Simulation of collision avoidance behavior in crowd movement by data-driven approach

• URL: http://arxiv.org/abs/2605.31210v1
• Date: Fri, 29 May 2026 12:15:52 GMT
• Title: Simulation of collision avoidance behavior in crowd movement by data-driven approach
• Abstract summary: We establish a novel data-driven crowd simulation model that incorporates the pedestrian collision mechanism into the loss function to reduce collisions.<n>A new lateral-acceleration-based collision loss function and a Voronoi-based motion feature extraction approach are proposed.<n>Results show that the proposed lateral-acceleration-based collision loss significantly reduces opposite-direction pedestrian collision rates to levels comparable with controlled experiments.
• Score: 6.88605480904119
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Crowd movement simulation is essential for pedestrian safety management and facility layout optimization. Data-driven models enhance trajectory prediction accuracy under Euclidean metrics, yet they suffer from excessively high collision rates, especially in bidirectional and multidirectional flows. In this paper, we establish a novel data-driven crowd simulation model that incorporates the pedestrian collision mechanism into the loss function to reduce collisions. A new lateral-acceleration-based collision loss function and a Voronoi-based motion feature extraction approach are proposed. The model is based on a Generative Adversarial Network (GAN) architecture and is termed CPGAN (Collision-Penalized GAN). We evaluate CPGAN in bidirectional flow scenarios, which involve frequent collision avoidance behaviors. Results show that the proposed lateral-acceleration-based collision loss significantly reduces opposite-direction pedestrian collision rates to levels comparable with controlled experiments. CPGAN effectively simulates bidirectional flow, reproducing lane formation and N-t curves. The research outcomes can provide inspiration for integrating pedestrian dynamics mechanisms into loss functions in data-driven crowd simulation.

Related papers

• KinematicRL: A Sim-to-Real Reinforcement Learning Framework For Social Navigation With Kinodynamic Feasibility [27.73146983499444]
  Deep Reinforcement Learning (DRL) has shown promise for social navigation, yet its real-world deployment remains hindered by a persistent sim-to-real gap.<n>This work presents a unified framework that addresses these limitations to produce dynamically feasible navigation policies.<n> Experiments in real-world environments demonstrate that, when combined with the proposed tracking pipeline, KinematicRL can be deployed on a real differential drive robot with minimal modifications.
  arXiv  Detail & Related papers  (2026-06-10T13:07:03Z)
• Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing [73.88859384645264]
  Out-Of-Distribution (OOD) generalization to various racetracks utilizes Machine Learning (ML) to encode the mathematical relation between sensor data and vehicle actuation for end-to-end control.<n>This paper presents a DRL method that parameterizes nonlinear vehicle dynamics from the spectral distribution of depth measurements with a non-geometric, physics-informed reward, to infer vehicle time-optimal and overtaking racing controls with an Artificial Neural Network (ANN)<n>The policy outperforms human demonstrations by 12% in OOD tracks on proportionally scaled hardware, by maximizing the friction circle with tire dynamics that resemble an empirical Pacejka tire model.
  arXiv  Detail & Related papers  (2026-04-10T17:12:07Z)
• Surveillance Video-Based Traffic Accident Detection Using Transformer Architecture [2.621034368312571]
  Traffic accidents represent a leading cause of mortality globally, with incidence rates due to increasing population, urbanization and motorization.<n>Traditional computer methods for accident detection struggle with limited understanding and poor cross-domain generalization.<n>We propose an accident detection model based on a transformer architecture using pre-extracted spatial video features.
  arXiv  Detail & Related papers  (2025-12-12T07:57:36Z)
• ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving [64.42138266293202]
  ResAD is a Normalized Residual Trajectory Modeling framework.<n>It reframes the learning task to predict the residual deviation from an inertial reference.<n>On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy.
  arXiv  Detail & Related papers  (2025-10-09T17:59:36Z)
• Causal Composition Diffusion Model for Closed-loop Traffic Generation [31.52951126032351]
  We introduce the Causal Compositional Diffusion Model (CCDiff), a structure-guided diffusion framework to address these challenges.<n>We first formulate the learning of controllable and realistic closed-loop simulation as a constrained optimization problem.<n>Then, CCDiff maximizes controllability while adhering to realism by automatically identifying and injecting causal structures directly into the diffusion process.
  arXiv  Detail & Related papers  (2024-12-23T19:20:29Z)
• MSCT: Addressing Time-Varying Confounding with Marginal Structural Causal Transformer for Counterfactual Post-Crash Traffic Prediction [24.3907895281179]
  This paper presents a novel deep learning model designed for counterfactual post-crash traffic prediction. The proposed model is treatment-aware, with a specific focus on comprehending and predicting traffic speed under hypothetical crash intervention strategies. The model is validated using both synthetic and real-world data, demonstrating that MSCT outperforms state-of-the-art models in multi-step-ahead prediction performance.
  arXiv  Detail & Related papers  (2024-07-19T06:42:41Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• STGlow: A Flow-based Generative Framework with Dual Graphormer for Pedestrian Trajectory Prediction [22.553356096143734]
  We propose a novel generative flow based framework with dual graphormer for pedestrian trajectory prediction (STGlow) Our method can more precisely model the underlying data distribution by optimizing the exact log-likelihood of motion behaviors. Experimental results on several benchmarks demonstrate that our method achieves much better performance compared to previous state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-21T07:29:24Z)
• Safety-compliant Generative Adversarial Networks for Human Trajectory Forecasting [95.82600221180415]
  Human forecasting in crowds presents the challenges of modelling social interactions and outputting collision-free multimodal distribution. We introduce SGANv2, an improved safety-compliant SGAN architecture equipped with motion-temporal interaction modelling and a transformer-based discriminator design.
  arXiv  Detail & Related papers  (2022-09-25T15:18:56Z)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)
• Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance [110.63037190641414]
  We propose to learn congestion patterns explicitly and devise a novel "Sense--Learn--Reason--Predict" framework. By decomposing the learning phases into two stages, a "student" can learn contextual cues from a "teacher" while generating collision-free trajectories. In experiments, we demonstrate that the proposed model is able to generate collision-free trajectory predictions in a synthetic dataset.
  arXiv  Detail & Related papers  (2021-03-26T02:42:33Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.