Data-driven anomaly identification for cameras and lidars mounted on vehicles
Cyber-physical systems (CPS) have advanced rapidly and fueled the growth of automated driving in electric vehicles (EVs) that rely on AI and machine learning for functions like image recognition and navigation. However, the integration of sensors such as cameras, LiDARs, and radars makes these syste...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/181562 |
| _version_ | 1826120006294831104 |
|---|---|
| author | Lim, Steven YongHeng |
| author2 | Su Rong |
| author_facet | Su Rong Lim, Steven YongHeng |
| author_sort | Lim, Steven YongHeng |
| collection | NTU |
| description | Cyber-physical systems (CPS) have advanced rapidly and fueled the growth of automated driving in electric vehicles (EVs) that rely on AI and machine learning for functions like image recognition and navigation. However, the integration of sensors such as cameras, LiDARs, and radars makes these systems vulnerable to cyber attacks, posing potentially fatal threats. This project focuses on data-driven approaches to detect anomalies and mitigate these attack vectors in autonomous vehicles. By analysing data from cameras and LiDARs, and employing machine learning techniques, the aim is to promote early detection and lower the risk of threats like LiDAR spoofing and jamming attacks can bring. As the industry pushes toward fully autonomous driving, robust cybersecurity measures are essential to ensure the safety and reliability of these systems. |
| first_indexed | 2025-03-09T12:35:48Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/181562 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-03-09T12:35:48Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1815622024-12-13T15:45:45Z Data-driven anomaly identification for cameras and lidars mounted on vehicles Lim, Steven YongHeng Su Rong School of Electrical and Electronic Engineering RSu@ntu.edu.sg Computer and Information Science Engineering Cyber-physical systems (CPS) have advanced rapidly and fueled the growth of automated driving in electric vehicles (EVs) that rely on AI and machine learning for functions like image recognition and navigation. However, the integration of sensors such as cameras, LiDARs, and radars makes these systems vulnerable to cyber attacks, posing potentially fatal threats. This project focuses on data-driven approaches to detect anomalies and mitigate these attack vectors in autonomous vehicles. By analysing data from cameras and LiDARs, and employing machine learning techniques, the aim is to promote early detection and lower the risk of threats like LiDAR spoofing and jamming attacks can bring. As the industry pushes toward fully autonomous driving, robust cybersecurity measures are essential to ensure the safety and reliability of these systems. Bachelor's degree 2024-12-10T02:43:39Z 2024-12-10T02:43:39Z 2024 Final Year Project (FYP) Lim, S. Y. (2024). Data-driven anomaly identification for cameras and lidars mounted on vehicles. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181562 https://hdl.handle.net/10356/181562 en A1186-232 application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Engineering Lim, Steven YongHeng Data-driven anomaly identification for cameras and lidars mounted on vehicles |
| title | Data-driven anomaly identification for cameras and lidars mounted on vehicles |
| title_full | Data-driven anomaly identification for cameras and lidars mounted on vehicles |
| title_fullStr | Data-driven anomaly identification for cameras and lidars mounted on vehicles |
| title_full_unstemmed | Data-driven anomaly identification for cameras and lidars mounted on vehicles |
| title_short | Data-driven anomaly identification for cameras and lidars mounted on vehicles |
| title_sort | data driven anomaly identification for cameras and lidars mounted on vehicles |
| topic | Computer and Information Science Engineering |
| url | https://hdl.handle.net/10356/181562 |
| work_keys_str_mv | AT limstevenyongheng datadrivenanomalyidentificationforcamerasandlidarsmountedonvehicles |