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Autonomous Vehicles (AVs) have emerged as one of the most revolutionary developments in the field of vehicular technology. These smart vehicles significantly improve road safety, accessibility, and environmentally friendly driving practices through advanced sensing capabilities. Unfortunately, this technology can be compromised through adversarial attacks that can target various AV applications like traffic sign recognition. Within this paper, we highlight the evolution of AVs through the ages. Following this, we highlight some contemporary adversarial attacks and defenses that have been carried out on AV applications like traffic sign recognition. Finally, we introduce open research areas/opportunities that can be investigated to improve the resiliency of ML-based traffic sign recognition in AVs against adversarial attacks.

Purpose

One of the most captivating interdisciplinary developments in the last 10 years is the emergence of autonomous vehicles. These vehicles possess advanced sensing capabilities and are designed to significantly improve road safety, enhance accessibility, and promote environmentally friendly driving, by leveraging the usage of machine learning. Unfortunately, machine learning-enabled applications in autonomous vehicles face safety concerns as adversarial attacks can endanger these systems. These attacks deliberately and strategically attempt to deceive a machine learning model, leading to incorrect predictions. This can put users, passengers, drivers, and the public at risk. In this research, we survey the current evolution of autonomous vehicles through multiple generations and look at contemporary adversarial …