Vulnerable Road User Safety Using Mobile Phones with Vehicle-to-VRU Communication
Pedestrians, bicyclists, and scooterists are Vulnerable Road Users (VRUs) in traffic accidents. The number of fatalities and injuries in traffic accidents involving vulnerable road users has been steadily increasing in the last two decades in the U.S., even though road vehicles now have perception s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/13/2/331 |
| _version_ | 1797344234637885440 |
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| author | Sukru Yaren Gelbal Bilin Aksun-Guvenc Levent Guvenc |
| author_facet | Sukru Yaren Gelbal Bilin Aksun-Guvenc Levent Guvenc |
| author_sort | Sukru Yaren Gelbal |
| collection | DOAJ |
| description | Pedestrians, bicyclists, and scooterists are Vulnerable Road Users (VRUs) in traffic accidents. The number of fatalities and injuries in traffic accidents involving vulnerable road users has been steadily increasing in the last two decades in the U.S., even though road vehicles now have perception sensors like cameras to detect risk and issue collision warnings or apply emergency braking. Perception sensors like cameras are highly affected by lighting and weather conditions. Cameras, radar, and lidar cannot detect vulnerable road users in partially occluded and occluded situations. This paper proposes the use of Vehicle-to-VRU communication to inform nearby vehicles of VRUs on trajectories with a potential collision risk. An Android smartphone app with low-energy Bluetooth (BLE) advertising is developed and used for this communication. The same app is also used to collect motion data of VRUs for training. VRU motion data are smoothed using a Kalman filter, and an LSTM neural network is used for future motion prediction. This information is used in an algorithm comparing Time-To-collision-Zone (TTZ) for the vehicle and VRU, and issues driver warnings with different severity levels. The warning severity level is based on the analysis of real data from a smart intersection for close vehicle and VRU interactions. The resulting driver warning system is demonstrated using proof-of-concept experiments. The method can easily be extended to a VRU collision-mitigation system. |
| first_indexed | 2024-03-08T10:59:25Z |
| format | Article |
| id | doaj.art-aaa3bca5434e4498933c6851bd98f84c |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-08T10:59:25Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-aaa3bca5434e4498933c6851bd98f84c2024-01-26T16:13:44ZengMDPI AGElectronics2079-92922024-01-0113233110.3390/electronics13020331Vulnerable Road User Safety Using Mobile Phones with Vehicle-to-VRU CommunicationSukru Yaren Gelbal0Bilin Aksun-Guvenc1Levent Guvenc2Automated Driving Laboratory, Ohio State University, Columbus, OH 43212, USAAutomated Driving Laboratory, Ohio State University, Columbus, OH 43212, USAAutomated Driving Laboratory, Ohio State University, Columbus, OH 43212, USAPedestrians, bicyclists, and scooterists are Vulnerable Road Users (VRUs) in traffic accidents. The number of fatalities and injuries in traffic accidents involving vulnerable road users has been steadily increasing in the last two decades in the U.S., even though road vehicles now have perception sensors like cameras to detect risk and issue collision warnings or apply emergency braking. Perception sensors like cameras are highly affected by lighting and weather conditions. Cameras, radar, and lidar cannot detect vulnerable road users in partially occluded and occluded situations. This paper proposes the use of Vehicle-to-VRU communication to inform nearby vehicles of VRUs on trajectories with a potential collision risk. An Android smartphone app with low-energy Bluetooth (BLE) advertising is developed and used for this communication. The same app is also used to collect motion data of VRUs for training. VRU motion data are smoothed using a Kalman filter, and an LSTM neural network is used for future motion prediction. This information is used in an algorithm comparing Time-To-collision-Zone (TTZ) for the vehicle and VRU, and issues driver warnings with different severity levels. The warning severity level is based on the analysis of real data from a smart intersection for close vehicle and VRU interactions. The resulting driver warning system is demonstrated using proof-of-concept experiments. The method can easily be extended to a VRU collision-mitigation system.https://www.mdpi.com/2079-9292/13/2/331vulnerable road user safetyvehicle-to-VRU communicationpedestrian collision warning |
| spellingShingle | Sukru Yaren Gelbal Bilin Aksun-Guvenc Levent Guvenc Vulnerable Road User Safety Using Mobile Phones with Vehicle-to-VRU Communication Electronics vulnerable road user safety vehicle-to-VRU communication pedestrian collision warning |
| title | Vulnerable Road User Safety Using Mobile Phones with Vehicle-to-VRU Communication |
| title_full | Vulnerable Road User Safety Using Mobile Phones with Vehicle-to-VRU Communication |
| title_fullStr | Vulnerable Road User Safety Using Mobile Phones with Vehicle-to-VRU Communication |
| title_full_unstemmed | Vulnerable Road User Safety Using Mobile Phones with Vehicle-to-VRU Communication |
| title_short | Vulnerable Road User Safety Using Mobile Phones with Vehicle-to-VRU Communication |
| title_sort | vulnerable road user safety using mobile phones with vehicle to vru communication |
| topic | vulnerable road user safety vehicle-to-VRU communication pedestrian collision warning |
| url | https://www.mdpi.com/2079-9292/13/2/331 |
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