Driving pattern recognition with vehicle CAN-data

Driving pattern recognition with vehicle CAN-data

Master Thesis

Neural Network is increasingly applied in various domains within the automotive industry. One of many is the field of predictive maintenance, which tries to estimate the current state of car parts to early access needs for maintenance and residual car value estimation. For these application signal bus system (CAN) data can be used, giving a huge amount of time series data to estimate the state of a car component. Problematic about this data is, that time series signals can vary strongly depending on driving situations, user and environmental factors, and therefore are not necessarily comparable. Knowledge about similar and different situations within the data, would enable a smarter selection of data for training and evaluation, therefore increase training data quality and performance of the model. Further, knowledge about repeating events within the data, would enable condition monitoring based on behavioral changes in well-comparable situations.

Being said that, the objective behind this work is to explore algorithms to find often occurring events within the multidimensional CAN-data. Due to the huge size of the data and high dimensionality, efficient algorithms need to be found and tested regarding the quality of the results. For this investigation, a case study from the automotive domain is considered, where the model is taking multivariate time series from the signal bus system as features in order to find often occurring and rare events such as a specific driving maneuver within testing data or commute trips within real car usage CAN data.


  • Knowledge in unsupervised Machine Learning.
  • Experience in python programming language.
  • (Bonus) Knowledge in Motif Detection.


  • Identify patterns in the multi-dimensional time series.
  • Hierarchically cluster the patterns into classes.
  • Find and evaluate rate event algorithms
  • Evaluate the models in terms of capabilities, computation expense and performance.
  • (Bonus) label the cluster classes

If you are interested in this topic, please sent an email to one of the supervisors (see right panel).