Pedestrians In Urban Environment Research Articles

From Visions to Reality: Investigating the Interplay of Vehicle Kinematics and Light-band eHMI in a Real Vehicle Study

Highly automated vehicles (HAVs) will interact with pedestrians in urban environments. This requires efficient communication tools to ensure mutual understanding. Past research showed that pedestrians mostly used vehicle kinematics to communicate with vehicles, e.g., the vehicle’s speed and distance. However, pedestrians required further explicit communication signals when the traffic situation was ambiguous. Light-band external human–machine interfaces (eHMIs) transmit additional explicit communication signals to pedestrians, e.g., the vehicle’s yielding intent. To this point, the precise interplay of vehicle kinematics and eHMIs for HAVs has not yet been determined. Nevertheless, previous research showed that combining both means of communication has great potential to increase pedestrian perceived safety and to ensure a safe interaction. Only a little research used real vehicle studies to investigate the interaction between pedestrians and HAVs in a close-to-reality experimental setting. However, this would ensure the transferability of experimental results to future urban traffic. Therefore, this study aimed to address this research gap by investigating the effects of vehicle kinematics, eHMIs, and their interplay in a real-world pedestrian crossing on pedestrians’ behaviors and subjective evaluations. In this field experiment, we applied a light-band eHMI on a Wizard-of-Oz test vehicle, an actual vehicle instructed as an HAV. We investigated the effects of vehicle kinematics (early yielding vs. late yielding) and the eHMI status (no eHMI, static eHMI, dynamic eHMI) on pedestrians’ crossing behavior and subjective evaluation in a low-speed real-world setting. The static eHMI displayed the vehicle automation status by a static illuminated eHMI. The dynamic eHMI conveyed the automation status and the vehicle’s yielding intent. This study focused particularly on the interplay of vehicle kinematics and eHMI status. We assumed that the crossing initiation was shorter when a dynamic eHMI was combined with an early yielding compared to a late yielding in this real-world setting. Moreover, we hypothesized that pedestrians’ subjective evaluations are more positive for a well-coordinated interplay of eHMI and vehicle kinematics. The results showed that pedestrians initiated their crossing earlier with dynamic eHMI vs. no eHMI or static eHMI. Furthermore, they perceived a dynamic eHMI as safer and more trustworthy compared to no eHMI or a static eHMI. Combining an early yielding and dynamic eHMI increased participants’ perceived safety of the vehicle behavior and trust and improved pedestrians’ affective evaluations compared to a late yielding with dynamic eHMI. Overall, this real vehicle study highlighted the importance of implicit and explicit communication signals and their well-coordinated interplay for pedestrians’ future interactions with HAVs in a real-world setting.

Effects of noise on pedestrians in urban environments where road traffic is the main source of sound.

Research combining the measurement of objective variables with surveys of people's perception of noise on city streets is useful in terms of understanding the impact of urban noise on the population and improving the environment. Although previous investigations have analysed the factors that may influence the noise annoyance of citizens, it is usually considered as a global aspect. This paper presents research based on in situ surveys and objective variables (urban, meteorological and noise indicators) to evaluate some specific effects of noise on pedestrians in urban environments where road traffic is the main source of sound. The results show significant relationships of the effects of noise and perceptions of how noisy urban environments are with variables such as building height, road category and temperature, with correlation coefficients ranging from 0.37 to 0.64. Significant correlations between these subjective variables and the acoustic variables were also found, with explanations of variability that reached values of up to 50 %. A multivariate analysis revealed that both urban variables (especially the category of street) and environmental variables can be an alternative or a complement to models predicting the effects and perception of environmental noise based only on acoustic variables.

Reference-free video-to-real distance approximation-based urban social distancing analytics amid COVID-19 pandemic

IntroductionThe rapidly evolving COVID-19 pandemic has dramatically reshaped urban travel patterns. In this research, we explore the relationship between “social distancing,” a concept that has gained worldwide familiarity, and urban mobility during the pandemic. Understanding social distancing behavior will allow urban planners and engineers to better understand the new norm of urban mobility amid the pandemic, and what patterns might hold for individual mobility post-pandemic or in the event of a future pandemic. MethodsThere are still few efforts to obtain precise information on social distancing patterns of pedestrians in urban environments. This is largely attributed to numerous burdens in safely deploying any effective field data collection approaches during the crisis. This paper aims to fill that gap by developing a data-driven analytical framework that leverages existing public video data sources and advanced computer vision techniques to monitor the evolution of social distancing patterns in urban areas. Specifically, the proposed framework develops a deep-learning approach with a pre-trained convolutional neural network to mine the massive amount of public video data captured in urban areas. Real-time traffic camera data collected in New York City (NYC) was used as a case study to demonstrate the feasibility and validity of using the proposed approach to analyze pedestrian social distancing patterns. ResultsThe results show that microscopic pedestrian social distancing patterns can be quantified by using a generalized real-distance approximation method. The estimated distance between individuals can be compared to social distancing guidelines to evaluate policy compliance and effectiveness during a pandemic. Quantifying social distancing adherence will provide decision-makers with a better understanding of prevailing social contact challenges. It also provides insights into the development of response strategies and plans for phased reopening for similar future scenarios.

Pedestrian counting estimation based on fractal dimension

Counting the number of pedestrians in urban environments has become an area of interest over the past few years. Its applications include studies to control vehicular traffic lights, urban planning, market studies, and detection of abnormal behaviors. However, these tasks require the use of intelligent algorithms of high computational demand that need to be trained in the environment being studied. This article presents a novel method to estimate pedestrian flow in uncontrolled environments by using the fractal dimension measured through the box-counting algorithm, which does not require the use of image pre-processing and intelligent algorithms. Four scenarios were used to validate the method presented in this article, of which the last scene was a low-light surveillance video, showing experimental results with a mean relative error of 4.92% when counting pedestrians. After comparing the results with other techniques that depend on intelligent algorithms, we can confirm that this method achieves improved performance in the estimation of pedestrian traffic.

Landmark-enhanced route itineraries for navigation of blind pedestrians in urban environment

Visual impairment limits a person mainly in the ability to move freely and independently. Even with many navigation aids and tools currently on the market, almost one-third of the visually impaired do not travel alone without a guide. One of the most useful aids are the landmark-enhanced itineraries of the route created by orientation and mobility specialists. We describe the design of a system, which is based on a specific, efficiently collected geographical data, and which automatically generates human-like landmark-enhanced itineraries for navigation of visually impaired users. The studies we conducted (quantitative n = 16, qualitative n = 6) showed usability and efficiency of the system. We also provide a set of design recommendations to increase the usability of the system along with specific examples of usage of particular landmarks. Moreover, we report on 14-day long diary study (n = 3) where visually impaired participants used the system freely throughout the city center with a success rate of 84% without using GPS. Finally, based on an over-the-phone survey (n = 18), we provide a methodology to identify the essential area, where geographical data needs to be collected to enable visually impaired pedestrians to travel around the city center efficiently.

Mapping the black carbon exposure of pedestrians and cyclists in urban environments

Background The exposure to air pollution of pedestrians and cyclists in urban environments can differ significantly from street to street. Measurements from central monitoring stations are unable to describe this variability well. Mobile air quality measurements are needed, but do require the collection of huge data sets to offer a good spatiotemporal coverage. Aims Volunteers or city personnel working most of their time on the streets, can assist in collecting a huge set of exposure data at a feasible cost in targeted or opportunistic measurement campaigns. These data can be used to build a street-level exposure map with increasing level of detail and precision. To do so, user friendly measurement devices and software, privacy protection and automated data processing to update the map each time new data is collected, are required. Methods The Black Carbon Mapper (BCM) uses compact black carbon monitors, GPS and netbooks with software to transmit data and synchronize instruments. New measurements are processed by an automated data processing infrastructure. Processing steps include measurement enhancement and validation, background correction and spatiotemporal data aggregation. An interactive online air quality map is offered with the average BC exposure and summary statistics for multiple time intervals for each street section. Results A targeted case study has been conducted in the city of Ghent with volunteers biking on three routes. The BCM has also been used for a six month opportunistic measurement campaign with three teams of city guards in Antwerp. In this case processing of GPS data is more challenging. In both case studies, the differences in exposure between streets can clearly be distinguished. Conclusions The BCM has demonstrated to be a powerful platform to map the exposure of cyclists and pedestrians in urban environments. By involving city personnel and volunteers, large amounts of exposure measurements can be collected at a feasible cost when paying attention to usability, privacy and automated data processing.

Architectural evaluation of simulated pedestrian spatial behaviour

The purpose of the software system described in this article is to support architects and urban designers to better assess the impact of pedestrian behaviour on planned urban spaces and streetscapes. A new method for modelling and simulating pedestrians in urban environments is proposed. It analyses pedestrian behaviour with a combined focus on movement trajectories, walking speed and the angle between the movement vector and the gaze vector of individuals in large groups of simulated pedestrians. The system learns a statistical model characterizing normal behaviour, based on sample observations of regular pedestrian movements without the impact of significant visual attractions in the environment. Sudden changes of the pedestrians' behavioural characteristics, caused by the visual detection of ‘attractive’ objects, are considered as abnormal behaviour. The simulated environment can be automatically generated using scanned line drawings of two-dimensional street maps or public spaces. In the simulation model, a variety of scenarios can be defined and modified by altering different parameters. Using the example of Wheeler Place in Newcastle (Australia), our pilot experiments demonstrate how pedestrian behaviour characteristics can depend on selected abstract features in urban space. Among 10 different example scenarios, the analyser was able to identify the most frequently visited positions associated with attractive objects.

Factors influencing the detectability of pedestrians in urban environments

Factors influencing the detectability of pedestrians in urban environments

A new autonomous agent approach for the simulation of pedestrians in urban environments

In this paper, a new approach to autonomous agent navigation in real-time applications is presented. Effective driving simulators demand a realistic simulation of both the driven vehicle and the su...

A new autonomous agent approach for the simulation of pedestrians in urban environments

In this paper, a new approach to autonomous agent navigation in real-time applications is presented. Effective driving simulators demand a realistic simulation of both the driven vehicle and the surrounding environment. For example, a variety of pede

Autonomous pedestrians

We address the challenging problem of emulating the rich complexity of real pedestrians in urban environments. Our artificial life approach integrates motor, perceptual, behavioral, and cognitive components within a comprehensive model of pedestrians as individuals. Featuring innovations in these components, as well as in their combination, our model yields results of unprecedented fidelity and complexity for fully autonomous multihuman simulation in a large urban environment. We represent the environment using hierarchical data structures, which efficiently support the perceptual queries that influence the behavioral responses of the autonomous pedestrians and sustain their ability to plan their actions on local and global scales.

Presentation media for route instructions for pedestrians in urban environments

GPS-based route-guidance systems are used for various types of transport, such as airplanes, boats, cars and motorbikes. Such systems have also been developed for pedestrians, focusing initially on hikers in rough terrain. Recently, some devices were developed for pedestrians in urban environments. This study focuses on the ways in which route instructions can be presented to urban pedestrians with such GPS-receivers.

PEDFLOW: Development of an Autonomous Agent Model of Pedestrian Flow

The need for an autonomous agent approach for the modeling of pedestrians in urban environments is discussed, and PEDFLOW is placed in the context of existing models. PEDFLOW is a microscopic model of pedestrians’ movement; each pedestrian is represented as an agent capable of making its own decisions on the basis of a part of the observable scene local to that pedestrian. The model, implemented in Java, provides a framework in which agents are visualized as squares in a grid and movement is modeled as a change of grid position with a delay that characterizes the speed of the agent. A single rule set that is made specific to each agent by the incorporation of parameters characterizing types of pedestrians is used. The rules originate from computer-aided analysis of video footage and are transformed into a form that can be efficiently processed by the agent. By adding tools to extract measures of pedestrian flow, the PEDFLOW model will be made useful to urban planners to evaluate infrastructural changes intended to promote walking in the urban environment.

Walking pedestrian recognition

In previous years, many methods providing the ability to recognize rigid obstacles-sedans and trucks-have been developed. These methods provide the driver with relevant information. They are able to cope reliably with scenarios on motorways. Nevertheless, not much attention has been given to image processing approaches to increase the safety of pedestrians in urban environments. In the paper, a method for the detection, tracking, and final recognition of pedestrians crossing the moving observer's trajectory is suggested. A combination of data- and model-driven approaches is realized. The initial detection process is based on a fusion of texture analysis, model-based grouping of, most likely, the geometric features of pedestrians, and inverse-perspective mapping (binocular vision). Additionally, motion patterns of limb movements are analyzed to determine initial object-hypotheses. The tracking of the quasirigid part of the body is performed by different algorithms that have been successfully employed for the tracking of sedans, trucks, motorbikes, and pedestrians. The final classification is obtained by a temporal analysis of the walking process.