We present an interaction scenario-based design space for autonomous vehicles (AVs) and external road users (ERU)s, emphasizing the gaps and fragmented approach in current standards development. We first provide an overview of research in AV-ERU interactions and ongoing standards and policy development efforts. We then outline the AV-ERU ecosystem and identify key information modes that encapsulate potential interaction scenarios, such as states, intent, and responses of AVs. We describe the elements of the design space, including the interfaces, interactors and agents, modalities of interaction, and design considerations. We demonstrate the application of our design space and map existing standards to highlight its significance as a comprehensive tool for future standards development. We discuss the need for collaborative efforts among stakeholders to expedite and reduce bias in the establishment of standards.

Lane changes of autonomous vehicles (AV) should not only succeed in making the maneuver but also provide a positive interaction experience for other drivers. As lane changes involve complex interactions, identification of a set of behaviors for autonomous vehicle lane change communication can be difficult to define. This study investigates different movements communicating AV lane change intent in order to identify which effectively communicates and positively affects other drivers' decisions. We utilized a virtual reality environment wherein 14 participants were each placed in the driver's seat of a car and experienced four different AV lane change signals. Our findings suggest that expressive lane change behaviors such as lateral movement have high levels of legibility at the cost of high perceived aggressiveness. We propose further investigation into how balancing key parameters of lateral movement can balance in legibility and aggressiveness that provide the best AV interaction experience for human drivers

With the advent of autonomous vehicles (AVs) on public roads, the frequency of interactions between these AVs and pedestrians will increase. One example of such an interaction is at unsignalized crosswalks, where pedestrians and vehicles must negotiate for the right of way. Studies show that these interactions often use social communication channels. This paper addresses how AVs can fill this communication gap, focusing on the impact of pedestrian self-identifiability. Using VR, we designed two novel awareness-conveying behaviors, and a control condition with no awareness behavior. We then conducted a within-subjects VR study with 19 participants in which they traversed a crosswalk in front of a driverless vehicle in each experimental condition and rated their experience across seven probes. Results indicated that an awareness-conveying behavior significantly increased pedestrians' sense of safety and that increases in self-identifiability further improved pedestrians' experience without resulting in a heightened sense of surveillance from the vehicle.

As autonomous vehicles (AV) become increasingly common on our roads, it is important for first responders - police officers, firefighters, and emergency medical services to learn new interaction protocols as they can no longer rely on those applied to human-driven vehicles. This study identifies critical pain points and concerns of first responders interacting with AVs on the road. We explore 7 different designs that communicate that an AV is in park and is safe to approach and analyze how first responders perceive these designs in terms of clarity and safety. We conducted qualitative interviews with 9 first responders and gained insights on how the needs of first responders can be integrated within the AV design process. As a result, we identify an AV safe park state communication protocol that would be ideal for first responders. Additionally, we derive a guideline for effective communication methods that can be used in the design of these vehicles establishing research methods that involve emergency responders within the loop.

While great strides have been taken in advancing the field of Human-Robot Interaction (HRI), challenges abound in understanding and improving how Autonomous Vehicles (AVs) will interact with and within society. Through this paper, the authors attempt to paint the picture of challenges unique to the study and advancement of interfaces between AVs and vulnerable road users (VRUs). In turn, these gaps in research highlight the opportunities for academia, industry, and public policy to collaborate and advance the state of the art of AV-VRU interaction, and the need for a dedicated forum for sharing insights across these various sectors.

In this direct replication of Mueller and Oppenheimer’s (2014) Study 1, participants watched a lecture while taking notes with a laptop ( n = 74) or longhand ( n = 68). After a brief distraction and without the opportunity to study, they took a quiz. As in the original study, laptop participants took notes containing more words spoken verbatim by the lecturer and more words overall than did longhand participants. However, laptop participants did not perform better than longhand participants on the quiz. Exploratory meta-analyses of eight similar studies echoed this pattern. In addition, in both the original study and our replication, higher word count was associated with better quiz performance, and higher verbatim overlap was associated with worse quiz performance, but the latter finding was not robust in our replication. Overall, results do not support the idea that longhand note taking improves immediate learning via better encoding of information.

Emotions are crucial for human social interactions and thus people communicate emotions through a variety of modalities: kinesthetic (through facial expressions, body posture and gestures), auditory (the acoustic features of speech) and semantic (the content of what they say). Sometimes however, communication channels for certain modalities can be unavailable (e.g., in the case of texting), and sometimes they can be compromised, due to a disorder such as Parkinson's disease (PD) that may affect facial, gestural and speech expressions of emotions. To address this, we developed a prototype for an emoting robot that can detect emotions in one modality, specifically in the content of speech, and then express them in another modality, specifically through gestures. The system consists of two components: detection and expression of emotions. In this paper we present the development of the expression component of the emoting system. We focus on its dynamical properties that use a spring model for smooth transitions between emotion expressions over time. This novel method compensates for varying utterance frequency and prediction errors coming from the emotion recognition component. We also describe the input the dynamical expression component receives from the emotion detection component, the development and validation of the output comprising of the gestures instantiated in the robot, and the implementation of the system. We present results from a human validation study that shows people perceive the robot gestures, generated by the system, as expressing the emotions in the speech content. Also, we show that people's perceptions of the accuracy of emotion expression is significantly higher for a mass-spring dynamical system than a system without a mass-spring when specific detection errors are present. We discuss and suggest future developments of the system and further validation experiments. This paper is part of a larger project to develop a prototype for a socially assistive robot for PD persons. The goal is to present the technical implementation of one robot capability: emotion expression.

Women are underrepresented in robotics, and this may be partly due to the educational emphasis on mechanical applications rather than social applications of robotics. This study aimed to investigate whether teaching robotics using social robots increased girls' engagement compared to using more mechanical vex robots. 20 girls were recruited from school robotics classes. They were taught 30 minutes of VEX robotics and 30 minutes of social robotics in a counter-balanced order. Engagement was measured using questionnaires and observations. Results showed that girls were significantly more engaged in the social robot classes than the vex robot classes. This pilot study suggests a possible way to encourage more girls to study robotics.