The Challenges of the Implementation of Artificial Intelligence (AI) in Transportation

The implementation of Artificial Intelligence (AI) into transportation systems signifies a fundamental change that holds the potential to improve overall sustainability, safety, and efficiency. However, this transformation is not without its challenges, particularly concerning human factors and ergonomics. Firstly, the ergonomic integration of AI systems in transportation requires careful consideration of human-AI interaction. Unlike traditional systems, AI-driven technologies often involve complex algorithms that are not inherently intuitive to human operators. This complexity can lead to a disconnect between how the system operates and how users understand it. For instance, in autonomous vehicles, drivers may be required to take control in certain scenarios. The ergonomic challenge here lies in ensuring that the transition between AI and human control is seamless and that the user is adequately informed and prepared to take over. Another significant challenge is addressing the variability in human behavior and expectations. AI systems, designed based on standard models of behavior, may not adequately account for the wide range of human responses and interactions. This gap can lead to scenarios where AI systems behave in unexpected or counterintuitive ways to human users, potentially causing con-fusion and reducing the overall efficiency and safety of the transportation system. CAE Advanced Air Mobility (AAM) research case study focuses on the implementation and certification of AAM in the FAA/EASA environment. The reliability and trustworthiness of AI systems also pose a major ergonomic challenge. To fully integrate AI into transportation, users must trust these systems. This trust depends on the transparency and explainability of AI decision-making processes. Nevertheless, numerous AI algorithms, especially those rooted in deep learning, are frequently regarded as ‘black boxes’ owing to their intricacy and absence of interpretability. Developing AI systems that are both advanced and trans-parent is a significant hurdle that needs to be addressed. Data privacy and security are also paramount concerns. AI systems in transportation rely heavily on data, including personal and sensitive information. Ensuring the privacy and security of this data while utilizing it for AI processes is a complex challenge, requiring robust encryption methods and strict data handling policies. Moreover, the ergonomic aspect involves designing systems that not only protect data but also respect user privacy preferences and norms. The implementation of AI in transportation is a multifaceted challenge, requiring a holistic approach that considers human factors and ergonomics. As we move towards more AI-integrated transportation systems, it is essential to address these challenges through interdisciplinary research, collaboration, and a user-centered design approach.