Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

Abstract—Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, inter- active motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behaviour, from prediction of individual pedestrians’ likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behaviour, high-level psychological and social modelling of pedestrian behaviour still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behaviour, but much work is still needed to translate them into quantitative algorithms for practical AV control

Spatial Behavior in Groups: an Agent-Based Approach

We present an agent-based model with the aim of studying how macro-level dynamics of spatial distances among interacting individuals in a closed space emerge from micro-level dyadic and local interactions. Our agents moved on a lattice (referred to as a room) using a model implemented in a computer program called P-Space in order to minimize their dissatisfaction, defined as a function of the discrepancy between the real distance and the ideal, or desired, distance between agents. Ideal distances evolved in accordance with the agent\'s personal and social space, which changed throughout the dynamics of the interactions among the agents. In the first set of simulations we studied the effects of the parameters of the function that generated ideal distances, and in a second set we explored how group macro-level behavior depended on model parameters and other variables. We learned that certain parameter values yielded consistent patterns in the agents\' personal and social spaces, which in turn led to avoidance and approaching behaviors in the agents. We also found that the spatial behavior of the group of agents as a whole was influenced by the values of the model parameters, as well as by other variables such as the number of agents. Our work demonstrates that the bottom-up approach is a useful way of explaining macro-level spatial behavior. The proposed model is also shown to be a powerful tool for simulating the spatial behavior of groups of interacting individuals.Spatial Behavior, Proxemics, Agent-Based Modeling, Minimum Dissatisfaction Model, Small Groups, Social Interaction

Simulation of space acquisition process of pedestrians using Proxemic Floor Field Model

We propose the Proxemic Floor Field Model as an extension of the Floor Field Model, which is one of the successful models describing the pedestrian dynamics. Proxemic Floor Field is the Floor Field which corresponds to the effect of repulsion force between others. By introducing the Proxemic Floor Field and threshold, we investigate the process that pedestrians enter a certain area. The results of simulations are evaluated by simple approximate analyses and newly introduced indices. The difference in pedestrian behavior due to the disposition of the entrance is also confirmed, namely, the entrance in the corner of the area leads to the long entrance time because of the obstruction by pedestrians settling on the boundary cells

Pedestrian Evacuation: Vulnerable Group Member Influence on the Group Leaders’ Decision-Making and the Impact on Evacuation Time

As pedestrian evacuations of buildings, outdoor venues, and special events occur, dynamic interactions between pedestrians and vehicles during egress are possible. To model pedestrian and vehicle evacuations, simulation models have evolved to incorporate more realistic crowd characteristics and behaviors to provide improved results. Past studies using modeling and simulation, specifically agent-based modeling, have explored pedestrian behaviors such as decision-making, navigation within a virtual environment, group formations, intra-group interactions, inter-group dynamics, crowd behaviors such as queuing and herding, and pedestrianvehicle interactions. These studies have led to relevant insights helpful to improving the accuracy of evacuation times for normal and emergency egress for preparedness and management purposes. As evacuating crowds are composed of individual pedestrians and social or familial groups, this project contributes to the study of pedestrian evacuation by exploring the incorporation of a subgroup not often considered in this area. Vulnerable individuals, such as the physically disabled, elderly, and children, can change the decision-making dynamic of a group leader while evacuating to safety. Current agent-based simulation models explore the intra- and inter- action and the effects on evacuation times; however, the vulnerable group members\u27 influence is neglected. This project presents enhancements to pedestrian evacuations with vehicle interaction using an agent-based simulation model that includes the presence of vulnerable group members and their impact on decision-making and evacuation times. This project explores how changing behaviors due to the presence of vulnerable group members can collectively cause delays and increase evacuation times. Utilizing verification and validation methods, the credibility and reliability of the simulation model and its results are increased. The results show that the group leaders\u27 decision-making differs when leading a vulnerable group versus a non-vulnerable group. Also, evacuation times increase with increased percentages of vulnerable groups within an evacuating crowd. A simulation tool can be utilized by end-users to explore specific evacuation scenarios in preparation for upcoming events and glean insight into how evacuation times may vary with differing crowd population sizes and compositions. Including vulnerable pedestrians in simulation models for evacuations would improve output accuracy and ultimately improve event training and preparation for future evacuations

A Memory-Based Evacuation Navigation Model in Complex High-Rise Buildings

In contemporary society, safety issues are the main focus in the field of pedestrian and evacuation dynamics. As for complex high-rise buildings, the navigation strategies of evacuees still need to be further studied. Previous types of research has contributed to the construction of evacuation navigation model in complex high-rise buildings, where pedestrians are regarded as having an omniscient view in most of these models. In reality, evacuees’ perception is always limited, especially when the scenario is complex. In this contribution, pedestrians’ perception procedure is considered by computing the visible space so that the occlusion of the visual field can be estimated. In addition, human memory progress is modeled. Not all parts of environmental information would be remembered. Driven by evacuees’ memory data, a proposed dynamical shortest path algorithm will be periodically implemented or suddenly triggered by incidents during the simulation. For the pedestrians who have no enough knowledge about the evacuation scenario, a communication system is utilized so that information can be obtained from well-knowledged pedestrian, and an autonomous way-finding system will be executed when useful information cannot be acquired through near evacuees. For the microscopic perspective, human following and avoidance behavior is modeled. Simulations in two different types of scenarios are conducted. The knowledge level of simulated agents is gradually evolved by in-room free exploration. Results in different conditions show that the proposed memory-based model can reproduce pedestrians’ observation, turn-back, communication, and searching behavior. The intense conflict caused by the bidirectional crowd is observed and analyzed. In addition, the effects of knowledge level are investigated. The presented model in this contribution can be promising and useful in safety engineering

Identification of social relation within pedestrian dyads

This study focuses on social pedestrian groups in public spaces and makes an effort to identify the type of social relation between the group members. As a first step for this identification problem, we focus on dyads (i.e. 2 people groups). Moreover, as a mutually exclusive categorization of social relations, we consider the domain-based approach of Bugental, which precisely corresponds to social relations of colleagues, couples, friends and families, and identify each dyad with one of those relations. For this purpose, we use anonymized trajectory data and derive a set of observables thereof, namely, inter-personal distance, group velocity, velocity difference and height difference. Subsequently, we use the probability density functions (pdf) of these observables as a tool to understand the nature of the relation between pedestrians. To that end, we propose different ways of using the pdfs. Namely, we introduce a probabilistic Bayesian approach and contrast it to a functional metric one and evaluate the performance of both methods with appropriate assessment measures. This study stands out as the first attempt to automatically recognize social relation between pedestrian groups. Additionally, in doing that it uses completely anonymous data and proves that social relation is still possible to recognize with a good accuracy without invading privacy. In particular, our findings indicate that significant recognition rates can be attained for certain categories and with certain methods. Specifically, we show that a very good recognition rate is achieved in distinguishing colleagues from leisure-oriented dyads (families, couples and friends), whereas the distinction between the leisure-oriented dyads results to be inherently harder, but still possible at reasonable rates, in particular if families are restricted to parent-child groups. In general, we establish that the Bayesian method outperforms the functional metric one due, probably, to the difficulty of the latter to learn observable pdfs from individual trajectories

GPGPU Computing for Microscopic Simulations of Crowd Dynamics

We compare GPGPU implementations of two popular models of crowd dynamics. Specifically, we consider a continuous social force model, based on differential equations (molecular dynamics) and a discrete social distances model based on non-homogeneous cellular automata. For comparative purposes both models have been implemented in two versions: on the one hand using GPGPU technology, on the other hand using CPU only. We compare some significant characteristics of each model, for example: performance, memory consumption and issues of visualization. We also propose and test some possibilities for tuning the proposed algorithms for efficient GPU computations

Virtual reality crowd simulation: effects of agent density on user experience and behaviour

Agent-based crowd simulations are used for modelling building and space usage, allowing designers to explore hypothetical real-world scenarios, including extraordinary events such as evacuations. Existing work which engages virtual reality (VR) as a platform for crowd simulations has been primarily focussed on the validation of simulation models through observation; the use of interactions such as gaze to enhance a sense of immersion; or studies of proxemics. In this work, we extend previous studies of proxemics and examine the effects of varying crowd density on user experience and behaviour. We have created a simulation in which participants walk freely and perform a routine manual task, whilst interacting with agents controlled by a typical social force simulation model. We examine and report the effects of crowd density on both affective state and behaviour. Our results show a significant increase in negative affect with density, measured using a self-report scale. We further show significant differences in some aspects of user behaviours, using video analysis, and discuss how our results relate to VR simulation design for mixed human–agent scenarios