Interaction between pedestrians and automated vehicles A Wizard of Oz experiment

a b s t r a c tHeart rate data are often collected in human factors studies, including those into vehicle automation. Advances in open hardware platforms and off-the-shelf photoplethysmogram (PPG) sensors allow the non-intrusive collection of heart rate data at very low cost. However, the signal is not trivial to analyse, since the morphology of PPG waveforms differs from electrocardiogram (ECG) waveforms and shows different noise patterns. Few validated open source available algorithms exist that handle PPG data well, as most of these algorithms are specifically designed for ECG data.In this paper we present the validation of a novel algorithm named HeartPy, useful for the analysis of heart rate data collected in noisy settings, such as when driving a car or when in a simulator. We benchmark the performance on two types of datasets and show that the developed algorithm performs well. Further research steps are discussed.

This paper describes the functioning and development of HeartPy: a heart rate analysis toolkit designed for photoplethysmogram (PPG) data. Most openly available algorithms focus on electrocardiogram (ECG) data, which has very different signal properties and morphology, creating a problem with analysis. ECG-based algorithms generally don't function well on PPG data, especially noisy PPG data collected in experimental studies. To counter this, we developed HeartPy to be a noise-resistant algorithm that handles PPG data well. It has been implemented in Python and C. Arduino IDE sketches for popular boards (Arduino, Teensy) are available to enable data collection as well. This provides both pc-based and wearable implementations of the software, which allows rapid reuse by researchers looking for a validated heart rate analysis toolkit for use in human factors studies.

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In the vicinity of ramps, drivers make route choices, change lanes and in most cases also adjust their speeds. This can trigger anticipatory behaviour by the surrounding vehicles, which are also reflected in lane changes and/or changes in speed. This phenomenon is called turbulence and is widely recognised by the scientific literature and various design guidelines. However the knowledge about the characteristics of turbulence is limited. This study investigates the microscopic characteristics of driving behaviour around 14 different on-ramps (3), offramps (3) and weaving segments (8) in The Netherlands, based on unique empirical trajectory data collected from a video camera mounted underneath a hovering helicopter. The data analysis reveals that lane changes caused by merging and diverging vehicles create most turbulence, that an increase in the amount of traffic results in a higher level of turbulence and that an increase in the available length for merging and diverging results in a lower level of turbulence. The results of this study are useful for improving the road design guidelines and for modelling driving behaviour more realistically.

Partially and fully automated vehicles (AVs) are being developed and tested in different countries. These vehicles are being designed to reduce and ultimately eliminate the role of human drivers in the future. However, other road users, such as pedestrians and cyclists will still be present and would need to interact with these automated vehicles. Therefore, external communication interfaces could be added to the vehicle to communicate with pedestrians and other non-automated road users. The first aim of this study is to investigate how the physical appearance of the AV and a mounted external human-machine interface (eHMI) affect pedestrians' crossing intention. The second aim is to assess the perceived realism of Virtual reality based on 360°videos for pedestrian crossing behavior for research purposes. The speed, time gap, and an eHMIs were included in the study as independent factors. Fifty-five individuals participated in our experiment. Their crossing intentions were recorded, as well as their trust in automation and perceived behavioral control. A mixed binomial logistic regression model was applied on the data for analysis. The results show that the presence of a zebra crossing and larger gap size between the pedestrian and the vehicle increase the pedestrian's intention to cross. In contrast to our expectations, participants intended to cross less often when the speed of the vehicle was lower. Despite that the vehicle type affected the perceived risk of the participants, no significant difference was found in crossing intention. Participants who recognized the vehicle as an AV had, overall, lower intentions to cross. A strong positive relationship was found between crossing intentions and perceived behavioral control. A difference in trust was found between participants who recognized the vehicle as automated, but this did not lead to a difference in crossing intentions. We assessed the research methodology using the presence questionnaire, the simulation sickness survey, and by comparing the results with previous literature. The method scored highly on the presence questionnaire and only 2 out of 55 participants stopped prematurely. Thus, the research methodology is useful for crossing behavior experiments.

For decades researchers have been pointing out significant differences in the driving behavior between young and old and between male and female drivers. There are many studies concerning age and gender differences in risk perception, traffic accident involvement, traffic violations, alcohol consumption, and risky driving. However, little effort has been focused on studying the behavioral differences in overtaking maneuvers on two-lane highways. A considerable percentage of the fatal accidents on two-lane highways is directly related to overtaking maneuvers. Therefore, the main focus of this study is to understand better the overtaking behavior of different drivers classified by their age and gender. Data on the overtaking behavior of 100 drivers were collected with an interactive driving simulator. Several scenarios of two-lane rural highways with different geometric and traffic conditions were developed. The results show interesting and significant differences in the overtaking behavior of drivers depending on their age and gender. These differences are mainly in the frequency of overtaking maneuvers, overtaking time duration, following distances, critical overtaking gaps, and desired driving speeds. Geometric and traffic conditions were also found to have a significant impact on drivers’ overtaking behavior. The findings of this study contribute to the understanding of the overtaking behavior of different groups of drivers and thus have implications for road safety intervention programs and the development of effective risk reduction strategies adapted and targeted for different age and gender groups.