Scoping Out the Scalability Issues of Autonomous Vehicle-Pedestrian Interaction

TL;DR

The paper tackles how to scale AV external communication with pedestrians in complex traffic by performing a scoping review of 54 publications (2014–2022). It identifies seven scalability issues and groups them into multi-vehicle and multi-pedestrian contexts, highlighting Clarity of Recipients, Information Overload, and Multi-Lane Safety as the most pressing. The authors propose high-level design directions across three communication loci—vehicle, infrastructure, and pedestrian—to foster simplified, coordinated, and targeted messaging. These findings provide a groundwork and roadmap for developing scalable eHMIs aimed at improving safety and traffic efficiency in dense urban scenarios. The work emphasizes empirical validation and cross-locus integration as essential steps for real-world deployment.

Abstract

Autonomous vehicles (AVs) may use external interfaces, such as LED light bands, to communicate with pedestrians safely and intuitively. While previous research has demonstrated the effectiveness of these interfaces in simple traffic scenarios involving one pedestrian and one vehicle, their performance in more complex scenarios with multiple road users remains unclear. The scalability of AV external communication has therefore attracted increasing attention, prompting the need for further investigation. This scoping review synthesises information from 54 papers to identify seven key scalability issues in multi-vehicle and multi-pedestrian environments, with Clarity of Recipients, Information Overload, and Multi-Lane Safety emerging as the most pressing concerns. To guide future research in scalable AV-pedestrian interactions, we propose high-level design directions focused on three communication loci: vehicle, infrastructure, and pedestrian. Our work contributes the groundwork and a roadmap for designing simplified, coordinated, and targeted external AV communication, ultimately improving safety and efficiency in complex traffic scenarios.