Long-Term Human Trajectory Prediction using 3D Dynamic Scene Graphs for Complex Environments

Core Concepts

A novel approach for long-term human trajectory prediction in complex environments by leveraging 3D Dynamic Scene Graphs and Large Language Models to predict sequences of human interactions with the environment, which are then grounded into a probabilistic spatio-temporal distribution over future human positions.

Abstract

The authors present a novel approach called "Language-driven Probabilistic Long-term Prediction" (LP2) for long-term human trajectory prediction in complex environments. Existing methods are limited by their focus on short-term collision avoidance and their inability to model complex human-environment interactions. LP2 overcomes these limitations by predicting sequences of human interactions with the environment using Large Language Models (LLMs) conditioned on a 3D Dynamic Scene Graph (DSG) representation of the environment. The DSG encodes the geometry, semantics, and traversability of the environment into a hierarchical representation. The LLM-based Interaction Sequence Prediction (ISP) module predicts likely future interactions, which are then grounded into a probabilistic spatio-temporal distribution over human positions using a Continuous-Time Markov Chain (CTMC)-based Probabilistic Trajectory Prediction (PTP) module. The authors introduce a new semi-synthetic dataset of long-term human trajectories in complex indoor environments with rich scene and interaction annotations. Experiments show that LP2 achieves a 54% lower average negative log-likelihood (NLL) and a 26.5% lower Best-of-20 displacement error compared to the best non-privileged baselines for a time horizon of 60 seconds.

Stats

The average distance travelled in the past is 14.0 ± 11.9 m in the office and 7.8 ± 3.0 m in the home environment. The average distance travelled in the future is 18.6 ± 12.7 m in the office and 11.7 ± 6.5 m in the home environment. The average number of past interactions is 2.2 ± 0.5 in the office and 2.4 ± 0.5 in the home environment. The average number of future interactions is 2.3 ± 0.8 in the office and 2.2 ± 0.8 in the home environment. The average duration of the past observation is 67.6 ± 30.8 s in the office and 60.6 ± 30.2 s in the home environment. The average time spent interacting in the past is 55.2 ± 30.1 s in the office and 55.1 ± 30.4 s in the home environment. The average time spent interacting in the future is 43.7 ± 11.5 s in the office and 51.3 ± 6.6 s in the home environment.

Quotes

"For the first time, we study long-term human trajectory prediction with a prediction horizon of up to 60 s and including complex human-object interactions." "We present LP2, a novel method combining LLM-based zero-shot interaction sequence prediction and CTMC-based probabilistic trajectory prediction to infer multi-modal spatio-temporal distribution over future human positions." "We introduce a new semi-synthetic dataset of human trajectories in complex indoor environments with annotated human-object interactions."

Key Insights Distilled From

Long-Term Human Trajectory Prediction using 3D Dynamic Scene Graphs

by Nicolas Gorl... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00552.pdf

Deeper Inquiries

How could the proposed approach be extended to handle dynamic environments with multiple interacting agents?

In order to extend the proposed approach to handle dynamic environments with multiple interacting agents, several modifications and enhancements could be implemented: Multi-Agent Interaction Modeling: The approach could be adapted to incorporate multiple agents in the scene, each with their own trajectories and interactions. This would involve extending the scene graph representation to include information about multiple agents and their interactions with the environment. Interaction Graphs: Instead of focusing solely on individual human-object interactions, the approach could be expanded to model interactions between multiple agents and objects in the environment. This would require a more complex graph structure to capture the relationships and dependencies between different entities in the scene. Collaborative Prediction: The model could be designed to predict collaborative interactions between agents, such as coordinated movements or joint tasks. This would involve considering the intentions and goals of each agent and how they influence the overall trajectory of the group. Dynamic Scene Updates: To handle dynamic environments, the approach could be enhanced to dynamically update the scene graph and interaction predictions in real-time as the environment changes. This would require efficient algorithms for updating and maintaining the scene representation. Temporal Reasoning: Incorporating temporal reasoning mechanisms would be crucial for predicting interactions and trajectories in dynamic environments. This could involve modeling the evolution of interactions over time and predicting future states based on past observations. By incorporating these enhancements, the proposed approach could be extended to effectively handle dynamic environments with multiple interacting agents, enabling more accurate and robust long-term trajectory predictions.

What are the potential limitations of using LLMs for predicting long-term human interactions, and how could these be addressed?

While Large Language Models (LLMs) have shown promise in predicting human behavior and interactions, there are several potential limitations that need to be considered: Data Bias and Generalization: LLMs are prone to biases present in the training data, which can lead to biased predictions and lack of generalization to unseen scenarios. Addressing this limitation would require careful data curation, augmentation, and regularization techniques to ensure the model learns diverse and representative patterns. Complexity and Interpretability: LLMs are complex models with millions of parameters, making them challenging to interpret and understand. Simplifying the model architecture or incorporating interpretability techniques could help address this limitation and improve the transparency of the predictions. Long-Term Dependency: LLMs may struggle with capturing long-term dependencies in sequences, leading to errors in predicting interactions over extended time horizons. Techniques like hierarchical modeling or memory-augmented architectures could help the model retain information over longer sequences. Scalability and Efficiency: LLMs can be computationally expensive and resource-intensive, especially for real-time applications or large-scale datasets. Optimizing the model architecture, leveraging parallel computing, and using efficient inference strategies could mitigate these scalability limitations. Ethical Considerations: LLMs have raised concerns related to ethical issues such as bias, fairness, and privacy. Implementing ethical AI principles, conducting bias audits, and ensuring transparency in the model's decision-making process are essential for addressing these concerns. By addressing these limitations through careful model design, data preprocessing, and ethical considerations, the use of LLMs for predicting long-term human interactions can be enhanced in terms of accuracy, robustness, and ethical compliance.

How could the insights from this work on long-term human trajectory prediction be applied to other domains, such as assistive robotics or urban planning?

The insights gained from the work on long-term human trajectory prediction can be valuable for various domains, including assistive robotics and urban planning: Assistive Robotics: Path Planning: The trajectory prediction techniques can be applied to assistive robots to plan optimal paths for assisting humans in tasks like navigation, object manipulation, and interaction with the environment. Safety and Collision Avoidance: By predicting long-term human trajectories, assistive robots can proactively avoid collisions and ensure safe interactions with humans in dynamic environments. Task Assistance: Understanding human intentions and interactions can help robots anticipate user needs and provide timely assistance in tasks such as household chores, healthcare support, and mobility assistance. Urban Planning: Pedestrian Flow Analysis: Long-term trajectory prediction can be used to analyze pedestrian movements in urban areas, optimize traffic flow, and design pedestrian-friendly spaces. Public Space Design: Insights from trajectory prediction can inform the design of public spaces, transportation systems, and urban infrastructure to enhance accessibility, safety, and efficiency. Emergency Response Planning: Predicting human trajectories in emergency situations can aid in disaster response planning, evacuation route optimization, and crowd management strategies. By applying the principles and methodologies of long-term human trajectory prediction to assistive robotics and urban planning, it is possible to create more intelligent, adaptive, and human-centric systems that improve efficiency, safety, and quality of life in various real-world applications.