SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints (1806.01482v2)

Abstract: This paper addresses the problem of path prediction for multiple interacting agents in a scene, which is a crucial step for many autonomous platforms such as self-driving cars and social robots. We present \textit{SoPhie}; an interpretable framework based on Generative Adversarial Network (GAN), which leverages two sources of information, the path history of all the agents in a scene, and the scene context information, using images of the scene. To predict a future path for an agent, both physical and social information must be leveraged. Previous work has not been successful to jointly model physical and social interactions. Our approach blends a social attention mechanism with a physical attention that helps the model to learn where to look in a large scene and extract the most salient parts of the image relevant to the path. Whereas, the social attention component aggregates information across the different agent interactions and extracts the most important trajectory information from the surrounding neighbors. SoPhie also takes advantage of GAN to generates more realistic samples and to capture the uncertain nature of the future paths by modeling its distribution. All these mechanisms enable our approach to predict socially and physically plausible paths for the agents and to achieve state-of-the-art performance on several different trajectory forecasting benchmarks.

• The paper introduces SoPhie, a novel GAN framework that integrates social and physical constraints to predict realistic multi-agent trajectories.
• It employs dual attention mechanisms using CNNs for scene context and LSTMs for trajectory encoding, effectively capturing both static and dynamic cues.
• The approach achieves state-of-the-art results on benchmark datasets, demonstrating its potential for reliable path prediction in autonomous systems.

An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints

This paper introduces "SoPhie," an interpretable framework that leverages a Generative Adversarial Network (GAN) to predict future paths for multiple agents in a scene, incorporating both social interactions and physical constraints. The model innovates by integrating two distinct sources of information: the historical paths of agents and the scene context derived from images.

Contributions and Methodology

The paper identifies several challenges in predicting paths for agents interacting within a scene:

1. Physical Constraints: The need to process environmental information to navigate obstacles.
2. Social Interactions: The necessity to account for the social movements and behaviors of nearby agents.
3. Path Variability: The existence of multiple feasible paths toward a destination.

Previous methods have typically addressed one or two of these aspects but have not focused comprehensively on integrating both physical and social constraints.

SoPhie stands out by employing a hybrid approach that combines these interactions using a social attention mechanism to aggregate trajectory information from surrounding agents and a physical attention mechanism to focus on salient parts of the scene. It uses a GAN framework to generate realistic samples, capturing the uncertainty in future paths.

Feature Extraction

SoPhie extracts pertinent features using two main approaches:

• Visual Features: Extracted from scene images using a Convolutional Neural Network (CNN), specifically VGGnet-19.
• Temporal Features: Encoded using Long Short-Term Memory (LSTM) networks to capture the trajectory dependencies.

These feature extractors ensure that the model considers both static and dynamic information within the scene.

Attention Mechanisms

The attention mechanisms are crucial for making the model interpretable and effective:

• Physical Attention: Focuses on areas of the scene that are relevant to the agent's path, such as avoiding obstacles or staying on a feasible path.
• Social Attention: Targets the interactions among agents, emphasizing the influence of nearby agents in the prediction of the target agent's trajectory.

GAN Framework

The combination of these attention mechanisms within a GAN framework allows for the prediction of multiple plausible paths:

1. Generator: An LSTM-based network that produces a distribution of possible future states for each agent.
2. Discriminator: Another LSTM-based network that distinguishes between real and generated trajectories, refining the generator’s output.

This adversarial setup helps in modeling the inherent variability in agent movements, producing trajectories that conform to both physical constraints and social norms.

Experimental Results

The authors evaluated SoPhie on several benchmark datasets, including ETH, UCY, and the Stanford Drone Dataset. In terms of numerical performance, SoPhie achieved state-of-the-art results:

• For the ETH and UCY datasets, SoPhie demonstrated lower average displacement error (ADE) and final displacement error (FDE) metrics compared to existing models like Social GAN.
• On the Stanford Drone Dataset, SoPhie outperformed other models by a significant margin, highlighting its effectiveness in complex environments.

Implications

Practically, SoPhie can be highly beneficial for autonomous systems like self-driving cars and social robots, which require robust and reliable path prediction capabilities within dynamic environments. Theoretically, the integration of social and physical attention mechanisms within a GAN framework presents a solid advancement in trajectory prediction research.

Future Directions

Future research could extend SoPhie by exploring unsupervised learning techniques to further enhance its performance in unseen environments. Additionally, incorporating more complex agent behaviors and interactions, such as group dynamics or heterogeneous agent types (e.g., pedestrians and vehicles), could improve the model's versatility.

Conclusion

SoPhie represents a significant step forward in path prediction, demonstrating the power of combining social interactions and physical constraints within an interpretable GAN framework. This approach not only achieves high accuracy but also provides insights into the decision-making processes of autonomous agents in dynamic environments.