The Role of Data-Driven Priors in Multi-Agent Crowd Trajectory Estimation
Authors: Gang Qiao, Sejong Yoon, Mubbasir Kapadia, Vladimir Pavlovic
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Various combinations of priors and optimization algorithms are evaluated in comprehensive simulated experiments. Our experimental results reveal important insights, including the significance of the global flow prior and the lesser-than-expected influence of datadriven collision priors. |
| Researcher Affiliation | Academia | 1Rutgers University, 2The College of New Jersey {gq19, mk1353, vladimir}@cs.rutgers.edu, yoons@tcnj.edu |
| Pseudocode | Yes | Algorithm 1: Proposed Optimization Framework |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | The ground truth trajectories are obtained by running Steer Suite (Singh et al. 2009) library with social force AI (Helbing and Moln ar 1995), and are split into a training and testing sets. |
| Dataset Splits | No | The paper mentions splitting data into "training and testing sets" but does not specify a separate validation set or details about its split (e.g., percentages or methods like cross-validation). |
| Hardware Specification | No | The paper discusses computational time but does not provide any specific hardware details such as CPU, GPU models, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions tools and models like "Steer Suite (Singh et al. 2009)" and "social force AI (Helbing and Moln ar 1995)" as data sources, and refers to algorithms like "message-passing ADMM (MPA)", "interior point method (IPM)", and "unscented Kalman smoother (UKS)" for optimization. However, it does not specify any software names with version numbers that would be necessary for reproduction. |
| Experiment Setup | Yes | We set the parameter as: ut = 1 if the point is actually observed, otherwise ut = 0; Ckn = 1 by assuming homogeneous crowd, Cmv = 2.6m/s, Δt = 1.5s and λ = 1/(σ2 NNΔt2) 108.0. |