Learning to interpret novel eHMI: The effect of vehicle kinematics and eHMI familiarity on pedestrians' crossing behaviour

Abstract

In current urban traffic, pedestrians attempting to cross the road at un-signalised 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. Using an existing, well-recognised, 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 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 200ms, 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.

Variable-Drift Diffusion Models of Pedestrian Road-Crossing Decisions

“…This experiment was originally developed to study the combined impact of vehicle approach kinematics and different types of eHMI on human road-crossing behavior; full methodological details and non-modeling analyses of the same dataset are reported by Lee et al (2020). All procedures were approved by the relevant University of Leeds Research Ethics committee, reference LTTRAN- The experimental paradigm was adapted from (Lobjois & Cavallo, 2007): As shown in Fig.…”

“…The latter took the form of three quick flashes of the front headlights, chosen for being a commonly used signal for yielding intentions in the UK. As described by Lee et al (2020), a third group of participants experienced a slow pulsing light band eHMI, but since the effect of this eHMI on participant behavior was considerably smaller, we did not include these participants in our analyses here. The eHMIs were not mentioned at all in the information provided to the participants before the experiment, since a goal of the original study was to investigate how quickly participants would deduce the meaning of the eHMI signals.…”

“…The eHMIs were not mentioned at all in the information provided to the participants before the experiment, since a goal of the original study was to investigate how quickly participants would deduce the meaning of the eHMI signals. Lee et al (2020) found that the impact of the flashing headlights eHMI on crossing decisions was already present from the first experimental block, hence we are not considering learning effects here.…”

“…In the context of large-scale traffic simulation, logistic regression models have long been used to model pedestrian "gap acceptance" between vehicles in a stream of traffic (Brewer et al, 2006;Schroeder, 2008;Yannis et al, 2013), and the use of such models in automated vehicle algorithms has also been proposed (Jayaraman et al, 2021;Kapania et al, 2019). However, these models are limited to a discrete acceptance/rejection decision per gap, and do not account for the timing of roadcrossing decisions, which has implications for traffic flow and acceptance of automated vehicles (Dey et al, 2020;Lee et al, 2020;. The existing models also do not account for how pedestrians respond to vehicles yielding to them, a process which is known to be non-trivial: Human drivers tend to communicate with pedestrians both implicitly, for example, using exaggerated deceleration, and explicitly with communicative signals (Domeyer et al, 2019;Markkula et al, 2020).…”

“…The existing models also do not account for how pedestrians respond to vehicles yielding to them, a process which is known to be non-trivial: Human drivers tend to communicate with pedestrians both implicitly, for example, using exaggerated deceleration, and explicitly with communicative signals (Domeyer et al, 2019;Markkula et al, 2020). Studies are currently investigating the extent to which automated vehicles should indicate their intentions in similar ways, for example, by means of external human-machine interfaces (eHMIs), either leveraging conventional signals such as headlight flashes, or using novel designs (Faas et al, 2020;Lee et al, 2019;Lee et al, 2020). However, these communicative aspects of vehicle-pedestrian interactions remain poorly understood, and have not been the subject of computational modeling.…”