Nicole Perterer

Come drive with me: an ethnographic study of driver-passenger pairs to inform future in-car assistance

There is today a large number of ADAS used while driving. These systems are mainly technology driven and most often fail to make use of the social nature and the collaborative mechanisms between driver-passenger pairs. To inform the development of future automotive user interface designs we need to develop a deeper understanding of collaboration in general. In addition, we need to develop an understanding of how, and in what way, other platforms (e.g., the mobile phones) are and will be used in combination with these systems while driving. This paper presents the results of a participative ethnographic study with nine driver-passenger pairs recruited from two online car-sharing portals. Results are categorized in three areas: common ground as a base for successful in-car communication, types and strategies of front-seat passenger assistance, and lastly the impact of technology on collaboration.

Experiential perspectives on road congestions

Commuting can be perceived as both relaxing and stressful. An important cause for stress is getting stuck in traffic, which can especially when unexpected quickly turn commuting into a negative experience, often associated with frustration and the feeling of wasting time. Congestion experiences do not need to be frustrating and annoying. In our research, we aim to generate design solutions for turning the negative experiences into positive ones. We foster the experiential perspective on road congestions, and go beyond current automotive HCI research, which mainly focuses on safety, functionality, and usability. In this paper, we present our work-in progress on characterizing congestion experiences and needs, explored in a design workshop outlining future design directions for supporting positive experiences.

Three Strategies for Autonomous Car-to-Pedestrian Communication: A Survival Guide

With autonomous cars being released into the wild, the question is how they integrate with conventional participants in traffic, such as pedestrians. For accident-free traffic, it is necessary for autonomous cars to interact, collaborate, and negotiate with other agents. Knowledge from social robots provides a valuable source for new interaction paradigms for autonomous cars. We raise the question of how autonomous cars communicate their intentions to pedestrians and negotiate in conflict situations. We discuss the scenario and present three possible communication strategies that are informed from human-robot interaction (HRI).

Co-Navigator: an advanced navigation system for front-seat passengers

In-car navigation systems typically have the purpose to support the driver in a navigation task. While the field of automotive HCI research abounds in driver-focused navigation design relatively little attention has been directed to the front-seat passengers as a support to the driver for e.g., navigation. Based on our ethnographic research, we have designed and prototyped a system called Co-Navigator. It was devised to be operated by the front-seat passenger, in order to support the driver in navigation and driving related tasks. The Co-Navigator is an interactive, tablet based navigation app that provides different kinds of information such as landmarks and upcoming hazard warnings (e.g., construction sites or potholes). In this paper, we describe the prototype, its elements, and an explorative in-situ evaluation. Results show that the Co-Navigator is a valuable in-car navigation device for the front-seat passenger. Especially the map overview and hazard warnings were appreciated.

Trip experience sampling: assessing driver experience in the field

This paper introduces Trip Experience Sampling (TES)---a low-tech method to gather driver experiences in the car. It builds upon the original paper-and-pencil based experience sampling method and applies it to the automotive context. TES uses driver logbooks to survey user experience at the end of a trip while still sitting in the car. To proof the feasibility of TES we collected 475 trip experience samples from 20 participants in a field study. Our study showed that TES is a feasible and successful method to conduct user experience research in the car without distracting the driver. TES is a successful method in terms of return rate, trip diversity, data quality and subjective participants responses. The strengths of the method lie in the entire abandonment of technology making it a quantifiable field method, which is easy to conduct.

Activities and Technology Usage while Driving: A Field Study with Private Short-Distance Car Commuters

Despite its high costs and stressful traffic congestions, solo driving is still the number one commuting choice. In order to inform the development of safer or more enjoyable future automotive user interface designs, we need to understand what people do while commuting to and from work. In this paper, we present a contextual inquiry study with 13 private short-distance (PSD) car commuters in order to investigate their main activities in the car and what kind of technology they use. We were, especially, interested in strategies that may support or enhance activities while being caught in a traffic jam. Results show that most PSD car commuters had chosen a more relaxed and comfortable alternative instead of the fastest route, as well as took advantage of short waiting periods for collaborative activities.

The Impact of Spatial Properties on Collaboration: An Exploratory Study in the Automotive Domain

Interaction environments are characterized by their spatial properties, which guide, direct, and provide an opportunity to become a place for social encounters. For example, the car cabin comprises properties such as a special seating arrangement and hence physical barriers between the back and front row. In emphasizing notions of space and place, we present an initial study on how such spatial properties of the car cabin shape passenger collaboration. With this, we contribute to a better understanding of the automotive design space beyond driver and co-driver positions. In an exploratory lab study with 56 participants we observed collaborative practices in a hardware mock-up of an actual car. We found that social practices in cars need to be understood as connected to their inherent spatial manifestations, which are constraining and concurrently constituting them. We reflect upon the driver position as the crux of the matter, the meanings people ascribe to particular positions, and how we can use this knowledge to inform automotive interaction design.

Exploring Challenging Environments: Contextual Research in the Car and the Factory Through an HCI Lens

Nontraditional environments offer a variety of methodological challenges when exploring cooperation under very specific contextual conditions. We understand contexts as challenging when they exhibit very specific/unique characteristics that need to be explored beyond traditional and already better-understood working/office settings. Moreover, these challenging environments are contexts in which human-human interaction mediated by computing systems and human-machine collaboration is hard to observe. In this paper, we focus on two challenging environments: the highly context-dependent automotive environment and the complex context of a semiconductor factory. Both contexts offer potential in a variety of ways for novel computer-supported cooperative work research, such as driver/codriver cooperation and operator-robot cooperation. In this book chapter, two exemplary contexts “car” and “factory” will be characterized in terms of (1) research challenges posed by the context, (2) performed exploratory studies, and (3) methodological implications for the two exemplary contexts, as well as for CSCW and HCI research practices in general.

Automotive HMI test package: an exploitable approach to study in-car HMIs

This case study describes the development of a method package for evaluating in-car HMIs holistically. The goal is to provide a toolbox that is easy to replicate and allows evaluators to identify the effects of the system usage on the drivers state. Additionally it aims at finding interface flaws that cause distraction and negative experiences. We applied the toolbox in two example studies, which informed the further application of the HMI study approach. We learned that the combination of established expert and end user methods with a real test track leads to useful results that are easy to communicate to both scientific and public audiences.

Follow Me: Exploring Strategies and Challenges for Collaborative Driving

Current research on Vehicle-to-Vehicle (V2V) communication aims at improving interaction between different vehicles by communication technologies and is mainly focused on driver-to-driver interaction. But how do drivers and passengers of two vehicles that have the same destination communicate with each other? In such a collaborative driving scenario, several factors such as the environmental context or the behavior of the vehicle occupants may influence the communication. In order to explore how information is exchanged in collaborative driving, we conducted an exploratory in-situ study with seven groups of two driver/co-driver pairs each, located in two separate vehicles. During the ride, the participants had to drive collaboratively on a predefined route solving different subtasks. We found that different social (e.g., driving habits, unpredicted intentions) and contextual factors (e.g., night/rain conditions, size or color of the vehicle) influenced collaboration. Our findings provide a deeper understanding of collaborative driving and inform future V2V communication designs.