Road safety remains a major global challenge, with traffic accidents accounting for more than one million fatalities worldwide each year. A substantial body of literature indicates that many of these accidents have human error as a main contributory factor, highlighting the pivotal role of human factors in road safety analysis. Consequently, a comprehensive understanding of driver behaviour under varying traffic and environmental conditions is essential for the effective design, evaluation, and management of road infrastructure.

Several studies identify human factors as a key determinant of correct road perception (Campbell et al., 2012; Čičković, 2016; Green, 2018; Theeuwes, 2021) and, consequently, as a critical contributor to accident occurrence and safety assessment (Paliotto et al., 2024, 2025; Paliotto & Meocci, 2024). Among the various sources of human error, delays in responding to unexpected events within the road environment play a particularly significant role. These delays are commonly characterized by an increase of perception–reaction time (PRT) or any other change in it, defined as the time interval between the appearance of a stimulus and the initiation of the driver’s response, such as braking or steering. PRT is influenced by a wide range of factors (Campbell et al., 2012). Even relatively small increases in PRT can have substantial implications for road safety, as they directly affect stopping distance and a driver’s ability to avoid collisions (Green, 2000). Moreover, variability in PRT has been associated with degraded driving performance and an increased risk of accidents, particularly regarding near-miss events and traffic violations (Serda, 2020). The reaction to sudden stimuli can be due to an obstacle, an animal, or even another road user that is on the trajectory of the moving vehicles. One of the riskiest scenarios is the presence of a Vulnerable Road Users (VRUs) on the road and, such a circumstance reaches its apex considering pedestrians crossing the road. In that moment the pedestrian is crossing the road transversally, positioning exactly on the trajectory of the vehicle. A high number of accidents occurred between a car and a pedestrian who is crossing the road, both at signalized crosswalks, unsignalized crosswalks, or outside crosswalks. Previous research consistently indicates that a substantial proportion of pedestrian crashes occurs during road crossings away from designated facilities (Banerjee et al., 2021; Gitelman et al., 2012). Such situations are highly critical because pedestrian presence is often unexpected; drivers predominantly allocate their visual attention to vehicle interactions and roadway geometry rather than anticipating pedestrian movements in non-designated locations (Mukherjee et al., 2024) (Zhuang & Wu, 2011). Historical EU-wide crash studies from the CARE database show that between 40% and 50% of all pedestrian-motor vehicle collisions occur when the pedestrian is crossing away from a crossing facility (European Road Safety Observatory - Mobility & Transport - Road Safety, 2026)(Ambros et al., 2025) Similarly, historical data from the UK’s Department for Transport indicates that about 40% of pedestrian collisions occur away from designated facilities, whereas fewer than 10% occur directly on them. In Italy, according to comprehensive data from ACI-ISTAT (ISTAT & ACI, 2023, 2025), only about 19% of pedestrian fatalities occur while legally crossing on a designated crosswalk. However, the absolute number of serious injuries remains high on marked facilities. This phenomenon is deeply linked to a false sense of safety often experienced by pedestrians on designated crossings, which can degrade situational alertness. In reality, protection on these facilities is structurally weak and relies heavily on the mutual expectations and subsequent evasive actions of both the driver and the pedestrian (Ekman, 1996) (Kim & Yamashita, 2008). Consequently, understanding baseline driver reaction limits when these expectations fail—such as in midblock, occluded scenarios—is vital for designing effective safety interventions.

In this context, Bella and Silvestri (Bella & Silvestri, 2021) showed that drivers exhibit lower yielding rates and initiate evasive actions closer to the conflict point when pedestrians cross outside zebra crossings, indicating delayed detection and reduced expectancy. Similar findings were reported by Toxopeus et al. (Toxopeus et al., 2018), who highlighted that unexpected pedestrians crossing result in shorter available response times and critical interaction conditions. Research on driver–pedestrian communication further emphasizes the role of visual cues and expectancy (Huang et al., 2026). Rasouli et al. (Rasouli et al., 2017) demonstrated that although pedestrians tend to look toward oncoming traffic before crossing, driver reactions strongly depend on the ability to perceive the pedestrian and the available Time-to-Collision (TTC). This confirms that pedestrians appearing in non-designated areas reduce drivers’ predictive cues and delay decision-making processes.

Driving simulator studies combining eye-tracking and behavioural measures have further examined these dynamics. Banerjee et al. (Banerjee et al., 2021) reported that collision warning systems significantly influence driver attention allocation and braking behaviour, indicating that pedestrian detection and response may be increased in scenarios with technological support systems. Understanding driver behaviour and reactions in such circumstances is paramount to identifying effective countermeasures, including Advanced Driver Assistance Systems (ADAS) (Rigneault et al., 2014) like Automated Emergency Braking (AEB), which may anticipate the braking action, and pedestrian collision warning systems, which may optimize driver attention. The effects of warning systems on PRT were analysed by Bella and Silvestri (Bella & Silvestri, 2017), who demonstrated that both visual and auditory warnings significantly reduce reaction times. Notably, auditory warnings resulted in statistically lower PRT values than visual ones, as they allowed drivers to maintain visual focus on the roadway rather than redirecting attention toward an in-vehicle visual alert. Similar conclusions regarding the benefits of advanced driver support technologies have been validated in broader studies on Intelligent Transportation Systems (ITS) and ADAS (Rachakonda & Pawar, 2023; Scanlon et al., 2016).

This study focuses on a critical urban safety scenario involving a pedestrian suddenly crossing the roadway outside designated crosswalks, emerging from behind a parked car. Such situations, as highlighted by accident statistics (ISTAT & ACI, 2023, 2025) and previous research (Gitelman et al., 2012), are frequent in urban environments and are characterised by low driver expectancy, making pedestrian detection particularly challenging.

The objective of this research is to provide insights into the driver perception–reaction process under these critical conditions, with specific focus on the time intervals required to visually detect the pedestrian, perceive as hazard, and initiate a response. In addition, the study analyses the specific evasive manoeuvres adopted by drivers (i.e., braking vs. steering). Particular attention is dedicated to the vehicle speed, position, and drivers’ gaze location at the exact moment the pedestrian appears.

The experimental investigation was conducted at the Laboratory for Road Safety and Accident Investigation (LaSIS) of the University of Florence. LaSIS is equipped with a virtual reality driving simulator. It features a full-scale vehicle (Lancia Ypsilon) mounted on a Stewart platform with six degrees of freedom, capable of reproducing yaw, roll, and pitch movements, and a circular screen covering a 220° field of view. This setup allows for highly versatile testing of diverse geometric configurations within interactive environments under predetermined traffic and environmental conditions, utilizing standardized, controlled, and fully repeatable experimental procedures. To complement the driving simulator, participants wore Pupil Labs eye-tracking glasses (Tonsen et al., 2020) to monitor visual attention allocation. The experiment was part of a series of experiments built for the evaluation of driver reactions (Carini et al., 2026).

The scenario has been built considering an existing road, whose layout is composed of a single-carriageway, two-lane, two-way rural road followed by a single-carriageway, two-lane, two- way urban road section, which in the final part splits into two separated single lane carriageways separated by a raised median in the province of Grosseto (Italy). The cross section of the rural road has been defined according to the geometry of a real road. The road consists of two lanes each 4.00 m wide with very narrow shoulders (approximately 0.30 m).

The urban road consists of a single carriageway with one lane per direction, each approximately 3.75 m wide, except for a short final section where lane widths are reduced to 3.50 m and the lanes are separated by a central median. A minor urban road, used for the left-turn manoeuvre, has a width of approximately 3.00 m.

The environment around the road (buildings, trees, etc.) has been simplified to allow smooth simulation, while the main geometric and infrastructural elements have been retained to ensure a realistic driving experience. In all scenarios, a unidirectional traffic flow is present on the lane opposite to the one driven by the participants, to make the driving task more realistic. No vehicles are present in the participant’s lane, so as to avoid influencing the driver’s behaviour. In the urban section, pedestrians and cyclists were dynamically generated along the entire route to create a realistic driving environment and maintain a baseline cognitive workload. However, to avoid confounding variables or distractions, no other road users or cyclists were present in the immediate vicinity of the critical jaywalking event.

At the end of the urban section, an unexpected pedestrian crossing is introduced. The pedestrian starts the crossing manoeuvre suddenly, in the absence of a marked crosswalk. A planimetric view of the entire scenario, including both the extra-urban and urban sections, is provided in Figure 1.

Following the simulation runs, data collected from both the driving simulator and the eye-tracking system were processed and analysed. The raw experimental dataset is publicly available in open access-form together with the description of the procedure adopted to synchronizing the tools (Paliotto, 2025).

The analysis focused on two main aspects: drivers’ perceptual and perceptual–reaction processes, and the type of manoeuvre adopted in response to the critical event. Drivers’ perception–reaction time (PRT) was analysed by identifying two main temporal components:

Based on these components, the total perception–reaction time (ΔT_total) was computed as the sum of ΔT_see and ΔT_react, corresponding to the time interval between the pedestrian’s appearance and the driver’s first reaction.

The type of reaction was classified into four categories:

To clearly identify while a braking or steering action took place, the following thresholds have been considered, based also on the simulator technical characteristics:

Since steering measurements can be affected by signal noise, every identified 'first steering reaction' was manually validated through a review of the video recordings. A similar validation was not required for brake pressure.

To characterise the distributional properties of the three temporal variables (ΔT_see, ΔT_react, and ΔT_total) and to investigate potential differences in behavioural responses between the two experimental groups, an exploratory analysis was conducted using relative frequency histograms. The resulting distributions for each temporal variable, separately reported for groups B1 and B2, are shown in Figure 6. Among the simulations’ dataset, 3 participants of group B1 and 4 participants of group B2 do not react at all to the pedestrian. In this case, the data have been excluded by the analysis of , ΔT_react, and ΔT_total.

In group B1, the distributions appear comparatively more compact and exhibit clearer central tendencies across all temporal variables. The values of ΔT_see are predominantly concentrated within the lower time intervals, indicating relatively rapid and consistent visual detection of the pedestrian. Similarly, ΔT_react shows a marked concentration within a limited range, suggesting a more homogeneous reaction process among participants. The distribution of ΔT_total also appears relatively balanced, with most observations clustered around intermediate values, reflecting a consistent overall response sequence.

Conversely, group B2 is characterised by broader and more irregular distributions for ΔT_see and ΔT_total, while ΔT_react follows a normal distribution around (0.8,1]. The distribution of ΔT_total shows a greater spread towards higher time intervals and indicating longer and more heterogeneous response processes.

Overall, the distributions suggest greater inter-individual variability and less consistent temporal responses in group B2 compared to group B1, where reaction patterns appear more stable and concentrated.

To formally evaluate the normality of the data distributions and to support the selection of appropriate statistical tests, the Anderson-Darling test was applied to each variable for both experimental groups. The null hypothesis (H_o), assuming a normal distribution of the data, was considered acceptable for p-values < 0.05. The results are reported in Table 6.

The results indicated that, for all variables, the normality assumption was violated in at least one of the two groups. Specifically, ΔT_see did not follow a normal distribution in group B1, whereas ΔT_total deviated from normality in group B2.

Following the assessment of normality using the Anderson-Darling test, the assumption of homogeneity of variances between the two experimental groups is evaluated. To this end, Levene’s test was applied to all temporal variables under investigation.

As reported in Table 7, Levene’s test did not reveal statistically significant differences in variance between groups B1 and B2 for ΔT_react and ΔT_total (p ≥ 0.05), while a difference in variance is evident for ΔT_see.

As the Anderson-Darling test indicated deviations from normality in at least one group for each temporal variable, the assumptions required for parametric testing were not consistently met. Consequently, between-group comparisons were performed using the non-parametric Mann–Whitney U test. Statistical significance was assessed using a conventional significance level of α = 0.05. The results of the Mann–Whitney U test are reported in Table 8.

For ΔT_see, the Mann–Whitney U test yielded a U statistic of 113 with a p-value lower than 0.001, indicating a statistically significant difference between the two groups. This result suggests that the time required to visually detect the pedestrian differs systematically across the experimental conditions, likely reflecting variations in approach dynamics and visual processing demands. ΔT_see is larger in B2 than in B1, suggesting the need for more time to identify the pedestrian.

A statistically significant difference was also observed for ΔT_reaction (U = 300, p = 0.0196), indicating that the time required to initiate a control response following pedestrian detection is not equivalent between groups B1 and B2.

In contrast, no statistically significant difference was found for ΔT_total (U = 154, p = 0.148). However, this is mainly derived by the different distribution among the previous two times: ΔT_see is larger in B2 than in B1, while ΔT_reaction is larger in B1 than in B2, so these two effects are overlaying and canceling each other out when building the sum.

Taken together, these results indicate that perception and reaction-related processes differ between groups B1 and B2; however, such differences do not translate into a statistically significant variation in the total perception–reaction time. This finding highlights the importance of analysing the individual temporal components separately, as compensatory mechanisms may occur across different stages of the driver’s response.

Descriptive statistics indicate shorter perception–reaction times in scenario B1 compared to B2, particularly with respect to ΔT_total. Mean values show that drivers in B2 exhibit longer perception times (ΔT_see) and higher overall response durations, suggesting generally slower behavioural responses under this condition.

Standard deviation values reveal greater dispersion in scenario B2, especially for ΔTtotal, indicating increased inter-individual variability. The wide range between minimum and maximum values further supports the presence of diverse perceptual and response strategies among participants.

In Group B1, response times are generally shorter and more consistent across all variables. The distributions of ΔT_see and ΔT_total are relatively compact, suggesting a more homogeneous behavioural response among drivers.

In group B2, higher central values for ΔT_see and ΔT_total are observed and increased dispersion for all times. These visual patterns are consistent with the descriptive statistics presented in Table 5 and indicate a tendency toward a more heterogeneous perception–reaction behaviour compared to scenario B1.

Each recording was also deeply analysed to identify the reason of such a difference. The analysis underlines that the shorter times to see the pedestrian can be explained by the closer position of the gaze from the point of appearance. Most of the participants of scenario B1 were looking at the area of ​​the car from behind which the pedestrian starts to move, thus the perception of the pedestrian was faster than in scenario B2. Indeed, in scenario B2, drivers are closer to the crossing point and their gaze is already directed somewhere further away. This underlines the importance of having the gaze closer to the possible critical location. The validity of these results can be generalized to contexts beyond this specific experiment. Each critical location (pedestrian crossings, intersections, accesses, etc.) must be the focus point of the driver gaze; eye-catching elements capturing the attention of the drivers, must be avoided.

To further investigate the perceptual and reactive mechanisms involved in drivers’ responses to the sudden appearance of a pedestrian, the relationships between selected temporal variables and spatial or kinematic parameters were analysed, with the aim of identifying potential behavioural differences between the experimental groups B1 and B2.

The first relationship examined concerned the association between the driver’s reaction time following pedestrian perception (ΔT_react) and the spatial distance between the pedestrian and the driver at the time the pedestrian is seen (T_trigger+ΔT_see). Figure 7 illustrates the corresponding scatter plots for groups B1 and B2, together with the fitted linear regression lines and the associated coefficients of determination (R²).

The linear regression results reported in Figure 7 indicate a positive association between pedestrian distance at the time of appearance and driver reaction time (ΔT_react) in both groups. However, the coefficients of determination (R²) are low for group B2 and moderate for B1, indicating that the linear models explain only a limited portion of the observed variability in reaction times.

Given the non-normal distribution of the data and the variability typical of behavioural measures, Spearman’s rank correlation coefficient (ρ) was used to quantify the monotonic association between the two variables. Positive correlations were observed in both groups, with ρ = 0.54 in Group B1 and ρ = 0.21 in Group B2, which according to Cohen’s classification correspond to a moderate and a small-to-moderate association, respectively.

The relationship between vehicle speed at the time the pedestrian is seen (T_trigger+ΔT_see). and driver reaction time (ΔT_react) was also analysed for both experimental groups. Figure 8 shows the association between these variables for groups B1 and B2.

Spearman’s rank correlation analysis revealed weak positive associations in both groups, with correlation coefficients equal to ρ = 0.08 for group B1 and ρ = 0.12 for group B2. According to Cohen’s classification, these values correspond to small and very small correlations, indicating a limited monotonic relationship between vehicle speed and reaction time.

These findings are supported by the results of the simple linear regression analysis, which exhibited shallow slopes and very low coefficients of determination (R²) for both groups. The low R² values indicate that vehicle speed does not account for the observed variability in reaction times. Drivers do not react on the basis of speed.

Because greater speed may be also associated to greater distance, the combined effect of vehicle speed and distance was investigated using the Time To Collision (TTC), defined as the ratio between the distance of the ego vehicle from the pedestrian and the ego vehicle speed at the time the pedestrian is seen (T_trigger+ΔT_see).. TTC was thus used as an integrated measure of the temporal margin available to the driver at the onset of the critical event.

Figure 9 illustrates the relationship between TTC and reaction time (ΔT_reaction) for groups B1 and B2.

Spearman’s rank correlation analysis revealed a very weak positive association in group B1 (ρ = 0.01) and a medium to weak positive association in group B2 (ρ = 0.21). According to Cohen’s classification, both values fall within the trivial to small correlation range.

Consistently, the linear regression analysis resulted in very low coefficients of determination (R²) for both groups, indicating that TTC accounts for only a limited proportion of the variability observed in drivers’ reaction times. The results reported in Figure 9 therefore indicate that, within the considered pedestrian appearance scenario, TTC has a limited association with time to react and does not substantially explain the observed variability in response behaviour.

From a theoretical perspective, conditions B1 and B2 can be integrated, as inter-group variability is adequately captured within the Time-To-Collision (TTC) metric. Consequently, it may be hypothesized that lower TTC values correlate with decreased reaction times across specific levels of criticality, consistent with prior empirical findings. This has been analysed and it is represented by Figure 10.

In this case, the results show a quite more powerful correlation among the data with a Spearman’s rank correlation ρ = 0.36. However, the relationship between TTC and reaction times typically attenuates beyond a given threshold, ultimately reaching a plateau defined by the lower limit of human driver reaction capabilities on one side, and the absence of urgency on the other.

Drivers’ reaction types were also analysed for both experimental groups. Figure 10 presents the percentage distribution of the different reaction categories observed in groups B1 and B2.

In group B1, responses involving braking represent the predominant reaction type. In particular, the “Braking” manoeuvre accounts for the 60% of the observed responses, followed by “Braking first” actions, which represent about 28% of cases. Steering-based reactions are absent, while a limited proportion of cases (12%) is classified as “No response”, indicating the absence of a clear evasive response.

In group B2, reaction strategies appear more heterogeneous. Only-braking response still constitute a substantial share of driver behaviour (40%), but a higher occurrence of “No response” responses is observed (16%), suggesting delayed or absent reactions in a considerable proportion of trials. Steering manoeuvres account for approximately 20% of responses, the same as “Braking first” actions are less frequent (20%), and “Steering first” represents a marginal proportion.

To further explore how reaction strategies relate to response timing, the time to react (ΔT_react) was examined across the different reaction categories, excluding cases classified as “Nothing”. Figure 11 presents boxplots of reaction time by reaction type, reported separately for groups B1 and B2.

In group B1, only braking-related reaction types are observed. Reaction times associated with “Braking first” and “Braking” appear comparable, with similar central values, indicating limited differences in the time to react between these two manoeuvres. In group B2, a broader range of reaction strategies is observed, including both steering- and braking-based responses. Steering manoeuvres tend to be associated with shorter times to react compared to braking manoeuvres, while braking-related responses exhibit higher central values and greater dispersion, reflecting increased variability in response timing.

The boxplots also indicate a larger overall spread of reaction times in group B2 compared to group B1, particularly for braking manoeuvres. Given the limited number of observations within some reaction categories, these results are intended for descriptive purposes only, and no statistical comparisons are performed.

To further investigate differences in driving behaviour between the two groups, the average driving behaviour is analysed separately for each group. For groups B1 and B2, mean profiles of vehicle speed, accelerator pedal input, brake pedal input, and steering wheel activity are computed as a function of distance along the route and are shown in Figure 12 and Figure 13. The figures focus exclusively on the final segment of the route, which corresponds to the road section where the pedestrian appears.

Although the average values ​​do not exactly reflect the trend of the parameters for each user, the two graphs, which are very different from each other, highlight how in the two scenarios, overall the two reactions are very different near the crossing point, and very similar in the preceding section.

In group B1, during the segment preceding the pedestrian appearance, drivers generally maintained speeds that were consistent with the imposed limit, with average values slightly below 50 km/h. Following trigger activation, braking responses were typically progressive and allowed drivers to substantially reduce speed, resulting in the vehicle slowing down sufficiently to avoid the collision or being brought to a stop before reaching the pedestrian in all observed cases.

In contrast, in group B2, although the urban speed limit was set to 30 km/h, the average approach speed was notably higher than the imposed limit and comparable to that observed in group B1. In this group, braking responses tended to occur later and were often more abrupt, leading to a reduced available reaction window, which in many cases appears insufficient to stop the vehicle before the point of collision with the pedestrian.

The differences observed in the speed profiles and vehicle control responses are reflected in the outcomes of the pedestrian interaction. While no pedestrian impacts were observed in group B1, pedestrian impacts occurred in approximately 80% of the simulations in group B2. These results suggest that higher approach speeds, combined with reduced reaction margins, are associated with more critical safety outcomes in the pedestrian appearance scenario.

The adopted methodological framework proved effective in identifying both general behavioural patterns and inter-individual variability in driver responses to the sudden appearance of a pedestrian outside designated crosswalks. The combined use of descriptive statistics, distributional analyses, and non-parametric tests ensured a robust interpretation of perception–reaction processes, accounting for the non-normality and variability typical of behavioural data in safety-critical driving scenarios (Green, 2000; Han et al., 2021).

The analysis of perception time (ΔT_see) revealed significantly longer detection times in group B2. The comparison of the two groups in the rural area, where speed limits were 80 km/h for both the groups, and where other perceptual tasks and activities were requested to the participants, reveals that no differences are present between group B1 and group B2 in terms of perception abilities. Consequently, the difference in the perception time, lays in the different position of the pedestrian in the field of view of the driver, when the pedestrian starts crossing the carriageway. The analysis indicates that shorter detection times for the pedestrian are due to the proximity of the driver's gaze to the point of appearance. In scenario B1, about half of the participants were looking toward the parked vehicle, while the others were looking at the road. Even in the latter situation, the parked car was located very close to the centre of the field of view, because of the distance, thus, when the pedestrian appeared, she was not in the peripheral field of view. This leads to a faster perception than in scenario B2. In B2, drivers were closer to the crossing point, but their gaze was already directed further down the road. This underscores the critical importance of aligning a driver's focal point with potential hazard locations. These results can be generalized beyond this specific study: every potential conflict point (pedestrian crossings, intersections, etc.) must remain the driver's primary focus, and "eye-catching" distractions must be minimized.

The broader distributions and increased dispersion observed in group B2 further indicate higher inter-individual variability in perceptual processing, consistent with reduced driver expectancy and less systematic visual scanning behaviour, as reported in previous hazard perception studies (Crundall, 2016; Hirano et al., 2023).

The reaction time following the pedestrian detection (ΔT_react) has shown few differences between groups. This finding suggests that this type of reaction is more dictated by human physical constraints rather than the specific nature of the critical situation, as both the reaction times distributions are very similar. It must be underlined that both the scenarios were structured to be critical if the driver drives with a speed close to the speed limits.

Finally, no statistically significant difference was observed for the total perception–reaction time (ΔT_total), indicating that perceptual and motor components may partially compensate when the response process is considered. This supports previous evidence that aggregated PRT measures may mask differences occurring at individual response stages (Green, 2000).

Reaction strategy analysis further highlighted differences between groups. In group B1, braking-related manoeuvres were predominant and consistently effective in avoiding pedestrian impacts. In contrast, group B2 exhibited more heterogeneous responses, including a substantial proportion of delayed or absent evasive actions, a behaviour previously associated with increased collision likelihood in pedestrian scenarios(Jurecki & Stańczyk, 2014). While braking has often been reported as the dominant response to pedestrian hazards, steering-based reactions may emerge more rapidly in situations where drivers are already engaged in steering actions, as shown by Li et al. (Li et al., 2019).

Correlation and regression analyses indicated that reaction time is moderately associated with pedestrian distance from the ego vehicle at the time they are seen for group B1. This cannot be said for group B2 where there a correlation cannot be seen. Considering the TTC, when the two groups are considered alone, the relationship between TTC and reaction time (Δt_react) is very poor, but it increases considering both groups together. This finding is partially consistent with previous studies reporting that the greater the TTC the greater the reaction time (Jurecki et al., 2017).

Overall, the results confirm that pedestrians who cross outside designated crosswalks represent a highly critical safety condition, especially when combined with higher approach speeds and reduced reaction margins. In agreement with prior human-factors-based safety research, the findings emphasise the predominance of perceptual and behavioural processes over purely kinematic variables in shaping driver responses (Crundall, 2016). These results underline the need for interventions aimed at improving early pedestrian detection and driver awareness. Moreover, efforts must be made to improve also pedestrian awareness of the dangers of crossing the road under such circumstances.