When crossing a road, pedestrians must detect traffic, combine data coming from different perceptual modalities, evaluate the time envelope for safely cross the street, and monitor the position of oncoming vehicles to perform corrective actions if needed. This study analyzed the influence of noise emitted by vehicles, or its absence, on pedestrians’ crossing decision-making. Experiments were performed in a virtual environment using two road scenarios. Participants were presented with stimuli of approaching vehicles that varied regarding speed, movement patterns, and auditory condition: one concerning the approaching of an electric vehicle, another regarding the approaching of a gasoline combustion vehicle, and, finally, a condition regarding the absence of auditory cues. Participants were tasked with indicating the moment when they decided to cross the street. The results show that, despite the noise variations caused by the type of vehicle and its speed pattern, the participants’ decision to cross was mostly based on vehicle distance. When a vehicle approaches the crosswalk from a short distance and with no occlusion to the pedestrian’s visibility, the sound does not seem to influence the pedestrians’ crossing decision-making.

Many of our sequential activities require that behaviors must be both precisely timed and put in the proper order. This paper presents a neuro-computational model based on the theoretical framework of Dynamic Neural Fields that supports the rapid learning and flexible adaptation of coupled order-timing representations of sequential events. A key assumption is that elapsed time is encoded in the monotonic buildup of self-stabilized neural population activity representing event memory. A stable activation gradient over subpopulations carries the information of an entire sequence. With robotics applications in mind, we test the model in simulations of a learning by observation paradigm, in which the cognitive agent first memorizes the order and relative timing of observed events and, subsequently, recalls the information from memory taking potential speed constraints into account. Model robustness is tested by systematically varying sequence complexity along the temporal and the ordinal dimension. Furthermore, an adaptation rule is proposed that allows the agent to adjust in a single trial a learned timing pattern to a changing temporal context. The simulation results are discussed with respect to our goal to endow autonomous robots with the capacity to efficiently learn complex sequences with time constraints, supporting more natural human-robot interactions.

A significant percentage of pedestrians walk in social groups (friends, families, or acquaintances who walk together). Although patterns generated by social interactions among group members have been shown to affect crowd dynamics, studies on the effect of social interactions at different crossing phases under low pedestrian density are limited. This study aims to comprehensively examine the influence of size and sex composition on pedestrians’ behaviors when walking alone and with friends in different phases before, during, and after the road crossing. For this, experiences were carried out with controlled small groups of friends (varying size and sex composition) at three unsignalized crosswalks with low pedestrian density. The average speed and distance between the young pedestrians in six segments of the trajectories (two in each phase), extracted from video recordings, were analyzed with linear mixed models. Results show that pedestrians reduce their speed when approaching the curb, they accelerate while on the crosswalk, and reduce again when they reach the other side. In all phases, the average speed of the groups was lower than the single pedestrians, and the females’ groups walked slower than the males, except during the crossing, where no sex-related differences were found. On the contrary, before the crossing, the distance increased and decreased from the second segment in the crosswalk. The smallest distance was observed between the female groups and dyads. These findings have relevant implications for research on pedestrian behavior, helping to better understand the complexity of pedestrian dynamics and improve pedestrian safety.