An Enhanced Classification Technique for Mitigating Unexpected Noise Intrusions in Autonomous Vehicles

Autonomous vehicle, which must trust the judgment of the entire system without human participation, have many disadvantages. Traffic sign classification is one of those disadvantages and a challenge to overcome. Cybersecurity breaches or changes in the surrounding environment are critical obstacles to the traffic sign classification function. Therefore, this paper proposes an image reconstruction method based on mask autoencoder to improve classification performance. Additionally, classification accuracy is also improved by extracting characters from traffic signs. A case study proves that image classification accuracy improves even when various abnormalities are added to the input of traffic sign classification performance.