Universal adversarial network attacks on traffic light recognition of Apollo autonomous driving system
Autonomous Vehicles are becoming increasingly important and relevant in today’s world. Their applications can be found everywhere, from public transport to overcome land and workforce constraints to personal uses for convenience to business uses for freight transportation and utility services sec...
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| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2022
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/156782 |
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| 總結: | Autonomous Vehicles are becoming increasingly important and relevant in today’s
world. Their applications can be found everywhere, from public transport to
overcome land and workforce constraints to personal uses for convenience to
business uses for freight transportation and utility services sectors. Therefore,
emphasising the importance of safety in these autonomous vehicles. Autonomous
vehicles use Autonomous Driving Systems (ADS), which requires inputs from
multiple camera sensors to be passed into a machine learning model to output the
results that directly control the car movements. This paper focuses on the safety of
these machine learning models. A black-box Universal Adversarial Network (UAN)
is first trained to create a universal perturbation, which will be used to attack the
machine learning model that recognises traffic light signals. Eventually producing a
wrong traffic signal as an output. Multiple variations of the UAN are produced to
study their effect on the accuracy of these machine learning models. This
vulnerability will also be studied in a realistic environment using Baidu Apollo ADS
and LGSVL. Lastly, basic defences of Apollo ADS will be explored. |
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