Naturalistic Driving Observation for Energetic Optimization and AccidentAvoidance

Naturalistic Driving Observation

Content and learning outcomes

The module provides a comprehensive overview of current methods of monitoring driver behavior and their areas of application including driver assist systems. Students will learn processes and boundary conditions to develop methods of driver behavior observation and will acquire the skills to independently analyze system relationships, develop evaluation methods as well as apply and arrange these in the context of statistical relevance. You will develop a detailed understanding of how driver behavior is observed and how this can be applied to future developments in automotive engineering, in particular Car2X communication.

The course is based on current examples and research areas of existing and budding methods and techniques of driver behavior observation, their components (measurement technology, databases, simulation models, protocols) and includes current issues regarding application (Car2X communication , optimizing fuel consumption, accident avoidance), opportunities (technical implementation, application areas away from the industry) and limitations (data processing, data protection, environmental influences). Students have the opportunity to gain some practical experience applying the methodology.

Module format and content

Credits6 ECTS (4 course hours per week)
Module supervisorProf. Dr. rer. nat. Stefanie Marker
Teaching formatCombination of lecture and practical tutorial, group work and group discussion. Supplemented with online support via ISIS, office hours. Students can complete part of the course online (blended learning).
Dates and durationThe module is offered winter semester and can be completed in one semester.
Requirements(a) mandatory: good knowledge of German and English; strong transferable basic skills in automotive technology (acquired by successful completion of the module "Fundamentals of Vehicle Technology" and "Fundamentals of Vehicle Dynamics"
 (b) preferred: basic knowledge in the fields of "big data," vehicle safety and dynamic simulations; oral and written presentation skills for technical results, good teamwork skills and social competence
Exam requirementsExam-equivalent performance (PS): Practical exercises (two exercises in groups of two including a presentation) and a final test are graded. These are equally weighted in the final grade.