In the transition towards higher levels of vehicle automation, one of the key concerns with regards to human factors is to avoid mode confusion, when drivers misinterpret the driving mode and therewith misjudge their own tasks and responsibility. To enhance mode awareness, a clear human centered Human Machine Interface (HMI) is essential. The HMI should support the driver tasks of both supervising the driving environment when needed and self-regulating their non-driving related activities (NDRAs). Such support may be provided by either presenting continuous information on automation reliability, from which the driver needs to infer what task is required, or by presenting continuous information on the currently required driving task and allowed NDRA directly. Additionally, it can be valuable to provide continuous information to support anticipation of upcoming changes in the automation mode and its associated reliability or required and allowed driver task(s). Information that could support anticipation includes the available time until a change in mode (i.e. time budget), information on the upcoming mode, and reasons for changing to the upcoming mode. The current work investigates the effects of communicating this potentially valuable information through HMI design. Participants received information from an HMI during simulated drives in a simulated car presented online (using Microsoft Teams) with an experimenter virtually accompanying and guiding each session. The HMI either communicated on automation reliability or on the driver task, and either included information supporting anticipation or did not include such information. Participants were thinking aloud during the simulated drives and reported on their experience and preferences afterwards. Anticipatory information supported understanding about upcoming changes without causing information overload or overreliance. Moreover, anticipatory information and information on automation reliability, and especially a combination of the two, best supported understandability and usability. Recommendations are provided for future work on facilitating supervision and NDRA self-regulation during automated driving through HMI design.@en

What will cyclists do in future conflict situations with automated cars at intersections when the cyclist has the right of way? In order to explore this, short high-quality animation videos of conflicts between a car and a cyclist at five different intersections were developed. These videos were ‘shot’ from the perspective of the cyclist and ended when a collision was imminent should the car or the bicyclist not slow down. After each video participants indicated whether they would slow down or continue cycling, how confident they were about this decision, what they thought the car would do, and how confident they were about what the car would do. The appearance of the approaching car was varied as within-subjects variable with 3 levels (Car type): automated car, automated car displaying its intentions to the cyclists, and traditional car. In all situations the cyclist had right of way. Of each conflict, three versions were made that differed in the moment that the video ended by cutting off fractions from the longest version, thus creating videos with an early, mid, and late moment for the cyclist to decide to continue cycling or to slow down (Decision moment). Before the video experiment started the participants watched an introductory video about automated vehicles that served as prime. This video was either positive, negative, or neutral about automated vehicles (Prime type). Both Decision moment and Prime type were between subject variables. After the experiment participants completed a short questionnaire about trust in technology and trust in automated vehicles. 1009 participants divided in nine groups (one per Decision moment and Prime) completed the online experiment in which they watched fifteen videos (5 conflicts × 3 car types). The results show that participants more often yielded when the approaching car was an automated car than when it was a traditional car. However, when the approaching car was an automated car that could communicate its intentions, they yielded less often than for a traditional car. The earlier the Decision moment, the more often participants yielded but this increase in yielding did not differ between the three car types. Participants yielded more often for automated cars (both types) after they watched the negative prime video before the experiment than when they watched the positive video. The less participants trusted technology, and the capabilities of automated vehicles in particular, the more they were inclined to slow down in the conflict situations with automated cars. The association between trust and yielding was stronger for trust in the capabilities of automated vehicles than for trust in technology in general.

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Cyclists may have incorrect expectations of the behaviour of automated vehicles in interactions with them, which could bring extra risks in traffic. This study investigated whether expectations and behavioural intentions of cyclists when interacting with automated cars differed from those with manually driven cars. A photo experiment was conducted with 35 participants who judged bicycle–car interactions from the perspective of the cyclist. Thirty photos were presented. An experimental design was used with between-subjects factor instruction (two levels: positive, neutral), and two within-subjects factors: car type (three levels: roof name plate, sticker–these two external features indicated automated cars; and traditional car), and series (two levels: first, second). Participants were asked how sure they were to be noticed by the car shown in the photos, whether the car would stop, and how they would behave themselves. A subset of nine participants was equipped with an eye-tracker. Findings generally point to cautious dispositions towards automated cars: participants were not more confident to be noticed when interacting with both types of automated cars than with manually driven cars. Participants were more confident that automated cars would stop for them during the second series and looked significantly longer at automated cars during the first.@en

In the future, cyclists will be sharing the roads with automated and traditional vehicles [1]. Interactions between cyclists and automated vehicles (AVs) may differ from interactions with traditional vehicles, because cyclists may hold incorrect expectations about how AVs will react to their presence, leading to confusing and risky situations. The objective of this study was to assess the (self-reported) behaviour and expectations of cyclists when encountering an AV with different external features as compared to a traditional vehicle. This study builds on a smaller questionnaire conducted by Hagenzieker et al. [2]. 607 participants from 15 countries completed an online questionnaire, in which they were shown 12 photos from a cyclist's point of view. Each photo involved a traditional vehicle or an AV recognisable by a specific feature (door sign or roof sign with the message 'self-driving'). Moreover, three descriptions of the capabilities of AVs (negative, neutral, positive) were provided in a between-subjects design. Participants had to report, for each photo, (1) how sure they were that the car had noticed them, (2) how sure they were that the car would stop, both on a scale from unsure to sure, and (3) what they would do on a scale from 'increase speed' to 'wait'. Personal characteristics (trust in automation and sensation seeking) were also measured. The results showed that participants were more sure to be noticed by an AV with a door sign than by a traditional vehicle. They were also more sure that the car would stop when the vehicle was an AV. The type of AV description showed statistically significant interaction effects with vehicle type. Furthermore, results differed per country group. For example, European respondents were more sure to be noticed by the car than respondents from North America. Finally, trust and sensation seeking scores showed positive correlations with participants' answers to the three questions.@en

The transition period towards large-scale or full automated driving will pose specific challenges. One of the challenges concerns the interaction of automated vehicles with vulnerable road users. So far, most studies into this type of interactions took the perspective of the car. The current study, however, takes the perspective of the vulnerable road user. More specifically, it explores how cyclists perceive and expect automated vehicles to ‘behave’ and how cyclists would react. Expectations are important determinants of traffic behaviour. Incorrect expectations could lead to overly trustful or hesitant behaviour and subsequent unsafe interactions. Recently, Hagenzieker and colleagues (2017) conducted a photo experiment in which regular cyclists had to judge photos of traffic situations where they encountered manually-driven cars and automated cars (recognisable by either a sticker on the side of the car or a roof name plate on top of the car). Participants judged 30 photos twice, in random order. In that study a subset of nine participants were equipped with an eye tracker in order to study their eye movements while judging the photos. This study further examined these eye tracking data, comparing timeto- first-fixation, dwell time from the start of the first fixation, and total number of revisits in interactions with automated and with manually-driven cars. Results indicate no differences in time-to-first-fixation nor in the number of revisits between situations with automated cars and traditional cars. Dwell times revealed an effect of familiarity, showing that cyclists spent more time looking at cars during the first round of photos compared to the second round. In particular, they spent more time looking at automated cars which were identifiable by a ticker on the side. The results are discussed and suggestions for future research are given.@en