Adversarial training has emerged as a promising defense mechanism involving the addition of adversarial examples into training data to enhance model robustness. Despite its potential, research on adversarial machine learning attack and resistance methods in the digital domain remains limited, requiring further investigation into its efficacy, specifically in defending against misclassification in smart vehicles. This study investigates the application of adversarial training to combat misclassification errors in smart vehicles, highlighting its necessity due to these systems’ vulnerability to adversarial machine learning attacks, especially through their connection to external networks like the Controller Area Network. This paper also analyzes the significant challenges, defense mechanisms, and vulnerabilities specific to smart vehicle systems, offering insights to guide future research and improve the reliability of autonomous driving technologies.