Can Interfaces Facilitate Communication in Autonomous Vehicle-Pedestrian Interaction?

Mahadevan, Karthik; Somanath, Sowmya; Sharlin, Ehud

doi:10.1145/3173386.3176909

Cited by 19 publications

(9 citation statements)

References 4 publications

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“…Considering the effectiveness of the way to communicate between the AV and the pedestrians, which corresponds to the third research question, this is in line with past research outcomes (Petzoldt, Schleinitz, & Banse, 2018;Lagström et al, 2015;Mahadevan et al, 2018;Matthews et al, 2017), the current study shows that in general, the communication system helps pedestrians to recognize the intention of the AV and that they are willing to cross the road according to that information.…”

Section: Discussionsupporting

confidence: 89%

“…It has been suggested that dedicated communication of AVs with pedestrians could help pedestrians overcome their fear of AVs (Keferböck & Riener, 2015;Mahadevan, Somanath, & Sharlin, 2018;Matthews, Chowdhary, & Kieson, 2017). However, only a few studies have been done toward defining which type of communication is most beneficial.…”

Section: Interaction Of Pedestrians With Avsmentioning

confidence: 99%

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Pedestrians’ road crossing behaviour in front of automated vehicles: Results from a pedestrian simulation experiment using agent-based modelling

Rad

Correia

Hagenzieker

2020

Transportation Research Part F: Traffic Psychology and Behaviou

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The objective of this research is to explore the relation between personal characteristics of pedestrians and their crossing behaviour in front of an automated vehicle (AV). For this purpose, a simulation experiment was developed using Agent-Based Modelling (ABM) techniques. Sixty participants were asked to cross the road in a virtual environment displayed on a computer screen, allowing to record their crossing behaviour when in the presence of AVs and conventional vehicles (CVs). In some experimental configurations, the AVs communicated their intention to continue or not to continue their trajectories through the use of lights. The ABM allowed controlling the behaviour of the vehicles when interacting with the simulated avatar of the respondents. The subjects of the experiment were also asked to fill in a questionnaire about usual behaviour in traffic, as well as attitudes and risk perceptions toward crossing roads. The questionnaire data were used to estimate individual specific behavioural latent variables by means of principal component analysis which resulted in three main factors named: violations, lapses, and trust in AVs. The results of generalized linear mixed models applied to the data showed that besides the distance from the approaching vehicle and existence of a zebra crossing, pedestrians' crossing decisions are significantly affected by the participants' age, familiarity with AVs, the communication between the AV and the pedestrian, and whether the approaching vehicle is an AV. Moreover, the introduction of the latent factors as explanatory variables into the regression models indicated that individual specific characteristics like willingness to take risks and violate traffic rules, and trust in AVs can have additional explanatory power in the crossing decisions.

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Section: Discussionsupporting

confidence: 89%

Section: Interaction Of Pedestrians With Avsmentioning

confidence: 99%

Pedestrians’ road crossing behaviour in front of automated vehicles: Results from a pedestrian simulation experiment using agent-based modelling

Rad

Correia

Hagenzieker

2020

Transportation Research Part F: Traffic Psychology and Behaviou

View full text Add to dashboard Cite

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“…They also observed that pedestrians needed more time to understand the intention of a vehicle without eHMI in mixed traffic situations [157]. Communicating the intent and awareness of automated vehicles has been considered in a positive way [136] [137]. Habibovic et al [86], [7] argued that, for safety reasons, communication should never be commandbased.…”

Section: B Signalling Interaction Modelsmentioning

confidence: 99%

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

Camara

Bellotto

Coşar

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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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 and kinematic obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour 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 lowlevel image detection to high-level psychological models. This selfcontained Part II covers the higher levels of this stack, consisting of models of pedestrian behaviour, 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 behaviour, high-level psychological and social modelling of pedestrian behaviour 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 behaviour, but much work is still needed to translate them into quantitative algorithms for practical AV control.

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“…As autonomous vehicles encounter other road users, they too need to communicate what they are going to do next. This has mostly been explored in physically structured locations such as crosswalks and intersections, with specific scenarios like a pedestrian who needs to know whether the car will stop for them and whether it is safe to pass in front of it [1,4,13,21,24].…”

mentioning

confidence: 99%

Why Autonomous Driving Is So Hard: The Social Dimension of Traffic

Pelikan

2021

Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction

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Smooth traffic presupposes fine coordination between different actors, such as pedestrians, cyclists and car drivers. When autonomous vehicles join regular traffic, they need to coordinate with humans on the road. Prior work has often studied and designed for interaction with autonomous vehicles in structured environments such as traffic intersections. This paper describes aspects of coordination also in less structured situations during mundane maneuvers such as overtaking. Taking an ethnomethodological and conversation analytic approach, the paper analyzes video recordings of self-driving shuttle buses in Sweden. Initial findings suggest that the shuttle buses currently do not comply with cyclists' expectations of social coordination in traffic. The paper highlights that communication and coordination with human road users is crucial for smooth flow of traffic and successful deployment of autonomous vehicles also in less structured traffic environments. CCS CONCEPTS• Human-centered computing → Field studies; Collaborative and social computing.

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Can Interfaces Facilitate Communication in Autonomous Vehicle-Pedestrian Interaction?

Cited by 19 publications

References 4 publications

Pedestrians’ road crossing behaviour in front of automated vehicles: Results from a pedestrian simulation experiment using agent-based modelling

Pedestrians’ road crossing behaviour in front of automated vehicles: Results from a pedestrian simulation experiment using agent-based modelling

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

Why Autonomous Driving Is So Hard: The Social Dimension of Traffic

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