Defining interactions: a conceptual framework for understanding interactive behaviour in human and automated road traffic

Rapid advances in technology for highly automated vehicles (HAVs) have raised concerns about coexistence of HAVs and human road users. Although there is a long tradition of research into human road user interactions, there is a lack of shared models and terminology to support cross-disciplinary research and development towards safe and acceptable interaction-capable HAVs. Here, we review the main themes and findings in previous theoretical and empirical interaction research, and find large variability in perspectives and terminologies. We unify these perspectives in a structured, cross-theoretical conceptual framework, describing what road traffic interactions are, how they arise, and how they get resolved. Two key contributions are: (1) a stringent definition of “interaction”, as “a situation where the behaviour of at least two road users can be interpreted as being influenced by the possibility that they are both intending to occupy the same region of space at the same time in the near future”, and (2) a taxonomy of the types of behaviours that road users exhibit in interactions. We hope that this conceptual framework will be useful in the development of improved empirical methodology, theoretical models, and technical requirements on vehicle automation

Methodologies to Understand the Road User Needs When Interacting with Automated Vehicles

Interactions among road users play an important role for road safety and fluent traffic. In order to design appropriate interaction strategies for automated vehicles, observational studies were conducted in Athens (Greece), Munich (Germany), Leeds (UK) and in Rockville, MD (USA). Naturalistic behaviour was studied, as it may expose interesting scenarios not encountered in controlled conditions. Video and LiDAR recordings were used to extract kinematic information of all road users involved in an interaction and to develop appropriate kinematic models that can be used to predict other’s behaviour or plan the behaviour of an automated vehicle. Manual on-site observations of interactions provided additional behavioural information that may not have been visible via the overhead camera or LiDAR recordings. Verbal protocols were also applied to get a more direct recording of the human thought process. Real-time verbal reports deliver a richness of information that is inaccessible by purely quantitative data but they may pose excessive cognitive workload and remain incomplete. A retrospective commentary was applied in complex traffic environment, which however carries an increased risk of omission, rationalization and reconstruction. This is why it was applied while the participants were watching videos from their eye gaze recording. The commentaries revealed signals and cues used in interactions and in drivers’ decision-making, that cannot be captured by objective methods. Multiple methods need to be combined, objective and qualitative ones, depending on the specific objectives of each future study

How Do We Study Pedestrian Interaction with Automated Vehicles? Preliminary Findings from the European interACT Project

This paper provides an overview of a set of behavioural studies, conducted as part of the European project interACT, to understand road user behaviour in current urban settings. The paper reports on a number of methodologies used to understand how humans currently interact in urban traffic, in order to establish what information would be useful for the design of future AVs, when interacting with other road users, especially pedestrians. In addition to summarising the results from a number of observation studies, we report on preliminary results from Virtual Reality studies, investigating if, in the absence of a human vehicle controller, externally presented interfaces can be used for communication between AVs and pedestrians. Finally, an overview of the mathematical and computational modelling techniques used to understand how AV and pedestrian behaviour can be both cooperative, and effective is provided. The hope is that future AVs can be designed with an understanding of how humans cooperate and communicate in mixed traffic, promoting good traffic flow, user acceptance and user trust

Transforming Cars into Computers: Interdisciplinary Opportunities for HCI

Road and highway infrastructures are being transformed in anticipation of self-driving vehicles. During the transition to fully autonomous road networks people and driverless cars will interact with each other in mixed traffic situations. Vehicles are currently equipped with two types of communication devices one auditory (a horn) and the other visual (signalling lights). In many instances, human drivers use these devices in combination with embodied interaction such as eye contact and gesture when communicating with other road users. Hence, horn and signalling devices currently in use may not be enough to communicate with others in traffic settings; especially when driverless vehicles become responsible for the main driving activity. Driverless vehicles require new interaction types that support Human-AV interaction in an easy to understand and intuitive way. With the transformation of cars into computers new opportunities for research present themselves to the HCI community

Communicating Issues in Automated Driving to Surrounding Traffic - How should an Automated Vehicle Communicate a Minimum Risk Maneuver via eHMI and/or dHMI?

Cooperative automated vehicles (CAV) are not able to drive automated in all situations. Each vehicle has or is going to have its own operational design domain (ODD), which exactly specifies which situations can be handled, and which cannot. Vehicles of higher levels of automation according to SAE J3016 will try to take the driver back into the control loop if the vehicle approaches the border of its ODD by issuing a transition of control (ToC). If the driver is not responding, the vehicle will perform a minimum risk maneuver (MRM), where the CAV is stopping. Instead of looking at the internal HMI of single CAVs, the H2020 project TransAID focusses on the effects of automation limitations on traffic efficiency and safety. Besides helping the CAV to reduce negative impacts of such situations by infrastructure measures, also informing the surrounding vehicles about a CAV’s current issues and about its plans to solve them will most likely improve such situations. To approach this assumption, DLR conducted a first virtual reality study, where e.g. a 360° externally mounted LED light-band as external HMI (eHMI) of a CAV and specific vehicle movements as dynamic HMI (dHMI) are used in case it needs to perform an MRM. In the study, ten participants tested different variants and combinations. Preliminary results show that the use of an eHMI is a useful and informative approach

Understanding interactions between Automated Road Transport Systems and other road users: A video analysis

If automated vehicles (AVs) are to move efficiently through the traffic environment, there is a need for them to interact and communicate with other road users in a comprehensible and predictable manner. For this reason, an understanding of the interaction requirements of other road users is needed. The current study investigated these requirements through an analysis of 22 h of video footage of the CityMobil2 AV demonstrations in La Rochelle (France) and Trikala (Greece). Manual and automated video-analysis techniques were used to identify typical interaction patterns between AVs and other road users. Results indicate that road infrastructure and road user factors had a major impact on the type of interactions that arose between AVs and other road users. Road infrastructure features such as road width, and the presence or absence of zebra crossings had an impact on road users’ trajectory decisions while approaching an AV. Where possible, pedestrians and cyclists appeared to leave as much space as possible between their trajectories and that of the AV. However, in situations where the infrastructure did not allow for the separation of traffic, risky behaviours were more likely to emerge, with cyclists, in particular, travelling closely alongside the AVs on narrow parts of the road, rather than waiting for the AV to pass. In addition, the types of interaction varied considerably across socio-demographic groups, with females and older users more likely to show cautionary behaviour around the AVs than males, or younger road users. Overall, the results highlight the importance of implementing the correct infrastructure to support the safe introduction of AVs, while also ensuring that the behaviour of the AV matches other road users’ expectations as closely as possible in order to avoid traffic conflicts

Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behavior as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behavior, from prediction of individual pedestrians' likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behavior, high-level psychological and social modelling of pedestrian behavior still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behavior, but much work is still needed to translate them into quantitative algorithms for practical AV control

D3.3 Final functional Human Factors recommendations

This report documents the Human Factors (HF) recommendations developed and used for the design of demonstrator vehicles within the AdaptIVe project. The proposed HF-recommendations, therefore, mostly address the automation levels (SAE) 1-3, in highway, urban, and close-distance scenarios. The recommendations developed in this work were predominantly designed to meet AdaptIVe project requirements, and they should be carefully verified prior to use in further projects/applications. However, this report can provide general Human Factors guidelines for the User-Centred Design (UCD) of automated vehicles

H-Mode 2D. Eine haptisch-multimodale Bedienweise für die kooperative Führung teil- und hochautomatisierter Fahrzeuge

Vor dem Hintergrund wachsender technischer Möglichkeiten im Bereich der Assistenz und Automation entstehen vielfältige Herausforderungen, Risiken und Chancen in der Gestaltung des assistierten, teil- und hochautomatisierten Fahrens. Eine der größten Herausforderungen besteht darin, eine Vielzahl von komplexen technischen Funktionen so zu integrieren und dem Menschen anzubieten, dass sie intuitiv als ein zusammenhängendes, mit dem Fahrer kooperierendes System verstanden und jederzeit zuverlässig, sicher und angenehm bedient werden können. Dabei verschwimmen die Grenzen zwischen Assistenz und Automation zunehmend und es wird notwendig, einander ergänzende Assistenz- und Automationsgrade zu definieren [1]. Somit ist es sinnvoll, einen stärkeren Fokus auf die Einbeziehung des Menschen im Sinne einer kognitiven Kompatibilität und im Hinblick auf das Vertrauen zwischen Mensch und Automation bzw. Assistenz (vgl. [2, 3] und Kap. 58) sowie auch dem Menschen im Entwicklungsprozess zu legen [4]. Die kooperative Fahrzeugführung adressiert diese Fragestellungen und beschreibt als generisches Konzept die generellen Freiheitsgrade des Zusammenwirkens von Mensch und Automation z. B. auf den verschiedenen Ebenen der Fahrzeugführung (vgl. Abb. 60.11). Der im vorliegenden Kapitel beschriebene H-Mode ist eine konkrete Umsetzung einer kooperativen Fahrzeugführung