Smart goggle for personal mobility
Overtaking prediction system for smart vehicles has always been a famous research topic worldwide, by taking into considerations the driver’s gaze information as well as the data collected by sensors installed inside the vehicles. In Singapore, Personal Mobility Devices (PMDs) are often the choice o...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/138829 |
| _version_ | 1826114343813513216 |
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| author | Lim, Tau Meng |
| author2 | Guan Yong Liang |
| author_facet | Guan Yong Liang Lim, Tau Meng |
| author_sort | Lim, Tau Meng |
| collection | NTU |
| description | Overtaking prediction system for smart vehicles has always been a famous research topic worldwide, by taking into considerations the driver’s gaze information as well as the data collected by sensors installed inside the vehicles. In Singapore, Personal Mobility Devices (PMDs) are often the choice of transportation for its citizens, as it can help to reduce the total emission of greenhouse gases in the “Clean City”. Unfortunately, the usage of PMDs in Singapore is often related with accidents and injuries. Therefore, this project aims to develop an “Overtaking Prediction System” for PMDs, using both rider’s gaze information and his eye view (camera) as input. Using a pre-trained YOLO Machine Learning model and a self-developed Overtaking Prediction System, the rider’s overtaking intention can then be used to turn on a “Turn Left” or a “Turn Right” signalling light on PMD, without requiring rider’s action. Doing this can help to prevent collision between a fast-moving or reckless rider and other people around him. Real-time experiments involving 7 participants of ages ranging from 23 to 31 years old, or a total of 234 overtaking events, are carried out to examine the accuracy of the smart system, and the result is shown as 84.62%. This shows that PMD rider’s gaze data can be a good source of input to determine his overtaking intention. This project also serves as the first step to design Advanced Driver Assistance Systems (ADAS) for PMDs, which have not existed in the market currently. |
| first_indexed | 2024-10-01T03:37:36Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/138829 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:37:36Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1388292023-07-07T18:31:59Z Smart goggle for personal mobility Lim, Tau Meng Guan Yong Liang Justin Dauwels School of Electrical and Electronic Engineering eylguan@ntu.edu.sg Engineering Overtaking prediction system for smart vehicles has always been a famous research topic worldwide, by taking into considerations the driver’s gaze information as well as the data collected by sensors installed inside the vehicles. In Singapore, Personal Mobility Devices (PMDs) are often the choice of transportation for its citizens, as it can help to reduce the total emission of greenhouse gases in the “Clean City”. Unfortunately, the usage of PMDs in Singapore is often related with accidents and injuries. Therefore, this project aims to develop an “Overtaking Prediction System” for PMDs, using both rider’s gaze information and his eye view (camera) as input. Using a pre-trained YOLO Machine Learning model and a self-developed Overtaking Prediction System, the rider’s overtaking intention can then be used to turn on a “Turn Left” or a “Turn Right” signalling light on PMD, without requiring rider’s action. Doing this can help to prevent collision between a fast-moving or reckless rider and other people around him. Real-time experiments involving 7 participants of ages ranging from 23 to 31 years old, or a total of 234 overtaking events, are carried out to examine the accuracy of the smart system, and the result is shown as 84.62%. This shows that PMD rider’s gaze data can be a good source of input to determine his overtaking intention. This project also serves as the first step to design Advanced Driver Assistance Systems (ADAS) for PMDs, which have not existed in the market currently. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-13T04:39:32Z 2020-05-13T04:39:32Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/138829 en B3096-191 application/pdf Nanyang Technological University |
| spellingShingle | Engineering Lim, Tau Meng Smart goggle for personal mobility |
| title | Smart goggle for personal mobility |
| title_full | Smart goggle for personal mobility |
| title_fullStr | Smart goggle for personal mobility |
| title_full_unstemmed | Smart goggle for personal mobility |
| title_short | Smart goggle for personal mobility |
| title_sort | smart goggle for personal mobility |
| topic | Engineering |
| url | https://hdl.handle.net/10356/138829 |
| work_keys_str_mv | AT limtaumeng smartgoggleforpersonalmobility |