Goal-Guided Diffusion Model with Tree Sampling for Accurate and Efficient Multi-Modal Pedestrian Trajectory Prediction

Основні поняття

The proposed GDTS framework integrates goal estimation and a novel two-stage tree sampling diffusion model to generate accurate and diverse multi-modal pedestrian trajectory predictions in real-time.

Анотація

The paper presents a novel framework called GDTS (Goal-Guided Diffusion Model with Tree Sampling) for multi-modal pedestrian trajectory prediction. The key components are:

1. Goal Estimation Module:

   • Predicts the probability distribution of the pedestrian's goal position using a U-Net architecture.
   • Samples multiple possible goals from this distribution to ensure diversity in the predictions.

2. Trajectory Prediction Module:

   • Takes the history trajectory and the estimated goals as input.
   • Leverages a diffusion-based model to generate multi-modal future trajectory predictions.
   • Introduces a two-stage tree sampling algorithm to accelerate the inference speed without compromising accuracy:
     • Trunk stage: Uses a common feature to generate a roughly denoised initial trajectory.
     • Branch stage: Further refines the initial trajectory using diverse features to obtain multiple modalities.

The experiments on the ETH/UCY and Stanford Drone datasets demonstrate that GDTS achieves state-of-the-art performance in terms of prediction accuracy while maintaining real-time inference speed. The ablation studies validate the effectiveness of the proposed tree sampling algorithm and the combination of diffusion models.

Статистика

The paper reports the following key metrics:

Average Displacement Error (ADE20) and Final Displacement Error (FDE20) on the Stanford Drone Dataset and ETH/UCY dataset.
Inference time for the proposed method and baselines.

Цитати

"Accurate prediction of pedestrian trajectories is crucial for improving the safety of autonomous driving."
"To address these challenges and facilitate the use of diffusion models in multi-modal trajectory prediction, we propose GDTS, a novel Goal-Guided Diffusion Model with Tree Sampling for multi-modal trajectory prediction."
"Experimental results demonstrate that our proposed framework achieves comparable state-of-the-art performance with real-time inference speed in public datasets."

Ключові висновки, отримані з

GDTS: Goal-Guided Diffusion Model with Tree Sampling for Multi-Modal Pedestrian Trajectory Prediction

by Ge Sun, Shen... о arxiv.org 09-19-2024

https://arxiv.org/pdf/2311.14922.pdf

Глибші Запити

How can the proposed GDTS framework be extended to handle interactions between multiple pedestrians and other dynamic agents in the scene?

To extend the GDTS framework for handling interactions between multiple pedestrians and other dynamic agents, several strategies can be implemented:

Multi-Agent Interaction Modeling: Incorporating a multi-agent interaction model can enhance the framework's ability to predict trajectories by considering the influence of surrounding agents. This could involve using attention mechanisms or graph neural networks to model the relationships and interactions between pedestrians and other dynamic agents in the scene. By capturing the social dynamics and potential collision avoidance behaviors, the model can generate more realistic trajectory predictions.

Recurrent Neural Networks (RNNs): Integrating RNNs or Long Short-Term Memory (LSTM) networks can help in capturing temporal dependencies and interactions over time. By processing sequences of past trajectories from multiple agents, the model can learn to predict future movements based on the observed behaviors of all agents involved.

Goal Estimation Enhancement: The goal estimation module can be enhanced to consider not only the target pedestrian's goals but also the goals of other agents. This could involve predicting a distribution of potential goals for each agent based on their observed behaviors and interactions, allowing the model to account for the influence of other agents' movements on the target pedestrian's trajectory.

Simulation of Social Forces: Implementing a social force model can help simulate the effects of social interactions on pedestrian movement. By modeling forces that represent attraction to goals and repulsion from other agents, the framework can generate trajectories that reflect realistic social behaviors.

Data Augmentation: Utilizing datasets that include diverse scenarios with multiple interacting agents can improve the model's robustness. By training on a variety of interaction patterns, the model can learn to generalize better to unseen situations.

By incorporating these strategies, the GDTS framework can effectively handle the complexities of multi-agent interactions, leading to improved accuracy in pedestrian trajectory predictions.

What are the potential limitations of the goal estimation module, and how could it be further improved to enhance the overall prediction accuracy?

The goal estimation module in the GDTS framework has several potential limitations:

Static Goal Assumption: The current implementation assumes that pedestrian goals remain relatively static during the prediction horizon. This may not always hold true in dynamic environments where pedestrians frequently change their goals based on real-time interactions or environmental cues. To address this, the module could be enhanced to incorporate a dynamic goal prediction mechanism that updates the estimated goals based on observed behaviors and interactions.

Limited Contextual Awareness: The goal estimation module primarily relies on historical trajectory data and the semantic map. However, it may not fully utilize contextual information such as the presence of obstacles, other pedestrians, or environmental changes. Integrating additional contextual features, such as proximity to other agents or environmental dynamics, could improve the accuracy of goal predictions.

Noise Sensitivity: The module may be sensitive to noise in the input data, such as inaccuracies in the historical trajectory or semantic map. Implementing robust filtering techniques or using ensemble methods could help mitigate the impact of noise and improve the reliability of goal estimations.

Training Data Limitations: The performance of the goal estimation module is heavily dependent on the quality and diversity of the training data. If the training dataset lacks sufficient examples of varied pedestrian behaviors or interactions, the model may struggle to generalize. Expanding the training dataset to include a wider range of scenarios and behaviors can enhance the model's robustness.

Integration with Trajectory Prediction: The current separation between goal estimation and trajectory prediction may lead to suboptimal performance. A more integrated approach, where the trajectory prediction module continuously refines the goal estimates based on predicted trajectories, could lead to more accurate and coherent predictions.

By addressing these limitations through dynamic goal prediction, enhanced contextual awareness, noise robustness, improved training data, and better integration with trajectory prediction, the overall accuracy of the GDTS framework can be significantly enhanced.

What insights from this work on pedestrian trajectory prediction could be applied to other time-series forecasting problems in different domains, such as finance or climate modeling?

The insights gained from the GDTS framework for pedestrian trajectory prediction can be effectively applied to other time-series forecasting problems across various domains, including finance and climate modeling:

Multi-Modal Prediction: The ability to generate multi-modal predictions, as demonstrated in GDTS, is crucial in fields like finance where multiple future scenarios (e.g., stock price movements) can occur. By leveraging goal estimation and diffusion models, similar frameworks can be developed to predict various potential outcomes based on historical data and contextual factors.

Incorporation of External Factors: Just as the GDTS framework integrates semantic maps and historical trajectories, other domains can benefit from incorporating external factors that influence the target variable. For instance, in finance, macroeconomic indicators, market sentiment, and geopolitical events can be integrated into the prediction models to enhance accuracy.

Dynamic Goal Adjustment: The concept of dynamically adjusting goals based on real-time data can be applied to finance, where market conditions change rapidly. Implementing adaptive models that update predictions based on new information can improve forecasting accuracy in volatile environments.

Handling Stochasticity: The inherent stochasticity of human motion addressed in GDTS is also present in financial markets and climate systems. Techniques developed for modeling uncertainty and generating diverse predictions can be adapted to forecast stock prices, economic indicators, or climate variables, allowing for better risk assessment and decision-making.

Temporal Dependencies: The use of recurrent architectures to capture temporal dependencies in pedestrian movements can be translated to other time-series forecasting tasks. In finance, for example, LSTM networks can be employed to model the sequential nature of stock prices, while in climate modeling, they can help capture seasonal patterns and trends.

Real-Time Inference: The emphasis on real-time inference speed in GDTS is particularly relevant in finance, where timely predictions can significantly impact trading strategies. Developing efficient algorithms that maintain high accuracy while ensuring rapid processing can enhance decision-making in fast-paced environments.

By applying these insights, researchers and practitioners in finance, climate modeling, and other time-series forecasting domains can develop more robust, accurate, and adaptive predictive models that account for the complexities and uncertainties inherent in their respective fields.