Dynamic Identification Method for Potential Threat Vehicles beyond Line of Sight in Expressway Scenarios
Due to the challenge of limited line of sight in the perception system of intelligent driving vehicles (cameras, radar, body sensors, etc.), which can only perceive threats within a limited range, potential threats outside the line of sight cannot be fed back to the driver. Therefore, this article p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/23/12899 |
| _version_ | 1797400416129908736 |
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| author | Fumin Zou Chenxi Xia Feng Guo Xinjian Cai Qiqin Cai Guanghao Luo Ting Ye |
| author_facet | Fumin Zou Chenxi Xia Feng Guo Xinjian Cai Qiqin Cai Guanghao Luo Ting Ye |
| author_sort | Fumin Zou |
| collection | DOAJ |
| description | Due to the challenge of limited line of sight in the perception system of intelligent driving vehicles (cameras, radar, body sensors, etc.), which can only perceive threats within a limited range, potential threats outside the line of sight cannot be fed back to the driver. Therefore, this article proposes a safety perception detection method for beyond the line of sight for intelligent driving. This method can improve driving safety, enabling drivers to perceive potential threats to vehicles in the rear areas beyond the line of sight earlier and make decisions in advance. Firstly, the electronic toll collection (ETC) transaction data are preprocessed to construct the vehicle trajectory speed dataset; then, wavelet transform (WT) is used to decompose and reconstruct the speed dataset, and lightweight gradient noosting machine learning (LightGBM) is adopted to train and learn the features of the vehicle section speed. On this basis, we also consider the features of vehicle type, traffic flow, and other characteristics, and construct a quantitative method to identify potential threat vehicles (PTVs) based on a fuzzy set to realize the dynamic safety assessment of vehicles, so as to effectively detect PTVs within the over-the-horizon range behind the driver. We simulated an expressway scenario using an ETC simulation platform to evaluate the detection of over-the-horizon PTVs. The simulation results indicate that the method can accurately detect PTVs of different types and under different road scenarios with an identification accuracy of 97.66%, which verifies the effectiveness of the method in this study. This result provides important theoretical and practical support for intelligent driving safety assistance in vehicle–road collaboration scenarios. |
| first_indexed | 2024-03-09T01:55:12Z |
| format | Article |
| id | doaj.art-d42fb0afe28c4f7ab2b8310bae9d6e25 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T01:55:12Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d42fb0afe28c4f7ab2b8310bae9d6e252023-12-08T15:12:06ZengMDPI AGApplied Sciences2076-34172023-12-0113231289910.3390/app132312899Dynamic Identification Method for Potential Threat Vehicles beyond Line of Sight in Expressway ScenariosFumin Zou0Chenxi Xia1Feng Guo2Xinjian Cai3Qiqin Cai4Guanghao Luo5Ting Ye6Fujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, ChinaFujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, ChinaFujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, ChinaRenewable Energy Technology Research Institute, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Mechanical Engineering and Automation, Huaqiao University, Xiamen 362021, ChinaFujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, ChinaFujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, ChinaDue to the challenge of limited line of sight in the perception system of intelligent driving vehicles (cameras, radar, body sensors, etc.), which can only perceive threats within a limited range, potential threats outside the line of sight cannot be fed back to the driver. Therefore, this article proposes a safety perception detection method for beyond the line of sight for intelligent driving. This method can improve driving safety, enabling drivers to perceive potential threats to vehicles in the rear areas beyond the line of sight earlier and make decisions in advance. Firstly, the electronic toll collection (ETC) transaction data are preprocessed to construct the vehicle trajectory speed dataset; then, wavelet transform (WT) is used to decompose and reconstruct the speed dataset, and lightweight gradient noosting machine learning (LightGBM) is adopted to train and learn the features of the vehicle section speed. On this basis, we also consider the features of vehicle type, traffic flow, and other characteristics, and construct a quantitative method to identify potential threat vehicles (PTVs) based on a fuzzy set to realize the dynamic safety assessment of vehicles, so as to effectively detect PTVs within the over-the-horizon range behind the driver. We simulated an expressway scenario using an ETC simulation platform to evaluate the detection of over-the-horizon PTVs. The simulation results indicate that the method can accurately detect PTVs of different types and under different road scenarios with an identification accuracy of 97.66%, which verifies the effectiveness of the method in this study. This result provides important theoretical and practical support for intelligent driving safety assistance in vehicle–road collaboration scenarios.https://www.mdpi.com/2076-3417/13/23/12899expresswayETC dataover-the-horizonvehicle detection of potential threatsintelligent driving |
| spellingShingle | Fumin Zou Chenxi Xia Feng Guo Xinjian Cai Qiqin Cai Guanghao Luo Ting Ye Dynamic Identification Method for Potential Threat Vehicles beyond Line of Sight in Expressway Scenarios Applied Sciences expressway ETC data over-the-horizon vehicle detection of potential threats intelligent driving |
| title | Dynamic Identification Method for Potential Threat Vehicles beyond Line of Sight in Expressway Scenarios |
| title_full | Dynamic Identification Method for Potential Threat Vehicles beyond Line of Sight in Expressway Scenarios |
| title_fullStr | Dynamic Identification Method for Potential Threat Vehicles beyond Line of Sight in Expressway Scenarios |
| title_full_unstemmed | Dynamic Identification Method for Potential Threat Vehicles beyond Line of Sight in Expressway Scenarios |
| title_short | Dynamic Identification Method for Potential Threat Vehicles beyond Line of Sight in Expressway Scenarios |
| title_sort | dynamic identification method for potential threat vehicles beyond line of sight in expressway scenarios |
| topic | expressway ETC data over-the-horizon vehicle detection of potential threats intelligent driving |
| url | https://www.mdpi.com/2076-3417/13/23/12899 |
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