Alexander Steinke M.Sc.

Working area(s)

Trajectory planning in automated driving, Optimization concerning driving comfort

Contact

work +49 6151 16-25183
fax +49 6151 16-25172

Work S3|10 508
Landgraf-Georg-Str. 4
64283 Darmstadt

For several years intense development and research has been underway on automated traffic, in which commercial and passenger vehicles move fully autonomously without human intervention. Among other benefits, it is hoped that this will lead to fewer accidents and a reduction in urban traffic volume.

Autonomous driving is also often associated with the possibility of performing other activities while beeing driven. However, it is often overlooked that one is not in an office or bedroom, but in a highly dynamic vehicle. A widespread phenomenon is kinetosis, also known as motion sickness. Low-frequency vibrations can cause dizziness and nausea. Activities unrelated to driving, such as reading, further intensify this effect.

In addition to the stress caused by acceleration, safety aspects also have an impact on the feeling of comfort. For example, driving too close to the vehicle in front or high speed differences to the traffic flow can cause discomfort.

In the current traffic environment, human drivers are usually in charge of getting other passengers quickly and comfortably to their destination. Professional chauffeurs – such as those in upper-class limousine services – are specially trained for this purpose and develop an intuitive feeling for comfort, even if this can be rated quite subjectively depending on the passenger. If the driver is replaced, trajectory planning takes over the tasks of course and speed planning.

This research project is initially aimed at developing a model-based description of driving comfort. To achieve this, not only human driving styles will be analyzed, but also insights from kinetosis research will be used. Using suitable metrics, including aspects such as travel time and energy consumption, a comfort optimized trajectory planning will be designed, implemented and finally validated in participant studies.

The planning algorithm builds on existing work on time-optimized trajectory planning. Concepts of time-optimal planning are then used to calculate evasion trajectories for collision avoidance in case of emergency situations.

Topic Type Status
Powertrain control of an overactuated vehicle bachelor thesis in process
Optimal trajectory planning in automated driving with varying model fidelity of vehicle dynamics master thesis in process
Calculation of feasible halting trajectories in road traffic project seminar completed
Inverse optimal control on the example of trajectory planning in automated driving. master thesis in process
Setup and control of a parallel robot project seminar completed
Optimal trajectory planning in automated driving considering driving comfort and kinetosis master thesis in process
Development of a temperature control for a test chamber master thesis (extern) in process