On the Performance of Pedestrian Content Distribution

Mobile communication devices may be used for spreading multimedia data without support of an infrastructure. Such a scheme, where the data is carried by people walking around and relayed from device to device by means of short range radio, could potentially form a public content distribution system that spans vast urban areas. There are basically only three system parameters that can be determined in the design: the transmission range of the nodes, the setup time when nodes make a contact, and their storage capacity. The transport mechanism is the flow of people and it can be studied but not engineered. The question addressed in this paper is how well pedestrian content distribution may work. We answer this question by modeling the mobility of people moving around in a city, constrained by a given topology. The model is supplemented by simulation of similar or related scenarios for validation and extension. Our conclusion is that contents spread well with pedestrian speeds already at low arrival rates into a studied region. Our contributions are both the results on the feasibility of pedestrian content distribution and the queuing analytic model that captures the flow of people

Benchmark footbridge for vibration serviceability assessment under vertical component of pedestrian load

Vibration serviceability criteria are governing the design and determining the cost of modern, slender footbridges. Efficient and reliable evaluation of dynamic performance of these structures usually requires a detailed insight into the structural behaviour under human induced dynamic loading. Design procedures are becoming ever more sophisticated and versatile and for their successful use a thorough verification on a range of structures is required. The verification is currently hampered by a lack of experimental data that are presented in the form directly usable in the verification process

What Makes a Place? Building Bespoke Place Dependent Object Detectors for Robotics

This paper is about enabling robots to improve their perceptual performance through repeated use in their operating environment, creating local expert detectors fitted to the places through which a robot moves. We leverage the concept of 'experiences' in visual perception for robotics, accounting for bias in the data a robot sees by fitting object detector models to a particular place. The key question we seek to answer in this paper is simply: how do we define a place? We build bespoke pedestrian detector models for autonomous driving, highlighting the necessary trade off between generalisation and model capacity as we vary the extent of the place we fit to. We demonstrate a sizeable performance gain over a current state-of-the-art detector when using computationally lightweight bespoke place-fitted detector models.Comment: IROS 201

GPU accelerated Nature Inspired Methods for Modelling Large Scale Bi-Directional Pedestrian Movement

Pedestrian movement, although ubiquitous and well-studied, is still not that well understood due to the complicating nature of the embedded social dynamics. Interest among researchers in simulating pedestrian movement and interactions has grown significantly in part due to increased computational and visualization capabilities afforded by high power computing. Different approaches have been adopted to simulate pedestrian movement under various circumstances and interactions. In the present work, bi-directional crowd movement is simulated where an equal numbers of individuals try to reach the opposite sides of an environment. Two movement methods are considered. First a Least Effort Model (LEM) is investigated where agents try to take an optimal path with as minimal changes from their intended path as possible. Following this, a modified form of Ant Colony Optimization (ACO) is proposed, where individuals are guided by a goal of reaching the other side in a least effort mode as well as a pheromone trail left by predecessors. The basic idea is to increase agent interaction, thereby more closely reflecting a real world scenario. The methodology utilizes Graphics Processing Units (GPUs) for general purpose computing using the CUDA platform. Because of the inherent parallel properties associated with pedestrian movement such as proximate interactions of individuals on a 2D grid, GPUs are well suited. The main feature of the implementation undertaken here is that the parallelism is data driven. The data driven implementation leads to a speedup up to 18x compared to its sequential counterpart running on a single threaded CPU. The numbers of pedestrians considered in the model ranged from 2K to 100K representing numbers typical of mass gathering events. A detailed discussion addresses implementation challenges faced and averted