Deep learning approach to driving behavior prediction with data fusion.

Deep learning approach to driving behavior prediction with data fusion.



Reliable driving behavior prediction is of great importance in autonomous driving because the interaction between vehicles is complex and non-deterministic. To address this problem, multimodal data from different sources must be used in real time to ensure reliable prediction. In the last decades, different approaches, such as parametric models, Markov models, reinforcement learning (RL) and inverse RL, have been developed.

In this work, new methods for driving behavior prediction are investigated depending on the selection of focus areas:

  • Comprehensive literature review ("Review of SOTA").
  • Development of a data fusion framework, hardware (e.g. sensors, GPS, cameras)
  • Development of a data fusion framework, software (data simulation/generation, cloud data, data fusion).
  • Development, training and validation of (RL-based) deep learning approaches for driving behavior prediction.
  • Optimization of neural networks, e.g. feature selection, network pruning & quantization, knowledge distillation
  • Application and optimization of NN on the test bench


  • Literature research on the SOTA technique in the area of interest
  • Conceptual design of the work in terms of workload and publication potential
  • Target-oriented development, implementation of the Minimum Viable Product (MVP) for driving behavior prediction
  • Evaluation and analysis of the results and writing of the final paper


  • Motivation and interest in solving technical problems independently
  • Knowledge of machine learning, ideally reinforcement learning and data fusion
  • Experience with programming (Python, C++ , Java...)
  • Analytical skills, problem solving and communication skills