Master Thesis: Episodic Memory for Behavior Prediction & Accident Anticipation - Traffic Simulation
Core
Investigate how episodic memory mechanisms can improve behavior prediction and accident anticipation in immersive urban traffic environments using AI and VR simulations.
Role type
Master Thesis Researcher (AI/ML)
Builds
Memory-augmented behavior prediction models and explainable prediction frameworks for traffic safety
Domain
Artificial Intelligence, Machine Learning, Traffic Simulation, Human-Centric Intelligence
Deliverable
research
Required skills
Python, Machine Learning, Deep Learning, PyTorch or TensorFlow, Data Analysis, Software Engineering
Preferred skills
VR/XR development, Unity or Unreal Engine, Computer Vision, Simulation environments, Eye-tracking data, Retrieval systems, Autonomous driving research
Responsibilities
Design and implement memory-augmented behavior prediction models; Evaluate personal, location-based, and situation-based episodic memory mechanisms; Integrate simulation data into a retrieval-based prediction framework; Compare AI-based risk prediction with human attention patterns through user studies; Investigate explainability of memory-based prediction systems