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Being able to safely control autonomous heavy machinery is of uttermost importance for the conversion of traditional machines to autonomous machines. With the continuous growth of autonomous vehicles around the globe, an increasing effort has been put into certifying autonomous vehicles in terms of reliability and safety. In this thesis, we will investigate the problem with a deviation from the planned path for an autonomous hauler from Volvo Construction Equipment. The autonomous hauler has an error within the kinematic model, the feed-forward curvature-steering controller, due to a slip-effect that comes with the third wheel-axle. The deviation can especially be seen in sharp curves, where the deviation needs to be decreased in order to make the autonomous hauler more dependable and achieve an increased accuracy when following any given path. The aim of the thesis is to develop a fully functional Artificial Neural Network that has a new steering angle as output. The hypothesis for this thesis is to use an ANN to mimic the steering of a human driver, since a real driver compensates for the slipping behavior; both because the operator knows where on the road the machine is and also in the way that a human thinks many steps ahead whilst driving. This proposed ANN will have a monitor function which ensures that the steering angle command operates within its boundaries. Hence this thesis implies that it is indeed possible to ensure that the ANN performs reliably with the help of a monitor function in a simulated environment and can thus be used in dependable systems.

The study aimed to identify and analyse methods for establishing external communication between humans and autonomous/teleoperated machines/vehicles using various Human-Machine Interfaces (HMIs). The study was divided into three phases. The purpose of the first phase was to identify and highlight previously tested/researched methods for establishing external communication by conducting a literature review. The findings from the literature review were categorised into six points of interest: machine indications, test delivery methods, HMI technologies/types, symbols, textual/numerical messages, and colours associated with different indications. Based on these findings, four HMIs (projection, display, LED-strip, and auditory) were selected for evaluation in a virtual reality environment for the second phase of the study, which has the purpose of identifying which of the human-machine interfaces can effectively communicate the intentions of autonomous/teleoperated machines to humans. The results of phase two indicate that the participants preferred projection as the most effective individual HMI, and when given the option to combine two HMIs, projection combined with auditory was the most preferred combination. The participants were also asked to pick three HMIs of their choosing, resulting in the projection, display and audible HMI combination being the preferred option. The evaluation of HMIs in a virtual reality environment contributes to improving dependability and identifying usability issues.  The objective of the third and final phase was to gather all the findings from the previous phases and subsequently refine the report until it was considered finalised. Future work includes enhancing the realism of the VR environment, refining machine behaviour and scenarios, enabling multiple participants to simultaneously interact with the environment, and exploring alternative evaluation methods. Addressing these areas will lead to more realistic evaluations and advancements in human-machine interaction research.

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