Active vehicle pitch motion for communication in automated driving

The number of automated vehicles (AVs) is expected to successively increase in the near future. This development has a considerable impact on the informal communication between AVs and pedestrians. Informal communication with the driver will become obsolete during the interaction with AVs. Literature suggests that external human machine interfaces (eHMIs) might substitute the communication between drivers and pedestrians. In the study, we additionally test a recently discussed type of communication in terms of artificial vehicle motion, namely active pitch motion, as an informal communication cue for AVs.N = 54 participants approached AVs in a virtual inner-city traffic environment. We explored the effect of three communication concepts: an artificial vehicle motion, namely active pitch motion, eHMI and the combination of both. Moreover, vehicle types (sports car, limousine, SUV) were varied. A mixed-method approach was applied to investigate the participantś crossing behavior and subjective safety feeling. Furthermore, eye movement parameters were recorded as indicators for mental workload.The results revealed that any communication concept drove beneficial effects on the crossing behavior. The participants crossed the road earlier when an active pitch motion was present, as this was interpreted as a stronger braking. Further, the eHMI and a combination of eHMI and active pitch motion had a positive effect on the crossing behavior. The active pitch motion showed no effect on the subjective safety feeling, while eHMI and the combination enhanced the pedestrianś safety feeling while crossing. The use of communication resulted in less mental workload, as evidenced by eye-tracking parameters. Variations of vehicle types did not result in significant main effects but revealed interactions between parameters. The active pitch motion revealed no learning. In contrast, it took participants several trials for the eHMI and the combination condition to affect their crossing behavior. To sum up, this study indicates that communication between AVs and pedestrians can benefit from the consideration of vehicle motion.     

Interaction between pedestrians and automated vehicles: Exploring a motion-based approach for virtual reality experiments

External human machine interfaces (eHMI) might contribute to an enhanced traffic flow and road safety by providing relevant information to surrounding road users. To quantify the effect of eHMI on traffic flow, the majority of studies required participants to indicate their crossing decision in an explicit manner, such as pressing a button. While this approach proved to be efficient, the transfer to real-world behavior is unclear. Here, we propose a more realistic, motion-based approach allowing pedestrians to actually cross the road in front of a vehicle in a virtual reality environment. Participants (N = 51) encountered simulated automated vehicles (AVs) in two scenarios. We investigated the effect of different eHMIs on traffic flow and road safety. Pedestrians’́ body movements were obtained using a motion capturing system with six sensors. Our approach was validated using a two-step procedure. First, we assessed crossing behavior and subjective safety feeling while approaching AVs with and without eHMI. Second, we tested to which extent objective crossing behavior matched self-reported safety feeling. For this purpose, we evaluated if subjective safety feeling can be reliably predicted from actual crossing behavior using a functional data analysis. The proposed motion-based approach proved a valid investigation method for eHMI designs. The results indicated that eHMIs have a beneficial effect on traffic flow and road safety. Regarding traffic flow, participants crossed the road earlier and felt significantly safer when encountering an AV with an eHMI compared to no eHMI. In addition, in situations in which only some of the AVs were equipped with an eHMI, participants’ crossing behavior and safety feeling became more conservative for encounters without eHMI, indicating higher road safety. Further, subjective safety feeling was significantly predicted from actual crossing behavior. These findings highlight that eHMIs are beneficial for pedestrians’ crossing decision, both from an objective and subjective perspective.     

After you?! – Use of external human-machine interfaces in road bottleneck scenarios

In the near future, automated vehicles (AVs) will enter the urban transport system. This fact will lead to mixed traffic consisting of AVs, human car drivers and vulnerable road users. Since the AV’s passenger no longer has to monitor the driving scene, conventional communication does not exist anymore, which is essential for traffic efficiency and safety. In research, there are plenty of studies focusing on how AVs could communicate with pedestrians. One approach is to use external human-machine interfaces (eHMIs) on the AV’s surface. In contrast to the studies dealing with AV-pedestrian communication, this paper focuses on communication strategies of AVs with drivers of regular vehicles in different road bottleneck scenarios. The eHMI development and design is building on previously defined requirements and on fundamentals of human visual perception. After designing several eHMI drafts, we conducted a user survey with 29 participants resulting in the final eHMI concept. The evaluation of the evolved eHMI was conducted in a driving simulator experiment with 43 participants investigating the AV-human driver interaction at road bottlenecks. The participants were assigned either to the experimental group being faced with the eHMI or to the baseline group without explicit communication. The results show significantly shorter passing times and fewer crashes among the human drivers in the group with the eHMI. Additionally, the paper researches the aftereffects of an automation failure, where the AV first yields the right of way and then changes its strategy and insisted on priority. Experiencing the automation failure is reflected in increased passing times, reduced acceptance ratings and a lower perceived usefulness. In conclusion, especially in unregulated bottleneck scenarios flawless communication via eHMIs increases traffic efficiency and safety.     

Pedestrians’ road crossing behaviour in front of automated vehicles: Results from a pedestrian simulation experiment using agent-based modelling

The objective of this research is to explore the relation between personal characteristics of pedestrians and their crossing behaviour in front of an automated vehicle (AV). For this purpose, a simulation experiment was developed using Agent-Based Modelling (ABM) techniques. Sixty participants were asked to cross the road in a virtual environment displayed on a computer screen, allowing to record their crossing behaviour when in the presence of AVs and conventional vehicles (CVs). In some experimental configurations, the AVs communicated their intention to continue or not to continue their trajectories through the use of lights. The ABM allowed controlling the behaviour of the vehicles when interacting with the simulated avatar of the respondents. The subjects of the experiment were also asked to fill in a questionnaire about usual behaviour in traffic, as well as attitudes and risk perceptions toward crossing roads. The questionnaire data were used to estimate individual specific behavioural latent variables by means of principal component analysis which resulted in three main factors named: violations, lapses, and trust in AVs. The results of generalized linear mixed models applied to the data showed that besides the distance from the approaching vehicle and existence of a zebra crossing, pedestrians’ crossing decisions are significantly affected by the participants’ age, familiarity with AVs, the communication between the AV and the pedestrian, and whether the approaching vehicle is an AV. Moreover, the introduction of the latent factors as explanatory variables into the regression models indicated that individual specific characteristics like willingness to take risks and violate traffic rules, and trust in AVs can have additional explanatory power in the crossing decisions.     

Evaluating the influence of approaching vehicles on pedestrian’s visual patterns and crossing behaviors at an uncontrolled crosswalk

The visual behaviors and movement characteristics of pedestrians are related to their surrounding potential safety hazards, such as approaching vehicles. This study primarily aimed to investigate the visual patterns and walking behaviors of pedestrians interacting with approaching vehicles. Field experiments were conducted at two uncontrolled crosswalks located at the Cuihua and Yanta roads in Xi’an, China. The visual performance of pedestrians was assessed using the eye tracking system from SensoMotoric Instruments (SMI). Moreover, motion trajectories of the pedestrians and approaching vehicles were obtained using an unmanned aerial vehicle. Subsequently, the visual attributes and movement trajectories of pedestrians and motion trajectories of approaching vehicles were statistically analyzed. The results showed that approaching vehicles distracted the fixation of crossing pedestrians significantly, and occupied 29.5% of the total duration of fixation; that is, pedestrians always directed more fixation points to the approaching vehicles compared to other stimuli. As a vehicle approached, pedestrians’ fixation shifted from other areas of interest to the vehicle. Moreover, an increase in the velocity of the vehicle and a closer distance between pedestrian and the vehicle resulted in an increase in the pedestrians’ duration of fixation on the approaching vehicle, and they implemented more saccades. Furthermore, approaching vehicle’s velocity and distance between pedestrian and approaching vehicle are not significantly associated with pedestrian’s movement attributes. These findings provide insights into the crossing behavior of pedestrians during pedestrian-vehicle interactions, which could assist future researchers and policy makers.     

Interaction between pedestrians and automated vehicles: A Wizard of Oz experiment

Automated vehicles (AVs) will be introduced on public roads in the future, meaning that traditional vehicles and AVs will be sharing the urban space. There is currently little knowledge about the interaction between pedestrians and AVs from the point of view of the pedestrian in a real-life environment. Pedestrians may not know with which type of vehicle they are interacting, potentially leading to stress and altered crossing decisions. For example, pedestrians may show elevated stress and conservative crossing behavior when the AV driver does not make eye contact and performs a non-driving task instead. It is also possible that pedestrians assume that an AV would always yield (leading to short critical gaps). This study aimed to determine pedestrians’ crossing decisions when interacting with an AV as compared to when interacting with a traditional vehicle. We performed a study on a closed road section where participants (N = 24) encountered a Wizard of Oz AV and a traditional vehicle in a within-subject design. In the Wizard of Oz setup, a fake ‘driver’ sat on the driver seat while the vehicle was driven by the passenger by means of a joystick. Twenty scenarios were studied regarding vehicle conditions (traditional vehicle, ‘driver’ reading a newspaper, inattentive driver in a vehicle with “self-driving” sign on the roof, inattentive driver in a vehicle with “self-driving” signs on the hood and door, attentive driver), vehicle behavior (stopping vs. not stopping), and approach direction (left vs. right). Participants experienced each scenario once, in a randomized order. This allowed assessing the behavior of participants when interacting with AVs for the first time (no previous training or experience). Post-experiment interviews showed that about half of the participants thought that the vehicle was (sometimes) driven automatically. Measurements of the participants’ critical gap (i.e., the gap below which the participant will not attempt to begin crossing the street) and self-reported level of stress showed no statistically significant differences between the vehicle conditions. However, results from a post-experiment questionnaire indicated that most participants did perceive differences in vehicle appearance, and reported to have been influenced by these features. Future research could adopt more fine-grained behavioral measures, such as eye tracking, to determine how pedestrians react to AVs. Furthermore, we recommend examining the effectiveness of dynamic AV-to-pedestrian communication, such as artificial lights and gestures.     

To cross or not to cross: Impact of visual and auditory cues on pedestrians’ crossing decision-making

The controlled study of pedestrians’ crossing decision-making is relevant to the search for better safety conditions for this class of vulnerable road users. Several risk factors have been identified in the literature related to the crosswalks’ surrounding environment, the socio-demographic characteristics of the pedestrians crossing the road and the place where the crosswalks are inserted, as well as situational variables, such as speed and distance of the approaching vehicle during the crossing. In this work, the roles of visual and auditory cues in crossing decisions were analysed, comparing different speeds and distances, and taking into consideration different speed patterns of the approaching vehicle, aiming to identify what can affect pedestrians’ crossing behaviour. Experiments were performed in a virtual environment. Participants were presented with 10 different stimuli featuring a vehicle approaching with different speeds and movement patterns, combined with 2 auditory conditions: one concerning a vehicle with a gasoline combustion engine and another one with no sound cues. Participants were tasked with indicating the moment they decided to cross the street when they thought it was safe to do so by pressing a response button. Percentage of crossings, response time (RT), and time-to-passage (TTP) were recorded and subsequently analysed. The results showed that lower speeds and higher distances lead to higher percentages of crossings and RTs. The auditory condition did not significantly affect participants’ responses, leading to the conclusion that participants’ crossing decision was especially based on their visual perception of the movement characteristics of the approaching vehicle, particularly its speed and distance. These results may have relevance for the development of communication strategies between the vehicles, especially the automated ones, and pedestrians.     

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.     

Pedestrian interaction with automated vehicles at uncontrolled intersections

Automated Vehicles (AVs) are being developed rapidly and tested on public roads, but pedestrians’ interaction with AV is not comprehensively understood or thoroughly investigated to ensure safe operations and the public’s trust of AVs. In this study, we aimed to provide another research evidence to enhance such understanding with the use of external interfaces for facilitating the interaction between pedestrians and AVs. We developed five external interfaces, including text, symbol, animated-eye, a combination of text and symbol, and speed. These interfaces communicated five types of information, including (1) intent of AV; 2) advice to pedestrians of what to do, (3) AV’s awareness of pedestrians, (4) combination of intent and advice, and (5) vehicle movement (i.e., speed). We tested the interfaces through two field studies at uncontrolled intersections with crosswalks. The Wizard of Oz method was used, in which an experimenter worked as a driver in an instrumented vehicle and wore an outfit to be invisible to the pedestrians, thus rendering the set-up to simulate an AV interacting with a pedestrian. The interfaces were displayed on an LED panel mounted on the AV. Results showed that the AV’s external interface did not change pedestrians’ response time in comparison with the baseline without any interface. There was no statistically significant difference in response time among the external interfaces either. According to the post-experimental interview, vehicle movement pattern (e.g., vehicle speed) continued to be a significant cue for pedestrians to decide when to cross the intersections. Participants perceived the communication of the AV’s intent and vehicle speed as more beneficial than the communication of AV’s awareness. The subjective ratings showed positive effects of those interfaces that were easy to understand (e.g., text interface and speed interface), which also helped pedestrians feel safer when interacting with the AV.     

External HMI for self-driving vehicles: Which information shall be displayed?

The objective of this study was to investigate pedestrians’ informational needs towards self-driving vehicles (SDVs). Previous research has shown that external human-machine interfaces (eHMIs) compensate for pedestrian-driver communication when SDVs are integrated into traffic. However, detailed insights on which information the eHMI shall provide lack so far. In a mixed design study, N = 59 participants encountered a simulated driverless vehicle in different traffic scenarios (a. unsignalized intersection vs. b. parking lot; between-subject factor). We investigated the effect of no eHMI (baseline) vs. eHMIs displaying the automated driving system (ADS) status, and informing subsequently about its perception of the pedestrian and/or its intent for the next maneuver ((1) no eHMI, (2) status eHMI, (3) status + perception eHMI, (4) status + intent eHMI, (5) status + perception + intent eHMI; within-subject factor). A mixed-methods design was used to explore participants’ subjective feelings, traffic behavior, and underlying attitudes. The findings reveal that any eHMI contributes to a more positive feeling towards SDVs compared to the baseline condition without eHMI, consistent among traffic scenarios: participants felt significantly safer, reflected greater trust and user experience ratings, and perceived the SDV as more intelligent and transparent. The status indicator mainly drives these beneficial effects on subjective measures. Participants reported that the status information explains the absence of a driver steering the vehicle. Compared to the status eHMI, the status + perception eHMI reflects no further benefit regarding subjective feelings and even has a negative impact on traffic flow. Moreover, participants regarded the additional information on the vehicle’s perception as an obvious gimmick. On the contrary, the status + intent eHMI increases user experience, perceived intelligence, and transparency for pedestrians more than the mere status eHMI. Participants reported that additionally informing about the vehicle’s intent adds a further sense of safety. The present study failed to show any improvements in traffic flow but found evidence for individual crossing and clearing strategies among pedestrians. This work can inform the future design of eHMIs.     

Evaluating pedestrian interaction preferences with a game theoretic autonomous vehicle in virtual reality

Localisation and navigation of autonomous vehicles (AVs) in static environments are now solved problems, but how to control their interactions with other road users in mixed traffic environments, especially with pedestrians, remains an open question. Recent work has begun to apply game theory to model and control AV-pedestrian interactions as they compete for space on the road whilst trying to avoid collisions. But this game theory model has been developed only in unrealistic lab environments. To improve their realism, this study empirically examines pedestrian behaviour during road crossing in the presence of approaching autonomous vehicles in more realistic virtual reality (VR) environments. The autonomous vehicles are controlled using game theory, and this study seeks to find the best parameters for these controls to produce comfortable interactions for the pedestrians. In a first experiment, participants’ trajectories reveal a more cautious crossing behaviour in VR than in previous laboratory experiments. In two further experiments, a gradient descent approach is used to investigate participants’ preference for AV driving style. The results show that the majority of participants were not expecting the AV to stop in some scenarios, and there was no change in their crossing behaviour in two environments and with different car models suggestive of car and last-mile style vehicles. These results provide some initial estimates for game theoretic parameters needed by future AVs in their pedestrian interactions and more generally show how such parameters can be inferred from virtual reality experiments.     

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.     

Behavioral adaptations of human drivers interacting with automated vehicles

Advancements in technology are bringing automated vehicles (AVs) closer to wider deployment. However, in the early phases of their deployment, AVs will coexist and frequently interact with human-driven vehicles (HDVs). These interactions might lead to changes in the driving behavior of HDVs. A field test was conducted in the Netherlands with 18 participants focusing on gap acceptance, car-following, and overtaking behaviors to understand such behavioral adaptations. The participants were asked to drive their vehicles in a controlled environment, interacting with an HDV and a Wizard of Oz AV. The effects of positive and negative information regarding AV behavior on the participants’ driving behavior and their trust in AVs were also studied. The results show that human drivers adopted significantly smaller critical gaps when interacting with the approaching AV as compared to when interacting with the approaching HDV. Drivers also maintained a significantly shorter headway after overtaking the AV in comparison to overtaking the HDV. Positive information about the behavior of the AV led to closer interactions in comparison to HDVs. Additionally, drivers showed higher trust in the interacting AV when they were provided with positive information regarding the AV in comparison to scenarios where no information was provided. These findings suggest the potential exploitation of AV technology by HDV drivers.     

External human-machine interfaces: Effects of message perspective

Future automated vehicles may be equipped with external Human-Machine Interfaces (eHMIs). Currently, little is known about the effect of the perspective of the eHMI message on crossing decisions of pedestrians. We performed an experiment to examine the effects of images depicting eHMI messages of different perspectives (egocentric from the pedestrian’s point of view: WALK, DON’T WALK, allocentric: BRAKING, DRIVING, and ambiguous: GO, STOP) on participants’ (N = 103) crossing decisions, response times, and eye movements. Considering that crossing the road can be cognitively demanding, we added a memory task in two-thirds of the trials. The results showed that egocentric messages yielded higher subjective clarity ratings than the other messages as well as higher objective clarity scores (i.e., more uniform crossing decisions) and faster response times than the allocentric BRAKING and the ambiguous STOP. When participants were subjected to the memory task, pupil diameter increased, and crossing decisions were reached faster as compared to trials without memory task. Regarding the ambiguous messages, most participants crossed for the GO message and did not cross for the STOP message, which points towards an egocentric perspective taken by the participant. More lengthy text messages (e.g., DON’T WALK) yielded a higher number of saccades but did not cause slower response times. We conclude that pedestrians find egocentric eHMI messages clearer than allocentric ones, and take an egocentric perspective if the message is ambiguous. Our results may have important implications, as the consensus among eHMI researchers appears to be that egocentric text-based eHMIs should not be used in traffic.     

Sharing the road with autonomous vehicles: A qualitative analysis of the perceptions of pedestrians and bicyclists

Public perception assessment is important for gaining a better understanding of the acceptance of autonomous vehicles (AVs) and identifying potential ways to resolve public concerns. This study investigated how pedestrians and bicyclists perceived AVs based on their knowledge and road sharing experiences, applying a combined inductive and deductive data analysis approach. Survey responses of pedestrians and bicyclists in Pittsburgh, Pennsylvania, USA collected by Bike Pittsburgh (BikePGH) in 2019, were analyzed in this research. AVs following traffic rules appropriately and AVs driving safer than the human drivers were the most notable positive perceptions towards AVs. Pedestrians and bicyclists showed comparatively fewer negative perceptions towards AVs than positive perceptions. Negative perceptions mostly included a lack of perceived safety and comfort around AVs and trust in the AV technology. Respondents also concerned about AV technology issues (e.g., slow and defensive driving, disruptive maneuver), while sharing the road with AVs. Perceptions of the respondents were significantly influenced by their views on AV safety, familiarity with the technology, the extent respondents followed AV on the news, and household automobile ownership. Regulating AV movement on roadways, developing safety assessment guidelines, and controlling oversights of improper practices by AV companies were the major suggestions from the survey participants. Findings of this study might help AV companies to identify potential improvement needed in AV technology to increase pedestrians and bicyclists acceptance, and policymakers to develop policy guidelines to ensure safe road sharing among pedestrians, bicyclists, and AVs.     

Effects of traffic context on eHMI icon comprehension

External human–machine-interfaces (eHMIs) might support the interaction between automated vehicles and pedestrians. The messages conveyed by eHMIs need to be understood quickly and correctly by their addressees. If implemented in the future, pedestrians will repeatedly encounter eHMIs in situations that feature different traffic context. So far, little is known about the influence of contextual cues like regulatory elements or (presumed) model behavior of fellow road users on the comprehension of eHMIs. In order to investigate possible effects of such contextual cues on comprehension, we conducted a picture-based online study among German residents (N = 175). Participants repeatedly interpreted three eHMI icons (“you can cross”, “do not cross”, and “pedestrian detected”) either without any context (control group) or within varying degrees of relevant context (experimental group). Context facilitated comprehensibility in terms of accuracy and subjective certainty. Relevant context was especially beneficial at first encounter. As soon as an icon’s meaning was internalized, the necessity of relevant context decreased. The effect of context should therefore be considered in future eHMI research as real-world comprehension might be underestimated otherwise.     

Effect of external HMI for automated vehicles (AVs) on drivers’ ability to infer the AV motion intention: A field experiment

A field experiment was conducted aiming to shed light on how drivers negotiate an ambiguous traffic situation when encountering an autonomous vehicle (AV) in the presence of a yielding intention signal (AV with eHMI) or not (AV without eHMI). A traffic conflict scenario was created with two opposing vehicles instructed to perform a left turn at a four-way junction, at the same point in time. Forty participants were assigned to two groups encountering either an AV with eHMI or an AV without eHMI. To check for equivalence across the two groups, both groups also encountered a conventional vehicle (CV). Results showed that the two groups performed similarly during encounters with a CV. During encounters with AV, however, participants provided with the eHMI maintained higher speed before the AV and finished their maneuver quicker than when they were not provided with eHMI. Additionally participants provided with eHMI rated higher their ability to infer the AV intention before coming to a full-stop than those provided without eHMI. The above findings indicate that the presence of eHMI on AV can accelerate drivers’ inferences about yielding intention of an AV, and have the potential to optimize AV-driver interaction in ambiguous traffic situations.     

社会性线索对内隐和外显元认知监控的影响

本研究以词图匹配材料为学习材料,以“大多数人选择的情况”为社会性线索,通过分析行为表现和眼动指标,探讨了社会性线索对内隐和外显元认知监控的影响。结果显示：（1）第一次信心判断时,外显信心判断显著高估,内隐信心判断较为准确。第二次信心判断时,在线索一致条件下,外显信心判断等级显著高于内隐信心判断。（2）在提供社会性线索阶段和第二次再认阶段,与线索一致条件相比,线索不一致条件下被提示项的注视时间更短,而未提示项的注视时间更长,且个体改变选择的次数显著增多。结果表明：内隐和外显元认知监测存在分离,内隐监测的准确性更高,外显监测受社会性线索的影响更大;而内隐和外显的元认知控制受社会性线索的影响趋同。     

Autonomous vehicle safety: Understanding perceptions of pedestrians and bicyclists

Autonomous vehicle (AV) technologies have been rapidly advancing. One benefit of AVs is that the technology could eliminate many driver errors and also mitigate many pedestrian and bicyclist collisions. Real-world AVs have been tested in many cities. Five companies are running around 50 AVs in Pittsburgh, following the autonomous testing guidelines. BikePGH, a non-profit organization located in Pittsburgh, Pennsylvania conducted a follow-up survey in 2019 (the first survey was conducted in 2017) to understand non-motorists’ opinions of AVs. This study examined how pedestrians and bicyclists perceived AV safety based on their understanding and experiences. At first, this study performed a comparison group test to determine which questions vary by participants’ AV safety rating. The responses were later analyzed with a data mining method known as ‘association rules mining.’ A new performance measure, known as the rule power factor, was then used to identify the significant patterns in the form of rules. The participants also provided their thoughts in responses to the open-ended questions. Using Latent Dirichlet Allocation (LDA), a topic modeling algorithm, 40 topic models were developed based on five open-ended questions. The findings show that the non-motorists showed comparatively fewer negative opinions towards AVs than positive assessments. The results also show that perception patterns vary by the participant’s rating on AV safety. Findings of this study would be beneficial for the AV stakeholders in making AVs and roadways safer for non-motorists.     

The safety and conspicuity of pedestrian crossing at roundabouts: The effect of median refuge island and zebra markings

Roundabouts are one of the most used road intersections because, compared to signalized ones, they reduce conflict points between traffic flows and moderate driving speed. Great attention should also be paid to vulnerable road users at roundabouts. According to accident statistics, in fact, accessibility of pedestrians and cyclists is not always ensured.This paper has evaluated the effects on the visibility of pedestrian crossing before and after the displacement of zebra markings, moved before intersections, and the introduction of media refuge islands and “Yield here to pedestrians” vertical signs. The above effects have been assessed by before-after analysis of speed and visual behaviour of drivers approaching the crosswalk.Moreover, the analysis of the drivers’ eye movements has highlighted the most salient elements of the pedestrian crossing. The relation between the drivers’ visual behaviour and the vehicle speed have also been calculated. Results have confirmed that the intervention carried out has increased both visibility and safety of the studied pedestrian crosswalks.Zebra markings and the median refuge island have turned out to be the most glanced elements, respectively seen by 93.75% and 56.25% of the drivers, followed by the “Yield here to pedestrians” vertical sign. The mean distance of first fixation of the crosswalk increased from 21.98 m before the intervention, to 40.69 m after it. The drivers perceived the pedestrian crossings from a longer distance after the intervention, and they continued to glance at the crosswalk while approaching it, enhancing their visual attention.