Crossing the line: Impact of pedestrian group behavior on individual crossing decisions in AV interactions

• The influence of other pedestrians is insufficiently investigated in eHMI research. • A VR study was conducted where participants crossed the street in front of an AV. • The efficiency of street crossings was positively influenced by other pedestrians. • The safety of street crossings was negatively influenced by other pedestrians. Research and development in automated driving are steadily progressing, with an increasing number of vehicles equipped with these systems participating in road traffic. Ensuring safe and robust interactions between automated vehicles and other road users is essential, especially in urban environments where interactions between vehicles and groups of pedestrians are common. Literature shows that external human–machine interfaces on automated vehicles might serve as a possible solution for such communication. Pedestrian groups have not been adequately considered in research on road crossings in front of automated vehicles equipped with external human–machine interfaces. This study investigates whether and how the behavior of a pedestrian group affects a single pedestrian’s behavior and perception of crossing in front of an automated vehicle equipped with an external human–machine interface. A virtual reality study with 44 participants revealed that pedestrians adjust their crossing behavior based on the behavior of others, leading to both improved efficiency and potential safety risks during crossings. Subjective measures revealed increased trust in automated vehicles with repeated exposure. However, trust remained essentially unchanged across different behaviors of the pedestrian group. After the experiment, almost three-quarters of the participants reported a subjective influence of the pedestrian group. Objectively, participants crossed earlier and more confidently when the group began to cross the street, improving efficiency in scenarios where the automated vehicle stopped. The same behavior raises safety concerns in scenarios where the automated vehicle is about to pass, as participants follow the pedestrian group early. These findings highlight the need for automated vehicle communication systems to consider group dynamics to ensure safety and efficiency.

Authors

Institutions

Publication Details

Journal
Transportation Research Part F Traffic Psychology and Behaviour
Published
2025-01-22
DOI
https://doi.org/10.1016/j.trf.2025.01.020
Citations
11
Primary Topic
Evacuation and Crowd Dynamics
Type
article
Field-Weighted Citation Impact
12.60

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Crossing the line: Impact of pedestrian group behavior on individual crossing decisions in AV interactions

Klaus Bengler, Maximilian Hübner, Jan-Niklas Birger Stockmann
11 citations
Transportation Research Part F Traffic Psychology and Behaviour
Evacuation and Crowd Dynamics
12.60
article

Crossing the line: Impact of pedestrian group behavior on individual crossing decisions in AV interactions

Klaus Bengler, Maximilian Hübner, Jan-Niklas Birger Stockmann
article en
11 citations

Abstract

• The influence of other pedestrians is insufficiently investigated in eHMI research. • A VR study was conducted where participants crossed the street in front of an AV. • The efficiency of street crossings was positively influenced by other pedestrians. • The safety of street crossings was negatively influenced by other pedestrians. Research and development in automated driving are steadily progressing, with an increasing number of vehicles equipped with these systems participating in road traffic. Ensuring safe and robust interactions between automated vehicles and other road users is essential, especially in urban environments where interactions between vehicles and groups of pedestrians are common. Literature shows that external human–machine interfaces on automated vehicles might serve as a possible solution for such communication. Pedestrian groups have not been adequately considered in research on road crossings in front of automated vehicles equipped with external human–machine interfaces. This study investigates whether and how the behavior of a pedestrian group affects a single pedestrian’s behavior and perception of crossing in front of an automated vehicle equipped with an external human–machine interface. A virtual reality study with 44 participants revealed that pedestrians adjust their crossing behavior based on the behavior of others, leading to both improved efficiency and potential safety risks during crossings. Subjective measures revealed increased trust in automated vehicles with repeated exposure. However, trust remained essentially unchanged across different behaviors of the pedestrian group. After the experiment, almost three-quarters of the participants reported a subjective influence of the pedestrian group. Objectively, participants crossed earlier and more confidently when the group began to cross the street, improving efficiency in scenarios where the automated vehicle stopped. The same behavior raises safety concerns in scenarios where the automated vehicle is about to pass, as participants follow the pedestrian group early. These findings highlight the need for automated vehicle communication systems to consider group dynamics to ensure safety and efficiency.

Transportation Research Part F Traffic Psychology and BehaviourVol. 109
Technical University of Munich (DE)
Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie
Sustainable cities and communities
Openalex Percentile: Top 1%
Evacuation and Crowd Dynamics
12.60
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.