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Introduction: In current urban traffic, pedestrians attempting to cross the road at un-signalized locations are thought to mostly use implicit communication, such as deceleration cues, to interpret a vehicle's intention to yield. There is less reliance on explicit driver- or vehicle-based messages, such as hand/head movements, or flashing lights/beeping horns. With the impending deployment of Automated Vehicles (AV), especially those at SAE Level 4 and 5, where the driver is no longer in control of the vehicle, there has been a surge in interest in the value of new forms of communication for AVs, for example, via different types of external Human Machine Interfaces (eHMIs). However, there is still much to be understood about how quickly a novel eHMI affects pedestrian crossing decisions, and whether it provides any additional aid, above and beyond implicit/kinematic information from the vehicle. The aim of this between-participant study, funded by the H2020 interACT project, was to investigate how the combination of kinematic information from a vehicle (e.g., Speed and Deceleration), and eHMI designs, play a role in assisting the crossing decision of pedestrians in a cave-based pedestrian simulator., Method: Using an existing, well-recognized, message for yielding (Flashing Headlights - FH) as a benchmark, this study also investigated how quickly a novel eHMI (Slow Pulsing Light Band - SPLB) was learned. To investigate the effect of eHMI visibility on crossing decisions, the distance at which each eHMI was perceivable was also measured., Results: Results showed that, compared to SPLB, the FH led to earlier crossings during vehicle deceleration, especially at lower approaching speeds, and smaller time gaps. However, although FH was visible earlier than SPLB, this visibility does not appear to be the only reason for earlier crossings, with message familiarity thought to play a role. Participants were found to learn the meaning conveyed by FH relatively quickly, crossing around 1 second earlier in its presence (compared to the no eHMI condition), across the three blocks of trials. On the other hand, it took participants at least one block of 12 trials for the new SPLB signal to affect crossing, which only accelerated crossing initiations by around 200 ms, compared to the no eHMI condition. The role of comprehension, long-term exposure, and familiarity of novel messages in this context is therefore important, if AVs are to provide safe, trustworthy communication messages, which will enhance traffic flow and efficiency., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

• Pedestrians' road-crossing decisions and head movement patterns in response to different vehicle kinematics in mixed traffic were investigated. • A distributed platform incorporating a CAVE-based pedestrian simulator and a desktop driving simulator was developed to enable pedestrian-vehicle interaction in VR. • Pedestrians displayed different head movement patterns prior to crossing and non-crossing decisions, suggesting this to be a promising indicator of pedestrians' road-crossing intention. • The 'last moment check' behaviour in pedestrians' head movements before crossing initiation, can be used by future AVs for early recognition of pedestrians' road-crossing intention. • The similar road-crossing percentage and head-turning patterns of pedestrians demonstrated that the kinematic difference between human-driven and predefined vehicles did not impact pedestrians' behaviour prior to road crossings. The impending deployment of automated vehicles (AVs) will lead to mixed traffic conditions, where pedestrians will be required to interact with both AVs and human-driven vehicles. For traffic flow to be safe and efficient, AVs' understanding of pedestrians' behaviour and intention is as important as pedestrians' perception of AVs' status and intent. To investigate pedestrians' road-crossing decisions and interactive behaviour in mixed traffic, a distributed simulation study was developed by linking a CAVE-based pedestrian simulator and a desktop driving simulator. Twenty-five pairs of pedestrians and drivers were recruited, and each pair experienced 32 trials, where pedestrians decided to cross (or not) before an approaching vehicle at an un-signalised, single-lane, road. The driving pattern of the approaching vehicle (controlled by either a predefined program or human driver) and braking mode (braking/non-braking) were manipulated. For the predefined vehicles, the braking pattern was subdivided into hard braking and soft braking to provide more kinematic variability. Human drivers were also instructed to yield, or not, in different trials. Pedestrians' road-crossing decisions and head movements were recorded and analysed. Results revealed a significant difference in head-turning patterns between crossing and non-crossing manoeuvres, demonstrating pedestrians' head movements as a valid indicator of their road-crossing intentions. Moreover, results identified a 'last moment check' behaviour before pedestrians' crossing initiation, with a significant increase in head-turning during the last 2 s. Finally, pedestrians made a similar percentage of road crossings and displayed a similar pattern of head movements, in response to human-driven and predefined vehicles, suggesting that the difference of implicit cues in the current mixed traffic setup does not impact their behaviour prior to road crossings. The findings from this study extend our knowledge of how pedestrians behave when crossing the road in mixed traffic, particularly in terms of their head-turning behaviour. We hope this information can be used by future AVs to better predict pedestrians' road-crossing intentions in urban settings. [ABSTRACT FROM AUTHOR]