Bio-inspired intent communication for automated vehicles

Various external human-machine interfaces (eHMIs) have been proposed that communicate the intent of automated vehicles (AVs) to vulnerable road users. However, there is no consensus on which eHMI concept is most suitable for intent communication. In nature, animals have evolved the ability to communicate intent via visual signals. Inspired by intent communication in nature, this paper investigated three novel and potentially intuitive eHMI designs that rely on posture, gesture, and colouration, respectively. In an online crowdsourcing study, 1141 participants viewed videos featuring a yielding or non-yielding AV with one of the three bio-inspired eHMIs, as well as a green/red lightbar eHMI, a walk/don’t walk text-based eHMI, and a baseline condition (i.e., no eHMI). Participants were asked to press and hold a key when they felt safe to cross and to answer rating questions. Together, these measures were used to determine the intuitiveness of the tested eHMIs. Results showed that the lightbar eHMI and text-based eHMI were more intuitive than the three bio-inspired eHMIs, which, in turn, were more intuitive than the baseline condition. An exception was the bio-inspired colouration eHMI, which produced a performance score that was equivalent to the text-based eHMI when communicating ‘non-yielding’. Further research is necessary to examine whether these observations hold in more complex traffic situations. Additionally, we recommend combining features from different eHMIs, such as the full-body communication of the bio-inspired colouration eHMI with the colours of the lightbar eHMI.     

Modelling Cognitive Bias in Crowdsourcing Systems

Crowdsourcing is the process of obtaining input or enlisting the services of a crowd of people with the aim of finding the best solution to a proposed problem. The method with which the collected input is aggregated affects the outcome of the crowdsourcing process. In this paper, we introduce a modelling framework through which we compare several aggregation methods for crowdsourcing systems. The work reveals a surprising result where confidence-related approaches lack in performance when compared to other approaches such as simple plurality voting or approaches which consider respondent competence. This inadequacy stems from a psychological phenomenon brought forth by David Dunning and Justin Kruger related to people’s bias in assessing their own cognitive abilities.     

Summarization of videos by analyzing affective state of the user through crowdsource

Video summarization is one of the key techniques to access and manage a huge chunk of videos. Video summarization is used to extract the effective contents of a video sequence to generate a concise representation of its content. The involvement of crowdsourcing models in recent years is effectively used by researchers in E-commerce domain to increase the quality of the data contents. In this paper, we present a video summarization framework based on users emotion while they watch videos by analyzing cerebral activities through Electroencephalogram (EEG) signals. Three emotions, namely happy, sad and neutral have been extracted from the EEG signals. Video frames have been synchronized with EEG signals and tagged with various emotions. Finally, a crowdsourcing model has been used for effective summarization of the videos. The qualitative assessment of video summarization has been conducted with the help of user ratings using online Google Forms application. EEG signals of 28 users have been recorded while video streams of different emotions. An average accuracy of 83.93% has been recorded in emotion classification using crowdsourcing. Output summarized videos include dynamic video skims and the corresponding audio stream for better understanding.     

The effect of drivers’ eye contact on pedestrians’ perceived safety

Many fatal accidents that involve pedestrians occur at road crossings, and are attributed to a breakdown of communication between pedestrians and drivers. Thus, it is important to investigate how forms of communication in traffic, such as eye contact, influence crossing decisions. Thus far, there is little information about the effect of drivers’ eye contact on pedestrians’ perceived safety to cross the road. Existing studies treat eye contact as immutable, i.e., it is either present or absent in the whole interaction, an approach that overlooks the effect of the timing of eye contact. We present an online crowdsourced study that addresses this research gap. 1835 participants viewed 13 videos of an approaching car twice, in random order, and held a key whenever they felt safe to cross. The videos differed in terms of whether the car yielded or not, whether the car driver made eye contact or not, and the times when the driver made eye contact. Participants also answered questions about their perceived intuitiveness of the driver’s eye contact behavior. The results showed that eye contact made people feel considerably safer to cross compared to no eye contact (an increase in keypress percentage from 31% to 50% was observed). In addition, the initiation and termination of eye contact affected perceived safety to cross more strongly than continuous eye contact and a lack of it, respectively. The car’s motion, however, was a more dominant factor. Additionally, the driver’s eye contact when the car braked was considered intuitive, and when it drove off, counterintuitive. In summary, this study demonstrates for the first time how drivers’ eye contact affects pedestrians’ perceived safety as a function of time in a dynamic scenario and questions the notion in recent literature that eye contact in road interactions is dispensable. These findings may be of interest in the development of automated vehicles (AVs), where the driver of the AV might not always be paying attention to the environment.     

Word knowledge in the crowd: Measuring vocabulary size and word prevalence in a massive online experiment

We use the results of a large online experiment on word knowledge in Dutch to investigate variables influencing vocabulary size in a large population and to examine the effect of word prevalence—the percentage of a population knowing a word—as a measure of word occurrence. Nearly 300,000 participants were presented with about 70 word stimuli (selected from a list of 53,000 words) in an adapted lexical decision task. We identify age, education, and multilingualism as the most important factors influencing vocabulary size. The results suggest that the accumulation of vocabulary throughout life and in multiple languages mirrors the logarithmic growth of number of types with number of tokens observed in text corpora (Herdan's law). Moreover, the vocabulary that multilinguals acquire in related languages seems to increase their first language (L1) vocabulary size and outweighs the loss caused by decreased exposure to L1. In addition, we show that corpus word frequency and prevalence are complementary measures of word occurrence covering a broad range of language experiences. Prevalence is shown to be the strongest independent predictor of word processing times in the Dutch Lexicon Project, making it an important variable for psycholinguistic research.     

Crowdsourcing and personality measurement equivalence: A warning about countries whose primary language is not English

In the search to find cheaper, faster approaches for data collection, crowdsourcing methods (i.e., online labor portals that allow independent workers to complete surveys for compensation) have risen in popularity as a tool for personality researchers, despite a lack of evidence regarding the equivalence of crowdsourcing with traditional data collection methods. The purpose of this study was to evaluate crowdsourcing as a data collection tool by examining the measurement equivalence of crowdsourced data (i.e., from Amazon.com’s MTurk) with more traditional samples (i.e., an undergraduate sample and a sample of organizational employees). Our results (using a popular measure of Big Five personality) provided evidence of measurement equivalence across all three samples, with one important exception: crowdsourced data (from MTurk) only exhibited measurement invariance with traditional data collection methods when responses were restricted to participants from native-English speaking countries. Although MTurk appears to be an easy, cost-effective data collection tool, our results suggest that MTurk data are similar to traditionally-collected data only when the MTurk sample is restricted to IP addresses from English-speaking countries.     

Megastudies,crowdsourcing, and large datasets in psycholinguistics: An overview of recent developments

This paper introduces and summarizes the special issue on megastudies, crowdsourcing, and large datasets in psycholinguistics. We provide a brief historical overview and show how the papers in this issue have extended the field by compiling new databases and making important theoretical contributions. In addition, we discuss several studies that use text corpora to build distributional semantic models to tackle various interesting problems in psycholinguistics. Finally, as is the case across the papers, we highlight some methodological issues that are brought forth via the analyses of such datasets.     

Take-over requests in highly automated driving: A crowdsourcing survey on auditory,vibrotactile, and visual displays

An important research question in the domain of highly automated driving is how to aid drivers in transitions between manual and automated control. Until highly automated cars are available, knowledge on this topic has to be obtained via simulators and self-report questionnaires. Using crowdsourcing, we surveyed 1692 people on auditory, visual, and vibrotactile take-over requests (TORs) in highly automated driving. The survey presented recordings of auditory messages and illustrations of visual and vibrational messages in traffic scenarios of various urgency levels. Multimodal TORs were the most preferred option in high-urgency scenarios. Auditory TORs were the most preferred option in low-urgency scenarios and as a confirmation message that the system is ready to switch from manual to automated mode. For low-urgency scenarios, visual-only TORs were more preferred than vibration-only TORs. Beeps with shorter interpulse intervals were perceived as more urgent, with Stevens’ power law yielding an accurate fit to the data. Spoken messages were more accepted than abstract sounds, and the female voice was more preferred than the male voice. Preferences and perceived urgency ratings were similar in middle- and high-income countries. In summary, this international survey showed that people’s preferences for TOR types in highly automated driving depend on the urgency of the situation.