Naturalistic Driving Observation for Energetic Optimization and AccidentAvoidance

Key Research Areas of our Academic Chair

The subject of Naturalistic Driving Observation (NDO) offers a wide range of applications, which are being used with increasing intensity in the research of active safety. Using recorded data, we apply methodological approaches and statistical analyses to draw conclusions about energy efficiency with the aim of answering the following research questions:

What possibilities does NDO offer to identify relevant pre-crash situations? Which solutions can prevent future accidents?

  • Can critical sequences of a typical pre-crash situation be identified?
  • Which situations require closer attention?
  • Which critical situations can be avoided or eased by a change to drivability or to the surroundings (e.g. structural measures)?

What potential does NDO offer besides accident research to the energetic optimization of (electric) drive trains? What are the options and limits of individual adjustments?

  • How can individual manner of driving be measured or simulated?
  • How big are the impacts of individual manner of driving?
  • What effort is needed to capture the individual manner of driving, assess it, and develop recommendations for an individually customized, more economical manner of driving?

How can an automated assessment of the data set be implemented?

  • How can different data sets be synchronized?
  • What should an ideal NDO database look like?
  • Which situations (critical sequences) are especially or not at all applicable to an automated assessment?

The figure symbolizes the approach for NDOs. Our focus is on developing and using an NDO database that is compatible or linked to other NDO databases (e.g. BASt, TU Chemnitz, DLR Braunschweig). This database will also serve as a platform for own assessments, focusing on the initial phase of research. It will feature the development of a measuring system (simultaneous usage of several data loggers to identify vehicle and surrounding parameters as well as video and audio recording), as well as the testing and evaluation of the system and the data recording itself. Our aim is to develop a system that not only works for vehicles, but for bicycles and pedelecs as well.

The primary focus of the analysis (database usage) is the (semi-)automated assessment and evaluation of data to extract sequences from the data sets that are essential to the research questions. These are used to interpret research results from the areas of energy efficiency and accident avoidance. External applications or cooperations (e.g. psychology or material engineering) will be pursued where possible.

Contact information

Office TIB 13