Real-Time Heterogeneous Collaborative Perception in Edge-Enabled Vehicular Environments
Vehicular sensing has reached new heights due to advances in external perception systems enabled by the increasing number and type of sensors in vehicles, as well as the availability of on-board computing. These changes have led to improvements in driver safety and have also created a highly heterog...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/10852339/ |
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| author | Samuel Thornton Nithin Santhanam Rajeev Chhajer Sujit Dey |
| author_facet | Samuel Thornton Nithin Santhanam Rajeev Chhajer Sujit Dey |
| author_sort | Samuel Thornton |
| collection | DOAJ |
| description | Vehicular sensing has reached new heights due to advances in external perception systems enabled by the increasing number and type of sensors in vehicles, as well as the availability of on-board computing. These changes have led to improvements in driver safety and have also created a highly heterogeneous environment of vehicles on the road today in terms of sensing and computing. Using collaborative perception, the information obtained by vehicles with sensing capabilities can be expanded and improved, and older vehicles that lack external sensors and computing capabilities can be informed of potential hazards, opening the opportunity to improve traffic efficiency and safety on the roads. However, achieving real-time collaborative perception is a difficult task due to the dynamic availability of vehicular sensing and computing and the highly variable nature of vehicular communications. To address these challenges, we propose a Heterogeneous Adaptive Collaborative Perception (HAdCoP) framework which utilizes a Context-aware Latency Prediction Network (CaLPeN) to intelligently select which vehicles should transmit their sensor data, the specific individual and collaborative perception tasks, and the amount of computational offloading that should be utilized given information about the current state of the environment. Additionally, we propose an Adaptive Perception Frequency (APF) model to determine the optimal end-to-end latency requirement according to the current state of the environment. The proposed CaLPeN model outperforms six implemented comparison models in terms of effective mean average precision (EmAP), beating the next best model's performance by 5.5% on average when tested on the OPV2V perception dataset using two different combinations of wireless communication conditions and vehicular sensor/computing distributions. |
| format | Article |
| id | doaj-art-f582f83ba0dc4200af82e752117ecaa4 |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-f582f83ba0dc4200af82e752117ecaa42025-02-11T00:01:51ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302025-01-01647148610.1109/OJVT.2025.353336810852339Real-Time Heterogeneous Collaborative Perception in Edge-Enabled Vehicular EnvironmentsSamuel Thornton0https://orcid.org/0000-0002-3087-2577Nithin Santhanam1Rajeev Chhajer2Sujit Dey3https://orcid.org/0000-0001-9671-3950Department of Electrical and Computer Engineering, University of California, San Diego, CA, USAHonda Research Institute USA Inc., Columbus, OH, USAHonda Research Institute USA Inc., Columbus, OH, USADepartment of Electrical and Computer Engineering, University of California, San Diego, CA, USAVehicular sensing has reached new heights due to advances in external perception systems enabled by the increasing number and type of sensors in vehicles, as well as the availability of on-board computing. These changes have led to improvements in driver safety and have also created a highly heterogeneous environment of vehicles on the road today in terms of sensing and computing. Using collaborative perception, the information obtained by vehicles with sensing capabilities can be expanded and improved, and older vehicles that lack external sensors and computing capabilities can be informed of potential hazards, opening the opportunity to improve traffic efficiency and safety on the roads. However, achieving real-time collaborative perception is a difficult task due to the dynamic availability of vehicular sensing and computing and the highly variable nature of vehicular communications. To address these challenges, we propose a Heterogeneous Adaptive Collaborative Perception (HAdCoP) framework which utilizes a Context-aware Latency Prediction Network (CaLPeN) to intelligently select which vehicles should transmit their sensor data, the specific individual and collaborative perception tasks, and the amount of computational offloading that should be utilized given information about the current state of the environment. Additionally, we propose an Adaptive Perception Frequency (APF) model to determine the optimal end-to-end latency requirement according to the current state of the environment. The proposed CaLPeN model outperforms six implemented comparison models in terms of effective mean average precision (EmAP), beating the next best model's performance by 5.5% on average when tested on the OPV2V perception dataset using two different combinations of wireless communication conditions and vehicular sensor/computing distributions.https://ieeexplore.ieee.org/document/10852339/Connected vehiclescollaborative perceptionedge computingmachine learning |
| spellingShingle | Samuel Thornton Nithin Santhanam Rajeev Chhajer Sujit Dey Real-Time Heterogeneous Collaborative Perception in Edge-Enabled Vehicular Environments IEEE Open Journal of Vehicular Technology Connected vehicles collaborative perception edge computing machine learning |
| title | Real-Time Heterogeneous Collaborative Perception in Edge-Enabled Vehicular Environments |
| title_full | Real-Time Heterogeneous Collaborative Perception in Edge-Enabled Vehicular Environments |
| title_fullStr | Real-Time Heterogeneous Collaborative Perception in Edge-Enabled Vehicular Environments |
| title_full_unstemmed | Real-Time Heterogeneous Collaborative Perception in Edge-Enabled Vehicular Environments |
| title_short | Real-Time Heterogeneous Collaborative Perception in Edge-Enabled Vehicular Environments |
| title_sort | real time heterogeneous collaborative perception in edge enabled vehicular environments |
| topic | Connected vehicles collaborative perception edge computing machine learning |
| url | https://ieeexplore.ieee.org/document/10852339/ |
| work_keys_str_mv | AT samuelthornton realtimeheterogeneouscollaborativeperceptioninedgeenabledvehicularenvironments AT nithinsanthanam realtimeheterogeneouscollaborativeperceptioninedgeenabledvehicularenvironments AT rajeevchhajer realtimeheterogeneouscollaborativeperceptioninedgeenabledvehicularenvironments AT sujitdey realtimeheterogeneouscollaborativeperceptioninedgeenabledvehicularenvironments |