Currently, autonomous vehicles are being evaluated by how well they interact with humans without evaluating how well humans interact with them. Since people are not going to unanimously switch over to using autonomous vehicles, attention must be given to how well these new vehicles signal intent to human drivers from the driver’s point of view. Ineffective communication will lead to unnecessary discomfort among drivers caused by an underlying uncertainty about what an autonomous vehicle is or isn’t about to do. Recent studies suggest that humans tend to fixate on areas of higher uncertainty so scenarios that have a higher number of vehicle fixations can be reasoned to be more uncertain. We provide a framework for measuring human uncertainty and use the framework to measure the effect of empathetic vs non-empathetic agents. We used a simulated driving environment to create recorded scenarios and manipulate the autonomous vehicle to include either an empathetic or non-empathetic agent. The driving interaction is composed of two vehicles approaching an uncontrolled intersection. These scenarios were played to twelve participants while their gaze was recorded to track what the participants were fixating on. The overall intent was to provide an analytical framework as a tool for evaluating autonomous driving features; and in this case, we choose to evaluate how effective it was for vehicles to have empathetic behaviors included in the autonomous vehicle decision making. A t-test analysis of the gaze indicated that empathy did not in fact reduce uncertainty although additional testing of this hypothesis will be needed due to the small sample size.

A novel CFD algorithm called LEAP is currently being developed by the Kasbaoui Research Group (KRG) using the Immersed Boundary Method (IBM) to describe complex geometries. To validate the algorithm, this research project focused on testing the algorithm in three dimensions by simulating a sphere placed in a moving fluid. The simulation results were compared against the experimentally derived Schiller-Naumann Correlation. Over the course of 36 trials, various spatial and temporal resolutions were tested at specific Reynolds numbers between 10 and 300. It was observed that numerical errors decreased with increasing spatial and temporal resolution. This result was expected as increased resolution should give results closer to experimental values. Having shown the accuracy and robustness of this method, KRG will continue to develop this algorithm to explore more complex geometries such as aircraft engines or human lungs.

The following analysis was conducted at the Arizona State University open loop wind tunnel. Two 1/24-th scale NASCAR models were placed in a wind tunnel test section and were adjusted to study drafting that commonly occurs at superspeedway racetracks. The purpose of the experiment was to determine how drafting affects a leading and trailing car through changes in distance. A wind tunnel model was developed consisting of two 2019 NASCAR Chevy Camaro race car models, two bar-style load cells, and a programmed Arduino UNO. Two trials were run at each drafting distance, 0, 0.5, 1, 1.5, and 2 car lengths apart. Each trial was run at a wind tunnel velocity of 78 mph (35 m/s) and force data was collected to represent the drag effects at each drafting location. Based on previously published experimentation, this analysis provided important data that related drafting effects in scale model race cars to full-scale vehicles. The experiment showed that scale model testing can be accurately completed when the wind tunnel Reynolds number is of the same magnitude as a full-scale NASCAR. However, the wind tunnel data collected was proven to be fully laminar flow and did not compare to the flow characteristics of typically turbulent flow seen in superspeedway races. Overall, the analytical drag analysis of drafting NASCAR models proved that wind tunnel testing is only accurate when many parameters are met and should only be used as a method of validation to full-scale testing.