Master Thesis: Episodic Memory for Behavior Prediction & Accident Anticipation - Traffic Simulation

Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Artificial Intelligence · Data Science

In short

This Master Thesis project at Ericsson in Lund, Sweden, explores how episodic memory mechanisms can improve AI-driven behavior prediction and accident anticipation in traffic simulations. The role involves designing and evaluating memory-augmented models using VR-based simulations to enhance AI's understanding of hazards and future events.

Responsibilities

Design and implement memory-augmented behavior prediction models.
Evaluate three complementary episodic memory mechanisms: personal, location-based, and situation-based.
Integrate simulation data (trajectories, interactions, context, events, gaze) into a retrieval-based prediction framework.
Compare AI-based risk prediction with human attention and decision-making via user studies.
Investigate explainability and interpretability of memory-based prediction systems.
Explore how memory-augmented world models can support safer and more intelligent future systems.

Requirements

Knowledge in Computer Science, Electrical Engineering, Artificial Intelligence, or a related field.
Experience with Python programming.
Interest in machine learning, deep learning, and AI research.
Familiarity with PyTorch or TensorFlow.
Understanding of data analysis and software engineering practices.
Curiosity, creativity, and willingness to explore new research ideas.

Desired Qualifications

Experience with VR/XR development, Unity or Unreal Engine.
Experience with computer vision, simulation environments, eye-tracking data, retrieval systems, digital twins, or autonomous driving research.
Students who enjoy combining machine learning, simulation, human behavior analysis, and user-centered experimentation.

Skills

PythonPyTorchTensorFlowMachine LearningDeep LearningAIComputer VisionSimulationUnityUnreal Engine

#AI#Traffic Simulation#Episodic Memory#Behavior Prediction#Accident Anticipation#Master Thesis

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