Social Attention: Modeling Attention in Human Crowds (1710.04689v2)

Abstract: Robots that navigate through human crowds need to be able to plan safe, efficient, and human predictable trajectories. This is a particularly challenging problem as it requires the robot to predict future human trajectories within a crowd where everyone implicitly cooperates with each other to avoid collisions. Previous approaches to human trajectory prediction have modeled the interactions between humans as a function of proximity. However, that is not necessarily true as some people in our immediate vicinity moving in the same direction might not be as important as other people that are further away, but that might collide with us in the future. In this work, we propose Social Attention, a novel trajectory prediction model that captures the relative importance of each person when navigating in the crowd, irrespective of their proximity. We demonstrate the performance of our method against a state-of-the-art approach on two publicly available crowd datasets and analyze the trained attention model to gain a better understanding of which surrounding agents humans attend to, when navigating in a crowd.

• The paper introduces a novel RNN attention mechanism that shifts focus from mere spatial proximity to dynamic, interaction-based trajectory prediction.
• It integrates RNNs with an attention-weighted spatio-temporal graph to capture both temporal consistency and nuanced human interactions.
• Evaluation on ETH and UCY datasets demonstrates improved average displacement error, paving the way for safer and more natural robot navigation.

Social Attention: Modeling Attention in Human Crowds

The paper presents a novel approach to predicting human trajectories in crowded environments, a task crucial for enabling socially compliant navigation in robots. This research introduces the "Social Attention" model, which reflects a shift from traditional proximity-based trajectory predictions. Instead, it captures the nuanced dynamics of human interactions beyond mere spatial locality.

Context and Motivation

Robot navigation among humans involves predicting human motions with due consideration for complex interactions. Conventionally, models have depended heavily on spatial proximity to infer these trajectories. However, this assumption often fails in realistic scenarios where the influence is dictated by factors such as velocity and potential future collisions, not just proximity. This insight motivates the proposed Social Attention model.

Technical Approach

The paper proposes an RNN-based architecture augmented with an attention mechanism. This integrates the components of spatial and temporal dynamics to allow for nuanced interaction modeling. The architecture strategically employs shared parameters across nodes and edges within a spatio-temporal graph (st-graph). The attention mechanism learns to weigh the influence of all agents in the environment dynamically, providing flexibility to highlight interactions that are not immediately apparent through mere spatial analysis.

Key Contributions

1. Attention Mechanism: Unlike methods counting only on spatial adjacency, this model leverages learned attention weights to account for various dynamic features. This improves the predictive capabilities of the model by considering complex patterns of interaction.
2. Integration with RNNs: Using a mixture of recurrent neural networks, the model captures both temporal consistency and interaction effects, providing a comprehensive framework for trajectory prediction.
3. Improved Performance: Evaluation on the ETH and UCY datasets demonstrates that Social Attention outperforms existing methods, such as Social LSTM, especially in scenarios with intricate human interactions. The average displacement error across datasets showed consistent improvements.
4. Qualitative Analysis: The learned attention weights provide interpretable insights into which agents influence individual trajectories, demonstrating scenarios where pedestrians strategically focus their attention based on predicted future states rather than immediate proximity.

Implications and Future Developments

The implications of this work extend to both robotic autonomy and human-computer interaction domains. Accurate human trajectory prediction is critical for any system interfacing with human crowds, particularly autonomous robots in dynamic and unpredictable settings. Ensuring robots navigate safely and naturally is a step closer with models like Social Attention.

For future directions, the paper suggests integrating static obstacles into the attention model, increasing its applicability in environments with complex, unmoving elements. Furthermore, real-world deployment and testing on robotic platforms remain critical to assess model performance in live settings fully.

The use of attention introduces a scalable approach to capturing diverse interaction dynamics, paving the way for further exploration into generalized human-robot interaction models. As robotic systems continue to develop in complexity and capability, such trajectory prediction models will likely form the backbone of robust, socially aware navigation frameworks.