Awesome Traffic Agent Trajectory Prediction: Curated Research and Code

This bibliographic collection curated by Psychic-DL indexes research papers and source code for traffic agent trajectory prediction as of late 2025. It organizes deep learning literature into categories to facilitate the study of vehicle and pedestrian movement modeling.

Use Cases

• Conducting literature reviews by filtering through paper titles and publication years
• Implementing baseline models using the provided source code links for trajectory prediction
• Categorizing deep learning approaches based on the methodology tags assigned to each entry

Strengths

• Includes direct links to source code repositories for implementation
• Updated through December 2025 to include recent deep learning advancements
• Organized by specific traffic-agent sub-topics for easier navigation

Limitations

• Contains no raw trajectory coordinates or sensor data for model training
• Lacks a standardized tabular format, requiring manual parsing of markdown text

Provenance

Source

Psychic-DL via GitHub

Collection Method

Manual curation of academic literature and open-source projects

Freshness

Last updated December 2025.

Geography

Global