: 6640 issue: 7 year: 2021 ident: 82356_CR20 publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2021.3059674 – volume: 28 start-page: 4883 issue: 10 year: 2019 ident: 82356_CR42 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2019.2913079 – volume: 81 start-page: 3553 issue: 3 year: 2022 ident: 82356_CR50 publication-title: Multimedia Tools Appl. doi: 10.1007/s11042-021-11437-3 – volume: 11 start-page: 1514 issue: 4 year: 2021 ident: 82356_CR10 publication-title: Appl. Sci. doi: 10.3390/app11041514 – volume: 23 start-page: 3335 issue: 6 year: 2023 ident: 82356_CR37 publication-title: Sensors doi: 10.3390/s23063335 – volume: 23 start-page: 5110 issue: 11 year: 2023 ident: 82356_CR7 publication-title: Sensors doi: 10.3390/s23115110 – volume: 15 start-page: 1210 issue: 5 year: 2023 ident: 82356_CR33 publication-title: Remote Sens. doi: 10.3390/rs15051210 – volume: 24 start-page: 3541 issue: 3 year: 2023 ident: 82356_CR48 publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2022.3231259 – volume: 15 start-page: 2628 issue: 3 year: 2023 ident: 82356_CR39 publication-title: Sustainability doi: 10.3390/su15032628 – volume: 104 start-page: 116667 year: 2022 ident: 82356_CR18 publication-title: Sig. Process. Image Commun. doi: 10.1016/j.image.2022.116667 – ident: 82356_CR19 doi: 10.1007/978-3-031-61066-0_14 – volume: 22 start-page: 5061 issue: 13 year: 2022 ident: 82356_CR21 publication-title: Sensors doi: 10.3390/s22135061 – volume: 23 start-page: 2845 issue: 5 year: 2023 ident: 82356_CR36 publication-title: Sensors doi: 10.3390/s23052845 – ident: #cr-split#-82356_CR15.2 – volume: 22 start-page: 5946 issue: 16 year: 2022 ident: 82356_CR6 publication-title: Sensors doi: 10.3390/s22165946 – ident: 82356_CR8 doi: 10.1109/WACV48630.2021.00232 – volume: 8 start-page: 2527 issue: 3 year: 2023 ident: 82356_CR51 publication-title: IEEE Trans. Intell. Veh. doi: 10.1109/TIV.2023.3235007 – volume: 21 start-page: 7461 issue: 22 year: 2021 ident: 82356_CR2 publication-title: Sensors doi: 10.3390/s21227461 – volume: 11 start-page: 29 issue: 1 year: 2020 ident: 82356_CR11 publication-title: Appl. Sci. doi: 10.3390/app11010029 – volume: 313 start-page: 119368 year: 2024 ident: 82356_CR25 publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2024.119368 – volume: 2023 start-page: 5349965 issue: 1 year: 2023 ident: 82356_CR26 publication-title: J. Adv. Transp. – volume: 83 start-page: 48907 issue: 16 year: 2024 ident: 82356_CR24 publication-title: Multimedia Tools Appl. doi: 10.1007/s11042-023-17456-6 – volume: 77 start-page: 172 year: 2022 ident: 82356_CR43 publication-title: Inform. Fusion doi: 10.1016/j.inffus.2021.07.004 – ident: #cr-split#-82356_CR45.2 |
| SSID | ssj0000529419 |
| Score | 2.4899936 |
| Snippet | Autonomous vehicles, often known as self-driving cars, have emerged as a disruptive technology with the promise of safer, more efficient, and convenient... Abstract Autonomous vehicles, often known as self-driving cars, have emerged as a disruptive technology with the promise of safer, more efficient, and... |
| SourceID | doaj pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 31130 |
| SubjectTerms | 639/166 639/705/117 Autonomous vehicles Dense net (D net) Dust storms Energy Valley Optimizer (EVO) Fog Humanities and Social Sciences Image processing Information processing Lidar Multi-sensor fusion multidisciplinary Noise reduction Pedestrians Pipelines Rainfall Science Science (multidisciplinary) Segmentation Self-driving vehicles Sensors YOLO V7 model |
| SummonAdditionalLinks | – databaseName: Science Database dbid: M2P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELaggMSF9yNQkJG4gdX4kdg-IUBUXKiKBFJvkePY20qQLJvdSvx7ZhxvquXRC9fYicaZGfvzPAl5iTXb8NxmXSUiU6aVzHrjWO2jBPwAkCE3m9BHR-bkxB5ng9uYwyq3e2LaqLvBo438QAKU19ZUxrxZ_mDYNQq9q7mFxlVyDZANx5CuT-J4trGgF0txm3NlgKyDEc4rzCkTihkBxLBy5zxKZfv_hjX_DJn8zW-ajqPD2_-7kDvkVgai9O0kOXfJldDfIzem1pQ_75NFysxlI9xyhxWNGzSqUdd3dAyL7zlfqaeAeKnbrDExYtiM9Dyc4sdoilJkQ4s2HgoL8WiPpxhiv6BdCEv6mfZT-Pn4gHw9_PDl_UeWmzIwXym-Zk5H5-BW5YK0QZnOVbyOtetaH7UpreuqTljhpXVeitj6mptYB1XFaDxADy0fkr1-6MNjQoPRQQlXlZicq6rWGBUit20q0WqFLgjfsqbxuWI5Ns741iTPuTTNxM4G2NkkdjZlQV7N7yyneh2Xzn6HHJ9nYq3t9GBYLZqsuo0GGAV0tuiQVKWrTRUV4KZWOa1N621B9reMbvIGMDYXXC7Ii3kYVBf9Ma4PwJQ0xwDAqmVBHk3iNVMisYqSlUCh2RG8HVJ3R_qz01QenPPaAJADul5vZfSCrn__iyeXL-MpuSlQbThm8--TvfVqE56R6_58fTaunie9-wUPeDRx priority: 102 providerName: ProQuest |
| Title | Multi-sensor fusion and segmentation for autonomous vehicle multi-object tracking using deep Q networks |
| URI | https://link.springer.com/article/10.1038/s41598-024-82356-0 https://www.ncbi.nlm.nih.gov/pubmed/39732930 https://www.proquest.com/docview/3149798588 https://www.proquest.com/docview/3149881263 https://pubmed.ncbi.nlm.nih.gov/PMC11682159 https://doaj.org/article/7007e42b304940a685f4820b4a778bc9 |
| Volume | 14 |
| WOSCitedRecordID | wos001385898400048&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M7P dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: 7X7 dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M2P dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lj9MwELZgFyQuiDeFpTISN7A2sZ14fGTRruCwVUEglVPkOHZ3JUhXTbsS_54ZJy1bnhcuPiRO9Mkz1szYM98w9oI428hui6aQUWiolbAenCh9VOg_oMswNJswkwnMZnZ6pdUX5YT19MD9wh0aNGJBy5qug3TmSiiiRqtVa2cM1D6V7mXGXgmmelZvaXVuhyoZBHTYoaWiajKpBUiEIbIdS5QI-3_nZf6aLPnTjWkyRCd32O3Bg-Sve-R32bXQ3mM3-56S3-6zeSqpFR2Gp4slj2s6DeOubXgX5l-HQqOWo6vK3XpFFQ0Y-vPLcEY_4ym9UCxqOpzhiMPTQTqn3Pg5b0K44O952-eNdw_Yp5Pjj2_eiqGbgvCFzlfCmegchkMuKBs0NK7Iy1i6pvbRQGZdUzTSSq-s80rG2pc5xDLoIkbw6DMY9ZDttYs2PGY8AInEFRlV1eqiBtAh5rZO3KpWmhHLNytb-YFqnDpefKnSlbeCqpdGhdKokjSqbMRebr-56Ik2_jr7iAS2nUkk2ekBqk41qE71L9UZsYONuKth53aVwpDRWCgARuz59jXuObpIcW1AoaQ5gJ5RqUbsUa8dWySK6I-sQoSwozc7UHfftOdnidc7z0tADwxxvdqo2A9cf16LJ_9jLZ6yW5L2Rk7F-gdsb7Vch2fshr9cnXfLMbtuZiaNMGb7R8eT6Ydx2nA4nsopjQbH_em70-nn77E6K8I |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELZKAcGFN2WhgJHgBFY3tpPYB4R4Va1aVkUqUm_GcextJZosm92i_il-IzNOstXy6K0HrhtvNHa-GX_2vAh5jjXbcN9mZcoDk6oQTDtlWeaCAP4AlKFrNpGPRurgQO-tkJ99LgyGVfY2MRrqsnZ4R74hgMrnWqVKvZl8Z9g1Cr2rfQuNFhY7_vQHHNma19sf4Pu-4Hzz4_77LdZ1FWAulcmM2TxYC8cC64X2UpU2TbKQ2bJwIVdDbcu05Jo7oa0TPBQuS1TIvExDUA72zlzAey-RyxIri2GoIN9b3Omg10wmusvNgWXYaGB_xBw2LpniMHk2XNr_YpuAv3HbP0M0f_PTxu1v8-b_tnC3yI2OaNO3rWbcJiu-ukOutq03T--Sccw8Zg2c4uspDXO8NKS2Kmnjx8ddPlZFgdFTO59h4kc9b-iJP8SX0RiFyeoC77AoLJxDfwPFFIIxLb2f0M-0asPrm3vky4XM8j5ZrerKPyDUq9xLbtMhJh_LtFBK-pDoIpag1TwfkKSHgnFdRXZsDPLNxMgAoUwLHwPwMRE-ZjggLxf_mbT1SM4d_Q4RthiJtcTjD_V0bDrTZHKgiSBngQ5XObSZSoMEXlhIm-eqcHpA1ntgmc7ANeYMVQPybPEYTBP6m2zl4aPEMQoIZCYGZK2F80ISgVWitAAJ1RLQl0RdflIdHcby50mSKSCqINerXifO5Pr3Wjw8fxpPybWt_U-7Znd7tPOIXOeosglWLlgnq7Pp3D8mV9zJ7KiZPok6T8nXi9aVX7K6lAU |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFLZGB4gX7ozCACPBE1hrHCexHxACRkU1qIoE0ngyjmN3k0ZSmnZof41fxzlO0qlc9rYHXhMnOjk5l88-N0KeYM829NusSLhnQuYxU1YallofA34AyNAOm8jGY7m_ryYb5GdXC4NplZ1NDIa6qCyeke_EAOUzJRPYsPk2LWKyO3w5-85wghRGWrtxGo2I7LmTH7B9q1-MduFfP-V8-PbTm3esnTDAbCKiBTOZNwa2CMbFyglZmCRKfWqK3PpMDpQpkoIrbmNlbMx9btNI-tSJxHtpwY9mMbz3AtkESC54j2xORh8mX1YnPBhDE5FqK3WAKTs1eEusaOOCSQ6sYIM1bxiGBvwN6f6ZsPlb1DY4w-G1_5mN18nVFoLTV43O3CAbrrxJLjVDOU9ukWmoSWY17O-rOfVLPE6kpixo7abf2kqtkgLWp2a5wJKQalnTY3eAL6MhP5NVOZ5uUWCixUgExeKCKS2cm9GPtGwS7-vb5PO5fOUd0iur0t0l1MnMCW6SAZYliySXUjgfqTw0p1U865OoEwtt217tODLkSIecgVjqRpQ0iJIOoqQHffJs9cys6VRy5urXKG2rldhlPFyo5lPdGi2dAYAEOnMMxYqBSWXiBSDGXJgsk7lVfbLdCZluTV-tTyWsTx6vboPRwkiUKR38lLBGArRM4z7ZakR7RUmM_aNUDBTKNaFfI3X9Tnl4EBqjR1EqAcICXc87_Til69-8uHf2Zzwil0FF9PvReO8-ucJReyNsabBNeov50j0gF-3x4rCeP2wNACVfz1tZfgGSPJ5O |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Multi-sensor+fusion+and+segmentation+for+autonomous+vehicle+multi-object+tracking+using+deep+Q+networks&rft.jtitle=Scientific+reports&rft.au=K.+Vinoth&rft.au=P.+Sasikumar&rft.date=2024-12-28&rft.pub=Nature+Portfolio&rft.eissn=2045-2322&rft.volume=14&rft.issue=1&rft.spage=1&rft.epage=32&rft_id=info:doi/10.1038%2Fs41598-024-82356-0&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_7007e42b304940a685f4820b4a778bc9 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon |