Modeling Cooperative Navigation in Dense Human Crowds

Vemula, Anirudh; Muelling, Katharina; Oh, Jean

doi:10.48550/arxiv.1705.06201

Cited by 1 publication

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to take into account multimodal behavior for motion planning in human crowds, mixture models were introduced later on [18]. Another learning based interaction model was presented by Vemula et al [5], where a GP model is used to forecast future agent velocities and destinations. The predictions are made based on a grid-based representation of the world which collects information about pedestrian positions and heading in the agent's surroundings.…”

Section: Related Workmentioning

confidence: 99%

“…The main factors influencing pedestrian motion are interactions among pedestrians, the environment, and the location of their destination. By taking into account the interaction between pedestrians, the accuracy of the motion models can be significantly increased [1]- [5]. It was shown that using interaction-aware motion models for dynamic agent This work has received funding from the European Union Seventh Framework Programme FP7, project EUROPA2, Grant No.…”

Section: Introductionmentioning

confidence: 99%

“…(ii) The approaches are not scalable to dense crowds since they use pairwise interactions between all agents [1]- [3], which leads to a quadratic complexity in number of agents, and therefore real-time computation is only feasible for a small number of agents. (iii) Static obstacles are neglected [1], [4], [5] and (iv) knowledge about a set of potential destinations is assumed [1], [3], [5].…”

Section: Introductionmentioning

confidence: 99%

“…Given these inputs, the pedestrian's future motion is predicted in a receding horizon manner. Compared to other approaches, which do forward simulation through repetitive use of 1-step predictions provided by the model [4], [5], we already do the forecast for the whole prediction horizon, and therefore do not need a forward simulation. One possibility to deploy the presented model on a robotic platform is to predict trajectories of surrounding pedestrians and plan a collision-free path for the robot given the predicted trajectories.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Data-driven Model for Interaction-Aware Pedestrian Motion Prediction in Object Cluttered Environments

Pfeiffer

Paolo

Sommer

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

This paper reports on a data-driven, interactionaware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior is mostly influenced by their surrounding pedestrians and by the static obstacles in their vicinity. In this paper we introduce a new model based on Long-Short Term Memory (LSTM) neural networks, which is able to learn human motion behavior from demonstrated data. To the best of our knowledge, this is the first approach using LSTMs, that incorporates both static obstacles and surrounding pedestrians for trajectory forecasting. As part of the model, we introduce a new way of encoding surrounding pedestrians based on a 1d-grid in polar angle space. We evaluate the benefit of interaction-aware motion prediction and the added value of incorporating static obstacles on both simulation and real-world datasets by comparing with state-of-the-art approaches. The results show, that our new approach outperforms the other approaches while being very computationally efficient and that taking into account static obstacles for motion predictions significantly improves the prediction accuracy, especially in cluttered environments.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%