Handing over the wheel, giving up your privacy?

With the increase in automation of vehicles and the rise of driver monitoring systems in those vehicles, privacy and data protection concerns become more relevant for the automotive sector. Monitoring systems could contribute to road safety, by for instance warning the driver if he is dozing off. But keeping such a close eye on the user of the vehicle has legal implications. Within the European Union, the data gathered through the monitoring system, and the automated vehicle as a whole, will have to be collected and processed in conformity with the General Data Protection Regulation. By means of a use case, the different types of data, including health data, and the different requirements applicable to the collecting and processing of those types of data will be explored. Thereby, this contribution will give insights into the consequences of the GDPR for the collecting and processing of data gathered by automated vehicles

Towards hybrid driver state monitoring : review, future perspectives and the role of consumer electronics

The purpose of this paper is to bring together multiple literature sources which present innovative methodologies for the assessment of driver state, driving context and performance by means of technology within a vehicle and consumer electronic devices. It also provides an overview of ongoing research and trends in the area of driver state monitoring. As part of this review a model of a hybrid driver state monitoring system is proposed. The model incorporates technology within a vehicle and multiple broughtin devices for enhanced validity and reliability of recorded data. Additionally, the model draws upon requirement of data fusion in order to generate unified driver state indicator(-s) that could be used to modify in-vehicle information and safety systems hence, make them driver state adaptable. Such modification could help to reach optimal driving performance in a particular driving situation. To conclude, we discuss the advantages of integrating hybrid driver state monitoring system into a vehicle and suggest future areas of research

Exploring data protection challenges of automated driving

With the increase in automation of vehicles and the rise of driver monitoring systems in those vehicles, data protection becomes more relevant for the automotive sector. Monitoring systems could contribute to road safety by, for instance, warning the driver if he is dozing off. However, keeping such a close eye on the user of the vehicle has legal implications. Within the European Union, the data gathered through the monitoring system, and the automated vehicle as a whole, will have to be collected and processed in conformity with the General Data Protection Regulation. By means of a use case, the different types of data collected by the automated vehicle, including health data, and the different requirements applicable to the collecting and processing of those types of data are explored. A three-step approach to ensuring data protection in automated vehicles is discussed. In addition, the possibilities to ensure data protection at a European level via the (type-) approval requirements will be explored

Adaptive Regenerative Braking in Electric Vehicles

Elektrofahrzeuge fahren lokal emissionsfrei und tragen damit dazu bei, die Emissionen in Städten zu reduzieren. Zusätzlich, zeichnen sich Elektrofahrzeuge durch ein dynamisches Fahrverhalten aus. Nachteilig wirkt sich bei den meisten Elektrofahrzeugen, die geringe Reichweite auf die Akzeptanz bei Neuwagenkäufern aus. Eine der Maßnahmen zur Erhöhung der Reichweite von Elektrofahrzeuge ist das regenerative Bremsen. Hierbei wird die kinetische Energie des Fahrzeugs durch generatorisches Bremsen als elektrische Energie zurückgewonnen. Diese zurückgewonnene Energie erhöht die Reichweite des Autos. In dieser Dissertation, wird ein adaptives regeneratives Bremssystem vorgestellt. Dieses System wählt abhängig vom Fahrertyp und der aktuellen Verkehrssituation ein geeignetes regeneratives Bremsniveau aus. Um ein solches System zu realisieren, wurden Verfahren entwickelt, welche einerseits den Fahrertyp und andererseits die Fahrerintention durch Analyse des Fahrbetriebs ermitteln. Dazu wurde u.a. ein mehrdimensionales verstecktes Markov-Modell (MDHMM) entwickelt. Bei Verwendung des Fahrertyps und der Intention des Fahrers, kann so eine geeignete Bremsstufe ausgewählt werden, die die physikalische Begrenzung der Fahrzeugkomponenten berücksichtigt. Durch den Einsatz des entwickelten Systems, kann gezeigt werden, dass eine Erhöhung der Reichweite erreicht werden kann, ohne den Komfort des Fahrers zu beeinträchtigen