Human Trajectory Forecasting in Crowds: A Deep Learning Perspective (2007.03639v3)

Abstract: Since the past few decades, human trajectory forecasting has been a field of active research owing to its numerous real-world applications: evacuation situation analysis, deployment of intelligent transport systems, traffic operations, to name a few. Early works handcrafted this representation based on domain knowledge. However, social interactions in crowded environments are not only diverse but often subtle. Recently, deep learning methods have outperformed their handcrafted counterparts, as they learned about human-human interactions in a more generic data-driven fashion. In this work, we present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two knowledge-based data-driven methods to effectively capture these social interactions. To objectively compare the performance of these interaction-based forecasting models, we develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting. We propose novel performance metrics that evaluate the ability of a model to output socially acceptable trajectories. Experiments on TrajNet++ validate the need for our proposed metrics, and our method outperforms competitive baselines on both real-world and synthetic datasets.

• The paper introduces novel deep learning-based interaction models that enhance pedestrian trajectory forecasting in crowded environments.
• It presents the TrajNet++ benchmark and innovative collision-based metrics to objectively evaluate social norms in trajectory predictions.
• An exploratory use of layer-wise relevance propagation improves model interpretability, advancing safe deployment in real-world scenarios.

Human Trajectory Forecasting in Crowds: A Deep Learning Perspective

The paper "Human Trajectory Forecasting in Crowds: A Deep Learning Perspective" by Parth Kothari, Sven Kreiss, and Alexandre Alahi presents an extensive analysis and evaluation framework for computational models that predict human trajectories in crowded environments using deep learning techniques. Such models seek to capture and forecast interactions amongst humans in various social settings, with potential applications spanning evacuation planning, traffic management, and intelligent transportation systems.

Summary of Contributions

1. Deep Learning-based Interaction Modeling: The paper revisits existing designs of neural network (NN)-based modules which model social interactions. The authors propose novel knowledge-driven interaction models, offering enhanced predictions of pedestrian behavior. Unlike handcrafted methods which rely heavily on preset rules, these NN-based approaches adapt through data-driven learning, capturing a wider array of subtle interactions.
2. TrajNet++ Benchmark: TrajNet++ is introduced as a robust benchmark designed to objectively evaluate trajectory forecasting models. The benchmark emphasizes interaction-rich scenarios where human-human interaction dynamics are prominent. Notably, this work highlights the lack of interaction-centric evaluations in prior works and addresses it by composing datasets predominantly comprising interactive pedestrian scenes.
3. Novel Evaluation Metrics: The authors propose new performance metrics such as collision-based metrics which assess the social acceptability of trajectory forecasts. These metrics measure the extent to which predicted trajectories conform to spatial norms such as collision avoidance, thereby moving beyond traditional distance-based metrics like ADE and FDE.
4. Exploratory Implementation of Layer-wise Relevance Propagation (LRP): By employing LRP, a method typically used in classification tasks, the paper extends its application to explain the decision-making processes in NN regression tasks, particularly in trajectory forecasting models. This allows for better interpretability of model outputs, a crucial factor in deploying models in real-world, safety-critical scenarios.

Numerical Findings and Discussion

The analyses span both synthetic and real-world datasets. Numerical results validate the efficacy of proposed models and benchmarks:

• Collision Avoidance: Experiments reveal that incorporating relative velocities in model inputs significantly improves collision avoidance outcomes, a critical social norm in dense environments. The D-Grid model demonstrates a superior ability to minimize prediction collisions (Col-I) versus other configurations.
• Computational Efficiency: Domain-knowledge based models not only improve prediction quality, especially in collision scenarios, but also offer computational efficiency, making them viable for real-world deployment.
• Training and Generalization: A revised training strategy—where only the primary pedestrian trajectory guides the optimization—enhances model performance, illustrating the benefit of targeted learning in interaction-focused settings.

Implications and Future Directions

The research presents several implications for future developments in AI-powered trajectory forecasting:

• Domain Knowledge Integration: Effectively integrating domain knowledge, such as social norms of movement, into data-driven models can improve both interpretability and performance. This work sets a precedent for future efforts to harmonize data-driven and rule-based approaches.
• Benchmarking Standards: With TrajNet++, the community is provided with an interaction-centered benchmark and a comprehensive evaluation framework—components essential for fostering robust model comparisons and subsequent advancements.
• Explainable AI: The application of LRP in regression contexts exemplifies a step towards explainable AI in safety-critical applications, calling for continued exploration into transparency mechanisms for complex neural architectures.

By addressing crucial gaps in modeling, benchmarking, and evaluation, this research marks a significant stride in advancing human trajectory forecasting in social environments. The findings emphasize the importance of robust evaluation setups and highlight underexplored aspects such as collision aversion in trajectory prediction, creating pathways for enriching predictive models with enhanced social awareness and practical applicability. Future work may focus on refining these models to account for more dynamic and multifaceted crowd scenarios, as well as exploring privacy and ethical considerations within diverse application contexts.