AI-Based Digital Twin Consistency Monitoring and Performance Validation for Intelligent Infrastructure

02.03.2026, Abschlussarbeiten, Bachelor- und Masterarbeiten

Motivation & Relevance

Digital twins are increasingly used to represent and simulate traffic environments. Their reliability depends on continuous alignment with real-world observations. Inconsistencies between sensor data and the digital twin can arise from perception errors, synchronization drift, or model inaccuracies. Systematic validation of digital twins against real infrastructure data is still an open research challenge.

This thesis focuses strongly on the digital twin as the central model.

Project Description

In this thesis, you will design AI-based validation mechanisms between a traffic digital twin and real-world roadside infrastructure data.

The system will:

align digital twin object states with real-world detections
detect spatial, temporal and dynamic inconsistencies
identify missing or hallucinated objects
quantify long-term drift and model degradation
define digital twin performance KPIs
monitor performance evolution over time
generate validation reports and reliability metrics

The focus is on model consistency, long-term validation, and system reliability assessment.

Your Tasks

Develop real ↔ digital twin alignment methods
Design discrepancy detection algorithms
Define validation metrics
Implement long-term performance monitoring
Evaluate robustness under varying traffic scenarios

Your Profile

Master’s student in CS, Robotics, EE or similar
Strong programming and analytical skills
Interest in digital twins and model validation
Experience with simulation environments is a plus

What you will gain

Deep insight into digital twin validation
Experience with real-world infrastructure data
Knowledge of long-term AI system evaluation
Research potential for publication

I am looking forward to receiving your application. Please include a very brief motivational statement and your grade transcript

Kontakt: erik-leo.hass@tum.de