SteerBench: a benchmark suite for evaluating steering behaviors

Singh, Shawn; Kapadia, Mubbasir; Faloutsos, Petros; Reinman, Glenn

doi:10.1002/cav.277

Cited by 64 publications

(53 citation statements)

References 26 publications

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“…This last metric has two steps; a probability distribution is estimated for states which best represent the collected data and, then, the simulation system makes predictions from these states. Finally, the work by Singh et al [2009] proposes a benchmark for evaluating steering behaviors. The authors select a set of metrics of evaluation such as collision, distance, turning based metrics and a set of test cases (e.g.…”

Section: Validation Techniques Based Directly On Acquired Datamentioning

confidence: 99%

Modeling, Evaluation, and Scale on Artificial Pedestrians

et al. 2017

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Modeling pedestrian dynamics and their implementation in a computer are challenging and important issues in the knowledge areas of transportation and computer simulation. The aim of this paper is to provide a bibliographic outlook so that the reader could have a quick access to the most relevant works related with this problem. We have used three main axes to organise the paper contents: pedestrian models, validation techniques and multiscale approaches. The backbone of the paper is the classification of existing pedestrian models; we have organised the works in the literature under five categories, according to the techniques used for implementing the operational level in each pedestrian model. Then, the main existing validation methods, oriented to evaluate the behavioural quality of the simulation systems, are reviewed. Furthermore, we review the key issues that arise when facing multiscale pedestrian modeling, where we firstly focus on the behavioural scale (combinations of micro and macro pedestrian models) and secondly, on the scale size (from individuals to crowds). The paper begins introducing the main characteristics of walking dynamics and its analysis tools and concludes with a discussion about the contributions that different knowledge fields can do in a near future to this exciting area.

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Section: Validation Techniques Based Directly On Acquired Datamentioning

confidence: 99%

Modeling, Evaluation, and Scale on Artificial Pedestrians

et al. 2017

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“…A number of approaches have been proposed in the crowd animation area [Shao and Terzopoulos 2005;Singh et al 2009;Berg et al 2008]. The examples given in Figure 15 only include the simple behavior of stopping and computing a new random path every time an agent collides with another agent during path following.…”

Section: Resultsmentioning

confidence: 99%

Dynamic and Robust Local Clearance Triangulations

Kallmann

2014

ACM Trans. Graph.

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The Local Clearance Triangulation (LCT) of polygonal obstacles is a cell decomposition designed for the efficient computation of locally shortest paths with clearance. This paper presents a revised definition of LCTs, new theoretical results and optimizations, and new algorithms introducing dynamic updates and robustness. Given an input obstacle set with n vertices, a theoretical analysis is proposed showing that LCTs generate a triangular decomposition of O(n) cells, guaranteeing that discrete search algorithms can compute paths in optimal times. In addition, several examples are presented indicating that the number of triangles is low in practice, close to 2n, and a new technique is described for reducing the number of triangles when the maximum query clearance is known in advance. Algorithms for repairing the local clearance property dynamically are also introduced, leading to efficient LCT updates for addressing dynamic changes in the obstacle set. Dynamic updates automatically handle intersecting and overlapping segments with guaranteed robustness, using techniques that combine one exact geometric predicate with adjustment of illegal floating point coordinates. The presented results demonstrate that LCTs are efficient and highly flexible for representing dynamic polygonal environments with clearance information.

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“…For example, several metrics have been developed in Geographic Information Science (GIS) for measuring and comparing the shape, scale, and complexity of movement sequences and paths on streetscapes [165,254], and other metrics of relevance to the efficiency of computation and rendering in crowd simulations have been produced in computer graphics research [268]. Many of these metrics are now massively efficient over the huge volumes of data that streetscape simulations often produce [256,[269][270][271].…”

Section: Acquiring and Generating Peoplescape Datamentioning

confidence: 99%

“…[402]. Model steering behaviors can be brokered by individual entities or negotiated among groups [181,229,268,354,363,381,402,403].…”

Section: Steering To Avoid and Avail Of Interactionmentioning

confidence: 99%

Computational Streetscapes

Torrens

2016

Computation

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Abstract:Streetscapes have presented a long-standing interest in many fields. Recently, there has been a resurgence of attention on streetscape issues, catalyzed in large part by computing. Because of computing, there is more understanding, vistas, data, and analysis of and on streetscape phenomena than ever before. This diversity of lenses trained on streetscapes permits us to address long-standing questions, such as how people use information while mobile, how interactions with people and things occur on streets, how we might safeguard crowds, how we can design services to assist pedestrians, and how we could better support special populations as they traverse cities. Amid each of these avenues of inquiry, computing is facilitating new ways of posing these questions, particularly by expanding the scope of what-if exploration that is possible. With assistance from computing, consideration of streetscapes now reaches across scales, from the neurological interactions that form among place cells in the brain up to informatics that afford real-time views of activity over whole urban spaces. For some streetscape phenomena, computing allows us to build realistic but synthetic facsimiles in computation, which can function as artificial laboratories for testing ideas. In this paper, I review the domain science for studying streetscapes from vantages in physics, urban studies, animation and the visual arts, psychology, biology, and behavioral geography. I also review the computational developments shaping streetscape science, with particular emphasis on modeling and simulation as informed by data acquisition and generation, data models, path-planning heuristics, artificial intelligence for navigation and way-finding, timing, synthetic vision, steering routines, kinematics, and geometrical treatment of collision detection and avoidance. I also discuss the implications that the advances in computing streetscapes might have on emerging developments in cyber-physical systems and new developments in urban computing and mobile computing.

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SteerBench: a benchmark suite for evaluating steering behaviors

Cited by 64 publications

References 26 publications

Modeling, Evaluation, and Scale on Artificial Pedestrians

Modeling, Evaluation, and Scale on Artificial Pedestrians

Dynamic and Robust Local Clearance Triangulations

Computational Streetscapes

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