Individual assistance during automated driving through driver modelling

The automotive industry has been following the objective of increasing assistance for improved usability and safety over the past decades. Currently, the focus of automotive development concentrates on implementation of automation, promising the possibility of vehicles holding temporary responsibility of the driving task, while the driver is free to focus on other tasks. However, especially in Level 3 automated driving, the human has to act as a fallback, if technical systems fail or reach limits. This transferal of responsibility in any direction can cause miscommunication, incomplete driver mental models and accidents if mismanaged. Therefore, a number of questions regarding the human-machine-interaction have to be addressed, in order to guarantee safe transitions between humans and vehicles. Firstly, the spectrum of human capabilities demands definition in the context of Level 3 driving. For empirical investigation, this requires monitoring systems for vehicles in order to identify factors influencing driver capability. Secondly, humans need assistance in the transition phases between automation and manual control. Based on the available information for an automated vehicle, a prediction of driver reactions is necessary to understand how a driver can be assisted, to reduce mismanagement of information. This is relevant for individuals as well as all varieties of driver types. Especially professional drivers, e.g. truck drivers, will experience long exposures to automation. Through extensive driving experience, possibly leading to other interaction than non-professional drivers, assistance needs to cater to their requirements. This dissertation focuses on the evaluation of professional driver capabilities in the context of Level 3 take-over. It proposes different computational models that offer the possibility of predicting driver behaviour, in order to improve usability and offer adaptive assistance during transition phases for automated driving. Thereby, assistance is improved and communication between the driver and vehicle is enhanced, to reduce accidents. In order to examine driver behaviour and capability for Level 3 automation, two highway scenario studies were directed, analysed and utilised for predictive computational driver models. The first study was conducted in a high-fidelity moving-base simulator, displaying critical take-over situations. Amongst others, analysis of the empirical data showed that premature visibility of take-over situations generated significantly quicker reaction times. The data gathered of 88 participants was later utilised to analyse different data-driven computational models to predict reaction times of drivers. Based on remote eye-tracking data, vehicular environmental information and non-driving related task interaction, four different machine learning algorithms were compared in a four-class classification problem. Information was obtained from 238 features, reaching a misclassification rate of 38% for support vector machines. The empirical data of the simulator study was later validated in a test track study with a prototypic Level 3 vehicle. As premature visibility was a determining factor in the capacity of drivers to react quickly, expectancy was assigned as an independent variable. The statistical analysis displayed no significant effect of expectancy of different take-over situations on the reaction time. Overall, the 3-interval of time to first reaction of the simulator study ranged from 0 to 2.82 seconds, while the test track study displayed slightly quicker reactions up to 2.34 seconds. Finally, based on the findings of the two studies, an adaptive assistance during automation was developed. This system is designed to monitor the surroundings of any automated vehicle through vehicular sensors. The system relates environmental metrics to situations in which individual drivers self-initiate take-over. Thereby, it acts as an example for a seamless assistance system that is capable of individualisation and learning to enrich the driving experience and safety of drivers.