The technological advancements in the automotive industry have enabled the automation of numerous routine driving tasks. As a result, the art of driving has become a control task with a strong supervisory character, including the common human factor issues. Haptic Shared Control has been shown to be useful in keeping the driver in-the-loop by providing continuous haptic guidance on the steering wheel. Nonetheless, it has been reported that haptic support often induces control conflicts caused by the limited amount of information that can be conveyed through haptic forces. As a consequence, it is often burdensome to develop a correct mental model of the underlying controller and to establish accurate situation awareness. This study presents the results of the conceptual development of a novel visual feedback system inspired by the principle of Ecological Interface Design. By displaying the future trajectory with respect to both the physical limitations of the vehicle and the intentional constraints imposed by the road, the driver is able to establish a better understanding of the space of possibilities in a certain driving scenario. The new visual feedback is combined with current haptic feedback solutions, with the goal of guiding the driver through force-feedback, while visualizing the space of possibilities as defined by the work domain constraints. A human-in-the-loop experiment was conducted to evaluate the effects of the novel feedback system on driver behavior and acceptance during an obstacle avoidance task. In analogy with previous findings, the results showed that haptic guidance was beneficial during obstacle avoidance in terms of response time, control activity and effort, where the addition of EID-inspired visual feedback revealed an improved task execution, together with a significant reduction of control activity and conflicts.

Context: To investigate the effectiveness of nonlinear calculation of the near and far angle for driver modelling in curve negotiation, simulated data is analyzed based of Mars driver model and the nonlinear realization of Mars model given in Scholtens. Aims: It is shown that due to the dynamic calculation of the near and far angle, the simulated driver is less sensitive to change of the Kp or Kc parameter value during a drive. Static calculation of the near and far angle showed a wider range of drivers that can be identified before the start of the drive, but is less adaptable to change during the drive. Methods: The range for the two parameters Kc and Kp was determined by setting two boundaries, whilst keeping the other parameters constant: the lateral position is bound to an absolute value of 1.8 meter and the steering wheel reversal rate to a maximum of 6. A ten by ten resolution range is subsequently established to form the basis for the simulations. The simulated results from the nonlinear and linear model are assessed on three criteria: realism, identifiability and descriptiveness. The three criteria show the realistic fit of the simulated data in comparison to real driver data. Results: Static calculation of the near and far angle can capture a broader set of human drivers front end if the parameter set of the driver is know front end. A dynamic calculation is more adaptable to an unknown driver set and is therefor in a smaller need of knowing the driver before the drive starts.

Haptic shared control enables continuous interaction between driver and automatic controller by means of torques on the steering wheel that guide towards a reference trajectory. Current haptic shared control systems induce conflicts when the reference trajectory of the automation differs from the drivers own desired trajectory, which can lead to rejection of the automation. This study investigated whether conflicts can be reduced by individualizing the automation. The approach of the study is twofold. First, a Four Design Choices Architecture (FDCA) is implemented that allows for separate tuning of the reference in a haptic shared controller that determines the total torques by separating feedback and feed-forward torques. Second, a driver-model was used to identify the individual steering parameters and determine the corresponding individualized reference in the curved road-sections. The combination of the FDCA controller and individualized reference was evaluated in an experiment using a fixed-based driving simulator. Sixteen subjects participated in a two-day experiment, where the first day was used to collect manual driving data to identify the individual and average steering parameters to construct the references. On the second day participants drove with four conditions: manual control, a conventional shared controller architecture using feedback torques towards a center-line reference, an individual tuned reference with FDCA controller, and an average tuned reference with FDCA controller. The results showed that the driver-model was able to identify differences in individual curve negotiation styles. Both FDCA controllers resulted in higher objective acceptance and lower workload; conflicts in torques and driver torques decreased significantly compared to manual control and the conventional controller. The usefulness and satisfaction scores were only marginally higher for the individualized FDCA compared to the average FDCA. Interestingly, only the individualized FDCA was perceived more satisfactory and useful than the conventional controller and manual control. Thus, the individualized FDCA can reduce conflicts and increase acceptance compared to the conventional shared controller