Objective: The aim of this study was to investigate the effect of autonomous-vehicle-to-pedestrian (AV2P) communication through augmented reality (AR) interfaces on the road crossing behaviour of pedestrians, and research whether subjective results from a previous Cave Automatic Virtual Environment (CAVE) study replicated in a real world AR experiment.
Background: Previous studies investigating the effects of AV2P communication have mostly been conducted through virtual reality (VR) providing researchers with safe experimentation methods and high experimental control, but also resulting in a common limitation: the lack of ecological validity and realism, thereby affecting participants’ behaviour and causing distractions. This study therefore introduces AR experiments that have been conducted in a real world environment to increase ecological validity.
Methods: An AR experiment was conducted in which 28 participants were situated in the real world with the objective to cross the road. The virtual vehicle, that was projected through a Varjo XR-3 head mounted display, approached from the right at a speed of 30 km/h while 4 interfaces (2x world-locked, head-locked, and vehicle-locked) appeared to communicate the vehicle’s intention towards the participants, in addition to a no-interface baseline. Participants were tasked with indicating when they were willing to cross through the push of a remote button from which their Willingness to cross and Decision certainty could be derived. Subjective data was collected after the trials and after the experiment through interviews and a questionnaire respectively.
Results: Results suggest a positive effect of the AV2P interfaces on the Willingness to cross and Decision certainty, although statistically not significant. In other words, Willingness to cross increases when the vehicle indicates that it will yield, and decreases when the vehicle communicates that it will not yield. Decision certainty also increases when an interface is present compared to the no-interface baseline. Moreover, participants indicated using the interfaces as a tool to validate their own decisions. Compared to the CAVE study, subjective intuitiveness ratings replicate in terms of observing higher intuitiveness of the interfaces than the no-interface baseline. However, the intuitiveness ratings were higher in the CAVE study than the real world AR experiment. Furthermore, the order of the top 3 most preferred interfaces ranking is in the opposite order. Both differences suggest that the increased ecological validity of the real world AR experiment introduces new insights into participants’ perception of interfaces. The Van der Laan acceptance scale shows that participants believe interfaces to be useful and satisfying overall.
Conclusion: The experiments suggest that AV2P interfaces have a positive effect on the crossing behaviour of pedestrians. Furthermore, participants indicate using the interfaces as a tool to validate their own decision, which increases confidence in their decisions. Although results partially replicate a previous virtual environment study, there are differences that suggest that real world AR experiments provide valuable insights into participants’ perception of interfaces in a more realistic experiment.

Objective: In this thesis, we explore whether augmented and diminished reality interfaces, which, respectively, add and remove information from the environment, improve a pedestrian's feeling of road crossing safety, and how this information should be conveyed to the pedestrian.

Background: Literature shows that view occlusion is a prominent cause in pedestrian collisions. The research focus is currently on vehicle technology and pedestrian warning systems. Whether aiding pedestrians with camera views from unobstructed positions helps to overcome the view occlusion problem is unclear.

Methods: Twenty-eight participants engaged in a virtual reality urban road crossing scenario, in which they took on the role of a pedestrian. The pedestrian was situated on the curb and positioned such that the view on the road was largely obstructed. An autonomous vehicle approached and drove past from the left of the pedestrian. Through a head-mounted display, the participants experienced seven prototypes: baseline (i.e., no display), see-through display, transparent car, and both a head-locked and body-locked display with and without view guidance. The order in which participants encountered the prototypes was determined by a balanced Latin square, and each interface was tested by means of six trials with a non-yielding and a yielding scenario randomly selected such that in total three non-yielding and three yielding scenarios occurred in each block. The participants were instructed to continuously indicate whether they felt safe to cross by pressing a button. The interface's acceptance, workload and preference were measured with questionnaires.

Results: The participants' perceived feeling of safety revealed improved performance for all interfaces compared to the baseline condition. For the baseline condition, in which the vulnerable road user did not have access to occlusion-free information, the perceived feeling of safety was the lowest on average and decreased the earliest in the autonomous vehicle approaching phase, as well as scoring the lowest rating on acceptance. The see-through display and the transparent car interfaces, which used a combination of augmented and diminished reality properties to convey the information in a world-anchored manner to the pedestrians, achieved a higher acceptance and perceived feeling of safety than the head-locked and body-locked display interfaces.

Conclusion: A vulnerable road user's perceived feeling of safety can be increased by means of camera views from unobstructed positions to help overcome the view occlusion problem in common road crossing scenarios. This study's findings suggest a positive effect for diminished reality techniques for pedestrians, and future research could examine this technology further in more demanding scenarios.

Vulnerable road users account for more than 50% of traffic fatalities, and among these, pedestrians are the most susceptible to fatalities due to their distraction and misperception of other road users. To mitigate their plight, systems that warn drivers and pedestrians in case of a possible collision have been developed. Among systems that focus on the pedestrian's perspective, existing concepts are capable of predicting collisions but lack elements that monitor the visual attention of pedestrians. We address this gap by developing a gaze-based pedestrian warning system based on the Tobii Pro Glasses 2, a head-mounted eye-tracker. The system consists of: (1) a custom trained fast neural network (YOLO v4) on the KITTI object detection dataset that processes the video feed of the eye-tracker to detect approaching vehicles and (2) a module that uses the pedestrian's gaze to identify whether their attention falls on the closest moving vehicle that is approaching the pedestrian, both in real-time. If the pedestrian does not look at the approaching vehicle, they are given an auditory alert that warns them of a possible collision. In a pilot study conducted on a busy road in an urban environment, the system was evaluated under different pedestrian walking speeds and gaze behaviours to test the algorithm's robustness. The pilot study revealed that our system alerted the inattentive pedestrian with an accuracy of 67%. The mean vehicle detection accuracy and a mean moving vehicle identification accuracy from the pilot were 93% and 60%, respectively, a promising result given the use of only a mono camera. Despite the use of computer vision techniques, the system worked at an inference speed of 50 FPS due to the multi-processing capabilities of our algorithm. Our efforts are a first step in developing pedestrian warning systems based on eye-tracking technology to improve road safety in the future. The algorithm (Python-based) code used for this work has been made publicly available.