Generative AI for Product Design: Getting the Right Design and the Design Right

Generative AI (GenAI) models excel in their ability to recognize patterns in existing data and generate new and unexpected content. Recent advances have motivated applications of GenAI tools (e.g., Stable Diffusion, ChatGPT) to professional practice across industries, including product design. While these generative capabilities may seem enticing on the surface, certain barriers limit their practical application for real-world use in industry settings. In this position paper, we articulate and situate these barriers within two phases of the product design process, namely "getting the right design" and "getting the design right," and propose a research agenda to stimulate discussions around opportunities for realizing the full potential of GenAI tools in product design

How Do Changes in the External Environment Affect Driving Engagement in Automated Driving? – An Exploratory Study

We developed a new method for simultaneously assessing theworkload of a driver and a non-driver engaged in natural conversation either inthe vehicle or over a cell phone. For both the driver and non-driver, talking wasfound to be more demanding than listening and the pattern was identical for bothpassenger conversations and cell phone conversations. Operating the vehicleincreased the workload for the driver over and above the conversation task. Theeffects of driving (or not) and talking (or not) were found to be additive. The datareveal a pattern of dynamic fluctuation in workload in driver/non-driverconversational dyads. Driving is performed while processing various internal driver and external cues from the driving environment (e.g., subtle vibrations, lateral and longitudinal acceleration). The present study was conducted for the purpose of identifying how much external cues affect driver’s gaze behavior in an automated driving environment. Fifteen participants drove a commercially available vehicle with longitudinal and lateral automation on an oval test track. Participants were asked to drive the vehicle with and without automation, with or without a side-task, and either with their hands-on or hands-off-wheel. Driver’s gaze behavior, handson-wheel status and driving conditions were annotated from video data. The results showed that during automated driving and side-task performance, eyes-on-road time was significantly greater after entering a curve than before and as a result of changes in speed. These differences were not observed in automated driving mode when no side-task is performed. Also, these were more sensitive than hands-on or hands-off-wheel conditions. The results also suggest that drivers may process nonvisual information (e.g., vestibular information produced by changes in lateral and longitudinal vehicle acceleration) prior to or even during the implementation of a visual resource allocation strategy. The present study suggests driver awareness can be aided without requiring the driver to grab the steering wheel

The Generation of Involuntary Mental Imagery in an Ecologically-Valid Task.

Laboratory tasks (e.g., the flanker task) reveal that incidental stimuli (e.g., distractors) can reliably trigger involuntary conscious imagery. Can such involuntary effects, involving competing representations, arise during dual-task conditions? Another concern about these laboratory tasks is whether such effects arise in highly ecologically-valid conditions. For example, do these effects arise from tasks involving dynamic stimuli (e.g., simulations of semi-automated driving experiences)? The data from our experiment suggest that the answer to our two questions is yes. Subjects were presented with video footage of the kinds of events that one would observe if one were seated in the driver's seat of a semi-automated vehicle. Before being presented with this video footage, subjects had been trained to respond to street signs according to laboratory techniques that cause stimulus-elicited involuntary imagery. After training, in the Respond condition, subjects responded to the signs; in the Suppress condition, subjects were instructed to not respond to the signs in the video footage. Subjects in the Suppress condition reported involuntary imagery on a substantive proportion of the trials. Such involuntary effects arose even under dual-task conditions (while performing the n-back task or psychomotor vigilance task). The present laboratory task has implications for semi-automated driving, because the safe interaction between driver and vehicle requires that the communicative signals from vehicle to driver be effective at activating the appropriate cognitions and behavioral inclinations. In addition, our data from the dual-task conditions provide constraints for theoretical models of cognitive resources

Dynamics of Pedestrian Crossing Decisions Based on Vehicle Trajectories in Large-Scale Simulated and Real-World Data

Humans, as both pedestrians and drivers, generally skillfully navigate traffic intersections. Despite the uncertainty, danger, and the non-verbal nature of communication commonly found in these interactions, there are surprisingly few collisions considering the total number of interactions. As the role of automation technology in vehicles grows, it becomes increasingly critical to understand the relationship between pedestrian and driver behavior: how pedestrians perceive the actions of a vehicle/driver and how pedestrians make crossing decisions. The relationship between time-to-arrival (TTA) and pedestrian gap acceptance (i.e., whether a pedestrian chooses to cross under a given window of time to cross) has been extensively investigated. However, the dynamic nature of vehicle trajectories in the context of non-verbal communication has not been systematically explored. Our work provides evidence that trajectory dynamics, such as changes in TTA, can be powerful signals in the non-verbal communication between drivers and pedestrians. Moreover, we investigate these effects in both simulated and realworld datasets, both larger than have previously been considered in literature to the best of our knowledge