Critical Systems Engineering - Socio-technical Vehicle Systems (The Car that Cares)

Due to the increasing automation human-machine-interaction becomes more and more important. In this project, the socio-technical relationship between the individual driver and the technical systems of the vehicle is investigated regarding a future car “that cares”. Areas of research also include the surrounding traffic and road infrastructures.
The scope of the CSE subproject The Car That Cares (CtC) is the development of socio - technical vehicle systems, which continuously adapt to internal (e.g. driver 's health, current tasks) and external conditions (e.g. weather, traffic) to offer a situation-related cooperative interaction with the driver of the car. The project addresses elderly drivers (aged 65 and over) who are increasing in numbers due to the demographic change. On the one hand, elderly drivers have a higher risk of accidents due to various restrictions accompanying the decline in age (such as perception or attention shift) that are crucial for driving. On the other hand, elderly drivers have a personal desire and a social need to stay mobile and independent as long as possible. High-resolution information about the drivers ‘cognitive status is essential to prevent overload situations and to enhance their performance.


We want to assess the cognitive, emotional, and health status of the driver within the car by a multi-sensor approach. Systems are developed to support the driver and provide recommendations to improve safety, comfort, and health through adaptive multimodal user interfaces.
The focus of our research group is the development of robust procedures for the detection and context-specific modulation of the neurocognitive status of the elderly driver. We want to improve processing capabilities and reaction times to support appropriate and safe actions even in traffic situations with increasing complexity and under time pressure.


Therefore, our research group develops and refines systems for the detection and enhancement of cognitive performance and evaluates their suitability for application:

  • We develop a special mobile fNIRS system that makes it possible to measure cognitive load reliably in realistic environments. The mobile fNIRS will be more robust against environmental influences (movement of the subject, stray light) and achieve higher signal quality.
  • Transcranial alternating current (tACS) is investigated as a method for modulating the neurocognitive driver's status, also for the elderly. In tACS, electrodes glued to the head generate an alternating current field that influences intrinsic brain waves. tACS can modify certain brain waves and thus, among other things, increase cognitive capabilities (e.g. in learning tasks). We investigate the applicability of tACS to the driving context. Current scenarios are the enhancement of attention or reaction times during monotonous or tedious driving tasks.
  • In the Living Lab State Characterization and Identification (LL SCI) neurocognitive measurement methods for studies in the driving context are prepared. These methods are going to be used to evaluate previous results and to gain further insights into the neuronal activities in complex driving situations. In addition to systems like fNIRS and tACS, imaging techniques such as MEG and MRI are used and appropriate MRI and MEG suitable driving simulator hardware (steering wheels and pedals) are developed. These components will be integrated into a processing system together with a driving simulator and used in studies.

Grants and cooperations

The interdisciplinary research center for the design of safety-critical socio-technical systems investigates the role of humans in the control of complex transport systems on land and water. Cooperation partners are OFFIS e.V. in Oldenburg, DLR Institute for Traffic Systems Engineering in Braunschweig and the network SafeTRANS.
Currently, the project is funded in the second phase by the state of Lower Saxony with EUR 2 million. The project runtime was extended by additional 18 months (2017-2018).


  • [article] bibtex
    S. Blum, S. Debener, R. Emkes, N. Volkening, S. Fudickar, and M. G. Bleichner, "EEG Recording and Online Signal Processing on Android: A Multiapp Framework for Brain-Computer Interfaces on Smartphone," BioMed Research International Hindawi, vol. 2017, p. 12, 2017.
    @article{Blum2017, abstract = {Objective. Our aim was the development and validation of a modular signal processing and classification application enabling online electroencephalography (EEG) signal processing on off-the-shelf mobile Android devices. The software application SCALA (Signal ProCessing and CLassification on Android) supports a standardized communication interface to exchange information with external software and hardware. Approach. In order to implement a closed-loop brain-computer interface (BCI) on the smartphone, we used a multiapp framework, which integrates applications for stimulus presentation, data acquisition, data processing, classification, and delivery of feedback to the user. Main Results. We have implemented the open source signal processing application SCALA. We present timing test results supporting sufficient temporal precision of audio events. We also validate SCALA with a well-established auditory selective attention paradigm and report above chance level classification results for all participants. Regarding the 24-channel EEG signal quality, evaluation results confirm typical sound onset auditory evoked potentials as well as cognitive event-related potentials that differentiate between correct and incorrect task performance feedback. Significance. We present a fully smartphone-operated, modular closed-loop BCI system that can be combined with different EEG amplifiers and can easily implement other paradigms.},
      author = {Blum, Sarah and Debener, Stefan and Emkes, Reiner and Volkening, Nils and Fudickar, Sebastian and Bleichner, Martin G},
      doi = {10.1155/2017/3072870},
      file = {:C$\backslash$:/Users/NilsV/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Blum et al. - Unknown - EEG Recording and Online Signal Processing on Android A Multiapp Framework for Brain-Computer Interfaces on Smar.pdf:pdf},
      journal = {BioMed Research International Hindawi},
      keywords = {AMTCSE,AMTUNI,accepted,full paper},
      mendeley-tags = {AMTCSE,AMTUNI,accepted,full paper},
      pages = {12},
      title = {{EEG Recording and Online Signal Processing on Android: A Multiapp Framework for Brain-Computer Interfaces on Smartphone}},
      volume = {2017},
      year = {2017}
  • [inproceedings] bibtex
    N. Volkening, A. Unni, B. S. Löffler, S. Fudickar, J. W. Rieger, and A. Hein, "Characterizing the Influence of Muscle Activity in fNIRS Brain Activation Measurements," in Proc. IFAC-PapersOnLine, 2016, pp. 84-88.
    @inproceedings{Volkening2016, abstract = {Driving is a complex and cognitively demanding task. It is essential to assess the cognitive state of the driver in order to design cognitive technical systems that can adapt to different driver cognitive states. Our research attempts to assess these states using functional Near Infrared Spectroscopy (fNIRS) by measuring brain activity in a virtual reality driving simulator. However, the fNIRS brain activation measurements could be influenced by muscle activity and we wanted to investigate this phenomenon. For this, we designed a paradigm with two conditions (listen, teeth clench) which show a significant contrast in the influence of muscle activity. We observed that the muscle hemodynamic response can show a higher magnitude of signal change compared to brain hemodynamic response. The muscle hemodynamic response showed an increase in deoxygenated hemoglobin (HbR) whereas the brain hemodynamic response showed a decrease in HbR. Moreover, the dynamics of the brain and muscle hemodynamic response differed. The brain response showed the same latency for oxygenated hemoglobin (HbO) and HbR while the muscle HbR response had a slower latency compared to HbO. We concluded that the fNIRS brain activation measurements could indeed be influenced by muscle activity. We were also able to determine some characteristics of the muscle hemodynamic response.},
      author = {Volkening, Nils and Unni, Anirudh and L{\"{o}}ffler, Birte Sofie and Fudickar, Sebastian and Rieger, Jochen W. and Hein, Andreas},
      booktitle = {IFAC-PapersOnLine},
      doi = {10.1016/j.ifacol.2016.08.013},
      file = {:C$\backslash$:/Users/NilsV/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Volkening et al. - 2016 - Characterizing the Influence of Muscle Activity in fNIRS Brain Activation Measurements.pdf:pdf},
      issn = {24058963},
      keywords = {OFFIS=G-AIT/AHT/CSE,UNIAMT,UNICSE,accepted,full paper},
      mendeley-tags = {OFFIS=G-AIT/AHT/CSE,UNIAMT,UNICSE,accepted,full paper},
      number = {11},
      pages = {84--88},
      title = {{Characterizing the Influence of Muscle Activity in fNIRS Brain Activation Measurements}},
      volume = {49},
      year = {2016} }
  • [inproceedings] bibtex | Go to document Go to document
    B. S. Löffler, S. Fudickar, C. S. Herrmann, and A. Hein, "Ein Blick in das Gehirn: Messung und Verbesserung der Fahrleistung älterer Autofahrender mit Hilfe von Neuroimaging und Elektrostimulation," in Proc. Trends in Neuroergonomics, Berlin, 2015, pp. 166-169.
    @inproceedings{bwmms, abstract = {In diesem Beitrag wird ein Forschungsansatz vorgestellt, der es {\"{a}}lteren Autofahrern erm{\"{o}}glichen soll, bis in ein sehr hohes Alter ihre Fahrleistung zu erhalten. Motivation ist die, aufgrund des demografischen Wandels zu erwartende zuk{\"{u}}nftige hohe Zahl {\"{a}}lterer Autofahrer. Trotz des erh{\"{o}}hten Unfallrisikos ist in Deutschland keine regelm{\"{a}}{\ss}ige {\"{U}}berpr{\"{u}}fung der Fahreignung dieser Gruppe vorgesehen – bem{\"{a}}ngelt werden von der Zielgruppe und Fachleuten vor allem die fehlende Objektivit{\"{a}}t der Beurteilung und die assoziierten hohen Kosten. Neurotechnik wird auf Forschungsebene bereits im Auto und im Simulator eingesetzt und hat als Methode zur objektiven Fahrerzustandserkennung, auch von {\"{A}}lteren, Potential. Dieser Beitrag gibt einen {\"{U}}berblick {\"{u}}ber Assessment-Strategien der Fahreignung, und beschreibt ein angestrebtes Studiendesign, bei dem die Gehirnaktivit{\"{a}}ten mit Hilfe von EEG w{\"{a}}hrend der Fahrt aufgezeichnet und die M{\"{o}}glichkeit der Verbesserung der Fahrleistung durch Feedback und transkranielle Wechselstromstimulation im Auto erforscht werden soll. Der erste Ansatz und Pilotversuch zu letzterem Aspekt wird kurz vorgestellt.},
      address = {Berlin},
      author = {L{\"{o}}ffler, Birte Sofie and Fudickar, Sebastian and Herrmann, Christoph S. and Hein, Andreas},
      booktitle = {Trends in Neuroergonomics},
      doi = {10.14279/depositonce-4887},
      editor = {{Carolin Wienrich, Thorsten Zander},
      Klaus Gramann},
      file = {:C$\backslash$:/Users/NilsV/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/L{\"{o}}ffler B, Fudickar S, Herrmann CS - 2015 - Ein Blick in das Gehirn Messung und Verbesserung der Fahrleistung {\"{a}}lterer Autofahrender mit.pdf:pdf},
      isbn = {9783798328037},
      keywords = {UNIAMT,UNICSE,accepted},
      mendeley-tags = {UNIAMT,UNICSE,accepted},
      organization = {Technische Universit{\"{a}}t Berlin},
      pages = {166--169},
      publisher = {Universit{\"{a}}tsverlag der TU Berlin},
      series = {11. Berliner Werkstatt Mensch-Maschine-Systeme},
      title = {{Ein Blick in das Gehirn: Messung und Verbesserung der Fahrleistung {\"{a}}lterer Autofahrender mit Hilfe von Neuroimaging und Elektrostimulation}},
      url = {{\%}5Cn},
      year = {2015} }
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