Moving in a Crowd: Safe and Efficient Navigation among Heterogeneous Agents
Authors: Julio Godoy, Ioannis Karamouzas, Stephen J. Guy, Maria Gini
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation experiments performed in many scenarios demonstrate that an agent using our approach computes safer and more time-efficient paths as compared to those generated without our inference approach and a state-of-the-art local navigation framework. and Third, we experimentally validate our approach and show that it leads to safer and more efficient motions in a variety of scenarios as compared to a state-of-the-art collision avoidance framework [van den Berg et al., 2011], and to planning without estimation of the models and goals. |
| Researcher Affiliation | Academia | Julio Godoy, Ioannis Karamouzas, Stephen J. Guy, and Maria Gini Department of Computer Science and Engineering University of Minnesota 200 Union St SE, Minneapolis MN 55455 {godoy, ioannis, sjguy, gini}@cs.umn.edu |
| Pseudocode | Yes | A pseudocode of our approach can be seen in Algorithm 1. and Algorithm 2 shows the model selection procedure. and Algorithm 3 shows the pseudocode of the planning process using an action set, Actions, for the entire time horizon T. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, such as a specific repository link or an explicit code release statement. |
| Open Datasets | No | The paper does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes simulation scenarios but does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | Yes | All simulations ran in real-time on a Core i7 CPU with 8 GB of memory. |
| Software Dependencies | No | The paper states, "We implemented our inference approach and our two planning methods, Vel Plan and Social Plan, in C++," but does not provide specific version numbers for C++ or any other ancillary software dependencies or libraries used. |
| Experiment Setup | Yes | We updated the position of every t = 0.1s, which was also the planning time limit. We empirically set the values of the time horizon T to 50 timesteps, γ to 0.1%, and used a σ value of 10 4 (Eq. 1), as they provided the best performance compared to larger values. |