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Microscopic Crowd Simulation : Evaluation and Development of Algorithms David Wolinski

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Microscopic Crowd Simulation : Evaluation and Development of Algorithms David Wolinski Microscopic crowd simulation : evaluation and development of algorithms David Wolinski To cite this version: David Wolinski. Microscopic crowd simulation : evaluation and development of algorithms. Data Structures and Algorithms [cs.DS]. Université Rennes 1, 2016. English. NNT : 2016REN1S036. tel-01420105 HAL Id: tel-01420105 https://tel.archives-ouvertes.fr/tel-01420105 Submitted on 20 Dec 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. ANNEE´ 2016 THESE` / UNIVERSITE´ DE RENNES 1 sous le sceau de l’Universite´ Bretagne Loire pour le grade de DOCTEUR DE L’UNIVERSITE´ DE RENNES 1 Mention : Informatique Ecole doctorale Matisse pr´esent´ee par David Wolinski pr´epar´ee a` l’unit´e de recherche UMR 6074 IRISA et au centre INRIA - Rennes Bretagne Atlantique ISTIC Th`ese soutenue `a Rennes le 22 janvier 2016 devant le jury compos´e de : Microscopic Crowd Yiorgos Chrysanthou Professeur, University of Cyprus / rapporteur Pierre Degond Simulation: Evaluation and Professeur, Imperial College London / rapporteur Ming Lin Development of Algorithms Professeur, University of North Carolina / examinateur Armin Seyfried Professeur, Julich¨ Supercomputing Centre / examinateur Kadi Bouatouch Professeur, Universit´e de Rennes 1 / examinateur Julien Pettre´ Charg´e de recherche, Universit´e de Rennes 1 / directeur de th`ese Contents List of Figures vii List of Tables ix 1 Introduction 1 1 Problem .................................... 2 2 Approach .................................... 3 3 Contributions ................................. 4 2 Background 7 1 Introduction .................................. 7 2 Autonomous Agent-based Algorithms ..................... 8 2.1 First-Order Algorithms ......................... 8 2.2 Second-Order Algorithms ....................... 9 2.2.1 Repulsive-Forces from Future Collisions ........... 9 2.2.2 Collision-Free Velocities ................... 10 2.2.3 Other Predictive Approaches ................ 11 2.3 Summary ................................ 12 3 Centralized Algorithms ............................. 13 3.1 Cellular Automata ........................... 13 3.2 Data-Driven Algorithms ........................ 14 3.3 Tiles and Patches ........................... 16 3.4 Macroscopic Algorithms ........................ 18 4 Conclusion ................................... 20 3 Craal: Parameter Estimation and Comparative Evaluation of Crowd Simulations 21 1 Introduction .................................. 22 2 Related Work .................................. 23 2.1 Evaluation ............................... 23 2.2 Parameters ............................... 24 2.3 Discussion ............................... 26 3 Optimization Framework ............................ 26 3.1 Overview of Approach ......................... 26 3.2 Optimization Metrics ......................... 29 3.2.1 Microscopic Data Metrics .................. 29 3.2.2 Macroscopic Data Metrics .................. 29 iii CONTENTS 3.3 Optimization Techniques ....................... 30 3.3.1 Greedy approach (G) ..................... 30 3.3.2 Simulated annealing (SA) .................. 30 3.3.3 Genetic algorithm (GA) ................... 30 3.3.4 Covariance Matrix Adapation (CMA) ............ 30 4 Results ..................................... 31 4.1 Data Categories ............................ 31 4.1.1 Microscopic data ....................... 31 4.1.2 Macroscopic data ...................... 33 4.1.3 Sketch-like data ....................... 35 4.2 Benchmarks .............................. 35 5 Analysis and Conclusions ............................ 39 4 WarpDriver: Context-Aware Probabilistic Motion Prediction for Crowd Simulation 43 1 Introduction .................................. 44 2 Summary of Related Work ........................... 45 3 Overview .................................... 47 4 Notations and Setup .............................. 48 5 Perception: collision probability Fields ..................... 49 5.1 The Intrinsic Field ........................... 50 5.2 Warp Operators ............................ 50 5.2.1 Agent-Related Operators .................. 50 5.2.2 Context-Related Operators ................. 51 5.2.3 Composition of Warp Operators ............... 52 5.3 Combining collision probability Fields ................. 53 6 Solving the Collision-Avoidance Problem ................... 53 7 Results ..................................... 54 7.1 Large and Dense Cases ......................... 54 7.1.1 Test case 1: Big Groups ................... 55 7.1.2 Test case 2: Crossing .................... 55 7.1.3 Analysis ........................... 55 7.2 Non-Linear Scenarios ......................... 57 7.2.1 Test case 3: Curved Flows .................. 57 7.2.2 Test case 4: Curved Obstacle ................ 58 7.2.3 Analysis ........................... 59 7.3 History-based Anticipation ....................... 59 7.3.1 Test case 5: Zig-Zags .................... 60 7.3.2 Test case 6: Danger Corridor ................ 60 7.3.3 Analysis ........................... 61 7.4 Highly-constrained Case ........................ 63 7.4.1 Test case 7: Plane ...................... 63 7.4.2 Analysis ........................... 64 7.5 Benchmarks .............................. 64 8 Discussion and Limitations ........................... 65 9 Conclusion ................................... 66 iv CONTENTS 5 Applications to Evaluation and Parameter Estimation 69 1 Application to Insect Simulation ........................ 71 1.1 Introduction .............................. 71 1.1.1 Working Arrangements ................... 72 1.2 Related Work .............................. 72 1.2.1 Graphics Point of View ................... 73 1.2.2 Biology Point of View .................... 73 1.2.3 Discussion .......................... 74 1.3 Approach Overview .......................... 75 1.3.1 Pre-Processing Stage: Data ................. 75 1.3.2 Runtime Stage: Three Levels of Simulation ......... 76 1.4 Results ................................. 79 1.4.1 Simulation .......................... 79 1.4.2 Evaluation .......................... 81 1.5 Conclusion ............................... 83 2 Application to Pedestrian Tracking ...................... 85 2.1 Introduction .............................. 85 2.1.1 Working Arrangements ................... 86 2.2 Related Work .............................. 86 2.2.1 General Object Tracking ................... 86 2.2.2 Crowd Motion Priors ..................... 89 2.2.3 Summary ........................... 89 2.3 Mixture Motion Model ......................... 90 2.3.1 Overview and Notations ................... 90 2.3.2 Particle Filter for Tracking .................. 91 2.3.3 Parameterized Motion Model ................ 92 2.3.4 Mixture of Motion Models .................. 92 2.4 Implementation and Results ...................... 95 2.4.1 Motion Models ........................ 95 2.4.2 Evaluation .......................... 97 2.5 Limitations, Conclusions, and Future Work .............. 99 6 Conclusion and Future Work 101 1 Contributions ................................. 101 2 Future Work .................................. 103 3 Summary .................................... 104 7 Résumé en Français 105 1 Problème .................................... 106 2 Approche .................................... 107 3 Contributions ................................. 108 Appendices i A Craal: Parameter Estimation and Comparative Evaluation of Crowd Simulations i 1 Metrics ..................................... i v CONTENTS 1.1 Microscopic Data Metrics ....................... i 1.2 Macroscopic Data Metrics ....................... ii 2 Optimization Techniques ............................ ii 2.1 Greedy algorithm ............................ ii 2.2 Simulated annealing .......................... ii 2.2.1 Genetic algorithm ...................... iii 2.2.2 Covariance Matrix Adaptation ................ iv 3 Optimization Comparison ........................... iv 4 Initial Parameters for Optimization ...................... viii B WarpDriver: Context-Aware Probabilistic Motion Prediction for Crowd Simulation xiii 1 Warp Operators ................................ xiii 1.1 Agent-Related Operators ....................... xiii 1.2 Context-Related Operators ...................... xiv Bibliography xxviii vi List of Figures 2.1 Boids flocking rules. .............................. 9 2.2 Second-order and first-order algorithms. .................... 10 2.3 Steering away from future collisions. ...................... 10 2.4 Quantizing orientation and speed changes for collision avoidance. ....... 11 2.5 Algorithms reasoning in velocity-space. .................... 11 2.6 Other predictive algorithms. .......................... 12 2.7 Matrix of preferred transitions for an agent. .................. 13 2.8 Conflict between two agents. .......................... 13 2.9 Data-driven algorithms’ usual workflow. .................... 14 2.10 Data-driven data-base example. ........................ 15 2.11 Heterogeneous data-driven crowd. ....................... 16 2.12 Tile-based algorithm examples. ........................ 17 2.13
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