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Pedestrian-Vehicle Interaction in Shared Space: Insights for Autonomous Vehicles

Yiyuan Wang, Luke Hespanhol, Stewart Worrall, Martin Tomitsch

TL;DR

The paper addresses how autonomous vehicles can socially integrate into pedestrian-heavy shared spaces by examining real-world pedestrian–vehicle interactions. It analyzes a long-term video dataset of a small automation-capable EV driven manually across three shared spaces, coding pedestrian responses to derive patterns in movement, gaze, etiquette, and prosocial actions. Key findings show movement and proxemics as primary communication channels, substantial group effects, and instances of information propagation among pedestrians, along with safety risks associated with inattentiveness. The study offers early, practical insights for AV external interaction designs that preserve ambience while enhancing safety, outlining directions for non-intrusive warnings and social-norm-compatible behaviours. Overall, it provides foundational empirical evidence to guide future AVs operating in shared spaces that prioritize pedestrians and social dynamics.

Abstract

Shared space reduces segregation between vehicles and pedestrians and encourages them to share roads without imposed traffic rules. The behaviour of road users (RUs) is then controlled by social norms, and interactions are more versatile than on traditional roads. Autonomous vehicles (AVs) will need to adapt to these norms to become socially acceptable RUs in shared spaces. However, to date, there is not much research into pedestrian-vehicle interaction in shared-space environments, and prior efforts have predominantly focused on traditional roads and crossing scenarios. We present a video observation investigating pedestrian reactions to a small, automation-capable vehicle driven manually in shared spaces based on a long-term naturalistic driving dataset. We report various pedestrian reactions (from movement adjustment to prosocial behaviour) and situations pertinent to shared spaces at this early stage. Insights drawn can serve as a foundation to support future AVs navigating shared spaces, especially those with a high pedestrian focus.

Pedestrian-Vehicle Interaction in Shared Space: Insights for Autonomous Vehicles

TL;DR

The paper addresses how autonomous vehicles can socially integrate into pedestrian-heavy shared spaces by examining real-world pedestrian–vehicle interactions. It analyzes a long-term video dataset of a small automation-capable EV driven manually across three shared spaces, coding pedestrian responses to derive patterns in movement, gaze, etiquette, and prosocial actions. Key findings show movement and proxemics as primary communication channels, substantial group effects, and instances of information propagation among pedestrians, along with safety risks associated with inattentiveness. The study offers early, practical insights for AV external interaction designs that preserve ambience while enhancing safety, outlining directions for non-intrusive warnings and social-norm-compatible behaviours. Overall, it provides foundational empirical evidence to guide future AVs operating in shared spaces that prioritize pedestrians and social dynamics.

Abstract

Shared space reduces segregation between vehicles and pedestrians and encourages them to share roads without imposed traffic rules. The behaviour of road users (RUs) is then controlled by social norms, and interactions are more versatile than on traditional roads. Autonomous vehicles (AVs) will need to adapt to these norms to become socially acceptable RUs in shared spaces. However, to date, there is not much research into pedestrian-vehicle interaction in shared-space environments, and prior efforts have predominantly focused on traditional roads and crossing scenarios. We present a video observation investigating pedestrian reactions to a small, automation-capable vehicle driven manually in shared spaces based on a long-term naturalistic driving dataset. We report various pedestrian reactions (from movement adjustment to prosocial behaviour) and situations pertinent to shared spaces at this early stage. Insights drawn can serve as a foundation to support future AVs navigating shared spaces, especially those with a high pedestrian focus.
Paper Structure (28 sections, 5 figures, 1 table)

This paper contains 28 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Field Observations Informing AV-pedestrian Interaction
  4. Pedestrian Experiences in Shared Space
  5. AVs: Social Actors in Future Traffic
  6. Method
  7. Research Questions
  8. Data
  9. Procedure
  10. Results
  11. Pedestrian Behaviours
  12. Movement Adjustment
  13. Curiosity
  14. Etiquette
  15. Prosocial Behaviour
  16. ...and 13 more sections

Figures (5)

  • Figure 1: Apparatus and environment for data collection.
  • Figure 2: A summary of behavioural codes. Left column: pedestrian states (mutually exclusive); middle column: pedestrian behaviours (mutually exclusive); right column: pedestrian behaviours accompanying the behaviours in the middle column. Both sum of left column and sum of middle column equal the total number of interactions.
  • Figure 3: Pedestrian states as shown in the sub-captions. We use cyan bounding boxes to highlight pedestrians referred to. More descriptions: (a) the group of pedestrians adapted trajectory and split into two groups; (b) one pedestrian was alerting the other pedestrian; (c) the pedestrian moved to the side; (d) the pedestrian saw the vehicle and stopped walking; (e,f,i) the pedestrians were gazing at the vehicle; (g) the pedestrians were chatting; (h) the group of pedestrians were gazing and smiling.
  • Figure 4: (a) The girl was smiling and gesturing to give way to the vehicle. (b) One player in the group was guiding the vehicle to pass through the crowd. (c) An elderly woman was asking the wheelchair user and the two helpers to make way for the vehicle.
  • Figure 5: (a) Weekly number of interactions. (b) Percentage of pedestrians in group and alone across five behaviours: Gaze, Adapt trajectory, Make way for vehicle, Smile, and Stop and give way.