Journal of Science Policy & Governance
Volume 26, Issue 01 | June 16, 2025

Policy Memo
Certified to Drive: A Policy Proposal for Mandatory Training on Semi-Automated Vehicles 

Soumita Mukherjee1, Varun Darshana Parekh2, Nikhil Tayal3
  1. College of Information Sciences and Technology, The Pennsylvania State University, Pennsylvania, USA
  2. Department of Computer Science, The Pennsylvania State University, Pennsylvania, USA
  3. Tezmee.Inc, San Jose, California, USA
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​Corresponding author: [email protected] ​​​
Keywords: automation; ADAS; training; safety; policy
https://doi.org/10.38126/JSPG260107 ​

Executive Summary

According to the Society of Automotive Engineers (SAE) J3016 standard, Advanced Driver Assistance Systems (ADAS) refer to a suite of technologies that automate specific driving tasks (such as acceleration, braking, and steering) while still requiring different levels of active driver oversight. While nearly 92.7% of new U.S. vehicles now include at least one automated driving feature, the transformative potential lies in the integration of advanced systems—specifically, ADAS Level 2. While these systems promise enhanced safety and efficiency, they have also created a significant knowledge gap. Drivers often lack the necessary training to understand system limitations and intervene effectively, leading to accidents and liability issues that undermine the benefits of automation. This paper highlights the urgent need for policies to address this gap. Although the Boeing 737 Max incidents resulted from a mix of design shortcomings, regulatory oversights, and systemic issues, they also highlight a critical gap in pilot training on managing automated systems during abnormal conditions. This example demonstrates the urgent need for focused, concise training on human-automation interaction—a need that is equally critical for operators of Level 2 ADAS-equipped vehicles, as discussed in detail later in this article. The lack of structured education for semi-automated vehicle (SAV) operators mirrors similar risks in other industries, where formal training is critical for safe operation. Two policy recommendations are proposed. First, governments should create concise, official resources in accessible and official format to educate drivers on system capabilities and limitations. Second, mandatory training and certification programs should be introduced, combining theoretical and hands-on components to prepare drivers for real-world scenarios. These measures will improve driver understanding, reduce misuse, and foster public trust in semi-automated vehicle technologies. By addressing the knowledge gap, policymakers can ensure a safer, more responsible transition to automation, maximizing its benefits while minimizing risks to public safety.

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Soumita Mukherjee is a PhD student specializing in the ethics and policy of automated systems. She is focused on bridging the gap between technological advancements and their societal impacts by exploring the implications of automation on public safety, accountability, and regulation. As an advocate for responsible innovation, she actively contributes to policy discussions on emerging technologies, ensuring that ethical considerations guide the integration of automation into everyday life. She is also a current Governors Science Technology Policy Fellow with Commonwealth of Pennsylvania, and an active member of Science Policy Society at Penn State. 

Varun Parekh is a Ph.D. candidate in the Microsystems Design Lab, Department of Computer Science and Engineering at The Pennsylvania State University. As an active member of the on-campus Science Policy Society, he has engaged in numerous advocacy efforts related to science policy and tech diplomacy. His research interests focus on computer architecture for AI, including near-memory computing architectures, chiplet placement, and efficient thermal management of resource-intensive hardware. 
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Nikhil Tayal is a professional with over 15 experience working in industry, currently working on his own startup Tezmee. Inc, which provides a platform for learning experiences for partially automated vehicles. The idea for this paper was conceived by the third author. ​

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