Road safety is still a challenging issue. In 2020, 1.35 million people have died as a result of traffic accidents, where the number one cause of death for young adults between the age of 5 and 29 is car accidents. In an attempt to improve road safety, the automotive industry has developed numerous types of Advanced Driver Assistance Systems (ADAS). These systems are in general effective in improving safety. However, these systems will only be used if and only if drivers perceive the assistance as intuitive and cooperative. It is recently found that 61% of drivers sometimes switch off the assistance, 23% feel that current assistance are annoying and bothersome, whereas only 21% find them helpful. A safe system that is not used has no safety benefits. A promising way to improve driver acceptance and to increase safety is to employ haptic shared control (HSC), which is an effective way of keeping drivers in the active control loop. Support in the form of HSC benefits situation awareness and ensures effective monitoring of the environment and automation. However, torque conflict resulting from opposing intentions of driver and automation is reported to be a bottleneck for drivers' acceptance of HSC. Particularly, such conflicts are found to be most debilitating in curves. With each driver having an individual driving style, with different preferences and skill levels, the current standard 'one-size-fits-all' assistance approach to HSC, and driver support in general, is not satisfactory for every individual. An effective approach to increase acceptance in ADAS, and a reliable way to align the automation to the driver's preferences, is through personalisation. Here, personalisation is generally defined as 'making something suitable for the needs and preferences of a particular person'. For HSC, personalisation can be effectively realised by adapting the system's adopted trajectory to that of the driver. Therefore, the personalisation of HSC requires a driver modelling approach that predicts an individual driver's behaviour. Before this thesis, the personalisation of HSC was attempted by adjusting the gains of a corrective feedback HSC, as though it were a driver steering model itself. What was missing was 1) a HSC that allows for personalisation, i.e., a framework where a personalisable reference trajectory is independent of the haptic controller and, 2) a computational driver model or a data-driven driver classification approach that is able to describe individual drivers. When this thesis was started, a theoretical HSC concept, the 'Four-Design-Choice-Architecture' (FDCA) was introduced within our group. This promising concept was, however, not realised or implemented yet. As for modelling individual drivers, it was not known what type of driver steering and trajectory model(s) are suitable to generate personalised trajectories, if any, due to the lack of a standardised way to compare and evaluate the output performance of driver behaviour models with different structures and complexities. It was not known exactly how to achieve successful personalisation in curves, nor was the needed level of personalisation understood, i.e., adapting to the intricacies of each individual or adapting to a more general style. Moreover, whether personalisation in itself improves the acceptance of HSC systems, was still to be verified. These challenges are addressed in the four parts of this thesis: 1) Driver model assessment: The development of an assessment method and application on prominent control-theoretic driver models in the literature. %This was done to gain in-depth understanding of what is needed to model and describe individual drivers. 2) Driver trajectory classification: Understanding and categorising the types of individual driver trajectories present in the driving population. 3) Driver prepositioning: Understanding and modelling driver prepositioning behaviour, a behaviour found to be an essential, yet mostly overlooked aspect of curve-driving behaviour. 4) Application to Haptic Shared Control: Apply and evaluate personalised haptic shared control. This thesis has achieved it's highest level goal, which is to improve the acceptance of the haptic shared control driver support. This thesis provides an improved understanding and new insights into 1) how the novel FDC HSC has solved much of the acceptance issue put forward, and 2) an understanding of how to personalise with the FDC HSC. In terms of modelling tools and methods, this thesis has contributed with: 1) a model assessment procedure that can highlight the strengths and weaknesses of any control theoretic model, 2) a trajectory classifier, which can categorise different types of drivers, 3) a prepositioning path model, which, when combined with the Van Paassen control-theoretic driver model results in the first individual control-theoretic driver model, i.e., a model that can capture all main styles of individual driver behaviour and 4) the first personalisable HSC, where the developed modelling methods are applied to evaluate personalised haptic shared control. The findings and insights from this thesis have contributed to design guidelines and, can accelerate future research. Some examples include 1) using the individualised driver steering model, personalisation of ADAS can now be done in real-time, 2) using the developed trajectory classifier, explicit personalisation can be achieved, i.e., the driver can select the type of trajectory guidance he may want, and, 3) the driver trajectory modelling methods developed in this thesis can be used for the personalisation of path-planning in fully autonomous-vehicles.