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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>F1Tenth Autonomous Training Platform, Courseware and Community Activities</title>
      <link>https://rip.trb.org/View/2586804</link>
      <description><![CDATA[This is a continuation of a successful Safety21 project on developing a training community for engineering and ethical skills for developing future autonomous vehicles. This project includes three components - (1) autonomous driving course development with a 1/10th-scale autonomous racecar where students learn advanced algorithms and software development for perception, planning and control of autonomous driving; (2) Community Activities spanning 80 universities which have one or more F1tenth platforms and participate in the international autonomous racing competitions. The research team will host a minimum of 5 competitions in the top robotics, transportation and cyber-physical systems conferences; (3) Development of an ethical framework for using machine learning in life-critical systems.]]></description>
      <pubDate>Thu, 07 Aug 2025 14:10:52 GMT</pubDate>
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      <title>Planning and Policy for Safer Roads with Autonomous Vehicles: Moral Decision Making Behavior in Dilemma-inducing Situations</title>
      <link>https://rip.trb.org/View/2440031</link>
      <description><![CDATA[The unparalleled technological advances in vehicle automation and artificial intelligence have made autonomous vehicle (AV) technically available for extensive road tests and, even recently, for limited commercial mobility services. Notwithstanding these advances, a critical challenge to integration of AV into the real-world transportation systems as well as our personal and professional lives is establishing ethical regulations for AV. In particular, such ethical principles would determine how an AV makes moral decisions in dilemma-inducing situations, for instance, whether it should hit a teenager pedestrian to spare two senior passengers onboard.

This research project looks at this problem not from the philosophical perspective, which prescribes moral behavior of an AV. Instead, this project describes the public expectation and perception of a moral AV, which is the perspective of econom(etr)ics, psychology, and cognitive science disciplines. To do so, the studies in economics and psychology explore the process of human decision making focusing on the morality dimension of decisions, since many of decisions humans routinely make can have a moral aspect. A recent example is the decision of receiving vaccination at a cost (e.g., side effects for the receiver) to immune the community and the society. This research project aims at understanding the public expectations of moral AVs by unravelling the cognitive process of human decisions making considering the decisions’ morality aspect. This objective will be accomplished in two consecutive tasks explained below.

Task 1: Designing and Conducting a Survey Using Stated Preferences (SP) Experiment. For the purpose of analyzing consumers’ choice behavior (e.g., travel behavior), the SP experimental design method provides a rigorous and efficient tool, which is extensively applied in the relevant literature. Applying this tool, this task designs a survey to collect an empirical dataset on human subjects. The PI plans to accomplish the required IRB certificate for data collection.

Task 2: Developing a Modeling Framework on Humans’ Decision Making. This task focuses on developing methods built on econom(etr)ics, psychology, and cognitive science disciplines, to be capable of capturing morality dimension of decisions. One of such methods is choice theory-based model of latent class choice, which is capable of capturing “reason-based” morality. Another example is random regret model, which can capture “emotion-based” morality (since regret is an emotion). The models are then empirically estimated on the dataset collected in Task 1.

]]></description>
      <pubDate>Sun, 13 Oct 2024 10:57:46 GMT</pubDate>
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