| Summary: | Digital Twin is a Metaverse virtual model that replicates the physical scenes accurately. This
replication can achieve purposes of simulation or prediction of the physical world. Augmented
reality (AR) assisted driving is an application that relies on Real-Time Digital Twinning. When
driving, the images of the physical scenes, such as signboards, are captured by the Internet of
Vehicles (IoV). A Service Provider (SP) will then upload the captured images to a Service
Provider Base Station (SPBS), which then be replicated in a virtual model. Information will be
calculated and displayed back to assist drivers in an AR format.
However, these applications may invite adversarial attacks. Actions include placing adversarial
patches onto the captured images before being replicated onto Metaverse. These tiny patches
on physical objects may cause the virtual model to generate false information, and this
misinformation can be detrimental to the drivers on the road.
The first portion of this project is to introduce an adversarial patch detection model placed in
the SPBS. The second portion introduces a reinforcement learning model. The model tasks are
to allocate channels between IoV and SPBS efficiently and select an appropriate resolution size
for the image sent for uploading. As AR-assisted driving is in real-time, these tasks aim to
maximize the detection model's mean average precision (mAP) while minimizing the upload
latency and idle count.
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