MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction (2108.09274v1)

Abstract: Pedestrian trajectory prediction is challenging due to its uncertain and multimodal nature. While generative adversarial networks can learn a distribution over future trajectories, they tend to predict out-of-distribution samples when the distribution of future trajectories is a mixture of multiple, possibly disconnected modes. To address this issue, we propose a multi-generator model for pedestrian trajectory prediction. Each generator specializes in learning a distribution over trajectories routing towards one of the primary modes in the scene, while a second network learns a categorical distribution over these generators, conditioned on the dynamics and scene input. This architecture allows us to effectively sample from specialized generators and to significantly reduce the out-of-distribution samples compared to single generator methods.

• The paper introduces a multi-generator GAN model that mitigates out-of-distribution samples by assigning specific trajectory modes to specialized generators.
• It incorporates a novel Path Mode Network (PM-Net) that dynamically selects the optimal generator based on scene information to boost prediction accuracy.
• Experimental results on real-world and synthetic datasets demonstrate improved precision and recall, underscoring its potential for safety-critical applications like autonomous navigation.

MG-GAN: A Multi-Generator Approach to Pedestrian Trajectory Prediction

The paper presents MG-GAN, a sophisticated approach for pedestrian trajectory prediction utilizing a multi-generator model within the framework of Generative Adversarial Networks (GANs). The research addresses the significant challenge of predicting pedestrian paths in dynamic environments, which inherently exhibit uncertainty and multimodal behaviors due to various influencing factors such as scene layouts and social interactions.

Key Contributions and Methodology

The primary contribution of the paper is the introduction of a multi-generator GAN model designed to tackle the issue of out-of-distribution (OOD) sample generation, a common problem in single-generator GAN frameworks, especially in scenarios with disconnected trajectory distributions. The key innovations of the paper include:

1. Multi-Generator Architecture: The model divides trajectory prediction tasks among multiple generators, each of which is specialized in a specific mode of possible trajectories. By approaching the trajectory distribution as a mixture of several continuous yet distinct distributions, MG-GAN is capable of capturing the complex nature of pedestrian paths more accurately.
2. Path Mode Network (PM-Net): A novel addition, PM-Net, learns the probability distribution over the generators conditioned on scene information, allowing the model to choose the most appropriate generator(s) for a given scenario.
3. Training Mechanism: The paper proposes an efficient training mechanism akin to expectation-maximization, where the PM-Net and generators are alternately trained to enhance specialization and accuracy in predictions.

Experimental Evaluation

The authors validate MG-GAN across several datasets, including real-world pedestrian trajectory datasets and a tailored synthetic dataset that facilitated controlled experimentation over trajectory distribution. The results are considerably promising, showcasing:

• A reduction in the generation of OOD samples, which enhances the practical applicability for safety-critical systems such as autonomous vehicles.
• Superior performance in terms of precision and recall, particularly on datasets where the trajectory distributions are inherently multimodal and display disconnected modes.

Implications and Future Directions

The implementation of MG-GAN signifies an important step towards handling complexities associated with multimodal trajectory prediction. The multi-generator approach fundamentally enables a more nuanced understanding and modeling of realistic human movement, potentially impacting applications in autonomous navigation systems, urban planning, and crowd management.

The research invites further exploration into adaptive multi-generator methods for other forecasting tasks that display inherent multimodal characteristics, such as traffic flow and robotics path planning. Moreover, the integration of additional contextual information into the PM-Net could provide a more holistic approach to trajectory prediction, further enhancing precision and robustness in dynamic environments.

The paper not only contributes a novel architectural framework for trajectory forecasting but also sets a new benchmark in evaluating predictive models through precision and recall. Future advancements can build on this framework to address more complex scenarios involving higher dimensions of dynamic interactions and real-time responsive capabilities.