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.     

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.     

Pedestrians at the kerb – Recognising the action intentions of humans

Aim of the presented research is the development of a cognitive driver assistance system, which can capture the traffic situation, analyse it, and warn the driver in case a pedestrian is a potential hazard. Hence parameters have to be identified by which the intention of the pedestrian can be unambiguously predicted. Two approaches to the topic are addressed. First, the pedestrian’s perspective was taken. The question was how crossing decisions were influenced by the parameters distance and velocity of the car. Following a signal, participants had to choose to cross the road in front of or behind the car. The data analysis showed that pedestrians relied on the distance of the car rather than the time to collision for their decision. In the second experiment the observer’s perspective raised the question what parameters humans use to predict pedestrians’ intentions. Videos of natural traffic scenes were presented. Participants had to make statements about whether the shown pedestrian would cross the street during the next moment. In a baseline and four experimental conditions, certain information was masked in the videos. Just the condition in which only the trajectory information of the pedestrian was available produced a higher error rate.     

How do the recognizability and driving styles of automated vehicles affect human drivers’ gap acceptance at T- Intersections?

Future traffic will be composed of both human-driven vehicles (HDVs) and automated vehicles (AVs). To accurately predict the performance of mixed traffic, an important aspect is describing HDV behavior when interacting with AVs. A few exploratory studies show that HDVs change their behavior when interacting with AVs, being influenced by factors such as recognizability and driving style of AVs. Unsignalized priority intersections can significantly affect traffic flow efficiency and safety of the road network. To understand HDV behavior in mixed traffic at unsignalized priority T-intersections, a driving simulator experiment was set up in which 95 drivers took part in it. The route in the driving simulator included three T-intersections where the drivers had to give priority to traffic on the major road. The participants drove different scenarios which varied in whether the AVs were recognizable or not, and in their driving style (Aggressive or Defensive). The results showed that in mixed traffic having recognizable aggressive AVs, drivers accepted significantly larger gaps (and had larger critical gaps) when merging in front of AVs as compared to mixed traffic having either recognizable defensive AVs or recognizable mixed AVs (composed of both aggressive and defensive). This was not the case when merging in front of an HDV in the same scenarios. Drivers had significantly smaller critical gaps when driving in traffic having non-recognizable aggressive AVs compared to non-recognizable defensive AVs. The findings suggest that human drivers change their gap acceptance behavior in mixed traffic depending on the combined effect of recognizability and driving style of AVs, including accepting shorter gaps in front of non-recognizable aggressive AVs and changing their original driving behavior. This could have implications for traffic efficiency and safety at such priority intersections. Decision makers must carefully consider such behavioral adaptations before implementing any policy changes related to AVs and the infrastructure.     

“What Makes a Cooperative Driver?” Identifying parameters of implicit and explicit forms of communication in a lane change scenario

An automated vehicle needs to learn how human road users communicate with each other in order to avoid misunderstandings and prevent giving a negative outward image during interactions. The aim of the present work is to develop an autonomous driving system which communicates its intentions to change lanes based on implicit and explicit rules used by human drivers. To reach this goal, we aimed at gaining a deeper understanding of which aspects of lane change behaviour makes them cooperative from the perspective of other drivers. Therefore a vehicle used various lane change announcement strategies by varying combinations of driving parameters in a static driving simulator. (First study: Start indicator signal, Waittime, lane change duration; Second study: Longitudinal acceleration). It’s impact on the perception and behaviour of other road users was observed in two studies (N = 25 per study). The results showed that the earlier the merging vehicle was indicating its intentions, the more cooperative it was perceived. When turning on the indicator at a later time participants considered it as more cooperative to merge with a slower or faster lane change duration or to wait longer in the lane before starting to move to the other lane. An early longitudinal acceleration when starting to change lanes is perceived more cooperative. These findings can be used to model a lane change strategy based on human behaviour, which will eventually lead to more acceptable and safer interactions between automated and non-automated road users.     

Illusion of safety? Safety-related perceptions of pedestrians and car drivers around 3D crosswalks

Facilitating safe pedestrian road crossings is a major prerequisite for safe urban environments. In multiple cities around the world, 3D crosswalks have been painted, which provoke an optical illusion, of e.g., a crosswalk floating above the road, in car drivers who approach the crosswalk. However, to date, no detailed study of road users’ safety related perceptions on 3D crosswalks has been conducted. Hence, we investigated car drivers’ and pedestrians’ perceptions of a 3D crosswalk, and how they rate its safety in comparison to traditional (non-3D illusion) crosswalks. In an on-site questionnaire survey, we interviewed 201 pedestrians and 102 car drivers in the direct vicinity of a newly painted 3D crosswalk located in Yangon, Myanmar. Our results show that only 53.9 % of the car drivers report to have consciously perceived the 3D effect of the crosswalk. Nonetheless, both, pedestrians and car drivers rate the 3D crosswalk as safer for road crossing than a traditional crosswalk. A high share of pedestrians (43.3 %) report taking a detour to use the 3D crosswalk for road crossing. Approximately one third (31.3 %) of pedestrians and 48.0 % of car drivers interviewed have talked to their friends about the 3D crosswalk, indicating a high potential for using 3D crosswalks as a marketing tool for road safety actors to generate attention for pedestrian safety. Unrelated to our main research question, we found that pedestrians prefer to cross in groups, as it increases the perceived likelihood of cars yielding to them. Overall, the data points to significant increases in the perceived safety of drivers as well pedestrians around the 3D crosswalk. Future studies need to investigate how these perceptions translate to actual safety related behavior.     

“Please watch right” – Evaluation of a speech-based on-demand assistance system for urban intersections

In a driving simulator study we evaluated a speech-based driver assistance system for urban intersections (called Assistance on Demand AoD system) which supports the driver in monitoring and decision making. The system provides recommendations for suitable time gaps to enter the intersection based on the observation of crossing traffic. Following an “on-demand”-concept, the driver activates the assistance only if support is desired.In one drive, drivers used the AoD system in every situation they experienced to guarantee that every driver had the same exposure to the system when evaluating it. During another drive, drivers were free to decide if they want to use the system or not. The experimental study compared the AoD system with driving manually and with driving supported by a more conventional visual-based system which was always active at intersections (system showing colored arrows in a simulated head-up display (HUD) to visualize the crossing traffic). This resulted in four drives the drivers had to perform. Every drive consisted of several intersections with varying traffic conditions. The drivers had to turn left at every intersection.A total of 24 drivers took part in the study; one group with 14 middle-aged drivers and another group with ten high-aged drivers. Several questionnaires and online ratings were used to assess drivers’ acceptance, perceived usefulness, benefits and specific characteristics of both system variants. In addition, driving behaviour with regard to gap choice and drivers’ monitoring behaviour (using head tracking data) were analyzed.The results show that the AoD system reaches high acceptance ratings and is preferred compared to the visual, always active system. Using the speech modality for communication and the on-demand concept were both highly appreciated by the drivers. With regard to driving behaviour, the AoD system is comparably safe as manual driving while at the same time making driving easier by facilitating the monitoring of vehicles while waiting at an intersection.     

Analysing driver’s decision in dilemma zone at signalized intersections under disordered traffic conditions

Delay in the decision-making process of stop or go during the amber phase of the signal cycle often leads to abrupt hard deceleration or red light violations at signalized intersections. The indecisiveness or the dilemma in decision making often results in compromised safety of the road users. The present study attempts to analyze the driver’s behaviour in order to make the decision of stop or go and developed a binary logistic regression model while considering different traffic behaviour parameters exhibited and observed after the onset of the amber phase. Empirical vehicular trajectory data from three signalized intersections covering 121 signal cycles and 1347 vehicles are used in the study. The study presents two dilemma zone identification models based on distance from the stop line, focusing on easy-to-use and static driver assistance and dynamic-realtime driver assistance systems. Both the models are observed to show good fit and prediction accuracy. The models are validated internally and externally for their adaptability in the field. The effect of different traffic parameters on the dilemma zone is explored, and a possible real-time application of the dynamic model as a driver assistance system in decision-making is explored.