Spatio-temporal Keyframe Control of Traffic Simulation using Coarse-to-Fine Optimization

We present a novel traffic trajectory editing method which uses spatio-temporal keyframes to control vehicles during the simulation to generate desired traffic trajectories. By taking self-motivation, path following and collision avoidance into account, the proposed force-based traffic simulation framework updates vehicle's motions in both the Frenet coordinates and the Cartesian coordinates. With the way-points from users, lane-level navigation can be generated by reference path planning. With a given keyframe, the coarse-to-fine optimization is proposed to efficiently generate the plausible trajectory which can satisfy the spatio-temporal constraints. At first, a directed state-time graph constructed along the reference path is used to search for a coarse-grained trajectory by mapping the keyframe as the goal. Then, using the information extracted from the coarse trajectory as initialization, adjoint-based optimization is applied to generate a finer trajectory with smooth motions based on our force-based simulation. We validate our method with extensive experiments

Visual modeling and simulation of multiscale phenomena

Many large-scale systems seen in real life, such as human crowds, fluids, and granular materials, exhibit complicated motion at many different scales, from a characteristic global behavior to important small-scale detail. Such multiscale systems are computationally expensive for traditional simulation techniques to capture over the full range of scales. In this dissertation, I present novel techniques for scalable and efficient simulation of these large, complex phenomena for visual computing applications. These techniques are based on a new approach of representing a complex system by coupling together separate models for its large-scale and fine-scale dynamics. In fluid simulation, it remains a challenge to efficiently simulate fine local detail such as foam, ripples, and turbulence without compromising the accuracy of the large-scale flow. I present two techniques for this problem that combine physically-based numerical simulation for the global flow with efficient local models for detail. For surface features, I propose the use of texture synthesis, guided by the physical characteristics of the macroscopic flow. For turbulence in the fluid motion itself, I present a technique that tracks the transfer of energy from the mean flow to the turbulent fluctuations and synthesizes these fluctuations procedurally, allowing extremely efficient visual simulation of turbulent fluids. Another large class of problems which are not easily handled by traditional approaches is the simulation of very large aggregates of discrete entities, such as dense pedestrian crowds and granular materials. I present a technique for crowd simulation that couples a discrete per-agent model of individual navigation with a novel continuum formulation for the collective motion of pedestrians. This approach allows simulation of dense crowds of a hundred thousand agents at near-real-time rates on desktop computers. I also present a technique for simulating granular materials, which generalizes this model and introduces a novel computational scheme for friction. This method efficiently reproduces a wide range of granular behavior and allows two-way interaction with simulated solid bodies. In all of these cases, the proposed techniques are typically an order of magnitude faster than comparable existing methods. Through these applications to a diverse set of challenging simulation problems, I demonstrate the benefits of the proposed approach, showing that it is a powerful and versatile technique for the simulation of a broad range of large and complex systems

TraInterSim: Adaptive and Planning-Aware Hybrid-Driven Traffic Intersection Simulation

Traffic intersections are important scenes that can be seen almost everywhere in the traffic system. Currently, most simulation methods perform well at highways and urban traffic networks. In intersection scenarios, the challenge lies in the lack of clearly defined lanes, where agents with various motion plannings converge in the central area from different directions. Traditional model-based methods are difficult to drive agents to move realistically at intersections without enough predefined lanes, while data-driven methods often require a large amount of high-quality input data. Simultaneously, tedious parameter tuning is inevitable involved to obtain the desired simulation results. In this paper, we present a novel adaptive and planning-aware hybrid-driven method (TraInterSim) to simulate traffic intersection scenarios. Our hybrid-driven method combines an optimization-based data-driven scheme with a velocity continuity model. It guides the agent's movements using real-world data and can generate those behaviors not present in the input data. Our optimization method fully considers velocity continuity, desired speed, direction guidance, and planning-aware collision avoidance. Agents can perceive others' motion planning and relative distance to avoid possible collisions. To preserve the individual flexibility of different agents, the parameters in our method are automatically adjusted during the simulation. TraInterSim can generate realistic behaviors of heterogeneous agents in different traffic intersection scenarios in interactive rates. Through extensive experiments as well as user studies, we validate the effectiveness and rationality of the proposed simulation method.Comment: 13 pages, 12 figure

Authoring virtual crowds: a survey

Recent advancements in crowd simulation unravel a wide range of functionalities for virtual agents, delivering highly-realistic,natural virtual crowds. Such systems are of particular importance to a variety of applications in fields such as: entertainment(e.g., movies, computer games); architectural and urban planning; and simulations for sports and training. However, providingtheir capabilities to untrained users necessitates the development of authoring frameworks. Authoring virtual crowds is acomplex and multi-level task, varying from assuming control and assisting users to realise their creative intents, to deliveringintuitive and easy to use interfaces, facilitating such control. In this paper, we present a categorisation of the authorable crowdsimulation components, ranging from high-level behaviours and path-planning to local movements, as well as animation andvisualisation. We provide a review of the most relevant methods in each area, emphasising the amount and nature of influencethat the users have over the final result. Moreover, we discuss the currently available authoring tools (e.g., graphical userinterfaces, drag-and-drop), identifying the trends of early and recent work. Finally, we suggest promising directions for futureresearch that mainly stem from the rise of learning-based methods, and the need for a unified authoring framework.This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska Curie grant agreement No 860768 (CLIPE project). This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital PolicyPeer ReviewedPostprint (author's final draft

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Smart Cities: Inverse Design of 3D Urban Procedural Models with Traffic and Weather Simulation

Urbanization, the demographic transition from rural to urban, has changed how we envision and share the world. From just one-fourth of the population living in cities one hundred years ago, now more than half of the population does, and this ratio is expected to grow in the near future. Creating more sustainable, accessible, safe, and enjoyable cities has become an imperative

FASTSWARM: A Data-driven FrAmework for Real-time Flying InSecT SWARM Simulation

Insect swarms are common phenomena in nature and therefore have been actively pursued in computer animation. Realistic insect swarm simulation is difficult due to two challenges: high‐fidelity behaviors and large scales, which make the simulation practice subject to laborious manual work and excessive trial‐and‐error processes. To address both challenges, we present a novel data‐driven framework, FASTSWARM, to model complex behaviors of flying insects based on real‐world data and simulate plausible animations of flying insect swarms. FASTSWARM has a linear time complexity and achieves real‐time performance for large swarms. The high‐fidelity behavior model of FASTSWARM explicitly takes into consideration the most common behaviors of flying insects, including the interactions among insects such as repulsion and attraction, self‐propelled behaviors such as target following and obstacle avoidance, and other characteristics such as random movements. To achieve scalability, an energy minimization problem is formed with different behaviors modeled as energy terms, where the minimizer is the desired behavior. The minimizer is computed from the real‐world data, which ensures the plausibility of the simulation results. Extensive simulation results and evaluations show that FASTSWARM is versatile in simulating various swarm behaviors, high fidelity measured by various metrics, easily controllable in inducing user controls and highly scalable