A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Area of Science:

• Computer Vision
• Artificial Intelligence
• Robotics

Background:

• Pedestrian trajectory prediction is crucial for automotive safety and advanced driver assistance systems (ADAS).
• Accurate prediction of pedestrian movements remains a significant technological challenge in various applications.
• Advancements in sensor technology and signal processing are enhancing prediction capabilities.

Purpose of the Study:

• To review recent deep learning-based solutions for pedestrian trajectory prediction.
• To provide an overview of sensors, processing methodologies, datasets, and performance metrics.
• To identify research gaps and suggest future research directions in the field.

Main Methods:

• Review of state-of-the-art deep learning models for trajectory prediction.
• Analysis of sensor technologies and signal processing techniques used.
• Compilation and overview of relevant datasets and evaluation metrics.

Main Results:

• Deep learning methods show significant promise in improving pedestrian trajectory prediction.
• A comprehensive overview of current methodologies, datasets, and applications is presented.
• Key research gaps and potential future research avenues are highlighted.

Conclusions:

• Deep learning is a pivotal technology for advancing pedestrian trajectory prediction.
• Further research is needed to address existing challenges and explore new directions.
• This review serves as a valuable resource for researchers and developers in the field.