Thesis on AI-Driven Map Matching and Path Prediction on Semantically Enriched Road Networks

Map matching and path prediction are core capabilities for autonomous driving. Our team at the BMW Group explores data-driven approaches that combine symbolic reasoning and machine learning, operating on real-world map data to improve robustness, accuracy, and interpretability in complex urban environments.

Responsibilities

Support modeling road connectivity and constraints in RDF and implementing rule sets to compute the most probable path using a rule-based reasoner; Build features from observations and the road graph, learning graph embeddings and training models to predict the next link or path; Contribute to formulating path prediction as a reinforcement learning problem, integrating graph embeddings and training agents such as DQN or actor-critic; Assist in developing sequence-to-sequence or transformer-based models to align GPS trajectories to graph-aligned edge sequences and comparing them to baselines; Experiment with graph-aware attention or constrained decoding to inject RDF structure and semantics into learning models; Support designing metrics and scenarios, measuring accuracy, robustness, efficiency, and interpretability, and running ablations on embeddings and semantic attributes

Requirements

Studies in computer science, data science, electrical engineering or a related field; Solid background in machine learning, including supervised learning and basic reinforcement learning; Experience with Python and machine learning or deep learning frameworks such as PyTorch or TensorFlow, plus data processing libraries; Familiarity with graph representations and knowledge graphs such as RDF and SPARQL; Understanding of graph neural networks or representation learning methods; Software engineering skills for reproducible experiments, including version control, clean code, and benchmarking; Good German and English skills

Nice to have

Comfort with graph or knowledge graph embeddings

Benefits

Comprehensive mentoring & onboarding; Personal & professional development; Flexible working hours; Mobile work; Attractive & fair compensation; Student apartments (subject to availability & only at the Munich location)