Human Motion Trajectory Prediction: A Survey (1905.06113v3)

Abstract: With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.

• The paper presents an extensive survey that categorizes human motion trajectory prediction methods into physics-based, pattern-based, and planning-based approaches.
• The paper evaluates each method’s strengths and limitations, emphasizing challenges in handling complex environments and extended prediction horizons.
• The paper outlines future research directions that integrate richer contextual cues and standardized metrics to enhance prediction robustness in autonomous systems.

An Overview of Human Motion Trajectory Prediction

The paper "Human Motion Trajectory Prediction: A Survey" by Rudenko et al. provides an extensive review of methods dedicated to predicting human motion trajectories, focusing on various autonomous systems. Central to these systems is the ability to anticipate human behavior, which is critical for applications such as self-driving vehicles, service robots, and surveillance systems. The survey offers a comprehensive analysis of the state of the art, classifying approaches based on motion modeling techniques and contextual information, while highlighting open challenges and future directions.

Taxonomy of Prediction Methods

The authors propose a detailed taxonomy that categorizes methods into three main groups based on their modeling approach: physics-based, pattern-based, and planning-based. Each of these methodologies presents unique advantages and limitations, particularly when contextual cues are integrated.

1. Physics-based Methods: These leverage explicit dynamic models grounded in Newtonian laws. They are subdivided into single-model and multi-model approaches, differing in complexity and adaptability. Physics-based models are often praised for their simplicity and efficiency but may struggle with extended prediction horizons and complex environments. Extensions to these models allow integration of contextual information like social and environmental factors, enhancing predictive performance.
2. Pattern-based Methods: These employ statistical learning to identify motion patterns from data. They are divided into sequential and non-sequential models. Sequential methods include advanced techniques like RNNs and LSTMs for time series prediction, adeptly modeling temporal dependencies. Non-sequential methods focus on entire trajectory distributions, often using clustering. Pattern-based approaches excel in environments with rich datasets but face challenges in generalization across diverse contexts.
3. Planning-based Methods: These assume rationality in human actions, using cost or reward-based models. The two categories here are forward planning, which uses predefined goals and optimization criteria, and inverse planning, which infers goals from observed behavior. Planning-based methods are especially robust in structured environments where clear objectives exist.

Evaluation Metrics and Datasets

The paper critiques current evaluation techniques, emphasizing the need for standardization in metrics and testing scenarios. Geometric accuracy metrics and probabilistic metrics are considered, with a call for broader adoption of probabilistic methods to better capture the inherent uncertainty in human motion. Commonly used datasets are discussed, with suggestions for new datasets that introduce varying levels of complexity and richness in contextual information.

Implications and Future Research Directions

The survey identifies several areas ripe for further exploration. An emphasis is placed on incorporating broader and richer contextual cues, such as dynamic interactions and environmental semantics, to improve predictive accuracy and robustness. Additionally, the integration of prediction algorithms with robotic planning and control systems is highlighted as a key area for development. Robustness and scalability remain crucial challenges, alongside enhancing transferability across different environments and scenarios.

Conclusion

Rudenko et al.'s survey provides valuable insights into the trajectory prediction landscape, offering a foundation for future advancements. Through systematic consideration of modeling approaches and contextual awareness, the paper underscores the complexity and multifaceted nature of predicting human motion, suggesting that while significant progress has been made, there remains considerable scope for innovation and improvement in real-world applications.